diff --git a/.clang-tidy b/.clang-tidy
index 3078beacc..952c0cca8 100644
--- a/.clang-tidy
+++ b/.clang-tidy
@@ -12,6 +12,7 @@ Checks: >
     -readability-implicit-bool-conversion,
     -readability-magic-numbers,
     -readability-uppercase-literal-suffix,
+    -readability-simplify-boolean-expr,
     clang-analyzer-*,
     -clang-analyzer-security.insecureAPI.DeprecatedOrUnsafeBufferHandling,
     performance-*,
diff --git a/.devops/full-cuda.Dockerfile b/.devops/full-cuda.Dockerfile
index 77a9ddc14..059fd2695 100644
--- a/.devops/full-cuda.Dockerfile
+++ b/.devops/full-cuda.Dockerfile
@@ -12,7 +12,7 @@ FROM ${BASE_CUDA_DEV_CONTAINER} as build
 ARG CUDA_DOCKER_ARCH=all
 
 RUN apt-get update && \
-    apt-get install -y build-essential python3 python3-pip git
+    apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev
 
 COPY requirements.txt   requirements.txt
 COPY requirements       requirements
@@ -26,8 +26,10 @@ COPY . .
 
 # Set nvcc architecture
 ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
-# Enable cuBLAS
-ENV LLAMA_CUBLAS=1
+# Enable CUDA
+ENV LLAMA_CUDA=1
+# Enable cURL
+ENV LLAMA_CURL=1
 
 RUN make
 
diff --git a/.devops/full-rocm.Dockerfile b/.devops/full-rocm.Dockerfile
index 8b9633dc4..6ecf3bcc7 100644
--- a/.devops/full-rocm.Dockerfile
+++ b/.devops/full-rocm.Dockerfile
@@ -40,6 +40,11 @@ ENV LLAMA_HIPBLAS=1
 ENV CC=/opt/rocm/llvm/bin/clang
 ENV CXX=/opt/rocm/llvm/bin/clang++
 
+# Enable cURL
+ENV LLAMA_CURL=1
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev
+
 RUN make
 
 ENTRYPOINT ["/app/.devops/tools.sh"]
diff --git a/.devops/full.Dockerfile b/.devops/full.Dockerfile
index cef1297d3..432fb5dad 100644
--- a/.devops/full.Dockerfile
+++ b/.devops/full.Dockerfile
@@ -3,7 +3,7 @@ ARG UBUNTU_VERSION=22.04
 FROM ubuntu:$UBUNTU_VERSION as build
 
 RUN apt-get update && \
-    apt-get install -y build-essential python3 python3-pip git
+    apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev
 
 COPY requirements.txt   requirements.txt
 COPY requirements       requirements
@@ -15,6 +15,9 @@ WORKDIR /app
 
 COPY . .
 
+ENV LLAMA_CURL=1
+
+
 RUN make
 
 ENV LC_ALL=C.utf8
diff --git a/.devops/llama-cpp-clblast.srpm.spec b/.devops/llama-cpp-clblast.srpm.spec
index 076f29695..774f63ddd 100644
--- a/.devops/llama-cpp-clblast.srpm.spec
+++ b/.devops/llama-cpp-clblast.srpm.spec
@@ -1,5 +1,5 @@
 # SRPM for building from source and packaging an RPM for RPM-based distros.
-# https://fedoraproject.org/wiki/How_to_create_an_RPM_package
+# https://docs.fedoraproject.org/en-US/quick-docs/creating-rpm-packages
 # Built and maintained by John Boero - boeroboy@gmail.com
 # In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal
 
diff --git a/.devops/llama-cpp-cublas.srpm.spec b/.devops/llama-cpp-cuda.srpm.spec
similarity index 79%
rename from .devops/llama-cpp-cublas.srpm.spec
rename to .devops/llama-cpp-cuda.srpm.spec
index f847ebb1e..ba9cb7cbb 100644
--- a/.devops/llama-cpp-cublas.srpm.spec
+++ b/.devops/llama-cpp-cuda.srpm.spec
@@ -1,5 +1,5 @@
 # SRPM for building from source and packaging an RPM for RPM-based distros.
-# https://fedoraproject.org/wiki/How_to_create_an_RPM_package
+# https://docs.fedoraproject.org/en-US/quick-docs/creating-rpm-packages
 # Built and maintained by John Boero - boeroboy@gmail.com
 # In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal
 
@@ -12,7 +12,7 @@
 # 4. OpenCL/CLBLAST support simply requires the ICD loader and basic opencl libraries.
 #    It is up to the user to install the correct vendor-specific support.
 
-Name:           llama.cpp-cublas
+Name:           llama.cpp-cuda
 Version:        %( date "+%%Y%%m%%d" )
 Release:        1%{?dist}
 Summary:        CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL)
@@ -32,16 +32,16 @@ CPU inference for Meta's Lllama2 models using default options.
 %setup -n llama.cpp-master
 
 %build
-make -j LLAMA_CUBLAS=1
+make -j LLAMA_CUDA=1
 
 %install
 mkdir -p %{buildroot}%{_bindir}/
-cp -p main %{buildroot}%{_bindir}/llamacppcublas
-cp -p server %{buildroot}%{_bindir}/llamacppcublasserver
-cp -p simple %{buildroot}%{_bindir}/llamacppcublassimple
+cp -p main %{buildroot}%{_bindir}/llamacppcuda
+cp -p server %{buildroot}%{_bindir}/llamacppcudaserver
+cp -p simple %{buildroot}%{_bindir}/llamacppcudasimple
 
 mkdir -p %{buildroot}/usr/lib/systemd/system
-%{__cat} <<EOF  > %{buildroot}/usr/lib/systemd/system/llamacublas.service
+%{__cat} <<EOF  > %{buildroot}/usr/lib/systemd/system/llamacuda.service
 [Unit]
 Description=Llama.cpp server, CPU only (no GPU support in this build).
 After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target
@@ -49,7 +49,7 @@ After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.t
 [Service]
 Type=simple
 EnvironmentFile=/etc/sysconfig/llama
-ExecStart=/usr/bin/llamacppcublasserver $LLAMA_ARGS
+ExecStart=/usr/bin/llamacppcudaserver $LLAMA_ARGS
 ExecReload=/bin/kill -s HUP $MAINPID
 Restart=never
 
@@ -67,10 +67,10 @@ rm -rf %{buildroot}
 rm -rf %{_builddir}/*
 
 %files
-%{_bindir}/llamacppcublas
-%{_bindir}/llamacppcublasserver
-%{_bindir}/llamacppcublassimple
-/usr/lib/systemd/system/llamacublas.service
+%{_bindir}/llamacppcuda
+%{_bindir}/llamacppcudaserver
+%{_bindir}/llamacppcudasimple
+/usr/lib/systemd/system/llamacuda.service
 %config /etc/sysconfig/llama
 
 %pre
diff --git a/.devops/llama-cpp.srpm.spec b/.devops/llama-cpp.srpm.spec
index 446213d69..1d9e4f425 100644
--- a/.devops/llama-cpp.srpm.spec
+++ b/.devops/llama-cpp.srpm.spec
@@ -1,5 +1,5 @@
 # SRPM for building from source and packaging an RPM for RPM-based distros.
-# https://fedoraproject.org/wiki/How_to_create_an_RPM_package
+# https://docs.fedoraproject.org/en-US/quick-docs/creating-rpm-packages
 # Built and maintained by John Boero - boeroboy@gmail.com
 # In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal
 
diff --git a/.devops/main-cuda.Dockerfile b/.devops/main-cuda.Dockerfile
index 2b7faf7c1..b937a4829 100644
--- a/.devops/main-cuda.Dockerfile
+++ b/.devops/main-cuda.Dockerfile
@@ -20,8 +20,8 @@ COPY . .
 
 # Set nvcc architecture
 ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
-# Enable cuBLAS
-ENV LLAMA_CUBLAS=1
+# Enable CUDA
+ENV LLAMA_CUDA=1
 
 RUN make
 
diff --git a/.devops/main-intel.Dockerfile b/.devops/main-intel.Dockerfile
new file mode 100644
index 000000000..572e5d8ea
--- /dev/null
+++ b/.devops/main-intel.Dockerfile
@@ -0,0 +1,28 @@
+ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04
+
+FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
+
+ARG LLAMA_SYCL_F16=OFF
+RUN apt-get update && \
+    apt-get install -y git
+
+WORKDIR /app
+
+COPY . .
+
+RUN mkdir build && \
+    cd build && \
+    if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
+        echo "LLAMA_SYCL_F16 is set" && \
+        export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \
+    fi && \
+    cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \
+    cmake --build . --config Release --target main
+
+FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime
+
+COPY --from=build /app/build/bin/main /main
+
+ENV LC_ALL=C.utf8
+
+ENTRYPOINT [ "/main" ]
diff --git a/.devops/main-vulkan.Dockerfile b/.devops/main-vulkan.Dockerfile
new file mode 100644
index 000000000..bca460365
--- /dev/null
+++ b/.devops/main-vulkan.Dockerfile
@@ -0,0 +1,29 @@
+ARG UBUNTU_VERSION=jammy
+
+FROM ubuntu:$UBUNTU_VERSION as build
+
+# Install build tools
+RUN apt update && apt install -y git build-essential cmake wget
+
+# Install Vulkan SDK
+RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
+    wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
+    apt update -y && \
+    apt-get install -y vulkan-sdk
+
+# Build it
+WORKDIR /app
+COPY . .
+RUN mkdir build && \
+    cd build && \
+    cmake .. -DLLAMA_VULKAN=1 && \
+    cmake --build . --config Release --target main
+
+# Clean up
+WORKDIR /
+RUN cp /app/build/bin/main /main && \
+    rm -rf /app
+
+ENV LC_ALL=C.utf8
+
+ENTRYPOINT [ "/main" ]
diff --git a/.devops/nix/docker.nix b/.devops/nix/docker.nix
new file mode 100644
index 000000000..d607b4575
--- /dev/null
+++ b/.devops/nix/docker.nix
@@ -0,0 +1,37 @@
+{
+  lib,
+  dockerTools,
+  buildEnv,
+  llama-cpp,
+  interactive ? true,
+  coreutils,
+}:
+
+# A tar that can be fed into `docker load`:
+#
+# $ nix build .#llamaPackages.docker
+# $ docker load < result
+
+# For details and variations cf.
+# - https://nixos.org/manual/nixpkgs/unstable/#ssec-pkgs-dockerTools-buildLayeredImage
+# - https://discourse.nixos.org/t/a-faster-dockertools-buildimage-prototype/16922
+# - https://nixery.dev/
+
+# Approximate (compressed) sizes, at the time of writing, are:
+#
+# .#llamaPackages.docker: 125M;
+# .#llamaPackagesCuda.docker: 537M;
+# .#legacyPackages.aarch64-linux.llamaPackagesXavier.docker: 415M.
+
+dockerTools.buildLayeredImage {
+  name = llama-cpp.pname;
+  tag = "latest";
+
+  contents =
+    [ llama-cpp ]
+    ++ lib.optionals interactive [
+      coreutils
+      dockerTools.binSh
+      dockerTools.caCertificates
+    ];
+}
diff --git a/.devops/nix/nixpkgs-instances.nix b/.devops/nix/nixpkgs-instances.nix
index 6e9872b28..4a2f81c4b 100644
--- a/.devops/nix/nixpkgs-instances.nix
+++ b/.devops/nix/nixpkgs-instances.nix
@@ -7,6 +7,18 @@
     { system, ... }:
     {
       _module.args = {
+        # Note: bringing up https://zimbatm.com/notes/1000-instances-of-nixpkgs
+        # again, the below creates several nixpkgs instances which the
+        # flake-centric CLI will be forced to evaluate e.g. on `nix flake show`.
+        #
+        # This is currently "slow" and "expensive", on a certain scale.
+        # This also isn't "right" in that this hinders dependency injection at
+        # the level of flake inputs. This might get removed in the foreseeable
+        # future.
+        #
+        # Note that you can use these expressions without Nix
+        # (`pkgs.callPackage ./devops/nix/scope.nix { }` is the entry point).
+
         pkgsCuda = import inputs.nixpkgs {
           inherit system;
           # Ensure dependencies use CUDA consistently (e.g. that openmpi, ucc,
diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix
index 43bdbd755..2c0ae4e2a 100644
--- a/.devops/nix/package.nix
+++ b/.devops/nix/package.nix
@@ -1,31 +1,43 @@
 {
   lib,
+  glibc,
   config,
   stdenv,
   mkShell,
+  runCommand,
   cmake,
   ninja,
   pkg-config,
   git,
   python3,
   mpi,
-  openblas, # TODO: Use the generic `blas` so users could switch between alternative implementations
+  blas,
   cudaPackages,
   darwin,
   rocmPackages,
+  vulkan-headers,
+  vulkan-loader,
   clblast,
   useBlas ? builtins.all (x: !x) [
     useCuda
     useMetalKit
     useOpenCL
     useRocm
-  ],
+    useVulkan
+  ] && blas.meta.available,
   useCuda ? config.cudaSupport,
   useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin && !useOpenCL,
   useMpi ? false, # Increases the runtime closure size by ~700M
   useOpenCL ? false,
   useRocm ? config.rocmSupport,
+  useVulkan ? false,
   llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake
+
+  # It's necessary to consistently use backendStdenv when building with CUDA support,
+  # otherwise we get libstdc++ errors downstream.
+  effectiveStdenv ? if useCuda then cudaPackages.backendStdenv else stdenv,
+  enableStatic ? effectiveStdenv.hostPlatform.isStatic,
+  precompileMetalShaders ? false
 }@inputs:
 
 let
@@ -37,10 +49,7 @@ let
     versionOlder
     ;
 
-  # It's necessary to consistently use backendStdenv when building with CUDA support,
-  # otherwise we get libstdc++ errors downstream.
   stdenv = throw "Use effectiveStdenv instead";
-  effectiveStdenv = if useCuda then cudaPackages.backendStdenv else inputs.stdenv;
 
   suffices =
     lib.optionals useBlas [ "BLAS" ]
@@ -48,7 +57,8 @@ let
     ++ lib.optionals useMetalKit [ "MetalKit" ]
     ++ lib.optionals useMpi [ "MPI" ]
     ++ lib.optionals useOpenCL [ "OpenCL" ]
-    ++ lib.optionals useRocm [ "ROCm" ];
+    ++ lib.optionals useRocm [ "ROCm" ]
+    ++ lib.optionals useVulkan [ "Vulkan" ];
 
   pnameSuffix =
     strings.optionalString (suffices != [ ])
@@ -57,10 +67,15 @@ let
     strings.optionalString (suffices != [ ])
       ", accelerated with ${strings.concatStringsSep ", " suffices}";
 
+  executableSuffix = effectiveStdenv.hostPlatform.extensions.executable;
+
   # TODO: package the Python in this repository in a Nix-like way.
   # It'd be nice to migrate to buildPythonPackage, as well as ensure this repo
   # is PEP 517-compatible, and ensure the correct .dist-info is generated.
   # https://peps.python.org/pep-0517/
+  #
+  # TODO: Package up each Python script or service appropriately, by making
+  # them into "entrypoints"
   llama-python = python3.withPackages (
     ps: [
       ps.numpy
@@ -73,11 +88,17 @@ let
     ps: [
       ps.numpy
       ps.sentencepiece
+      ps.tiktoken
       ps.torchWithoutCuda
       ps.transformers
     ]
   );
 
+  xcrunHost = runCommand "xcrunHost" {} ''
+    mkdir -p $out/bin
+    ln -s /usr/bin/xcrun $out/bin
+  '';
+
   # apple_sdk is supposed to choose sane defaults, no need to handle isAarch64
   # separately
   darwinBuildInputs =
@@ -107,6 +128,11 @@ let
     hipblas
     rocblas
   ];
+
+  vulkanBuildInputs = [
+    vulkan-headers
+    vulkan-loader
+  ];
 in
 
 effectiveStdenv.mkDerivation (
@@ -114,27 +140,40 @@ effectiveStdenv.mkDerivation (
     pname = "llama-cpp${pnameSuffix}";
     version = llamaVersion;
 
+    # Note: none of the files discarded here are visible in the sandbox or
+    # affect the output hash. This also means they can be modified without
+    # triggering a rebuild.
     src = lib.cleanSourceWith {
       filter =
         name: type:
-        !(builtins.any (_: _) [
+        let
+          noneOf = builtins.all (x: !x);
+          baseName = baseNameOf name;
+        in
+        noneOf [
           (lib.hasSuffix ".nix" name) # Ignore *.nix files when computing outPaths
-          (name == "README.md") # Ignore *.md changes whe computing outPaths
-          (lib.hasPrefix "." name) # Skip hidden files and directories
-        ]);
+          (lib.hasSuffix ".md" name) # Ignore *.md changes whe computing outPaths
+          (lib.hasPrefix "." baseName) # Skip hidden files and directories
+          (baseName == "flake.lock")
+        ];
       src = lib.cleanSource ../../.;
     };
 
     postPatch = ''
       substituteInPlace ./ggml-metal.m \
         --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";"
-
-      # TODO: Package up each Python script or service appropriately.
-      # If we were to migrate to buildPythonPackage and prepare the `pyproject.toml`,
-      # we could make those *.py into setuptools' entrypoints
-      substituteInPlace ./*.py --replace "/usr/bin/env python" "${llama-python}/bin/python"
+      substituteInPlace ./ggml-metal.m \
+        --replace '[bundle pathForResource:@"default" ofType:@"metallib"];' "@\"$out/bin/default.metallib\";"
     '';
 
+    # With PR#6015 https://github.com/ggerganov/llama.cpp/pull/6015,
+    # `default.metallib` may be compiled with Metal compiler from XCode
+    # and we need to escape sandbox on MacOS to access Metal compiler.
+    # `xcrun` is used find the path of the Metal compiler, which is varible
+    # and not on $PATH
+    # see https://github.com/ggerganov/llama.cpp/pull/6118 for discussion
+    __noChroot = effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders;
+
     nativeBuildInputs =
       [
         cmake
@@ -148,6 +187,11 @@ effectiveStdenv.mkDerivation (
         # TODO: Replace with autoAddDriverRunpath
         # once https://github.com/NixOS/nixpkgs/pull/275241 has been merged
         cudaPackages.autoAddOpenGLRunpathHook
+      ]
+      ++ optionals (effectiveStdenv.hostPlatform.isGnu && enableStatic) [
+        glibc.static
+      ] ++ optionals (effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders) [
+        xcrunHost
       ];
 
     buildInputs =
@@ -155,20 +199,24 @@ effectiveStdenv.mkDerivation (
       ++ optionals useCuda cudaBuildInputs
       ++ optionals useMpi [ mpi ]
       ++ optionals useOpenCL [ clblast ]
-      ++ optionals useRocm rocmBuildInputs;
+      ++ optionals useRocm rocmBuildInputs
+      ++ optionals useBlas [ blas ]
+      ++ optionals useVulkan vulkanBuildInputs;
 
     cmakeFlags =
       [
-        (cmakeBool "LLAMA_NATIVE" true)
+        (cmakeBool "LLAMA_NATIVE" false)
         (cmakeBool "LLAMA_BUILD_SERVER" true)
-        (cmakeBool "BUILD_SHARED_LIBS" true)
+        (cmakeBool "BUILD_SHARED_LIBS" (!enableStatic))
         (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true)
         (cmakeBool "LLAMA_BLAS" useBlas)
         (cmakeBool "LLAMA_CLBLAST" useOpenCL)
-        (cmakeBool "LLAMA_CUBLAS" useCuda)
+        (cmakeBool "LLAMA_CUDA" useCuda)
         (cmakeBool "LLAMA_HIPBLAS" useRocm)
         (cmakeBool "LLAMA_METAL" useMetalKit)
         (cmakeBool "LLAMA_MPI" useMpi)
+        (cmakeBool "LLAMA_VULKAN" useVulkan)
+        (cmakeBool "LLAMA_STATIC" enableStatic)
       ]
       ++ optionals useCuda [
         (
@@ -188,14 +236,16 @@ effectiveStdenv.mkDerivation (
         # Should likely use `rocmPackages.clr.gpuTargets`.
         "-DAMDGPU_TARGETS=gfx803;gfx900;gfx906:xnack-;gfx908:xnack-;gfx90a:xnack+;gfx90a:xnack-;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102"
       ]
-      ++ optionals useMetalKit [ (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1") ]
-      ++ optionals useBlas [ (lib.cmakeFeature "LLAMA_BLAS_VENDOR" "OpenBLAS") ];
+      ++ optionals useMetalKit [
+        (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1")
+        (cmakeBool "LLAMA_METAL_EMBED_LIBRARY" (!precompileMetalShaders))
+      ];
 
     # TODO(SomeoneSerge): It's better to add proper install targets at the CMake level,
     # if they haven't been added yet.
     postInstall = ''
-      mv $out/bin/main $out/bin/llama
-      mv $out/bin/server $out/bin/llama-server
+      mv $out/bin/main${executableSuffix} $out/bin/llama${executableSuffix}
+      mv $out/bin/server${executableSuffix} $out/bin/llama-server${executableSuffix}
       mkdir -p $out/include
       cp $src/llama.h $out/include/
     '';
@@ -209,6 +259,7 @@ effectiveStdenv.mkDerivation (
         useMpi
         useOpenCL
         useRocm
+        useVulkan
         ;
 
       shell = mkShell {
@@ -216,6 +267,9 @@ effectiveStdenv.mkDerivation (
         description = "contains numpy and sentencepiece";
         buildInputs = [ llama-python ];
         inputsFrom = [ finalAttrs.finalPackage ];
+        shellHook = ''
+          addToSearchPath "LD_LIBRARY_PATH" "${lib.getLib effectiveStdenv.cc.cc}/lib"
+        '';
       };
 
       shell-extra = mkShell {
diff --git a/.devops/nix/scope.nix b/.devops/nix/scope.nix
index 7932ac1e8..78530c9e8 100644
--- a/.devops/nix/scope.nix
+++ b/.devops/nix/scope.nix
@@ -4,9 +4,16 @@
   llamaVersion ? "0.0.0",
 }:
 
+# We're using `makeScope` instead of just writing out an attrset
+# because it allows users to apply overlays later using `overrideScope'`.
+# Cf. https://noogle.dev/f/lib/makeScope
+
 lib.makeScope newScope (
   self: {
     inherit llamaVersion;
     llama-cpp = self.callPackage ./package.nix { };
+    docker = self.callPackage ./docker.nix { };
+    docker-min = self.callPackage ./docker.nix { interactive = false; };
+    sif = self.callPackage ./sif.nix { };
   }
 )
diff --git a/.devops/nix/sif.nix b/.devops/nix/sif.nix
new file mode 100644
index 000000000..7a5e1dd0f
--- /dev/null
+++ b/.devops/nix/sif.nix
@@ -0,0 +1,27 @@
+{
+  lib,
+  singularity-tools,
+  llama-cpp,
+  bashInteractive,
+  interactive ? false,
+}:
+
+let
+  optionalInt = cond: x: if cond then x else 0;
+in
+singularity-tools.buildImage rec {
+  inherit (llama-cpp) name;
+  contents = [ llama-cpp ] ++ lib.optionals interactive [ bashInteractive ];
+
+  # These are excessive (but safe) for most variants. Building singularity
+  # images requires superuser privileges, so we build them inside a VM in a
+  # writable image of pre-determined size.
+  #
+  # ROCm is currently affected by https://github.com/NixOS/nixpkgs/issues/276846
+  #
+  # Expected image sizes:
+  # - cpu/blas: 150M,
+  # - cuda, all gencodes: 560M,
+  diskSize = 4096 + optionalInt llama-cpp.useRocm 16384;
+  memSize = diskSize;
+}
diff --git a/.devops/server-cuda.Dockerfile b/.devops/server-cuda.Dockerfile
new file mode 100644
index 000000000..59a52ba21
--- /dev/null
+++ b/.devops/server-cuda.Dockerfile
@@ -0,0 +1,37 @@
+ARG UBUNTU_VERSION=22.04
+# This needs to generally match the container host's environment.
+ARG CUDA_VERSION=11.7.1
+# Target the CUDA build image
+ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
+# Target the CUDA runtime image
+ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
+
+FROM ${BASE_CUDA_DEV_CONTAINER} as build
+
+# Unless otherwise specified, we make a fat build.
+ARG CUDA_DOCKER_ARCH=all
+
+RUN apt-get update && \
+    apt-get install -y build-essential git libcurl4-openssl-dev
+
+WORKDIR /app
+
+COPY . .
+
+# Set nvcc architecture
+ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
+# Enable CUDA
+ENV LLAMA_CUDA=1
+# Enable cURL
+ENV LLAMA_CURL=1
+
+RUN make
+
+FROM ${BASE_CUDA_RUN_CONTAINER} as runtime
+
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev
+
+COPY --from=build /app/server /server
+
+ENTRYPOINT [ "/server" ]
diff --git a/.devops/server-intel.Dockerfile b/.devops/server-intel.Dockerfile
new file mode 100644
index 000000000..304487335
--- /dev/null
+++ b/.devops/server-intel.Dockerfile
@@ -0,0 +1,31 @@
+ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04
+
+FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
+
+ARG LLAMA_SYCL_F16=OFF
+RUN apt-get update && \
+    apt-get install -y git libcurl4-openssl-dev
+
+WORKDIR /app
+
+COPY . .
+
+RUN mkdir build && \
+    cd build && \
+    if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
+        echo "LLAMA_SYCL_F16 is set" && \
+        export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \
+    fi && \
+    cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
+    cmake --build . --config Release --target server
+
+FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime
+
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev
+
+COPY --from=build /app/build/bin/server /server
+
+ENV LC_ALL=C.utf8
+
+ENTRYPOINT [ "/server" ]
diff --git a/.devops/server-rocm.Dockerfile b/.devops/server-rocm.Dockerfile
new file mode 100644
index 000000000..c02a31dd8
--- /dev/null
+++ b/.devops/server-rocm.Dockerfile
@@ -0,0 +1,50 @@
+ARG UBUNTU_VERSION=22.04
+
+# This needs to generally match the container host's environment.
+ARG ROCM_VERSION=5.6
+
+# Target the CUDA build image
+ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
+
+FROM ${BASE_ROCM_DEV_CONTAINER} as build
+
+# Unless otherwise specified, we make a fat build.
+# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
+# This is mostly tied to rocBLAS supported archs.
+ARG ROCM_DOCKER_ARCH=\
+    gfx803 \
+    gfx900 \
+    gfx906 \
+    gfx908 \
+    gfx90a \
+    gfx1010 \
+    gfx1030 \
+    gfx1100 \
+    gfx1101 \
+    gfx1102
+
+COPY requirements.txt   requirements.txt
+COPY requirements       requirements
+
+RUN pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.txt
+
+WORKDIR /app
+
+COPY . .
+
+# Set nvcc architecture
+ENV GPU_TARGETS=${ROCM_DOCKER_ARCH}
+# Enable ROCm
+ENV LLAMA_HIPBLAS=1
+ENV CC=/opt/rocm/llvm/bin/clang
+ENV CXX=/opt/rocm/llvm/bin/clang++
+
+# Enable cURL
+ENV LLAMA_CURL=1
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev
+
+RUN make
+
+ENTRYPOINT [ "/app/server" ]
diff --git a/.devops/server-vulkan.Dockerfile b/.devops/server-vulkan.Dockerfile
new file mode 100644
index 000000000..7e5a5283b
--- /dev/null
+++ b/.devops/server-vulkan.Dockerfile
@@ -0,0 +1,33 @@
+ARG UBUNTU_VERSION=jammy
+
+FROM ubuntu:$UBUNTU_VERSION as build
+
+# Install build tools
+RUN apt update && apt install -y git build-essential cmake wget
+
+# Install Vulkan SDK
+RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
+    wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
+    apt update -y && \
+    apt-get install -y vulkan-sdk
+
+# Install cURL
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev
+
+# Build it
+WORKDIR /app
+COPY . .
+RUN mkdir build && \
+    cd build && \
+    cmake .. -DLLAMA_VULKAN=1 -DLLAMA_CURL=1 && \
+    cmake --build . --config Release --target server
+
+# Clean up
+WORKDIR /
+RUN cp /app/build/bin/server /server && \
+    rm -rf /app
+
+ENV LC_ALL=C.utf8
+
+ENTRYPOINT [ "/server" ]
diff --git a/.devops/server.Dockerfile b/.devops/server.Dockerfile
new file mode 100644
index 000000000..be964e0e8
--- /dev/null
+++ b/.devops/server.Dockerfile
@@ -0,0 +1,25 @@
+ARG UBUNTU_VERSION=22.04
+
+FROM ubuntu:$UBUNTU_VERSION as build
+
+RUN apt-get update && \
+    apt-get install -y build-essential git libcurl4-openssl-dev
+
+WORKDIR /app
+
+COPY . .
+
+ENV LLAMA_CURL=1
+
+RUN make
+
+FROM ubuntu:$UBUNTU_VERSION as runtime
+
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev
+
+COPY --from=build /app/server /server
+
+ENV LC_ALL=C.utf8
+
+ENTRYPOINT [ "/server" ]
diff --git a/.ecrc b/.ecrc
index b682057dd..a3351f4e6 100644
--- a/.ecrc
+++ b/.ecrc
@@ -1,4 +1,5 @@
 {
+  "Exclude": ["^\\.gitmodules$"],
   "Disable": {
     "IndentSize": true
   }
diff --git a/.flake8 b/.flake8
index 113ca5fd3..18fba2c15 100644
--- a/.flake8
+++ b/.flake8
@@ -1,2 +1,3 @@
 [flake8]
 max-line-length = 125
+ignore = W503
diff --git a/.github/ISSUE_TEMPLATE/bug.md b/.github/ISSUE_TEMPLATE/bug.md
index ce69e6395..49812832c 100644
--- a/.github/ISSUE_TEMPLATE/bug.md
+++ b/.github/ISSUE_TEMPLATE/bug.md
@@ -7,3 +7,5 @@ assignees: ''
 ---
 
 Please include information about your system, the steps to reproduce the bug, and the version of llama.cpp that you are using. If possible, please provide a minimal code example that reproduces the bug.
+
+If the bug concerns the server, please try to reproduce it first using the [server test scenario framework](https://github.com/ggerganov/llama.cpp/tree/master/examples/server/tests).
diff --git a/.github/workflows/bench.yml b/.github/workflows/bench.yml
new file mode 100644
index 000000000..758796632
--- /dev/null
+++ b/.github/workflows/bench.yml
@@ -0,0 +1,300 @@
+# Benchmark
+name: Benchmark
+
+on:
+  workflow_dispatch:
+    inputs:
+      gpu-series:
+        description: 'Azure GPU series to run with'
+        required: true
+        type: choice
+        options:
+          - Standard_NC4as_T4_v3
+          - Standard_NC24ads_A100_v4
+          - Standard_NC80adis_H100_v5
+      sha:
+        description: 'Commit SHA1 to build'
+        required: false
+        type: string
+      duration:
+        description: 'Duration of the bench'
+        type: string
+        default: 10m
+
+  push:
+    branches:
+      - master
+    paths: ['llama.cpp', 'ggml.c', 'ggml-backend.c', 'ggml-quants.c', '**/*.cu', 'examples/server/*.h*', 'examples/server/*.cpp']
+  pull_request_target:
+    types: [opened, synchronize, reopened]
+    paths: ['llama.cpp', 'ggml.c', 'ggml-backend.c', 'ggml-quants.c', '**/*.cu', 'examples/server/*.h*', 'examples/server/*.cpp']
+  schedule:
+    -  cron: '04 2 * * *'
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}-${{ github.event.inputs.sha }}
+  cancel-in-progress: true
+
+jobs:
+  bench-server-baseline:
+    runs-on: Standard_NC4as_T4_v3
+    env:
+      RUNNER_LABEL: Standard_NC4as_T4_v3 # FIXME Do not find a way to not duplicate it
+      N_USERS: 8
+      DURATION: 10m
+
+    strategy:
+      matrix:
+        model: [phi-2]
+        ftype: [q4_0, q8_0, f16]
+        include:
+          - model: phi-2
+            ftype: q4_0
+            pr_comment_enabled: "true"
+
+    if: ${{ github.event.inputs.gpu-series == 'Standard_NC4as_T4_v3' || github.event.schedule || github.event.pull_request || github.head_ref == 'master' || github.ref_name == 'master' || github.event.push.ref == 'refs/heads/master' }}
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: Install python env
+        id: pipenv
+        run: |
+          cd examples/server/bench
+          python3 -m venv venv
+          source venv/bin/activate
+          pip install -r requirements.txt
+
+      - name: Prometheus
+        id: install_prometheus
+        run: |
+          wget --quiet https://github.com/prometheus/prometheus/releases/download/v2.51.0/prometheus-2.51.0.linux-amd64.tar.gz
+          tar xzf prometheus*.tar.gz --strip-components=1
+          ./prometheus --config.file=examples/server/bench/prometheus.yml &
+          while ! nc -z localhost 9090; do
+            sleep 0.1
+          done
+
+      - name: Set up Go
+        uses: actions/setup-go@v5
+        with:
+          go-version: '1.21'
+
+      - name: Install k6 and xk6-sse
+        id: k6_installation
+        run: |
+          cd examples/server/bench
+          go install go.k6.io/xk6/cmd/xk6@latest
+          xk6 build master \
+              --with github.com/phymbert/xk6-sse
+
+      - name: Build
+        id: cmake_build
+        run: |
+          set -eux
+          mkdir build
+          cd build
+          cmake .. \
+              -DLLAMA_NATIVE=OFF \
+              -DLLAMA_BUILD_SERVER=ON \
+              -DLLAMA_CURL=ON \
+              -DLLAMA_CUBLAS=ON \
+              -DCUDAToolkit_ROOT=/usr/local/cuda \
+              -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc \
+              -DCMAKE_CUDA_ARCHITECTURES=75 \
+              -DLLAMA_FATAL_WARNINGS=OFF \
+              -DLLAMA_ALL_WARNINGS=OFF \
+              -DCMAKE_BUILD_TYPE=Release;
+          cmake --build . --config Release -j $(nproc) --target server
+
+      - name: Download the dataset
+        id: download_dataset
+        run: |
+          cd examples/server/bench
+          wget --quiet https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+
+      - name: Server bench
+        id: server_bench
+        run: |
+          set -eux
+
+          cd examples/server/bench
+          source venv/bin/activate
+          python bench.py \
+              --runner-label ${{ env.RUNNER_LABEL }} \
+              --name ${{ github.job }} \
+              --branch ${{ github.head_ref || github.ref_name }} \
+              --commit ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha }} \
+              --scenario script.js \
+              --duration ${{ github.event.inputs.duration || env.DURATION }} \
+              --hf-repo ggml-org/models	 \
+              --hf-file ${{ matrix.model }}/ggml-model-${{ matrix.ftype }}.gguf \
+              --model-path-prefix /models \
+              --parallel ${{ env.N_USERS }} \
+              -ngl 33 \
+              --batch-size 2048 \
+              --ubatch-size	256 \
+              --ctx-size 16384 \
+              --n-prompts 1000 \
+              --max-prompt-tokens 1024 \
+              --max-tokens 2048
+
+          cat results.github.env >> $GITHUB_ENV
+
+          # Remove dataset as we do not want it in the artefact
+          rm ShareGPT_V3_unfiltered_cleaned_split.json
+
+      - uses: actions/upload-artifact@v4
+        with:
+          name: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }}
+          compression-level: 9
+          path: |
+            examples/server/bench/*.jpg
+            examples/server/bench/*.json
+            examples/server/bench/*.log
+
+      - name: Commit status
+        uses: Sibz/github-status-action@v1
+        with:
+          authToken: ${{secrets.GITHUB_TOKEN}}
+          sha: ${{ inputs.sha || github.event.pull_request.head.sha || github.sha }}
+          context: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }}
+          description: |
+            ${{ env.BENCH_RESULTS }}
+          state: 'success'
+
+      - name: Upload benchmark images
+        uses: devicons/public-upload-to-imgur@v2.2.2
+        continue-on-error: true # Important as it looks unstable: 503
+        id: imgur_step
+        with:
+          client_id: ${{secrets.IMGUR_CLIENT_ID}}
+          path: |
+            examples/server/bench/prompt_tokens_seconds.jpg
+            examples/server/bench/predicted_tokens_seconds.jpg
+            examples/server/bench/kv_cache_usage_ratio.jpg
+            examples/server/bench/requests_processing.jpg
+
+      - name: Extract mermaid
+        id: set_mermaid
+        run: |
+          set -eux
+
+          cd examples/server/bench
+          PROMPT_TOKENS_SECONDS=$(cat prompt_tokens_seconds.mermaid)
+          echo "PROMPT_TOKENS_SECONDS<<EOF" >> $GITHUB_ENV
+          echo "$PROMPT_TOKENS_SECONDS" >> $GITHUB_ENV
+          echo "EOF" >> $GITHUB_ENV
+
+          PREDICTED_TOKENS_SECONDS=$(cat predicted_tokens_seconds.mermaid)
+          echo "PREDICTED_TOKENS_SECONDS<<EOF" >> $GITHUB_ENV
+          echo "$PREDICTED_TOKENS_SECONDS" >> $GITHUB_ENV
+          echo "EOF" >> $GITHUB_ENV
+
+          KV_CACHE_USAGE_RATIO=$(cat kv_cache_usage_ratio.mermaid)
+          echo "KV_CACHE_USAGE_RATIO<<EOF" >> $GITHUB_ENV
+          echo "$KV_CACHE_USAGE_RATIO" >> $GITHUB_ENV
+          echo "EOF" >> $GITHUB_ENV
+
+          REQUESTS_PROCESSING=$(cat requests_processing.mermaid)
+          echo "REQUESTS_PROCESSING<<EOF" >> $GITHUB_ENV
+          echo "$REQUESTS_PROCESSING" >> $GITHUB_ENV
+          echo "EOF" >> $GITHUB_ENV
+
+      - name: Extract image url
+        id: extract_image_url
+        continue-on-error: true
+        run: |
+          set -eux
+
+          echo "IMAGE_O=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[0] }}" >> $GITHUB_ENV
+          echo "IMAGE_1=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[1] }}" >> $GITHUB_ENV
+          echo "IMAGE_2=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[2] }}" >> $GITHUB_ENV
+          echo "IMAGE_3=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[3] }}" >> $GITHUB_ENV
+
+      - name: Comment PR
+        uses: mshick/add-pr-comment@v2
+        id: comment_pr
+        if: ${{ github.event.pull_request != '' && matrix.pr_comment_enabled == 'true' }}
+        with:
+          message-id: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }}
+          message: |
+            <p align="center">
+
+            📈 **llama.cpp server** for _${{ github.job }}_ on _${{ env.RUNNER_LABEL }}_ for `${{ matrix.model }}`-`${{ matrix.ftype }}`: **${{ env.BENCH_ITERATIONS}} iterations** 🚀
+
+            </p>
+
+            <details>
+
+            <summary>Expand details for performance related PR only</summary>
+
+            - Concurrent users: ${{ env.N_USERS }}, duration: ${{ github.event.inputs.duration || env.DURATION }}
+            - HTTP request          : avg=${{ env.HTTP_REQ_DURATION_AVG }}ms        p(95)=${{ env.HTTP_REQ_DURATION_P_95_ }}ms fails=${{ env.HTTP_REQ_FAILED_PASSES }}, finish reason: stop=${{ env.LLAMACPP_COMPLETIONS_STOP_RATE_PASSES }} truncated=${{ env.LLAMACPP_COMPLETIONS_TRUNCATED_RATE_PASSES }}
+            - Prompt processing (pp): avg=${{ env.LLAMACPP_PROMPT_PROCESSING_SECOND_AVG }}tk/s p(95)=${{ env.LLAMACPP_PROMPT_PROCESSING_SECOND_P_95_ }}tk/s
+            - Token generation  (tg): avg=${{ env.LLAMACPP_TOKENS_SECOND_AVG }}tk/s p(95)=${{ env.LLAMACPP_TOKENS_SECOND_P_95_ }}tk/s
+            - ${{ env.BENCH_GRAPH_XLABEL }}
+
+
+            <p align="center">
+
+            <img width="100%" height="100%" src="${{ env.IMAGE_O }}" alt="prompt_tokens_seconds" />
+
+            <details>
+
+            <summary>More</summary>
+
+            ```mermaid
+            ${{ env.PROMPT_TOKENS_SECONDS }}
+            ```
+
+            </details>
+
+            <img width="100%" height="100%" src="${{ env.IMAGE_1 }}" alt="predicted_tokens_seconds"/>
+
+            <details>
+                <summary>More</summary>
+
+            ```mermaid
+            ${{ env.PREDICTED_TOKENS_SECONDS }}
+            ```
+
+            </details>
+
+            </p>
+
+            <details>
+
+            <summary>Details</summary>
+
+            <p align="center">
+
+            <img width="100%" height="100%" src="${{ env.IMAGE_2 }}" alt="kv_cache_usage_ratio" />
+
+            <details>
+                <summary>More</summary>
+
+            ```mermaid
+            ${{ env.KV_CACHE_USAGE_RATIO }}
+            ```
+
+            </details>
+
+            <img width="100%" height="100%" src="${{ env.IMAGE_3 }}" alt="requests_processing"/>
+
+            <details>
+                <summary>More</summary>
+
+            ```mermaid
+            ${{ env.REQUESTS_PROCESSING }}
+            ```
+
+            </details>
+
+            </p>
+            </details>
+            </details>
diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 0a28a1111..ff7238aba 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -15,19 +15,138 @@ on:
     types: [opened, synchronize, reopened]
     paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m']
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
 env:
   BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
   GGML_NLOOP: 3
   GGML_N_THREADS: 1
 
 jobs:
-  ubuntu-focal-make:
-    runs-on: ubuntu-20.04
+  macOS-latest-cmake-arm64:
+    runs-on: macos-14
 
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
+
+      - name: Dependencies
+        id: depends
+        continue-on-error: true
+        run: |
+          brew update
+
+      - name: Build
+        id: cmake_build
+        run: |
+          sysctl -a
+          mkdir build
+          cd build
+          cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL_EMBED_LIBRARY=ON -DLLAMA_CURL=ON ..
+          cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
+
+      - name: Test
+        id: cmake_test
+        run: |
+          cd build
+          ctest -L main --verbose --timeout 900
+
+      - name: Determine tag name
+        id: tag
+        shell: bash
+        run: |
+          BUILD_NUMBER="$(git rev-list --count HEAD)"
+          SHORT_HASH="$(git rev-parse --short=7 HEAD)"
+          if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then
+            echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT
+          else
+            SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-')
+            echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          cp LICENSE ./build/bin/
+          zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip ./build/bin/*
+
+      - name: Upload artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip
+          name: llama-bin-macos-arm64.zip
+
+  macOS-latest-cmake-x64:
+    runs-on: macos-latest
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+
+      - name: Dependencies
+        id: depends
+        continue-on-error: true
+        run: |
+          brew update
+
+      - name: Build
+        id: cmake_build
+        run: |
+          sysctl -a
+          mkdir build
+          cd build
+          cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL_EMBED_LIBRARY=ON -DLLAMA_CURL=ON ..
+          cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
+
+      - name: Test
+        id: cmake_test
+        run: |
+          cd build
+          ctest -L main --verbose --timeout 900
+
+      - name: Determine tag name
+        id: tag
+        shell: bash
+        run: |
+          BUILD_NUMBER="$(git rev-list --count HEAD)"
+          SHORT_HASH="$(git rev-parse --short=7 HEAD)"
+          if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then
+            echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT
+          else
+            SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-')
+            echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          cp LICENSE ./build/bin/
+          zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip ./build/bin/*
+
+      - name: Upload artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip
+          name: llama-bin-macos-x64.zip
+
+  ubuntu-focal-make:
+    runs-on: ubuntu-20.04
+    env:
+      LLAMA_NODE_AVAILABLE: true
+      LLAMA_PYTHON_AVAILABLE: true
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
 
       - name: Dependencies
         id: depends
@@ -35,8 +154,18 @@ jobs:
           sudo apt-get update
           sudo apt-get install build-essential gcc-8
 
+      - uses: actions/setup-node@v4
+        with:
+          node-version: "20"
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
       - name: Build
         id: make_build
+        env:
+            LLAMA_FATAL_WARNINGS: 1
         run: |
           CC=gcc-8 make -j $(nproc)
 
@@ -46,13 +175,35 @@ jobs:
           CC=gcc-8 make tests -j $(nproc)
           make test -j $(nproc)
 
+  ubuntu-focal-make-curl:
+    runs-on: ubuntu-20.04
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt-get update
+          sudo apt-get install build-essential gcc-8 libcurl4-openssl-dev
+
+      - name: Build
+        id: make_build
+        env:
+          LLAMA_FATAL_WARNINGS: 1
+          LLAMA_CURL: 1
+        run: |
+          CC=gcc-8 make -j $(nproc)
+
   ubuntu-latest-cmake:
     runs-on: ubuntu-latest
 
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Dependencies
         id: depends
@@ -65,49 +216,60 @@ jobs:
         run: |
           mkdir build
           cd build
-          cmake ..
+          cmake .. -DLLAMA_FATAL_WARNINGS=ON
           cmake --build . --config Release -j $(nproc)
 
       - name: Test
         id: cmake_test
         run: |
           cd build
-          ctest --verbose --timeout 900
+          ctest -L main --verbose --timeout 900
 
-  ubuntu-latest-cmake-sanitizer:
-    runs-on: ubuntu-latest
-
-    continue-on-error: true
-
-    strategy:
-      matrix:
-        sanitizer: [ADDRESS, THREAD, UNDEFINED]
-        build_type: [Debug, Release]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v3
-
-      - name: Dependencies
-        id: depends
-        run: |
-          sudo apt-get update
-          sudo apt-get install build-essential
-
-      - name: Build
-        id: cmake_build
-        run: |
-          mkdir build
-          cd build
-          cmake .. -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
-          cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
-
-      - name: Test
-        id: cmake_test
+      - name: Test llama2c conversion
+        id: llama2c_test
         run: |
           cd build
-          ctest --verbose --timeout 900
+          echo "Fetch tokenizer"
+          wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/tok512.bin
+          echo "Fetch llama2c model"
+          wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/stories260K.bin
+          ./bin/convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf
+          ./bin/main -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256
+
+#  ubuntu-latest-cmake-sanitizer:
+#    runs-on: ubuntu-latest
+#
+#    continue-on-error: true
+#
+#    strategy:
+#      matrix:
+#        sanitizer: [ADDRESS, THREAD, UNDEFINED]
+#        build_type: [Debug, Release]
+#
+#    steps:
+#      - name: Clone
+#        id: checkout
+#        uses: actions/checkout@v4
+#
+#      - name: Dependencies
+#        id: depends
+#        run: |
+#          sudo apt-get update
+#          sudo apt-get install build-essential
+#
+#      - name: Build
+#        id: cmake_build
+#        run: |
+#          mkdir build
+#          cd build
+#          cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
+#          cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
+#
+#      - name: Test
+#        id: cmake_test
+#        run: |
+#          cd build
+#          ctest -L main --verbose --timeout 900
 
   ubuntu-latest-cmake-mpi:
     runs-on: ubuntu-latest
@@ -121,7 +283,7 @@ jobs:
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Dependencies
         id: depends
@@ -141,7 +303,111 @@ jobs:
         id: cmake_test
         run: |
           cd build
-          ctest --verbose
+          ctest -L main --verbose
+
+  ubuntu-22-cmake-vulkan:
+    runs-on: ubuntu-22.04
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt-get update
+          sudo apt-get install build-essential libvulkan-dev
+
+      - name: Build
+        id: cmake_build
+        run: |
+          mkdir build
+          cd build
+          cmake -DLLAMA_VULKAN=ON ..
+          cmake --build . --config Release -j $(nproc)
+
+  ubuntu-22-cmake-sycl:
+    runs-on: ubuntu-22.04
+
+    continue-on-error: true
+
+    steps:
+      - uses: actions/checkout@v2
+
+      - name: add oneAPI to apt
+        shell: bash
+        run: |
+          cd /tmp
+          wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
+          sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
+          rm GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
+          sudo add-apt-repository "deb https://apt.repos.intel.com/oneapi all main"
+
+      - name: install oneAPI dpcpp compiler
+        shell: bash
+        run: |
+          sudo apt update
+          sudo apt install intel-oneapi-compiler-dpcpp-cpp
+
+      - name: install oneAPI MKL library
+        shell: bash
+        run: |
+          sudo apt install intel-oneapi-mkl-devel
+
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+
+      - name: Build
+        id: cmake_build
+        run: |
+          source /opt/intel/oneapi/setvars.sh
+          mkdir build
+          cd build
+          cmake -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ..
+          cmake --build . --config Release -j $(nproc)
+
+  ubuntu-22-cmake-sycl-fp16:
+    runs-on: ubuntu-22.04
+
+    continue-on-error: true
+
+    steps:
+      - uses: actions/checkout@v2
+
+      - name: add oneAPI to apt
+        shell: bash
+        run: |
+          cd /tmp
+          wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
+          sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
+          rm GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
+          sudo add-apt-repository "deb https://apt.repos.intel.com/oneapi all main"
+
+      - name: install oneAPI dpcpp compiler
+        shell: bash
+        run: |
+          sudo apt update
+          sudo apt install intel-oneapi-compiler-dpcpp-cpp
+
+      - name: install oneAPI MKL library
+        shell: bash
+        run: |
+          sudo apt install intel-oneapi-mkl-devel
+
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+
+      - name: Build
+        id: cmake_build
+        run: |
+          source /opt/intel/oneapi/setvars.sh
+          mkdir build
+          cd build
+          cmake -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON ..
+          cmake --build . --config Release -j $(nproc)
 
   # TODO: build with LLAMA_NO_METAL because test-backend-ops fail on "Apple Paravirtual device" and I don't know
   #       how to debug it.
@@ -152,7 +418,7 @@ jobs:
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Dependencies
         id: depends
@@ -162,6 +428,8 @@ jobs:
 
       - name: Build
         id: make_build
+        env:
+            LLAMA_FATAL_WARNINGS: 1
         run: |
           LLAMA_NO_METAL=1 make -j $(sysctl -n hw.logicalcpu)
 
@@ -181,7 +449,7 @@ jobs:
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Dependencies
         id: depends
@@ -195,14 +463,14 @@ jobs:
           sysctl -a
           mkdir build
           cd build
-          cmake -DLLAMA_METAL=OFF ..
+          cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL=OFF ..
           cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
 
       - name: Test
         id: cmake_test
         run: |
           cd build
-          ctest --verbose --timeout 900
+          ctest -L main --verbose --timeout 900
 
   macOS-latest-cmake-ios:
     runs-on: macos-latest
@@ -225,6 +493,7 @@ jobs:
           mkdir build
           cd build
           cmake -G Xcode .. \
+            -DLLAMA_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
             -DLLAMA_BUILD_SERVER=OFF \
@@ -253,6 +522,7 @@ jobs:
           mkdir build
           cd build
           cmake -G Xcode .. \
+            -DLLAMA_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
             -DLLAMA_BUILD_SERVER=OFF \
@@ -295,7 +565,8 @@ jobs:
       OPENBLAS_VERSION: 0.3.23
       OPENCL_VERSION: 2023.04.17
       CLBLAST_VERSION: 1.6.0
-      SDE_VERSION: 9.21.1-2023-04-24
+      SDE_VERSION: 9.33.0-2024-01-07
+      VULKAN_VERSION: 1.3.261.1
 
     strategy:
       matrix:
@@ -312,14 +583,26 @@ jobs:
             defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CLBLAST=ON -DBUILD_SHARED_LIBS=ON -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/clblast"'
           - build: 'openblas'
             defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_BLAS=ON -DBUILD_SHARED_LIBS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"'
+          - build: 'kompute'
+            defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON'
+          - build: 'vulkan'
+            defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_VULKAN=ON -DBUILD_SHARED_LIBS=ON'
+          - build: 'arm64'
+            defines: '-A ARM64 -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
 
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
 
+      - name: Clone Kompute submodule
+        id: clone_kompute
+        if: ${{ matrix.build == 'kompute' }}
+        run: |
+          git submodule update --init kompute
+
       - name: Download OpenCL SDK
         id: get_opencl
         if: ${{ matrix.build == 'clblast' }}
@@ -354,6 +637,15 @@ jobs:
           $lib =  $(join-path $msvc 'bin\Hostx64\x64\lib.exe')
           & $lib /machine:x64 "/def:${env:RUNNER_TEMP}/openblas/lib/libopenblas.def" "/out:${env:RUNNER_TEMP}/openblas/lib/openblas.lib" /name:openblas.dll
 
+      - name: Install Vulkan SDK
+        id: get_vulkan
+        if: ${{ matrix.build == 'kompute' || matrix.build == 'vulkan' }}
+        run: |
+          curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/VulkanSDK-${env:VULKAN_VERSION}-Installer.exe"
+          & "$env:RUNNER_TEMP\VulkanSDK-Installer.exe" --accept-licenses --default-answer --confirm-command install
+          Add-Content $env:GITHUB_ENV "VULKAN_SDK=C:\VulkanSDK\${env:VULKAN_VERSION}"
+          Add-Content $env:GITHUB_PATH "C:\VulkanSDK\${env:VULKAN_VERSION}\bin"
+
       - name: Build
         id: cmake_build
         run: |
@@ -391,22 +683,23 @@ jobs:
 
       - name: Test
         id: cmake_test
-        if: ${{ matrix.build != 'clblast' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} # not all machines have native AVX-512
+        # not all machines have native AVX-512
+        if: ${{ matrix.build != 'arm64' && matrix.build != 'clblast' && matrix.build != 'kompute' && matrix.build != 'vulkan' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }}
         run: |
           cd build
-          ctest -C Release --verbose --timeout 900
+          ctest -L main -C Release --verbose --timeout 900
 
       - name: Test (Intel SDE)
         id: cmake_test_sde
         if: ${{ matrix.build == 'avx512' && env.HAS_AVX512F == '0' }} # use Intel SDE for AVX-512 emulation
         run: |
-          curl.exe -o $env:RUNNER_TEMP/sde.tar.xz -L "https://downloadmirror.intel.com/777395/sde-external-${env:SDE_VERSION}-win.tar.xz"
+          curl.exe -o $env:RUNNER_TEMP/sde.tar.xz -L "https://downloadmirror.intel.com/813591/sde-external-${env:SDE_VERSION}-win.tar.xz"
           # for some weird reason windows tar doesn't like sde tar.xz
           7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar.xz
           7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar
           $sde = $(join-path $env:RUNNER_TEMP sde-external-${env:SDE_VERSION}-win/sde.exe)
           cd build
-          & $sde -future -- ctest -C Release --verbose --timeout 900
+          & $sde -future -- ctest -L main -C Release --verbose --timeout 900
 
       - name: Determine tag name
         id: tag
@@ -430,23 +723,23 @@ jobs:
 
       - name: Upload artifacts
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
-          path: |
-            llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-x64.zip
+          path: llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-x64.zip
+          name: llama-bin-win-${{ matrix.build }}-x64.zip
 
-  windows-latest-cmake-cublas:
+  windows-latest-cmake-cuda:
     runs-on: windows-latest
 
     strategy:
       matrix:
         cuda: ['12.2.0', '11.7.1']
-        build: ['cublas']
+        build: ['cuda']
 
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
 
@@ -462,7 +755,7 @@ jobs:
         run: |
           mkdir build
           cd build
-          cmake .. -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CUBLAS=ON -DBUILD_SHARED_LIBS=ON
+          cmake .. -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CUDA=ON -DBUILD_SHARED_LIBS=ON
           cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS}
 
       - name: Determine tag name
@@ -486,10 +779,10 @@ jobs:
 
       - name: Upload artifacts
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
-          path: |
-            llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip
+          path: llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip
+          name: llama-bin-win-cu${{ matrix.cuda }}-x64.zip
 
       - name: Copy and pack Cuda runtime
         run: |
@@ -500,26 +793,101 @@ jobs:
 
       - name: Upload Cuda runtime
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
-          path: |
-            cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip
+          path: cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip
+          name: cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip
+
+  windows-latest-cmake-sycl:
+    runs-on: windows-latest
+
+    defaults:
+      run:
+        shell: bash
+
+    env:
+      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/62641e01-1e8d-4ace-91d6-ae03f7f8a71f/w_BaseKit_p_2024.0.0.49563_offline.exe
+      WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Install
+        run:  scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL
+
+      - name: Build
+        id: cmake_build
+        run:  examples/sycl/win-build-sycl.bat
+
+      - name: Determine tag name
+        id: tag
+        shell: bash
+        run: |
+          BUILD_NUMBER="$(git rev-list --count HEAD)"
+          SHORT_HASH="$(git rev-parse --short=7 HEAD)"
+          if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then
+            echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT
+          else
+            SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-')
+            echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          7z a llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip ./build/bin/*
+
+      - name: Upload artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip
+          name: llama-bin-win-sycl-x64.zip
 
   ios-xcode-build:
     runs-on: macos-latest
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Build Xcode project
         run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' build
 
+  android-build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Clone
+        uses: actions/checkout@v4
+
+      - name: Set up JDK
+        uses: actions/setup-java@v3
+        with:
+          java-version: 17
+          distribution: zulu
+
+      - name: Setup Android SDK
+        uses: android-actions/setup-android@v3
+        with:
+          log-accepted-android-sdk-licenses: false
+
+      - name: Build
+        run: |
+          cd examples/llama.android
+
+          ./gradlew build --no-daemon
+
 #  freeBSD-latest:
 #    runs-on: macos-12
 #    steps:
 #    - name: Clone
-#      uses: actions/checkout@v3
+#      uses: actions/checkout@v4
 #
 #    - name: Build
 #      uses: cross-platform-actions/action@v0.19.0
@@ -543,12 +911,14 @@ jobs:
       - macOS-latest-make
       - macOS-latest-cmake
       - windows-latest-cmake
-      - windows-latest-cmake-cublas
+      - windows-latest-cmake-cuda
+      - macOS-latest-cmake-arm64
+      - macOS-latest-cmake-x64
 
     steps:
       - name: Clone
         id: checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
 
@@ -567,7 +937,7 @@ jobs:
 
       - name: Download artifacts
         id: download-artifact
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
 
       - name: Create release
         id: create_release
@@ -608,7 +978,7 @@ jobs:
 #
 #    steps:
 #      - name: Clone
-#        uses: actions/checkout@v3
+#        uses: actions/checkout@v4
 #
 #      - name: Dependencies
 #        run: |
@@ -632,7 +1002,7 @@ jobs:
 #
 #    steps:
 #      - name: Clone
-#        uses: actions/checkout@v3
+#        uses: actions/checkout@v4
 #
 #      - name: Dependencies
 #        run: |
@@ -656,7 +1026,7 @@ jobs:
 #
 #    steps:
 #      - name: Clone
-#        uses: actions/checkout@v3
+#        uses: actions/checkout@v4
 #
 #      - name: Dependencies
 #        run: |
@@ -686,7 +1056,7 @@ jobs:
 #
 #    steps:
 #      - name: Clone
-#        uses: actions/checkout@v3
+#        uses: actions/checkout@v4
 #
 #      - name: Add msbuild to PATH
 #        uses: microsoft/setup-msbuild@v1
@@ -702,7 +1072,7 @@ jobs:
 #          msbuild ALL_BUILD.vcxproj -t:build -p:configuration=${{ matrix.build }} -p:platform=${{ matrix.arch }}
 #
 #      - name: Upload binaries
-#        uses: actions/upload-artifact@v1
+#        uses: actions/upload-artifact@v4
 #        with:
 #          name: llama-bin-${{ matrix.arch }}
 #          path: build/bin/${{ matrix.build }}
@@ -725,7 +1095,7 @@ jobs:
 #
 #    steps:
 #      - name: Clone
-#        uses: actions/checkout@v3
+#        uses: actions/checkout@v4
 #
 #      - name: Add msbuild to PATH
 #        uses: microsoft/setup-msbuild@v1
@@ -757,7 +1127,7 @@ jobs:
 #
 #      - name: Upload binaries
 #        if: matrix.blas == 'ON'
-#        uses: actions/upload-artifact@v1
+#        uses: actions/upload-artifact@v4
 #        with:
 #          name: llama-blas-bin-${{ matrix.arch }}
 #          path: build/bin/${{ matrix.build }}
@@ -771,7 +1141,7 @@ jobs:
 #
 #    steps:
 #      - name: Clone
-#        uses: actions/checkout@v3
+#        uses: actions/checkout@v4
 #
 #      - name: Dependencies
 #        run: |
diff --git a/.github/workflows/close-issue.yml b/.github/workflows/close-issue.yml
new file mode 100644
index 000000000..7f21daec0
--- /dev/null
+++ b/.github/workflows/close-issue.yml
@@ -0,0 +1,23 @@
+name: Close inactive issues
+on:
+  schedule:
+    - cron: "42 0 * * *"
+
+jobs:
+  close-issues:
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+      pull-requests: write
+    steps:
+      - uses: actions/stale@v5
+        with:
+          exempt-issue-labels: "refactor,help wanted,good first issue,research"
+          days-before-issue-stale: 30
+          days-before-issue-close: 14
+          stale-issue-label: "stale"
+          close-issue-message: "This issue was closed because it has been inactive for 14 days since being marked as stale."
+          days-before-pr-stale: -1
+          days-before-pr-close: -1
+          operations-per-run: 10000
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
diff --git a/.github/workflows/code-coverage.yml b/.github/workflows/code-coverage.yml
index 392db8a08..f12c558f8 100644
--- a/.github/workflows/code-coverage.yml
+++ b/.github/workflows/code-coverage.yml
@@ -5,12 +5,16 @@ env:
   GGML_NLOOP: 3
   GGML_N_THREADS: 1
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
 jobs:
   run:
     runs-on: ubuntu-20.04
     steps:
       - name: Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Dependencies
         run: |
diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 87904b75e..eefd87878 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -15,6 +15,10 @@ on:
     branches:
       - master
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
 jobs:
   push_to_registry:
     name: Push Docker image to Docker Hub
@@ -28,16 +32,21 @@ jobs:
         config:
           - { tag: "light", dockerfile: ".devops/main.Dockerfile", platforms: "linux/amd64,linux/arm64" }
           - { tag: "full", dockerfile: ".devops/full.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          - { tag: "server", dockerfile: ".devops/server.Dockerfile", platforms: "linux/amd64,linux/arm64" }
           # NOTE(canardletter): The CUDA builds on arm64 are very slow, so I
           #                     have disabled them for now until the reason why
           #                     is understood.
           - { tag: "light-cuda", dockerfile: ".devops/main-cuda.Dockerfile", platforms: "linux/amd64" }
           - { tag: "full-cuda", dockerfile: ".devops/full-cuda.Dockerfile", platforms: "linux/amd64" }
+          - { tag: "server-cuda", dockerfile: ".devops/server-cuda.Dockerfile", platforms: "linux/amd64" }
           - { tag: "light-rocm", dockerfile: ".devops/main-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
           - { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          - { tag: "server-rocm", dockerfile: ".devops/server-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          - { tag: "light-intel", dockerfile: ".devops/main-intel.Dockerfile", platforms: "linux/amd64" }
+          - { tag: "server-intel", dockerfile: ".devops/server-intel.Dockerfile", platforms: "linux/amd64" }
     steps:
       - name: Check out the repo
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v2
diff --git a/.github/workflows/editorconfig.yml b/.github/workflows/editorconfig.yml
index b4e535acf..ae86e9927 100644
--- a/.github/workflows/editorconfig.yml
+++ b/.github/workflows/editorconfig.yml
@@ -1,6 +1,12 @@
 name: EditorConfig Checker
 
 on:
+  workflow_dispatch: # allows manual triggering
+    inputs:
+      create_release:
+        description: 'Create new release'
+        required: true
+        type: boolean
   push:
     branches:
       - master
@@ -8,10 +14,14 @@ on:
     branches:
       - master
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
 jobs:
   editorconfig:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - uses: editorconfig-checker/action-editorconfig-checker@main
       - run: editorconfig-checker
diff --git a/.github/workflows/gguf-publish.yml b/.github/workflows/gguf-publish.yml
index 57db17512..3ca4d3058 100644
--- a/.github/workflows/gguf-publish.yml
+++ b/.github/workflows/gguf-publish.yml
@@ -24,9 +24,9 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v5
       with:
         python-version: '3.9.x'
     - name: Install dependencies
diff --git a/.github/workflows/nix-ci-aarch64.yml b/.github/workflows/nix-ci-aarch64.yml
new file mode 100644
index 000000000..4aa4b2379
--- /dev/null
+++ b/.github/workflows/nix-ci-aarch64.yml
@@ -0,0 +1,65 @@
+name: Nix aarch64 builds
+
+on:
+  workflow_dispatch: # allows manual triggering
+  schedule:
+    # Rebuild daily rather than on every push because QEMU is expensive (e.g.
+    # 1.5h instead of minutes with the cold cache).
+    #
+    # randint(0, 59), randint(0, 23)
+    - cron: '26 12 * * *'
+  # But also rebuild if we touched any of the Nix expressions:
+  push:
+    branches:
+      - master
+    paths: ['**/*.nix', 'flake.lock']
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths: ['**/*.nix', 'flake.lock']
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  nix-build-aarch64:
+    runs-on: ubuntu-latest
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+    - name: Install QEMU
+      # Copy-paste from https://github.com/orgs/community/discussions/8305#discussioncomment-5888654
+      run: |
+        sudo apt-get update
+        sudo apt-get install -y qemu-user-static qemu-system-aarch64
+        sudo usermod -a -G kvm $USER
+    - name: Install Nix
+      uses: DeterminateSystems/nix-installer-action@v9
+      with:
+        github-token: ${{ secrets.GITHUB_TOKEN }}
+        extra-conf: |
+          extra-platforms = aarch64-linux
+          extra-system-features = nixos-test kvm
+          extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org
+          extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
+    - uses: DeterminateSystems/magic-nix-cache-action@v2
+      with:
+        upstream-cache: https://${{ matrix.cachixName }}.cachix.org
+    - name: Set-up cachix to push the results to
+      uses: cachix/cachix-action@v13
+      with:
+        authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}'
+        name: llama-cpp
+    - name: Show all output paths
+      run: >
+          nix run github:nix-community/nix-eval-jobs
+          -- --gc-roots-dir gcroot
+          --flake
+          ".#packages.aarch64-linux"
+    - name: Build
+      run: >
+          nix run github:Mic92/nix-fast-build
+          -- --skip-cached --no-nom
+          --systems aarch64-linux
+          --flake
+          ".#checks.aarch64-linux"
diff --git a/.github/workflows/nix-ci.yml b/.github/workflows/nix-ci.yml
index a38c6ead4..8955f38d0 100644
--- a/.github/workflows/nix-ci.yml
+++ b/.github/workflows/nix-ci.yml
@@ -5,10 +5,12 @@ on:
   push:
     branches:
       - master
-    paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', '**/*.sh', '**/*.py', '**/*.nix']
   pull_request:
     types: [opened, synchronize, reopened]
-    paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', '**/*.sh', '**/*.py', '**/*.nix']
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
 
 jobs:
   nix-eval:
@@ -25,8 +27,8 @@ jobs:
       with:
         github-token: ${{ secrets.GITHUB_TOKEN }}
         extra-conf: |
-          extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org
-          extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
+          extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org
+          extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
     - uses: DeterminateSystems/magic-nix-cache-action@v2
       with:
         upstream-cache: https://${{ matrix.cachixName }}.cachix.org
@@ -39,7 +41,6 @@ jobs:
           --flake
           ".#packages.$(nix eval --raw --impure --expr builtins.currentSystem)"
   nix-build:
-    if: ${{ vars.CACHIX_NAME != '' }}
     strategy:
       fail-fast: false
       matrix:
@@ -53,8 +54,8 @@ jobs:
       with:
         github-token: ${{ secrets.GITHUB_TOKEN }}
         extra-conf: |
-          extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org
-          extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
+          extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org
+          extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
     - uses: DeterminateSystems/magic-nix-cache-action@v2
       with:
         upstream-cache: https://${{ matrix.cachixName }}.cachix.org
@@ -62,51 +63,10 @@ jobs:
       uses: cachix/cachix-action@v13
       with:
         authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}'
-        name: ${{ vars.CACHIX_NAME }}
+        name: llama-cpp
     - name: Build
       run: >
           nix run github:Mic92/nix-fast-build
           -- --skip-cached --no-nom
           --flake
           ".#checks.$(nix eval --raw --impure --expr builtins.currentSystem)"
-  nix-build-aarch64:
-    if: ${{ vars.CACHIX_NAME != '' }}
-    runs-on: ubuntu-latest
-    steps:
-    - name: Checkout repository
-      uses: actions/checkout@v4
-    - name: Install QEMU
-      # Copy-paste from https://github.com/orgs/community/discussions/8305#discussioncomment-5888654
-      run: |
-        sudo apt-get install -y qemu-user-static qemu-system-aarch64
-        sudo usermod -a -G kvm $USER
-    - name: Install Nix
-      uses: DeterminateSystems/nix-installer-action@v9
-      with:
-        github-token: ${{ secrets.GITHUB_TOKEN }}
-        extra-conf: |
-          extra-platforms = aarch64-linux
-          extra-system-features = nixos-test kvm
-          extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org
-          extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
-    - uses: DeterminateSystems/magic-nix-cache-action@v2
-      with:
-        upstream-cache: https://${{ matrix.cachixName }}.cachix.org
-    - name: Set-up cachix to push the results to
-      uses: cachix/cachix-action@v13
-      with:
-        authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}'
-        name: ${{ vars.CACHIX_NAME }}
-    - name: Show all output paths
-      run: >
-          nix run github:nix-community/nix-eval-jobs
-          -- --gc-roots-dir gcroot
-          --flake
-          ".#packages.aarch64-linux"
-    - name: Build
-      run: >
-          nix run github:Mic92/nix-fast-build
-          -- --skip-cached --no-nom
-          --systems aarch64-linux
-          --flake
-          ".#checks.aarch64-linux"
diff --git a/.github/workflows/nix-flake-update.yml b/.github/workflows/nix-flake-update.yml
index fa9360841..3a6a96e26 100644
--- a/.github/workflows/nix-flake-update.yml
+++ b/.github/workflows/nix-flake-update.yml
@@ -19,4 +19,4 @@ jobs:
           pr-labels: |
             nix
           pr-reviewers: philiptaron,SomeoneSerge
-          token: ${{ secrets.GITHUB_TOKEN }}
+          token: ${{ secrets.FLAKE_TOKEN }}
diff --git a/.github/workflows/python-check-requirements.yml b/.github/workflows/python-check-requirements.yml
index 92e1108b3..4e0374fc6 100644
--- a/.github/workflows/python-check-requirements.yml
+++ b/.github/workflows/python-check-requirements.yml
@@ -3,27 +3,33 @@ name: Python check requirements.txt
 on:
   push:
     paths:
+      - '.github/workflows/python-check-requirements.yml'
       - 'scripts/check-requirements.sh'
       - 'convert*.py'
       - 'requirements.txt'
       - 'requirements/*.txt'
   pull_request:
     paths:
+      - '.github/workflows/python-check-requirements.yml'
       - 'scripts/check-requirements.sh'
       - 'convert*.py'
       - 'requirements.txt'
       - 'requirements/*.txt'
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
 jobs:
   python-check-requirements:
     runs-on: ubuntu-latest
     name: check-requirements
     steps:
       - name: Check out source repository
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
       - name: Set up Python environment
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: "3.11"
       - name: Run check-requirements.sh script
-        run:  bash scripts/check-requirements.sh nocleanup
+        run:  bash scripts/check-requirements.sh
diff --git a/.github/workflows/python-lint.yml b/.github/workflows/python-lint.yml
index 56d17b66c..f4ae65495 100644
--- a/.github/workflows/python-lint.yml
+++ b/.github/workflows/python-lint.yml
@@ -2,19 +2,23 @@ name: flake8 Lint
 
 on: [push, pull_request]
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
 jobs:
   flake8-lint:
     runs-on: ubuntu-latest
     name: Lint
     steps:
       - name: Check out source repository
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
       - name: Set up Python environment
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: "3.11"
       - name: flake8 Lint
         uses: py-actions/flake8@v2
         with:
-            ignore: "E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704"
+            ignore: "E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503"
             exclude: "examples/*,examples/*/**,*/**/__init__.py"
diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml
new file mode 100644
index 000000000..521cc29ae
--- /dev/null
+++ b/.github/workflows/server.yml
@@ -0,0 +1,178 @@
+# Server build and tests
+name: Server
+
+on:
+  workflow_dispatch: # allows manual triggering
+    inputs:
+      sha:
+        description: 'Commit SHA1 to build'
+        required: false
+        type: string
+      slow_tests:
+        description: 'Run slow tests'
+        required: true
+        type: boolean
+  push:
+    branches:
+      - master
+    paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
+  pull_request_target:
+    types: [opened, synchronize, reopened]
+    paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
+  schedule:
+    -  cron: '2 4 * * *'
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  server:
+    runs-on: ubuntu-latest
+
+    strategy:
+      matrix:
+        # TODO: temporary disabled due to linux kernel issues
+        #sanitizer: [ADDRESS, THREAD, UNDEFINED]
+        sanitizer: [UNDEFINED]
+        build_type: [Debug]
+        include:
+          - build_type: Release
+            sanitizer: ""
+      fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
+
+    container:
+      image: ubuntu:latest
+      ports:
+        - 8888
+      options: --cpus 4
+
+    steps:
+      - name: Dependencies
+        id: depends
+        run: |
+          apt-get update
+          apt-get -y install \
+            build-essential \
+            xxd \
+            git \
+            cmake \
+            python3-pip \
+            curl \
+            wget \
+            language-pack-en \
+            libcurl4-openssl-dev
+
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: Verify server deps
+        id: verify_server_deps
+        run: |
+          git config --global --add safe.directory $(realpath .)
+          cd examples/server
+          git ls-files --others --modified
+          git status
+          ./deps.sh
+          git status
+          not_ignored_files="$(git ls-files --others --modified)"
+          echo "Modified files: ${not_ignored_files}"
+          if [ -n "${not_ignored_files}" ]; then
+            echo "Repository is dirty or server deps are not built as expected"
+            echo "${not_ignored_files}"
+            exit 1
+          fi
+
+      - name: Build
+        id: cmake_build
+        run: |
+          mkdir build
+          cd build
+          cmake .. \
+              -DLLAMA_NATIVE=OFF \
+              -DLLAMA_BUILD_SERVER=ON \
+              -DLLAMA_CURL=ON \
+              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
+              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
+          cmake --build . --config ${{ matrix.build_type }} -j $(nproc) --target server
+
+      - name: Tests dependencies
+        id: test_dependencies
+        run: |
+          pip install -r examples/server/tests/requirements.txt
+
+      - name: Tests
+        id: server_integration_tests
+        if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }}
+        run: |
+          cd examples/server/tests
+          PORT=8888 ./tests.sh
+
+      - name: Slow tests
+        id: server_integration_tests_slow
+        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
+        run: |
+          cd examples/server/tests
+          PORT=8888 ./tests.sh --stop --no-skipped --no-capture --tags slow
+
+
+  server-windows:
+    runs-on: windows-latest
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: libCURL
+        id: get_libcurl
+        env:
+          CURL_VERSION: 8.6.0_6
+        run: |
+          curl.exe -o $env:RUNNER_TEMP/curl.zip -L "https://curl.se/windows/dl-${env:CURL_VERSION}/curl-${env:CURL_VERSION}-win64-mingw.zip"
+          mkdir $env:RUNNER_TEMP/libcurl
+          tar.exe -xvf $env:RUNNER_TEMP/curl.zip --strip-components=1 -C $env:RUNNER_TEMP/libcurl
+
+      - name: Build
+        id: cmake_build
+        run: |
+          mkdir build
+          cd build
+          cmake .. -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include"
+          cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS} --target server
+
+      - name: Python setup
+        id: setup_python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Tests dependencies
+        id: test_dependencies
+        run: |
+          pip install -r examples/server/tests/requirements.txt
+
+      - name: Copy Libcurl
+        id: prepare_libcurl
+        run: |
+          cp $env:RUNNER_TEMP/libcurl/bin/libcurl-x64.dll ./build/bin/Release/libcurl-x64.dll
+
+      - name: Tests
+        id: server_integration_tests
+        if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }}
+        run: |
+          cd examples/server/tests
+          behave.exe --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp
+
+      - name: Slow tests
+        id: server_integration_tests_slow
+        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
+        run: |
+          cd examples/server/tests
+          behave.exe --stop --no-skipped --no-capture --tags slow
diff --git a/.github/workflows/tidy-post.yml b/.github/workflows/tidy-post.yml
deleted file mode 100644
index 03652760c..000000000
--- a/.github/workflows/tidy-post.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-name: clang-tidy review post comments
-
-on:
-  workflow_dispatch:
-    workflows: ["clang-tidy-review"]
-    types:
-      - completed
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-
-    steps:
-      - uses: ZedThree/clang-tidy-review/post@v0.13.0
-        # lgtm_comment_body, max_comments, and annotations need to be set on the posting workflow in a split setup
-        with:
-          # adjust options as necessary
-          lgtm_comment_body: ''
-          annotations: false
-          max_comments: 25
diff --git a/.github/workflows/tidy-review.yml b/.github/workflows/tidy-review.yml
deleted file mode 100644
index a4bc8d976..000000000
--- a/.github/workflows/tidy-review.yml
+++ /dev/null
@@ -1,23 +0,0 @@
-name: clang-tidy-review
-
-on:
-  pull_request:
-    branches:
-      - master
-
-jobs:
-  clang-tidy-review:
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v3
-
-    - uses: ZedThree/clang-tidy-review@v0.13.0
-      id: review
-      with:
-        lgtm_comment_body: ''
-        build_dir: build
-        cmake_command: cmake . -B build -DCMAKE_EXPORT_COMPILE_COMMANDS=on
-        split_workflow: true
-
-    - uses: ZedThree/clang-tidy-review/upload@v0.13.0
diff --git a/.github/workflows/zig-build.yml b/.github/workflows/zig-build.yml
index 68a698ab9..747c35cc0 100644
--- a/.github/workflows/zig-build.yml
+++ b/.github/workflows/zig-build.yml
@@ -6,6 +6,10 @@ on:
     branches:
       - master
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
 jobs:
   build:
     strategy:
@@ -14,7 +18,7 @@ jobs:
         runs-on: [ubuntu-latest, macos-latest, windows-latest]
     runs-on: ${{ matrix.runs-on }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           submodules: recursive
           fetch-depth: 0
diff --git a/.gitignore b/.gitignore
index cf1b692e9..9fb5b80c3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,7 +11,10 @@
 *.gcda
 *.dot
 *.bat
+*.tmp
 *.metallib
+*.etag
+*.lastModified
 .DS_Store
 .build/
 .cache/
@@ -23,11 +26,15 @@
 .clang-tidy
 .vs/
 .vscode/
+.idea/
+
+ggml-metal-embed.metal
 
 lcov-report/
 gcovr-report/
 
-build*/
+build*
+cmake-build-*
 out/
 tmp/
 
@@ -43,12 +50,18 @@ models-mnt
 /embedding
 /gguf
 /gguf-llama-simple
+/gguf-split
+/gritlm
+/imatrix
 /infill
 /libllama.so
 /llama-bench
 /llava-cli
 /lookahead
 /lookup
+/lookup-create
+/lookup-merge
+/lookup-stats
 /main
 /metal
 /passkey
@@ -64,6 +77,7 @@ models-mnt
 /batched-bench
 /export-lora
 /finetune
+/retrieval
 /speculative
 /parallel
 /train-text-from-scratch
@@ -88,19 +102,4 @@ examples/jeopardy/results.txt
 
 poetry.lock
 poetry.toml
-
-# Test binaries
-/tests/test-grammar-parser
-/tests/test-llama-grammar
-/tests/test-double-float
-/tests/test-grad0
-/tests/test-opt
-/tests/test-quantize-fns
-/tests/test-quantize-perf
-/tests/test-sampling
-/tests/test-tokenizer-0-llama
-/tests/test-tokenizer-0-falcon
-/tests/test-tokenizer-1-llama
-/tests/test-tokenizer-1-bpe
-/tests/test-rope
-/tests/test-backend-ops
+nppBackup
diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 000000000..b7e8b8ff2
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "kompute"]
+	path = kompute
+	url = https://github.com/nomic-ai/kompute.git
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 668669c6d..19fdfa46c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,5 +1,6 @@
-cmake_minimum_required(VERSION 3.13)  # for add_link_options
+cmake_minimum_required(VERSION 3.14)  # for add_link_options and implicit target directories.
 project("llama.cpp" C CXX)
+include(CheckIncludeFileCXX)
 
 set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
 
@@ -47,12 +48,16 @@ option(BUILD_SHARED_LIBS                "build shared libraries"
 option(LLAMA_STATIC                     "llama: static link libraries"                          OFF)
 option(LLAMA_NATIVE                     "llama: enable -march=native flag"                      ON)
 option(LLAMA_LTO                        "llama: enable link time optimization"                  OFF)
+option(LLAMA_CCACHE                     "llama: use ccache if available"                        ON)
 
 # debug
 option(LLAMA_ALL_WARNINGS               "llama: enable all compiler warnings"                   ON)
 option(LLAMA_ALL_WARNINGS_3RD_PARTY     "llama: enable all compiler warnings in 3rd party libs" OFF)
 option(LLAMA_GPROF                      "llama: enable gprof"                                   OFF)
 
+# build
+option(LLAMA_FATAL_WARNINGS             "llama: enable -Werror flag"                            OFF)
+
 # sanitizers
 option(LLAMA_SANITIZE_THREAD            "llama: enable thread sanitizer"                        OFF)
 option(LLAMA_SANITIZE_ADDRESS           "llama: enable address sanitizer"                       OFF)
@@ -76,12 +81,16 @@ if (NOT MSVC)
     option(LLAMA_F16C                        "llama: enable F16C"                               ${INS_ENB})
 endif()
 
+if (WIN32)
+    set(LLAMA_WIN_VER "0x602" CACHE STRING "llama: Windows Version")
+endif()
+
 # 3rd party libs
 option(LLAMA_ACCELERATE                      "llama: enable Accelerate framework"               ON)
 option(LLAMA_BLAS                            "llama: use BLAS"                                  OFF)
 set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor")
-option(LLAMA_CUBLAS                          "llama: use CUDA"                                  OFF)
-#option(LLAMA_CUDA_CUBLAS                     "llama: use cuBLAS for prompt processing"          OFF)
+option(LLAMA_CUDA                            "llama: use CUDA"                                  OFF)
+option(LLAMA_CUBLAS                          "llama: use CUDA (deprecated, use LLAMA_CUDA)"     OFF)
 option(LLAMA_CUDA_FORCE_DMMV                 "llama: use dmmv instead of mmvq CUDA kernels"     OFF)
 option(LLAMA_CUDA_FORCE_MMQ                  "llama: use mmq kernels instead of cuBLAS"         OFF)
 set(LLAMA_CUDA_DMMV_X      "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
@@ -90,19 +99,39 @@ option(LLAMA_CUDA_F16                        "llama: use 16 bit floats for some
 set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
 set(LLAMA_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING
                                              "llama: max. batch size for using peer access")
+option(LLAMA_CUDA_NO_PEER_COPY               "llama: do not use peer to peer copies"            OFF)
+option(LLAMA_CURL                            "llama: use libcurl to download model from an URL" OFF)
 option(LLAMA_HIPBLAS                         "llama: use hipBLAS"                               OFF)
 option(LLAMA_HIP_UMA                         "llama: use HIP unified memory architecture"       OFF)
 option(LLAMA_CLBLAST                         "llama: use CLBlast"                               OFF)
+option(LLAMA_VULKAN                          "llama: use Vulkan"                                OFF)
+option(LLAMA_VULKAN_CHECK_RESULTS            "llama: run Vulkan op checks"                      OFF)
+option(LLAMA_VULKAN_DEBUG                    "llama: enable Vulkan debug output"                OFF)
+option(LLAMA_VULKAN_VALIDATE                 "llama: enable Vulkan validation"                  OFF)
+option(LLAMA_VULKAN_RUN_TESTS                "llama: run Vulkan tests"                          OFF)
 option(LLAMA_METAL                           "llama: use Metal"                                 ${LLAMA_METAL_DEFAULT})
 option(LLAMA_METAL_NDEBUG                    "llama: disable Metal debugging"                   OFF)
 option(LLAMA_METAL_SHADER_DEBUG              "llama: compile Metal with -fno-fast-math"         OFF)
+option(LLAMA_METAL_EMBED_LIBRARY             "llama: embed Metal library"                       OFF)
+set(LLAMA_METAL_MACOSX_VERSION_MIN "" CACHE STRING
+                                             "llama: metal minimum macOS version")
+set(LLAMA_METAL_STD "" CACHE STRING          "llama: metal standard version (-std flag)")
+option(LLAMA_KOMPUTE                         "llama: use Kompute"                               OFF)
 option(LLAMA_MPI                             "llama: use MPI"                                   OFF)
 option(LLAMA_QKK_64                          "llama: use super-block size of 64 for k-quants"   OFF)
+option(LLAMA_SYCL                            "llama: use SYCL"                                  OFF)
+option(LLAMA_SYCL_F16                        "llama: use 16 bit floats for sycl calculations"   OFF)
+set(LLAMA_SYCL_TARGET   "INTEL" CACHE STRING "llama: sycl target device")
+option(LLAMA_CPU_HBM                         "llama: use memkind for CPU HBM"                   OFF)
+set(LLAMA_SCHED_MAX_COPIES  "4" CACHE STRING "llama: max input copies for pipeline parallelism")
 
 option(LLAMA_BUILD_TESTS                     "llama: build tests"    ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_EXAMPLES                  "llama: build examples" ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_SERVER                    "llama: build server example"                      ON)
 
+# add perf arguments
+option(LLAMA_PERF                            "llama: enable perf"                               OFF)
+
 # Required for relocatable CMake package
 include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
 
@@ -110,14 +139,22 @@ include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
 # Compile flags
 #
 
-set(CMAKE_CXX_STANDARD 11)
+if (LLAMA_SYCL)
+    set(CMAKE_CXX_STANDARD 17)
+else()
+    set(CMAKE_CXX_STANDARD 11)
+endif()
+
 set(CMAKE_CXX_STANDARD_REQUIRED true)
 set(CMAKE_C_STANDARD 11)
 set(CMAKE_C_STANDARD_REQUIRED true)
 set(THREADS_PREFER_PTHREAD_FLAG ON)
+
 find_package(Threads REQUIRED)
 include(CheckCXXCompilerFlag)
 
+add_compile_definitions(GGML_SCHED_MAX_COPIES=${LLAMA_SCHED_MAX_COPIES})
+
 # enable libstdc++ assertions for debug builds
 if (CMAKE_SYSTEM_NAME MATCHES "Linux")
     add_compile_definitions($<$<CONFIG:Debug>:_GLIBCXX_ASSERTIONS>)
@@ -126,17 +163,17 @@ endif()
 if (NOT MSVC)
     if (LLAMA_SANITIZE_THREAD)
         add_compile_options(-fsanitize=thread)
-        link_libraries(-fsanitize=thread)
+        link_libraries     (-fsanitize=thread)
     endif()
 
     if (LLAMA_SANITIZE_ADDRESS)
         add_compile_options(-fsanitize=address -fno-omit-frame-pointer)
-        link_libraries(-fsanitize=address)
+        link_libraries     (-fsanitize=address)
     endif()
 
     if (LLAMA_SANITIZE_UNDEFINED)
         add_compile_options(-fsanitize=undefined)
-        link_libraries(-fsanitize=undefined)
+        link_libraries     (-fsanitize=undefined)
     endif()
 endif()
 
@@ -168,40 +205,80 @@ if (LLAMA_METAL)
         add_compile_definitions(GGML_METAL_NDEBUG)
     endif()
 
-    # get full path to the file
-    #add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")
-
-    # copy ggml-metal.metal to bin directory
+    # copy ggml-common.h and ggml-metal.metal to bin directory
+    configure_file(ggml-common.h    ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h    COPYONLY)
     configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY)
 
-    if (LLAMA_METAL_SHADER_DEBUG)
-        # custom command to do the following:
-        #   xcrun -sdk macosx metal    -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
-        #   xcrun -sdk macosx metallib                   ggml-metal.air   -o default.metallib
-        #
-        # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works
-        #       disabling fast math is needed in order to pass tests/test-backend-ops
-        # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1
-        # note: unfortunately, we have to call it default.metallib instead of ggml.metallib
-        #       ref: https://github.com/ggerganov/whisper.cpp/issues/1720
-        set(XC_FLAGS -fno-fast-math -fno-inline -g)
-        if (LLAMA_QKK_64)
-            set(XC_FLAGS ${XC_FLAGS} -DQK_K=64)
+    if (LLAMA_METAL_EMBED_LIBRARY)
+        enable_language(ASM)
+        add_compile_definitions(GGML_METAL_EMBED_LIBRARY)
+
+        set(METALLIB_COMMON "${CMAKE_CURRENT_SOURCE_DIR}/ggml-common.h")
+        set(METALLIB_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal")
+
+        file(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/autogenerated")
+
+        # merge ggml-common.h and ggml-metal.metal into a single file
+        set(METALLIB_EMBED_ASM    "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.s")
+        set(METALLIB_SOURCE_EMBED "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.metal")
+
+        add_custom_command(
+            OUTPUT ${METALLIB_EMBED_ASM}
+            COMMAND echo "Embedding Metal library"
+            COMMAND sed -e '/\#include \"ggml-common.h\"/r ${METALLIB_COMMON}' -e '/\#include \"ggml-common.h\"/d' < ${METALLIB_SOURCE} > ${METALLIB_SOURCE_EMBED}
+            COMMAND echo ".section __DATA,__ggml_metallib"          >  ${METALLIB_EMBED_ASM}
+            COMMAND echo ".globl _ggml_metallib_start"              >> ${METALLIB_EMBED_ASM}
+            COMMAND echo "_ggml_metallib_start:"                    >> ${METALLIB_EMBED_ASM}
+            COMMAND echo ".incbin \\\"${METALLIB_SOURCE_EMBED}\\\"" >> ${METALLIB_EMBED_ASM}
+            COMMAND echo ".globl _ggml_metallib_end"                >> ${METALLIB_EMBED_ASM}
+            COMMAND echo "_ggml_metallib_end:"                      >> ${METALLIB_EMBED_ASM}
+            DEPENDS ggml-metal.metal ggml-common.h
+            COMMENT "Generate assembly for embedded Metal library"
+        )
+
+        set(GGML_SOURCES_METAL ${GGML_SOURCES_METAL} ${METALLIB_EMBED_ASM})
+    else()
+        if (LLAMA_METAL_SHADER_DEBUG)
+            # custom command to do the following:
+            #   xcrun -sdk macosx metal    -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
+            #   xcrun -sdk macosx metallib                   ggml-metal.air   -o default.metallib
+            #
+            # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works
+            #       disabling fast math is needed in order to pass tests/test-backend-ops
+            # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1
+            # note: unfortunately, we have to call it default.metallib instead of ggml.metallib
+            #       ref: https://github.com/ggerganov/whisper.cpp/issues/1720
+            set(XC_FLAGS -fno-fast-math -fno-inline -g)
+        else()
+            set(XC_FLAGS -O3)
+        endif()
+
+        # Append macOS metal versioning flags
+        if (LLAMA_METAL_MACOSX_VERSION_MIN)
+            message(STATUS "Adding -mmacosx-version-min=${LLAMA_METAL_MACOSX_VERSION_MIN} flag to metal compilation")
+            list(APPEND XC_FLAGS -mmacosx-version-min=${LLAMA_METAL_MACOSX_VERSION_MIN})
+        endif()
+        if (LLAMA_METAL_STD)
+            message(STATUS "Adding -std=${LLAMA_METAL_STD} flag to metal compilation")
+            list(APPEND XC_FLAGS -std=${LLAMA_METAL_STD})
         endif()
 
         add_custom_command(
             OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
             COMMAND xcrun -sdk macosx metal    ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
             COMMAND xcrun -sdk macosx metallib                ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air   -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
-            DEPENDS ggml-metal.metal
+            COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
+            COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h
+            COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal
+            DEPENDS ggml-metal.metal ggml-common.h
             COMMENT "Compiling Metal kernels"
-        )
+            )
 
         add_custom_target(
             ggml-metal ALL
             DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
-        )
-    endif()
+            )
+    endif() # LLAMA_METAL_EMBED_LIBRARY
 
     set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
         ${FOUNDATION_LIBRARY}
@@ -273,14 +350,17 @@ if (LLAMA_BLAS)
         endif()
 
         message(STATUS "BLAS found, Includes: ${BLAS_INCLUDE_DIRS}")
+
         add_compile_options(${BLAS_LINKER_FLAGS})
+
         add_compile_definitions(GGML_USE_OPENBLAS)
+
         if (${BLAS_INCLUDE_DIRS} MATCHES "mkl" AND (${LLAMA_BLAS_VENDOR} MATCHES "Generic" OR ${LLAMA_BLAS_VENDOR} MATCHES "Intel"))
             add_compile_definitions(GGML_BLAS_USE_MKL)
         endif()
-        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${BLAS_LIBRARIES})
-        set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${BLAS_INCLUDE_DIRS})
 
+        set(LLAMA_EXTRA_LIBS     ${LLAMA_EXTRA_LIBS}     ${BLAS_LIBRARIES})
+        set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${BLAS_INCLUDE_DIRS})
     else()
         message(WARNING "BLAS not found, please refer to "
         "https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors"
@@ -293,21 +373,25 @@ if (LLAMA_QKK_64)
 endif()
 
 if (LLAMA_CUBLAS)
+    message(WARNING "LLAMA_CUBLAS is deprecated and will be removed in the future.\nUse LLAMA_CUDA instead")
+    set(LLAMA_CUDA ON)
+endif()
+
+if (LLAMA_CUDA)
     cmake_minimum_required(VERSION 3.17)
 
     find_package(CUDAToolkit)
     if (CUDAToolkit_FOUND)
-        message(STATUS "cuBLAS found")
+        message(STATUS "CUDA found")
 
         enable_language(CUDA)
 
         set(GGML_HEADERS_CUDA ggml-cuda.h)
-        set(GGML_SOURCES_CUDA ggml-cuda.cu)
 
-        add_compile_definitions(GGML_USE_CUBLAS)
-#        if (LLAMA_CUDA_CUBLAS)
-#            add_compile_definitions(GGML_CUDA_CUBLAS)
-#        endif()
+        file(GLOB GGML_SOURCES_CUDA "ggml-cuda/*.cu")
+        list(APPEND GGML_SOURCES_CUDA "ggml-cuda.cu")
+
+        add_compile_definitions(GGML_USE_CUDA)
         if (LLAMA_CUDA_FORCE_DMMV)
             add_compile_definitions(GGML_CUDA_FORCE_DMMV)
         endif()
@@ -324,6 +408,9 @@ if (LLAMA_CUBLAS)
         endif()
         add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
         add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${LLAMA_CUDA_PEER_MAX_BATCH_SIZE})
+        if (LLAMA_CUDA_NO_PEER_COPY)
+            add_compile_definitions(GGML_CUDA_NO_PEER_COPY)
+        endif()
 
         if (LLAMA_STATIC)
             if (WIN32)
@@ -353,7 +440,7 @@ if (LLAMA_CUBLAS)
     message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}")
 
     else()
-        message(WARNING "cuBLAS not found")
+        message(WARNING "CUDA not found")
     endif()
 endif()
 
@@ -362,15 +449,20 @@ if (LLAMA_MPI)
     find_package(MPI)
     if (MPI_C_FOUND)
         message(STATUS "MPI found")
+
         set(GGML_HEADERS_MPI ggml-mpi.h)
-        set(GGML_SOURCES_MPI ggml-mpi.c ggml-mpi.h)
+        set(GGML_SOURCES_MPI ggml-mpi.c)
+
         add_compile_definitions(GGML_USE_MPI)
         add_compile_definitions(${MPI_C_COMPILE_DEFINITIONS})
+
         if (NOT MSVC)
             add_compile_options(-Wno-cast-qual)
         endif()
+
         set(LLAMA_EXTRA_LIBS     ${LLAMA_EXTRA_LIBS}     ${MPI_C_LIBRARIES})
         set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${MPI_C_INCLUDE_DIRS})
+
         # Even if you're only using the C header, C++ programs may bring in MPI
         # C++ functions, so more linkage is needed
         if (MPI_CXX_FOUND)
@@ -397,49 +489,301 @@ if (LLAMA_CLBLAST)
     endif()
 endif()
 
+if (LLAMA_VULKAN)
+    find_package(Vulkan)
+    if (Vulkan_FOUND)
+        message(STATUS "Vulkan found")
+
+        set(GGML_HEADERS_VULKAN ggml-vulkan.h)
+        set(GGML_SOURCES_VULKAN ggml-vulkan.cpp)
+
+        add_compile_definitions(GGML_USE_VULKAN)
+
+        if (LLAMA_VULKAN_CHECK_RESULTS)
+            add_compile_definitions(GGML_VULKAN_CHECK_RESULTS)
+        endif()
+
+        if (LLAMA_VULKAN_DEBUG)
+            add_compile_definitions(GGML_VULKAN_DEBUG)
+        endif()
+
+        if (LLAMA_VULKAN_VALIDATE)
+            add_compile_definitions(GGML_VULKAN_VALIDATE)
+        endif()
+
+        if (LLAMA_VULKAN_RUN_TESTS)
+            add_compile_definitions(GGML_VULKAN_RUN_TESTS)
+        endif()
+
+        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} Vulkan::Vulkan)
+    else()
+        message(WARNING "Vulkan not found")
+    endif()
+endif()
+
 if (LLAMA_HIPBLAS)
     list(APPEND CMAKE_PREFIX_PATH /opt/rocm)
 
     if (NOT ${CMAKE_C_COMPILER_ID} MATCHES "Clang")
         message(WARNING "Only LLVM is supported for HIP, hint: CC=/opt/rocm/llvm/bin/clang")
     endif()
+
     if (NOT ${CMAKE_CXX_COMPILER_ID} MATCHES "Clang")
         message(WARNING "Only LLVM is supported for HIP, hint: CXX=/opt/rocm/llvm/bin/clang++")
     endif()
 
-    find_package(hip)
-    find_package(hipblas)
-    find_package(rocblas)
+    find_package(hip     REQUIRED)
+    find_package(hipblas REQUIRED)
+    find_package(rocblas REQUIRED)
 
-    if (${hipblas_FOUND} AND ${hip_FOUND})
-        message(STATUS "HIP and hipBLAS found")
-        add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUBLAS)
-        if (LLAMA_HIP_UMA)
-            add_compile_definitions(GGML_HIP_UMA)
-        endif()
-        add_library(ggml-rocm OBJECT ggml-cuda.cu ggml-cuda.h)
-        if (BUILD_SHARED_LIBS)
-            set_target_properties(ggml-rocm PROPERTIES POSITION_INDEPENDENT_CODE ON)
-        endif()
-        if (LLAMA_CUDA_FORCE_DMMV)
-            target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_FORCE_DMMV)
-        endif()
-        if (LLAMA_CUDA_FORCE_MMQ)
-            target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_FORCE_MMQ)
-        endif()
-        target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
-        target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
-        target_compile_definitions(ggml-rocm PRIVATE K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
-        set_source_files_properties(ggml-cuda.cu PROPERTIES LANGUAGE CXX)
-        target_link_libraries(ggml-rocm PRIVATE hip::device PUBLIC hip::host roc::rocblas roc::hipblas)
+    message(STATUS "HIP and hipBLAS found")
 
-        if (LLAMA_STATIC)
-            message(FATAL_ERROR "Static linking not supported for HIP/ROCm")
-        endif()
-        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ggml-rocm)
-    else()
-        message(WARNING "hipBLAS or HIP not found. Try setting CMAKE_PREFIX_PATH=/opt/rocm")
+    set(GGML_HEADERS_ROCM ggml-cuda.h)
+
+    file(GLOB GGML_SOURCES_ROCM "ggml-cuda/*.cu")
+    list(APPEND GGML_SOURCES_ROCM "ggml-cuda.cu")
+
+    add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUDA)
+
+    if (LLAMA_HIP_UMA)
+        add_compile_definitions(GGML_HIP_UMA)
     endif()
+
+    if (LLAMA_CUDA_FORCE_DMMV)
+        add_compile_definitions(GGML_CUDA_FORCE_DMMV)
+    endif()
+
+    if (LLAMA_CUDA_FORCE_MMQ)
+        add_compile_definitions(GGML_CUDA_FORCE_MMQ)
+    endif()
+
+    if (LLAMA_CUDA_NO_PEER_COPY)
+        add_compile_definitions(GGML_CUDA_NO_PEER_COPY)
+    endif()
+
+    add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
+    add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
+    add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
+
+    set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE CXX)
+
+    if (LLAMA_STATIC)
+        message(FATAL_ERROR "Static linking not supported for HIP/ROCm")
+    endif()
+
+    set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} hip::device PUBLIC hip::host roc::rocblas roc::hipblas)
+endif()
+
+if (LLAMA_SYCL)
+    if (NOT LLAMA_SYCL_TARGET MATCHES "^(INTEL|NVIDIA)$")
+        message(FATAL_ERROR "Invalid backend chosen, supported options are INTEL or NVIDIA")
+    endif()
+
+    if ( NOT DEFINED ENV{ONEAPI_ROOT})
+        message(FATAL_ERROR "Not detect ENV {ONEAPI_ROOT}, please install oneAPI & source it, like: source /opt/intel/oneapi/setvars.sh")
+    endif()
+    #todo: AOT
+
+    find_package(IntelSYCL REQUIRED)
+
+    message(STATUS "SYCL found")
+
+    add_compile_definitions(GGML_USE_SYCL)
+
+    if (LLAMA_SYCL_F16)
+        add_compile_definitions(GGML_SYCL_F16)
+    endif()
+
+    add_compile_options(-I./) #include DPCT
+    add_compile_options(-I/${SYCL_INCLUDE_DIR})
+
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl -L${MKLROOT}/lib")
+    if (LLAMA_SYCL_TARGET STREQUAL "NVIDIA")
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda")
+    endif()
+
+    set(GGML_HEADERS_SYCL ggml-sycl.h)
+    set(GGML_SOURCES_SYCL ggml-sycl.cpp)
+
+    if (WIN32)
+        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl sycl7 OpenCL mkl_sycl_blas_dll.lib mkl_intel_ilp64_dll.lib mkl_sequential_dll.lib mkl_core_dll.lib)
+    else()
+        if (LLAMA_SYCL_TARGET STREQUAL "INTEL")
+            set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
+        elseif (LLAMA_SYCL_TARGET STREQUAL "NVIDIA")
+            set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl pthread m dl onemkl)
+        endif()
+    endif()
+endif()
+
+if (LLAMA_KOMPUTE)
+    add_compile_definitions(VULKAN_HPP_DISPATCH_LOADER_DYNAMIC=1)
+    find_package(Vulkan COMPONENTS glslc REQUIRED)
+    find_program(glslc_executable NAMES glslc HINTS Vulkan::glslc)
+    if (NOT glslc_executable)
+        message(FATAL_ERROR "glslc not found")
+    endif()
+
+    function(compile_shader)
+        set(options)
+        set(oneValueArgs)
+        set(multiValueArgs SOURCES)
+        cmake_parse_arguments(compile_shader "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+        foreach(source ${compile_shader_SOURCES})
+            get_filename_component(filename ${source} NAME)
+            set(spv_file ${filename}.spv)
+            add_custom_command(
+                OUTPUT ${spv_file}
+                DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/${source}
+                ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/common.comp
+                ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_getrows.comp
+                ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n_pre.comp
+                ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n.comp
+                COMMAND ${glslc_executable} --target-env=vulkan1.2 -o ${spv_file} ${CMAKE_CURRENT_SOURCE_DIR}/${source}
+                COMMENT "Compiling ${source} to ${spv_file}"
+                )
+
+            get_filename_component(RAW_FILE_NAME ${spv_file} NAME)
+            set(FILE_NAME "shader${RAW_FILE_NAME}")
+            string(REPLACE ".comp.spv" ".h" HEADER_FILE ${FILE_NAME})
+            string(TOUPPER ${HEADER_FILE} HEADER_FILE_DEFINE)
+            string(REPLACE "." "_" HEADER_FILE_DEFINE "${HEADER_FILE_DEFINE}")
+            set(OUTPUT_HEADER_FILE "${HEADER_FILE}")
+            message(STATUS "${HEADER_FILE} generating ${HEADER_FILE_DEFINE}")
+            if(CMAKE_GENERATOR MATCHES "Visual Studio")
+                add_custom_command(
+                    OUTPUT ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_BINARY_DIR}/bin/$<CONFIG>/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
+                    DEPENDS ${spv_file} xxd
+                    COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/$<CONFIG>/xxd"
+                    )
+            else()
+                add_custom_command(
+                    OUTPUT ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_BINARY_DIR}/bin/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE}
+                    COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
+                    DEPENDS ${spv_file} xxd
+                    COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/xxd"
+                    )
+            endif()
+        endforeach()
+    endfunction()
+
+    if (EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/kompute/CMakeLists.txt")
+        message(STATUS "Kompute found")
+        set(KOMPUTE_OPT_LOG_LEVEL Error CACHE STRING "Kompute log level")
+        add_subdirectory(kompute)
+
+        # Compile our shaders
+        compile_shader(SOURCES
+            kompute-shaders/op_scale.comp
+            kompute-shaders/op_scale_8.comp
+            kompute-shaders/op_add.comp
+            kompute-shaders/op_addrow.comp
+            kompute-shaders/op_mul.comp
+            kompute-shaders/op_silu.comp
+            kompute-shaders/op_relu.comp
+            kompute-shaders/op_gelu.comp
+            kompute-shaders/op_softmax.comp
+            kompute-shaders/op_norm.comp
+            kompute-shaders/op_rmsnorm.comp
+            kompute-shaders/op_diagmask.comp
+            kompute-shaders/op_mul_mat_mat_f32.comp
+            kompute-shaders/op_mul_mat_f16.comp
+            kompute-shaders/op_mul_mat_q8_0.comp
+            kompute-shaders/op_mul_mat_q4_0.comp
+            kompute-shaders/op_mul_mat_q4_1.comp
+            kompute-shaders/op_mul_mat_q6_k.comp
+            kompute-shaders/op_getrows_f16.comp
+            kompute-shaders/op_getrows_q4_0.comp
+            kompute-shaders/op_getrows_q4_1.comp
+            kompute-shaders/op_getrows_q6_k.comp
+            kompute-shaders/op_rope_f16.comp
+            kompute-shaders/op_rope_f32.comp
+            kompute-shaders/op_cpy_f16_f16.comp
+            kompute-shaders/op_cpy_f16_f32.comp
+            kompute-shaders/op_cpy_f32_f16.comp
+            kompute-shaders/op_cpy_f32_f32.comp
+        )
+
+        # Create a custom target for our generated shaders
+        add_custom_target(generated_shaders DEPENDS
+            shaderop_scale.h
+            shaderop_scale_8.h
+            shaderop_add.h
+            shaderop_addrow.h
+            shaderop_mul.h
+            shaderop_silu.h
+            shaderop_relu.h
+            shaderop_gelu.h
+            shaderop_softmax.h
+            shaderop_norm.h
+            shaderop_rmsnorm.h
+            shaderop_diagmask.h
+            shaderop_mul_mat_mat_f32.h
+            shaderop_mul_mat_f16.h
+            shaderop_mul_mat_q8_0.h
+            shaderop_mul_mat_q4_0.h
+            shaderop_mul_mat_q4_1.h
+            shaderop_mul_mat_q6_k.h
+            shaderop_getrows_f16.h
+            shaderop_getrows_q4_0.h
+            shaderop_getrows_q4_1.h
+            shaderop_getrows_q6_k.h
+            shaderop_rope_f16.h
+            shaderop_rope_f32.h
+            shaderop_cpy_f16_f16.h
+            shaderop_cpy_f16_f32.h
+            shaderop_cpy_f32_f16.h
+            shaderop_cpy_f32_f32.h
+        )
+
+        # Create a custom command that depends on the generated_shaders
+        add_custom_command(
+            OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp
+            COMMAND ${CMAKE_COMMAND} -E touch ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp
+            DEPENDS generated_shaders
+            COMMENT "Ensuring shaders are generated before compiling ggml-kompute.cpp"
+        )
+
+        # Add the stamp to the main sources to ensure dependency tracking
+        set(GGML_SOURCES_KOMPUTE ggml-kompute.cpp ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp)
+        set(GGML_HEADERS_KOMPUTE ggml-kompute.h   ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp)
+
+        add_compile_definitions(GGML_USE_KOMPUTE)
+
+        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} kompute)
+        set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${CMAKE_BINARY_DIR})
+    else()
+        message(WARNING "Kompute not found")
+    endif()
+endif()
+
+if (LLAMA_CPU_HBM)
+    find_library(memkind memkind REQUIRED)
+
+    add_compile_definitions(GGML_USE_CPU_HBM)
+
+    target_link_libraries(ggml PUBLIC memkind)
+endif()
+
+if (LLAMA_PERF)
+    add_compile_definitions(GGML_PERF)
 endif()
 
 function(get_flags CCID CCVER)
@@ -454,17 +798,17 @@ function(get_flags CCID CCVER)
             (CCID STREQUAL "Clang"      AND CCVER VERSION_GREATER_EQUAL 3.8.0) OR
             (CCID STREQUAL "AppleClang" AND CCVER VERSION_GREATER_EQUAL 7.3.0)
         )
-            set(C_FLAGS ${C_FLAGS} -Wdouble-promotion)
+            list(APPEND C_FLAGS -Wdouble-promotion)
         endif()
     elseif (CCID STREQUAL "GNU")
         set(C_FLAGS   -Wdouble-promotion)
         set(CXX_FLAGS -Wno-array-bounds)
 
         if (CCVER VERSION_GREATER_EQUAL 7.1.0)
-            set(CXX_FLAGS ${CXX_FLAGS} -Wno-format-truncation)
+            list(APPEND CXX_FLAGS -Wno-format-truncation)
         endif()
         if (CCVER VERSION_GREATER_EQUAL 8.1.0)
-            set(CXX_FLAGS ${CXX_FLAGS} -Wextra-semi)
+            list(APPEND CXX_FLAGS -Wextra-semi)
         endif()
     endif()
 
@@ -472,15 +816,24 @@ function(get_flags CCID CCVER)
     set(GF_CXX_FLAGS ${CXX_FLAGS} PARENT_SCOPE)
 endfunction()
 
+if (LLAMA_FATAL_WARNINGS)
+    if (CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
+        list(APPEND C_FLAGS   -Werror)
+        list(APPEND CXX_FLAGS -Werror)
+    elseif (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
+        add_compile_options(/WX)
+    endif()
+endif()
+
 if (LLAMA_ALL_WARNINGS)
     if (NOT MSVC)
-        set(WARNING_FLAGS -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function)
-        set(C_FLAGS       -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes
-                          -Werror=implicit-int -Werror=implicit-function-declaration)
-        set(CXX_FLAGS     -Wmissing-declarations -Wmissing-noreturn)
+        list(APPEND WARNING_FLAGS -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function)
+        list(APPEND C_FLAGS       -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes
+                                  -Werror=implicit-int -Werror=implicit-function-declaration)
+        list(APPEND CXX_FLAGS     -Wmissing-declarations -Wmissing-noreturn)
 
-        set(C_FLAGS   ${WARNING_FLAGS} ${C_FLAGS})
-        set(CXX_FLAGS ${WARNING_FLAGS} ${CXX_FLAGS})
+        list(APPEND C_FLAGS   ${WARNING_FLAGS})
+        list(APPEND CXX_FLAGS ${WARNING_FLAGS})
 
         get_flags(${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION})
 
@@ -493,16 +846,19 @@ if (LLAMA_ALL_WARNINGS)
     endif()
 endif()
 
-if (LLAMA_CUBLAS)
-    set(CUDA_FLAGS ${CXX_FLAGS} -use_fast_math)
-    if (NOT MSVC)
-        set(CUDA_FLAGS ${CUDA_FLAGS} -Wno-pedantic)
+set(CUDA_CXX_FLAGS "")
+
+if (LLAMA_CUDA)
+    set(CUDA_FLAGS -use_fast_math)
+
+    if (LLAMA_FATAL_WARNINGS)
+        list(APPEND CUDA_FLAGS -Werror all-warnings)
     endif()
 
     if (LLAMA_ALL_WARNINGS AND NOT MSVC)
         set(NVCC_CMD ${CMAKE_CUDA_COMPILER} .c)
         if (NOT CMAKE_CUDA_HOST_COMPILER STREQUAL "")
-            set(NVCC_CMD ${NVCC_CMD} -ccbin ${CMAKE_CUDA_HOST_COMPILER})
+            list(APPEND NVCC_CMD -ccbin ${CMAKE_CUDA_HOST_COMPILER})
         endif()
 
         execute_process(
@@ -530,13 +886,12 @@ if (LLAMA_CUBLAS)
         message("-- CUDA host compiler is ${CUDA_CCID} ${CUDA_CCVER}")
 
         get_flags(${CUDA_CCID} ${CUDA_CCVER})
-        list(JOIN GF_CXX_FLAGS " " CUDA_CXX_FLAGS)  # pass host compiler flags as a single argument
-        if (NOT CUDA_CXX_FLAGS STREQUAL "")
-            set(CUDA_FLAGS ${CUDA_FLAGS} -Xcompiler ${CUDA_CXX_FLAGS})
-        endif()
+        list(APPEND CUDA_CXX_FLAGS ${CXX_FLAGS} ${GF_CXX_FLAGS})  # This is passed to -Xcompiler later
     endif()
 
-    add_compile_options("$<$<COMPILE_LANGUAGE:CUDA>:${CUDA_FLAGS}>")
+    if (NOT MSVC)
+        list(APPEND CUDA_CXX_FLAGS -Wno-pedantic)
+    endif()
 endif()
 
 if (WIN32)
@@ -557,12 +912,24 @@ if (LLAMA_LTO)
     endif()
 endif()
 
+if (LLAMA_CCACHE)
+    find_program(LLAMA_CCACHE_FOUND ccache)
+    if (LLAMA_CCACHE_FOUND)
+        set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE ccache)
+        set(ENV{CCACHE_SLOPPINESS} time_macros)
+        message(STATUS "ccache found, compilation results will be cached. Disable with LLAMA_CCACHE=OFF.")
+    else()
+        message(STATUS "Warning: ccache not found - consider installing it for faster compilation or disable this warning with LLAMA_CCACHE=OFF")
+    endif ()
+endif()
+
 # this version of Apple ld64 is buggy
 execute_process(
     COMMAND ${CMAKE_C_COMPILER} ${CMAKE_EXE_LINKER_FLAGS} -Wl,-v
     ERROR_VARIABLE output
     OUTPUT_QUIET
 )
+
 if (output MATCHES "dyld-1015\.7")
     add_compile_definitions(HAVE_BUGGY_APPLE_LINKER)
 endif()
@@ -572,10 +939,10 @@ endif()
 #       feel free to update the Makefile for your architecture and send a pull request or issue
 message(STATUS "CMAKE_SYSTEM_PROCESSOR: ${CMAKE_SYSTEM_PROCESSOR}")
 if (MSVC)
-  string(TOLOWER "${CMAKE_GENERATOR_PLATFORM}" CMAKE_GENERATOR_PLATFORM_LWR)
-  message(STATUS "CMAKE_GENERATOR_PLATFORM: ${CMAKE_GENERATOR_PLATFORM}")
+    string(TOLOWER "${CMAKE_GENERATOR_PLATFORM}" CMAKE_GENERATOR_PLATFORM_LWR)
+    message(STATUS "CMAKE_GENERATOR_PLATFORM: ${CMAKE_GENERATOR_PLATFORM}")
 else ()
-  set(CMAKE_GENERATOR_PLATFORM_LWR "")
+    set(CMAKE_GENERATOR_PLATFORM_LWR "")
 endif ()
 
 if (NOT MSVC)
@@ -590,33 +957,55 @@ if (NOT MSVC)
     endif()
 endif()
 
-if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64") OR ("${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "arm64"))
+set(ARCH_FLAGS "")
+
+if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR CMAKE_GENERATOR_PLATFORM_LWR STREQUAL "arm64" OR
+    (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
+     CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$"))
     message(STATUS "ARM detected")
     if (MSVC)
+        add_compile_definitions(__aarch64__) # MSVC defines _M_ARM64 instead
         add_compile_definitions(__ARM_NEON)
         add_compile_definitions(__ARM_FEATURE_FMA)
-        add_compile_definitions(__ARM_FEATURE_DOTPROD)
-        # add_compile_definitions(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) # MSVC doesn't support vdupq_n_f16, vld1q_f16, vst1q_f16
-        add_compile_definitions(__aarch64__) # MSVC defines _M_ARM64 instead
+
+        set(CMAKE_REQUIRED_FLAGS_PREV ${CMAKE_REQUIRED_FLAGS})
+        string(JOIN " " CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS} "/arch:armv8.2")
+        check_cxx_source_compiles("#include <arm_neon.h>\nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD)
+        if (GGML_COMPILER_SUPPORT_DOTPROD)
+            add_compile_definitions(__ARM_FEATURE_DOTPROD)
+        endif ()
+        check_cxx_source_compiles("#include <arm_neon.h>\nint main() { float16_t _a; float16x8_t _s = vdupq_n_f16(_a); return 0; }" GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC)
+        if (GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC)
+            add_compile_definitions(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+        endif ()
+        set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_PREV})
     else()
         check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E)
         if (NOT "${COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "")
-            add_compile_options(-mfp16-format=ieee)
+            list(APPEND ARCH_FLAGS -mfp16-format=ieee)
         endif()
         if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6")
             # Raspberry Pi 1, Zero
-            add_compile_options(-mfpu=neon-fp-armv8 -mno-unaligned-access)
+            list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access)
         endif()
         if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv7")
-            # Raspberry Pi 2
-            add_compile_options(-mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations)
+            if ("${CMAKE_SYSTEM_NAME}" STREQUAL "Android")
+                # Android armeabi-v7a
+                list(APPEND ARCH_FLAGS -mfpu=neon-vfpv4 -mno-unaligned-access -funsafe-math-optimizations)
+            else()
+                # Raspberry Pi 2
+                list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations)
+            endif()
         endif()
         if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv8")
+            # Android arm64-v8a
             # Raspberry Pi 3, 4, Zero 2 (32-bit)
-            add_compile_options(-mno-unaligned-access)
+            list(APPEND ARCH_FLAGS -mno-unaligned-access)
         endif()
     endif()
-elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "^(x86_64|i686|amd64|x64)$" )
+elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR
+        (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
+         CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64)$"))
     message(STATUS "x86 detected")
     if (MSVC)
         # instruction set detection for MSVC only
@@ -624,8 +1013,7 @@ elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GE
             include(cmake/FindSIMD.cmake)
         endif ()
         if (LLAMA_AVX512)
-            add_compile_options($<$<COMPILE_LANGUAGE:C>:/arch:AVX512>)
-            add_compile_options($<$<COMPILE_LANGUAGE:CXX>:/arch:AVX512>)
+            list(APPEND ARCH_FLAGS /arch:AVX512)
             # MSVC has no compile-time flags enabling specific
             # AVX512 extensions, neither it defines the
             # macros corresponding to the extensions.
@@ -639,54 +1027,64 @@ elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GE
                 add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AVX512VNNI__>)
             endif()
         elseif (LLAMA_AVX2)
-            add_compile_options($<$<COMPILE_LANGUAGE:C>:/arch:AVX2>)
-            add_compile_options($<$<COMPILE_LANGUAGE:CXX>:/arch:AVX2>)
+            list(APPEND ARCH_FLAGS /arch:AVX2)
         elseif (LLAMA_AVX)
-            add_compile_options($<$<COMPILE_LANGUAGE:C>:/arch:AVX>)
-            add_compile_options($<$<COMPILE_LANGUAGE:CXX>:/arch:AVX>)
+            list(APPEND ARCH_FLAGS /arch:AVX)
         endif()
     else()
         if (LLAMA_NATIVE)
-            add_compile_options(-march=native)
+            list(APPEND ARCH_FLAGS -march=native)
         endif()
         if (LLAMA_F16C)
-            add_compile_options(-mf16c)
+            list(APPEND ARCH_FLAGS -mf16c)
         endif()
         if (LLAMA_FMA)
-            add_compile_options(-mfma)
+            list(APPEND ARCH_FLAGS -mfma)
         endif()
         if (LLAMA_AVX)
-            add_compile_options(-mavx)
+            list(APPEND ARCH_FLAGS -mavx)
         endif()
         if (LLAMA_AVX2)
-            add_compile_options(-mavx2)
+            list(APPEND ARCH_FLAGS -mavx2)
         endif()
         if (LLAMA_AVX512)
-            add_compile_options(-mavx512f)
-            add_compile_options(-mavx512bw)
+            list(APPEND ARCH_FLAGS -mavx512f)
+            list(APPEND ARCH_FLAGS -mavx512bw)
         endif()
         if (LLAMA_AVX512_VBMI)
-            add_compile_options(-mavx512vbmi)
+            list(APPEND ARCH_FLAGS -mavx512vbmi)
         endif()
         if (LLAMA_AVX512_VNNI)
-            add_compile_options(-mavx512vnni)
+            list(APPEND ARCH_FLAGS -mavx512vnni)
         endif()
     endif()
 elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64")
     message(STATUS "PowerPC detected")
     if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64le")
-        add_compile_options(-mcpu=powerpc64le)
+        list(APPEND ARCH_FLAGS -mcpu=powerpc64le)
     else()
-        add_compile_options(-mcpu=native -mtune=native)
+        list(APPEND ARCH_FLAGS -mcpu=native -mtune=native)
         #TODO: Add  targets for Power8/Power9 (Altivec/VSX) and Power10(MMA) and query for big endian systems (ppc64/le/be)
     endif()
 else()
     message(STATUS "Unknown architecture")
 endif()
 
+add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:${ARCH_FLAGS}>")
+add_compile_options("$<$<COMPILE_LANGUAGE:C>:${ARCH_FLAGS}>")
+
+if (LLAMA_CUDA)
+    list(APPEND CUDA_CXX_FLAGS ${ARCH_FLAGS})
+    list(JOIN CUDA_CXX_FLAGS " " CUDA_CXX_FLAGS_JOINED)  # pass host compiler flags as a single argument
+    if (NOT CUDA_CXX_FLAGS_JOINED STREQUAL "")
+        list(APPEND CUDA_FLAGS -Xcompiler ${CUDA_CXX_FLAGS_JOINED})
+    endif()
+    add_compile_options("$<$<COMPILE_LANGUAGE:CUDA>:${CUDA_FLAGS}>")
+endif()
+
 if (MINGW)
     # Target Windows 8 for PrefetchVirtualMemory
-    add_compile_definitions(_WIN32_WINNT=0x602)
+    add_compile_definitions(_WIN32_WINNT=${LLAMA_WIN_VER})
 endif()
 
 #
@@ -744,11 +1142,6 @@ endif()
 
 # ggml
 
-if (GGML_USE_CPU_HBM)
-    add_definitions(-DGGML_USE_CPU_HBM)
-    find_library(memkind memkind REQUIRED)
-endif()
-
 add_library(ggml OBJECT
             ggml.c
             ggml.h
@@ -758,21 +1151,24 @@ add_library(ggml OBJECT
             ggml-backend.h
             ggml-quants.c
             ggml-quants.h
-            ${GGML_SOURCES_CUDA}   ${GGML_HEADERS_CUDA}
-            ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL}
-            ${GGML_SOURCES_METAL}  ${GGML_HEADERS_METAL}
-            ${GGML_SOURCES_MPI}    ${GGML_HEADERS_MPI}
-            ${GGML_SOURCES_EXTRA}  ${GGML_HEADERS_EXTRA}
+            ${GGML_SOURCES_CUDA}    ${GGML_HEADERS_CUDA}
+            ${GGML_SOURCES_OPENCL}  ${GGML_HEADERS_OPENCL}
+            ${GGML_SOURCES_METAL}   ${GGML_HEADERS_METAL}
+            ${GGML_SOURCES_MPI}     ${GGML_HEADERS_MPI}
+            ${GGML_SOURCES_EXTRA}   ${GGML_HEADERS_EXTRA}
+            ${GGML_SOURCES_SYCL}    ${GGML_HEADERS_SYCL}
+            ${GGML_SOURCES_KOMPUTE} ${GGML_HEADERS_KOMPUTE}
+            ${GGML_SOURCES_VULKAN}  ${GGML_HEADERS_VULKAN}
+            ${GGML_SOURCES_ROCM}    ${GGML_HEADERS_ROCM}
             )
 
 target_include_directories(ggml PUBLIC . ${LLAMA_EXTRA_INCLUDES})
-target_compile_features(ggml PUBLIC c_std_11) # don't bump
+target_compile_features   (ggml PUBLIC c_std_11) # don't bump
+
 target_link_libraries(ggml PUBLIC Threads::Threads ${LLAMA_EXTRA_LIBS})
-if (GGML_USE_CPU_HBM)
-    target_link_libraries(ggml PUBLIC memkind)
-endif()
 
 add_library(ggml_static STATIC $<TARGET_OBJECTS:ggml>)
+
 if (BUILD_SHARED_LIBS)
     set_target_properties(ggml PROPERTIES POSITION_INDEPENDENT_CODE ON)
     add_library(ggml_shared SHARED $<TARGET_OBJECTS:ggml>)
@@ -785,10 +1181,14 @@ endif()
 add_library(llama
             llama.cpp
             llama.h
+            unicode.h
+            unicode.cpp
+            unicode-data.cpp
             )
 
 target_include_directories(llama PUBLIC .)
-target_compile_features(llama PUBLIC cxx_std_11) # don't bump
+target_compile_features   (llama PUBLIC cxx_std_11) # don't bump
+
 target_link_libraries(llama PRIVATE
     ggml
     ${LLAMA_EXTRA_LIBS}
@@ -838,8 +1238,8 @@ install(FILES ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake
               ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfigVersion.cmake
         DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama)
 
-set(GGML_PUBLIC_HEADERS "ggml.h"
-        "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}"
+set(GGML_PUBLIC_HEADERS "ggml.h" "ggml-alloc.h" "ggml-backend.h"
+        "${GGML_HEADERS_CUDA}"  "${GGML_HEADERS_OPENCL}"
         "${GGML_HEADERS_METAL}" "${GGML_HEADERS_MPI}" "${GGML_HEADERS_EXTRA}")
 
 set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}")
@@ -879,6 +1279,12 @@ if (LLAMA_METAL)
             GROUP_READ
             WORLD_READ
         DESTINATION ${CMAKE_INSTALL_BINDIR})
+    if (NOT LLAMA_METAL_EMBED_LIBRARY)
+        install(
+            FILES ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
+            DESTINATION ${CMAKE_INSTALL_BINDIR}
+        )
+    endif()
 endif()
 
 #
diff --git a/Makefile b/Makefile
index 4c7e175bf..bdd5ef335 100644
--- a/Makefile
+++ b/Makefile
@@ -1,15 +1,16 @@
 # Define the default target now so that it is always the first target
 BUILD_TARGETS = \
-	main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
-	simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search  \
-	speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey tests/test-c.o
+	main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
+	simple batched batched-bench save-load-state server gguf gguf-split llama-bench libllava.a llava-cli baby-llama beam-search  \
+	retrieval speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm tests/test-c.o
 
 # Binaries only useful for tests
 TEST_TARGETS = \
 	tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt \
 	tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama          \
 	tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe tests/test-rope      \
-	tests/test-backend-ops
+	tests/test-backend-ops tests/test-model-load-cancel tests/test-autorelease                                 \
+	tests/test-json-schema-to-grammar
 
 # Code coverage output files
 COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report
@@ -43,10 +44,6 @@ ifeq ($(UNAME_S),Darwin)
 	endif
 endif
 
-ifneq '' '$(or $(filter clean,$(MAKECMDGOALS)),$(LLAMA_METAL))'
-BUILD_TARGETS += metal
-endif
-
 default: $(BUILD_TARGETS)
 
 test: $(TEST_TARGETS)
@@ -101,9 +98,10 @@ endif
 #
 
 # keep standard at C11 and C++11
-MK_CPPFLAGS = -I. -Icommon
-MK_CFLAGS   = -std=c11   -fPIC
-MK_CXXFLAGS = -std=c++11 -fPIC
+MK_CPPFLAGS  = -I. -Icommon
+MK_CFLAGS    = -std=c11   -fPIC
+MK_CXXFLAGS  = -std=c++11 -fPIC
+MK_NVCCFLAGS = -std=c++11
 
 # -Ofast tends to produce faster code, but may not be available for some compilers.
 ifdef LLAMA_FAST
@@ -113,8 +111,21 @@ MK_NVCCFLAGS  += -O3
 else
 MK_CFLAGS     += -O3
 MK_CXXFLAGS   += -O3
+MK_NVCCFLAGS  += -O3
 endif
 
+ifndef LLAMA_NO_CCACHE
+CCACHE := $(shell which ccache)
+ifdef CCACHE
+export CCACHE_SLOPPINESS = time_macros
+$(info I ccache found, compilation results will be cached. Disable with LLAMA_NO_CCACHE.)
+CC    := $(CCACHE) $(CC)
+CXX   := $(CCACHE) $(CXX)
+else
+$(info I ccache not found. Consider installing it for faster compilation.)
+endif # CCACHE
+endif # LLAMA_NO_CCACHE
+
 # clock_gettime came in POSIX.1b (1993)
 # CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional
 # posix_memalign came in POSIX.1-2001 / SUSv3
@@ -157,13 +168,17 @@ ifeq ($(UNAME_S),OpenBSD)
 	MK_CPPFLAGS += -D_BSD_SOURCE
 endif
 
+ifdef LLAMA_SCHED_MAX_COPIES
+	MK_CPPFLAGS += -DGGML_SCHED_MAX_COPIES=$(LLAMA_SCHED_MAX_COPIES)
+endif
+
 ifdef LLAMA_DEBUG
 	MK_CFLAGS   += -O0 -g
 	MK_CXXFLAGS += -O0 -g
 	MK_LDFLAGS  += -g
 
 	ifeq ($(UNAME_S),Linux)
-		MK_CXXFLAGS += -Wp,-D_GLIBCXX_ASSERTIONS
+		MK_CPPFLAGS += -D_GLIBCXX_ASSERTIONS
 	endif
 else
 	MK_CPPFLAGS += -DNDEBUG
@@ -191,6 +206,10 @@ ifdef LLAMA_SERVER_VERBOSE
 	MK_CPPFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE)
 endif
 
+ifdef LLAMA_SERVER_SSL
+	MK_CPPFLAGS += -DCPPHTTPLIB_OPENSSL_SUPPORT
+	MK_LDFLAGS += -lssl -lcrypto
+endif
 
 ifdef LLAMA_CODE_COVERAGE
 	MK_CXXFLAGS += -fprofile-arcs -ftest-coverage -dumpbase ''
@@ -206,6 +225,11 @@ MK_CFLAGS    += $(WARN_FLAGS) -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmis
 				-Werror=implicit-function-declaration
 MK_CXXFLAGS  += $(WARN_FLAGS) -Wmissing-declarations -Wmissing-noreturn
 
+ifeq ($(LLAMA_FATAL_WARNINGS),1)
+	MK_CFLAGS   += -Werror
+	MK_CXXFLAGS += -Werror
+endif
+
 # this version of Apple ld64 is buggy
 ifneq '' '$(findstring dyld-1015.7,$(shell $(CC) $(LDFLAGS) -Wl,-v 2>&1))'
 	MK_CPPFLAGS += -DHAVE_BUGGY_APPLE_LINKER
@@ -366,10 +390,24 @@ ifdef LLAMA_BLIS
 endif # LLAMA_BLIS
 
 ifdef LLAMA_CUBLAS
-	MK_CPPFLAGS  += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include -I/usr/local/cuda/targets/aarch64-linux/include
-	MK_LDFLAGS   += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib -L/usr/local/cuda/targets/aarch64-linux/lib -L/usr/lib/wsl/lib
+# LLAMA_CUBLAS is deprecated and will be removed in the future
+	LLAMA_CUDA := 1
+endif
+
+ifdef LLAMA_CUDA
+	ifneq ('', '$(wildcard /opt/cuda)')
+		CUDA_PATH ?= /opt/cuda
+	else
+		CUDA_PATH ?= /usr/local/cuda
+	endif
+	MK_CPPFLAGS  += -DGGML_USE_CUDA -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include
+	MK_LDFLAGS   += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L/usr/lib/wsl/lib
 	OBJS         += ggml-cuda.o
-	MK_NVCCFLAGS  = -use_fast_math
+	OBJS         += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu))
+	MK_NVCCFLAGS += -use_fast_math
+ifdef LLAMA_FATAL_WARNINGS
+	MK_NVCCFLAGS += -Werror all-warnings
+endif # LLAMA_FATAL_WARNINGS
 ifndef JETSON_EOL_MODULE_DETECT
 	MK_NVCCFLAGS += --forward-unknown-to-host-compiler
 endif # JETSON_EOL_MODULE_DETECT
@@ -377,9 +415,9 @@ ifdef LLAMA_DEBUG
 	MK_NVCCFLAGS += -lineinfo
 endif # LLAMA_DEBUG
 ifdef LLAMA_CUDA_NVCC
-	NVCC = $(LLAMA_CUDA_NVCC)
+	NVCC = $(CCACHE) $(LLAMA_CUDA_NVCC)
 else
-	NVCC = nvcc
+	NVCC = $(CCACHE) nvcc
 endif #LLAMA_CUDA_NVCC
 ifdef CUDA_DOCKER_ARCH
 	MK_NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH)
@@ -420,19 +458,30 @@ ifdef LLAMA_CUDA_PEER_MAX_BATCH_SIZE
 else
 	MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128
 endif # LLAMA_CUDA_PEER_MAX_BATCH_SIZE
-#ifdef LLAMA_CUDA_CUBLAS
-#	MK_NVCCFLAGS += -DGGML_CUDA_CUBLAS
-#endif # LLAMA_CUDA_CUBLAS
+ifdef LLAMA_CUDA_NO_PEER_COPY
+	MK_NVCCFLAGS += -DGGML_CUDA_NO_PEER_COPY
+endif # LLAMA_CUDA_NO_PEER_COPY
 ifdef LLAMA_CUDA_CCBIN
 	MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
 endif
-ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
+
 ifdef JETSON_EOL_MODULE_DETECT
-	$(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
+define NVCC_COMPILE
+	$(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUDA -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
+endef # NVCC_COMPILE
 else
-	$(NVCC) $(BASE_CXXFLAGS) $(NVCCFLAGS) -Wno-pedantic -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
+define NVCC_COMPILE
+	$(NVCC) $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
+endef # NVCC_COMPILE
 endif # JETSON_EOL_MODULE_DETECT
-endif # LLAMA_CUBLAS
+
+ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/common.cuh
+	$(NVCC_COMPILE)
+
+ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh)
+	$(NVCC_COMPILE)
+
+endif # LLAMA_CUDA
 
 ifdef LLAMA_CLBLAST
 
@@ -452,8 +501,32 @@ ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 endif # LLAMA_CLBLAST
 
-ifdef LLAMA_HIPBLAS
+ifdef LLAMA_VULKAN
+	MK_CPPFLAGS  += -DGGML_USE_VULKAN
+	MK_LDFLAGS += -lvulkan
+	OBJS    += ggml-vulkan.o
 
+ifdef LLAMA_VULKAN_CHECK_RESULTS
+	MK_CPPFLAGS  += -DGGML_VULKAN_CHECK_RESULTS
+endif
+
+ifdef LLAMA_VULKAN_DEBUG
+	MK_CPPFLAGS  += -DGGML_VULKAN_DEBUG
+endif
+
+ifdef LLAMA_VULKAN_VALIDATE
+	MK_CPPFLAGS  += -DGGML_VULKAN_VALIDATE
+endif
+
+ifdef LLAMA_VULKAN_RUN_TESTS
+	MK_CPPFLAGS  += -DGGML_VULKAN_RUN_TESTS
+endif
+
+ggml-vulkan.o: ggml-vulkan.cpp ggml-vulkan.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+endif # LLAMA_VULKAN
+
+ifdef LLAMA_HIPBLAS
 	ifeq ($(wildcard /opt/rocm),)
 		ROCM_PATH	?= /usr
 		GPU_TARGETS ?= $(shell $(shell which amdgpu-arch))
@@ -461,11 +534,11 @@ ifdef LLAMA_HIPBLAS
 		ROCM_PATH	?= /opt/rocm
 		GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch)
 	endif
-	HIPCC                   ?= $(ROCM_PATH)/bin/hipcc
+	HIPCC                   ?= $(CCACHE) $(ROCM_PATH)/bin/hipcc
 	LLAMA_CUDA_DMMV_X       ?= 32
 	LLAMA_CUDA_MMV_Y        ?= 1
 	LLAMA_CUDA_KQUANTS_ITER ?= 2
-	MK_CPPFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS
+	MK_CPPFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUDA
 ifdef LLAMA_HIP_UMA
 	MK_CPPFLAGS += -DGGML_HIP_UMA
 endif # LLAMA_HIP_UMA
@@ -478,9 +551,18 @@ endif # LLAMA_HIP_UMA
 ifdef LLAMA_CUDA_FORCE_DMMV
 	HIPFLAGS 	+= -DGGML_CUDA_FORCE_DMMV
 endif # LLAMA_CUDA_FORCE_DMMV
+ifdef LLAMA_CUDA_NO_PEER_COPY
+	HIPFLAGS 	+= -DGGML_CUDA_NO_PEER_COPY
+endif # LLAMA_CUDA_NO_PEER_COPY
 	OBJS        += ggml-cuda.o
-ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
+	OBJS        += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu))
+
+ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh)
 	$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
+
+ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/common.cuh
+	$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
+
 endif # LLAMA_HIPBLAS
 
 ifdef LLAMA_METAL
@@ -490,11 +572,30 @@ ifdef LLAMA_METAL
 ifdef LLAMA_METAL_NDEBUG
 	MK_CPPFLAGS += -DGGML_METAL_NDEBUG
 endif
+ifdef LLAMA_METAL_EMBED_LIBRARY
+	MK_CPPFLAGS += -DGGML_METAL_EMBED_LIBRARY
+	OBJS        += ggml-metal-embed.o
+endif
 endif # LLAMA_METAL
 
 ifdef LLAMA_METAL
-ggml-metal.o: ggml-metal.m ggml-metal.h
+ggml-metal.o: ggml-metal.m ggml-metal.h ggml.h
 	$(CC) $(CFLAGS) -c $< -o $@
+
+ifdef LLAMA_METAL_EMBED_LIBRARY
+ggml-metal-embed.o: ggml-metal.metal ggml-common.h
+	@echo "Embedding Metal library"
+	@sed -e '/#include "ggml-common.h"/r ggml-common.h' -e '/#include "ggml-common.h"/d' < ggml-metal.metal > ggml-metal-embed.metal
+	$(eval TEMP_ASSEMBLY=$(shell mktemp))
+	@echo ".section __DATA, __ggml_metallib"   >  $(TEMP_ASSEMBLY)
+	@echo ".globl _ggml_metallib_start"        >> $(TEMP_ASSEMBLY)
+	@echo "_ggml_metallib_start:"              >> $(TEMP_ASSEMBLY)
+	@echo ".incbin \"ggml-metal-embed.metal\"" >> $(TEMP_ASSEMBLY)
+	@echo ".globl _ggml_metallib_end"          >> $(TEMP_ASSEMBLY)
+	@echo "_ggml_metallib_end:"                >> $(TEMP_ASSEMBLY)
+	@$(AS) $(TEMP_ASSEMBLY) -o $@
+	@rm -f ${TEMP_ASSEMBLY}
+endif
 endif # LLAMA_METAL
 
 ifdef LLAMA_MPI
@@ -506,17 +607,23 @@ GF_CC := $(CC)
 include scripts/get-flags.mk
 
 # combine build flags with cmdline overrides
-override CFLAGS    := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CFLAGS) $(GF_CFLAGS) $(CFLAGS)
-BASE_CXXFLAGS      := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CXXFLAGS) $(CXXFLAGS)
-override CXXFLAGS  := $(BASE_CXXFLAGS) $(HOST_CXXFLAGS) $(GF_CXXFLAGS)
+override CPPFLAGS  := $(MK_CPPFLAGS) $(CPPFLAGS)
+override CFLAGS    := $(CPPFLAGS) $(MK_CFLAGS) $(GF_CFLAGS) $(CFLAGS)
+BASE_CXXFLAGS      := $(MK_CXXFLAGS) $(CXXFLAGS)
+override CXXFLAGS  := $(BASE_CXXFLAGS) $(HOST_CXXFLAGS) $(GF_CXXFLAGS) $(CPPFLAGS)
 override NVCCFLAGS := $(MK_NVCCFLAGS) $(NVCCFLAGS)
 override LDFLAGS   := $(MK_LDFLAGS) $(LDFLAGS)
 
 # identify CUDA host compiler
-ifdef LLAMA_CUBLAS
+ifdef LLAMA_CUDA
 GF_CC := $(NVCC) $(NVCCFLAGS) 2>/dev/null .c -Xcompiler
 include scripts/get-flags.mk
-CUDA_CXXFLAGS := $(GF_CXXFLAGS)
+CUDA_CXXFLAGS := $(BASE_CXXFLAGS) $(GF_CXXFLAGS) -Wno-pedantic
+endif
+
+ifdef LLAMA_CURL
+override CXXFLAGS := $(CXXFLAGS) -DLLAMA_USE_CURL
+override LDFLAGS  := $(LDFLAGS) -lcurl
 endif
 
 #
@@ -531,10 +638,28 @@ $(info I CFLAGS:    $(CFLAGS))
 $(info I CXXFLAGS:  $(CXXFLAGS))
 $(info I NVCCFLAGS: $(NVCCFLAGS))
 $(info I LDFLAGS:   $(LDFLAGS))
-$(info I CC:        $(shell $(CC) --version | head -n 1))
-$(info I CXX:       $(shell $(CXX) --version | head -n 1))
+$(info I CC:        $(shell $(CC)   --version | head -n 1))
+$(info I CXX:       $(shell $(CXX)  --version | head -n 1))
+ifdef LLAMA_CUDA
+$(info I NVCC:      $(shell $(NVCC) --version | tail -n 1))
+CUDA_VERSION := $(shell $(NVCC) --version | grep -oP 'release (\K[0-9]+\.[0-9])')
+ifeq ($(shell awk -v "v=$(CUDA_VERSION)" 'BEGIN { print (v < 11.7) }'),1)
+ifndef CUDA_DOCKER_ARCH
+ifndef CUDA_POWER_ARCH
+$(error I ERROR: For CUDA versions < 11.7 a target CUDA architecture must be explicitly provided via CUDA_DOCKER_ARCH)
+endif # CUDA_POWER_ARCH
+endif # CUDA_DOCKER_ARCH
+endif # eq ($(shell echo "$(CUDA_VERSION) < 11.7" | bc),1)
+endif # LLAMA_CUDA
 $(info )
 
+ifdef LLAMA_CUBLAS
+$(info !!!!)
+$(info LLAMA_CUBLAS is deprecated and will be removed in the future. Use LLAMA_CUDA instead.)
+$(info !!!!)
+$(info )
+endif
+
 #
 # Build library
 #
@@ -548,12 +673,18 @@ ggml-alloc.o: ggml-alloc.c ggml.h ggml-alloc.h
 ggml-backend.o: ggml-backend.c ggml.h ggml-backend.h
 	$(CC)  $(CFLAGS)   -c $< -o $@
 
-ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h
+ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h ggml-common.h
 	$(CC) $(CFLAGS)    -c $< -o $@
 
-OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o
+unicode.o: unicode.cpp unicode.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-llama.o: llama.cpp ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
+unicode-data.o: unicode-data.cpp unicode-data.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+
+OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o unicode-data.o
+
+llama.o: llama.cpp unicode.h ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
 COMMON_H_DEPS = common/common.h common/sampling.h common/log.h
@@ -571,107 +702,174 @@ console.o: common/console.cpp common/console.h
 grammar-parser.o: common/grammar-parser.cpp common/grammar-parser.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
+json-schema-to-grammar.o: common/json-schema-to-grammar.cpp common/json-schema-to-grammar.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+
 train.o: common/train.cpp common/train.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
+ngram-cache.o: common/ngram-cache.cpp common/ngram-cache.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+
 libllama.so: llama.o ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
 
+libllama.a: llama.o ggml.o $(OBJS) $(COMMON_DEPS)
+	ar rcs libllama.a llama.o ggml.o $(OBJS) $(COMMON_DEPS)
+
 clean:
-	rm -vrf *.o tests/*.o *.so *.dll benchmark-matmult common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS)
+	rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult lookup-create lookup-merge lookup-stats common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS)
+	rm -vrf ggml-cuda/*.o
+	find examples pocs -type f -name "*.o" -delete
 
 #
 # Examples
 #
 
+# $< is the first prerequisite, i.e. the source file.
+# Explicitly compile this to an object file so that it can be cached with ccache.
+# The source file is then filtered out from $^ (the list of all prerequisites) and the object file is added instead.
+
+# Helper function that replaces .c, .cpp, and .cu file endings with .o:
+GET_OBJ_FILE = $(patsubst %.c,%.o,$(patsubst %.cpp,%.o,$(patsubst %.cu,%.o,$(1))))
+
 main: examples/main/main.cpp                                  ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 	@echo
 	@echo '====  Run ./main -h for help.  ===='
 	@echo
 
 infill: examples/infill/infill.cpp                            ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 simple: examples/simple/simple.cpp                            ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tokenize: examples/tokenize/tokenize.cpp                      ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 batched: examples/batched/batched.cpp                         ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 batched-bench: examples/batched-bench/batched-bench.cpp       build-info.o ggml.o llama.o common.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 quantize: examples/quantize/quantize.cpp                      build-info.o ggml.o llama.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 quantize-stats: examples/quantize-stats/quantize-stats.cpp    build-info.o ggml.o llama.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 perplexity: examples/perplexity/perplexity.cpp                ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
+imatrix: examples/imatrix/imatrix.cpp                         ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 embedding: examples/embedding/embedding.cpp                   ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
+gritlm: examples/gritlm/gritlm.cpp                         ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
-	$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) -Wno-cast-qual
+server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp json-schema-to-grammar.o common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
 
 gguf: examples/gguf/gguf.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
+gguf-split: examples/gguf-split/gguf-split.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp ggml.o llama.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 llama-bench: examples/llama-bench/llama-bench.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 libllava.a: examples/llava/llava.cpp examples/llava/llava.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h common/base64.hpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual
 
 llava-cli: examples/llava/llava-cli.cpp examples/llava/clip.h examples/llava/clip.cpp examples/llava/llava.h examples/llava/llava.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp  -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual
+	$(CXX) $(CXXFLAGS) -c examples/llava/llava.cpp -o $(call GET_OBJ_FILE, examples/llava/llava.cpp)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $< examples/llava/clip.cpp examples/llava/llava.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) $(call GET_OBJ_FILE, examples/llava/llava.cpp) -o $@ $(LDFLAGS)
 
 baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 beam-search: examples/beam-search/beam-search.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 export-lora: examples/export-lora/export-lora.cpp ggml.o common/common.h $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
+retrieval: examples/retrieval/retrieval.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+lookup: examples/lookup/lookup.cpp ggml.o llama.o ngram-cache.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c examples/lookup/lookup-create.cpp -o $(call GET_OBJ_FILE, examples/lookup/lookup-create.cpp)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, examples/lookup/lookup-create.cpp) -o lookup-create $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c examples/lookup/lookup-merge.cpp -o $(call GET_OBJ_FILE, examples/lookup/lookup-merge.cpp)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, examples/lookup/lookup-merge.cpp) -o lookup-merge $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c examples/lookup/lookup-stats.cpp -o $(call GET_OBJ_FILE, examples/lookup/lookup-stats.cpp)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, examples/lookup/lookup-stats.cpp) -o lookup-stats $(LDFLAGS)
 
 passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-ifdef LLAMA_METAL
-metal: examples/metal/metal.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-endif
+gbnf-validator: examples/gbnf-validator/gbnf-validator.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 ifeq ($(UNAME_S),Darwin)
 swift: examples/batched.swift
@@ -679,7 +877,7 @@ swift: examples/batched.swift
 endif
 
 common/build-info.cpp: $(wildcard .git/index) scripts/build-info.sh
-	@sh scripts/build-info.sh $(CC) > $@.tmp
+	@sh scripts/build-info.sh "$(CC)" > $@.tmp
 	@if ! cmp -s $@.tmp $@; then \
 		mv $@.tmp $@; \
 	else \
@@ -696,7 +894,8 @@ build-info.o: common/build-info.cpp
 tests: $(TEST_TARGETS)
 
 benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.o ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 run-benchmark-matmult: benchmark-matmult
 	./$@
@@ -704,52 +903,84 @@ run-benchmark-matmult: benchmark-matmult
 .PHONY: run-benchmark-matmult swift
 
 vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 q8dot: pocs/vdot/q8dot.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o grammar-parser.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o grammar-parser.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-double-float: tests/test-double-float.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
+tests/test-json-schema-to-grammar: tests/test-json-schema-to-grammar.cpp json-schema-to-grammar.o ggml.o llama.o grammar-parser.o $(OBJS)
+	$(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-grad0: tests/test-grad0.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-opt: tests/test-opt.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-rope: tests/test-rope.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-c.o: tests/test-c.c llama.h
 	$(CC) $(CFLAGS) -c $(filter-out %.h,$^) -o $@
 
 tests/test-backend-ops: tests/test-backend-ops.cpp ggml.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
+tests/test-model-load-cancel: tests/test-model-load-cancel.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
+tests/test-autorelease: tests/test-autorelease.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
+tests/test-chat-template: tests/test-chat-template.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
diff --git a/Package.swift b/Package.swift
index 583e2e276..8b7195869 100644
--- a/Package.swift
+++ b/Package.swift
@@ -13,17 +13,33 @@ let package = Package(
     products: [
         .library(name: "llama", targets: ["llama"]),
     ],
-    dependencies: [
-        .package(url: "https://github.com/ggerganov/ggml.git", .branch("master"))
-    ],
     targets: [
         .target(
             name: "llama",
-            dependencies: ["ggml"],
             path: ".",
-            exclude: ["ggml-metal.metal"],
+            exclude: [
+               "cmake",
+               "examples",
+               "scripts",
+               "models",
+               "tests",
+               "CMakeLists.txt",
+               "ggml-cuda.cu",
+               "ggml-cuda.h",
+               "Makefile"
+            ],
             sources: [
+                "ggml.c",
                 "llama.cpp",
+                "unicode.cpp",
+                "unicode-data.cpp",
+                "ggml-alloc.c",
+                "ggml-backend.c",
+                "ggml-quants.c",
+                "ggml-metal.m",
+            ],
+            resources: [
+                .process("ggml-metal.metal")
             ],
             publicHeadersPath: "spm-headers",
             cSettings: [
diff --git a/README-sycl.md b/README-sycl.md
new file mode 100644
index 000000000..1324f632d
--- /dev/null
+++ b/README-sycl.md
@@ -0,0 +1,597 @@
+# llama.cpp for SYCL
+
+- [Background](#background)
+- [News](#news)
+- [OS](#os)
+- [Supported Devices](#supported-devices)
+- [Docker](#docker)
+- [Linux](#linux)
+- [Windows](#windows)
+- [Environment Variable](#environment-variable)
+- [Known Issue](#known-issue)
+- [Q&A](#q&a)
+- [Todo](#todo)
+
+## Background
+
+**SYCL** is a high-level parallel programming model designed to improve developers productivity writing code across various hardware accelerators such as CPUs, GPUs, and FPGAs. It is a single-source language designed for heterogeneous computing and based on standard C++17.
+
+**oneAPI** is an open ecosystem and a standard-based specification, supporting multiple architectures including but not limited to intel CPUs, GPUs and FPGAs. The key components of the oneAPI ecosystem include:
+
+- **DPCPP** *(Data Parallel C++)*: The primary oneAPI SYCL implementation, which includes the icpx/icx Compilers.
+- **oneAPI Libraries**: A set of highly optimized libraries targeting multiple domains *(e.g. oneMKL - Math Kernel Library)*.
+- **oneAPI LevelZero**: A high performance low level interface for fine-grained control over intel iGPUs and dGPUs.
+- **Nvidia & AMD Plugins**: These are plugins extending oneAPI's DPCPP support to SYCL on Nvidia and AMD GPU targets.
+
+### Llama.cpp + SYCL
+This SYCL "backend" follows the same design found in other llama.cpp BLAS-based paths such as *OpenBLAS, cuBLAS, CLBlast etc..*. The oneAPI's [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) open-source migration tool (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) was used for this purpose.
+
+The llama.cpp SYCL backend supports:
+- Intel GPUs.
+- Nvidia GPUs.
+
+*Upcoming support: AMD GPUs*.
+
+When targetting **Intel CPUs**, it is recommended to  use llama.cpp for [x86_64](README.md#intel-onemkl) approach.
+
+## News
+
+- 2024.3
+  - A blog is published: **Run LLM on all Intel GPUs Using llama.cpp**: [intel.com](https://www.intel.com/content/www/us/en/developer/articles/technical/run-llm-on-all-gpus-using-llama-cpp-artical.html) or [medium.com](https://medium.com/@jianyu_neo/run-llm-on-all-intel-gpus-using-llama-cpp-fd2e2dcbd9bd).
+  - New base line is ready: [tag b2437](https://github.com/ggerganov/llama.cpp/tree/b2437).
+  - Support multiple cards: **--split-mode**: [none|layer]; not support [row], it's on developing.
+  - Support to assign main GPU by **--main-gpu**, replace $GGML_SYCL_DEVICE.
+  - Support detecting all GPUs with level-zero and same top **Max compute units**.
+  - Support OPs
+    - hardsigmoid
+    - hardswish
+    - pool2d
+
+- 2024.1
+  - Create SYCL backend for Intel GPU.
+  - Support Windows build
+
+## OS
+
+|OS|Status|Verified|
+|-|-|-|
+|Linux|Support|Ubuntu 22.04, Fedora Silverblue 39|
+|Windows|Support|Windows 11|
+
+
+## Supported devices
+
+### Intel GPUs
+
+The oneAPI Math Kernel Library, which the oneAPI base-toolkit includes, supports intel GPUs. In order to make it "visible", simply run the following:
+```sh
+source /opt/intel/oneapi/setvars.sh
+```
+
+- **Tested devices**
+
+|Intel GPU| Status | Verified Model|
+|-|-|-|
+|Intel Data Center Max Series| Support| Max 1550|
+|Intel Data Center Flex Series| Support| Flex 170|
+|Intel Arc Series| Support| Arc 770, 730M|
+|Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake|
+|Intel iGPU| Support| iGPU in i5-1250P, i7-1260P, i7-1165G7|
+
+*Notes:*
+
+- Device memory can be a limitation when running a large model on an intel GPU. The loaded model size, *`llm_load_tensors: buffer_size`*, is displayed in the log when running `./bin/main`.
+
+- Please make sure the GPU shared memory from the host is large enough to account for the model's size. For e.g. the *llama-2-7b.Q4_0* requires at least 8.0GB for integrated GPUs and 4.0GB for discrete GPUs.
+
+- If the iGPU has less than 80  EUs *(Execution Unit)*, the inference speed will likely be too slow for practical use.
+
+### Nvidia GPUs
+The BLAS acceleration on Nvidia GPUs through oneAPI can be obtained using the Nvidia plugins for oneAPI and the cuBLAS backend of the upstream oneMKL library. Details and instructions on how to setup the runtime and library can be found in [this section](#i-setup-environment)
+
+- **Tested devices**
+
+|Nvidia GPU| Status | Verified Model|
+|-|-|-|
+|Ampere Series| Support| A100, A4000|
+|Ampere Series *(Mobile)*| Support| RTX 40 Series|
+
+*Notes:*
+  - Support for Nvidia targets through oneAPI is currently limited to Linux platforms.
+
+  - Please make sure the native oneAPI MKL *(dedicated to intel CPUs and GPUs)* is not "visible" at this stage to properly setup and use the built-from-source oneMKL with cuBLAS backend in llama.cpp for Nvidia GPUs.
+
+
+## Docker
+The docker build option is currently limited to *intel GPU* targets.
+### Build image
+```sh
+# Using FP16
+docker build -t llama-cpp-sycl --build-arg="LLAMA_SYCL_F16=ON" -f .devops/main-intel.Dockerfile .
+```
+
+*Notes*:
+
+To build in default FP32 *(Slower than FP16 alternative)*, you can remove the `--build-arg="LLAMA_SYCL_F16=ON"` argument from the previous command.
+
+You can also use the `.devops/server-intel.Dockerfile`, which builds the *"server"* alternative.
+
+### Run container
+
+```sh
+# First, find all the DRI cards
+ls -la /dev/dri
+# Then, pick the card that you want to use (here for e.g. /dev/dri/card1).
+docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-sycl -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
+```
+
+*Notes:*
+- Docker has been tested successfully on native Linux. WSL support has not been verified yet.
+- You may need to install Intel GPU driver on the **host** machine *(Please refer to the [Linux configuration](#linux) for details)*.
+
+## Linux
+
+### I. Setup Environment
+
+1. **Install GPU drivers**
+
+  - **Intel GPU**
+
+Intel data center GPUs drivers installation guide and download page can be found here: [Get intel dGPU Drivers](https://dgpu-docs.intel.com/driver/installation.html#ubuntu-install-steps).
+
+*Note*: for client GPUs *(iGPU & Arc A-Series)*, please refer to the [client iGPU driver installation](https://dgpu-docs.intel.com/driver/client/overview.html).
+
+Once installed, add the user(s) to the `video` and `render` groups.
+
+```sh
+sudo usermod -aG render $USER
+sudo usermod -aG video $USER
+```
+
+*Note*: logout/re-login for the changes to take effect.
+
+Verify installation through `clinfo`:
+
+```sh
+sudo apt install clinfo
+sudo clinfo -l
+```
+
+Sample output:
+
+```sh
+Platform #0: Intel(R) OpenCL Graphics
+ `-- Device #0: Intel(R) Arc(TM) A770 Graphics
+
+Platform #0: Intel(R) OpenCL HD Graphics
+ `-- Device #0: Intel(R) Iris(R) Xe Graphics [0x9a49]
+```
+
+- **Nvidia GPU**
+
+In order to target Nvidia GPUs through SYCL, please make sure the CUDA/CUBLAS native requirements *-found [here](README.md#cublas)-* are installed.
+Installation can be verified by running the following:
+```sh
+nvidia-smi
+```
+Please make sure at least one CUDA device is available, which can be displayed like this *(here an A100-40GB Nvidia GPU)*:
+```
++---------------------------------------------------------------------------------------+
+| NVIDIA-SMI 535.54.03              Driver Version: 535.54.03    CUDA Version: 12.2     |
+|-----------------------------------------+----------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
+|                                         |                      |               MIG M. |
+|=========================================+======================+======================|
+|   0  NVIDIA A100-PCIE-40GB          On  | 00000000:8D:00.0 Off |                    0 |
+| N/A   36C    P0              57W / 250W |      4MiB / 40960MiB |      0%      Default |
+|                                         |                      |             Disabled |
++-----------------------------------------+----------------------+----------------------+
+```
+
+
+2. **Install Intel® oneAPI Base toolkit**
+
+- **Base installation**
+
+The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page.
+
+Please follow the instructions for downloading and installing the Toolkit for Linux, and preferably keep the default installation values unchanged, notably the installation path *(`/opt/intel/oneapi` by default)*.
+
+Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable.
+
+Upon a successful installation, SYCL is enabled for the available intel devices, along with relevant libraries such as oneAPI MKL for intel GPUs.
+
+- **Adding support to Nvidia GPUs**
+
+**oneAPI**: In order to enable SYCL support on Nvidia GPUs, please install the [Codeplay oneAPI Plugin for Nvidia GPUs](https://developer.codeplay.com/products/oneapi/nvidia/download). User should also make sure the plugin version matches the installed base toolkit one *(previous step)* for a seamless "oneAPI on Nvidia GPU" setup.
+
+
+**oneMKL**: The current oneMKL releases *(shipped with the oneAPI base-toolkit)* do not contain the cuBLAS backend. A build from source of the upstream [oneMKL](https://github.com/oneapi-src/oneMKL) with the *cuBLAS* backend enabled is thus required to run it on Nvidia GPUs.
+
+```sh
+git clone https://github.com/oneapi-src/oneMKL
+cd oneMKL
+mkdir -p buildWithCublas && cd buildWithCublas
+cmake ../ -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON -DTARGET_DOMAINS=blas
+make
+```
+
+
+3. **Verify installation and environment**
+
+In order to check the available SYCL devices on the machine, please use the `sycl-ls` command.
+```sh
+source /opt/intel/oneapi/setvars.sh
+sycl-ls
+```
+
+- **Intel GPU**
+
+When targeting an intel GPU, the user should expect one or more level-zero devices among the available SYCL devices. Please make sure that at least one GPU is present, for instance [`ext_oneapi_level_zero:gpu:0`] in the sample output below:
+
+```
+[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2  [2023.16.10.0.17_160000]
+[opencl:cpu:1] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i7-13700K OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000]
+[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO  [23.30.26918.50]
+[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Arc(TM) A770 Graphics 1.3 [1.3.26918]
+```
+
+- **Nvidia GPU**
+
+Similarly, user targetting Nvidia GPUs should expect at least one SYCL-CUDA device [`ext_oneapi_cuda:gpu`] as bellow:
+```
+[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2  [2023.16.12.0.12_195853.xmain-hotfix]
+[opencl:cpu:1] Intel(R) OpenCL, Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz OpenCL 3.0 (Build 0) [2023.16.12.0.12_195853.xmain-hotfix]
+[ext_oneapi_cuda:gpu:0] NVIDIA CUDA BACKEND, NVIDIA A100-PCIE-40GB 8.0 [CUDA 12.2]
+```
+
+### II. Build llama.cpp
+
+#### Intel GPU
+```sh
+# Export relevant ENV variables
+source /opt/intel/oneapi/setvars.sh
+
+# Build LLAMA with MKL BLAS acceleration for intel GPU
+mkdir -p build && cd build
+
+# Option 1: Use FP16 for better performance in long-prompt  inference
+cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
+
+# Option 2: Use FP32 by default
+cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
+```
+
+#### Nvidia GPU
+```sh
+# Export relevant ENV variables
+export LD_LIBRARY_PATH=/path/to/oneMKL/buildWithCublas/lib:$LD_LIBRARY_PATH
+export LIBRARY_PATH=/path/to/oneMKL/buildWithCublas/lib:$LIBRARY_PATH
+export CPLUS_INCLUDE_DIR=/path/to/oneMKL/buildWithCublas/include:$CPLUS_INCLUDE_DIR
+export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$CPLUS_INCLUDE_DIR
+
+# Build LLAMA with Nvidia BLAS acceleration through SYCL
+mkdir -p build && cd build
+
+# Option 1: Use FP16 for better performance in long-prompt  inference
+cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
+
+# Option 2: Use FP32 by default
+cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
+```
+
+### III. Run the inference
+
+1. Retrieve and prepare model
+
+You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example.
+
+2. Enable oneAPI running environment
+
+```sh
+source /opt/intel/oneapi/setvars.sh
+```
+
+3. List devices information
+
+Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:
+
+```sh
+./build/bin/ls-sycl-device
+```
+A example of such log in a system with 1 *intel CPU* and 1 *intel GPU* can look like the following:
+```
+found 6 SYCL devices:
+|  |                  |                                             |Compute   |Max compute|Max work|Max sub|               |
+|ID|       Device Type|                                         Name|capability|units      |group   |group  |Global mem size|
+|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
+| 0|[level_zero:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       1.3|        512|    1024|     32|    16225243136|
+| 1|[level_zero:gpu:1]|                    Intel(R) UHD Graphics 770|       1.3|         32|     512|     32|    53651849216|
+| 2|    [opencl:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       3.0|        512|    1024|     32|    16225243136|
+| 3|    [opencl:gpu:1]|                    Intel(R) UHD Graphics 770|       3.0|         32|     512|     32|    53651849216|
+| 4|    [opencl:cpu:0]|         13th Gen Intel(R) Core(TM) i7-13700K|       3.0|         24|    8192|     64|    67064815616|
+| 5|    [opencl:acc:0]|               Intel(R) FPGA Emulation Device|       1.2|         24|67108864|     64|    67064815616|
+```
+
+|Attribute|Note|
+|-|-|
+|compute capability 1.3|Level-zero driver/runtime, recommended |
+|compute capability 3.0|OpenCL driver/runtime, slower than level-zero in most cases|
+
+4. Launch inference
+
+There are two device selection modes:
+
+- Single device: Use one device target specified by the user.
+- Multiple devices: Automatically select the devices with the same largest Max compute-units.
+
+|Device selection|Parameter|
+|-|-|
+|Single device|--split-mode none --main-gpu DEVICE_ID |
+|Multiple devices|--split-mode layer (default)|
+
+Examples:
+
+- Use device 0:
+
+```sh
+ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm none -mg 0
+```
+or run by script:
+
+```sh
+./examples/sycl/run_llama2.sh 0
+```
+
+- Use multiple devices:
+
+```sh
+ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer
+```
+
+Otherwise, you can run the script:
+
+```sh
+./examples/sycl/run_llama2.sh
+```
+
+*Notes:*
+
+- By default, `mmap` is used to read the model file. In some cases, it causes runtime hang issues. Please disable it by passing `--no-mmap` to the `/bin/main` if faced with the issue.
+- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow:
+
+```sh
+detect 1 SYCL GPUs: [0] with top Max compute units:512
+```
+Or
+```sh
+use 1 SYCL GPUs: [0] with Max compute units:512
+```
+
+## Windows
+
+### I. Setup Environment
+
+1. Install GPU driver
+
+Intel GPU drivers instructions guide and download page can be found here: [Get intel GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html).
+
+2. Install Visual Studio
+
+If you already have a recent version of Microsoft Visual Studio, you can skip this step. Otherwise, please refer to the official download page for [Microsoft Visual Studio](https://visualstudio.microsoft.com/).
+
+3. Install Intel® oneAPI Base toolkit
+
+The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page.
+
+Please follow the instructions for downloading and installing the Toolkit for Windows, and preferably keep the default installation values unchanged, notably the installation path *(`C:\Program Files (x86)\Intel\oneAPI` by default)*.
+
+Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable.
+
+b. Enable oneAPI running environment:
+
+- Type "oneAPI" in the search bar, then open the `Intel oneAPI command prompt for Intel 64 for Visual Studio 2022` App.
+
+- On the command prompt, enable the runtime environment with the following:
+```
+"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
+```
+
+c. Verify installation
+
+In the oneAPI command line, run the following to print the available SYCL devices:
+
+```
+sycl-ls
+```
+
+There should be one or more *level-zero* GPU devices displayed as **[ext_oneapi_level_zero:gpu]**. Below is example of such output detecting an *intel Iris Xe* GPU as a Level-zero SYCL device:
+
+Output (example):
+```
+[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2  [2023.16.10.0.17_160000]
+[opencl:cpu:1] Intel(R) OpenCL, 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000]
+[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Iris(R) Xe Graphics OpenCL 3.0 NEO  [31.0.101.5186]
+[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044]
+```
+
+4. Install build tools
+
+a. Download & install cmake for Windows: https://cmake.org/download/
+
+b. Download & install mingw-w64 make for Windows provided by w64devkit
+
+- Download the 1.19.0 version of [w64devkit](https://github.com/skeeto/w64devkit/releases/download/v1.19.0/w64devkit-1.19.0.zip).
+
+- Extract `w64devkit` on your pc.
+
+- Add the **bin** folder path in the Windows system PATH environment (for e.g. `C:\xxx\w64devkit\bin\`).
+
+### II. Build llama.cpp
+
+On the oneAPI command line window, step into the llama.cpp main directory and run the following:
+
+```
+mkdir -p build
+cd build
+@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
+
+cmake -G "MinGW Makefiles" ..  -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
+
+make
+```
+
+Otherwise, run the `win-build-sycl.bat` wrapper which encapsulates the former instructions:
+```sh
+.\examples\sycl\win-build-sycl.bat
+```
+
+*Notes:*
+
+- By default, calling `make` will build all target binary files. In case of a minimal experimental setup, the user can build the inference executable only through `make main`.
+
+### III. Run the inference
+
+1. Retrieve and prepare model
+
+You can refer to the general [*Prepare and Quantize*](README#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example.
+
+2. Enable oneAPI running environment
+
+On the oneAPI command line window, run the following and step into the llama.cpp directory:
+```
+"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
+```
+
+3. List devices information
+
+Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:
+
+```
+build\bin\ls-sycl-device.exe
+```
+
+The output of this command in a system with 1 *intel CPU* and 1 *intel GPU* would look like the following:
+```
+found 6 SYCL devices:
+|  |                  |                                             |Compute   |Max compute|Max work|Max sub|               |
+|ID|       Device Type|                                         Name|capability|units      |group   |group  |Global mem size|
+|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
+| 0|[level_zero:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       1.3|        512|    1024|     32|    16225243136|
+| 1|[level_zero:gpu:1]|                    Intel(R) UHD Graphics 770|       1.3|         32|     512|     32|    53651849216|
+| 2|    [opencl:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       3.0|        512|    1024|     32|    16225243136|
+| 3|    [opencl:gpu:1]|                    Intel(R) UHD Graphics 770|       3.0|         32|     512|     32|    53651849216|
+| 4|    [opencl:cpu:0]|         13th Gen Intel(R) Core(TM) i7-13700K|       3.0|         24|    8192|     64|    67064815616|
+| 5|    [opencl:acc:0]|               Intel(R) FPGA Emulation Device|       1.2|         24|67108864|     64|    67064815616|
+
+```
+
+|Attribute|Note|
+|-|-|
+|compute capability 1.3|Level-zero running time, recommended |
+|compute capability 3.0|OpenCL running time, slower than level-zero in most cases|
+
+
+4. Launch inference
+
+There are two device selection modes:
+
+- Single device: Use one device assigned by user.
+- Multiple devices: Automatically choose the devices with the same biggest Max compute units.
+
+|Device selection|Parameter|
+|-|-|
+|Single device|--split-mode none --main-gpu DEVICE_ID |
+|Multiple devices|--split-mode layer (default)|
+
+Examples:
+
+- Use device 0:
+
+```
+build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm none -mg 0
+```
+
+- Use multiple devices:
+
+```
+build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm layer
+```
+Otherwise, run the following wrapper script:
+
+```
+.\examples\sycl\win-run-llama2.bat
+```
+
+Note:
+
+- By default, `mmap` is used to read the model file. In some cases, it causes runtime hang issues. Please disable it by passing `--no-mmap` to the `main.exe` if faced with the issue.
+- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow:
+
+```sh
+detect 1 SYCL GPUs: [0] with top Max compute units:512
+```
+Or
+```sh
+use 1 SYCL GPUs: [0] with Max compute units:512
+```
+
+## Environment Variable
+
+#### Build
+
+|Name|Value|Function|
+|-|-|-|
+|LLAMA_SYCL|ON (mandatory)|Enable build with SYCL code path.|
+|LLAMA_SYCL_TARGET | INTEL *(default)* \| NVIDIA|Set the SYCL target device type.|
+|LLAMA_SYCL_F16|OFF *(default)* \|ON *(optional)*|Enable FP16 build with SYCL code path.|
+|CMAKE_C_COMPILER|icx|Set *icx* compiler for SYCL code path.|
+|CMAKE_CXX_COMPILER|icpx *(Linux)*, icx *(Windows)*|Set `icpx/icx` compiler for SYCL code path.|
+
+#### Runtime
+
+|Name|Value|Function|
+|-|-|-|
+|GGML_SYCL_DEBUG|0 (default) or 1|Enable log function by macro: GGML_SYCL_DEBUG|
+|ZES_ENABLE_SYSMAN| 0 (default) or 1|Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer|
+
+## Known Issues
+
+- Hanging during startup
+
+  llama.cpp uses *mmap* as the default mode for reading the model file and copying it to the GPU. In some systems, `memcpy` might behave abnormally and therefore hang.
+
+  - **Solution**: add `--no-mmap` or `--mmap 0` flag to the `main` executable.
+
+- `Split-mode:[row]` is not supported.
+
+## Q&A
+
+- Error:  `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`.
+
+  - Potential cause: Unavailable oneAPI installation or not set ENV variables.
+  - Solution: Install *oneAPI base toolkit* and enable its ENV through: `source /opt/intel/oneapi/setvars.sh`.
+
+- General compiler error:
+
+  - Remove build folder or try a clean-build.
+
+- I can **not** see `[ext_oneapi_level_zero:gpu]` afer installing the GPU driver on Linux.
+
+  Please double-check with `sudo sycl-ls`.
+
+  If it's present in the list, please add video/render group to your user then **logout/login** or restart your system:
+
+  ```
+  sudo usermod -aG render $USER
+  sudo usermod -aG video $USER
+  ```
+  Otherwise, please double-check the GPU driver installation steps.
+
+### **GitHub contribution**:
+Please add the **[SYCL]** prefix/tag in issues/PRs titles to help the SYCL-team check/address them without delay.
+
+## Todo
+
+- Support row layer split for multiple card runs.
diff --git a/README.md b/README.md
index 866aa87b4..bd3f9cff5 100644
--- a/README.md
+++ b/README.md
@@ -6,16 +6,25 @@
 
 [Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml)
 
-Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
+Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++
+
+### Recent API changes
+
+- [2024 Mar 26] Logits and embeddings API updated for compactness https://github.com/ggerganov/llama.cpp/pull/6122
+- [2024 Mar 13] Add `llama_synchronize()` + `llama_context_params.n_ubatch` https://github.com/ggerganov/llama.cpp/pull/6017
+- [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_seq_max()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328
+- [2024 Mar 4] Embeddings API updated https://github.com/ggerganov/llama.cpp/pull/5796
+- [2024 Mar 3] `struct llama_context_params` https://github.com/ggerganov/llama.cpp/pull/5849
 
 ### Hot topics
 
-- New SOTA quantized models, including pure 2-bits: https://huggingface.co/ikawrakow
-- Collecting Apple Silicon performance stats:
-  - M-series: https://github.com/ggerganov/llama.cpp/discussions/4167
-  - A-series: https://github.com/ggerganov/llama.cpp/discussions/4508
-- Added Mixtral support: https://github.com/ggerganov/llama.cpp/pull/4406
-- Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216
+- **MoE memory layout has been updated - reconvert models for `mmap` support and regenerate `imatrix` https://github.com/ggerganov/llama.cpp/pull/6387**
+- Model sharding instructions using `gguf-split` https://github.com/ggerganov/llama.cpp/discussions/6404
+- Fix major bug in Metal batched inference https://github.com/ggerganov/llama.cpp/pull/6225
+- Multi-GPU pipeline parallelism support https://github.com/ggerganov/llama.cpp/pull/6017
+- Looking for contributions to add Deepseek support: https://github.com/ggerganov/llama.cpp/issues/5981
+- Quantization blind testing: https://github.com/ggerganov/llama.cpp/discussions/5962
+- Initial Mamba support has been added: https://github.com/ggerganov/llama.cpp/pull/5328
 
 ----
 
@@ -31,17 +40,14 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
         <li><a href="#get-the-code">Get the Code</a></li>
         <li><a href="#build">Build</a></li>
         <li><a href="#blas-build">BLAS Build</a></li>
-        <li><a href="#prepare-data--run">Prepare Data & Run</a></li>
+        <li><a href="#prepare-and-quantize">Prepare and Quantize</a></li>
+        <li><a href="#run-the-quantized-model">Run the quantized model</a></li>
         <li><a href="#memorydisk-requirements">Memory/Disk Requirements</a></li>
         <li><a href="#quantization">Quantization</a></li>
         <li><a href="#interactive-mode">Interactive mode</a></li>
         <li><a href="#constrained-output-with-grammars">Constrained output with grammars</a></li>
-        <li><a href="#instruction-mode-with-alpaca">Instruction mode with Alpaca</a></li>
-        <li><a href="#using-openllama">Using OpenLLaMA</a></li>
-        <li><a href="#using-gpt4all">Using GPT4All</a></li>
-        <li><a href="#using-pygmalion-7b--metharme-7b">Using Pygmalion 7B & Metharme 7B</a></li>
-        <li><a href="#obtaining-the-facebook-llama-original-model-and-stanford-alpaca-model-data">Obtaining the Facebook LLaMA original model and Stanford Alpaca model data</a></li>
-        <li><a href="#verifying-the-model-files">Verifying the model files</a></li>
+        <li><a href="#instruct-mode">Instruct mode</a></li>
+        <li><a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a></li>
         <li><a href="#seminal-papers-and-background-on-the-models">Seminal papers and background on the models</a></li>
         <li><a href="#perplexity-measuring-model-quality">Perplexity (measuring model quality)</a></li>
         <li><a href="#android">Android</a></li>
@@ -56,18 +62,20 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
 
 ## Description
 
-The main goal of `llama.cpp` is to run the LLaMA model using 4-bit integer quantization on a MacBook
+The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide
+variety of hardware - locally and in the cloud.
 
-- Plain C/C++ implementation without dependencies
-- Apple silicon first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
+- Plain C/C++ implementation without any dependencies
+- Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
 - AVX, AVX2 and AVX512 support for x86 architectures
-- Mixed F16 / F32 precision
-- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit and 8-bit integer quantization support
-- CUDA, Metal and OpenCL GPU backend support
+- 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
+- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP)
+- Vulkan, SYCL, and (partial) OpenCL backend support
+- CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
 
-The original implementation of `llama.cpp` was [hacked in an evening](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022).
-Since then, the project has improved significantly thanks to many contributions. This project is mainly for educational purposes and serves
-as the main playground for developing new features for the [ggml](https://github.com/ggerganov/ggml) library.
+Since its [inception](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022), the project has
+improved significantly thanks to many contributions. It is the main playground for developing new features for the
+[ggml](https://github.com/ggerganov/ggml) library.
 
 **Supported platforms:**
 
@@ -75,44 +83,55 @@ as the main playground for developing new features for the [ggml](https://github
 - [X] Linux
 - [X] Windows (via CMake)
 - [X] Docker
+- [X] FreeBSD
 
 **Supported models:**
 
+Typically finetunes of the base models below are supported as well.
+
 - [X] LLaMA 🦙
 - [x] LLaMA 2 🦙🦙
+- [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
 - [X] Falcon
-- [X] [Alpaca](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca)
-- [X] [GPT4All](https://github.com/ggerganov/llama.cpp#using-gpt4all)
 - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
 - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
-- [X] [Vicuna](https://github.com/ggerganov/llama.cpp/discussions/643#discussioncomment-5533894)
 - [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
-- [X] [OpenBuddy 🐶 (Multilingual)](https://github.com/OpenBuddy/OpenBuddy)
-- [X] [Pygmalion/Metharme](#using-pygmalion-7b--metharme-7b)
-- [X] [WizardLM](https://github.com/nlpxucan/WizardLM)
 - [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft)
 - [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila)
 - [X] [Starcoder models](https://github.com/ggerganov/llama.cpp/pull/3187)
-- [X] [Mistral AI v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
 - [X] [Refact](https://huggingface.co/smallcloudai/Refact-1_6B-fim)
 - [X] [Persimmon 8B](https://github.com/ggerganov/llama.cpp/pull/3410)
 - [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417)
 - [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553)
 - [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi)
-- [X] [StableLM-3b-4e1t](https://github.com/ggerganov/llama.cpp/pull/3586)
+- [X] [StableLM models](https://huggingface.co/stabilityai)
 - [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek)
 - [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
-- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
 - [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557)
+- [x] [Phi models](https://huggingface.co/models?search=microsoft/phi)
 - [x] [GPT-2](https://huggingface.co/gpt2)
+- [x] [Orion 14B](https://github.com/ggerganov/llama.cpp/pull/5118)
+- [x] [InternLM2](https://huggingface.co/models?search=internlm2)
+- [x] [CodeShell](https://github.com/WisdomShell/codeshell)
+- [x] [Gemma](https://ai.google.dev/gemma)
+- [x] [Mamba](https://github.com/state-spaces/mamba)
+- [x] [Xverse](https://huggingface.co/models?search=xverse)
+- [x] [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
+- [x] [SEA-LION](https://huggingface.co/models?search=sea-lion)
 
 **Multimodal models:**
 
-- [x] [Llava 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e)
-- [x] [Bakllava](https://huggingface.co/models?search=SkunkworksAI/Bakllava)
+- [x] [LLaVA 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e), [LLaVA 1.6 models](https://huggingface.co/collections/liuhaotian/llava-16-65b9e40155f60fd046a5ccf2)
+- [x] [BakLLaVA](https://huggingface.co/models?search=SkunkworksAI/Bakllava)
 - [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5)
 - [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V)
+- [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM)
+- [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL)
 
+**HTTP server**
+
+[llama.cpp web server](./examples/server) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients.
 
 **Bindings:**
 
@@ -120,30 +139,57 @@ as the main playground for developing new features for the [ggml](https://github
 - Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp)
 - Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp)
 - JS/TS (llama.cpp server client): [lgrammel/modelfusion](https://modelfusion.dev/integration/model-provider/llamacpp)
+- JavaScript/Wasm (works in browser): [tangledgroup/llama-cpp-wasm](https://github.com/tangledgroup/llama-cpp-wasm)
+- Typescript/Wasm (nicer API, available on npm): [ngxson/wllama](https://github.com/ngxson/wllama)
 - Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
-- Rust: [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
+- Rust (more features): [edgenai/llama_cpp-rs](https://github.com/edgenai/llama_cpp-rs)
+- Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
+- Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs)
 - C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
 - Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s)
 - Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj)
 - React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn)
 - Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp)
 - Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig)
+- Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart)
+- PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggerganov/llama.cpp/pull/6326)
 
 **UI:**
 
-- [nat/openplayground](https://github.com/nat/openplayground)
-- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui)
-- [withcatai/catai](https://github.com/withcatai/catai)
-- [semperai/amica](https://github.com/semperai/amica)
-- [psugihara/FreeChat](https://github.com/psugihara/FreeChat)
-- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal)
+Unless otherwise noted these projects are open-source with permissive licensing:
+
 - [iohub/collama](https://github.com/iohub/coLLaMA)
+- [janhq/jan](https://github.com/janhq/jan) (AGPL)
+- [nat/openplayground](https://github.com/nat/openplayground)
+- [Faraday](https://faraday.dev/) (proprietary)
+- [LMStudio](https://lmstudio.ai/) (proprietary)
+- [LocalAI](https://github.com/mudler/LocalAI) (MIT)
+- [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL)
+- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile)
+- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all)
+- [ollama/ollama](https://github.com/ollama/ollama)
+- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL)
+- [psugihara/FreeChat](https://github.com/psugihara/FreeChat)
+- [cztomsik/ava](https://github.com/cztomsik/ava) (MIT)
+- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal)
+- [pythops/tenere](https://github.com/pythops/tenere) (AGPL)
+- [RecurseChat](https://recurse.chat/) (proprietary)
+- [semperai/amica](https://github.com/semperai/amica)
+- [withcatai/catai](https://github.com/withcatai/catai)
+- [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT)
+- [Msty](https://msty.app) (proprietary)
+- [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT)
+- [KanTV](https://github.com/zhouwg/kantv?tab=readme-ov-file)(Apachev2.0 or later)
+- [Dot](https://github.com/alexpinel/Dot) (GPL)
+- [MindMac](https://mindmac.app) (proprietary)
+
+*(to have a project listed here, it should clearly state that it depends on `llama.cpp`)*
 
 ---
 
 Here is a typical run using LLaMA v2 13B on M2 Ultra:
 
-```java
+```
 $ make -j && ./main -m models/llama-13b-v2/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e
 I llama.cpp build info:
 I UNAME_S:  Darwin
@@ -227,7 +273,7 @@ https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8
 
 ## Usage
 
-Here are the end-to-end binary build and model conversion steps for the LLaMA-7B model.
+Here are the end-to-end binary build and model conversion steps for most supported models.
 
 ### Get the Code
 
@@ -288,7 +334,7 @@ In order to build llama.cpp you have three different options.
         sudo pkg install gmake automake autoconf pkgconf llvm15 clinfo clover \
             opencl clblast openblas
 
-            gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4
+        gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4
         ```
 
     **Notes:** With this packages you can build llama.cpp with OPENBLAS and
@@ -388,55 +434,52 @@ Building the program with BLAS support may lead to some performance improvements
 
   Check [BLIS.md](docs/BLIS.md) for more information.
 
+- #### SYCL
+  SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators.
+
+  llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU).
+
+  For detailed info, please refer to [llama.cpp for SYCL](README-sycl.md).
+
 - #### Intel oneMKL
+  Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./README-sycl.md).
+
   - Using manual oneAPI installation:
     By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps:
       ```bash
       mkdir build
       cd build
-      source /opt/intel/oneapi/setvars.sh # You can skip this step if  in oneapi-runtime docker image, only required for manual installation
+      source /opt/intel/oneapi/setvars.sh # You can skip this step if  in oneapi-basekit docker image, only required for manual installation
       cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON
       cmake --build . --config Release
       ```
 
   - Using oneAPI docker image:
-    If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-runtime](https://hub.docker.com/r/intel/oneapi-runtime)
-
-      ```bash
-      mkdir build
-      cd build
-      cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON
-      cmake --build . --config Release
-      ```
-
-  Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni.
+    If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-basekit](https://hub.docker.com/r/intel/oneapi-basekit). Then, you can use the commands given above.
 
   Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information.
 
-- #### cuBLAS
+- #### CUDA
 
-  This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
+  This provides GPU acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
 
   For Jetson user, if you have Jetson Orin, you can try this: [Offical Support](https://www.jetson-ai-lab.com/tutorial_text-generation.html). If you are using an old model(nano/TX2), need some additional operations before compiling.
 
   - Using `make`:
     ```bash
-    make LLAMA_CUBLAS=1
+    make LLAMA_CUDA=1
     ```
   - Using `CMake`:
 
     ```bash
     mkdir build
     cd build
-    cmake .. -DLLAMA_CUBLAS=ON
+    cmake .. -DLLAMA_CUDA=ON
     cmake --build . --config Release
     ```
 
   The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance:
 
-<!---
-  | LLAMA_CUDA_CUBLAS       | Boolean                |   false | Use cuBLAS instead of custom CUDA kernels for prompt processing. Faster for all quantization formats except for q4_0 and q8_0, especially for k-quants. Increases VRAM usage (700 MiB for 7b, 970 MiB for 13b, 1430 MiB for 33b). |
---->
   | Option                         | Legal values           | Default | Description |
   |--------------------------------|------------------------|---------|-------------|
   | LLAMA_CUDA_FORCE_DMMV          | Boolean                |   false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. |
@@ -596,34 +639,87 @@ Building the program with BLAS support may lead to some performance improvements
 
   You can get a list of platforms and devices from the `clinfo -l` command, etc.
 
-### Prepare Data & Run
+- #### Vulkan
+
+  **With docker**:
+
+  You don't need to install Vulkan SDK. It will be installed inside the container.
+
+  ```sh
+  # Build the image
+  docker build -t llama-cpp-vulkan -f .devops/main-vulkan.Dockerfile .
+
+  # Then, use it:
+  docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
+  ```
+
+  **Without docker**:
+
+  Firstly, you need to make sure you have installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html)
+
+  For example, on Ubuntu 22.04 (jammy), use the command below:
+
+  ```bash
+  wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add -
+  wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
+  apt update -y
+  apt-get install -y vulkan-sdk
+  # To verify the installation, use the command below:
+  vulkaninfo
+  ```
+
+  Alternatively your package manager might be able to provide the appropiate libraries. For example for Ubuntu 22.04 you can install `libvulkan-dev` instead.
+
+  Then, build llama.cpp using the cmake command below:
+
+  ```bash
+  mkdir -p build
+  cd build
+  cmake .. -DLLAMA_VULKAN=1
+  cmake --build . --config Release
+  # Test the output binary (with "-ngl 33" to offload all layers to GPU)
+  ./bin/main -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4
+
+  # You should see in the output, ggml_vulkan detected your GPU. For example:
+  # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32
+  ```
+
+### Prepare and Quantize
+
+To obtain the official LLaMA 2 weights please see the <a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a> section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face.
 
 ```bash
-# obtain the original LLaMA model weights and place them in ./models
+# obtain the official LLaMA model weights and place them in ./models
 ls ./models
-65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model
-  # [Optional] for models using BPE tokenizers
-  ls ./models
-  65B 30B 13B 7B vocab.json
+llama-2-7b tokenizer_checklist.chk tokenizer.model
+# [Optional] for models using BPE tokenizers
+ls ./models
+<folder containing weights and tokenizer json> vocab.json
+# [Optional] for PyTorch .bin models like Mistral-7B
+ls ./models
+<folder containing weights and tokenizer json>
 
 # install Python dependencies
 python3 -m pip install -r requirements.txt
 
-# convert the 7B model to ggml FP16 format
-python3 convert.py models/7B/
+# convert the model to ggml FP16 format
+python3 convert.py models/mymodel/
 
-  # [Optional] for models using BPE tokenizers
-  python convert.py models/7B/ --vocabtype bpe
+# [Optional] for models using BPE tokenizers
+python convert.py models/mymodel/ --vocab-type bpe
 
-# quantize the model to 4-bits (using q4_0 method)
-./quantize ./models/7B/ggml-model-f16.gguf ./models/7B/ggml-model-q4_0.gguf q4_0
+# quantize the model to 4-bits (using Q4_K_M method)
+./quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M
 
-# update the gguf filetype to current if older version is unsupported by another application
-./quantize ./models/7B/ggml-model-q4_0.gguf ./models/7B/ggml-model-q4_0-v2.gguf COPY
+# update the gguf filetype to current version if older version is now unsupported
+./quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY
+```
 
+### Run the quantized model
 
-# run the inference
-./main -m ./models/7B/ggml-model-q4_0.gguf -n 128
+```bash
+# start inference on a gguf model
+./main -m ./models/mymodel/ggml-model-Q4_K_M.gguf -n 128
 ```
 
 When running the larger models, make sure you have enough disk space to store all the intermediate files.
@@ -644,7 +740,7 @@ From the unzipped folder, open a terminal/cmd window here and place a pre-conver
 
 As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same.
 
-| Model | Original size | Quantized size (4-bit) |
+| Model | Original size | Quantized size (Q4_0) |
 |------:|--------------:|-----------------------:|
 |    7B |         13 GB |                 3.9 GB |
 |   13B |         24 GB |                 7.8 GB |
@@ -671,9 +767,21 @@ Several quantization methods are supported. They differ in the resulting model d
 |   13B | bits/weight  |   16.0 |    4.5 |    5.0 |    5.5 |    6.0 |    8.5 |
 
 - [k-quants](https://github.com/ggerganov/llama.cpp/pull/1684)
-- recent k-quants improvements
+- recent k-quants improvements and new i-quants
   - [#2707](https://github.com/ggerganov/llama.cpp/pull/2707)
   - [#2807](https://github.com/ggerganov/llama.cpp/pull/2807)
+  - [#4773 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4773)
+  - [#4856 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4856)
+  - [#4861 - importance matrix](https://github.com/ggerganov/llama.cpp/pull/4861)
+  - [#4872 - MoE models](https://github.com/ggerganov/llama.cpp/pull/4872)
+  - [#4897 - 2-bit quantization](https://github.com/ggerganov/llama.cpp/pull/4897)
+  - [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930)
+  - [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957)
+  - [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969)
+  - [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996)
+  - [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060)
+  - [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196)
+  - [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361)
 
 ### Perplexity (measuring model quality)
 
@@ -685,7 +793,7 @@ The time per token is measured on a MacBook M1 Pro 32GB RAM using 4 and 8 thread
 
 #### How to run
 
-1. Download/extract: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
+1. Download/extract: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
 2. Run `./perplexity -m models/7B/ggml-model-q4_0.gguf -f wiki.test.raw`
 3. Output:
 ```
@@ -698,7 +806,7 @@ And after 4.45 hours, you will have the final perplexity.
 ### Interactive mode
 
 If you want a more ChatGPT-like experience, you can run in interactive mode by passing `-i` as a parameter.
-In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMa emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`.
+In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMA emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`.
 
 Here is an example of a few-shot interaction, invoked with the command
 
@@ -748,9 +856,9 @@ The `grammars/` folder contains a handful of sample grammars. To write your own,
 
 For authoring more complex JSON grammars, you can also check out https://grammar.intrinsiclabs.ai/, a browser app that lets you write TypeScript interfaces which it compiles to GBNF grammars that you can save for local use. Note that the app is built and maintained by members of the community, please file any issues or FRs on [its repo](http://github.com/intrinsiclabsai/gbnfgen) and not this one.
 
-### Instruction mode with Alpaca
+### Instruct mode
 
-1. First, download the `ggml` Alpaca model into the `./models` folder
+1. First, download and place the `ggml` model into the `./models` folder
 2. Run the `main` tool like this:
 
 ```
@@ -762,7 +870,7 @@ Sample run:
 ```
 == Running in interactive mode. ==
  - Press Ctrl+C to interject at any time.
- - Press Return to return control to LLaMa.
+ - Press Return to return control to LLaMA.
  - If you want to submit another line, end your input in '\'.
 
  Below is an instruction that describes a task. Write a response that appropriately completes the request.
@@ -776,50 +884,6 @@ cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach.
 >
 ```
 
-### Using [OpenLLaMA](https://github.com/openlm-research/open_llama)
-
-OpenLLaMA is an openly licensed reproduction of Meta's original LLaMA model. It uses the same architecture and is a drop-in replacement for the original LLaMA weights.
-
-- Download the [3B](https://huggingface.co/openlm-research/open_llama_3b), [7B](https://huggingface.co/openlm-research/open_llama_7b), or [13B](https://huggingface.co/openlm-research/open_llama_13b) model from Hugging Face.
-- Convert the model to ggml FP16 format using `python convert.py <path to OpenLLaMA directory>`
-
-### Using [GPT4All](https://github.com/nomic-ai/gpt4all)
-
-*Note: these instructions are likely obsoleted by the GGUF update*
-
-- Obtain the `tokenizer.model` file from LLaMA model and put it to `models`
-- Obtain the `added_tokens.json` file from Alpaca model and put it to `models`
-- Obtain the `gpt4all-lora-quantized.bin` file from GPT4All model and put it to `models/gpt4all-7B`
-- It is distributed in the old `ggml` format which is now obsoleted
-- You have to convert it to the new format using `convert.py`:
-
-```bash
-python3 convert.py models/gpt4all-7B/gpt4all-lora-quantized.bin
-```
-
-- You can now use the newly generated `models/gpt4all-7B/ggml-model-q4_0.bin` model in exactly the same way as all other models
-
-- The newer GPT4All-J model is not yet supported!
-
-### Using Pygmalion 7B & Metharme 7B
-
-- Obtain the [LLaMA weights](#obtaining-the-facebook-llama-original-model-and-stanford-alpaca-model-data)
-- Obtain the [Pygmalion 7B](https://huggingface.co/PygmalionAI/pygmalion-7b/) or [Metharme 7B](https://huggingface.co/PygmalionAI/metharme-7b) XOR encoded weights
-- Convert the LLaMA model with [the latest HF convert script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py)
-- Merge the XOR files with the converted LLaMA weights by running the [xor_codec](https://huggingface.co/PygmalionAI/pygmalion-7b/blob/main/xor_codec.py) script
-- Convert to `ggml` format using the `convert.py` script in this repo:
-```bash
-python3 convert.py pygmalion-7b/ --outtype q4_1
-```
-> The Pygmalion 7B & Metharme 7B weights are saved in [bfloat16](https://en.wikipedia.org/wiki/Bfloat16_floating-point_format) precision. If you wish to convert to `ggml` without quantizating, please specify the `--outtype` as `f32` instead of `f16`.
-
-
-### Obtaining the Facebook LLaMA original model and Stanford Alpaca model data
-
-- **Under no circumstances should IPFS, magnet links, or any other links to model downloads be shared anywhere in this repository, including in issues, discussions, or pull requests. They will be immediately deleted.**
-- The LLaMA models are officially distributed by Facebook and will **never** be provided through this repository.
-- Refer to [Facebook's LLaMA repository](https://github.com/facebookresearch/llama/pull/73/files) if you need to request access to the model data.
-
 ### Obtaining and using the Facebook LLaMA 2 model
 
 - Refer to [Facebook's LLaMA download page](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) if you want to access the model data.
@@ -831,20 +895,6 @@ python3 convert.py pygmalion-7b/ --outtype q4_1
   - [LLaMA 2 13B chat](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF)
   - [LLaMA 2 70B chat](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGUF)
 
-### Verifying the model files
-
-Please verify the [sha256 checksums](SHA256SUMS) of all downloaded model files to confirm that you have the correct model data files before creating an issue relating to your model files.
-- The following python script will verify if you have all possible latest files in your self-installed `./models` subdirectory:
-
-```bash
-# run the verification script
-./scripts/verify-checksum-models.py
-```
-
-- On linux or macOS it is also possible to run the following commands to verify if you have all possible latest files in your self-installed `./models` subdirectory:
-    - On Linux: `sha256sum --ignore-missing -c SHA256SUMS`
-    - on macOS: `shasum -a 256 --ignore-missing -c SHA256SUMS`
-
 ### Seminal papers and background on the models
 
 If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:
@@ -867,6 +917,9 @@ First, install the essential packages for termux:
 pkg install clang wget git cmake
 ```
 Second, obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake:
+
+You can execute the following commands on your computer to avoid downloading the NDK to your mobile. Of course, you can also do this in Termux.
+
 ```
 $ mkdir build-android
 $ cd build-android
@@ -875,7 +928,28 @@ $ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROI
 $ make
 ```
 Install [termux](https://termux.dev/) on your device and run `termux-setup-storage` to get access to your SD card.
-Finally, copy the `llama` binary and the model files to your device storage. Here is a demo of an interactive session running on Pixel 5 phone:
+Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission:
+
+(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`)
+```
+$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/
+$cd /data/data/com.termux/files/home/bin
+$chmod +x ./*
+```
+
+Download model [llama-2-7b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/blob/main/llama-2-7b-chat.Q4_K_M.gguf), and push it to `/sdcard/llama.cpp/`, then move it to `/data/data/com.termux/files/home/model/`
+
+```
+$mv /sdcard/llama.cpp/llama-2-7b-chat.Q4_K_M.gguf /data/data/com.termux/files/home/model/
+```
+
+Now, you can start chatting:
+```
+$cd /data/data/com.termux/files/home/bin
+$./main -m ../model/llama-2-7b-chat.Q4_K_M.gguf -n 128 -cml
+```
+
+Here is a demo of an interactive session running on Pixel 5 phone:
 
 https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
 
@@ -929,17 +1003,20 @@ Place your desired model into the `~/llama.cpp/models/` directory and execute th
 * Create a folder to store big models & intermediate files (ex. /llama/models)
 
 #### Images
-We have two Docker images available for this project:
+We have three Docker images available for this project:
 
 1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`)
 2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`)
+3. `ghcr.io/ggerganov/llama.cpp:server`: This image only includes the server executable file. (platforms: `linux/amd64`, `linux/arm64`)
 
 Additionally, there the following images, similar to the above:
 
 - `ghcr.io/ggerganov/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA support. (platforms: `linux/amd64`)
 - `ghcr.io/ggerganov/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA support. (platforms: `linux/amd64`)
+- `ghcr.io/ggerganov/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA support. (platforms: `linux/amd64`)
 - `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
 - `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
+- `ghcr.io/ggerganov/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
 
 The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).
 
@@ -965,6 +1042,12 @@ or with a light image:
 docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
 ```
 
+or with a server image:
+
+```bash
+docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggerganov/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512
+```
+
 ### Docker With CUDA
 
 Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) properly installed on Linux, or is using a GPU enabled cloud, `cuBLAS` should be accessible inside the container.
@@ -974,6 +1057,7 @@ Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia
 ```bash
 docker build -t local/llama.cpp:full-cuda -f .devops/full-cuda.Dockerfile .
 docker build -t local/llama.cpp:light-cuda -f .devops/main-cuda.Dockerfile .
+docker build -t local/llama.cpp:server-cuda -f .devops/server-cuda.Dockerfile .
 ```
 
 You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture.
@@ -987,6 +1071,7 @@ The resulting images, are essentially the same as the non-CUDA images:
 
 1. `local/llama.cpp:full-cuda`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
 2. `local/llama.cpp:light-cuda`: This image only includes the main executable file.
+3. `local/llama.cpp:server-cuda`: This image only includes the server executable file.
 
 #### Usage
 
@@ -995,6 +1080,7 @@ After building locally, Usage is similar to the non-CUDA examples, but you'll ne
 ```bash
 docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
 docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
+docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1
 ```
 
 ### Contributing
diff --git a/SECURITY.md b/SECURITY.md
new file mode 100644
index 000000000..14504b1bf
--- /dev/null
+++ b/SECURITY.md
@@ -0,0 +1,67 @@
+# Security Policy
+
+ - [**Using llama.cpp securely**](#using-llamacpp-securely)
+   - [Untrusted models](#untrusted-models)
+   - [Untrusted inputs](#untrusted-inputs)
+   - [Data privacy](#data-privacy)
+   - [Untrusted environments or networks](#untrusted-environments-or-networks)
+   - [Multi-Tenant environments](#multi-tenant-environments)
+ - [**Reporting a vulnerability**](#reporting-a-vulnerability)
+
+## Using llama.cpp securely
+
+### Untrusted models
+Be careful when running untrusted models. This classification includes models created by unknown developers or utilizing data obtained from unknown sources.
+
+*Always execute untrusted models within a secure, isolated environment such as a sandbox* (e.g., containers, virtual machines). This helps protect your system from potentially malicious code.
+
+> [!NOTE]
+> The trustworthiness of a model is not binary. You must always determine the proper level of caution depending on the specific model and how it matches your use case and risk tolerance.
+
+### Untrusted inputs
+
+Some models accept various input formats (text, images, audio, etc.). The libraries converting these inputs have varying security levels, so it's crucial to isolate the model and carefully pre-process inputs to mitigate script injection risks.
+
+For maximum security when handling untrusted inputs, you may need to employ the following:
+
+* Sandboxing: Isolate the environment where the inference happens.
+* Pre-analysis: Check how the model performs by default when exposed to prompt injection (e.g. using [fuzzing for prompt injection](https://github.com/FonduAI/awesome-prompt-injection?tab=readme-ov-file#tools)). This will give you leads on how hard you will have to work on the next topics.
+* Updates: Keep both LLaMA C++ and your libraries updated with the latest security patches.
+* Input Sanitation: Before feeding data to the model, sanitize inputs rigorously. This involves techniques such as:
+    * Validation: Enforce strict rules on allowed characters and data types.
+    * Filtering: Remove potentially malicious scripts or code fragments.
+    * Encoding: Convert special characters into safe representations.
+    * Verification: Run tooling that identifies potential script injections (e.g. [models that detect prompt injection attempts](https://python.langchain.com/docs/guides/safety/hugging_face_prompt_injection)).
+
+### Data privacy
+
+To protect sensitive data from potential leaks or unauthorized access, it is crucial to sandbox the model execution. This means running the model in a secure, isolated environment, which helps mitigate many attack vectors.
+
+### Untrusted environments or networks
+
+If you can't run your models in a secure and isolated environment or if it must be exposed to an untrusted network, make sure to take the following security precautions:
+* Confirm the hash of any downloaded artifact (e.g. pre-trained model weights) matches a known-good value
+* Encrypt your data if sending it over the network.
+
+### Multi-Tenant environments
+
+If you intend to run multiple models in parallel with shared memory, it is your responsibility to ensure the models do not interact or access each other's data. The primary areas of concern are tenant isolation, resource allocation, model sharing and hardware attacks.
+
+1. Tenant Isolation: Models should run separately with strong isolation methods to prevent unwanted data access. Separating networks is crucial for isolation, as it prevents unauthorized access to data or models and malicious users from sending graphs to execute under another tenant's identity.
+
+1. Resource Allocation: A denial of service caused by one model can impact the overall system health. Implement safeguards like rate limits, access controls, and health monitoring.
+
+1. Model Sharing: In a multitenant model sharing design, tenants and users must understand the security risks of running code provided by others. Since there are no reliable methods to detect malicious models, sandboxing the model execution is the recommended approach to mitigate the risk.
+
+1. Hardware Attacks: GPUs or TPUs can also be attacked. [Researches](https://scholar.google.com/scholar?q=gpu+side+channel) has shown that side channel attacks on GPUs are possible, which can make data leak from other models or processes running on the same system at the same time.
+
+## Reporting a vulnerability
+
+Beware that none of the topics under [Using llama.cpp securely](#using-llamacpp-securely) are considered vulnerabilities of LLaMA C++.
+
+<!-- normal version -->
+However, If you have discovered a security vulnerability in this project, please report it privately. **Do not disclose it as a public issue.** This gives us time to work with you to fix the issue before public exposure, reducing the chance that the exploit will be used before a patch is released.
+
+Please disclose it as a private [security advisory](https://github.com/ggerganov/llama.cpp/security/advisories/new).
+
+A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure.
diff --git a/SHA256SUMS b/SHA256SUMS
deleted file mode 100644
index ca4d5a4a5..000000000
--- a/SHA256SUMS
+++ /dev/null
@@ -1,40 +0,0 @@
-700df0d3013b703a806d2ae7f1bfb8e59814e3d06ae78be0c66368a50059f33d  models/7B/consolidated.00.pth
-666a4bb533b303bdaf89e1b6a3b6f93535d868de31d903afdc20983dc526c847  models/7B/ggml-model-f16.bin
-ec2f2d1f0dfb73b72a4cbac7fa121abbe04c37ab327125a38248f930c0f09ddf  models/7B/ggml-model-q4_0.bin
-ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/7B/ggml-model-q4_1.bin
-ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/7B/ggml-model-q5_0.bin
-ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/7B/ggml-model-q5_1.bin
-7e89e242ddc0dd6f060b43ca219ce8b3e8f08959a72cb3c0855df8bb04d46265  models/7B/params.json
-745bf4e29a4dd6f411e72976d92b452da1b49168a4f41c951cfcc8051823cf08  models/13B/consolidated.00.pth
-d5ccbcc465c71c0de439a5aeffebe8344c68a519bce70bc7f9f92654ee567085  models/13B/consolidated.01.pth
-2b206e9b21fb1076f11cafc624e2af97c9e48ea09312a0962153acc20d45f808  models/13B/ggml-model-f16.bin
-fad169e6f0f575402cf75945961cb4a8ecd824ba4da6be2af831f320c4348fa5  models/13B/ggml-model-q4_0.bin
-ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/13B/ggml-model-q4_1.bin
-ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/13B/ggml-model-q5_0.bin
-ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/13B/ggml-model-q5_1.bin
-4ab77bec4d4405ccb66a97b282574c89a94417e3c32e5f68f37e2876fc21322f  models/13B/params.json
-e23294a58552d8cdec5b7e8abb87993b97ea6eced4178ff2697c02472539d067  models/30B/consolidated.00.pth
-4e077b7136c7ae2302e954860cf64930458d3076fcde9443f4d0e939e95903ff  models/30B/consolidated.01.pth
-24a87f01028cbd3a12de551dcedb712346c0b5cbdeff1454e0ddf2df9b675378  models/30B/consolidated.02.pth
-1adfcef71420886119544949767f6a56cb6339b4d5fcde755d80fe68b49de93b  models/30B/consolidated.03.pth
-7e1b524061a9f4b27c22a12d6d2a5bf13b8ebbea73e99f218809351ed9cf7d37  models/30B/ggml-model-f16.bin
-d2a441403944819492ec8c2002cc36fa38468149bfb4b7b4c52afc7bd9a7166d  models/30B/ggml-model-q4_0.bin
-ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/30B/ggml-model-q4_1.bin
-ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/30B/ggml-model-q5_0.bin
-ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/30B/ggml-model-q5_1.bin
-2c07118ea98d69dbe7810d88520e30288fa994751b337f8fca02b171955f44cb  models/30B/params.json
-135c563f6b3938114458183afb01adc9a63bef3d8ff7cccc3977e5d3664ecafe  models/65B/consolidated.00.pth
-9a600b37b19d38c7e43809485f70d17d1dc12206c07efa83bc72bb498a568bde  models/65B/consolidated.01.pth
-e7babf7c5606f165a3756f527cb0fedc4f83e67ef1290391e52fb1cce5f26770  models/65B/consolidated.02.pth
-73176ffb426b40482f2aa67ae1217ef79fbbd1fff5482bae5060cdc5a24ab70e  models/65B/consolidated.03.pth
-882e6431d0b08a8bc66261a0d3607da21cbaeafa96a24e7e59777632dbdac225  models/65B/consolidated.04.pth
-a287c0dfe49081626567c7fe87f74cce5831f58e459b427b5e05567641f47b78  models/65B/consolidated.05.pth
-72b4eba67a1a3b18cb67a85b70f8f1640caae9b40033ea943fb166bd80a7b36b  models/65B/consolidated.06.pth
-d27f5b0677d7ff129ceacd73fd461c4d06910ad7787cf217b249948c3f3bc638  models/65B/consolidated.07.pth
-60758f2384d74e423dffddfd020ffed9d3bb186ebc54506f9c4a787d0f5367b0  models/65B/ggml-model-f16.bin
-cde053439fa4910ae454407e2717cc46cc2c2b4995c00c93297a2b52e790fa92  models/65B/ggml-model-q4_0.bin
-ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/65B/ggml-model-q4_1.bin
-ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/65B/ggml-model-q5_0.bin
-ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff  models/65B/ggml-model-q5_1.bin
-999ed1659b469ccc2a941714c0a9656fa571d17c9f7c8c7589817ca90edef51b  models/65B/params.json
-9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347  models/tokenizer.model
diff --git a/awq-py/README.md b/awq-py/README.md
deleted file mode 100644
index 59354f4e3..000000000
--- a/awq-py/README.md
+++ /dev/null
@@ -1,116 +0,0 @@
-# AWQ: Activation-aware Weight Quantization for LLM - version apply to llamacpp
-[[Paper](https://arxiv.org/abs/2306.00978)][[Original Repo](https://github.com/mit-han-lab/llm-awq)][[Easy-to-use Repo](https://github.com/casper-hansen/AutoAWQ)]
-
-**Supported models:**
-
-- [X] LLaMA
-- [x] LLaMA 2
-- [X] MPT
-- [X] Mistral AI v0.1
-- [ ] Bloom
-- [ ] Mixtral MoE
-
-**TODO:**
-- [x] Update version work with both MPT and MPT-AWQ model
-- [ ] Add OPT model
-- [ ] Add Bloom model
-- [ ] Add Mixtral MoE
-- [ ] Support w3, w2
-
-
-## Contents
-
-- [Install](##Install)
-- [Convert](##Convert)
-- [Quantize](##Quantize)
-- [Test](##Test)
-- [Benchmark](##Benchmark)
-- [Results](##Results)
-
-## Install
-Install requirements
-```bash
-pip install -r requirements.txt
-```
-Get the pre-computed AWQ search results for multiple model families, including LLaMA, LLaMA2, MPT, OPT
-```bash
-git clone https://huggingface.co/datasets/mit-han-lab/awq-model-zoo awq_cache
-```
-
-## Convert
-Example for llama model
-```bash
-# For llama7b and llama2 models
-python convert.py models/llama-7b/ --awq-path awq_cache/llama-7b-w4-g128.pt --outfile models/llama_7b_fp16.gguf
-# For mistral and mpt models
-python convert-hf-to-gguf.py models/mpt-7b/ --awq-path awq_cache/llama-7b-w4-g128.pt --outfile models/mpt_7b_fp16.gguf
-```
-
-## Quantize
-```bash
-# We only benchmark and confirm the results on q4_0, q4_1, and q2_k types.
-./quantize models/llama_7b_fp16.gguf models/llama_7b_q4_0.gguf q4_0
-```
-
-## Test
-```bash
-# For all models.
-./build/bin/main -m models/llama_7b_q4_0.gguf -n 128 --prompt "Once upon a time"
-```
-
-## Benchmark
-The perplexity measurements in table above are done against the `wikitext2` test dataset (https://paperswithcode.com/dataset/wikitext-2), with context length of 512.
-```bash
-# For llama and llama2, and mistral models.
-./perplexity -m models/llama_7b_q4_0.gguf -f datasets/wikitext-2-raw/wiki.test.raw
-```
-
-## Results
-Results are run on OpenBLAS (CPU) and CuBLAS (GPU) for fair comparison
-We use three types of llamacpp quantization methods to work with our version, including q4_0, q4_1, and q2_k
-
-### Llama 7B (Build with OpenBLAS)
-
-| Model      | Measure      | F16    | Q4_0   | Q4_1   | Q2_K   |
-|-----------:|--------------|-------:|-------:|-------:|-------:|
-|Llama 7B    | perplexity   | 5.9066 | 6.1214 | 6.0643 | 6.5808 |
-|Llama 7B    | file size    |  12.9G  |   3.5G |   3.9G |   2.7G |
-|Llama 7B    | bits/weight  |   16.0 |    4.5 |    5.0 |    2.6 |
-|AWQ-LLama 7B| perplexity   | 5.9175 | 6.0252 | 5.9987 | 6.3692 |
-|AWQ-LLama 7B| file size    |  12.9G  |   3.5G |   3.9G |   2.7G |
-|AWQ-LLama 7B| bits/weight  |   16.0 |    4.5 |    5.0 |    2.6 |
-
-
-### Llama2 7B (Build with CuBLAS)
-
-| Model       | Measure      | F16    | Q4_0   | Q4_1   | Q2_K   |
-|------------:|--------------|-------:|-------:|-------:|-------:|
-|Llama2 7B    | perplexity   | 5.8664 | 6.0260 | 6.0656 | 6.4496 |
-|Llama2 7B    | file size    |  12.9G  |   3.5G |   3.9G |   2.7G |
-|Llama2 7B    | bits/weight  |   16.0 |    4.5 |    5.0 |    2.6 |
-|AWQ-LLama2 7B| perplexity   | 5.8801 | 6.0054 | 5.9849 | 6.3650 |
-|AWQ-LLama2 7B| file size    |  12.9G  |   3.5G |   3.9G |   2.7G |
-|AWQ-LLama2 7B| bits/weight  |   16.0 |    4.5 |    5.0 |    2.6 |
-
-
-### Mistral 7B v0.1 (Build with CuBLAS)
-
-| Model        | Measure      | F16    | Q4_0   | Q4_1   | Q2_K   |
-|-------------:|--------------|-------:|-------:|-------:|-------:|
-|Mistral 7B    | perplexity   | 5.6931 | 5.8202 | 5.8268 | 6.1645 |
-|Mistral 7B    | file size     |  14.5G |   4.1G |   4.5G |   3.1G |
-|Mistral 7B    | bits/weight  |   16.0 |    4.5 |    5.0 |    2.6 |
-|AWQ-Mistral 7B| perplexity   | 5.6934 | 5.8020 | 5.7691 | 6.0426 |
-|AWQ-Mistral 7B| file size     |  14.5G |   4.1G |   4.5G |   3.1G |
-|AWQ-Mistral 7B| bits/weight  |   16.0 |    4.5 |    5.0 |    2.6 |
-
-### MPT 7B (Build with OpenBLAS)
-
-| Model    | Measure      | F16    | Q4_0   | Q4_1   | Q2_K    |
-|---------:|--------------|-------:|-------:|-------:|--------:|
-|MPT 7B    | perplexity   | 8.4369 | 8.7956 | 8.6265 | 11.4913 |
-|MPT 7B    | file size    |  13.7G  |   3.9G |   4.3G |   2.8G  |
-|MPT 7B    | bits/weight  |   16.0 |    4.5 |    5.0 |    2.6  |
-|AWQ-MPT 7B| perplexity   | 8.4944 | 8.7053 |  8.6750 | 10.2873|
-|AWQ-MPT 7B| file size    |  13.7G  |   3.9G |   4.3G |   2.8G  |
-|AWQ-MPT 7B| bits/weight  |   16.0 |    4.5 |    5.0 |    2.6  |
diff --git a/awq-py/awq/apply_awq.py b/awq-py/awq/apply_awq.py
deleted file mode 100644
index 11132c5d2..000000000
--- a/awq-py/awq/apply_awq.py
+++ /dev/null
@@ -1,254 +0,0 @@
-"""
-Implements the AWQ for llama.cpp use cases.
-Original paper: https://arxiv.org/abs/2306.00978
-
-This code is based on versions of the AWQ implementation found in the following repositories:
-* https://github.com/mit-han-lab/llm-awq
-* https://github.com/casper-hansen/AutoAWQ
-"""
-
-import os
-import torch
-import torch.nn as nn
-
-from transformers import AutoModelForCausalLM, AutoConfig
-from transformers.models.bloom.modeling_bloom import BloomGelu
-from transformers.models.llama.modeling_llama import LlamaRMSNorm
-from transformers.activations import GELUActivation
-
-
-class ScaledActivation(nn.Module):
-    """
-    ScaledActivation module wraps an existing activation function and applies a
-    scale factor to its output.
-
-    Args:
-        module (nn.Module): The activation function to be scaled.
-        scales (torch.Tensor): A tensor of size (num_features,) containing the initial
-            scale factors for each feature.
-
-    Returns:
-        torch.Tensor: The scaled output of the activation function.
-    """
-
-    def __init__(self, module, scales):
-        super().__init__()
-        self.act = module
-        self.scales = nn.Parameter(scales.data)
-
-    def forward(self, x):
-        return self.act(x) / self.scales.view(1, 1, -1).to(x.device)
-
-
-def set_op_by_name(layer, name, new_module):
-    """
-    Set the new module for given module's name.
-
-    Args:
-        layer (nn.Module): The layer in which to replace the submodule.
-        name (str): The path to the submodule to be replaced, using dot notation
-            to access nested modules.
-        new_module (nn.Module): The new module to replace the existing one.
-    """
-    levels = name.split(".")
-    if len(levels) > 1:
-        mod_ = layer
-        for l_idx in range(len(levels) - 1):
-            if levels[l_idx].isdigit():
-                mod_ = mod_[int(levels[l_idx])]
-            else:
-                mod_ = getattr(mod_, levels[l_idx])
-        setattr(mod_, levels[-1], new_module)
-    else:
-        setattr(layer, name, new_module)
-
-
-def get_op_by_name(module, op_name):
-    """
-    Retrieves a submodule within a given layer based on its name.
-
-    Args:
-        module (nn.Module): The layer containing the submodule to find.
-        op_name (str): The name of the submodule.
-
-    Returns:
-        nn.Module: The requested submodule found within the given layer.
-
-    Raises:
-        ValueError: If the specified submodule cannot be found within the layer.
-    """
-    for name, m in module.named_modules():
-        if name == op_name:
-            return m
-    raise ValueError(f"Cannot find op {op_name} in module {module}")
-
-
-@torch.no_grad()
-def scale_ln_fcs(ln, fcs, scales):
-    """
-    Scales the weights of a LayerNorm and a list of fully-connected layers proportionally.
-
-    Args:
-        ln (nn.LayerNorm): The LayerNorm module to be scaled.
-        fcs (List[nn.Linear]): A list of fully-connected layers to be scaled.
-        scales (torch.Tensor): A 1D tensor of size (num_features,).
-    """
-
-    if not isinstance(fcs, list):
-        fcs = [fcs]
-
-    scales = scales.to(ln.weight.device)
-
-    ln.weight.div_(scales)
-    if hasattr(ln, "bias") and ln.bias is not None:
-        ln.bias.div_(scales)
-
-    for fc in fcs:
-        fc.weight.mul_(scales.view(1, -1))
-
-    for p in ln.parameters():
-        assert torch.isnan(p).sum() == 0
-    for fc in fcs:
-        for p in fc.parameters():
-            assert torch.isnan(p).sum() == 0
-
-
-@torch.no_grad()
-def scale_fc_fc(fc1, fc2, scales):
-    """
-    Scales the weights of two fully-connected layers in a specific pattern.
-
-    Args:
-        fc1 (nn.Linear): The first fully-connected layer to be scaled.
-        fc2 (nn.Linear): The second fully-connected layer to be scaled.
-        scales (torch.Tensor): A 1D tensor of size (num_features,).
-    """
-    assert isinstance(fc1, nn.Linear)
-    assert isinstance(fc2, nn.Linear)
-
-    scales = scales.to(fc1.weight.device)
-
-    fc1.weight[-scales.size(0):].div_(scales.view(-1, 1))
-    if fc1.bias is not None:
-        fc1.bias.div_(scales.view(-1))
-
-    fc2.weight.mul_(scales.view(1, -1))
-
-    for p in fc1.parameters():
-        assert torch.isnan(p).sum() == 0
-    for p in fc2.parameters():
-        assert torch.isnan(p).sum() == 0
-
-
-@torch.no_grad()
-def scale_gelu_fc(gelu, fc, scales):
-    """
-    Scales the weight of a GELU activation and a fully-connected layer proportionally.
-
-    Args:
-        gelu (Union[nn.GELU, BloomGelu, GELUActivation]): The GELU activation module to be scaled.
-        fc (nn.Linear): The fully-connected layer to be scaled.
-        scales (torch.Tensor): A 1D tensor of size (num_features,).
-
-    Raises:
-        TypeError: If the `gelu` module is not of type `nn.GELU`, `BloomGelu`, or `GELUActivation`.
-        TypeError: If the `fc` module is not of type `nn.Linear`.
-    """
-    assert isinstance(gelu, (nn.GELU, BloomGelu, GELUActivation))
-    assert isinstance(fc, nn.Linear)
-
-    fc.weight.mul_(scales.view(1, -1).to(fc.weight.device))
-
-    for p in fc.parameters():
-        assert torch.isnan(p).sum() == 0
-
-
-def apply_scale(module, scales_list, input_feat_dict=None):
-    """
-    Applies different scaling strategies to layers based on their type and hierarchy within a given module.
-
-    Args:
-        module (nn.Module): The module containing the layers to be scaled.
-        scales_list (List[Tuple[str, List[str], torch.Tensor]]): A list of tuples containing:
-            * prev_op_name (str): The name of the preceding operation or module,
-                relative to which the layers to be scaled are located.
-            * layer_names (List[str]): A list of names of the layers to be scaled, relative to the preceding operation.
-            * scales (torch.Tensor): A 1D tensor of size (num_features,) containing the scaling factors for each feature.
-        input_feat_dict (Optional[Dict[str, torch.Tensor]]): A dictionary mapping layer names to their corresponding
-            input features (optional).
-    """
-    for prev_op_name, layer_names, scales in scales_list:
-        prev_op = get_op_by_name(module, prev_op_name)
-        layers = [get_op_by_name(module, name) for name in layer_names]
-
-        prev_op.cuda()
-        for layer in layers:
-            layer.cuda()
-        scales.cuda()
-
-        if isinstance(prev_op, nn.Linear):
-            assert len(layers) == 1
-            scale_fc_fc(prev_op, layers[0], scales)
-        elif isinstance(prev_op, (nn.LayerNorm, LlamaRMSNorm)) or "rmsnorm" in str(prev_op.__class__).lower():
-            scale_ln_fcs(prev_op, layers, scales)
-        elif isinstance(prev_op, (nn.GELU, BloomGelu, GELUActivation)):
-            new_module = ScaledActivation(prev_op, scales)
-            set_op_by_name(module, prev_op_name, new_module)
-            scale_gelu_fc(prev_op, layers[0], scales)
-        else:
-            raise NotImplementedError(f"prev_op {type(prev_op)} not supported yet!")
-
-        # apply the scaling to input feat if given; prepare it for clipping
-        if input_feat_dict is not None:
-            for layer_name in layer_names:
-                inp = input_feat_dict[layer_name]
-                inp.div_(scales.view(1, -1).to(inp.device))
-
-        prev_op.cpu()
-        for layer in layers:
-            layer.cpu()
-        scales.cpu()
-
-
-@torch.no_grad()
-def apply_clip(module, clip_list):
-    """
-    Applies element-wise clipping to the weight of a specific layer within a given module.
-
-    Args:
-        module (nn.Module): The module containing the layer to be clipped.
-        clip_list (List[Tuple[str, torch.Tensor]]): A list of tuples containing:
-            * name (str): The name of the layer to be clipped, relative to the root of the module.
-            * max_val (torch.Tensor): A 1D or 2D tensor defining the upper bound for each element of the layer's weight.
-    """
-    for name, max_val in clip_list:
-        layer = get_op_by_name(module, name)
-        layer.cuda()
-        max_val = max_val.to(layer.weight.device)
-        org_shape = layer.weight.shape
-        layer.weight.data = layer.weight.data.reshape(*max_val.shape[:2], -1)
-        layer.weight.data = torch.clamp(layer.weight.data, -max_val, max_val)
-        layer.weight.data = layer.weight.data.reshape(org_shape)
-        layer.cpu()
-
-
-def add_scale_weights(model_path, scale_path, tmp_path):
-    """
-    Adds pre-computed Activation Weight Quantization (AWQ) results to a model,
-    including scaling factors and clipping bounds.
-
-    Args:
-        model_path (str): Path to the pre-trained model to be equipped with AWQ.
-        scale_path (str): Path to the AWQ scale factors (.pt file).
-        tmp_path (str): Path to the temporary directory where the equipped model will be saved.
-    """
-    config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
-    model = AutoModelForCausalLM.from_pretrained(
-        model_path, config=config, trust_remote_code=True
-    )
-    model.eval()
-    awq_results = torch.load(str(scale_path), map_location="cpu")
-    apply_scale(model, awq_results["scale"])
-    apply_clip(model, awq_results["clip"])
-    model.save_pretrained(str(tmp_path))
-    os.system(f"cp {str(model_path)}/tokenizer* {str(tmp_path)}")
diff --git a/awq-py/requirements.txt b/awq-py/requirements.txt
deleted file mode 100644
index 991896116..000000000
--- a/awq-py/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-torch>=2.1.1
-transformers>=4.32.0
diff --git a/build.zig b/build.zig
index 699738f3d..7f36e5968 100644
--- a/build.zig
+++ b/build.zig
@@ -115,23 +115,27 @@ pub fn build(b: *std.build.Builder) !void {
     const ggml_alloc = make.obj("ggml-alloc", "ggml-alloc.c");
     const ggml_backend = make.obj("ggml-backend", "ggml-backend.c");
     const ggml_quants = make.obj("ggml-quants", "ggml-quants.c");
+    const unicode = make.obj("unicode", "unicode.cpp");
+    const unicode_data = make.obj("unicode-data", "unicode-data.cpp");
     const llama = make.obj("llama", "llama.cpp");
     const buildinfo = make.obj("common", "common/build-info.cpp");
     const common = make.obj("common", "common/common.cpp");
     const console = make.obj("console", "common/console.cpp");
     const sampling = make.obj("sampling", "common/sampling.cpp");
     const grammar_parser = make.obj("grammar-parser", "common/grammar-parser.cpp");
+    const json_schema_to_grammar = make.obj("json-schema-to-grammar", "common/json-schema-to-grammar.cpp");
     const train = make.obj("train", "common/train.cpp");
     const clip = make.obj("clip", "examples/llava/clip.cpp");
+    const llava = make.obj("llava", "examples/llava/llava.cpp");
 
-    _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, console, grammar_parser });
-    _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });
-    _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });
-    _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });
-    _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train });
-    _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train });
+    _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo, sampling, console, grammar_parser });
+    _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo });
+    _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo });
+    _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo });
+    _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo, train });
+    _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo, train });
 
-    const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, grammar_parser, clip });
+    const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo, sampling, grammar_parser, json_schema_to_grammar, clip, llava });
     if (server.target.isWindows()) {
         server.linkSystemLibrary("ws2_32");
     }
diff --git a/ci/README.md b/ci/README.md
index 65cfe63eb..406470519 100644
--- a/ci/README.md
+++ b/ci/README.md
@@ -22,4 +22,8 @@ bash ./ci/run.sh ./tmp/results ./tmp/mnt
 
 # with CUDA support
 GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
+
+# with SYCL support
+source /opt/intel/oneapi/setvars.sh
+GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
 ```
diff --git a/ci/run.sh b/ci/run.sh
index 47a254f4c..19776b5f7 100755
--- a/ci/run.sh
+++ b/ci/run.sh
@@ -10,6 +10,9 @@
 # # with CUDA support
 # GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
 #
+# # with SYCL support
+# GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
+#
 
 if [ -z "$2" ]; then
     echo "usage: $0 <output-dir> <mnt-dir>"
@@ -22,20 +25,33 @@ mkdir -p "$2"
 OUT=$(realpath "$1")
 MNT=$(realpath "$2")
 
-rm -v $OUT/*.log
-rm -v $OUT/*.exit
-rm -v $OUT/*.md
+rm -f "$OUT/*.log"
+rm -f "$OUT/*.exit"
+rm -f "$OUT/*.md"
 
 sd=`dirname $0`
 cd $sd/../
 SRC=`pwd`
 
-CMAKE_EXTRA=""
+CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON"
 
 if [ ! -z ${GG_BUILD_METAL} ]; then
     CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_METAL_SHADER_DEBUG=ON"
 fi
 
+if [ ! -z ${GG_BUILD_CUDA} ]; then
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_CUDA=1"
+fi
+
+if [ ! -z ${GG_BUILD_SYCL} ]; then
+    if [ -z ${ONEAPI_ROOT} ]; then
+        echo "Not detected ONEAPI_ROOT, please install oneAPI base toolkit and enable it by:"
+        echo "source /opt/intel/oneapi/setvars.sh"
+        exit 1
+    fi
+
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_SYCL=1 DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON"
+fi
 ## helpers
 
 # download a file if it does not exist or if it is outdated
@@ -90,7 +106,7 @@ function gg_run_ctest_debug {
     (time cmake -DCMAKE_BUILD_TYPE=Debug ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
     (time make -j                                          ) 2>&1 | tee -a $OUT/${ci}-make.log
 
-    (time ctest --output-on-failure -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log
+    (time ctest --output-on-failure -L main -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log
 
     set +e
 }
@@ -119,9 +135,9 @@ function gg_run_ctest_release {
     (time make -j                                            ) 2>&1 | tee -a $OUT/${ci}-make.log
 
     if [ -z ${GG_BUILD_LOW_PERF} ]; then
-        (time ctest --output-on-failure ) 2>&1 | tee -a $OUT/${ci}-ctest.log
+        (time ctest --output-on-failure -L main ) 2>&1 | tee -a $OUT/${ci}-ctest.log
     else
-        (time ctest --output-on-failure -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log
+        (time ctest --output-on-failure -L main -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log
     fi
 
     set +e
@@ -137,6 +153,61 @@ function gg_sum_ctest_release {
     gg_printf '```\n'
 }
 
+function gg_get_model {
+    local gguf_3b="$MNT/models/open-llama/3B-v2/ggml-model-f16.gguf"
+    local gguf_7b="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf"
+    if [[ -s $gguf_3b ]]; then
+        echo -n "$gguf_3b"
+    elif [[ -s $gguf_7b ]]; then
+        echo -n "$gguf_7b"
+    else
+        echo >&2 "No model found. Can't run gg_run_ctest_with_model."
+        exit 1
+    fi
+}
+
+function gg_run_ctest_with_model_debug {
+    cd ${SRC}
+
+    local model; model=$(gg_get_model)
+    cd build-ci-debug
+    set -e
+    (LLAMACPP_TEST_MODELFILE="$model" time ctest --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log
+    set +e
+    cd ..
+}
+
+function gg_run_ctest_with_model_release {
+    cd ${SRC}
+
+    local model; model=$(gg_get_model)
+    cd build-ci-release
+    set -e
+    (LLAMACPP_TEST_MODELFILE="$model" time ctest --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log
+    set +e
+    cd ..
+}
+
+function gg_sum_ctest_with_model_debug {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Runs ctest with model files in debug mode\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '```\n'
+    gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)"
+    gg_printf '```\n'
+}
+
+function gg_sum_ctest_with_model_release {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Runs ctest with model files in release mode\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '```\n'
+    gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)"
+    gg_printf '```\n'
+}
+
 # open_llama_3b_v2
 
 function gg_run_open_llama_3b_v2 {
@@ -149,7 +220,7 @@ function gg_run_open_llama_3b_v2 {
     gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/pytorch_model.bin
     gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/generation_config.json
 
-    gg_wget models-mnt/wikitext/ https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip
+    gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
     unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
     head -n 60 models-mnt/wikitext/wikitext-2-raw/wiki.test.raw > models-mnt/wikitext/wikitext-2-raw/wiki.test-60.raw
 
@@ -160,8 +231,8 @@ function gg_run_open_llama_3b_v2 {
 
     set -e
 
-    (time cmake -DCMAKE_BUILD_TYPE=Release -DLLAMA_QKK_64=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j                                              ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_QKK_64=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j                                                             ) 2>&1 | tee -a $OUT/${ci}-make.log
 
     python3 ../convert.py ${path_models}
 
@@ -202,17 +273,19 @@ function gg_run_open_llama_3b_v2 {
     (time ./bin/main --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
     (time ./bin/main --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
-    (time ./bin/perplexity --model ${model_f16}  -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+    (time ./bin/perplexity --model ${model_f16}  -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+
+    (time ./bin/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
 
     (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
 
@@ -241,6 +314,8 @@ function gg_run_open_llama_3b_v2 {
     check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
 
+    cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log
+
     # lora
     function compare_ppl {
         qnt="$1"
@@ -269,20 +344,19 @@ function gg_run_open_llama_3b_v2 {
     python3 ../convert-lora-to-ggml.py ${path_lora}
 
     # f16
-    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare}                            -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log
-    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log
+    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare}                            -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log
+    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log
     compare_ppl "f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
 
     # q8_0
-    (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare}                            -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log
-    (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log
+    (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare}                            -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log
+    (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log
     compare_ppl "q8_0 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
 
     # q8_0 + f16 lora-base
-    (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log
+    (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log
     compare_ppl "q8_0 / f16 base shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
 
-
     set +e
 }
 
@@ -292,6 +366,7 @@ function gg_sum_open_llama_3b_v2 {
     gg_printf 'OpenLLaMA 3B-v2:\n'
     gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
     gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
+    gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
     gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)"
     gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
     gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
@@ -327,7 +402,7 @@ function gg_run_open_llama_7b_v2 {
     gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00002-of-00002.bin
     gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/generation_config.json
 
-    gg_wget models-mnt/wikitext/ https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip
+    gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
     unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
 
     path_models="../models-mnt/open-llama/7B-v2"
@@ -337,8 +412,8 @@ function gg_run_open_llama_7b_v2 {
 
     set -e
 
-    (time cmake -DCMAKE_BUILD_TYPE=Release -DLLAMA_CUBLAS=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j                                              ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j                                                           ) 2>&1 | tee -a $OUT/${ci}-make.log
 
     python3 ../convert.py ${path_models}
 
@@ -391,6 +466,8 @@ function gg_run_open_llama_7b_v2 {
     (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
     (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
+    (time ./bin/imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
+
     (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
 
     function check_ppl {
@@ -418,6 +495,8 @@ function gg_run_open_llama_7b_v2 {
     check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
 
+    cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log
+
     # lora
     function compare_ppl {
         qnt="$1"
@@ -469,6 +548,7 @@ function gg_sum_open_llama_7b_v2 {
     gg_printf 'OpenLLaMA 7B-v2:\n'
     gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
     gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
+    gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
     gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)"
     gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
     gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
@@ -489,17 +569,69 @@ function gg_sum_open_llama_7b_v2 {
     #gg_printf '- shakespeare (q8_0 / f16 base lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)"
 }
 
+# bge-small
+
+function gg_run_embd_bge_small {
+    cd ${SRC}
+
+    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/config.json
+    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/tokenizer.json
+    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/tokenizer_config.json
+    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/special_tokens_map.json
+    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/resolve/main/pytorch_model.bin
+    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/sentence_bert_config.json
+    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/vocab.txt
+    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/modules.json
+    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/config.json
+
+    gg_wget models-mnt/bge-small/1_Pooling https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/1_Pooling/config.json
+
+    path_models="../models-mnt/bge-small"
+
+    rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
+
+    set -e
+
+    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j                                            ) 2>&1 | tee -a $OUT/${ci}-make.log
+
+    python3 ../convert-hf-to-gguf.py ${path_models}
+
+    model_f16="${path_models}/ggml-model-f16.gguf"
+    model_q8_0="${path_models}/ggml-model-q8_0.gguf"
+
+    ./bin/quantize ${model_f16} ${model_q8_0} q8_0
+
+    (time ./bin/embedding --model ${model_f16}  -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/embedding --model ${model_q8_0} -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+
+    set +e
+}
+
+function gg_sum_embd_bge_small {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'BGE Small (BERT):\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
+    gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
+}
+
 ## main
 
 if [ -z ${GG_BUILD_LOW_PERF} ]; then
+    # Create symlink: ./llama.cpp/models-mnt -> $MNT/models/models-mnt
     rm -rf ${SRC}/models-mnt
-
     mnt_models=${MNT}/models
     mkdir -p ${mnt_models}
     ln -sfn ${mnt_models} ${SRC}/models-mnt
 
-    python3 -m pip install -r ${SRC}/requirements.txt
-    python3 -m pip install --editable gguf-py
+    # Create a fresh python3 venv and enter it
+    python3 -m venv "$MNT/venv"
+    source "$MNT/venv/bin/activate"
+
+    pip install -r ${SRC}/requirements.txt --disable-pip-version-check
+    pip install --editable gguf-py --disable-pip-version-check
 fi
 
 ret=0
@@ -508,12 +640,16 @@ test $ret -eq 0 && gg_run ctest_debug
 test $ret -eq 0 && gg_run ctest_release
 
 if [ -z ${GG_BUILD_LOW_PERF} ]; then
+    test $ret -eq 0 && gg_run embd_bge_small
+
     if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then
         if [ -z ${GG_BUILD_CUDA} ]; then
             test $ret -eq 0 && gg_run open_llama_3b_v2
         else
             test $ret -eq 0 && gg_run open_llama_7b_v2
         fi
+        test $ret -eq 0 && gg_run ctest_with_model_debug
+        test $ret -eq 0 && gg_run ctest_with_model_release
     fi
 fi
 
diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt
index f79acfef1..1d840e5f7 100644
--- a/common/CMakeLists.txt
+++ b/common/CMakeLists.txt
@@ -19,7 +19,12 @@ if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git")
         endif()
     endif()
 
-    set(GIT_INDEX "${GIT_DIR}/index")
+    if(EXISTS "${GIT_DIR}/index")
+        set(GIT_INDEX "${GIT_DIR}/index")
+    else()
+        message(WARNING "Git index not found in git repository.")
+        set(GIT_INDEX "")
+    endif()
 else()
     message(WARNING "Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.")
     set(GIT_INDEX "")
@@ -42,6 +47,8 @@ if (BUILD_SHARED_LIBS)
     set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
 endif()
 
+set(TARGET json-schema-to-grammar)
+add_library(${TARGET} OBJECT json-schema-to-grammar.cpp json-schema-to-grammar.h)
 
 set(TARGET common)
 
@@ -55,14 +62,28 @@ add_library(${TARGET} STATIC
     console.cpp
     grammar-parser.h
     grammar-parser.cpp
+    json.hpp
     train.h
     train.cpp
+    ngram-cache.h
+    ngram-cache.cpp
     )
 
 if (BUILD_SHARED_LIBS)
     set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
 endif()
 
+set(LLAMA_COMMON_EXTRA_LIBS build_info)
+
+# Use curl to download model url
+if (LLAMA_CURL)
+    find_package(CURL REQUIRED)
+    add_definitions(-DLLAMA_USE_CURL)
+    include_directories(${CURL_INCLUDE_DIRS})
+    find_library(CURL_LIBRARY curl REQUIRED)
+    set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARY})
+endif ()
+
 target_include_directories(${TARGET} PUBLIC .)
 target_compile_features(${TARGET} PUBLIC cxx_std_11)
-target_link_libraries(${TARGET} PRIVATE build_info PUBLIC llama)
+target_link_libraries(${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama)
diff --git a/common/common.cpp b/common/common.cpp
index 4e89fe516..3e2df6e34 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -37,11 +37,37 @@
 #include <sys/stat.h>
 #include <unistd.h>
 #endif
+#if defined(LLAMA_USE_CURL)
+#include <curl/curl.h>
+#include <curl/easy.h>
+#include <thread>
+#include <future>
+#endif
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
+#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL))
+#define GGML_USE_CUDA_SYCL
+#endif
+
+#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN)
+#define GGML_USE_CUDA_SYCL_VULKAN
+#endif
+
+#if defined(LLAMA_USE_CURL)
+#ifdef __linux__
+#include <linux/limits.h>
+#elif defined(_WIN32)
+#define PATH_MAX MAX_PATH
+#else
+#include <sys/syslimits.h>
+#endif
+#define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
+#define LLAMA_CURL_MAX_HEADER_LENGTH 256
+#endif // LLAMA_USE_CURL
+
 int32_t get_num_physical_cores() {
 #ifdef __linux__
     // enumerate the set of thread siblings, num entries is num cores
@@ -78,7 +104,7 @@ int32_t get_num_physical_cores() {
     return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
 }
 
-void process_escapes(std::string& input) {
+void process_escapes(std::string & input) {
     std::size_t input_len = input.length();
     std::size_t output_idx = 0;
 
@@ -131,6 +157,1072 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
     return result;
 }
 
+bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) {
+    llama_sampling_params& sparams = params.sparams;
+
+    if (arg == "-s" || arg == "--seed") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.seed = std::stoul(argv[i]);
+        return true;
+    }
+    if (arg == "-t" || arg == "--threads") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_threads = std::stoi(argv[i]);
+        if (params.n_threads <= 0) {
+            params.n_threads = std::thread::hardware_concurrency();
+        }
+        return true;
+    }
+    if (arg == "-tb" || arg == "--threads-batch") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_threads_batch = std::stoi(argv[i]);
+        if (params.n_threads_batch <= 0) {
+            params.n_threads_batch = std::thread::hardware_concurrency();
+        }
+        return true;
+    }
+    if (arg == "-td" || arg == "--threads-draft") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_threads_draft = std::stoi(argv[i]);
+        if (params.n_threads_draft <= 0) {
+            params.n_threads_draft = std::thread::hardware_concurrency();
+        }
+        return true;
+    }
+    if (arg == "-tbd" || arg == "--threads-batch-draft") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_threads_batch_draft = std::stoi(argv[i]);
+        if (params.n_threads_batch_draft <= 0) {
+            params.n_threads_batch_draft = std::thread::hardware_concurrency();
+        }
+        return true;
+    }
+    if (arg == "-p" || arg == "--prompt") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.prompt = argv[i];
+        return true;
+    }
+    if (arg == "-e" || arg == "--escape") {
+        params.escape = true;
+        return true;
+    }
+    if (arg == "--prompt-cache") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.path_prompt_cache = argv[i];
+        return true;
+    }
+    if (arg == "--prompt-cache-all") {
+        params.prompt_cache_all = true;
+        return true;
+    }
+    if (arg == "--prompt-cache-ro") {
+        params.prompt_cache_ro = true;
+        return true;
+    }
+    if (arg == "-bf" || arg == "--binary-file") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::ifstream file(argv[i], std::ios::binary);
+        if (!file) {
+            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+            invalid_param = true;
+            return true;
+        }
+        // store the external file name in params
+        params.prompt_file = argv[i];
+        std::ostringstream ss;
+        ss << file.rdbuf();
+        params.prompt = ss.str();
+        fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), argv[i]);
+        return true;
+    }
+    if (arg == "-f" || arg == "--file") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::ifstream file(argv[i]);
+        if (!file) {
+            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+            invalid_param = true;
+            return true;
+        }
+        // store the external file name in params
+        params.prompt_file = argv[i];
+        std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
+        if (!params.prompt.empty() && params.prompt.back() == '\n') {
+            params.prompt.pop_back();
+        }
+        return true;
+    }
+    if (arg == "-n" || arg == "--n-predict") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_predict = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--top-k") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.top_k = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "-c" || arg == "--ctx-size") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_ctx = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--grp-attn-n" || arg == "-gan") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.grp_attn_n = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--grp-attn-w" || arg == "-gaw") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.grp_attn_w = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--rope-freq-base") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.rope_freq_base = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--rope-freq-scale") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.rope_freq_scale = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--rope-scaling") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::string value(argv[i]);
+        /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
+        else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
+        else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
+        else { invalid_param = true; }
+        return true;
+    }
+    if (arg == "--rope-scale") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.rope_freq_scale = 1.0f / std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--yarn-orig-ctx") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.yarn_orig_ctx = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--yarn-ext-factor") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.yarn_ext_factor = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--yarn-attn-factor") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.yarn_attn_factor = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--yarn-beta-fast") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.yarn_beta_fast = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--yarn-beta-slow") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.yarn_beta_slow = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--pooling") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::string value(argv[i]);
+        /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
+        else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
+        else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
+        else { invalid_param = true; }
+        return true;
+    }
+    if (arg == "--defrag-thold" || arg == "-dt") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.defrag_thold = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--samplers") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        const auto sampler_names = string_split(argv[i], ';');
+        sparams.samplers_sequence = sampler_types_from_names(sampler_names, true);
+        return true;
+    }
+    if (arg == "--sampling-seq") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.samplers_sequence = sampler_types_from_chars(argv[i]);
+        return true;
+    }
+    if (arg == "--top-p") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.top_p = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--min-p") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.min_p = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--temp") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.temp = std::stof(argv[i]);
+        sparams.temp = std::max(sparams.temp, 0.0f);
+        return true;
+    }
+    if (arg == "--tfs") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.tfs_z = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--typical") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.typical_p = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--repeat-last-n") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.penalty_last_n = std::stoi(argv[i]);
+        sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n);
+        return true;
+    }
+    if (arg == "--repeat-penalty") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.penalty_repeat = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--frequency-penalty") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.penalty_freq = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--presence-penalty") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.penalty_present = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--dynatemp-range") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.dynatemp_range = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--dynatemp-exp") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.dynatemp_exponent = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--mirostat") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.mirostat = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--mirostat-lr") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.mirostat_eta = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--mirostat-ent") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.mirostat_tau = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--cfg-negative-prompt") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.cfg_negative_prompt = argv[i];
+        return true;
+    }
+    if (arg == "--cfg-negative-prompt-file") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::ifstream file(argv[i]);
+        if (!file) {
+            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+            invalid_param = true;
+            return true;
+        }
+        std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(sparams.cfg_negative_prompt));
+        if (!sparams.cfg_negative_prompt.empty() && sparams.cfg_negative_prompt.back() == '\n') {
+            sparams.cfg_negative_prompt.pop_back();
+        }
+        return true;
+    }
+    if (arg == "--cfg-scale") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.cfg_scale = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "-b" || arg == "--batch-size") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_batch = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "-ub" || arg == "--ubatch-size") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_ubatch = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--keep") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_keep = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--draft") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_draft = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--chunks") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_chunks = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "-np" || arg == "--parallel") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_parallel = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "-ns" || arg == "--sequences") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_sequences = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--p-split" || arg == "-ps") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.p_split = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "-m" || arg == "--model") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.model = argv[i];
+        return true;
+    }
+    if (arg == "-md" || arg == "--model-draft") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.model_draft = argv[i];
+        return true;
+    }
+    if (arg == "-a" || arg == "--alias") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.model_alias = argv[i];
+        return true;
+    }
+    if (arg == "-mu" || arg == "--model-url") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.model_url = argv[i];
+        return true;
+    }
+    if (arg == "-hfr" || arg == "--hf-repo") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.hf_repo = argv[i];
+        return true;
+    }
+    if (arg == "-hff" || arg == "--hf-file") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.hf_file = argv[i];
+        return true;
+    }
+    if (arg == "--lora") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.lora_adapter.emplace_back(argv[i], 1.0f);
+        params.use_mmap = false;
+        return true;
+    }
+    if (arg == "--lora-scaled") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        const char* lora_adapter = argv[i];
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i]));
+        params.use_mmap = false;
+        return true;
+    }
+    if (arg == "--lora-base") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.lora_base = argv[i];
+        return true;
+    }
+    if (arg == "--control-vector") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.control_vectors.push_back({ 1.0f, argv[i], });
+        return true;
+    }
+    if (arg == "--control-vector-scaled") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        const char* fname = argv[i];
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.control_vectors.push_back({ std::stof(argv[i]), fname, });
+        return true;
+    }
+    if (arg == "--control-vector-layer-range") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.control_vector_layer_start = std::stoi(argv[i]);
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.control_vector_layer_end = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--mmproj") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.mmproj = argv[i];
+        return true;
+    }
+    if (arg == "--image") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.image = argv[i];
+        return true;
+    }
+    if (arg == "-i" || arg == "--interactive") {
+        params.interactive = true;
+        return true;
+    }
+    if (arg == "--embedding") {
+        params.embedding = true;
+        return true;
+    }
+    if (arg == "--interactive-first") {
+        params.interactive_first = true;
+        return true;
+    }
+    if (arg == "-ins" || arg == "--instruct") {
+        params.instruct = true;
+        return true;
+    }
+    if (arg == "-cml" || arg == "--chatml") {
+        params.chatml = true;
+        return true;
+    }
+    if (arg == "--infill") {
+        params.infill = true;
+        return true;
+    }
+    if (arg == "-dkvc" || arg == "--dump-kv-cache") {
+        params.dump_kv_cache = true;
+        return true;
+    }
+    if (arg == "-nkvo" || arg == "--no-kv-offload") {
+        params.no_kv_offload = true;
+        return true;
+    }
+    if (arg == "-ctk" || arg == "--cache-type-k") {
+        params.cache_type_k = argv[++i];
+        return true;
+    }
+    if (arg == "-ctv" || arg == "--cache-type-v") {
+        params.cache_type_v = argv[++i];
+        return true;
+    }
+    if (arg == "--multiline-input") {
+        params.multiline_input = true;
+        return true;
+    }
+    if (arg == "--simple-io") {
+        params.simple_io = true;
+        return true;
+    }
+    if (arg == "-cb" || arg == "--cont-batching") {
+        params.cont_batching = true;
+        return true;
+    }
+    if (arg == "--color") {
+        params.use_color = true;
+        return true;
+    }
+    if (arg == "--mlock") {
+        params.use_mlock = true;
+        return true;
+    }
+    if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_gpu_layers = std::stoi(argv[i]);
+        if (!llama_supports_gpu_offload()) {
+            fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n");
+            fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
+        }
+        return true;
+    }
+    if (arg == "--gpu-layers-draft" || arg == "-ngld" || arg == "--n-gpu-layers-draft") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_gpu_layers_draft = std::stoi(argv[i]);
+        if (!llama_supports_gpu_offload()) {
+            fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n");
+            fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
+        }
+        return true;
+    }
+    if (arg == "--main-gpu" || arg == "-mg") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.main_gpu = std::stoi(argv[i]);
+#ifndef GGML_USE_CUDA_SYCL
+        fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL. Setting the main GPU has no effect.\n");
+#endif // GGML_USE_CUDA_SYCL
+        return true;
+    }
+    if (arg == "--split-mode" || arg == "-sm") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::string arg_next = argv[i];
+        if (arg_next == "none") {
+            params.split_mode = LLAMA_SPLIT_MODE_NONE;
+        }
+        else if (arg_next == "layer") {
+            params.split_mode = LLAMA_SPLIT_MODE_LAYER;
+        }
+        else if (arg_next == "row") {
+#ifdef GGML_USE_SYCL
+            fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n");
+            exit(1);
+#endif // GGML_USE_SYCL
+            params.split_mode = LLAMA_SPLIT_MODE_ROW;
+        }
+        else {
+            invalid_param = true;
+            return true;
+        }
+#ifndef GGML_USE_CUDA_SYCL
+        fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL. Setting the split mode has no effect.\n");
+#endif // GGML_USE_CUDA_SYCL
+        return true;
+    }
+    if (arg == "--tensor-split" || arg == "-ts") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::string arg_next = argv[i];
+
+        // split string by , and /
+        const std::regex regex{ R"([,/]+)" };
+        std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 };
+        std::vector<std::string> split_arg{ it, {} };
+        if (split_arg.size() >= llama_max_devices()) {
+            invalid_param = true;
+            return true;
+        }
+        for (size_t i = 0; i < llama_max_devices(); ++i) {
+            if (i < split_arg.size()) {
+                params.tensor_split[i] = std::stof(split_arg[i]);
+            }
+            else {
+                params.tensor_split[i] = 0.0f;
+            }
+        }
+#ifndef GGML_USE_CUDA_SYCL_VULKAN
+        fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting a tensor split has no effect.\n");
+#endif // GGML_USE_CUDA_SYCL_VULKAN
+        return true;
+    }
+    if (arg == "--no-mmap") {
+        params.use_mmap = false;
+        return true;
+    }
+    if (arg == "--numa") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::string value(argv[i]);
+        /**/ if (value == "distribute" || value == "") { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
+        else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
+        else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
+        else { invalid_param = true; }
+        return true;
+    }
+    if (arg == "--verbose-prompt") {
+        params.verbose_prompt = true;
+        return true;
+    }
+    if (arg == "--no-display-prompt") {
+        params.display_prompt = false;
+        return true;
+    }
+    if (arg == "-r" || arg == "--reverse-prompt") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.antiprompt.emplace_back(argv[i]);
+        return true;
+    }
+    if (arg == "-ld" || arg == "--logdir") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.logdir = argv[i];
+
+        if (params.logdir.back() != DIRECTORY_SEPARATOR) {
+            params.logdir += DIRECTORY_SEPARATOR;
+        }
+        return true;
+    }
+    if (arg == "-lcs" || arg == "--lookup-cache-static") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.lookup_cache_static = argv[i];
+        return true;
+    }
+    if (arg == "-lcd" || arg == "--lookup-cache-dynamic") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.lookup_cache_dynamic = argv[i];
+        return true;
+    }
+    if (arg == "--save-all-logits" || arg == "--kl-divergence-base") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.logits_file = argv[i];
+        return true;
+    }
+    if (arg == "--perplexity" || arg == "--all-logits") {
+        params.logits_all = true;
+        return true;
+    }
+    if (arg == "--ppl-stride") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.ppl_stride = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "-ptc" || arg == "--print-token-count") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.n_print = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--ppl-output-type") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.ppl_output_type = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--hellaswag") {
+        params.hellaswag = true;
+        return true;
+    }
+    if (arg == "--hellaswag-tasks") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.hellaswag_tasks = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--winogrande") {
+        params.winogrande = true;
+        return true;
+    }
+    if (arg == "--winogrande-tasks") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.winogrande_tasks = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--multiple-choice") {
+        params.multiple_choice = true;
+        return true;
+    }
+    if (arg == "--multiple-choice-tasks") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.multiple_choice_tasks = std::stoi(argv[i]);
+        return true;
+    }
+    if (arg == "--kl-divergence") {
+        params.kl_divergence = true;
+        return true;
+    }
+    if (arg == "--ignore-eos") {
+        params.ignore_eos = true;
+        return true;
+    }
+    if (arg == "--penalize-nl") {
+        sparams.penalize_nl = true;
+        return true;
+    }
+    if (arg == "-l" || arg == "--logit-bias") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::stringstream ss(argv[i]);
+        llama_token key;
+        char sign;
+        std::string value_str;
+        try {
+            if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
+                sparams.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
+            }
+            else {
+                throw std::exception();
+            }
+        }
+        catch (const std::exception&) {
+            invalid_param = true;
+            return true;
+        }
+        return true;
+    }
+    if (arg == "-h" || arg == "--help") {
+        gpt_print_usage(argc, argv, gpt_params());
+        exit(0);
+    }
+    if (arg == "--version") {
+        fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
+        fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
+        exit(0);
+    }
+    if (arg == "--random-prompt") {
+        params.random_prompt = true;
+        return true;
+    }
+    if (arg == "--in-prefix-bos") {
+        params.input_prefix_bos = true;
+        return true;
+    }
+    if (arg == "--in-prefix") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.input_prefix = argv[i];
+        return true;
+    }
+    if (arg == "--in-suffix") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        params.input_suffix = argv[i];
+        return true;
+    }
+    if (arg == "--grammar") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        sparams.grammar = argv[i];
+        return true;
+    }
+    if (arg == "--grammar-file") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        std::ifstream file(argv[i]);
+        if (!file) {
+            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+            invalid_param = true;
+            return true;
+        }
+        std::copy(
+            std::istreambuf_iterator<char>(file),
+            std::istreambuf_iterator<char>(),
+            std::back_inserter(sparams.grammar)
+        );
+        return true;
+    }
+    if (arg == "--override-kv") {
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        char* sep = strchr(argv[i], '=');
+        if (sep == nullptr || sep - argv[i] >= 128) {
+            fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
+            invalid_param = true;
+            return true;
+        }
+        struct llama_model_kv_override kvo;
+        std::strncpy(kvo.key, argv[i], sep - argv[i]);
+        kvo.key[sep - argv[i]] = 0;
+        sep++;
+        if (strncmp(sep, "int:", 4) == 0) {
+            sep += 4;
+            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
+            kvo.int_value = std::atol(sep);
+        }
+        else if (strncmp(sep, "float:", 6) == 0) {
+            sep += 6;
+            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
+            kvo.float_value = std::atof(sep);
+        }
+        else if (strncmp(sep, "bool:", 5) == 0) {
+            sep += 5;
+            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
+            if (std::strcmp(sep, "true") == 0) {
+                kvo.bool_value = true;
+            }
+            else if (std::strcmp(sep, "false") == 0) {
+                kvo.bool_value = false;
+            }
+            else {
+                fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
+                invalid_param = true;
+                return true;
+            }
+        }
+        else {
+            fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
+            invalid_param = true;
+            return true;
+        }
+        params.kv_overrides.push_back(kvo);
+        return true;
+    }
+#ifndef LOG_DISABLE_LOGS
+    // Parse args for logging parameters
+    if (log_param_single_parse(argv[i])) {
+        // Do nothing, log_param_single_parse automatically does it's thing
+        //  and returns if a match was found and parsed.
+        return true;
+    }
+    if (log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i])) {
+        // We have a matching known parameter requiring an argument,
+        //  now we need to check if there is anything after this argv
+        //  and flag invalid_param or parse it.
+        if (++i >= argc) {
+            invalid_param = true;
+            return true;
+        }
+        if (!log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i - 1], argv[i])) {
+            invalid_param = true;
+            return true;
+        }
+        return true;
+    }
+    // End of Parse args for logging parameters
+#endif // LOG_DISABLE_LOGS
+
+    return false;
+}
+
 bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
     bool invalid_param = false;
     std::string arg;
@@ -143,642 +1235,15 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
             std::replace(arg.begin(), arg.end(), '_', '-');
         }
 
-        if (arg == "-s" || arg == "--seed") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.seed = std::stoul(argv[i]);
-        } else if (arg == "-t" || arg == "--threads") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_threads = std::stoi(argv[i]);
-            if (params.n_threads <= 0) {
-                params.n_threads = std::thread::hardware_concurrency();
-            }
-        } else if (arg == "-tb" || arg == "--threads-batch") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_threads_batch = std::stoi(argv[i]);
-            if (params.n_threads_batch <= 0) {
-                params.n_threads_batch = std::thread::hardware_concurrency();
-            }
-        } else if (arg == "-p" || arg == "--prompt") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.prompt = argv[i];
-        } else if (arg == "-e" || arg == "--escape") {
-            params.escape = true;
-        } else if (arg == "--prompt-cache") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.path_prompt_cache = argv[i];
-        } else if (arg == "--prompt-cache-all") {
-            params.prompt_cache_all = true;
-        } else if (arg == "--prompt-cache-ro") {
-            params.prompt_cache_ro = true;
-        } else if (arg == "-f" || arg == "--file") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            std::ifstream file(argv[i]);
-            if (!file) {
-                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
-                invalid_param = true;
-                break;
-            }
-            // store the external file name in params
-            params.prompt_file = argv[i];
-            std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
-            if (!params.prompt.empty() && params.prompt.back() == '\n') {
-                params.prompt.pop_back();
-            }
-        } else if (arg == "-n" || arg == "--n-predict") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_predict = std::stoi(argv[i]);
-        } else if (arg == "--top-k") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.top_k = std::stoi(argv[i]);
-        } else if (arg == "-c" || arg == "--ctx-size") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_ctx = std::stoi(argv[i]);
-        } else if (arg == "--grp-attn-n" || arg == "-gan") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-
-            params.grp_attn_n = std::stoi(argv[i]);
-        } else if (arg == "--grp-attn-w" || arg == "-gaw") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-
-            params.grp_attn_w = std::stoi(argv[i]);
-        } else if (arg == "--rope-freq-base") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.rope_freq_base = std::stof(argv[i]);
-        } else if (arg == "--rope-freq-scale") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.rope_freq_scale = std::stof(argv[i]);
-        } else if (arg == "--rope-scaling") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            std::string value(argv[i]);
-            /**/ if (value == "none")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; }
-            else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; }
-            else if (value == "yarn")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; }
-            else { invalid_param = true; break; }
-        } else if (arg == "--rope-scale") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.rope_freq_scale = 1.0f/std::stof(argv[i]);
-        } else if (arg == "--yarn-orig-ctx") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.yarn_orig_ctx = std::stoi(argv[i]);
-        } else if (arg == "--yarn-ext-factor") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.yarn_ext_factor = std::stof(argv[i]);
-        } else if (arg == "--yarn-attn-factor") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.yarn_attn_factor = std::stof(argv[i]);
-        } else if (arg == "--yarn-beta-fast") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.yarn_beta_fast = std::stof(argv[i]);
-        } else if (arg == "--yarn-beta-slow") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.yarn_beta_slow = std::stof(argv[i]);
-        } else if (arg == "--samplers") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.samplers_sequence = parse_samplers_input(argv[i]);
-        } else if (arg == "--sampling-seq") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.samplers_sequence = argv[i];
-        } else if (arg == "--top-p") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.top_p = std::stof(argv[i]);
-        } else if (arg == "--min-p") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.min_p = std::stof(argv[i]);
-        } else if (arg == "--temp") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.temp = std::stof(argv[i]);
-            sparams.temp = std::max(sparams.temp, 0.0f);
-        } else if (arg == "--tfs") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.tfs_z = std::stof(argv[i]);
-        } else if (arg == "--typical") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.typical_p = std::stof(argv[i]);
-        } else if (arg == "--repeat-last-n") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.penalty_last_n = std::stoi(argv[i]);
-            sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n);
-        } else if (arg == "--repeat-penalty") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.penalty_repeat = std::stof(argv[i]);
-        } else if (arg == "--frequency-penalty") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.penalty_freq = std::stof(argv[i]);
-        } else if (arg == "--presence-penalty") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.penalty_present = std::stof(argv[i]);
-        } else if (arg == "--mirostat") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.mirostat = std::stoi(argv[i]);
-        } else if (arg == "--mirostat-lr") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.mirostat_eta = std::stof(argv[i]);
-        } else if (arg == "--mirostat-ent") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.mirostat_tau = std::stof(argv[i]);
-        } else if (arg == "--cfg-negative-prompt") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.cfg_negative_prompt = argv[i];
-        } else if (arg == "--cfg-negative-prompt-file") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            std::ifstream file(argv[i]);
-            if (!file) {
-                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
-                invalid_param = true;
-                break;
-            }
-            std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(sparams.cfg_negative_prompt));
-            if (!sparams.cfg_negative_prompt.empty() && sparams.cfg_negative_prompt.back() == '\n') {
-                sparams.cfg_negative_prompt.pop_back();
-            }
-        } else if (arg == "--cfg-scale") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.cfg_scale = std::stof(argv[i]);
-        } else if (arg == "-b" || arg == "--batch-size") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_batch = std::stoi(argv[i]);
-        } else if (arg == "--keep") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_keep = std::stoi(argv[i]);
-        } else if (arg == "--draft") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_draft = std::stoi(argv[i]);
-        } else if (arg == "--chunks") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_chunks = std::stoi(argv[i]);
-        } else if (arg == "-np" || arg == "--parallel") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_parallel = std::stoi(argv[i]);
-        } else if (arg == "-ns" || arg == "--sequences") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.n_sequences = std::stoi(argv[i]);
-        } else if (arg == "--p-accept" || arg == "-pa") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.p_accept = std::stof(argv[i]);
-        } else if (arg == "--p-split" || arg == "-ps") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.p_split = std::stof(argv[i]);
-        } else if (arg == "-m" || arg == "--model") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.model = argv[i];
-        } else if (arg == "-md" || arg == "--model-draft") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.model_draft = argv[i];
-        } else if (arg == "-a" || arg == "--alias") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.model_alias = argv[i];
-        } else if (arg == "--lora") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f));
-            params.use_mmap = false;
-        } else if (arg == "--lora-scaled") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            const char * lora_adapter = argv[i];
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.lora_adapter.push_back(std::make_tuple(lora_adapter, std::stof(argv[i])));
-            params.use_mmap = false;
-        } else if (arg == "--lora-base") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.lora_base = argv[i];
-        } else if (arg == "--mmproj") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.mmproj = argv[i];
-        } else if (arg == "--image") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.image = argv[i];
-        } else if (arg == "-i" || arg == "--interactive") {
-            params.interactive = true;
-        } else if (arg == "--embedding") {
-            params.embedding = true;
-        } else if (arg == "--interactive-first") {
-            params.interactive_first = true;
-        } else if (arg == "-ins" || arg == "--instruct") {
-            params.instruct = true;
-        } else if (arg == "-cml" || arg == "--chatml") {
-            params.chatml = true;
-        } else if (arg == "--infill") {
-            params.infill = true;
-        } else if (arg == "-dkvc" || arg == "--dump-kv-cache") {
-            params.dump_kv_cache = true;
-        } else if (arg == "-nkvo" || arg == "--no-kv-offload") {
-            params.no_kv_offload = true;
-        } else if (arg == "-ctk" || arg == "--cache-type-k") {
-            params.cache_type_k = argv[++i];
-        } else if (arg == "-ctv" || arg == "--cache-type-v") {
-            params.cache_type_v = argv[++i];
-        } else if (arg == "--multiline-input") {
-            params.multiline_input = true;
-        } else if (arg == "--simple-io") {
-            params.simple_io = true;
-        } else if (arg == "-cb" || arg == "--cont-batching") {
-            params.cont_batching = true;
-        } else if (arg == "--color") {
-            params.use_color = true;
-        } else if (arg == "--mlock") {
-            params.use_mlock = true;
-        } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
-            params.n_gpu_layers = std::stoi(argv[i]);
-#else
-            fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n");
-            fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
-#endif
-        } else if (arg == "--gpu-layers-draft" || arg == "-ngld" || arg == "--n-gpu-layers-draft") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
-            params.n_gpu_layers_draft = std::stoi(argv[i]);
-#else
-            fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n");
-            fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
-#endif
-        } else if (arg == "--main-gpu" || arg == "-mg") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-#ifdef GGML_USE_CUBLAS
-            params.main_gpu = std::stoi(argv[i]);
-#else
-            fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\n");
-#endif
-        } else if (arg == "--tensor-split" || arg == "-ts") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-#ifdef GGML_USE_CUBLAS
-            std::string arg_next = argv[i];
-
-            // split string by , and /
-            const std::regex regex{R"([,/]+)"};
-            std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
-            std::vector<std::string> split_arg{it, {}};
-            GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
-
-            for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
-                if (i < split_arg.size()) {
-                    params.tensor_split[i] = std::stof(split_arg[i]);
-                } else {
-                    params.tensor_split[i] = 0.0f;
-                }
-            }
-#else
-            fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
-#endif // GGML_USE_CUBLAS
-        } else if (arg == "--no-mul-mat-q" || arg == "-nommq") {
-#ifdef GGML_USE_CUBLAS
-            params.mul_mat_q = false;
-#else
-            fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n");
-#endif // GGML_USE_CUBLAS
-        } else if (arg == "--no-mmap") {
-            params.use_mmap = false;
-        } else if (arg == "--numa") {
-            params.numa = true;
-        } else if (arg == "--verbose-prompt") {
-            params.verbose_prompt = true;
-        } else if (arg == "-r" || arg == "--reverse-prompt") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.antiprompt.push_back(argv[i]);
-        } else if (arg == "-ld" || arg == "--logdir") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.logdir = argv[i];
-
-            if (params.logdir.back() != DIRECTORY_SEPARATOR) {
-                params.logdir += DIRECTORY_SEPARATOR;
-            }
-        } else if (arg == "--perplexity" || arg == "--all-logits") {
-            params.logits_all = true;
-        } else if (arg == "--ppl-stride") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.ppl_stride = std::stoi(argv[i]);
-        } else if (arg == "--ppl-output-type") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.ppl_output_type = std::stoi(argv[i]);
-        } else if (arg == "--hellaswag") {
-            params.hellaswag = true;
-        } else if (arg == "--hellaswag-tasks") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.hellaswag_tasks = std::stoi(argv[i]);
-        } else if (arg == "--ignore-eos") {
-            params.ignore_eos = true;
-        } else if (arg == "--no-penalize-nl") {
-            sparams.penalize_nl = false;
-        } else if (arg == "-l" || arg == "--logit-bias") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            std::stringstream ss(argv[i]);
-            llama_token key;
-            char sign;
-            std::string value_str;
-            try {
-                if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
-                    sparams.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
-                } else {
-                    throw std::exception();
-                }
-            } catch (const std::exception&) {
-                invalid_param = true;
-                break;
-            }
-        } else if (arg == "-h" || arg == "--help") {
-            return false;
-
-        } else if (arg == "--version") {
-            fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
-            fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
-            exit(0);
-        } else if (arg == "--random-prompt") {
-            params.random_prompt = true;
-        } else if (arg == "--in-prefix-bos") {
-            params.input_prefix_bos = true;
-        } else if (arg == "--in-prefix") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.input_prefix = argv[i];
-        } else if (arg == "--in-suffix") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.input_suffix = argv[i];
-        } else if (arg == "--grammar") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            sparams.grammar = argv[i];
-        } else if (arg == "--grammar-file") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            std::ifstream file(argv[i]);
-            if (!file) {
-                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
-                invalid_param = true;
-                break;
-            }
-            std::copy(
-                std::istreambuf_iterator<char>(file),
-                std::istreambuf_iterator<char>(),
-                std::back_inserter(sparams.grammar)
-            );
-        } else if (arg == "--override-kv") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            char * sep = strchr(argv[i], '=');
-            if (sep == nullptr || sep - argv[i] >= 128) {
-                fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
-                invalid_param = true;
-                break;
-            }
-            struct llama_model_kv_override kvo;
-            std::strncpy(kvo.key, argv[i], sep - argv[i]);
-            kvo.key[sep - argv[i]] = 0;
-            sep++;
-            if (strncmp(sep, "int:", 4) == 0) {
-                sep += 4;
-                kvo.tag = LLAMA_KV_OVERRIDE_INT;
-                kvo.int_value = std::atol(sep);
-            } else if (strncmp(sep, "float:", 6) == 0) {
-                sep += 6;
-                kvo.tag = LLAMA_KV_OVERRIDE_FLOAT;
-                kvo.float_value = std::atof(sep);
-            } else if (strncmp(sep, "bool:", 5) == 0) {
-                sep += 5;
-                kvo.tag = LLAMA_KV_OVERRIDE_BOOL;
-                if (std::strcmp(sep, "true") == 0) {
-                    kvo.bool_value = true;
-                } else if (std::strcmp(sep, "false") == 0) {
-                    kvo.bool_value = false;
-                } else {
-                    fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
-                    invalid_param = true;
-                    break;
-                }
-            } else {
-                fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
-                invalid_param = true;
-                break;
-            }
-            params.kv_overrides.push_back(kvo);
-#ifndef LOG_DISABLE_LOGS
-        // Parse args for logging parameters
-        } else if ( log_param_single_parse( argv[i] ) ) {
-            // Do nothing, log_param_single_parse automatically does it's thing
-            //  and returns if a match was found and parsed.
-        } else if ( log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i] ) ) {
-            // We have a matching known parameter requiring an argument,
-            //  now we need to check if there is anything after this argv
-            //  and flag invalid_param or parse it.
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            if( !log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i-1], argv[i]) ) {
-                invalid_param = true;
-                break;
-            }
-        // End of Parse args for logging parameters
-#endif // LOG_DISABLE_LOGS
-        } else {
+        if (!gpt_params_find_arg(argc, argv, arg, params, i, invalid_param)) {
             throw std::invalid_argument("error: unknown argument: " + arg);
         }
     }
+
     if (invalid_param) {
         throw std::invalid_argument("error: invalid parameter for argument: " + arg);
     }
+
     if (params.prompt_cache_all &&
             (params.interactive || params.interactive_first ||
              params.instruct)) {
@@ -786,6 +1251,11 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
         throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
     }
 
+    // short-hand to avoid specifying --hf-file -> default it to --model
+    if (!params.hf_repo.empty() && params.hf_file.empty()) {
+        params.hf_file = params.model;
+    }
+
     if (params.escape) {
         process_escapes(params.prompt);
         process_escapes(params.input_prefix);
@@ -797,7 +1267,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
     }
 
     if (!params.kv_overrides.empty()) {
-        params.kv_overrides.emplace_back(llama_model_kv_override());
+        params.kv_overrides.emplace_back();
         params.kv_overrides.back().key[0] = 0;
     }
 
@@ -807,12 +1277,20 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
 void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     const llama_sampling_params & sparams = params.sparams;
 
+    std::string sampler_type_chars;
+    std::string sampler_type_names;
+    for (const auto sampler_type : sparams.samplers_sequence) {
+        sampler_type_chars += static_cast<char>(sampler_type);
+        sampler_type_names += sampler_type_to_name_string(sampler_type) + ";";
+    }
+    sampler_type_names.pop_back();
+
     printf("\n");
     printf("usage: %s [options]\n", argv[0]);
     printf("\n");
     printf("options:\n");
     printf("  -h, --help            show this help message and exit\n");
-    printf("      --version         show version and build info\n");
+    printf("  --version             show version and build info\n");
     printf("  -i, --interactive     run in interactive mode\n");
     printf("  --interactive-first   run in interactive mode and wait for input right away\n");
     printf("  -ins, --instruct      run in instruction mode (use with Alpaca models)\n");
@@ -826,6 +1304,10 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  -t N, --threads N     number of threads to use during generation (default: %d)\n", params.n_threads);
     printf("  -tb N, --threads-batch N\n");
     printf("                        number of threads to use during batch and prompt processing (default: same as --threads)\n");
+    printf("  -td N, --threads-draft N");
+    printf("                        number of threads to use during generation (default: same as --threads)\n");
+    printf("  -tbd N, --threads-batch-draft N\n");
+    printf("                        number of threads to use during batch and prompt processing (default: same as --threads-draft)\n");
     printf("  -p PROMPT, --prompt PROMPT\n");
     printf("                        prompt to start generation with (default: empty)\n");
     printf("  -e, --escape          process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
@@ -839,11 +1321,16 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  --in-suffix STRING    string to suffix after user inputs with (default: empty)\n");
     printf("  -f FNAME, --file FNAME\n");
     printf("                        prompt file to start generation.\n");
+    printf("  -bf FNAME, --binary-file FNAME\n");
+    printf("                        binary file containing multiple choice tasks.\n");
     printf("  -n N, --n-predict N   number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
     printf("  -c N, --ctx-size N    size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx);
-    printf("  -b N, --batch-size N  batch size for prompt processing (default: %d)\n", params.n_batch);
-    printf("  --samplers            samplers that will be used for generation in the order, separated by \';\', for example: \"top_k;tfs;typical;top_p;min_p;temp\"\n");
-    printf("  --sampling-seq        simplified sequence for samplers that will be used (default: %s)\n", sparams.samplers_sequence.c_str());
+    printf("  -b N, --batch-size N  logical maximum batch size (default: %d)\n", params.n_batch);
+    printf("  -ub N, --ubatch-size N\n");
+    printf("                        physical maximum batch size (default: %d)\n", params.n_ubatch);
+    printf("  --samplers            samplers that will be used for generation in the order, separated by \';\'\n");
+    printf("                        (default: %s)\n", sampler_type_names.c_str());
+    printf("  --sampling-seq        simplified sequence for samplers that will be used (default: %s)\n", sampler_type_chars.c_str());
     printf("  --top-k N             top-k sampling (default: %d, 0 = disabled)\n", sparams.top_k);
     printf("  --top-p N             top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)sparams.top_p);
     printf("  --min-p N             min-p sampling (default: %.1f, 0.0 = disabled)\n", (double)sparams.min_p);
@@ -853,6 +1340,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  --repeat-penalty N    penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)sparams.penalty_repeat);
     printf("  --presence-penalty N  repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_present);
     printf("  --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_freq);
+    printf("  --dynatemp-range N    dynamic temperature range (default: %.1f, 0.0 = disabled)\n", (double)sparams.dynatemp_range);
+    printf("  --dynatemp-exp N      dynamic temperature exponent (default: %.1f)\n", (double)sparams.dynatemp_exponent);
     printf("  --mirostat N          use Mirostat sampling.\n");
     printf("                        Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n");
     printf("                        (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", sparams.mirostat);
@@ -879,50 +1368,63 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  --yarn-attn-factor N  YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n");
     printf("  --yarn-beta-slow N    YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow);
     printf("  --yarn-beta-fast N    YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast);
+    printf("  --pooling {none,mean,cls}\n");
+    printf("                        pooling type for embeddings, use model default if unspecified\n");
+    printf("  -dt N, --defrag-thold N\n");
+    printf("                        KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
     printf("  --ignore-eos          ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
-    printf("  --no-penalize-nl      do not penalize newline token\n");
+    printf("  --penalize-nl         penalize newline tokens\n");
     printf("  --temp N              temperature (default: %.1f)\n", (double)sparams.temp);
-    printf("  --logits-all          return logits for all tokens in the batch (default: disabled)\n");
+    printf("  --all-logits          return logits for all tokens in the batch (default: disabled)\n");
     printf("  --hellaswag           compute HellaSwag score over random tasks from datafile supplied with -f\n");
     printf("  --hellaswag-tasks N   number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
+    printf("  --winogrande          compute Winogrande score over random tasks from datafile supplied with -f\n");
+    printf("  --winogrande-tasks N  number of tasks to use when computing the Winogrande score (default: %zu)\n", params.winogrande_tasks);
+    printf("  --multiple-choice     compute multiple choice score over random tasks from datafile supplied with -f\n");
+    printf("  --multiple-choice-tasks N number of tasks to use when computing the multiple choice score (default: %zu)\n", params.winogrande_tasks);
+    printf("  --kl-divergence       computes KL-divergence to logits provided via --kl-divergence-base\n");
     printf("  --keep N              number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
     printf("  --draft N             number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft);
     printf("  --chunks N            max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
     printf("  -np N, --parallel N   number of parallel sequences to decode (default: %d)\n", params.n_parallel);
     printf("  -ns N, --sequences N  number of sequences to decode (default: %d)\n", params.n_sequences);
-    printf("  -pa N, --p-accept N   speculative decoding accept probability (default: %.1f)\n", (double)params.p_accept);
     printf("  -ps N, --p-split N    speculative decoding split probability (default: %.1f)\n", (double)params.p_split);
     printf("  -cb, --cont-batching  enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
     printf("  --mmproj MMPROJ_FILE  path to a multimodal projector file for LLaVA. see examples/llava/README.md\n");
     printf("  --image IMAGE_FILE    path to an image file. use with multimodal models\n");
-    if (llama_mlock_supported()) {
+    if (llama_supports_mlock()) {
         printf("  --mlock               force system to keep model in RAM rather than swapping or compressing\n");
     }
-    if (llama_mmap_supported()) {
+    if (llama_supports_mmap()) {
         printf("  --no-mmap             do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
     }
-    printf("  --numa                attempt optimizations that help on some NUMA systems\n");
+    printf("  --numa TYPE           attempt optimizations that help on some NUMA systems\n");
+    printf("                          - distribute: spread execution evenly over all nodes\n");
+    printf("                          - isolate: only spawn threads on CPUs on the node that execution started on\n");
+    printf("                          - numactl: use the CPU map provided by numactl\n");
     printf("                        if run without this previously, it is recommended to drop the system page cache before using this\n");
     printf("                        see https://github.com/ggerganov/llama.cpp/issues/1437\n");
-#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
-    printf("  -ngl N, --n-gpu-layers N\n");
-    printf("                        number of layers to store in VRAM\n");
-    printf("  -ngld N, --n-gpu-layers-draft N\n");
-    printf("                        number of layers to store in VRAM for the draft model\n");
-    printf("  -ts SPLIT --tensor-split SPLIT\n");
-    printf("                        how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
-    printf("  -mg i, --main-gpu i   the GPU to use for scratch and small tensors\n");
-#ifdef GGML_USE_CUBLAS
-    printf("  -nommq, --no-mul-mat-q\n");
-    printf("                        use " GGML_CUBLAS_NAME " instead of custom mul_mat_q " GGML_CUDA_NAME " kernels.\n");
-    printf("                        Not recommended since this is both slower and uses more VRAM.\n");
-#endif // GGML_USE_CUBLAS
-#endif
+    if (llama_supports_gpu_offload()) {
+        printf("  -ngl N, --n-gpu-layers N\n");
+        printf("                        number of layers to store in VRAM\n");
+        printf("  -ngld N, --n-gpu-layers-draft N\n");
+        printf("                        number of layers to store in VRAM for the draft model\n");
+        printf("  -sm SPLIT_MODE, --split-mode SPLIT_MODE\n");
+        printf("                        how to split the model across multiple GPUs, one of:\n");
+        printf("                          - none: use one GPU only\n");
+        printf("                          - layer (default): split layers and KV across GPUs\n");
+        printf("                          - row: split rows across GPUs\n");
+        printf("  -ts SPLIT, --tensor-split SPLIT\n");
+        printf("                        fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n");
+        printf("  -mg i, --main-gpu i   the GPU to use for the model (with split-mode = none),\n");
+        printf("                        or for intermediate results and KV (with split-mode = row) (default: %d)\n", params.main_gpu);
+    }
+    printf("  --verbose-prompt      print a verbose prompt before generation (default: %s)\n", params.verbose_prompt ? "true" : "false");
+    printf("  --no-display-prompt   don't print prompt at generation (default: %s)\n", !params.display_prompt ? "true" : "false");
     printf("  -gan N, --grp-attn-n N\n");
     printf("                        group-attention factor (default: %d)\n", params.grp_attn_n);
     printf("  -gaw N, --grp-attn-w N\n");
     printf("                        group-attention width (default: %.1f)\n", (double)params.grp_attn_w);
-    printf("  --verbose-prompt      print prompt before generation\n");
     printf("  -dkvc, --dump-kv-cache\n");
     printf("                        verbose print of the KV cache\n");
     printf("  -nkvo, --no-kv-offload\n");
@@ -935,15 +1437,33 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  --lora FNAME          apply LoRA adapter (implies --no-mmap)\n");
     printf("  --lora-scaled FNAME S apply LoRA adapter with user defined scaling S (implies --no-mmap)\n");
     printf("  --lora-base FNAME     optional model to use as a base for the layers modified by the LoRA adapter\n");
+    printf("  --control-vector FNAME\n");
+    printf("                        add a control vector\n");
+    printf("  --control-vector-scaled FNAME S\n");
+    printf("                        add a control vector with user defined scaling S\n");
+    printf("  --control-vector-layer-range START END\n");
+    printf("                        layer range to apply the control vector(s) to, start and end inclusive\n");
     printf("  -m FNAME, --model FNAME\n");
     printf("                        model path (default: %s)\n", params.model.c_str());
     printf("  -md FNAME, --model-draft FNAME\n");
-    printf("                        draft model for speculative decoding\n");
+    printf("                        draft model for speculative decoding (default: unused)\n");
+    printf("  -mu MODEL_URL, --model-url MODEL_URL\n");
+    printf("                        model download url (default: unused)\n");
+    printf("  -hfr REPO, --hf-repo REPO\n");
+    printf("                        Hugging Face model repository (default: unused)\n");
+    printf("  -hff FILE, --hf-file FILE\n");
+    printf("                        Hugging Face model file (default: unused)\n");
     printf("  -ld LOGDIR, --logdir LOGDIR\n");
     printf("                        path under which to save YAML logs (no logging if unset)\n");
+    printf("  -lcs FNAME, --lookup-cache-static FNAME\n");
+    printf("                        path to static lookup cache to use for lookup decoding (not updated by generation)\n");
+    printf("  -lcd FNAME, --lookup-cache-dynamic FNAME\n");
+    printf("                        path to dynamic lookup cache to use for lookup decoding (updated by generation)\n");
     printf("  --override-kv KEY=TYPE:VALUE\n");
     printf("                        advanced option to override model metadata by key. may be specified multiple times.\n");
     printf("                        types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
+    printf("  -ptc N, --print-token-count N\n");
+    printf("                        print token count every N tokens (default: %d)\n", params.n_print);
     printf("\n");
 #ifndef LOG_DISABLE_LOGS
     log_print_usage();
@@ -981,45 +1501,101 @@ std::string gpt_random_prompt(std::mt19937 & rng) {
 }
 
 //
-// String parsing
+// String utils
 //
 
-std::string parse_samplers_input(std::string input) {
-    std::string output = "";
+std::vector<std::string> string_split(std::string input, char separator) {
+    std::vector<std::string> parts;
+    size_t separator_pos = input.find(separator);
+    while (separator_pos != std::string::npos) {
+        std::string part = input.substr(0, separator_pos);
+        parts.emplace_back(part);
+        input = input.substr(separator_pos + 1);
+        separator_pos = input.find(separator);
+    }
+    parts.emplace_back(input);
+    return parts;
+}
+
+std::vector<llama_sampler_type> sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names) {
+    std::unordered_map<std::string, llama_sampler_type> sampler_canonical_name_map {
+        {"top_k",       llama_sampler_type::TOP_K},
+        {"top_p",       llama_sampler_type::TOP_P},
+        {"typical_p",   llama_sampler_type::TYPICAL_P},
+        {"min_p",       llama_sampler_type::MIN_P},
+        {"tfs_z",       llama_sampler_type::TFS_Z},
+        {"temperature", llama_sampler_type::TEMPERATURE}
+    };
+
     // since samplers names are written multiple ways
     // make it ready for both system names and input names
-    std::unordered_map<std::string, char> samplers_symbols {
-        {"top_k",      'k'},
-        {"top-k",      'k'},
-        {"top_p",      'p'},
-        {"top-p",      'p'},
-        {"nucleus",    'p'},
-        {"typical_p",  'y'},
-        {"typical-p",  'y'},
-        {"typical",    'y'},
-        {"min_p",      'm'},
-        {"min-p",      'm'},
-        {"tfs_z",      'f'},
-        {"tfs-z",      'f'},
-        {"tfs",        'f'},
-        {"temp",       't'},
-        {"temperature",'t'}
+    std::unordered_map<std::string, llama_sampler_type> sampler_alt_name_map {
+        {"top-k",       llama_sampler_type::TOP_K},
+        {"top-p",       llama_sampler_type::TOP_P},
+        {"nucleus",     llama_sampler_type::TOP_P},
+        {"typical-p",   llama_sampler_type::TYPICAL_P},
+        {"typical",     llama_sampler_type::TYPICAL_P},
+        {"min-p",       llama_sampler_type::MIN_P},
+        {"tfs-z",       llama_sampler_type::TFS_Z},
+        {"tfs",         llama_sampler_type::TFS_Z},
+        {"temp",        llama_sampler_type::TEMPERATURE}
     };
-    // expected format example: "temp;top_k;tfs_z;typical_p;top_p;min_p"
-    size_t separator = input.find(';');
-    while (separator != input.npos) {
-        std::string name = input.substr(0,separator);
-        input = input.substr(separator+1);
-        separator = input.find(';');
 
-        if (samplers_symbols.find(name) != samplers_symbols.end()) {
-            output += samplers_symbols[name];
+    std::vector<llama_sampler_type> sampler_types;
+    sampler_types.reserve(names.size());
+    for (const auto & name : names)
+    {
+        auto sampler_item = sampler_canonical_name_map.find(name);
+        if (sampler_item != sampler_canonical_name_map.end())
+        {
+            sampler_types.push_back(sampler_item->second);
+        }
+        else
+        {
+            if (allow_alt_names)
+            {
+                sampler_item = sampler_alt_name_map.find(name);
+                if (sampler_item != sampler_alt_name_map.end())
+                {
+                    sampler_types.push_back(sampler_item->second);
+                }
+            }
         }
     }
-    if (samplers_symbols.find(input) != samplers_symbols.end()) {
-        output += samplers_symbols[input];
+    return sampler_types;
+}
+
+std::vector<llama_sampler_type> sampler_types_from_chars(const std::string & names_string) {
+    std::unordered_map<char, llama_sampler_type> sampler_name_map {
+        {'k', llama_sampler_type::TOP_K},
+        {'p', llama_sampler_type::TOP_P},
+        {'y', llama_sampler_type::TYPICAL_P},
+        {'m', llama_sampler_type::MIN_P},
+        {'f', llama_sampler_type::TFS_Z},
+        {'t', llama_sampler_type::TEMPERATURE}
+    };
+
+    std::vector<llama_sampler_type> sampler_types;
+    sampler_types.reserve(names_string.size());
+    for (const auto & c : names_string) {
+        const auto sampler_item = sampler_name_map.find(c);
+        if (sampler_item != sampler_name_map.end()) {
+            sampler_types.push_back(sampler_item->second);
+        }
+    }
+    return sampler_types;
+}
+
+std::string sampler_type_to_name_string(llama_sampler_type sampler_type) {
+    switch (sampler_type) {
+        case llama_sampler_type::TOP_K:       return "top_k";
+        case llama_sampler_type::TFS_Z:       return "tfs_z";
+        case llama_sampler_type::TYPICAL_P:   return "typical_p";
+        case llama_sampler_type::TOP_P:       return "top_p";
+        case llama_sampler_type::MIN_P:       return "min_p";
+        case llama_sampler_type::TEMPERATURE: return "temperature";
+        default : return "";
     }
-    return output;
 }
 
 //
@@ -1033,6 +1609,7 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params &
         mparams.n_gpu_layers = params.n_gpu_layers;
     }
     mparams.main_gpu        = params.main_gpu;
+    mparams.split_mode      = params.split_mode;
     mparams.tensor_split    = params.tensor_split;
     mparams.use_mmap        = params.use_mmap;
     mparams.use_mlock       = params.use_mlock;
@@ -1047,6 +1624,9 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params &
 }
 
 static ggml_type kv_cache_type_from_str(const std::string & s) {
+    if (s == "f32") {
+        return GGML_TYPE_F32;
+    }
     if (s == "f16") {
         return GGML_TYPE_F16;
     }
@@ -1059,6 +1639,9 @@ static ggml_type kv_cache_type_from_str(const std::string & s) {
     if (s == "q4_1") {
         return GGML_TYPE_Q4_1;
     }
+    if (s == "iq4_nl") {
+        return GGML_TYPE_IQ4_NL;
+    }
     if (s == "q5_0") {
         return GGML_TYPE_Q5_0;
     }
@@ -1073,13 +1656,14 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
     auto cparams = llama_context_default_params();
 
     cparams.n_ctx             = params.n_ctx;
+    cparams.n_seq_max         = params.n_parallel;
     cparams.n_batch           = params.n_batch;
+    cparams.n_ubatch          = params.n_ubatch;
     cparams.n_threads         = params.n_threads;
     cparams.n_threads_batch   = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
-    cparams.mul_mat_q         = params.mul_mat_q;
     cparams.seed              = params.seed;
     cparams.logits_all        = params.logits_all;
-    cparams.embedding         = params.embedding;
+    cparams.embeddings        = params.embedding;
     cparams.rope_scaling_type = params.rope_scaling_type;
     cparams.rope_freq_base    = params.rope_freq_base;
     cparams.rope_freq_scale   = params.rope_freq_scale;
@@ -1088,6 +1672,8 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
     cparams.yarn_beta_fast    = params.yarn_beta_fast;
     cparams.yarn_beta_slow    = params.yarn_beta_slow;
     cparams.yarn_orig_ctx     = params.yarn_orig_ctx;
+    cparams.pooling_type      = params.pooling_type;
+    cparams.defrag_thold      = params.defrag_thold;
     cparams.offload_kqv       = !params.no_kv_offload;
 
     cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
@@ -1117,10 +1703,364 @@ void llama_batch_add(
     batch.n_tokens++;
 }
 
+#ifdef LLAMA_USE_CURL
+
+static bool llama_download_file(CURL * curl, const char * url, const char * path) {
+    bool force_download = false;
+
+    // Set the URL, allow to follow http redirection
+    curl_easy_setopt(curl, CURLOPT_URL, url);
+    curl_easy_setopt(curl, CURLOPT_FOLLOWLOCATION, 1L);
+
+#if defined(_WIN32)
+    // CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of
+    //   operating system. Currently implemented under MS-Windows.
+    curl_easy_setopt(curl, CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
+#endif
+
+    // Check if the file already exists locally
+    struct stat model_file_info;
+    auto file_exists = (stat(path, &model_file_info) == 0);
+
+    // If the file exists, check for ${path_model}.etag or ${path_model}.lastModified files
+    char etag[LLAMA_CURL_MAX_HEADER_LENGTH] = {0};
+    char etag_path[PATH_MAX] = {0};
+    snprintf(etag_path, sizeof(etag_path), "%s.etag", path);
+
+    char last_modified[LLAMA_CURL_MAX_HEADER_LENGTH] = {0};
+    char last_modified_path[PATH_MAX] = {0};
+    snprintf(last_modified_path, sizeof(last_modified_path), "%s.lastModified", path);
+
+    if (file_exists) {
+        auto * f_etag = fopen(etag_path, "r");
+        if (f_etag) {
+            if (!fgets(etag, sizeof(etag), f_etag)) {
+                fprintf(stderr, "%s: unable to read file %s\n", __func__, etag_path);
+            } else {
+                fprintf(stderr, "%s: previous file found %s: %s\n", __func__, etag_path, etag);
+            }
+            fclose(f_etag);
+        }
+
+        auto * f_last_modified = fopen(last_modified_path, "r");
+        if (f_last_modified) {
+            if (!fgets(last_modified, sizeof(last_modified), f_last_modified)) {
+                fprintf(stderr, "%s: unable to read file %s\n", __func__, last_modified_path);
+            } else {
+                fprintf(stderr, "%s: previous file found %s: %s\n", __func__, last_modified_path,
+                        last_modified);
+            }
+            fclose(f_last_modified);
+        }
+    }
+
+    // Send a HEAD request to retrieve the etag and last-modified headers
+    struct llama_load_model_from_url_headers {
+        char etag[LLAMA_CURL_MAX_HEADER_LENGTH] = {0};
+        char last_modified[LLAMA_CURL_MAX_HEADER_LENGTH] = {0};
+    };
+    llama_load_model_from_url_headers headers;
+    {
+        typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
+        auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
+            llama_load_model_from_url_headers *headers = (llama_load_model_from_url_headers *) userdata;
+
+            // Convert header field name to lowercase
+            for (size_t i = 0; i < n_items && buffer[i] != ':'; ++i) {
+                buffer[i] = tolower(buffer[i]);
+            }
+
+            const char * etag_prefix = "etag: ";
+            if (strncmp(buffer, etag_prefix, strlen(etag_prefix)) == 0) {
+                strncpy(headers->etag, buffer + strlen(etag_prefix), n_items - strlen(etag_prefix) - 2); // Remove CRLF
+            }
+
+            const char * last_modified_prefix = "last-modified: ";
+            if (strncmp(buffer, last_modified_prefix, strlen(last_modified_prefix)) == 0) {
+                strncpy(headers->last_modified, buffer + strlen(last_modified_prefix),
+                        n_items - strlen(last_modified_prefix) - 2); // Remove CRLF
+            }
+            return n_items;
+        };
+
+        curl_easy_setopt(curl, CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
+        curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 1L); // hide head request progress
+        curl_easy_setopt(curl, CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
+        curl_easy_setopt(curl, CURLOPT_HEADERDATA, &headers);
+
+        CURLcode res = curl_easy_perform(curl);
+        if (res != CURLE_OK) {
+            curl_easy_cleanup(curl);
+            fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
+            return false;
+        }
+
+        long http_code = 0;
+        curl_easy_getinfo(curl, CURLINFO_RESPONSE_CODE, &http_code);
+        if (http_code != 200) {
+            // HEAD not supported, we don't know if the file has changed
+            // force trigger downloading
+            force_download = true;
+            fprintf(stderr, "%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
+        }
+    }
+
+    // If the ETag or the Last-Modified headers are different: trigger a new download
+    bool should_download = !file_exists
+        || force_download
+        || (strlen(headers.etag) > 0 && strcmp(etag, headers.etag) != 0)
+        || (strlen(headers.last_modified) > 0 && strcmp(last_modified, headers.last_modified) != 0);
+    if (should_download) {
+        char path_temporary[PATH_MAX] = {0};
+        snprintf(path_temporary, sizeof(path_temporary), "%s.downloadInProgress", path);
+        if (file_exists) {
+            fprintf(stderr, "%s: deleting previous downloaded file: %s\n", __func__, path);
+            if (remove(path) != 0) {
+                curl_easy_cleanup(curl);
+                fprintf(stderr, "%s: unable to delete file: %s\n", __func__, path);
+                return false;
+            }
+        }
+
+        // Set the output file
+        auto * outfile = fopen(path_temporary, "wb");
+        if (!outfile) {
+            curl_easy_cleanup(curl);
+            fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path);
+            return false;
+        }
+
+        typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * data, size_t size, size_t nmemb, void * fd);
+        auto write_callback = [](void * data, size_t size, size_t nmemb, void * fd) -> size_t {
+            return fwrite(data, size, nmemb, (FILE *)fd);
+        };
+        curl_easy_setopt(curl, CURLOPT_NOBODY, 0L);
+        curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
+        curl_easy_setopt(curl, CURLOPT_WRITEDATA, outfile);
+
+        //  display download progress
+        curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 0L);
+
+        // helper function to hide password in URL
+        auto llama_download_hide_password_in_url = [](const std::string & url) -> std::string {
+            std::size_t protocol_pos = url.find("://");
+            if (protocol_pos == std::string::npos) {
+                return url;  // Malformed URL
+            }
+
+            std::size_t at_pos = url.find('@', protocol_pos + 3);
+            if (at_pos == std::string::npos) {
+                return url;  // No password in URL
+            }
+
+            return url.substr(0, protocol_pos + 3) + "********" + url.substr(at_pos);
+        };
+
+        // start the download
+        fprintf(stderr, "%s: downloading from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
+                llama_download_hide_password_in_url(url).c_str(), path, headers.etag, headers.last_modified);
+        auto res = curl_easy_perform(curl);
+        if (res != CURLE_OK) {
+            fclose(outfile);
+            curl_easy_cleanup(curl);
+            fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
+            return false;
+        }
+
+        long http_code = 0;
+        curl_easy_getinfo (curl, CURLINFO_RESPONSE_CODE, &http_code);
+        if (http_code < 200 || http_code >= 400) {
+            fclose(outfile);
+            curl_easy_cleanup(curl);
+            fprintf(stderr, "%s: invalid http status code received: %ld\n", __func__, http_code);
+            return false;
+        }
+
+        // Clean up
+        fclose(outfile);
+
+        // Write the new ETag to the .etag file
+        if (strlen(headers.etag) > 0) {
+            auto * etag_file = fopen(etag_path, "w");
+            if (etag_file) {
+                fputs(headers.etag, etag_file);
+                fclose(etag_file);
+                fprintf(stderr, "%s: file etag saved %s: %s\n", __func__, etag_path, headers.etag);
+            }
+        }
+
+        // Write the new lastModified to the .etag file
+        if (strlen(headers.last_modified) > 0) {
+            auto * last_modified_file = fopen(last_modified_path, "w");
+            if (last_modified_file) {
+                fputs(headers.last_modified, last_modified_file);
+                fclose(last_modified_file);
+                fprintf(stderr, "%s: file last modified saved %s: %s\n", __func__, last_modified_path,
+                        headers.last_modified);
+            }
+        }
+
+        if (rename(path_temporary, path) != 0) {
+            curl_easy_cleanup(curl);
+            fprintf(stderr, "%s: unable to rename file: %s to %s\n", __func__, path_temporary, path);
+            return false;
+        }
+    }
+
+    return true;
+}
+
+struct llama_model * llama_load_model_from_url(
+        const char * model_url,
+        const char * path_model,
+        const struct llama_model_params & params) {
+    // Basic validation of the model_url
+    if (!model_url || strlen(model_url) == 0) {
+        fprintf(stderr, "%s: invalid model_url\n", __func__);
+        return NULL;
+    }
+
+    // Initialize libcurl
+    auto * curl = curl_easy_init();
+
+    if (!curl) {
+        fprintf(stderr, "%s: error initializing libcurl\n", __func__);
+        return NULL;
+    }
+
+    if (!llama_download_file(curl, model_url, path_model)) {
+        return NULL;
+    }
+
+    // check for additional GGUFs split to download
+    int n_split = 0;
+    {
+        struct gguf_init_params gguf_params = {
+            /*.no_alloc = */ true,
+            /*.ctx      = */ NULL,
+        };
+        auto * ctx_gguf = gguf_init_from_file(path_model, gguf_params);
+        if (!ctx_gguf) {
+            fprintf(stderr, "\n%s:  failed to load input GGUF from %s\n", __func__, path_model);
+            curl_easy_cleanup(curl);
+            return NULL;
+        }
+
+        auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT);
+        if (key_n_split >= 0) {
+            n_split = gguf_get_val_u16(ctx_gguf, key_n_split);
+        }
+
+        gguf_free(ctx_gguf);
+    }
+
+    curl_easy_cleanup(curl);
+
+    if (n_split > 1) {
+        char split_prefix[PATH_MAX] = {0};
+        char split_url_prefix[LLAMA_CURL_MAX_URL_LENGTH] = {0};
+
+        // Verify the first split file format
+        // and extract split URL and PATH prefixes
+        {
+            if (!llama_split_prefix(split_prefix, sizeof(split_prefix), path_model, 0, n_split)) {
+                fprintf(stderr, "\n%s: unexpected model file name: %s"
+                                " n_split=%d\n", __func__, path_model, n_split);
+                return NULL;
+            }
+
+            if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model_url, 0, n_split)) {
+                fprintf(stderr, "\n%s: unexpected model url: %s"
+                                " n_split=%d\n", __func__, model_url, n_split);
+                return NULL;
+            }
+        }
+
+        // Prepare download in parallel
+        std::vector<std::future<bool>> futures_download;
+        for (int idx = 1; idx < n_split; idx++) {
+            futures_download.push_back(std::async(std::launch::async, [&split_prefix, &split_url_prefix, &n_split](int download_idx) -> bool {
+                char split_path[PATH_MAX] = {0};
+                llama_split_path(split_path, sizeof(split_path), split_prefix, download_idx, n_split);
+
+                char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0};
+                llama_split_path(split_url, sizeof(split_url), split_url_prefix, download_idx, n_split);
+
+                auto * curl = curl_easy_init();
+                bool res = llama_download_file(curl, split_url, split_path);
+                curl_easy_cleanup(curl);
+
+                return res;
+            }, idx));
+        }
+
+        // Wait for all downloads to complete
+        for (auto & f : futures_download) {
+            if (!f.get()) {
+                return NULL;
+            }
+        }
+    }
+
+    return llama_load_model_from_file(path_model, params);
+}
+
+struct llama_model * llama_load_model_from_hf(
+        const char * repo,
+        const char * model,
+        const char * path_model,
+        const struct llama_model_params & params) {
+    // construct hugging face model url:
+    //
+    //  --repo ggml-org/models --file tinyllama-1.1b/ggml-model-f16.gguf
+    //    https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf
+    //
+    //  --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf
+    //    https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf
+    //
+
+    std::string model_url = "https://huggingface.co/";
+    model_url += repo;
+    model_url += "/resolve/main/";
+    model_url += model;
+
+    return llama_load_model_from_url(model_url.c_str(), path_model, params);
+}
+
+#else
+
+struct llama_model * llama_load_model_from_url(
+        const char * /*model_url*/,
+        const char * /*path_model*/,
+        const struct llama_model_params & /*params*/) {
+    fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__);
+    return nullptr;
+}
+
+struct llama_model * llama_load_model_from_hf(
+        const char * /*repo*/,
+        const char * /*model*/,
+        const char * /*path_model*/,
+        const struct llama_model_params & /*params*/) {
+    fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__);
+    return nullptr;
+}
+
+#endif // LLAMA_USE_CURL
+
 std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(gpt_params & params) {
     auto mparams = llama_model_params_from_gpt_params(params);
 
-    llama_model * model  = llama_load_model_from_file(params.model.c_str(), mparams);
+    llama_model * model = nullptr;
+
+    if (!params.hf_repo.empty() && !params.hf_file.empty()) {
+        model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), mparams);
+    } else if (!params.model_url.empty()) {
+        model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), mparams);
+    } else {
+        model = llama_load_model_from_file(params.model.c_str(), mparams);
+    }
+
     if (model == NULL) {
         fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
         return std::make_tuple(nullptr, nullptr);
@@ -1135,8 +2075,32 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
         return std::make_tuple(nullptr, nullptr);
     }
 
+    if (!params.control_vectors.empty()) {
+        if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
+        if (params.control_vector_layer_end   <= 0) params.control_vector_layer_end   = llama_n_layer(model);
+
+        const auto cvec = llama_control_vector_load(params.control_vectors);
+        if (cvec.n_embd == -1) {
+            llama_free(lctx);
+            llama_free_model(model);
+            return std::make_tuple(nullptr, nullptr);
+        }
+
+        int err = llama_control_vector_apply(lctx,
+                                             cvec.data.data(),
+                                             cvec.data.size(),
+                                             cvec.n_embd,
+                                             params.control_vector_layer_start,
+                                             params.control_vector_layer_end);
+        if (err) {
+            llama_free(lctx);
+            llama_free_model(model);
+            return std::make_tuple(nullptr, nullptr);
+        }
+    }
+
     for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) {
-        const std::string& lora_adapter = std::get<0>(params.lora_adapter[i]);
+        const std::string & lora_adapter = std::get<0>(params.lora_adapter[i]);
         float lora_scale = std::get<1>(params.lora_adapter[i]);
         int err = llama_model_apply_lora_from_file(model,
                                              lora_adapter.c_str(),
@@ -1163,6 +2127,7 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
         std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), };
         llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
         llama_kv_cache_clear(lctx);
+        llama_synchronize(lctx);
         llama_reset_timings(lctx);
     }
 
@@ -1417,9 +2382,10 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "cpu_has_avx512: %s\n",      ggml_cpu_has_avx512()      ? "true" : "false");
     fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false");
     fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false");
-    fprintf(stream, "cpu_has_blas: %s\n",        ggml_cpu_has_blas()        ? "true" : "false");
-    fprintf(stream, "cpu_has_cublas: %s\n",      ggml_cpu_has_cublas()      ? "true" : "false");
+    fprintf(stream, "cpu_has_cuda: %s\n",        ggml_cpu_has_cuda()        ? "true" : "false");
+    fprintf(stream, "cpu_has_vulkan: %s\n",      ggml_cpu_has_vulkan()      ? "true" : "false");
     fprintf(stream, "cpu_has_clblast: %s\n",     ggml_cpu_has_clblast()     ? "true" : "false");
+    fprintf(stream, "cpu_has_kompute: %s\n",     ggml_cpu_has_kompute()     ? "true" : "false");
     fprintf(stream, "cpu_has_fma: %s\n",         ggml_cpu_has_fma()         ? "true" : "false");
     fprintf(stream, "cpu_has_gpublas: %s\n",     ggml_cpu_has_gpublas()     ? "true" : "false");
     fprintf(stream, "cpu_has_neon: %s\n",        ggml_cpu_has_neon()        ? "true" : "false");
@@ -1429,6 +2395,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "cpu_has_blas: %s\n",        ggml_cpu_has_blas()        ? "true" : "false");
     fprintf(stream, "cpu_has_sse3: %s\n",        ggml_cpu_has_sse3()        ? "true" : "false");
     fprintf(stream, "cpu_has_vsx: %s\n",         ggml_cpu_has_vsx()         ? "true" : "false");
+    fprintf(stream, "cpu_has_matmul_int8: %s\n", ggml_cpu_has_matmul_int8() ? "true" : "false");
 
 #ifdef NDEBUG
     fprintf(stream, "debug: false\n");
@@ -1505,6 +2472,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     }
     fprintf(stream, "lora_base: %s\n", params.lora_base.c_str());
     fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
+    fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep);
     fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat);
     fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau);
     fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta);
@@ -1516,9 +2484,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
     fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs);
     fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
-    fprintf(stream, "no_mul_mat_q: %s # default: false\n", !params.mul_mat_q ? "true" : "false");
-    fprintf(stream, "no_penalize_nl: %s # default: false\n", !sparams.penalize_nl ? "true" : "false");
-    fprintf(stream, "numa: %s # default: false\n", params.numa ? "true" : "false");
+    fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false");
     fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
     fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
     fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present);
@@ -1543,21 +2509,22 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
 
     fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base);
     fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale);
-    fprintf(stream, "seed: %d # default: -1 (random seed)\n", params.seed);
+    fprintf(stream, "seed: %u # default: -1 (random seed)\n", params.seed);
     fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
     fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false");
     fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);
 
-    const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + LLAMA_MAX_DEVICES);
+    const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices());
     dump_vector_float_yaml(stream, "tensor_split", tensor_split_vector);
 
     fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z);
-    fprintf(stream, "threads: %d # default: %d\n", params.n_threads, std::thread::hardware_concurrency());
+    fprintf(stream, "threads: %d # default: %u\n", params.n_threads, std::thread::hardware_concurrency());
     fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k);
     fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p);
     fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p);
     fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p);
     fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
+    fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false");
 }
 
 //
@@ -1568,17 +2535,17 @@ void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size) {
     static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+";
 
     printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d",
-        view.n_cells, view.n_max_seq, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
+        view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
 
     llama_kv_cache_view_cell * c_curr = view.cells;
     llama_seq_id * cs_curr = view.cells_sequences;
 
-    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) {
+    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
         if (i % row_size == 0) {
             printf("\n%5d: ", i);
         }
         int seq_count = 0;
-        for (int j = 0; j < view.n_max_seq; j++) {
+        for (int j = 0; j < view.n_seq_max; j++) {
             if (cs_curr[j] >= 0) { seq_count++; }
         }
         putchar(slot_chars[std::min(sizeof(slot_chars) - 2, size_t(seq_count))]);
@@ -1591,18 +2558,19 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) {
     static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
 
     printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n",
-        view.n_cells, view.n_max_seq, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
+        view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
 
     std::unordered_map<llama_seq_id, size_t> seqs;
     llama_kv_cache_view_cell * c_curr = view.cells;
     llama_seq_id * cs_curr = view.cells_sequences;
 
-    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) {
-        for (int j = 0; j < view.n_max_seq; j++) {
+    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
+        for (int j = 0; j < view.n_seq_max; j++) {
             if (cs_curr[j] < 0) { continue; }
             if (seqs.find(cs_curr[j]) == seqs.end()) {
                 if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
-                seqs[cs_curr[j]] = seqs.size();
+                const size_t sz = seqs.size();
+                seqs[cs_curr[j]] = sz;
             }
         }
         if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
@@ -1616,11 +2584,11 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) {
 
     c_curr = view.cells;
     cs_curr = view.cells_sequences;
-    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) {
+    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
         if (i % row_size == 0) {
             printf("\n%5d: ", i);
         }
-        for (int j = 0; j < view.n_max_seq; j++) {
+        for (int j = 0; j < view.n_seq_max; j++) {
             if (cs_curr[j] >= 0) {
                 const auto & it = seqs.find(cs_curr[j]);
                 putchar(it != seqs.end() ? int(slot_chars[it->second]) : '+');
@@ -1633,3 +2601,188 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) {
 
     printf("\n=== Done dumping\n");
 }
+
+void llama_embd_normalize(const float * inp, float * out, int n) {
+    double sum = 0.0;
+    for (int i = 0; i < n; i++) {
+        sum += inp[i] * inp[i];
+    }
+    sum = sqrt(sum);
+
+    const float norm = sum > 0.0 ? 1.0f / sum : 0.0f;
+
+    for (int i = 0; i < n; i++) {
+        out[i] = inp[i] * norm;
+    }
+}
+
+float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n){
+    double sum  = 0.0;
+    double sum1 = 0.0;
+    double sum2 = 0.0;
+
+    for (int i = 0; i < n; i++) {
+        sum  += embd1[i] * embd2[i];
+        sum1 += embd1[i] * embd1[i];
+        sum2 += embd2[i] * embd2[i];
+    }
+
+    return sum / (sqrt(sum1) * sqrt(sum2));
+}
+
+//
+// Control vector utils
+//
+
+static llama_control_vector_data llama_control_vector_load_one(const llama_control_vector_load_info & load_info) {
+    int32_t n_tensors;
+
+    size_t n_bytes = 0;
+
+    uint32_t max_direction_layer = 0;
+
+    llama_control_vector_data result = { -1, {} };
+
+    // calculate size of ctx needed for tensors, ensure tensors are f32, and find max layer
+    {
+        struct ggml_init_params meta_params = {
+            /* .mem_size   = */ ggml_tensor_overhead() * 128 + ggml_graph_overhead(),
+            /* .mem_buffer = */ nullptr,
+            /* .no_alloc   = */ true,
+        };
+        ggml_context * meta_ctx = ggml_init(meta_params);
+        struct gguf_init_params meta_gguf_params = {
+            /* .no_alloc = */ true,
+            /* .ctx      = */ &meta_ctx,
+        };
+        struct gguf_context * meta_ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params);
+        if (!meta_ctx_gguf) {
+            fprintf(stderr, "%s: failed to load control vector from %s\n", __func__, load_info.fname.c_str());
+            ggml_free(meta_ctx);
+            return result;
+        }
+
+        n_tensors = gguf_get_n_tensors(meta_ctx_gguf);
+        for (int i = 0; i < n_tensors; i++) {
+            std::string name = gguf_get_tensor_name(meta_ctx_gguf, i);
+
+            // split on '.'
+            size_t dotpos = name.find('.');
+            if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") {
+                try {
+                    uint32_t layer = std::stoi(name.substr(dotpos + 1));
+                    if (layer == 0) {
+                        fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str());
+                        ggml_free(meta_ctx);
+                        gguf_free(meta_ctx_gguf);
+                        return result;
+                    }
+                    if (layer > max_direction_layer) {
+                        max_direction_layer = layer;
+                    }
+                } catch (...) {
+                    fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str());
+                    ggml_free(meta_ctx);
+                    gguf_free(meta_ctx_gguf);
+                    return result;
+                }
+            }
+
+            struct ggml_tensor * tensor_meta = ggml_get_tensor(meta_ctx, name.c_str());
+            if (tensor_meta->type != GGML_TYPE_F32 || ggml_n_dims(tensor_meta) != 1) {
+                fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str());
+                ggml_free(meta_ctx);
+                gguf_free(meta_ctx_gguf);
+                return result;
+            }
+            if (result.n_embd == -1) {
+                result.n_embd = ggml_nelements(tensor_meta);
+            } else if (ggml_nelements(tensor_meta) != result.n_embd) {
+                fprintf(stderr, "%s: direction tensor sizes mismatched in %s\n", __func__, load_info.fname.c_str());
+                ggml_free(meta_ctx);
+                gguf_free(meta_ctx_gguf);
+                return result;
+            }
+            n_bytes += ggml_nbytes(tensor_meta);
+        }
+        ggml_free(meta_ctx);
+        gguf_free(meta_ctx_gguf);
+    }
+
+    if (n_tensors == 0) {
+        fprintf(stderr, "%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str());
+        return result;
+    }
+
+    // load and scale tensors into final control vector context
+    struct ggml_init_params ggml_params = {
+        /* .mem_size   = */ ggml_tensor_overhead() * n_tensors + n_bytes,
+        /* .mem_buffer = */ nullptr,
+        /* .no_alloc   = */ false,
+    };
+    struct ggml_context * ctx = ggml_init(ggml_params);
+
+    struct gguf_init_params params = {
+        /*.no_alloc = */ false,
+        /*.ctx      = */ &ctx,
+    };
+    struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), params);
+    if (!ctx_gguf) {
+        fprintf(stderr, "%s: failed to load control vector from %s\n", __func__, load_info.fname.c_str());
+        ggml_free(ctx);
+        return result;
+    }
+
+    // do not store data for layer 0 (it's not used)
+    result.data.resize(result.n_embd * max_direction_layer);
+
+    for (uint32_t il = 1; il <= max_direction_layer; il++) {
+        const std::string name = "direction." + std::to_string(il);
+        const ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str());
+
+        float * dst = result.data.data() + result.n_embd * (il - 1);
+
+        if (tensor) {
+            const float * src = (const float *) tensor->data;
+            for (int j = 0; j < result.n_embd; j++) {
+                dst[j] = src[j] * load_info.strength;
+            }
+        } else {
+            for (int j = 0; j < result.n_embd; j++) {
+                dst[j] = 0.0f;
+            }
+        }
+    }
+
+    return result;
+}
+
+llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos) {
+    llama_control_vector_data result = { -1, {} };
+
+    for (const auto & info : load_infos) {
+        auto cur = llama_control_vector_load_one(info);
+
+        if (cur.n_embd == -1) {
+            return result;
+        }
+        if (result.n_embd != -1 && (result.n_embd != cur.n_embd || result.data.size() != cur.data.size())) {
+            fprintf(stderr, "%s: control vector in %s does not match previous vector dimensions\n", __func__, info.fname.c_str());
+            return result;
+        }
+
+        if (result.n_embd == -1) {
+            result = std::move(cur);
+        } else {
+            for (size_t i = 0; i < cur.data.size(); i++) {
+                result.data[i] += cur.data[i];
+            }
+        }
+    }
+
+    if (result.n_embd == -1) {
+        fprintf(stderr, "%s: no vectors passed\n", __func__);
+    }
+
+    return result;
+}
diff --git a/common/common.h b/common/common.h
index e2bbfc258..99ee90bc3 100644
--- a/common/common.h
+++ b/common/common.h
@@ -37,56 +37,73 @@ extern char const *LLAMA_COMMIT;
 extern char const *LLAMA_COMPILER;
 extern char const *LLAMA_BUILD_TARGET;
 
+struct llama_control_vector_load_info;
+
+int32_t get_num_physical_cores();
+
 //
 // CLI argument parsing
 //
-int32_t get_num_physical_cores();
 
 struct gpt_params {
-    uint32_t seed                           = -1;    // RNG seed
+    uint32_t seed                 = LLAMA_DEFAULT_SEED; // RNG seed
 
-    int32_t n_threads                       = get_num_physical_cores();
-    int32_t n_threads_batch                 = -1;    // number of threads to use for batch processing (-1 = use n_threads)
-    int32_t n_predict                       = -1;    // new tokens to predict
-    int32_t n_ctx                           = 512;   // context size
-    int32_t n_batch                         = 512;   // batch size for prompt processing (must be >=32 to use BLAS)
-    int32_t n_keep                          = 0;     // number of tokens to keep from initial prompt
-    int32_t n_draft                         = 8;     // number of tokens to draft during speculative decoding
-    int32_t n_chunks                        = -1;    // max number of chunks to process (-1 = unlimited)
-    int32_t n_parallel                      = 1;     // number of parallel sequences to decode
-    int32_t n_sequences                     = 1;     // number of sequences to decode
-    float   p_accept                        = 0.5f;  // speculative decoding accept probability
-    float   p_split                         = 0.1f;  // speculative decoding split probability
-    int32_t n_gpu_layers                    = -1;    // number of layers to store in VRAM (-1 - use default)
-    int32_t n_gpu_layers_draft              = -1;    // number of layers to store in VRAM for the draft model (-1 - use default)
-    int32_t main_gpu                        = 0;     // the GPU that is used for scratch and small tensors
-    float   tensor_split[LLAMA_MAX_DEVICES] = {0};   // how split tensors should be distributed across GPUs
-    int32_t n_beams                         = 0;     // if non-zero then use beam search of given width.
-    int32_t grp_attn_n                      = 1;     // group-attention factor
-    int32_t grp_attn_w                      = 512;   // group-attention width
-    float   rope_freq_base                  = 0.0f;  // RoPE base frequency
-    float   rope_freq_scale                 = 0.0f;  // RoPE frequency scaling factor
-    float   yarn_ext_factor                 = -1.0f; // YaRN extrapolation mix factor
-    float   yarn_attn_factor                = 1.0f;  // YaRN magnitude scaling factor
-    float   yarn_beta_fast                  = 32.0f; // YaRN low correction dim
-    float   yarn_beta_slow                  = 1.0f;  // YaRN high correction dim
-    int32_t yarn_orig_ctx                   = 0;     // YaRN original context length
-    int8_t  rope_scaling_type               = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment
-                                                                              //       pinging @cebtenzzre
+    int32_t n_threads             = get_num_physical_cores();
+    int32_t n_threads_draft       = -1;
+    int32_t n_threads_batch       = -1;    // number of threads to use for batch processing (-1 = use n_threads)
+    int32_t n_threads_batch_draft = -1;
+    int32_t n_predict             = -1;    // new tokens to predict
+    int32_t n_ctx                 = 512;   // context size
+    int32_t n_batch               = 2048;  // logical batch size for prompt processing (must be >=32 to use BLAS)
+    int32_t n_ubatch              = 512;   // physical batch size for prompt processing (must be >=32 to use BLAS)
+    int32_t n_keep                = 0;     // number of tokens to keep from initial prompt
+    int32_t n_draft               = 5;     // number of tokens to draft during speculative decoding
+    int32_t n_chunks              = -1;    // max number of chunks to process (-1 = unlimited)
+    int32_t n_parallel            = 1;     // number of parallel sequences to decode
+    int32_t n_sequences           = 1;     // number of sequences to decode
+    float   p_split               = 0.1f;  // speculative decoding split probability
+    int32_t n_gpu_layers          = -1;    // number of layers to store in VRAM (-1 - use default)
+    int32_t n_gpu_layers_draft    = -1;    // number of layers to store in VRAM for the draft model (-1 - use default)
+    llama_split_mode split_mode   = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
+    int32_t main_gpu              = 0;     // the GPU that is used for scratch and small tensors
+    float   tensor_split[128]     = {0};   // how split tensors should be distributed across GPUs
+    int32_t n_beams               = 0;     // if non-zero then use beam search of given width.
+    int32_t grp_attn_n            = 1;     // group-attention factor
+    int32_t grp_attn_w            = 512;   // group-attention width
+    int32_t n_print               = -1;    // print token count every n tokens (-1 = disabled)
+    float   rope_freq_base        = 0.0f;  // RoPE base frequency
+    float   rope_freq_scale       = 0.0f;  // RoPE frequency scaling factor
+    float   yarn_ext_factor       = -1.0f; // YaRN extrapolation mix factor
+    float   yarn_attn_factor      = 1.0f;  // YaRN magnitude scaling factor
+    float   yarn_beta_fast        = 32.0f; // YaRN low correction dim
+    float   yarn_beta_slow        = 1.0f;  // YaRN high correction dim
+    int32_t yarn_orig_ctx         = 0;     // YaRN original context length
+    float   defrag_thold          = -1.0f; // KV cache defragmentation threshold
+
+    ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
+
+    llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
+    llama_pooling_type      pooling_type      = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
 
     // // sampling parameters
     struct llama_sampling_params sparams;
 
-    std::string model             = "models/7B/ggml-model-f16.gguf"; // model path
-    std::string model_draft       = "";                              // draft model for speculative decoding
-    std::string model_alias       = "unknown"; // model alias
-    std::string prompt            = "";
-    std::string prompt_file       = "";  // store the external prompt file name
-    std::string path_prompt_cache = "";  // path to file for saving/loading prompt eval state
-    std::string input_prefix      = "";  // string to prefix user inputs with
-    std::string input_suffix      = "";  // string to suffix user inputs with
+    std::string model                = "models/7B/ggml-model-f16.gguf"; // model path
+    std::string model_draft          = "";  // draft model for speculative decoding
+    std::string model_alias          = "unknown"; // model alias
+    std::string model_url            = "";  // model url to download
+    std::string hf_repo              = "";  // HF repo
+    std::string hf_file              = "";  // HF file
+    std::string prompt               = "";
+    std::string prompt_file          = "";  // store the external prompt file name
+    std::string path_prompt_cache    = "";  // path to file for saving/loading prompt eval state
+    std::string input_prefix         = "";  // string to prefix user inputs with
+    std::string input_suffix         = "";  // string to suffix user inputs with
     std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
-    std::string logdir            = "";  // directory in which to save YAML log files
+    std::string logdir               = "";  // directory in which to save YAML log files
+    std::string lookup_cache_static  = ""; // path of static ngram cache file for lookup decoding
+    std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding
+    std::string logits_file          = "";  // file for saving *all* logits
 
     std::vector<llama_model_kv_override> kv_overrides;
 
@@ -94,6 +111,11 @@ struct gpt_params {
     std::vector<std::tuple<std::string, float>> lora_adapter; // lora adapter path with user defined scale
     std::string lora_base  = "";                              // base model path for the lora adapter
 
+    std::vector<llama_control_vector_load_info> control_vectors; // control vector with user defined scale
+
+    int32_t control_vector_layer_start = -1; // layer range for control vector
+    int32_t control_vector_layer_end   = -1; // layer range for control vector
+
     int  ppl_stride        = 0;     // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
     int  ppl_output_type   = 0;     // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
                                     //                                       (which is more convenient to use for plotting)
@@ -101,7 +123,14 @@ struct gpt_params {
     bool   hellaswag       = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
     size_t hellaswag_tasks = 400;   // number of tasks to use when computing the HellaSwag score
 
-    bool mul_mat_q         = true;  // if true, use mul_mat_q kernels instead of cuBLAS
+    bool   winogrande      = false; // compute Winogrande score over random tasks from datafile supplied in prompt
+    size_t winogrande_tasks= 0;     // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed
+
+    bool   multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt
+    size_t multiple_choice_tasks = 0;     // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed
+
+    bool   kl_divergence   = false; // compute KL-divergence
+
     bool random_prompt     = false; // do not randomize prompt if none provided
     bool use_color         = false; // use color to distinguish generations and inputs
     bool interactive       = false; // interactive mode
@@ -114,7 +143,7 @@ struct gpt_params {
     bool interactive_first = false; // wait for user input immediately
     bool multiline_input   = false; // reverse the usage of `\`
     bool simple_io         = false; // improves compatibility with subprocesses and limited consoles
-    bool cont_batching     = false; // insert new sequences for decoding on-the-fly
+    bool cont_batching     = true;  // insert new sequences for decoding on-the-fly
 
     bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
     bool ignore_eos        = false; // ignore generated EOS tokens
@@ -122,8 +151,8 @@ struct gpt_params {
     bool logits_all        = false; // return logits for all tokens in the batch
     bool use_mmap          = true;  // use mmap for faster loads
     bool use_mlock         = false; // use mlock to keep model in memory
-    bool numa              = false; // attempt optimizations that help on some NUMA systems
     bool verbose_prompt    = false; // print prompt tokens before generation
+    bool display_prompt    = true;  // print prompt before generation
     bool infill            = false; // use infill mode
     bool dump_kv_cache     = false; // dump the KV cache contents for debugging purposes
     bool no_kv_offload     = false; // disable KV offloading
@@ -142,6 +171,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
 
 void gpt_print_usage(int argc, char ** argv, const gpt_params & params);
 
+bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param);
+
 std::string get_system_info(const gpt_params & params);
 
 std::string gpt_random_prompt(std::mt19937 & rng);
@@ -149,10 +180,13 @@ std::string gpt_random_prompt(std::mt19937 & rng);
 void process_escapes(std::string& input);
 
 //
-// String parsing
+// String utils
 //
 
-std::string parse_samplers_input(std::string input);
+std::vector<llama_sampler_type> sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
+std::vector<llama_sampler_type> sampler_types_from_chars(const std::string & names_string);
+std::vector<std::string> string_split(std::string input, char separator);
+std::string sampler_type_to_name_string(llama_sampler_type sampler_type);
 
 //
 // Model utils
@@ -164,6 +198,9 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
 struct llama_model_params   llama_model_params_from_gpt_params  (const gpt_params & params);
 struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params);
 
+struct llama_model * llama_load_model_from_url(const char * model_url, const char * path_model, const struct llama_model_params & params);
+struct llama_model * llama_load_model_from_hf(const char * repo, const char * file, const char * path_model, const struct llama_model_params & params);
+
 // Batch utils
 
 void llama_batch_clear(struct llama_batch & batch);
@@ -243,3 +280,38 @@ void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80);
 // Dump the KV cache view showing individual sequences in each cell (long output).
 void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40);
 
+//
+// Embedding utils
+//
+
+void llama_embd_normalize(const float * inp, float * out, int n);
+
+float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n);
+
+//
+// Control vector utils
+//
+
+struct llama_control_vector_data {
+    int n_embd;
+
+    // stores data for layers [1, n_layer] where n_layer = data.size() / n_embd
+    std::vector<float> data;
+};
+
+struct llama_control_vector_load_info {
+    float strength;
+
+    std::string fname;
+};
+
+// Load control vectors, scale each by strength, and add them together.
+// On error, returns {-1, empty}
+llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos);
+
+//
+// Split utils
+//
+static const char * const LLM_KV_SPLIT_NO            = "split.no";
+static const char * const LLM_KV_SPLIT_COUNT         = "split.count";
+static const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
diff --git a/common/grammar-parser.cpp b/common/grammar-parser.cpp
index bf89a96f3..2a1301569 100644
--- a/common/grammar-parser.cpp
+++ b/common/grammar-parser.cpp
@@ -278,6 +278,22 @@ namespace grammar_parser {
             while (*pos) {
                 pos = parse_rule(state, pos);
             }
+            // Validate the state to ensure that all rules are defined
+            for (const auto & rule : state.rules) {
+                for (const auto & elem : rule) {
+                    if (elem.type == LLAMA_GRETYPE_RULE_REF) {
+                        // Ensure that the rule at that location exists
+                        if (elem.value >= state.rules.size() || state.rules[elem.value].empty()) {
+                            // Get the name of the rule that is missing
+                            for (const auto & kv : state.symbol_ids) {
+                                if (kv.second == elem.value) {
+                                    throw std::runtime_error("Undefined rule identifier '" + kv.first + "'");
+                                }
+                            }
+                        }
+                    }
+                }
+            }
             return state;
         } catch (const std::exception & err) {
             fprintf(stderr, "%s: error parsing grammar: %s\n", __func__, err.what());
diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp
new file mode 100644
index 000000000..0e4680346
--- /dev/null
+++ b/common/json-schema-to-grammar.cpp
@@ -0,0 +1,721 @@
+#include "json-schema-to-grammar.h"
+#include <algorithm>
+#include <fstream>
+#include <map>
+#include <regex>
+#include <sstream>
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+
+using json = nlohmann::ordered_json;
+
+const std::string SPACE_RULE = "\" \"?";
+
+std::unordered_map<std::string, std::string> PRIMITIVE_RULES = {
+    {"boolean", "(\"true\" | \"false\") space"},
+    {"number", "(\"-\"? ([0-9] | [1-9] [0-9]*)) (\".\" [0-9]+)? ([eE] [-+]? [0-9]+)? space"},
+    {"integer", "(\"-\"? ([0-9] | [1-9] [0-9]*)) space"},
+    {"value", "object | array | string | number | boolean"},
+    {"object", "\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space"},
+    {"array", "\"[\" space ( value (\",\" space value)* )? \"]\" space"},
+    {"uuid", "\"\\\"\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
+                "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
+                "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
+                "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
+                "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] \"\\\"\" space"},
+    {"string", " \"\\\"\" (\n"
+               "        [^\"\\\\] |\n"
+               "        \"\\\\\" ([\"\\\\/bfnrt] | \"u\" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])\n"
+               "      )* \"\\\"\" space"},
+    {"null", "\"null\" space"}
+};
+std::vector<std::string> OBJECT_RULE_NAMES = {"object", "array", "string", "number", "boolean", "null", "value"};
+
+std::unordered_map<std::string, std::string> DATE_RULES = {
+    {"date", "[0-9] [0-9] [0-9] [0-9] \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )"},
+    {"time", "([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9] [0-9] [0-9] )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )"},
+    {"date-time", "date \"T\" time"},
+    {"date-string", "\"\\\"\" date \"\\\"\" space"},
+    {"time-string", "\"\\\"\" time \"\\\"\" space"},
+    {"date-time-string", "\"\\\"\" date-time \"\\\"\" space"}
+};
+
+static bool is_reserved_name(const std::string & name) {
+    static std::unordered_set<std::string> RESERVED_NAMES;
+    if (RESERVED_NAMES.empty()) {
+        RESERVED_NAMES.insert("root");
+        for (const auto &p : PRIMITIVE_RULES) RESERVED_NAMES.insert(p.first);
+        for (const auto &p : DATE_RULES) RESERVED_NAMES.insert(p.first);
+    }
+    return RESERVED_NAMES.find(name) != RESERVED_NAMES.end();
+}
+
+std::regex INVALID_RULE_CHARS_RE("[^a-zA-Z0-9-]+");
+std::regex GRAMMAR_LITERAL_ESCAPE_RE("[\r\n\"]");
+std::regex GRAMMAR_RANGE_LITERAL_ESCAPE_RE("[\r\n\"\\]\\-\\\\]");
+std::unordered_map<char, std::string> GRAMMAR_LITERAL_ESCAPES = {
+    {'\r', "\\r"}, {'\n', "\\n"}, {'"', "\\\""}, {'-', "\\-"}, {']', "\\]"}
+};
+
+std::unordered_set<char> NON_LITERAL_SET = {'|', '.', '(', ')', '[', ']', '{', '}', '*', '+', '?'};
+std::unordered_set<char> ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = {'[', ']', '(', ')', '|', '{', '}', '*', '+', '?'};
+
+template <typename Iterator>
+std::string join(Iterator begin, Iterator end, const std::string & separator) {
+    std::ostringstream result;
+    if (begin != end) {
+        result << *begin;
+        for (Iterator it = begin + 1; it != end; ++it) {
+            result << separator << *it;
+        }
+    }
+    return result.str();
+}
+
+static std::vector<std::string> split(const std::string & str, const std::string & delimiter) {
+    std::vector<std::string> tokens;
+    size_t start = 0;
+    size_t end = str.find(delimiter);
+
+    while (end != std::string::npos) {
+        tokens.push_back(str.substr(start, end - start));
+        start = end + delimiter.length();
+        end = str.find(delimiter, start);
+    }
+
+    tokens.push_back(str.substr(start));
+
+    return tokens;
+}
+
+static std::string repeat(const std::string & str, size_t n) {
+    if (n == 0) {
+        return "";
+    }
+
+    std::string result;
+    result.reserve(str.length() * n);
+
+    for (size_t i = 0; i < n; ++i) {
+        result += str;
+    }
+
+    return result;
+}
+
+static std::string replacePattern(const std::string & input, const std::regex & regex, const std::function<std::string(const std::smatch  &)> & replacement) {
+    std::smatch match;
+    std::string result;
+
+    std::string::const_iterator searchStart(input.cbegin());
+    std::string::const_iterator searchEnd(input.cend());
+
+    while (std::regex_search(searchStart, searchEnd, match, regex)) {
+        result.append(searchStart, searchStart + match.position());
+        result.append(replacement(match));
+        searchStart = match.suffix().first;
+    }
+
+    result.append(searchStart, searchEnd);
+
+    return result;
+}
+
+static std::string format_literal(const std::string & literal) {
+    std::string escaped = replacePattern(literal, GRAMMAR_LITERAL_ESCAPE_RE, [&](const std::smatch & match) {
+        char c = match.str()[0];
+        return GRAMMAR_LITERAL_ESCAPES.at(c);
+    });
+    return "\"" + escaped + "\"";
+}
+
+
+class SchemaConverter {
+private:
+    std::function<json(const std::string &)> _fetch_json;
+    bool _dotall;
+    std::map<std::string, std::string> _rules;
+    std::unordered_map<std::string, json> _refs;
+    std::unordered_set<std::string> _refs_being_resolved;
+    std::vector<std::string> _errors;
+    std::vector<std::string> _warnings;
+
+    std::string _add_rule(const std::string & name, const std::string & rule) {
+        std::string esc_name = regex_replace(name, INVALID_RULE_CHARS_RE, "-");
+        if (_rules.find(esc_name) == _rules.end() || _rules[esc_name] == rule) {
+            _rules[esc_name] = rule;
+            return esc_name;
+        } else {
+            int i = 0;
+            while (_rules.find(esc_name + std::to_string(i)) != _rules.end() && _rules[esc_name + std::to_string(i)] != rule) {
+                i++;
+            }
+            std::string key = esc_name + std::to_string(i);
+            _rules[key] = rule;
+            return key;
+        }
+    }
+
+    std::string _generate_union_rule(const std::string & name, const std::vector<json> & alt_schemas) {
+        std::vector<std::string> rules;
+        for (size_t i = 0; i < alt_schemas.size(); i++) {
+            rules.push_back(visit(alt_schemas[i], name + (name.empty() ? "alternative-" : "-") + std::to_string(i)));
+        }
+        return join(rules.begin(), rules.end(), " | ");
+    }
+
+    std::string _visit_pattern(const std::string & pattern, const std::string & name) {
+        if (!(pattern.front() == '^' && pattern.back() == '$')) {
+            _errors.push_back("Pattern must start with '^' and end with '$'");
+            return "";
+        }
+        std::string sub_pattern = pattern.substr(1, pattern.length() - 2);
+        std::unordered_map<std::string, std::string> sub_rule_ids;
+
+        size_t i = 0;
+        size_t length = sub_pattern.length();
+
+        using literal_or_rule = std::pair<std::string, bool>;
+        auto to_rule = [&](const literal_or_rule & ls) {
+            auto is_literal = ls.second;
+            auto s = ls.first;
+            return is_literal ? "\"" + s + "\"" : s;
+        };
+        std::function<literal_or_rule()> transform = [&]() -> literal_or_rule {
+            size_t start = i;
+            std::vector<literal_or_rule> seq;
+
+            auto get_dot = [&]() {
+                std::string rule;
+                if (_dotall) {
+                    rule = "[\\U00000000-\\U0010FFFF]";
+                } else {
+                    rule = "[\\U00000000-\\x09\\x0B\\x0C\\x0E-\\U0010FFFF]";
+                }
+                return _add_rule("dot", rule);
+            };
+
+            // Joins the sequence, merging consecutive literals together.
+            auto join_seq = [&]() {
+                std::vector<literal_or_rule> ret;
+
+                std::string literal;
+                auto flush_literal = [&]() {
+                    if (literal.empty()) {
+                        return false;
+                    }
+                    ret.push_back(std::make_pair(literal, true));
+                    literal.clear();
+                    return true;
+                };
+
+                for (const auto & item : seq) {
+                    auto is_literal = item.second;
+                    if (is_literal) {
+                        literal += item.first;
+                    } else {
+                        flush_literal();
+                        ret.push_back(item);
+                    }
+                }
+                flush_literal();
+
+                std::vector<std::string> results;
+                for (const auto & item : ret) {
+                    results.push_back(to_rule(item));
+                }
+                return std::make_pair(join(results.begin(), results.end(), " "), false);
+            };
+
+            while (i < length) {
+                char c = sub_pattern[i];
+                if (c == '.') {
+                    seq.push_back(std::make_pair(get_dot(), false));
+                    i++;
+                } else if (c == '(') {
+                    i++;
+                    if (i < length) {
+                        if (sub_pattern[i] == '?') {
+                            _warnings.push_back("Unsupported pattern syntax");
+                        }
+                    }
+                    seq.push_back(std::make_pair("(" + to_rule(transform()) + ")", false));
+                } else if (c == ')') {
+                    i++;
+                    if (start > 0 && sub_pattern[start - 1] != '(') {
+                        _errors.push_back("Unbalanced parentheses");
+                    }
+                    return join_seq();
+                } else if (c == '[') {
+                    std::string square_brackets = std::string(1, c);
+                    i++;
+                    while (i < length && sub_pattern[i] != ']') {
+                        if (sub_pattern[i] == '\\') {
+                            square_brackets += sub_pattern.substr(i, 2);
+                            i += 2;
+                        } else {
+                            square_brackets += sub_pattern[i];
+                            i++;
+                        }
+                    }
+                    if (i >= length) {
+                        _errors.push_back("Unbalanced square brackets");
+                    }
+                    square_brackets += ']';
+                    i++;
+                    seq.push_back(std::make_pair(square_brackets, false));
+                } else if (c == '|') {
+                    seq.push_back(std::make_pair("|", false));
+                    i++;
+                } else if (c == '*' || c == '+' || c == '?') {
+                    seq.back() = std::make_pair(to_rule(seq.back()) + c, false);
+                    i++;
+                } else if (c == '{') {
+                    std::string curly_brackets = std::string(1, c);
+                    i++;
+                    while (i < length && sub_pattern[i] != '}') {
+                        curly_brackets += sub_pattern[i];
+                        i++;
+                    }
+                    if (i >= length) {
+                        _errors.push_back("Unbalanced curly brackets");
+                    }
+                    curly_brackets += '}';
+                    i++;
+                    auto nums = split(curly_brackets.substr(1, curly_brackets.length() - 2), ",");
+                    int min_times = 0;
+                    int max_times = std::numeric_limits<int>::max();
+                    try {
+                        if (nums.size() == 1) {
+                            min_times = max_times = std::stoi(nums[0]);
+                        } else if (nums.size() != 2) {
+                            _errors.push_back("Wrong number of values in curly brackets");
+                        } else {
+                            if (!nums[0].empty()) {
+                                min_times = std::stoi(nums[0]);
+                            }
+                            if (!nums[1].empty()) {
+                                max_times = std::stoi(nums[1]);
+                            }
+                        }
+                    } catch (const std::invalid_argument & e) {
+                        _errors.push_back("Invalid number in curly brackets");
+                        return std::make_pair("", false);
+                    }
+                    auto &last = seq.back();
+                    auto &sub = last.first;
+                    auto sub_is_literal = last.second;
+
+                    if (min_times == 0 && max_times == std::numeric_limits<int>::max()) {
+                        sub += "*";
+                    } else if (min_times == 0 && max_times == 1) {
+                        sub += "?";
+                    } else if (min_times == 1 && max_times == std::numeric_limits<int>::max()) {
+                        sub += "+";
+                    } else {
+                        if (!sub_is_literal) {
+                            std::string & sub_id = sub_rule_ids[sub];
+                            if (sub_id.empty()) {
+                                sub_id = _add_rule(name + "-" + std::to_string(sub_rule_ids.size()), sub);
+                            }
+                            sub = sub_id;
+                        }
+                        std::string result;
+                        if (sub_is_literal && min_times > 0) {
+                            result = "\"" + repeat(sub.substr(1, sub.length() - 2), min_times) + "\"";
+                        } else {
+                            for (int j = 0; j < min_times; j++) {
+                                if (j > 0) {
+                                    result += " ";
+                                }
+                                result += sub;
+                            }
+                        }
+                        if (min_times > 0 && min_times < max_times) {
+                            result += " ";
+                        }
+                        if (max_times == std::numeric_limits<int>::max()) {
+                            result += sub + "*";
+                        } else {
+                            for (int j = min_times; j < max_times; j++) {
+                                if (j > min_times) {
+                                    result += " ";
+                                }
+                                result += sub + "?";
+                            }
+                        }
+                        seq.back().first = result;
+                        seq.back().second = false;
+                    }
+                } else {
+                    std::string literal;
+                    auto is_non_literal = [&](char c) {
+                        return NON_LITERAL_SET.find(c) != NON_LITERAL_SET.end();
+                    };
+                    while (i < length) {
+                        if (sub_pattern[i] == '\\' && i < length - 1) {
+                            char next = sub_pattern[i + 1];
+                            if (ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS.find(next) != ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS.end()) {
+                                i++;
+                                literal += sub_pattern[i];
+                                i++;
+                            } else {
+                                literal += sub_pattern.substr(i, 2);
+                                i += 2;
+                            }
+                        } else if (sub_pattern[i] == '"') {
+                            literal += "\\\"";
+                            i++;
+                        } else if (!is_non_literal(sub_pattern[i]) &&
+                                (i == length - 1 || literal.empty() || sub_pattern[i + 1] == '.' || !is_non_literal(sub_pattern[i + 1]))) {
+                            literal += sub_pattern[i];
+                            i++;
+                        } else {
+                            break;
+                        }
+                    }
+                    if (!literal.empty()) {
+                        seq.push_back(std::make_pair(literal, true));
+                    }
+                }
+            }
+            return join_seq();
+        };
+        return _add_rule(name, "\"\\\"\" " + to_rule(transform()) + " \"\\\"\" space");
+    }
+
+    std::string _resolve_ref(const std::string & ref) {
+        std::string ref_name = ref.substr(ref.find_last_of('/') + 1);
+        if (_rules.find(ref_name) == _rules.end() && _refs_being_resolved.find(ref) == _refs_being_resolved.end()) {
+            _refs_being_resolved.insert(ref);
+            json resolved = _refs[ref];
+            ref_name = visit(resolved, ref_name);
+            _refs_being_resolved.erase(ref);
+        }
+        return ref_name;
+    }
+
+    std::string _build_object_rule(
+        const std::vector<std::pair<std::string, json>> & properties,
+        const std::unordered_set<std::string> & required,
+        const std::string & name,
+        const json & additional_properties)
+    {
+        std::vector<std::string> required_props;
+        std::vector<std::string> optional_props;
+        std::unordered_map<std::string, std::string> prop_kv_rule_names;
+        for (const auto & kv : properties) {
+            const auto &prop_name = kv.first;
+            const auto &prop_schema = kv.second;
+
+            std::string prop_rule_name = visit(prop_schema, name + (name.empty() ? "" : "-") + prop_name);
+            prop_kv_rule_names[prop_name] = _add_rule(
+                name + (name.empty() ? "" : "-") + prop_name + "-kv",
+                format_literal(json(prop_name).dump()) + " space \":\" space " + prop_rule_name
+            );
+            if (required.find(prop_name) != required.end()) {
+                required_props.push_back(prop_name);
+            } else {
+                optional_props.push_back(prop_name);
+            }
+        }
+        if (additional_properties.is_object() || (additional_properties.is_boolean() && additional_properties.get<bool>())) {
+            std::string sub_name = name + (name.empty() ? "" : "-") + "additional";
+            std::string value_rule = visit(additional_properties.is_object() ? additional_properties : json::object(), sub_name + "-value");
+            std::string kv_rule = _add_rule(sub_name + "-kv", _add_rule("string", PRIMITIVE_RULES.at("string")) + " \":\" space " + value_rule);
+            prop_kv_rule_names["*"] = kv_rule;
+            optional_props.push_back("*");
+        }
+
+        std::string rule = "\"{\" space ";
+        for (size_t i = 0; i < required_props.size(); i++) {
+            if (i > 0) {
+                rule += " \",\" space ";
+            }
+            rule += prop_kv_rule_names[required_props[i]];
+        }
+
+        if (!optional_props.empty()) {
+            rule += " (";
+            if (!required_props.empty()) {
+                rule += " \",\" space ( ";
+            }
+
+            std::function<std::string(const std::vector<std::string> &, bool)> get_recursive_refs = [&](const std::vector<std::string> & ks, bool first_is_optional) {
+                std::string res;
+                if (ks.empty()) {
+                    return res;
+                }
+                std::string k = ks[0];
+                std::string kv_rule_name = prop_kv_rule_names[k];
+                if (k == "*") {
+                    res = _add_rule(
+                        name + (name.empty() ? "" : "-") + "additional-kvs",
+                        kv_rule_name + " ( \",\" space " + kv_rule_name + " )*"
+                    );
+                } else if (first_is_optional) {
+                    res = "( \",\" space " + kv_rule_name + " )?";
+                } else {
+                    res = kv_rule_name;
+                }
+                if (ks.size() > 1) {
+                    res += " " + _add_rule(
+                        name + (name.empty() ? "" : "-") + k + "-rest",
+                        get_recursive_refs(std::vector<std::string>(ks.begin() + 1, ks.end()), true)
+                    );
+                }
+                return res;
+            };
+
+            for (size_t i = 0; i < optional_props.size(); i++) {
+                if (i > 0) {
+                    rule += " | ";
+                }
+                rule += get_recursive_refs(std::vector<std::string>(optional_props.begin() + i, optional_props.end()), false);
+            }
+            if (!required_props.empty()) {
+                rule += " )";
+            }
+            rule += " )?";
+        }
+
+        rule += " \"}\" space";
+
+        return rule;
+    }
+
+public:
+    SchemaConverter(
+        const std::function<json(const std::string &)> & fetch_json,
+        bool dotall)
+          : _fetch_json(fetch_json), _dotall(dotall)
+    {
+        _rules["space"] = SPACE_RULE;
+    }
+
+    void resolve_refs(json & schema, const std::string & url) {
+        /*
+        * Resolves all $ref fields in the given schema, fetching any remote schemas,
+        * replacing each $ref with absolute reference URL and populates _refs with the
+        * respective referenced (sub)schema dictionaries.
+        */
+        std::function<void(json &)> visit_refs = [&](json & n) {
+            if (n.is_array()) {
+                for (auto & x : n) {
+                    visit_refs(x);
+                }
+            } else if (n.is_object()) {
+                if (n.contains("$ref")) {
+                    std::string ref = n["$ref"];
+                    if (_refs.find(ref) == _refs.end()) {
+                        json target;
+                        if (ref.find("https://") == 0) {
+                            std::string base_url = ref.substr(0, ref.find('#'));
+                            auto it = _refs.find(base_url);
+                            if (it != _refs.end()) {
+                                target = it->second;
+                            } else {
+                                // Fetch the referenced schema and resolve its refs
+                                auto referenced = _fetch_json(ref);
+                                resolve_refs(referenced, base_url);
+                                _refs[base_url] = referenced;
+                            }
+                            if (ref.find('#') == std::string::npos || ref.substr(ref.find('#') + 1).empty()) {
+                                return;
+                            }
+                        } else if (ref.find("#/") == 0) {
+                            target = schema;
+                            n["$ref"] = url + ref;
+                            ref = url + ref;
+                        } else {
+                            _errors.push_back("Unsupported ref: " + ref);
+                            return;
+                        }
+                        std::string pointer = ref.substr(ref.find('#') + 1);
+                        std::vector<std::string> tokens = split(pointer, "/");
+                        for (size_t i = 1; i < tokens.size(); ++i) {
+                            std::string sel = tokens[i];
+                            if (target.is_null() || !target.contains(sel)) {
+                                _errors.push_back("Error resolving ref " + ref + ": " + sel + " not in " + target.dump());
+                                return;
+                            }
+                            target = target[sel];
+                        }
+                        _refs[ref] = target;
+                    }
+                } else {
+                    for (auto & kv : n.items()) {
+                        visit_refs(kv.value());
+                    }
+                }
+            }
+        };
+
+        visit_refs(schema);
+    }
+
+    std::string _generate_constant_rule(const json & value) {
+        return format_literal(value.dump());
+    }
+
+    std::string visit(const json & schema, const std::string & name) {
+        json schema_type = schema.contains("type") ? schema["type"] : json();
+        std::string schema_format = schema.contains("format") ? schema["format"].get<std::string>() : "";
+        std::string rule_name = is_reserved_name(name) ? name + "-" : name.empty() ? "root" : name;
+
+        if (schema.contains("$ref")) {
+            return _add_rule(rule_name, _resolve_ref(schema["$ref"]));
+        } else if (schema.contains("oneOf") || schema.contains("anyOf")) {
+            std::vector<json> alt_schemas = schema.contains("oneOf") ? schema["oneOf"].get<std::vector<json>>() : schema["anyOf"].get<std::vector<json>>();
+            return _add_rule(rule_name, _generate_union_rule(name, alt_schemas));
+        } else if (schema_type.is_array()) {
+            std::vector<json> schema_types;
+            for (const auto & t : schema_type) {
+                schema_types.push_back({{"type", t}});
+            }
+            return _add_rule(rule_name, _generate_union_rule(name, schema_types));
+        } else if (schema.contains("const")) {
+            return _add_rule(rule_name, _generate_constant_rule(schema["const"]));
+        } else if (schema.contains("enum")) {
+            std::vector<std::string> enum_values;
+            for (const auto & v : schema["enum"]) {
+                enum_values.push_back(_generate_constant_rule(v));
+            }
+            return _add_rule(rule_name, join(enum_values.begin(), enum_values.end(), " | "));
+        } else if ((schema_type.is_null() || schema_type == "object")
+                && (schema.contains("properties") ||
+                    (schema.contains("additionalProperties") && schema["additionalProperties"] != true))) {
+            std::unordered_set<std::string> required;
+            if (schema.contains("required") && schema["required"].is_array()) {
+                for (const auto & item : schema["required"]) {
+                    if (item.is_string()) {
+                        required.insert(item.get<std::string>());
+                    }
+                }
+            }
+            std::vector<std::pair<std::string, json>> properties;
+            if (schema.contains("properties")) {
+                for (const auto & prop : schema["properties"].items()) {
+                    properties.emplace_back(prop.key(), prop.value());
+                }
+            }
+            return _add_rule(rule_name,
+                _build_object_rule(
+                    properties, required, name,
+                    schema.contains("additionalProperties") ? schema["additionalProperties"] : json()));
+        } else if ((schema_type.is_null() || schema_type == "object") && schema.contains("allOf")) {
+            std::unordered_set<std::string> required;
+            std::vector<std::pair<std::string, json>> properties;
+            std::string hybrid_name = name;
+            std::function<void(const json &, bool)> add_component = [&](const json & comp_schema, bool is_required) {
+                if (comp_schema.contains("$ref")) {
+                    add_component(_refs[comp_schema["$ref"]], is_required);
+                } else if (comp_schema.contains("properties")) {
+                    for (const auto & prop : comp_schema["properties"].items()) {
+                        properties.emplace_back(prop.key(), prop.value());
+                        if (is_required) {
+                            required.insert(prop.key());
+                        }
+                    }
+                } else {
+                  // todo warning
+                }
+            };
+            for (auto & t : schema["allOf"]) {
+                if (t.contains("anyOf")) {
+                    for (auto & tt : t["anyOf"]) {
+                        add_component(tt, false);
+                    }
+                } else {
+                    add_component(t, true);
+                }
+            }
+            return _add_rule(rule_name, _build_object_rule(properties, required, hybrid_name, json()));
+        } else if ((schema_type.is_null() || schema_type == "array") && (schema.contains("items") || schema.contains("prefixItems"))) {
+            json items = schema.contains("items") ? schema["items"] : schema["prefixItems"];
+            if (items.is_array()) {
+                std::string rule = "\"[\" space ";
+                for (size_t i = 0; i < items.size(); i++) {
+                    if (i > 0) {
+                        rule += " \",\" space ";
+                    }
+                    rule += visit(items[i], name + (name.empty() ? "" : "-") + "tuple-" + std::to_string(i));
+                }
+                rule += " \"]\" space";
+                return _add_rule(rule_name, rule);
+            } else {
+                std::string item_rule_name = visit(items, name + (name.empty() ? "" : "-") + "item");
+                std::string list_item_operator = "( \",\" space " + item_rule_name + " )";
+                std::string successive_items;
+                int min_items = schema.contains("minItems") ? schema["minItems"].get<int>() : 0;
+                json max_items_json = schema.contains("maxItems") ? schema["maxItems"] : json();
+                int max_items = max_items_json.is_number_integer() ? max_items_json.get<int>() : -1;
+                if (min_items > 0) {
+                    successive_items += repeat(list_item_operator, min_items - 1);
+                    min_items--;
+                }
+                if (max_items >= 0 && max_items > min_items) {
+                    successive_items += repeat(list_item_operator + "?", max_items - min_items - 1);
+                } else {
+                    successive_items += list_item_operator + "*";
+                }
+                std::string rule;
+                if (min_items == 0) {
+                    rule =  "\"[\" space ( " + item_rule_name + " " + successive_items + " )? \"]\" space";
+                } else {
+                    rule =  "\"[\" space " + item_rule_name + " " + successive_items + " \"]\" space";
+                }
+                return _add_rule(rule_name, rule);
+            }
+        } else if ((schema_type.is_null() || schema_type == "string") && schema.contains("pattern")) {
+            return _visit_pattern(schema["pattern"], rule_name);
+        } else if ((schema_type.is_null() || schema_type == "string") && std::regex_match(schema_format, std::regex("^uuid[1-5]?$"))) {
+            return _add_rule(rule_name == "root" ? "root" : schema_format, PRIMITIVE_RULES.at("uuid"));
+        } else if ((schema_type.is_null() || schema_type == "string") && DATE_RULES.find(schema_format) != DATE_RULES.end()) {
+            for (const auto & kv : DATE_RULES) {
+                _add_rule(kv.first, kv.second);
+            }
+            return schema_format + "-string";
+        } else if (schema.empty() || schema_type == "object") {
+            for (const auto & n : OBJECT_RULE_NAMES) {
+                _add_rule(n, PRIMITIVE_RULES.at(n));
+            }
+            return _add_rule(rule_name, "object");
+        } else {
+            if (!schema_type.is_string() || PRIMITIVE_RULES.find(schema_type.get<std::string>()) == PRIMITIVE_RULES.end()) {
+                _errors.push_back("Unrecognized schema: " + schema.dump());
+                return "";
+            }
+            // TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero
+            return _add_rule(rule_name == "root" ? "root" : schema_type.get<std::string>(), PRIMITIVE_RULES.at(schema_type.get<std::string>()));
+        }
+    }
+
+    void check_errors() {
+        if (!_errors.empty()) {
+            throw std::runtime_error("JSON schema conversion failed:\n" + join(_errors.begin(), _errors.end(), "\n"));
+        }
+        if (!_warnings.empty()) {
+            fprintf(stderr, "WARNING: JSON schema conversion was incomplete: %s\n", join(_warnings.begin(), _warnings.end(), "; ").c_str());
+        }
+    }
+
+    std::string format_grammar() {
+        std::stringstream ss;
+        for (const auto & kv : _rules) {
+            ss << kv.first << " ::= " << kv.second << std::endl;
+        }
+        return ss.str();
+    }
+};
+
+std::string json_schema_to_grammar(const json & schema) {
+    SchemaConverter converter([](const std::string &) { return json::object(); }, /* dotall= */ false);
+    auto copy = schema;
+    converter.resolve_refs(copy, "input");
+    converter.visit(copy, "");
+    converter.check_errors();
+    return converter.format_grammar();
+}
diff --git a/common/json-schema-to-grammar.h b/common/json-schema-to-grammar.h
new file mode 100644
index 000000000..e1abed303
--- /dev/null
+++ b/common/json-schema-to-grammar.h
@@ -0,0 +1,4 @@
+#pragma once
+#include "json.hpp"
+
+std::string json_schema_to_grammar(const nlohmann::ordered_json& schema);
diff --git a/examples/server/json.hpp b/common/json.hpp
similarity index 93%
rename from examples/server/json.hpp
rename to common/json.hpp
index ea945f346..a858728c4 100644
--- a/examples/server/json.hpp
+++ b/common/json.hpp
@@ -1,9 +1,9 @@
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 /****************************************************************************\
@@ -27,7 +27,6 @@
 #endif  // JSON_NO_IO
 #include <iterator> // random_access_iterator_tag
 #include <memory> // unique_ptr
-#include <numeric> // accumulate
 #include <string> // string, stoi, to_string
 #include <utility> // declval, forward, move, pair, swap
 #include <vector> // vector
@@ -35,10 +34,10 @@
 // #include <nlohmann/adl_serializer.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -48,10 +47,10 @@
 // #include <nlohmann/detail/abi_macros.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -60,7 +59,7 @@
 
 #ifndef JSON_SKIP_LIBRARY_VERSION_CHECK
     #if defined(NLOHMANN_JSON_VERSION_MAJOR) && defined(NLOHMANN_JSON_VERSION_MINOR) && defined(NLOHMANN_JSON_VERSION_PATCH)
-        #if NLOHMANN_JSON_VERSION_MAJOR != 3 || NLOHMANN_JSON_VERSION_MINOR != 11 || NLOHMANN_JSON_VERSION_PATCH != 2
+        #if NLOHMANN_JSON_VERSION_MAJOR != 3 || NLOHMANN_JSON_VERSION_MINOR != 11 || NLOHMANN_JSON_VERSION_PATCH != 3
             #warning "Already included a different version of the library!"
         #endif
     #endif
@@ -68,7 +67,7 @@
 
 #define NLOHMANN_JSON_VERSION_MAJOR 3   // NOLINT(modernize-macro-to-enum)
 #define NLOHMANN_JSON_VERSION_MINOR 11  // NOLINT(modernize-macro-to-enum)
-#define NLOHMANN_JSON_VERSION_PATCH 2   // NOLINT(modernize-macro-to-enum)
+#define NLOHMANN_JSON_VERSION_PATCH 3   // NOLINT(modernize-macro-to-enum)
 
 #ifndef JSON_DIAGNOSTICS
     #define JSON_DIAGNOSTICS 0
@@ -150,10 +149,10 @@
 // #include <nlohmann/detail/conversions/from_json.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -173,16 +172,19 @@
 // #include <nlohmann/detail/exceptions.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
 
 #include <cstddef> // nullptr_t
 #include <exception> // exception
+#if JSON_DIAGNOSTICS
+    #include <numeric> // accumulate
+#endif
 #include <stdexcept> // runtime_error
 #include <string> // to_string
 #include <vector> // vector
@@ -190,10 +192,10 @@
 // #include <nlohmann/detail/value_t.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -206,10 +208,10 @@
 // #include <nlohmann/detail/macro_scope.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -218,10 +220,10 @@
 // #include <nlohmann/detail/meta/detected.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -231,10 +233,10 @@
 // #include <nlohmann/detail/meta/void_t.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -318,10 +320,10 @@ NLOHMANN_JSON_NAMESPACE_END
 
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-FileCopyrightText: 2016-2021 Evan Nemerson <evan@nemerson.com>
 // SPDX-License-Identifier: MIT
 
@@ -2483,6 +2485,14 @@ JSON_HEDLEY_DIAGNOSTIC_POP
     #endif
 #endif
 
+#ifndef JSON_HAS_STATIC_RTTI
+    #if !defined(_HAS_STATIC_RTTI) || _HAS_STATIC_RTTI != 0
+        #define JSON_HAS_STATIC_RTTI 1
+    #else
+        #define JSON_HAS_STATIC_RTTI 0
+    #endif
+#endif
+
 #ifdef JSON_HAS_CPP_17
     #define JSON_INLINE_VARIABLE inline
 #else
@@ -2590,12 +2600,13 @@ JSON_HEDLEY_DIAGNOSTIC_POP
              class NumberUnsignedType, class NumberFloatType,              \
              template<typename> class AllocatorType,                       \
              template<typename, typename = void> class JSONSerializer,     \
-             class BinaryType>
+             class BinaryType,                                             \
+             class CustomBaseClass>
 
 #define NLOHMANN_BASIC_JSON_TPL                                            \
     basic_json<ObjectType, ArrayType, StringType, BooleanType,             \
     NumberIntegerType, NumberUnsignedType, NumberFloatType,                \
-    AllocatorType, JSONSerializer, BinaryType>
+    AllocatorType, JSONSerializer, BinaryType, CustomBaseClass>
 
 // Macros to simplify conversion from/to types
 
@@ -2745,7 +2756,10 @@ JSON_HEDLEY_DIAGNOSTIC_POP
 
 #define NLOHMANN_DEFINE_TYPE_INTRUSIVE_WITH_DEFAULT(Type, ...)  \
     friend void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \
-    friend void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { Type nlohmann_json_default_obj; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) }
+    friend void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { const Type nlohmann_json_default_obj{}; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) }
+
+#define NLOHMANN_DEFINE_TYPE_INTRUSIVE_ONLY_SERIALIZE(Type, ...)  \
+    friend void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) }
 
 /*!
 @brief macro
@@ -2756,10 +2770,12 @@ JSON_HEDLEY_DIAGNOSTIC_POP
     inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \
     inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM, __VA_ARGS__)) }
 
+#define NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE_ONLY_SERIALIZE(Type, ...)  \
+    inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) }
+
 #define NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE_WITH_DEFAULT(Type, ...)  \
     inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \
-    inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { Type nlohmann_json_default_obj; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) }
-
+    inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { const Type nlohmann_json_default_obj{}; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) }
 
 // inspired from https://stackoverflow.com/a/26745591
 // allows to call any std function as if (e.g. with begin):
@@ -2923,10 +2939,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/string_escape.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -2998,10 +3014,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/input/position_t.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -3040,10 +3056,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/meta/cpp_future.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-FileCopyrightText: 2018 The Abseil Authors
 // SPDX-License-Identifier: MIT
 
@@ -3214,10 +3230,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/meta/type_traits.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -3226,14 +3242,15 @@ NLOHMANN_JSON_NAMESPACE_END
 #include <type_traits> // false_type, is_constructible, is_integral, is_same, true_type
 #include <utility> // declval
 #include <tuple> // tuple
+#include <string> // char_traits
 
 // #include <nlohmann/detail/iterators/iterator_traits.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -3298,10 +3315,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/meta/call_std/begin.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -3318,10 +3335,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/meta/call_std/end.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -3342,10 +3359,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/json_fwd.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 #ifndef INCLUDE_NLOHMANN_JSON_FWD_HPP_
@@ -3389,7 +3406,8 @@ NLOHMANN_JSON_NAMESPACE_END
     template<typename U> class AllocatorType = std::allocator,
     template<typename T, typename SFINAE = void> class JSONSerializer =
     adl_serializer,
-    class BinaryType = std::vector<std::uint8_t>>
+    class BinaryType = std::vector<std::uint8_t>, // cppcheck-suppress syntaxError
+    class CustomBaseClass = void>
     class basic_json;
 
     /// @brief JSON Pointer defines a string syntax for identifying a specific value within a JSON document
@@ -3577,6 +3595,63 @@ struct actual_object_comparator
 template<typename BasicJsonType>
 using actual_object_comparator_t = typename actual_object_comparator<BasicJsonType>::type;
 
+/////////////////
+// char_traits //
+/////////////////
+
+// Primary template of char_traits calls std char_traits
+template<typename T>
+struct char_traits : std::char_traits<T>
+{};
+
+// Explicitly define char traits for unsigned char since it is not standard
+template<>
+struct char_traits<unsigned char> : std::char_traits<char>
+{
+    using char_type = unsigned char;
+    using int_type = uint64_t;
+
+    // Redefine to_int_type function
+    static int_type to_int_type(char_type c) noexcept
+    {
+        return static_cast<int_type>(c);
+    }
+
+    static char_type to_char_type(int_type i) noexcept
+    {
+        return static_cast<char_type>(i);
+    }
+
+    static constexpr int_type eof() noexcept
+    {
+        return static_cast<int_type>(EOF);
+    }
+};
+
+// Explicitly define char traits for signed char since it is not standard
+template<>
+struct char_traits<signed char> : std::char_traits<char>
+{
+    using char_type = signed char;
+    using int_type = uint64_t;
+
+    // Redefine to_int_type function
+    static int_type to_int_type(char_type c) noexcept
+    {
+        return static_cast<int_type>(c);
+    }
+
+    static char_type to_char_type(int_type i) noexcept
+    {
+        return static_cast<char_type>(i);
+    }
+
+    static constexpr int_type eof() noexcept
+    {
+        return static_cast<int_type>(EOF);
+    }
+};
+
 ///////////////////
 // is_ functions //
 ///////////////////
@@ -3613,7 +3688,6 @@ template <typename... Ts>
 struct is_default_constructible<const std::tuple<Ts...>>
             : conjunction<is_default_constructible<Ts>...> {};
 
-
 template <typename T, typename... Args>
 struct is_constructible : std::is_constructible<T, Args...> {};
 
@@ -3629,7 +3703,6 @@ struct is_constructible<std::tuple<Ts...>> : is_default_constructible<std::tuple
 template <typename... Ts>
 struct is_constructible<const std::tuple<Ts...>> : is_default_constructible<const std::tuple<Ts...>> {};
 
-
 template<typename T, typename = void>
 struct is_iterator_traits : std::false_type {};
 
@@ -4039,7 +4112,6 @@ struct value_in_range_of_impl2<OfType, T, false, true>
     }
 };
 
-
 template<typename OfType, typename T>
 struct value_in_range_of_impl2<OfType, T, true, true>
 {
@@ -4138,10 +4210,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/string_concat.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -4165,28 +4237,28 @@ inline std::size_t concat_length()
 }
 
 template<typename... Args>
-inline std::size_t concat_length(const char* cstr, Args&& ... rest);
+inline std::size_t concat_length(const char* cstr, const Args& ... rest);
 
 template<typename StringType, typename... Args>
-inline std::size_t concat_length(const StringType& str, Args&& ... rest);
+inline std::size_t concat_length(const StringType& str, const Args& ... rest);
 
 template<typename... Args>
-inline std::size_t concat_length(const char /*c*/, Args&& ... rest)
+inline std::size_t concat_length(const char /*c*/, const Args& ... rest)
 {
-    return 1 + concat_length(std::forward<Args>(rest)...);
+    return 1 + concat_length(rest...);
 }
 
 template<typename... Args>
-inline std::size_t concat_length(const char* cstr, Args&& ... rest)
+inline std::size_t concat_length(const char* cstr, const Args& ... rest)
 {
     // cppcheck-suppress ignoredReturnValue
-    return ::strlen(cstr) + concat_length(std::forward<Args>(rest)...);
+    return ::strlen(cstr) + concat_length(rest...);
 }
 
 template<typename StringType, typename... Args>
-inline std::size_t concat_length(const StringType& str, Args&& ... rest)
+inline std::size_t concat_length(const StringType& str, const Args& ... rest)
 {
-    return str.size() + concat_length(std::forward<Args>(rest)...);
+    return str.size() + concat_length(rest...);
 }
 
 template<typename OutStringType>
@@ -4277,7 +4349,7 @@ template<typename OutStringType = std::string, typename... Args>
 inline OutStringType concat(Args && ... args)
 {
     OutStringType str;
-    str.reserve(concat_length(std::forward<Args>(args)...));
+    str.reserve(concat_length(args...));
     concat_into(str, std::forward<Args>(args)...);
     return str;
 }
@@ -4286,7 +4358,6 @@ inline OutStringType concat(Args && ... args)
 NLOHMANN_JSON_NAMESPACE_END
 
 
-
 NLOHMANN_JSON_NAMESPACE_BEGIN
 namespace detail
 {
@@ -4334,9 +4405,9 @@ class exception : public std::exception
             {
                 case value_t::array:
                 {
-                    for (std::size_t i = 0; i < current->m_parent->m_value.array->size(); ++i)
+                    for (std::size_t i = 0; i < current->m_parent->m_data.m_value.array->size(); ++i)
                     {
-                        if (&current->m_parent->m_value.array->operator[](i) == current)
+                        if (&current->m_parent->m_data.m_value.array->operator[](i) == current)
                         {
                             tokens.emplace_back(std::to_string(i));
                             break;
@@ -4347,7 +4418,7 @@ class exception : public std::exception
 
                 case value_t::object:
                 {
-                    for (const auto& element : *current->m_parent->m_value.object)
+                    for (const auto& element : *current->m_parent->m_data.m_value.object)
                     {
                         if (&element.second == current)
                         {
@@ -4410,17 +4481,17 @@ class parse_error : public exception
     template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
     static parse_error create(int id_, const position_t& pos, const std::string& what_arg, BasicJsonContext context)
     {
-        std::string w = concat(exception::name("parse_error", id_), "parse error",
-                               position_string(pos), ": ", exception::diagnostics(context), what_arg);
+        const std::string w = concat(exception::name("parse_error", id_), "parse error",
+                                     position_string(pos), ": ", exception::diagnostics(context), what_arg);
         return {id_, pos.chars_read_total, w.c_str()};
     }
 
     template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
     static parse_error create(int id_, std::size_t byte_, const std::string& what_arg, BasicJsonContext context)
     {
-        std::string w = concat(exception::name("parse_error", id_), "parse error",
-                               (byte_ != 0 ? (concat(" at byte ", std::to_string(byte_))) : ""),
-                               ": ", exception::diagnostics(context), what_arg);
+        const std::string w = concat(exception::name("parse_error", id_), "parse error",
+                                     (byte_ != 0 ? (concat(" at byte ", std::to_string(byte_))) : ""),
+                                     ": ", exception::diagnostics(context), what_arg);
         return {id_, byte_, w.c_str()};
     }
 
@@ -4454,7 +4525,7 @@ class invalid_iterator : public exception
     template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
     static invalid_iterator create(int id_, const std::string& what_arg, BasicJsonContext context)
     {
-        std::string w = concat(exception::name("invalid_iterator", id_), exception::diagnostics(context), what_arg);
+        const std::string w = concat(exception::name("invalid_iterator", id_), exception::diagnostics(context), what_arg);
         return {id_, w.c_str()};
     }
 
@@ -4472,7 +4543,7 @@ class type_error : public exception
     template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
     static type_error create(int id_, const std::string& what_arg, BasicJsonContext context)
     {
-        std::string w = concat(exception::name("type_error", id_), exception::diagnostics(context), what_arg);
+        const std::string w = concat(exception::name("type_error", id_), exception::diagnostics(context), what_arg);
         return {id_, w.c_str()};
     }
 
@@ -4489,7 +4560,7 @@ class out_of_range : public exception
     template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
     static out_of_range create(int id_, const std::string& what_arg, BasicJsonContext context)
     {
-        std::string w = concat(exception::name("out_of_range", id_), exception::diagnostics(context), what_arg);
+        const std::string w = concat(exception::name("out_of_range", id_), exception::diagnostics(context), what_arg);
         return {id_, w.c_str()};
     }
 
@@ -4506,7 +4577,7 @@ class other_error : public exception
     template<typename BasicJsonContext, enable_if_t<is_basic_json_context<BasicJsonContext>::value, int> = 0>
     static other_error create(int id_, const std::string& what_arg, BasicJsonContext context)
     {
-        std::string w = concat(exception::name("other_error", id_), exception::diagnostics(context), what_arg);
+        const std::string w = concat(exception::name("other_error", id_), exception::diagnostics(context), what_arg);
         return {id_, w.c_str()};
     }
 
@@ -4525,10 +4596,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/meta/identity_tag.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -4549,10 +4620,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/meta/std_fs.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -5055,10 +5126,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/conversions/to_json.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -5075,10 +5146,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/iterators/iteration_proxy.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -5147,10 +5218,10 @@ template<typename IteratorType> class iteration_proxy_value
     // older GCCs are a bit fussy and require explicit noexcept specifiers on defaulted functions
     iteration_proxy_value(iteration_proxy_value&&)
     noexcept(std::is_nothrow_move_constructible<IteratorType>::value
-             && std::is_nothrow_move_constructible<string_type>::value) = default;
+             && std::is_nothrow_move_constructible<string_type>::value) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor,cppcoreguidelines-noexcept-move-operations)
     iteration_proxy_value& operator=(iteration_proxy_value&&)
     noexcept(std::is_nothrow_move_assignable<IteratorType>::value
-             && std::is_nothrow_move_assignable<string_type>::value) = default;
+             && std::is_nothrow_move_assignable<string_type>::value) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor,cppcoreguidelines-noexcept-move-operations)
     ~iteration_proxy_value() = default;
 
     /// dereference operator (needed for range-based for)
@@ -5297,11 +5368,11 @@ namespace std
     #pragma clang diagnostic ignored "-Wmismatched-tags"
 #endif
 template<typename IteratorType>
-class tuple_size<::nlohmann::detail::iteration_proxy_value<IteratorType>>
+class tuple_size<::nlohmann::detail::iteration_proxy_value<IteratorType>> // NOLINT(cert-dcl58-cpp)
             : public std::integral_constant<std::size_t, 2> {};
 
 template<std::size_t N, typename IteratorType>
-class tuple_element<N, ::nlohmann::detail::iteration_proxy_value<IteratorType >>
+class tuple_element<N, ::nlohmann::detail::iteration_proxy_value<IteratorType >> // NOLINT(cert-dcl58-cpp)
 {
   public:
     using type = decltype(
@@ -5340,7 +5411,7 @@ namespace detail
 
 /*
  * Note all external_constructor<>::construct functions need to call
- * j.m_value.destroy(j.m_type) to avoid a memory leak in case j contains an
+ * j.m_data.m_value.destroy(j.m_data.m_type) to avoid a memory leak in case j contains an
  * allocated value (e.g., a string). See bug issue
  * https://github.com/nlohmann/json/issues/2865 for more information.
  */
@@ -5353,9 +5424,9 @@ struct external_constructor<value_t::boolean>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, typename BasicJsonType::boolean_t b) noexcept
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::boolean;
-        j.m_value = b;
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::boolean;
+        j.m_data.m_value = b;
         j.assert_invariant();
     }
 };
@@ -5366,18 +5437,18 @@ struct external_constructor<value_t::string>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, const typename BasicJsonType::string_t& s)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::string;
-        j.m_value = s;
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::string;
+        j.m_data.m_value = s;
         j.assert_invariant();
     }
 
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, typename BasicJsonType::string_t&& s)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::string;
-        j.m_value = std::move(s);
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::string;
+        j.m_data.m_value = std::move(s);
         j.assert_invariant();
     }
 
@@ -5386,9 +5457,9 @@ struct external_constructor<value_t::string>
                              int > = 0 >
     static void construct(BasicJsonType& j, const CompatibleStringType& str)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::string;
-        j.m_value.string = j.template create<typename BasicJsonType::string_t>(str);
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::string;
+        j.m_data.m_value.string = j.template create<typename BasicJsonType::string_t>(str);
         j.assert_invariant();
     }
 };
@@ -5399,18 +5470,18 @@ struct external_constructor<value_t::binary>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, const typename BasicJsonType::binary_t& b)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::binary;
-        j.m_value = typename BasicJsonType::binary_t(b);
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::binary;
+        j.m_data.m_value = typename BasicJsonType::binary_t(b);
         j.assert_invariant();
     }
 
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, typename BasicJsonType::binary_t&& b)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::binary;
-        j.m_value = typename BasicJsonType::binary_t(std::move(b));
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::binary;
+        j.m_data.m_value = typename BasicJsonType::binary_t(std::move(b));
         j.assert_invariant();
     }
 };
@@ -5421,9 +5492,9 @@ struct external_constructor<value_t::number_float>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, typename BasicJsonType::number_float_t val) noexcept
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::number_float;
-        j.m_value = val;
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::number_float;
+        j.m_data.m_value = val;
         j.assert_invariant();
     }
 };
@@ -5434,9 +5505,9 @@ struct external_constructor<value_t::number_unsigned>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, typename BasicJsonType::number_unsigned_t val) noexcept
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::number_unsigned;
-        j.m_value = val;
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::number_unsigned;
+        j.m_data.m_value = val;
         j.assert_invariant();
     }
 };
@@ -5447,9 +5518,9 @@ struct external_constructor<value_t::number_integer>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, typename BasicJsonType::number_integer_t val) noexcept
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::number_integer;
-        j.m_value = val;
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::number_integer;
+        j.m_data.m_value = val;
         j.assert_invariant();
     }
 };
@@ -5460,9 +5531,9 @@ struct external_constructor<value_t::array>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, const typename BasicJsonType::array_t& arr)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::array;
-        j.m_value = arr;
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::array;
+        j.m_data.m_value = arr;
         j.set_parents();
         j.assert_invariant();
     }
@@ -5470,9 +5541,9 @@ struct external_constructor<value_t::array>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, typename BasicJsonType::array_t&& arr)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::array;
-        j.m_value = std::move(arr);
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::array;
+        j.m_data.m_value = std::move(arr);
         j.set_parents();
         j.assert_invariant();
     }
@@ -5485,9 +5556,9 @@ struct external_constructor<value_t::array>
         using std::begin;
         using std::end;
 
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::array;
-        j.m_value.array = j.template create<typename BasicJsonType::array_t>(begin(arr), end(arr));
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::array;
+        j.m_data.m_value.array = j.template create<typename BasicJsonType::array_t>(begin(arr), end(arr));
         j.set_parents();
         j.assert_invariant();
     }
@@ -5495,14 +5566,14 @@ struct external_constructor<value_t::array>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, const std::vector<bool>& arr)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::array;
-        j.m_value = value_t::array;
-        j.m_value.array->reserve(arr.size());
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::array;
+        j.m_data.m_value = value_t::array;
+        j.m_data.m_value.array->reserve(arr.size());
         for (const bool x : arr)
         {
-            j.m_value.array->push_back(x);
-            j.set_parent(j.m_value.array->back());
+            j.m_data.m_value.array->push_back(x);
+            j.set_parent(j.m_data.m_value.array->back());
         }
         j.assert_invariant();
     }
@@ -5511,13 +5582,13 @@ struct external_constructor<value_t::array>
              enable_if_t<std::is_convertible<T, BasicJsonType>::value, int> = 0>
     static void construct(BasicJsonType& j, const std::valarray<T>& arr)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::array;
-        j.m_value = value_t::array;
-        j.m_value.array->resize(arr.size());
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::array;
+        j.m_data.m_value = value_t::array;
+        j.m_data.m_value.array->resize(arr.size());
         if (arr.size() > 0)
         {
-            std::copy(std::begin(arr), std::end(arr), j.m_value.array->begin());
+            std::copy(std::begin(arr), std::end(arr), j.m_data.m_value.array->begin());
         }
         j.set_parents();
         j.assert_invariant();
@@ -5530,9 +5601,9 @@ struct external_constructor<value_t::object>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, const typename BasicJsonType::object_t& obj)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::object;
-        j.m_value = obj;
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::object;
+        j.m_data.m_value = obj;
         j.set_parents();
         j.assert_invariant();
     }
@@ -5540,9 +5611,9 @@ struct external_constructor<value_t::object>
     template<typename BasicJsonType>
     static void construct(BasicJsonType& j, typename BasicJsonType::object_t&& obj)
     {
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::object;
-        j.m_value = std::move(obj);
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::object;
+        j.m_data.m_value = std::move(obj);
         j.set_parents();
         j.assert_invariant();
     }
@@ -5554,9 +5625,9 @@ struct external_constructor<value_t::object>
         using std::begin;
         using std::end;
 
-        j.m_value.destroy(j.m_type);
-        j.m_type = value_t::object;
-        j.m_value.object = j.template create<typename BasicJsonType::object_t>(begin(obj), end(obj));
+        j.m_data.m_value.destroy(j.m_data.m_type);
+        j.m_data.m_type = value_t::object;
+        j.m_data.m_value.object = j.template create<typename BasicJsonType::object_t>(begin(obj), end(obj));
         j.set_parents();
         j.assert_invariant();
     }
@@ -5626,7 +5697,8 @@ template<typename BasicJsonType, typename EnumType,
 inline void to_json(BasicJsonType& j, EnumType e) noexcept
 {
     using underlying_type = typename std::underlying_type<EnumType>::type;
-    external_constructor<value_t::number_integer>::construct(j, static_cast<underlying_type>(e));
+    static constexpr value_t integral_value_t = std::is_unsigned<underlying_type>::value ? value_t::number_unsigned : value_t::number_integer;
+    external_constructor<integral_value_t>::construct(j, static_cast<underlying_type>(e));
 }
 #endif  // JSON_DISABLE_ENUM_SERIALIZATION
 
@@ -5796,10 +5868,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/byte_container_with_subtype.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -5908,10 +5980,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/hash.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -6041,10 +6113,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/input/binary_reader.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -6067,10 +6139,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/input/input_adapters.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -6094,6 +6166,8 @@ NLOHMANN_JSON_NAMESPACE_END
 
 // #include <nlohmann/detail/macro_scope.hpp>
 
+// #include <nlohmann/detail/meta/type_traits.hpp>
+
 
 NLOHMANN_JSON_NAMESPACE_BEGIN
 namespace detail
@@ -6140,7 +6214,6 @@ class file_input_adapter
     std::FILE* m_file;
 };
 
-
 /*!
 Input adapter for a (caching) istream. Ignores a UFT Byte Order Mark at
 beginning of input. Does not support changing the underlying std::streambuf
@@ -6214,16 +6287,16 @@ class iterator_input_adapter
         : current(std::move(first)), end(std::move(last))
     {}
 
-    typename std::char_traits<char_type>::int_type get_character()
+    typename char_traits<char_type>::int_type get_character()
     {
         if (JSON_HEDLEY_LIKELY(current != end))
         {
-            auto result = std::char_traits<char_type>::to_int_type(*current);
+            auto result = char_traits<char_type>::to_int_type(*current);
             std::advance(current, 1);
             return result;
         }
 
-        return std::char_traits<char_type>::eof();
+        return char_traits<char_type>::eof();
     }
 
   private:
@@ -6239,7 +6312,6 @@ class iterator_input_adapter
     }
 };
 
-
 template<typename BaseInputAdapter, size_t T>
 struct wide_string_input_helper;
 
@@ -6363,7 +6435,7 @@ struct wide_string_input_helper<BaseInputAdapter, 2>
     }
 };
 
-// Wraps another input apdater to convert wide character types into individual bytes.
+// Wraps another input adapter to convert wide character types into individual bytes.
 template<typename BaseInputAdapter, typename WideCharType>
 class wide_string_input_adapter
 {
@@ -6408,7 +6480,6 @@ class wide_string_input_adapter
     std::size_t utf8_bytes_filled = 0;
 };
 
-
 template<typename IteratorType, typename Enable = void>
 struct iterator_input_adapter_factory
 {
@@ -6565,10 +6636,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/input/json_sax.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -6710,7 +6781,6 @@ struct json_sax
     virtual ~json_sax() = default;
 };
 
-
 namespace detail
 {
 /*!
@@ -6812,7 +6882,7 @@ class json_sax_dom_parser
         JSON_ASSERT(ref_stack.back()->is_object());
 
         // add null at given key and store the reference for later
-        object_element = &(ref_stack.back()->m_value.object->operator[](val));
+        object_element = &(ref_stack.back()->m_data.m_value.object->operator[](val));
         return true;
     }
 
@@ -6887,8 +6957,8 @@ class json_sax_dom_parser
 
         if (ref_stack.back()->is_array())
         {
-            ref_stack.back()->m_value.array->emplace_back(std::forward<Value>(v));
-            return &(ref_stack.back()->m_value.array->back());
+            ref_stack.back()->m_data.m_value.array->emplace_back(std::forward<Value>(v));
+            return &(ref_stack.back()->m_data.m_value.array->back());
         }
 
         JSON_ASSERT(ref_stack.back()->is_object());
@@ -7007,7 +7077,7 @@ class json_sax_dom_callback_parser
         // add discarded value at given key and store the reference for later
         if (keep && ref_stack.back())
         {
-            object_element = &(ref_stack.back()->m_value.object->operator[](val) = discarded);
+            object_element = &(ref_stack.back()->m_data.m_value.object->operator[](val) = discarded);
         }
 
         return true;
@@ -7092,7 +7162,7 @@ class json_sax_dom_callback_parser
         // remove discarded value
         if (!keep && !ref_stack.empty() && ref_stack.back()->is_array())
         {
-            ref_stack.back()->m_value.array->pop_back();
+            ref_stack.back()->m_data.m_value.array->pop_back();
         }
 
         return true;
@@ -7159,7 +7229,7 @@ class json_sax_dom_callback_parser
         if (ref_stack.empty())
         {
             root = std::move(value);
-            return {true, &root};
+            return {true, & root};
         }
 
         // skip this value if we already decided to skip the parent
@@ -7175,8 +7245,8 @@ class json_sax_dom_callback_parser
         // array
         if (ref_stack.back()->is_array())
         {
-            ref_stack.back()->m_value.array->emplace_back(std::move(value));
-            return {true, &(ref_stack.back()->m_value.array->back())};
+            ref_stack.back()->m_data.m_value.array->emplace_back(std::move(value));
+            return {true, & (ref_stack.back()->m_data.m_value.array->back())};
         }
 
         // object
@@ -7201,9 +7271,9 @@ class json_sax_dom_callback_parser
     /// stack to model hierarchy of values
     std::vector<BasicJsonType*> ref_stack {};
     /// stack to manage which values to keep
-    std::vector<bool> keep_stack {};
+    std::vector<bool> keep_stack {}; // NOLINT(readability-redundant-member-init)
     /// stack to manage which object keys to keep
-    std::vector<bool> key_keep_stack {};
+    std::vector<bool> key_keep_stack {}; // NOLINT(readability-redundant-member-init)
     /// helper to hold the reference for the next object element
     BasicJsonType* object_element = nullptr;
     /// whether a syntax error occurred
@@ -7298,10 +7368,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/input/lexer.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -7322,6 +7392,8 @@ NLOHMANN_JSON_NAMESPACE_END
 
 // #include <nlohmann/detail/macro_scope.hpp>
 
+// #include <nlohmann/detail/meta/type_traits.hpp>
+
 
 NLOHMANN_JSON_NAMESPACE_BEGIN
 namespace detail
@@ -7416,7 +7488,7 @@ class lexer : public lexer_base<BasicJsonType>
     using number_float_t = typename BasicJsonType::number_float_t;
     using string_t = typename BasicJsonType::string_t;
     using char_type = typename InputAdapterType::char_type;
-    using char_int_type = typename std::char_traits<char_type>::int_type;
+    using char_int_type = typename char_traits<char_type>::int_type;
 
   public:
     using token_type = typename lexer_base<BasicJsonType>::token_type;
@@ -7523,7 +7595,7 @@ class lexer : public lexer_base<BasicJsonType>
         for (auto range = ranges.begin(); range != ranges.end(); ++range)
         {
             get();
-            if (JSON_HEDLEY_LIKELY(*range <= current && current <= *(++range)))
+            if (JSON_HEDLEY_LIKELY(*range <= current && current <= *(++range))) // NOLINT(bugprone-inc-dec-in-conditions)
             {
                 add(current);
             }
@@ -7566,7 +7638,7 @@ class lexer : public lexer_base<BasicJsonType>
             switch (get())
             {
                 // end of file while parsing string
-                case std::char_traits<char_type>::eof():
+                case char_traits<char_type>::eof():
                 {
                     error_message = "invalid string: missing closing quote";
                     return token_type::parse_error;
@@ -8155,7 +8227,7 @@ class lexer : public lexer_base<BasicJsonType>
                     {
                         case '\n':
                         case '\r':
-                        case std::char_traits<char_type>::eof():
+                        case char_traits<char_type>::eof():
                         case '\0':
                             return true;
 
@@ -8172,7 +8244,7 @@ class lexer : public lexer_base<BasicJsonType>
                 {
                     switch (get())
                     {
-                        case std::char_traits<char_type>::eof():
+                        case char_traits<char_type>::eof():
                         case '\0':
                         {
                             error_message = "invalid comment; missing closing '*/'";
@@ -8601,10 +8673,10 @@ scan_number_done:
     token_type scan_literal(const char_type* literal_text, const std::size_t length,
                             token_type return_type)
     {
-        JSON_ASSERT(std::char_traits<char_type>::to_char_type(current) == literal_text[0]);
+        JSON_ASSERT(char_traits<char_type>::to_char_type(current) == literal_text[0]);
         for (std::size_t i = 1; i < length; ++i)
         {
-            if (JSON_HEDLEY_UNLIKELY(std::char_traits<char_type>::to_char_type(get()) != literal_text[i]))
+            if (JSON_HEDLEY_UNLIKELY(char_traits<char_type>::to_char_type(get()) != literal_text[i]))
             {
                 error_message = "invalid literal";
                 return token_type::parse_error;
@@ -8622,7 +8694,7 @@ scan_number_done:
     {
         token_buffer.clear();
         token_string.clear();
-        token_string.push_back(std::char_traits<char_type>::to_char_type(current));
+        token_string.push_back(char_traits<char_type>::to_char_type(current));
     }
 
     /*
@@ -8630,7 +8702,7 @@ scan_number_done:
 
     This function provides the interface to the used input adapter. It does
     not throw in case the input reached EOF, but returns a
-    `std::char_traits<char>::eof()` in that case.  Stores the scanned characters
+    `char_traits<char>::eof()` in that case.  Stores the scanned characters
     for use in error messages.
 
     @return character read from the input
@@ -8650,9 +8722,9 @@ scan_number_done:
             current = ia.get_character();
         }
 
-        if (JSON_HEDLEY_LIKELY(current != std::char_traits<char_type>::eof()))
+        if (JSON_HEDLEY_LIKELY(current != char_traits<char_type>::eof()))
         {
-            token_string.push_back(std::char_traits<char_type>::to_char_type(current));
+            token_string.push_back(char_traits<char_type>::to_char_type(current));
         }
 
         if (current == '\n')
@@ -8691,7 +8763,7 @@ scan_number_done:
             --position.chars_read_current_line;
         }
 
-        if (JSON_HEDLEY_LIKELY(current != std::char_traits<char_type>::eof()))
+        if (JSON_HEDLEY_LIKELY(current != char_traits<char_type>::eof()))
         {
             JSON_ASSERT(!token_string.empty());
             token_string.pop_back();
@@ -8885,7 +8957,7 @@ scan_number_done:
             // end of input (the null byte is needed when parsing from
             // string literals)
             case '\0':
-            case std::char_traits<char_type>::eof():
+            case char_traits<char_type>::eof():
                 return token_type::end_of_input;
 
             // error
@@ -8903,7 +8975,7 @@ scan_number_done:
     const bool ignore_comments = false;
 
     /// the current character
-    char_int_type current = std::char_traits<char_type>::eof();
+    char_int_type current = char_traits<char_type>::eof();
 
     /// whether the next get() call should just return current
     bool next_unget = false;
@@ -8937,10 +9009,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/meta/is_sax.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -9129,7 +9201,6 @@ static inline bool little_endianness(int num = 1) noexcept
     return *reinterpret_cast<char*>(&num) == 1;
 }
 
-
 ///////////////////
 // binary reader //
 ///////////////////
@@ -9147,7 +9218,7 @@ class binary_reader
     using binary_t = typename BasicJsonType::binary_t;
     using json_sax_t = SAX;
     using char_type = typename InputAdapterType::char_type;
-    using char_int_type = typename std::char_traits<char_type>::int_type;
+    using char_int_type = typename char_traits<char_type>::int_type;
 
   public:
     /*!
@@ -9220,7 +9291,7 @@ class binary_reader
                 get();
             }
 
-            if (JSON_HEDLEY_UNLIKELY(current != std::char_traits<char_type>::eof()))
+            if (JSON_HEDLEY_UNLIKELY(current != char_traits<char_type>::eof()))
             {
                 return sax->parse_error(chars_read, get_token_string(), parse_error::create(110, chars_read,
                                         exception_message(input_format, concat("expected end of input; last byte: 0x", get_token_string()), "value"), nullptr));
@@ -9303,7 +9374,7 @@ class binary_reader
                                     exception_message(input_format_t::bson, concat("string length must be at least 1, is ", std::to_string(len)), "string"), nullptr));
         }
 
-        return get_string(input_format_t::bson, len - static_cast<NumberType>(1), result) && get() != std::char_traits<char_type>::eof();
+        return get_string(input_format_t::bson, len - static_cast<NumberType>(1), result) && get() != char_traits<char_type>::eof();
     }
 
     /*!
@@ -9404,7 +9475,7 @@ class binary_reader
             {
                 std::array<char, 3> cr{{}};
                 static_cast<void>((std::snprintf)(cr.data(), cr.size(), "%.2hhX", static_cast<unsigned char>(element_type))); // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg)
-                std::string cr_str{cr.data()};
+                const std::string cr_str{cr.data()};
                 return sax->parse_error(element_type_parse_position, cr_str,
                                         parse_error::create(114, element_type_parse_position, concat("Unsupported BSON record type 0x", cr_str), nullptr));
             }
@@ -9497,7 +9568,7 @@ class binary_reader
         switch (get_char ? get() : current)
         {
             // EOF
-            case std::char_traits<char_type>::eof():
+            case char_traits<char_type>::eof():
                 return unexpect_eof(input_format_t::cbor, "value");
 
             // Integer 0x00..0x17 (0..23)
@@ -10272,7 +10343,7 @@ class binary_reader
         switch (get())
         {
             // EOF
-            case std::char_traits<char_type>::eof():
+            case char_traits<char_type>::eof():
                 return unexpect_eof(input_format_t::msgpack, "value");
 
             // positive fixint
@@ -11339,7 +11410,7 @@ class binary_reader
                                         exception_message(input_format, concat("expected '#' after type information; last byte: 0x", last_token), "size"), nullptr));
             }
 
-            bool is_error = get_ubjson_size_value(result.first, is_ndarray);
+            const bool is_error = get_ubjson_size_value(result.first, is_ndarray);
             if (input_format == input_format_t::bjdata && is_ndarray)
             {
                 if (inside_ndarray)
@@ -11354,7 +11425,7 @@ class binary_reader
 
         if (current == '#')
         {
-            bool is_error = get_ubjson_size_value(result.first, is_ndarray);
+            const bool is_error = get_ubjson_size_value(result.first, is_ndarray);
             if (input_format == input_format_t::bjdata && is_ndarray)
             {
                 return sax->parse_error(chars_read, get_token_string(), parse_error::create(112, chars_read,
@@ -11374,7 +11445,7 @@ class binary_reader
     {
         switch (prefix)
         {
-            case std::char_traits<char_type>::eof():  // EOF
+            case char_traits<char_type>::eof():  // EOF
                 return unexpect_eof(input_format, "value");
 
             case 'T':  // true
@@ -11819,7 +11890,7 @@ class binary_reader
 
     This function provides the interface to the used input adapter. It does
     not throw in case the input reached EOF, but returns a -'ve valued
-    `std::char_traits<char_type>::eof()` in that case.
+    `char_traits<char_type>::eof()` in that case.
 
     @return character read from the input
     */
@@ -11961,7 +12032,7 @@ class binary_reader
     JSON_HEDLEY_NON_NULL(3)
     bool unexpect_eof(const input_format_t format, const char* context) const
     {
-        if (JSON_HEDLEY_UNLIKELY(current == std::char_traits<char_type>::eof()))
+        if (JSON_HEDLEY_UNLIKELY(current == char_traits<char_type>::eof()))
         {
             return sax->parse_error(chars_read, "<end of file>",
                                     parse_error::create(110, chars_read, exception_message(format, "unexpected end of input", context), nullptr));
@@ -12028,7 +12099,7 @@ class binary_reader
     InputAdapterType ia;
 
     /// the current character
-    char_int_type current = std::char_traits<char_type>::eof();
+    char_int_type current = char_traits<char_type>::eof();
 
     /// the number of characters read
     std::size_t chars_read = 0;
@@ -12090,10 +12161,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/input/parser.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -12439,13 +12510,25 @@ class parser
                                                 m_lexer.get_token_string(),
                                                 parse_error::create(101, m_lexer.get_position(), exception_message(token_type::uninitialized, "value"), nullptr));
                     }
+                    case token_type::end_of_input:
+                    {
+                        if (JSON_HEDLEY_UNLIKELY(m_lexer.get_position().chars_read_total == 1))
+                        {
+                            return sax->parse_error(m_lexer.get_position(),
+                                                    m_lexer.get_token_string(),
+                                                    parse_error::create(101, m_lexer.get_position(),
+                                                            "attempting to parse an empty input; check that your input string or stream contains the expected JSON", nullptr));
+                        }
 
+                        return sax->parse_error(m_lexer.get_position(),
+                                                m_lexer.get_token_string(),
+                                                parse_error::create(101, m_lexer.get_position(), exception_message(token_type::literal_or_value, "value"), nullptr));
+                    }
                     case token_type::uninitialized:
                     case token_type::end_array:
                     case token_type::end_object:
                     case token_type::name_separator:
                     case token_type::value_separator:
-                    case token_type::end_of_input:
                     case token_type::literal_or_value:
                     default: // the last token was unexpected
                     {
@@ -12607,10 +12690,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/iterators/internal_iterator.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -12620,10 +12703,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/iterators/primitive_iterator.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -12779,10 +12862,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/iterators/iter_impl.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -12887,7 +12970,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
             {
@@ -12984,17 +13067,17 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
             {
-                m_it.object_iterator = m_object->m_value.object->begin();
+                m_it.object_iterator = m_object->m_data.m_value.object->begin();
                 break;
             }
 
             case value_t::array:
             {
-                m_it.array_iterator = m_object->m_value.array->begin();
+                m_it.array_iterator = m_object->m_data.m_value.array->begin();
                 break;
             }
 
@@ -13028,17 +13111,17 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
             {
-                m_it.object_iterator = m_object->m_value.object->end();
+                m_it.object_iterator = m_object->m_data.m_value.object->end();
                 break;
             }
 
             case value_t::array:
             {
-                m_it.array_iterator = m_object->m_value.array->end();
+                m_it.array_iterator = m_object->m_data.m_value.array->end();
                 break;
             }
 
@@ -13067,17 +13150,17 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
             {
-                JSON_ASSERT(m_it.object_iterator != m_object->m_value.object->end());
+                JSON_ASSERT(m_it.object_iterator != m_object->m_data.m_value.object->end());
                 return m_it.object_iterator->second;
             }
 
             case value_t::array:
             {
-                JSON_ASSERT(m_it.array_iterator != m_object->m_value.array->end());
+                JSON_ASSERT(m_it.array_iterator != m_object->m_data.m_value.array->end());
                 return *m_it.array_iterator;
             }
 
@@ -13111,17 +13194,17 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
             {
-                JSON_ASSERT(m_it.object_iterator != m_object->m_value.object->end());
+                JSON_ASSERT(m_it.object_iterator != m_object->m_data.m_value.object->end());
                 return &(m_it.object_iterator->second);
             }
 
             case value_t::array:
             {
-                JSON_ASSERT(m_it.array_iterator != m_object->m_value.array->end());
+                JSON_ASSERT(m_it.array_iterator != m_object->m_data.m_value.array->end());
                 return &*m_it.array_iterator;
             }
 
@@ -13164,7 +13247,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
             {
@@ -13215,7 +13298,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
             {
@@ -13262,7 +13345,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
 
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
                 return (m_it.object_iterator == other.m_it.object_iterator);
@@ -13307,7 +13390,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
 
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
                 JSON_THROW(invalid_iterator::create(213, "cannot compare order of object iterators", m_object));
@@ -13363,7 +13446,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
                 JSON_THROW(invalid_iterator::create(209, "cannot use offsets with object iterators", m_object));
@@ -13442,7 +13525,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
                 JSON_THROW(invalid_iterator::create(209, "cannot use offsets with object iterators", m_object));
@@ -13471,7 +13554,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci
     {
         JSON_ASSERT(m_object != nullptr);
 
-        switch (m_object->m_type)
+        switch (m_object->m_data.m_type)
         {
             case value_t::object:
                 JSON_THROW(invalid_iterator::create(208, "cannot use operator[] for object iterators", m_object));
@@ -13541,10 +13624,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/iterators/json_reverse_iterator.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -13673,13 +13756,55 @@ NLOHMANN_JSON_NAMESPACE_END
 
 // #include <nlohmann/detail/iterators/primitive_iterator.hpp>
 
+// #include <nlohmann/detail/json_custom_base_class.hpp>
+//     __ _____ _____ _____
+//  __|  |   __|     |   | |  JSON for Modern C++
+// |  |  |__   |  |  | | | |  version 3.11.3
+// |_____|_____|_____|_|___|  https://github.com/nlohmann/json
+//
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
+// SPDX-License-Identifier: MIT
+
+
+
+#include <type_traits> // conditional, is_same
+
+// #include <nlohmann/detail/abi_macros.hpp>
+
+
+NLOHMANN_JSON_NAMESPACE_BEGIN
+namespace detail
+{
+
+/*!
+@brief Default base class of the @ref basic_json class.
+
+So that the correct implementations of the copy / move ctors / assign operators
+of @ref basic_json do not require complex case distinctions
+(no base class / custom base class used as customization point),
+@ref basic_json always has a base class.
+By default, this class is used because it is empty and thus has no effect
+on the behavior of @ref basic_json.
+*/
+struct json_default_base {};
+
+template<class T>
+using json_base_class = typename std::conditional <
+                        std::is_same<T, void>::value,
+                        json_default_base,
+                        T
+                        >::type;
+
+}  // namespace detail
+NLOHMANN_JSON_NAMESPACE_END
+
 // #include <nlohmann/detail/json_pointer.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -13911,7 +14036,7 @@ class json_pointer
         const char* p = s.c_str();
         char* p_end = nullptr;
         errno = 0; // strtoull doesn't reset errno
-        unsigned long long res = std::strtoull(p, &p_end, 10); // NOLINT(runtime/int)
+        const unsigned long long res = std::strtoull(p, &p_end, 10); // NOLINT(runtime/int)
         if (p == p_end // invalid input or empty string
                 || errno == ERANGE // out of range
                 || JSON_HEDLEY_UNLIKELY(static_cast<std::size_t>(p_end - p) != s.size())) // incomplete read
@@ -14067,7 +14192,7 @@ class json_pointer
                     if (reference_token == "-")
                     {
                         // explicitly treat "-" as index beyond the end
-                        ptr = &ptr->operator[](ptr->m_value.array->size());
+                        ptr = &ptr->operator[](ptr->m_data.m_value.array->size());
                     }
                     else
                     {
@@ -14119,7 +14244,7 @@ class json_pointer
                     {
                         // "-" always fails the range check
                         JSON_THROW(detail::out_of_range::create(402, detail::concat(
-                                "array index '-' (", std::to_string(ptr->m_value.array->size()),
+                                "array index '-' (", std::to_string(ptr->m_data.m_value.array->size()),
                                 ") is out of range"), ptr));
                     }
 
@@ -14176,7 +14301,7 @@ class json_pointer
                     if (JSON_HEDLEY_UNLIKELY(reference_token == "-"))
                     {
                         // "-" cannot be used for const access
-                        JSON_THROW(detail::out_of_range::create(402, detail::concat("array index '-' (", std::to_string(ptr->m_value.array->size()), ") is out of range"), ptr));
+                        JSON_THROW(detail::out_of_range::create(402, detail::concat("array index '-' (", std::to_string(ptr->m_data.m_value.array->size()), ") is out of range"), ptr));
                     }
 
                     // use unchecked array access
@@ -14226,7 +14351,7 @@ class json_pointer
                     {
                         // "-" always fails the range check
                         JSON_THROW(detail::out_of_range::create(402, detail::concat(
-                                "array index '-' (", std::to_string(ptr->m_value.array->size()),
+                                "array index '-' (", std::to_string(ptr->m_data.m_value.array->size()),
                                 ") is out of range"), ptr));
                     }
 
@@ -14421,7 +14546,7 @@ class json_pointer
         {
             case detail::value_t::array:
             {
-                if (value.m_value.array->empty())
+                if (value.m_data.m_value.array->empty())
                 {
                     // flatten empty array as null
                     result[reference_string] = nullptr;
@@ -14429,10 +14554,10 @@ class json_pointer
                 else
                 {
                     // iterate array and use index as reference string
-                    for (std::size_t i = 0; i < value.m_value.array->size(); ++i)
+                    for (std::size_t i = 0; i < value.m_data.m_value.array->size(); ++i)
                     {
                         flatten(detail::concat(reference_string, '/', std::to_string(i)),
-                                value.m_value.array->operator[](i), result);
+                                value.m_data.m_value.array->operator[](i), result);
                     }
                 }
                 break;
@@ -14440,7 +14565,7 @@ class json_pointer
 
             case detail::value_t::object:
             {
-                if (value.m_value.object->empty())
+                if (value.m_data.m_value.object->empty())
                 {
                     // flatten empty object as null
                     result[reference_string] = nullptr;
@@ -14448,7 +14573,7 @@ class json_pointer
                 else
                 {
                     // iterate object and use keys as reference string
-                    for (const auto& element : *value.m_value.object)
+                    for (const auto& element : *value.m_data.m_value.object)
                     {
                         flatten(detail::concat(reference_string, '/', detail::escape(element.first)), element.second, result);
                     }
@@ -14495,7 +14620,7 @@ class json_pointer
         BasicJsonType result;
 
         // iterate the JSON object values
-        for (const auto& element : *value.m_value.object)
+        for (const auto& element : *value.m_data.m_value.object)
         {
             if (JSON_HEDLEY_UNLIKELY(!element.second.is_primitive()))
             {
@@ -14671,10 +14796,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/json_ref.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -14763,10 +14888,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/output/binary_writer.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -14789,10 +14914,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/output/output_adapters.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -14978,7 +15103,7 @@ class binary_writer
         {
             case value_t::object:
             {
-                write_bson_object(*j.m_value.object);
+                write_bson_object(*j.m_data.m_value.object);
                 break;
             }
 
@@ -15013,7 +15138,7 @@ class binary_writer
 
             case value_t::boolean:
             {
-                oa->write_character(j.m_value.boolean
+                oa->write_character(j.m_data.m_value.boolean
                                     ? to_char_type(0xF5)
                                     : to_char_type(0xF4));
                 break;
@@ -15021,42 +15146,42 @@ class binary_writer
 
             case value_t::number_integer:
             {
-                if (j.m_value.number_integer >= 0)
+                if (j.m_data.m_value.number_integer >= 0)
                 {
                     // CBOR does not differentiate between positive signed
                     // integers and unsigned integers. Therefore, we used the
                     // code from the value_t::number_unsigned case here.
-                    if (j.m_value.number_integer <= 0x17)
+                    if (j.m_data.m_value.number_integer <= 0x17)
                     {
-                        write_number(static_cast<std::uint8_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_integer <= (std::numeric_limits<std::uint8_t>::max)())
+                    else if (j.m_data.m_value.number_integer <= (std::numeric_limits<std::uint8_t>::max)())
                     {
                         oa->write_character(to_char_type(0x18));
-                        write_number(static_cast<std::uint8_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_integer <= (std::numeric_limits<std::uint16_t>::max)())
+                    else if (j.m_data.m_value.number_integer <= (std::numeric_limits<std::uint16_t>::max)())
                     {
                         oa->write_character(to_char_type(0x19));
-                        write_number(static_cast<std::uint16_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint16_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_integer <= (std::numeric_limits<std::uint32_t>::max)())
+                    else if (j.m_data.m_value.number_integer <= (std::numeric_limits<std::uint32_t>::max)())
                     {
                         oa->write_character(to_char_type(0x1A));
-                        write_number(static_cast<std::uint32_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint32_t>(j.m_data.m_value.number_integer));
                     }
                     else
                     {
                         oa->write_character(to_char_type(0x1B));
-                        write_number(static_cast<std::uint64_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint64_t>(j.m_data.m_value.number_integer));
                     }
                 }
                 else
                 {
                     // The conversions below encode the sign in the first
                     // byte, and the value is converted to a positive number.
-                    const auto positive_number = -1 - j.m_value.number_integer;
-                    if (j.m_value.number_integer >= -24)
+                    const auto positive_number = -1 - j.m_data.m_value.number_integer;
+                    if (j.m_data.m_value.number_integer >= -24)
                     {
                         write_number(static_cast<std::uint8_t>(0x20 + positive_number));
                     }
@@ -15086,52 +15211,52 @@ class binary_writer
 
             case value_t::number_unsigned:
             {
-                if (j.m_value.number_unsigned <= 0x17)
+                if (j.m_data.m_value.number_unsigned <= 0x17)
                 {
-                    write_number(static_cast<std::uint8_t>(j.m_value.number_unsigned));
+                    write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_unsigned));
                 }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint8_t>::max)())
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint8_t>::max)())
                 {
                     oa->write_character(to_char_type(0x18));
-                    write_number(static_cast<std::uint8_t>(j.m_value.number_unsigned));
+                    write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_unsigned));
                 }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint16_t>::max)())
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint16_t>::max)())
                 {
                     oa->write_character(to_char_type(0x19));
-                    write_number(static_cast<std::uint16_t>(j.m_value.number_unsigned));
+                    write_number(static_cast<std::uint16_t>(j.m_data.m_value.number_unsigned));
                 }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint32_t>::max)())
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint32_t>::max)())
                 {
                     oa->write_character(to_char_type(0x1A));
-                    write_number(static_cast<std::uint32_t>(j.m_value.number_unsigned));
+                    write_number(static_cast<std::uint32_t>(j.m_data.m_value.number_unsigned));
                 }
                 else
                 {
                     oa->write_character(to_char_type(0x1B));
-                    write_number(static_cast<std::uint64_t>(j.m_value.number_unsigned));
+                    write_number(static_cast<std::uint64_t>(j.m_data.m_value.number_unsigned));
                 }
                 break;
             }
 
             case value_t::number_float:
             {
-                if (std::isnan(j.m_value.number_float))
+                if (std::isnan(j.m_data.m_value.number_float))
                 {
                     // NaN is 0xf97e00 in CBOR
                     oa->write_character(to_char_type(0xF9));
                     oa->write_character(to_char_type(0x7E));
                     oa->write_character(to_char_type(0x00));
                 }
-                else if (std::isinf(j.m_value.number_float))
+                else if (std::isinf(j.m_data.m_value.number_float))
                 {
                     // Infinity is 0xf97c00, -Infinity is 0xf9fc00
                     oa->write_character(to_char_type(0xf9));
-                    oa->write_character(j.m_value.number_float > 0 ? to_char_type(0x7C) : to_char_type(0xFC));
+                    oa->write_character(j.m_data.m_value.number_float > 0 ? to_char_type(0x7C) : to_char_type(0xFC));
                     oa->write_character(to_char_type(0x00));
                 }
                 else
                 {
-                    write_compact_float(j.m_value.number_float, detail::input_format_t::cbor);
+                    write_compact_float(j.m_data.m_value.number_float, detail::input_format_t::cbor);
                 }
                 break;
             }
@@ -15139,7 +15264,7 @@ class binary_writer
             case value_t::string:
             {
                 // step 1: write control byte and the string length
-                const auto N = j.m_value.string->size();
+                const auto N = j.m_data.m_value.string->size();
                 if (N <= 0x17)
                 {
                     write_number(static_cast<std::uint8_t>(0x60 + N));
@@ -15169,15 +15294,15 @@ class binary_writer
 
                 // step 2: write the string
                 oa->write_characters(
-                    reinterpret_cast<const CharType*>(j.m_value.string->c_str()),
-                    j.m_value.string->size());
+                    reinterpret_cast<const CharType*>(j.m_data.m_value.string->c_str()),
+                    j.m_data.m_value.string->size());
                 break;
             }
 
             case value_t::array:
             {
                 // step 1: write control byte and the array size
-                const auto N = j.m_value.array->size();
+                const auto N = j.m_data.m_value.array->size();
                 if (N <= 0x17)
                 {
                     write_number(static_cast<std::uint8_t>(0x80 + N));
@@ -15206,7 +15331,7 @@ class binary_writer
                 // LCOV_EXCL_STOP
 
                 // step 2: write each element
-                for (const auto& el : *j.m_value.array)
+                for (const auto& el : *j.m_data.m_value.array)
                 {
                     write_cbor(el);
                 }
@@ -15215,32 +15340,32 @@ class binary_writer
 
             case value_t::binary:
             {
-                if (j.m_value.binary->has_subtype())
+                if (j.m_data.m_value.binary->has_subtype())
                 {
-                    if (j.m_value.binary->subtype() <= (std::numeric_limits<std::uint8_t>::max)())
+                    if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits<std::uint8_t>::max)())
                     {
                         write_number(static_cast<std::uint8_t>(0xd8));
-                        write_number(static_cast<std::uint8_t>(j.m_value.binary->subtype()));
+                        write_number(static_cast<std::uint8_t>(j.m_data.m_value.binary->subtype()));
                     }
-                    else if (j.m_value.binary->subtype() <= (std::numeric_limits<std::uint16_t>::max)())
+                    else if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits<std::uint16_t>::max)())
                     {
                         write_number(static_cast<std::uint8_t>(0xd9));
-                        write_number(static_cast<std::uint16_t>(j.m_value.binary->subtype()));
+                        write_number(static_cast<std::uint16_t>(j.m_data.m_value.binary->subtype()));
                     }
-                    else if (j.m_value.binary->subtype() <= (std::numeric_limits<std::uint32_t>::max)())
+                    else if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits<std::uint32_t>::max)())
                     {
                         write_number(static_cast<std::uint8_t>(0xda));
-                        write_number(static_cast<std::uint32_t>(j.m_value.binary->subtype()));
+                        write_number(static_cast<std::uint32_t>(j.m_data.m_value.binary->subtype()));
                     }
-                    else if (j.m_value.binary->subtype() <= (std::numeric_limits<std::uint64_t>::max)())
+                    else if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits<std::uint64_t>::max)())
                     {
                         write_number(static_cast<std::uint8_t>(0xdb));
-                        write_number(static_cast<std::uint64_t>(j.m_value.binary->subtype()));
+                        write_number(static_cast<std::uint64_t>(j.m_data.m_value.binary->subtype()));
                     }
                 }
 
                 // step 1: write control byte and the binary array size
-                const auto N = j.m_value.binary->size();
+                const auto N = j.m_data.m_value.binary->size();
                 if (N <= 0x17)
                 {
                     write_number(static_cast<std::uint8_t>(0x40 + N));
@@ -15270,7 +15395,7 @@ class binary_writer
 
                 // step 2: write each element
                 oa->write_characters(
-                    reinterpret_cast<const CharType*>(j.m_value.binary->data()),
+                    reinterpret_cast<const CharType*>(j.m_data.m_value.binary->data()),
                     N);
 
                 break;
@@ -15279,7 +15404,7 @@ class binary_writer
             case value_t::object:
             {
                 // step 1: write control byte and the object size
-                const auto N = j.m_value.object->size();
+                const auto N = j.m_data.m_value.object->size();
                 if (N <= 0x17)
                 {
                     write_number(static_cast<std::uint8_t>(0xA0 + N));
@@ -15308,7 +15433,7 @@ class binary_writer
                 // LCOV_EXCL_STOP
 
                 // step 2: write each element
-                for (const auto& el : *j.m_value.object)
+                for (const auto& el : *j.m_data.m_value.object)
                 {
                     write_cbor(el.first);
                     write_cbor(el.second);
@@ -15337,7 +15462,7 @@ class binary_writer
 
             case value_t::boolean: // true and false
             {
-                oa->write_character(j.m_value.boolean
+                oa->write_character(j.m_data.m_value.boolean
                                     ? to_char_type(0xC3)
                                     : to_char_type(0xC2));
                 break;
@@ -15345,75 +15470,75 @@ class binary_writer
 
             case value_t::number_integer:
             {
-                if (j.m_value.number_integer >= 0)
+                if (j.m_data.m_value.number_integer >= 0)
                 {
                     // MessagePack does not differentiate between positive
                     // signed integers and unsigned integers. Therefore, we used
                     // the code from the value_t::number_unsigned case here.
-                    if (j.m_value.number_unsigned < 128)
+                    if (j.m_data.m_value.number_unsigned < 128)
                     {
                         // positive fixnum
-                        write_number(static_cast<std::uint8_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint8_t>::max)())
+                    else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint8_t>::max)())
                     {
                         // uint 8
                         oa->write_character(to_char_type(0xCC));
-                        write_number(static_cast<std::uint8_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint16_t>::max)())
+                    else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint16_t>::max)())
                     {
                         // uint 16
                         oa->write_character(to_char_type(0xCD));
-                        write_number(static_cast<std::uint16_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint16_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint32_t>::max)())
+                    else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint32_t>::max)())
                     {
                         // uint 32
                         oa->write_character(to_char_type(0xCE));
-                        write_number(static_cast<std::uint32_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint32_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint64_t>::max)())
+                    else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint64_t>::max)())
                     {
                         // uint 64
                         oa->write_character(to_char_type(0xCF));
-                        write_number(static_cast<std::uint64_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::uint64_t>(j.m_data.m_value.number_integer));
                     }
                 }
                 else
                 {
-                    if (j.m_value.number_integer >= -32)
+                    if (j.m_data.m_value.number_integer >= -32)
                     {
                         // negative fixnum
-                        write_number(static_cast<std::int8_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::int8_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_integer >= (std::numeric_limits<std::int8_t>::min)() &&
-                             j.m_value.number_integer <= (std::numeric_limits<std::int8_t>::max)())
+                    else if (j.m_data.m_value.number_integer >= (std::numeric_limits<std::int8_t>::min)() &&
+                             j.m_data.m_value.number_integer <= (std::numeric_limits<std::int8_t>::max)())
                     {
                         // int 8
                         oa->write_character(to_char_type(0xD0));
-                        write_number(static_cast<std::int8_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::int8_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_integer >= (std::numeric_limits<std::int16_t>::min)() &&
-                             j.m_value.number_integer <= (std::numeric_limits<std::int16_t>::max)())
+                    else if (j.m_data.m_value.number_integer >= (std::numeric_limits<std::int16_t>::min)() &&
+                             j.m_data.m_value.number_integer <= (std::numeric_limits<std::int16_t>::max)())
                     {
                         // int 16
                         oa->write_character(to_char_type(0xD1));
-                        write_number(static_cast<std::int16_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::int16_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_integer >= (std::numeric_limits<std::int32_t>::min)() &&
-                             j.m_value.number_integer <= (std::numeric_limits<std::int32_t>::max)())
+                    else if (j.m_data.m_value.number_integer >= (std::numeric_limits<std::int32_t>::min)() &&
+                             j.m_data.m_value.number_integer <= (std::numeric_limits<std::int32_t>::max)())
                     {
                         // int 32
                         oa->write_character(to_char_type(0xD2));
-                        write_number(static_cast<std::int32_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::int32_t>(j.m_data.m_value.number_integer));
                     }
-                    else if (j.m_value.number_integer >= (std::numeric_limits<std::int64_t>::min)() &&
-                             j.m_value.number_integer <= (std::numeric_limits<std::int64_t>::max)())
+                    else if (j.m_data.m_value.number_integer >= (std::numeric_limits<std::int64_t>::min)() &&
+                             j.m_data.m_value.number_integer <= (std::numeric_limits<std::int64_t>::max)())
                     {
                         // int 64
                         oa->write_character(to_char_type(0xD3));
-                        write_number(static_cast<std::int64_t>(j.m_value.number_integer));
+                        write_number(static_cast<std::int64_t>(j.m_data.m_value.number_integer));
                     }
                 }
                 break;
@@ -15421,48 +15546,48 @@ class binary_writer
 
             case value_t::number_unsigned:
             {
-                if (j.m_value.number_unsigned < 128)
+                if (j.m_data.m_value.number_unsigned < 128)
                 {
                     // positive fixnum
-                    write_number(static_cast<std::uint8_t>(j.m_value.number_integer));
+                    write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_integer));
                 }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint8_t>::max)())
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint8_t>::max)())
                 {
                     // uint 8
                     oa->write_character(to_char_type(0xCC));
-                    write_number(static_cast<std::uint8_t>(j.m_value.number_integer));
+                    write_number(static_cast<std::uint8_t>(j.m_data.m_value.number_integer));
                 }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint16_t>::max)())
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint16_t>::max)())
                 {
                     // uint 16
                     oa->write_character(to_char_type(0xCD));
-                    write_number(static_cast<std::uint16_t>(j.m_value.number_integer));
+                    write_number(static_cast<std::uint16_t>(j.m_data.m_value.number_integer));
                 }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint32_t>::max)())
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint32_t>::max)())
                 {
                     // uint 32
                     oa->write_character(to_char_type(0xCE));
-                    write_number(static_cast<std::uint32_t>(j.m_value.number_integer));
+                    write_number(static_cast<std::uint32_t>(j.m_data.m_value.number_integer));
                 }
-                else if (j.m_value.number_unsigned <= (std::numeric_limits<std::uint64_t>::max)())
+                else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint64_t>::max)())
                 {
                     // uint 64
                     oa->write_character(to_char_type(0xCF));
-                    write_number(static_cast<std::uint64_t>(j.m_value.number_integer));
+                    write_number(static_cast<std::uint64_t>(j.m_data.m_value.number_integer));
                 }
                 break;
             }
 
             case value_t::number_float:
             {
-                write_compact_float(j.m_value.number_float, detail::input_format_t::msgpack);
+                write_compact_float(j.m_data.m_value.number_float, detail::input_format_t::msgpack);
                 break;
             }
 
             case value_t::string:
             {
                 // step 1: write control byte and the string length
-                const auto N = j.m_value.string->size();
+                const auto N = j.m_data.m_value.string->size();
                 if (N <= 31)
                 {
                     // fixstr
@@ -15489,15 +15614,15 @@ class binary_writer
 
                 // step 2: write the string
                 oa->write_characters(
-                    reinterpret_cast<const CharType*>(j.m_value.string->c_str()),
-                    j.m_value.string->size());
+                    reinterpret_cast<const CharType*>(j.m_data.m_value.string->c_str()),
+                    j.m_data.m_value.string->size());
                 break;
             }
 
             case value_t::array:
             {
                 // step 1: write control byte and the array size
-                const auto N = j.m_value.array->size();
+                const auto N = j.m_data.m_value.array->size();
                 if (N <= 15)
                 {
                     // fixarray
@@ -15517,7 +15642,7 @@ class binary_writer
                 }
 
                 // step 2: write each element
-                for (const auto& el : *j.m_value.array)
+                for (const auto& el : *j.m_data.m_value.array)
                 {
                     write_msgpack(el);
                 }
@@ -15528,10 +15653,10 @@ class binary_writer
             {
                 // step 0: determine if the binary type has a set subtype to
                 // determine whether or not to use the ext or fixext types
-                const bool use_ext = j.m_value.binary->has_subtype();
+                const bool use_ext = j.m_data.m_value.binary->has_subtype();
 
                 // step 1: write control byte and the byte string length
-                const auto N = j.m_value.binary->size();
+                const auto N = j.m_data.m_value.binary->size();
                 if (N <= (std::numeric_limits<std::uint8_t>::max)())
                 {
                     std::uint8_t output_type{};
@@ -15576,18 +15701,18 @@ class binary_writer
                 }
                 else if (N <= (std::numeric_limits<std::uint16_t>::max)())
                 {
-                    std::uint8_t output_type = use_ext
-                                               ? 0xC8 // ext 16
-                                               : 0xC5; // bin 16
+                    const std::uint8_t output_type = use_ext
+                                                     ? 0xC8 // ext 16
+                                                     : 0xC5; // bin 16
 
                     oa->write_character(to_char_type(output_type));
                     write_number(static_cast<std::uint16_t>(N));
                 }
                 else if (N <= (std::numeric_limits<std::uint32_t>::max)())
                 {
-                    std::uint8_t output_type = use_ext
-                                               ? 0xC9 // ext 32
-                                               : 0xC6; // bin 32
+                    const std::uint8_t output_type = use_ext
+                                                     ? 0xC9 // ext 32
+                                                     : 0xC6; // bin 32
 
                     oa->write_character(to_char_type(output_type));
                     write_number(static_cast<std::uint32_t>(N));
@@ -15596,12 +15721,12 @@ class binary_writer
                 // step 1.5: if this is an ext type, write the subtype
                 if (use_ext)
                 {
-                    write_number(static_cast<std::int8_t>(j.m_value.binary->subtype()));
+                    write_number(static_cast<std::int8_t>(j.m_data.m_value.binary->subtype()));
                 }
 
                 // step 2: write the byte string
                 oa->write_characters(
-                    reinterpret_cast<const CharType*>(j.m_value.binary->data()),
+                    reinterpret_cast<const CharType*>(j.m_data.m_value.binary->data()),
                     N);
 
                 break;
@@ -15610,7 +15735,7 @@ class binary_writer
             case value_t::object:
             {
                 // step 1: write control byte and the object size
-                const auto N = j.m_value.object->size();
+                const auto N = j.m_data.m_value.object->size();
                 if (N <= 15)
                 {
                     // fixmap
@@ -15630,7 +15755,7 @@ class binary_writer
                 }
 
                 // step 2: write each element
-                for (const auto& el : *j.m_value.object)
+                for (const auto& el : *j.m_data.m_value.object)
                 {
                     write_msgpack(el.first);
                     write_msgpack(el.second);
@@ -15670,7 +15795,7 @@ class binary_writer
             {
                 if (add_prefix)
                 {
-                    oa->write_character(j.m_value.boolean
+                    oa->write_character(j.m_data.m_value.boolean
                                         ? to_char_type('T')
                                         : to_char_type('F'));
                 }
@@ -15679,19 +15804,19 @@ class binary_writer
 
             case value_t::number_integer:
             {
-                write_number_with_ubjson_prefix(j.m_value.number_integer, add_prefix, use_bjdata);
+                write_number_with_ubjson_prefix(j.m_data.m_value.number_integer, add_prefix, use_bjdata);
                 break;
             }
 
             case value_t::number_unsigned:
             {
-                write_number_with_ubjson_prefix(j.m_value.number_unsigned, add_prefix, use_bjdata);
+                write_number_with_ubjson_prefix(j.m_data.m_value.number_unsigned, add_prefix, use_bjdata);
                 break;
             }
 
             case value_t::number_float:
             {
-                write_number_with_ubjson_prefix(j.m_value.number_float, add_prefix, use_bjdata);
+                write_number_with_ubjson_prefix(j.m_data.m_value.number_float, add_prefix, use_bjdata);
                 break;
             }
 
@@ -15701,10 +15826,10 @@ class binary_writer
                 {
                     oa->write_character(to_char_type('S'));
                 }
-                write_number_with_ubjson_prefix(j.m_value.string->size(), true, use_bjdata);
+                write_number_with_ubjson_prefix(j.m_data.m_value.string->size(), true, use_bjdata);
                 oa->write_characters(
-                    reinterpret_cast<const CharType*>(j.m_value.string->c_str()),
-                    j.m_value.string->size());
+                    reinterpret_cast<const CharType*>(j.m_data.m_value.string->c_str()),
+                    j.m_data.m_value.string->size());
                 break;
             }
 
@@ -15716,7 +15841,7 @@ class binary_writer
                 }
 
                 bool prefix_required = true;
-                if (use_type && !j.m_value.array->empty())
+                if (use_type && !j.m_data.m_value.array->empty())
                 {
                     JSON_ASSERT(use_count);
                     const CharType first_prefix = ubjson_prefix(j.front(), use_bjdata);
@@ -15739,10 +15864,10 @@ class binary_writer
                 if (use_count)
                 {
                     oa->write_character(to_char_type('#'));
-                    write_number_with_ubjson_prefix(j.m_value.array->size(), true, use_bjdata);
+                    write_number_with_ubjson_prefix(j.m_data.m_value.array->size(), true, use_bjdata);
                 }
 
-                for (const auto& el : *j.m_value.array)
+                for (const auto& el : *j.m_data.m_value.array)
                 {
                     write_ubjson(el, use_count, use_type, prefix_required, use_bjdata);
                 }
@@ -15762,7 +15887,7 @@ class binary_writer
                     oa->write_character(to_char_type('['));
                 }
 
-                if (use_type && !j.m_value.binary->empty())
+                if (use_type && !j.m_data.m_value.binary->empty())
                 {
                     JSON_ASSERT(use_count);
                     oa->write_character(to_char_type('$'));
@@ -15772,21 +15897,21 @@ class binary_writer
                 if (use_count)
                 {
                     oa->write_character(to_char_type('#'));
-                    write_number_with_ubjson_prefix(j.m_value.binary->size(), true, use_bjdata);
+                    write_number_with_ubjson_prefix(j.m_data.m_value.binary->size(), true, use_bjdata);
                 }
 
                 if (use_type)
                 {
                     oa->write_characters(
-                        reinterpret_cast<const CharType*>(j.m_value.binary->data()),
-                        j.m_value.binary->size());
+                        reinterpret_cast<const CharType*>(j.m_data.m_value.binary->data()),
+                        j.m_data.m_value.binary->size());
                 }
                 else
                 {
-                    for (size_t i = 0; i < j.m_value.binary->size(); ++i)
+                    for (size_t i = 0; i < j.m_data.m_value.binary->size(); ++i)
                     {
                         oa->write_character(to_char_type('U'));
-                        oa->write_character(j.m_value.binary->data()[i]);
+                        oa->write_character(j.m_data.m_value.binary->data()[i]);
                     }
                 }
 
@@ -15800,9 +15925,9 @@ class binary_writer
 
             case value_t::object:
             {
-                if (use_bjdata && j.m_value.object->size() == 3 && j.m_value.object->find("_ArrayType_") != j.m_value.object->end() && j.m_value.object->find("_ArraySize_") != j.m_value.object->end() && j.m_value.object->find("_ArrayData_") != j.m_value.object->end())
+                if (use_bjdata && j.m_data.m_value.object->size() == 3 && j.m_data.m_value.object->find("_ArrayType_") != j.m_data.m_value.object->end() && j.m_data.m_value.object->find("_ArraySize_") != j.m_data.m_value.object->end() && j.m_data.m_value.object->find("_ArrayData_") != j.m_data.m_value.object->end())
                 {
-                    if (!write_bjdata_ndarray(*j.m_value.object, use_count, use_type))  // decode bjdata ndarray in the JData format (https://github.com/NeuroJSON/jdata)
+                    if (!write_bjdata_ndarray(*j.m_data.m_value.object, use_count, use_type))  // decode bjdata ndarray in the JData format (https://github.com/NeuroJSON/jdata)
                     {
                         break;
                     }
@@ -15814,7 +15939,7 @@ class binary_writer
                 }
 
                 bool prefix_required = true;
-                if (use_type && !j.m_value.object->empty())
+                if (use_type && !j.m_data.m_value.object->empty())
                 {
                     JSON_ASSERT(use_count);
                     const CharType first_prefix = ubjson_prefix(j.front(), use_bjdata);
@@ -15837,10 +15962,10 @@ class binary_writer
                 if (use_count)
                 {
                     oa->write_character(to_char_type('#'));
-                    write_number_with_ubjson_prefix(j.m_value.object->size(), true, use_bjdata);
+                    write_number_with_ubjson_prefix(j.m_data.m_value.object->size(), true, use_bjdata);
                 }
 
-                for (const auto& el : *j.m_value.object)
+                for (const auto& el : *j.m_data.m_value.object)
                 {
                     write_number_with_ubjson_prefix(el.first.size(), true, use_bjdata);
                     oa->write_characters(
@@ -15990,19 +16115,19 @@ class binary_writer
     void write_bson_unsigned(const string_t& name,
                              const BasicJsonType& j)
     {
-        if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int32_t>::max)()))
+        if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int32_t>::max)()))
         {
             write_bson_entry_header(name, 0x10 /* int32 */);
-            write_number<std::int32_t>(static_cast<std::int32_t>(j.m_value.number_unsigned), true);
+            write_number<std::int32_t>(static_cast<std::int32_t>(j.m_data.m_value.number_unsigned), true);
         }
-        else if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int64_t>::max)()))
+        else if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int64_t>::max)()))
         {
             write_bson_entry_header(name, 0x12 /* int64 */);
-            write_number<std::int64_t>(static_cast<std::int64_t>(j.m_value.number_unsigned), true);
+            write_number<std::int64_t>(static_cast<std::int64_t>(j.m_data.m_value.number_unsigned), true);
         }
         else
         {
-            JSON_THROW(out_of_range::create(407, concat("integer number ", std::to_string(j.m_value.number_unsigned), " cannot be represented by BSON as it does not fit int64"), &j));
+            JSON_THROW(out_of_range::create(407, concat("integer number ", std::to_string(j.m_data.m_value.number_unsigned), " cannot be represented by BSON as it does not fit int64"), &j));
         }
     }
 
@@ -16083,13 +16208,13 @@ class binary_writer
         switch (j.type())
         {
             case value_t::object:
-                return header_size + calc_bson_object_size(*j.m_value.object);
+                return header_size + calc_bson_object_size(*j.m_data.m_value.object);
 
             case value_t::array:
-                return header_size + calc_bson_array_size(*j.m_value.array);
+                return header_size + calc_bson_array_size(*j.m_data.m_value.array);
 
             case value_t::binary:
-                return header_size + calc_bson_binary_size(*j.m_value.binary);
+                return header_size + calc_bson_binary_size(*j.m_data.m_value.binary);
 
             case value_t::boolean:
                 return header_size + 1ul;
@@ -16098,13 +16223,13 @@ class binary_writer
                 return header_size + 8ul;
 
             case value_t::number_integer:
-                return header_size + calc_bson_integer_size(j.m_value.number_integer);
+                return header_size + calc_bson_integer_size(j.m_data.m_value.number_integer);
 
             case value_t::number_unsigned:
-                return header_size + calc_bson_unsigned_size(j.m_value.number_unsigned);
+                return header_size + calc_bson_unsigned_size(j.m_data.m_value.number_unsigned);
 
             case value_t::string:
-                return header_size + calc_bson_string_size(*j.m_value.string);
+                return header_size + calc_bson_string_size(*j.m_data.m_value.string);
 
             case value_t::null:
                 return header_size + 0ul;
@@ -16130,28 +16255,28 @@ class binary_writer
         switch (j.type())
         {
             case value_t::object:
-                return write_bson_object_entry(name, *j.m_value.object);
+                return write_bson_object_entry(name, *j.m_data.m_value.object);
 
             case value_t::array:
-                return write_bson_array(name, *j.m_value.array);
+                return write_bson_array(name, *j.m_data.m_value.array);
 
             case value_t::binary:
-                return write_bson_binary(name, *j.m_value.binary);
+                return write_bson_binary(name, *j.m_data.m_value.binary);
 
             case value_t::boolean:
-                return write_bson_boolean(name, j.m_value.boolean);
+                return write_bson_boolean(name, j.m_data.m_value.boolean);
 
             case value_t::number_float:
-                return write_bson_double(name, j.m_value.number_float);
+                return write_bson_double(name, j.m_data.m_value.number_float);
 
             case value_t::number_integer:
-                return write_bson_integer(name, j.m_value.number_integer);
+                return write_bson_integer(name, j.m_data.m_value.number_integer);
 
             case value_t::number_unsigned:
                 return write_bson_unsigned(name, j);
 
             case value_t::string:
-                return write_bson_string(name, *j.m_value.string);
+                return write_bson_string(name, *j.m_data.m_value.string);
 
             case value_t::null:
                 return write_bson_null(name);
@@ -16173,8 +16298,8 @@ class binary_writer
     */
     static std::size_t calc_bson_object_size(const typename BasicJsonType::object_t& value)
     {
-        std::size_t document_size = std::accumulate(value.begin(), value.end(), static_cast<std::size_t>(0),
-                                    [](size_t result, const typename BasicJsonType::object_t::value_type & el)
+        const std::size_t document_size = std::accumulate(value.begin(), value.end(), static_cast<std::size_t>(0),
+                                          [](size_t result, const typename BasicJsonType::object_t::value_type & el)
         {
             return result += calc_bson_element_size(el.first, el.second);
         });
@@ -16424,35 +16549,35 @@ class binary_writer
                 return 'Z';
 
             case value_t::boolean:
-                return j.m_value.boolean ? 'T' : 'F';
+                return j.m_data.m_value.boolean ? 'T' : 'F';
 
             case value_t::number_integer:
             {
-                if ((std::numeric_limits<std::int8_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::int8_t>::max)())
+                if ((std::numeric_limits<std::int8_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::int8_t>::max)())
                 {
                     return 'i';
                 }
-                if ((std::numeric_limits<std::uint8_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::uint8_t>::max)())
+                if ((std::numeric_limits<std::uint8_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::uint8_t>::max)())
                 {
                     return 'U';
                 }
-                if ((std::numeric_limits<std::int16_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::int16_t>::max)())
+                if ((std::numeric_limits<std::int16_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::int16_t>::max)())
                 {
                     return 'I';
                 }
-                if (use_bjdata && ((std::numeric_limits<std::uint16_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::uint16_t>::max)()))
+                if (use_bjdata && ((std::numeric_limits<std::uint16_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::uint16_t>::max)()))
                 {
                     return 'u';
                 }
-                if ((std::numeric_limits<std::int32_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::int32_t>::max)())
+                if ((std::numeric_limits<std::int32_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::int32_t>::max)())
                 {
                     return 'l';
                 }
-                if (use_bjdata && ((std::numeric_limits<std::uint32_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::uint32_t>::max)()))
+                if (use_bjdata && ((std::numeric_limits<std::uint32_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::uint32_t>::max)()))
                 {
                     return 'm';
                 }
-                if ((std::numeric_limits<std::int64_t>::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits<std::int64_t>::max)())
+                if ((std::numeric_limits<std::int64_t>::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits<std::int64_t>::max)())
                 {
                     return 'L';
                 }
@@ -16462,35 +16587,35 @@ class binary_writer
 
             case value_t::number_unsigned:
             {
-                if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int8_t>::max)()))
+                if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int8_t>::max)()))
                 {
                     return 'i';
                 }
-                if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::uint8_t>::max)()))
+                if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::uint8_t>::max)()))
                 {
                     return 'U';
                 }
-                if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int16_t>::max)()))
+                if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int16_t>::max)()))
                 {
                     return 'I';
                 }
-                if (use_bjdata && j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::uint16_t>::max)()))
+                if (use_bjdata && j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::uint16_t>::max)()))
                 {
                     return 'u';
                 }
-                if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int32_t>::max)()))
+                if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int32_t>::max)()))
                 {
                     return 'l';
                 }
-                if (use_bjdata && j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::uint32_t>::max)()))
+                if (use_bjdata && j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::uint32_t>::max)()))
                 {
                     return 'm';
                 }
-                if (j.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int64_t>::max)()))
+                if (j.m_data.m_value.number_unsigned <= static_cast<std::uint64_t>((std::numeric_limits<std::int64_t>::max)()))
                 {
                     return 'L';
                 }
-                if (use_bjdata && j.m_value.number_unsigned <= (std::numeric_limits<std::uint64_t>::max)())
+                if (use_bjdata && j.m_data.m_value.number_unsigned <= (std::numeric_limits<std::uint64_t>::max)())
                 {
                     return 'M';
                 }
@@ -16499,7 +16624,7 @@ class binary_writer
             }
 
             case value_t::number_float:
-                return get_ubjson_float_prefix(j.m_value.number_float);
+                return get_ubjson_float_prefix(j.m_data.m_value.number_float);
 
             case value_t::string:
                 return 'S';
@@ -16548,7 +16673,7 @@ class binary_writer
         std::size_t len = (value.at(key).empty() ? 0 : 1);
         for (const auto& el : value.at(key))
         {
-            len *= static_cast<std::size_t>(el.m_value.number_unsigned);
+            len *= static_cast<std::size_t>(el.m_data.m_value.number_unsigned);
         }
 
         key = "_ArrayData_";
@@ -16570,70 +16695,70 @@ class binary_writer
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<std::uint8_t>(el.m_value.number_unsigned), true);
+                write_number(static_cast<std::uint8_t>(el.m_data.m_value.number_unsigned), true);
             }
         }
         else if (dtype == 'i')
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<std::int8_t>(el.m_value.number_integer), true);
+                write_number(static_cast<std::int8_t>(el.m_data.m_value.number_integer), true);
             }
         }
         else if (dtype == 'u')
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<std::uint16_t>(el.m_value.number_unsigned), true);
+                write_number(static_cast<std::uint16_t>(el.m_data.m_value.number_unsigned), true);
             }
         }
         else if (dtype == 'I')
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<std::int16_t>(el.m_value.number_integer), true);
+                write_number(static_cast<std::int16_t>(el.m_data.m_value.number_integer), true);
             }
         }
         else if (dtype == 'm')
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<std::uint32_t>(el.m_value.number_unsigned), true);
+                write_number(static_cast<std::uint32_t>(el.m_data.m_value.number_unsigned), true);
             }
         }
         else if (dtype == 'l')
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<std::int32_t>(el.m_value.number_integer), true);
+                write_number(static_cast<std::int32_t>(el.m_data.m_value.number_integer), true);
             }
         }
         else if (dtype == 'M')
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<std::uint64_t>(el.m_value.number_unsigned), true);
+                write_number(static_cast<std::uint64_t>(el.m_data.m_value.number_unsigned), true);
             }
         }
         else if (dtype == 'L')
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<std::int64_t>(el.m_value.number_integer), true);
+                write_number(static_cast<std::int64_t>(el.m_data.m_value.number_integer), true);
             }
         }
         else if (dtype == 'd')
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<float>(el.m_value.number_float), true);
+                write_number(static_cast<float>(el.m_data.m_value.number_float), true);
             }
         }
         else if (dtype == 'D')
         {
             for (const auto& el : value.at(key))
             {
-                write_number(static_cast<double>(el.m_value.number_float), true);
+                write_number(static_cast<double>(el.m_data.m_value.number_float), true);
             }
         }
         return false;
@@ -16757,11 +16882,11 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/output/serializer.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
 // SPDX-FileCopyrightText: 2008-2009 Björn Hoehrmann <bjoern@hoehrmann.de>
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -16782,11 +16907,11 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/detail/conversions/to_chars.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
 // SPDX-FileCopyrightText: 2009 Florian Loitsch <https://florian.loitsch.com/>
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -17692,7 +17817,7 @@ void grisu2(char* buf, int& len, int& decimal_exponent, FloatType value)
     // NB: If the neighbors are computed for single-precision numbers, there is a single float
     //     (7.0385307e-26f) which can't be recovered using strtod. The resulting double precision
     //     value is off by 1 ulp.
-#if 0
+#if 0 // NOLINT(readability-avoid-unconditional-preprocessor-if)
     const boundaries w = compute_boundaries(static_cast<double>(value));
 #else
     const boundaries w = compute_boundaries(value);
@@ -17994,11 +18119,11 @@ class serializer
               const unsigned int indent_step,
               const unsigned int current_indent = 0)
     {
-        switch (val.m_type)
+        switch (val.m_data.m_type)
         {
             case value_t::object:
             {
-                if (val.m_value.object->empty())
+                if (val.m_data.m_value.object->empty())
                 {
                     o->write_characters("{}", 2);
                     return;
@@ -18016,8 +18141,8 @@ class serializer
                     }
 
                     // first n-1 elements
-                    auto i = val.m_value.object->cbegin();
-                    for (std::size_t cnt = 0; cnt < val.m_value.object->size() - 1; ++cnt, ++i)
+                    auto i = val.m_data.m_value.object->cbegin();
+                    for (std::size_t cnt = 0; cnt < val.m_data.m_value.object->size() - 1; ++cnt, ++i)
                     {
                         o->write_characters(indent_string.c_str(), new_indent);
                         o->write_character('\"');
@@ -18028,8 +18153,8 @@ class serializer
                     }
 
                     // last element
-                    JSON_ASSERT(i != val.m_value.object->cend());
-                    JSON_ASSERT(std::next(i) == val.m_value.object->cend());
+                    JSON_ASSERT(i != val.m_data.m_value.object->cend());
+                    JSON_ASSERT(std::next(i) == val.m_data.m_value.object->cend());
                     o->write_characters(indent_string.c_str(), new_indent);
                     o->write_character('\"');
                     dump_escaped(i->first, ensure_ascii);
@@ -18045,8 +18170,8 @@ class serializer
                     o->write_character('{');
 
                     // first n-1 elements
-                    auto i = val.m_value.object->cbegin();
-                    for (std::size_t cnt = 0; cnt < val.m_value.object->size() - 1; ++cnt, ++i)
+                    auto i = val.m_data.m_value.object->cbegin();
+                    for (std::size_t cnt = 0; cnt < val.m_data.m_value.object->size() - 1; ++cnt, ++i)
                     {
                         o->write_character('\"');
                         dump_escaped(i->first, ensure_ascii);
@@ -18056,8 +18181,8 @@ class serializer
                     }
 
                     // last element
-                    JSON_ASSERT(i != val.m_value.object->cend());
-                    JSON_ASSERT(std::next(i) == val.m_value.object->cend());
+                    JSON_ASSERT(i != val.m_data.m_value.object->cend());
+                    JSON_ASSERT(std::next(i) == val.m_data.m_value.object->cend());
                     o->write_character('\"');
                     dump_escaped(i->first, ensure_ascii);
                     o->write_characters("\":", 2);
@@ -18071,7 +18196,7 @@ class serializer
 
             case value_t::array:
             {
-                if (val.m_value.array->empty())
+                if (val.m_data.m_value.array->empty())
                 {
                     o->write_characters("[]", 2);
                     return;
@@ -18089,8 +18214,8 @@ class serializer
                     }
 
                     // first n-1 elements
-                    for (auto i = val.m_value.array->cbegin();
-                            i != val.m_value.array->cend() - 1; ++i)
+                    for (auto i = val.m_data.m_value.array->cbegin();
+                            i != val.m_data.m_value.array->cend() - 1; ++i)
                     {
                         o->write_characters(indent_string.c_str(), new_indent);
                         dump(*i, true, ensure_ascii, indent_step, new_indent);
@@ -18098,9 +18223,9 @@ class serializer
                     }
 
                     // last element
-                    JSON_ASSERT(!val.m_value.array->empty());
+                    JSON_ASSERT(!val.m_data.m_value.array->empty());
                     o->write_characters(indent_string.c_str(), new_indent);
-                    dump(val.m_value.array->back(), true, ensure_ascii, indent_step, new_indent);
+                    dump(val.m_data.m_value.array->back(), true, ensure_ascii, indent_step, new_indent);
 
                     o->write_character('\n');
                     o->write_characters(indent_string.c_str(), current_indent);
@@ -18111,16 +18236,16 @@ class serializer
                     o->write_character('[');
 
                     // first n-1 elements
-                    for (auto i = val.m_value.array->cbegin();
-                            i != val.m_value.array->cend() - 1; ++i)
+                    for (auto i = val.m_data.m_value.array->cbegin();
+                            i != val.m_data.m_value.array->cend() - 1; ++i)
                     {
                         dump(*i, false, ensure_ascii, indent_step, current_indent);
                         o->write_character(',');
                     }
 
                     // last element
-                    JSON_ASSERT(!val.m_value.array->empty());
-                    dump(val.m_value.array->back(), false, ensure_ascii, indent_step, current_indent);
+                    JSON_ASSERT(!val.m_data.m_value.array->empty());
+                    dump(val.m_data.m_value.array->back(), false, ensure_ascii, indent_step, current_indent);
 
                     o->write_character(']');
                 }
@@ -18131,7 +18256,7 @@ class serializer
             case value_t::string:
             {
                 o->write_character('\"');
-                dump_escaped(*val.m_value.string, ensure_ascii);
+                dump_escaped(*val.m_data.m_value.string, ensure_ascii);
                 o->write_character('\"');
                 return;
             }
@@ -18153,24 +18278,24 @@ class serializer
 
                     o->write_characters("\"bytes\": [", 10);
 
-                    if (!val.m_value.binary->empty())
+                    if (!val.m_data.m_value.binary->empty())
                     {
-                        for (auto i = val.m_value.binary->cbegin();
-                                i != val.m_value.binary->cend() - 1; ++i)
+                        for (auto i = val.m_data.m_value.binary->cbegin();
+                                i != val.m_data.m_value.binary->cend() - 1; ++i)
                         {
                             dump_integer(*i);
                             o->write_characters(", ", 2);
                         }
-                        dump_integer(val.m_value.binary->back());
+                        dump_integer(val.m_data.m_value.binary->back());
                     }
 
                     o->write_characters("],\n", 3);
                     o->write_characters(indent_string.c_str(), new_indent);
 
                     o->write_characters("\"subtype\": ", 11);
-                    if (val.m_value.binary->has_subtype())
+                    if (val.m_data.m_value.binary->has_subtype())
                     {
-                        dump_integer(val.m_value.binary->subtype());
+                        dump_integer(val.m_data.m_value.binary->subtype());
                     }
                     else
                     {
@@ -18184,21 +18309,21 @@ class serializer
                 {
                     o->write_characters("{\"bytes\":[", 10);
 
-                    if (!val.m_value.binary->empty())
+                    if (!val.m_data.m_value.binary->empty())
                     {
-                        for (auto i = val.m_value.binary->cbegin();
-                                i != val.m_value.binary->cend() - 1; ++i)
+                        for (auto i = val.m_data.m_value.binary->cbegin();
+                                i != val.m_data.m_value.binary->cend() - 1; ++i)
                         {
                             dump_integer(*i);
                             o->write_character(',');
                         }
-                        dump_integer(val.m_value.binary->back());
+                        dump_integer(val.m_data.m_value.binary->back());
                     }
 
                     o->write_characters("],\"subtype\":", 12);
-                    if (val.m_value.binary->has_subtype())
+                    if (val.m_data.m_value.binary->has_subtype())
                     {
-                        dump_integer(val.m_value.binary->subtype());
+                        dump_integer(val.m_data.m_value.binary->subtype());
                         o->write_character('}');
                     }
                     else
@@ -18211,7 +18336,7 @@ class serializer
 
             case value_t::boolean:
             {
-                if (val.m_value.boolean)
+                if (val.m_data.m_value.boolean)
                 {
                     o->write_characters("true", 4);
                 }
@@ -18224,19 +18349,19 @@ class serializer
 
             case value_t::number_integer:
             {
-                dump_integer(val.m_value.number_integer);
+                dump_integer(val.m_data.m_value.number_integer);
                 return;
             }
 
             case value_t::number_unsigned:
             {
-                dump_integer(val.m_value.number_unsigned);
+                dump_integer(val.m_data.m_value.number_unsigned);
                 return;
             }
 
             case value_t::number_float:
             {
-                dump_float(val.m_value.number_float);
+                dump_float(val.m_data.m_value.number_float);
                 return;
             }
 
@@ -18810,8 +18935,8 @@ class serializer
                 ? (byte & 0x3fu) | (codep << 6u)
                 : (0xFFu >> type) & (byte);
 
-        std::size_t index = 256u + static_cast<size_t>(state) * 16u + static_cast<size_t>(type);
-        JSON_ASSERT(index < 400);
+        const std::size_t index = 256u + static_cast<size_t>(state) * 16u + static_cast<size_t>(type);
+        JSON_ASSERT(index < utf8d.size());
         state = utf8d[index];
         return state;
     }
@@ -18878,10 +19003,10 @@ NLOHMANN_JSON_NAMESPACE_END
 // #include <nlohmann/ordered_map.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -18998,7 +19123,7 @@ template <class Key, class T, class IgnoredLess = std::less<Key>,
 
     template<class KeyType, detail::enable_if_t<
                  detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
-    T & at(KeyType && key)
+    T & at(KeyType && key) // NOLINT(cppcoreguidelines-missing-std-forward)
     {
         for (auto it = this->begin(); it != this->end(); ++it)
         {
@@ -19026,7 +19151,7 @@ template <class Key, class T, class IgnoredLess = std::less<Key>,
 
     template<class KeyType, detail::enable_if_t<
                  detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
-    const T & at(KeyType && key) const
+    const T & at(KeyType && key) const // NOLINT(cppcoreguidelines-missing-std-forward)
     {
         for (auto it = this->begin(); it != this->end(); ++it)
         {
@@ -19060,7 +19185,7 @@ template <class Key, class T, class IgnoredLess = std::less<Key>,
 
     template<class KeyType, detail::enable_if_t<
                  detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
-    size_type erase(KeyType && key)
+    size_type erase(KeyType && key) // NOLINT(cppcoreguidelines-missing-std-forward)
     {
         for (auto it = this->begin(); it != this->end(); ++it)
         {
@@ -19151,7 +19276,7 @@ template <class Key, class T, class IgnoredLess = std::less<Key>,
 
     template<class KeyType, detail::enable_if_t<
                  detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
-    size_type count(KeyType && key) const
+    size_type count(KeyType && key) const // NOLINT(cppcoreguidelines-missing-std-forward)
     {
         for (auto it = this->begin(); it != this->end(); ++it)
         {
@@ -19177,7 +19302,7 @@ template <class Key, class T, class IgnoredLess = std::less<Key>,
 
     template<class KeyType, detail::enable_if_t<
                  detail::is_usable_as_key_type<key_compare, key_type, KeyType>::value, int> = 0>
-    iterator find(KeyType && key)
+    iterator find(KeyType && key) // NOLINT(cppcoreguidelines-missing-std-forward)
     {
         for (auto it = this->begin(); it != this->end(); ++it)
         {
@@ -19240,7 +19365,9 @@ NLOHMANN_JSON_NAMESPACE_END
 
 
 #if defined(JSON_HAS_CPP_17)
-    #include <any>
+    #if JSON_HAS_STATIC_RTTI
+        #include <any>
+    #endif
     #include <string_view>
 #endif
 
@@ -19271,6 +19398,7 @@ The invariants are checked by member function assert_invariant().
 */
 NLOHMANN_BASIC_JSON_TPL_DECLARATION
 class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-special-member-functions)
+    : public ::nlohmann::detail::json_base_class<CustomBaseClass>
 {
   private:
     template<detail::value_t> friend struct detail::external_constructor;
@@ -19297,6 +19425,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// workaround type for MSVC
     using basic_json_t = NLOHMANN_BASIC_JSON_TPL;
+    using json_base_class_t = ::nlohmann::detail::json_base_class<CustomBaseClass>;
 
   JSON_PRIVATE_UNLESS_TESTED:
     // convenience aliases for types residing in namespace detail;
@@ -19368,7 +19497,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @}
 
-
     /////////////////////
     // container types //
     /////////////////////
@@ -19410,7 +19538,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @}
 
-
     /// @brief returns the allocator associated with the container
     /// @sa https://json.nlohmann.me/api/basic_json/get_allocator/
     static allocator_type get_allocator()
@@ -19425,7 +19552,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     {
         basic_json result;
 
-        result["copyright"] = "(C) 2013-2022 Niels Lohmann";
+        result["copyright"] = "(C) 2013-2023 Niels Lohmann";
         result["name"] = "JSON for Modern C++";
         result["url"] = "https://github.com/nlohmann/json";
         result["version"]["string"] =
@@ -19473,7 +19600,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         result["compiler"] = {{"family", "unknown"}, {"version", "unknown"}};
 #endif
 
-
 #if defined(_MSVC_LANG)
         result["compiler"]["c++"] = std::to_string(_MSVC_LANG);
 #elif defined(__cplusplus)
@@ -19484,7 +19610,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         return result;
     }
 
-
     ///////////////////////////
     // JSON value data types //
     ///////////////////////////
@@ -19692,7 +19817,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                     object = nullptr;  // silence warning, see #821
                     if (JSON_HEDLEY_UNLIKELY(t == value_t::null))
                     {
-                        JSON_THROW(other_error::create(500, "961c151d2e87f2686a955a9be24d316f1362bf21 3.11.2", nullptr)); // LCOV_EXCL_LINE
+                        JSON_THROW(other_error::create(500, "961c151d2e87f2686a955a9be24d316f1362bf21 3.11.3", nullptr)); // LCOV_EXCL_LINE
                     }
                     break;
                 }
@@ -19731,6 +19856,16 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
         void destroy(value_t t)
         {
+            if (
+                (t == value_t::object && object == nullptr) ||
+                (t == value_t::array && array == nullptr) ||
+                (t == value_t::string && string == nullptr) ||
+                (t == value_t::binary && binary == nullptr)
+            )
+            {
+                //not initialized (e.g. due to exception in the ctor)
+                return;
+            }
             if (t == value_t::array || t == value_t::object)
             {
                 // flatten the current json_value to a heap-allocated stack
@@ -19761,18 +19896,18 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                     // its children to the stack to be processed later
                     if (current_item.is_array())
                     {
-                        std::move(current_item.m_value.array->begin(), current_item.m_value.array->end(), std::back_inserter(stack));
+                        std::move(current_item.m_data.m_value.array->begin(), current_item.m_data.m_value.array->end(), std::back_inserter(stack));
 
-                        current_item.m_value.array->clear();
+                        current_item.m_data.m_value.array->clear();
                     }
                     else if (current_item.is_object())
                     {
-                        for (auto&& it : *current_item.m_value.object)
+                        for (auto&& it : *current_item.m_data.m_value.object)
                         {
                             stack.push_back(std::move(it.second));
                         }
 
-                        current_item.m_value.object->clear();
+                        current_item.m_data.m_value.object->clear();
                     }
 
                     // it's now safe that current_item get destructed
@@ -19849,10 +19984,10 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     */
     void assert_invariant(bool check_parents = true) const noexcept
     {
-        JSON_ASSERT(m_type != value_t::object || m_value.object != nullptr);
-        JSON_ASSERT(m_type != value_t::array || m_value.array != nullptr);
-        JSON_ASSERT(m_type != value_t::string || m_value.string != nullptr);
-        JSON_ASSERT(m_type != value_t::binary || m_value.binary != nullptr);
+        JSON_ASSERT(m_data.m_type != value_t::object || m_data.m_value.object != nullptr);
+        JSON_ASSERT(m_data.m_type != value_t::array || m_data.m_value.array != nullptr);
+        JSON_ASSERT(m_data.m_type != value_t::string || m_data.m_value.string != nullptr);
+        JSON_ASSERT(m_data.m_type != value_t::binary || m_data.m_value.binary != nullptr);
 
 #if JSON_DIAGNOSTICS
         JSON_TRY
@@ -19871,11 +20006,11 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     void set_parents()
     {
 #if JSON_DIAGNOSTICS
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::array:
             {
-                for (auto& element : *m_value.array)
+                for (auto& element : *m_data.m_value.array)
                 {
                     element.m_parent = this;
                 }
@@ -19884,7 +20019,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
             case value_t::object:
             {
-                for (auto& element : *m_value.object)
+                for (auto& element : *m_data.m_value.object)
                 {
                     element.second.m_parent = this;
                 }
@@ -19925,7 +20060,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         {
             // see https://github.com/nlohmann/json/issues/2838
             JSON_ASSERT(type() == value_t::array);
-            if (JSON_HEDLEY_UNLIKELY(m_value.array->capacity() != old_capacity))
+            if (JSON_HEDLEY_UNLIKELY(m_data.m_value.array->capacity() != old_capacity))
             {
                 // capacity has changed: update all parents
                 set_parents();
@@ -19981,7 +20116,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @brief create an empty value with a given type
     /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
     basic_json(const value_t v)
-        : m_type(v), m_value(v)
+        : m_data(v)
     {
         assert_invariant();
     }
@@ -20055,12 +20190,12 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                 *this = nullptr;
                 break;
             case value_t::discarded:
-                m_type = value_t::discarded;
+                m_data.m_type = value_t::discarded;
                 break;
             default:            // LCOV_EXCL_LINE
                 JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE
         }
-        JSON_ASSERT(m_type == val.type());
+        JSON_ASSERT(m_data.m_type == val.type());
         set_parents();
         assert_invariant();
     }
@@ -20076,7 +20211,10 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         bool is_an_object = std::all_of(init.begin(), init.end(),
                                         [](const detail::json_ref<basic_json>& element_ref)
         {
-            return element_ref->is_array() && element_ref->size() == 2 && (*element_ref)[0].is_string();
+            // The cast is to ensure op[size_type] is called, bearing in mind size_type may not be int;
+            // (many string types can be constructed from 0 via its null-pointer guise, so we get a
+            // broken call to op[key_type], the wrong semantics and a 4804 warning on Windows)
+            return element_ref->is_array() && element_ref->size() == 2 && (*element_ref)[static_cast<size_type>(0)].is_string();
         });
 
         // adjust type if type deduction is not wanted
@@ -20098,22 +20236,22 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         if (is_an_object)
         {
             // the initializer list is a list of pairs -> create object
-            m_type = value_t::object;
-            m_value = value_t::object;
+            m_data.m_type = value_t::object;
+            m_data.m_value = value_t::object;
 
             for (auto& element_ref : init)
             {
                 auto element = element_ref.moved_or_copied();
-                m_value.object->emplace(
-                    std::move(*((*element.m_value.array)[0].m_value.string)),
-                    std::move((*element.m_value.array)[1]));
+                m_data.m_value.object->emplace(
+                    std::move(*((*element.m_data.m_value.array)[0].m_data.m_value.string)),
+                    std::move((*element.m_data.m_value.array)[1]));
             }
         }
         else
         {
             // the initializer list describes an array -> create array
-            m_type = value_t::array;
-            m_value.array = create<array_t>(init.begin(), init.end());
+            m_data.m_type = value_t::array;
+            m_data.m_value.array = create<array_t>(init.begin(), init.end());
         }
 
         set_parents();
@@ -20126,8 +20264,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     static basic_json binary(const typename binary_t::container_type& init)
     {
         auto res = basic_json();
-        res.m_type = value_t::binary;
-        res.m_value = init;
+        res.m_data.m_type = value_t::binary;
+        res.m_data.m_value = init;
         return res;
     }
 
@@ -20137,8 +20275,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     static basic_json binary(const typename binary_t::container_type& init, typename binary_t::subtype_type subtype)
     {
         auto res = basic_json();
-        res.m_type = value_t::binary;
-        res.m_value = binary_t(init, subtype);
+        res.m_data.m_type = value_t::binary;
+        res.m_data.m_value = binary_t(init, subtype);
         return res;
     }
 
@@ -20148,8 +20286,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     static basic_json binary(typename binary_t::container_type&& init)
     {
         auto res = basic_json();
-        res.m_type = value_t::binary;
-        res.m_value = std::move(init);
+        res.m_data.m_type = value_t::binary;
+        res.m_data.m_value = std::move(init);
         return res;
     }
 
@@ -20159,8 +20297,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     static basic_json binary(typename binary_t::container_type&& init, typename binary_t::subtype_type subtype)
     {
         auto res = basic_json();
-        res.m_type = value_t::binary;
-        res.m_value = binary_t(std::move(init), subtype);
+        res.m_data.m_type = value_t::binary;
+        res.m_data.m_value = binary_t(std::move(init), subtype);
         return res;
     }
 
@@ -20182,10 +20320,9 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @brief construct an array with count copies of given value
     /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
-    basic_json(size_type cnt, const basic_json& val)
-        : m_type(value_t::array)
+    basic_json(size_type cnt, const basic_json& val):
+        m_data{cnt, val}
     {
-        m_value.array = create<array_t>(cnt, val);
         set_parents();
         assert_invariant();
     }
@@ -20207,10 +20344,10 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         }
 
         // copy type from first iterator
-        m_type = first.m_object->m_type;
+        m_data.m_type = first.m_object->m_data.m_type;
 
         // check if iterator range is complete for primitive values
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::boolean:
             case value_t::number_float:
@@ -20235,55 +20372,55 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                 break;
         }
 
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::number_integer:
             {
-                m_value.number_integer = first.m_object->m_value.number_integer;
+                m_data.m_value.number_integer = first.m_object->m_data.m_value.number_integer;
                 break;
             }
 
             case value_t::number_unsigned:
             {
-                m_value.number_unsigned = first.m_object->m_value.number_unsigned;
+                m_data.m_value.number_unsigned = first.m_object->m_data.m_value.number_unsigned;
                 break;
             }
 
             case value_t::number_float:
             {
-                m_value.number_float = first.m_object->m_value.number_float;
+                m_data.m_value.number_float = first.m_object->m_data.m_value.number_float;
                 break;
             }
 
             case value_t::boolean:
             {
-                m_value.boolean = first.m_object->m_value.boolean;
+                m_data.m_value.boolean = first.m_object->m_data.m_value.boolean;
                 break;
             }
 
             case value_t::string:
             {
-                m_value = *first.m_object->m_value.string;
+                m_data.m_value = *first.m_object->m_data.m_value.string;
                 break;
             }
 
             case value_t::object:
             {
-                m_value.object = create<object_t>(first.m_it.object_iterator,
-                                                  last.m_it.object_iterator);
+                m_data.m_value.object = create<object_t>(first.m_it.object_iterator,
+                                        last.m_it.object_iterator);
                 break;
             }
 
             case value_t::array:
             {
-                m_value.array = create<array_t>(first.m_it.array_iterator,
-                                                last.m_it.array_iterator);
+                m_data.m_value.array = create<array_t>(first.m_it.array_iterator,
+                                                       last.m_it.array_iterator);
                 break;
             }
 
             case value_t::binary:
             {
-                m_value = *first.m_object->m_value.binary;
+                m_data.m_value = *first.m_object->m_data.m_value.binary;
                 break;
             }
 
@@ -20297,7 +20434,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         assert_invariant();
     }
 
-
     ///////////////////////////////////////
     // other constructors and destructor //
     ///////////////////////////////////////
@@ -20310,58 +20446,59 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @brief copy constructor
     /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
     basic_json(const basic_json& other)
-        : m_type(other.m_type)
+        : json_base_class_t(other)
     {
+        m_data.m_type = other.m_data.m_type;
         // check of passed value is valid
         other.assert_invariant();
 
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::object:
             {
-                m_value = *other.m_value.object;
+                m_data.m_value = *other.m_data.m_value.object;
                 break;
             }
 
             case value_t::array:
             {
-                m_value = *other.m_value.array;
+                m_data.m_value = *other.m_data.m_value.array;
                 break;
             }
 
             case value_t::string:
             {
-                m_value = *other.m_value.string;
+                m_data.m_value = *other.m_data.m_value.string;
                 break;
             }
 
             case value_t::boolean:
             {
-                m_value = other.m_value.boolean;
+                m_data.m_value = other.m_data.m_value.boolean;
                 break;
             }
 
             case value_t::number_integer:
             {
-                m_value = other.m_value.number_integer;
+                m_data.m_value = other.m_data.m_value.number_integer;
                 break;
             }
 
             case value_t::number_unsigned:
             {
-                m_value = other.m_value.number_unsigned;
+                m_data.m_value = other.m_data.m_value.number_unsigned;
                 break;
             }
 
             case value_t::number_float:
             {
-                m_value = other.m_value.number_float;
+                m_data.m_value = other.m_data.m_value.number_float;
                 break;
             }
 
             case value_t::binary:
             {
-                m_value = *other.m_value.binary;
+                m_data.m_value = *other.m_data.m_value.binary;
                 break;
             }
 
@@ -20378,15 +20515,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @brief move constructor
     /// @sa https://json.nlohmann.me/api/basic_json/basic_json/
     basic_json(basic_json&& other) noexcept
-        : m_type(std::move(other.m_type)),
-          m_value(std::move(other.m_value))
+        : json_base_class_t(std::forward<json_base_class_t>(other)),
+          m_data(std::move(other.m_data))
     {
         // check that passed value is valid
         other.assert_invariant(false);
 
         // invalidate payload
-        other.m_type = value_t::null;
-        other.m_value = {};
+        other.m_data.m_type = value_t::null;
+        other.m_data.m_value = {};
 
         set_parents();
         assert_invariant();
@@ -20398,15 +20535,17 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         std::is_nothrow_move_constructible<value_t>::value&&
         std::is_nothrow_move_assignable<value_t>::value&&
         std::is_nothrow_move_constructible<json_value>::value&&
-        std::is_nothrow_move_assignable<json_value>::value
+        std::is_nothrow_move_assignable<json_value>::value&&
+        std::is_nothrow_move_assignable<json_base_class_t>::value
     )
     {
         // check that passed value is valid
         other.assert_invariant();
 
         using std::swap;
-        swap(m_type, other.m_type);
-        swap(m_value, other.m_value);
+        swap(m_data.m_type, other.m_data.m_type);
+        swap(m_data.m_value, other.m_data.m_value);
+        json_base_class_t::operator=(std::move(other));
 
         set_parents();
         assert_invariant();
@@ -20418,7 +20557,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     ~basic_json() noexcept
     {
         assert_invariant(false);
-        m_value.destroy(m_type);
     }
 
     /// @}
@@ -20458,7 +20596,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @sa https://json.nlohmann.me/api/basic_json/type/
     constexpr value_t type() const noexcept
     {
-        return m_type;
+        return m_data.m_type;
     }
 
     /// @brief return whether type is primitive
@@ -20479,14 +20617,14 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @sa https://json.nlohmann.me/api/basic_json/is_null/
     constexpr bool is_null() const noexcept
     {
-        return m_type == value_t::null;
+        return m_data.m_type == value_t::null;
     }
 
     /// @brief return whether value is a boolean
     /// @sa https://json.nlohmann.me/api/basic_json/is_boolean/
     constexpr bool is_boolean() const noexcept
     {
-        return m_type == value_t::boolean;
+        return m_data.m_type == value_t::boolean;
     }
 
     /// @brief return whether value is a number
@@ -20500,63 +20638,63 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @sa https://json.nlohmann.me/api/basic_json/is_number_integer/
     constexpr bool is_number_integer() const noexcept
     {
-        return m_type == value_t::number_integer || m_type == value_t::number_unsigned;
+        return m_data.m_type == value_t::number_integer || m_data.m_type == value_t::number_unsigned;
     }
 
     /// @brief return whether value is an unsigned integer number
     /// @sa https://json.nlohmann.me/api/basic_json/is_number_unsigned/
     constexpr bool is_number_unsigned() const noexcept
     {
-        return m_type == value_t::number_unsigned;
+        return m_data.m_type == value_t::number_unsigned;
     }
 
     /// @brief return whether value is a floating-point number
     /// @sa https://json.nlohmann.me/api/basic_json/is_number_float/
     constexpr bool is_number_float() const noexcept
     {
-        return m_type == value_t::number_float;
+        return m_data.m_type == value_t::number_float;
     }
 
     /// @brief return whether value is an object
     /// @sa https://json.nlohmann.me/api/basic_json/is_object/
     constexpr bool is_object() const noexcept
     {
-        return m_type == value_t::object;
+        return m_data.m_type == value_t::object;
     }
 
     /// @brief return whether value is an array
     /// @sa https://json.nlohmann.me/api/basic_json/is_array/
     constexpr bool is_array() const noexcept
     {
-        return m_type == value_t::array;
+        return m_data.m_type == value_t::array;
     }
 
     /// @brief return whether value is a string
     /// @sa https://json.nlohmann.me/api/basic_json/is_string/
     constexpr bool is_string() const noexcept
     {
-        return m_type == value_t::string;
+        return m_data.m_type == value_t::string;
     }
 
     /// @brief return whether value is a binary array
     /// @sa https://json.nlohmann.me/api/basic_json/is_binary/
     constexpr bool is_binary() const noexcept
     {
-        return m_type == value_t::binary;
+        return m_data.m_type == value_t::binary;
     }
 
     /// @brief return whether value is discarded
     /// @sa https://json.nlohmann.me/api/basic_json/is_discarded/
     constexpr bool is_discarded() const noexcept
     {
-        return m_type == value_t::discarded;
+        return m_data.m_type == value_t::discarded;
     }
 
     /// @brief return the type of the JSON value (implicit)
     /// @sa https://json.nlohmann.me/api/basic_json/operator_value_t/
     constexpr operator value_t() const noexcept
     {
-        return m_type;
+        return m_data.m_type;
     }
 
     /// @}
@@ -20571,7 +20709,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     {
         if (JSON_HEDLEY_LIKELY(is_boolean()))
         {
-            return m_value.boolean;
+            return m_data.m_value.boolean;
         }
 
         JSON_THROW(type_error::create(302, detail::concat("type must be boolean, but is ", type_name()), this));
@@ -20580,97 +20718,97 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// get a pointer to the value (object)
     object_t* get_impl_ptr(object_t* /*unused*/) noexcept
     {
-        return is_object() ? m_value.object : nullptr;
+        return is_object() ? m_data.m_value.object : nullptr;
     }
 
     /// get a pointer to the value (object)
     constexpr const object_t* get_impl_ptr(const object_t* /*unused*/) const noexcept
     {
-        return is_object() ? m_value.object : nullptr;
+        return is_object() ? m_data.m_value.object : nullptr;
     }
 
     /// get a pointer to the value (array)
     array_t* get_impl_ptr(array_t* /*unused*/) noexcept
     {
-        return is_array() ? m_value.array : nullptr;
+        return is_array() ? m_data.m_value.array : nullptr;
     }
 
     /// get a pointer to the value (array)
     constexpr const array_t* get_impl_ptr(const array_t* /*unused*/) const noexcept
     {
-        return is_array() ? m_value.array : nullptr;
+        return is_array() ? m_data.m_value.array : nullptr;
     }
 
     /// get a pointer to the value (string)
     string_t* get_impl_ptr(string_t* /*unused*/) noexcept
     {
-        return is_string() ? m_value.string : nullptr;
+        return is_string() ? m_data.m_value.string : nullptr;
     }
 
     /// get a pointer to the value (string)
     constexpr const string_t* get_impl_ptr(const string_t* /*unused*/) const noexcept
     {
-        return is_string() ? m_value.string : nullptr;
+        return is_string() ? m_data.m_value.string : nullptr;
     }
 
     /// get a pointer to the value (boolean)
     boolean_t* get_impl_ptr(boolean_t* /*unused*/) noexcept
     {
-        return is_boolean() ? &m_value.boolean : nullptr;
+        return is_boolean() ? &m_data.m_value.boolean : nullptr;
     }
 
     /// get a pointer to the value (boolean)
     constexpr const boolean_t* get_impl_ptr(const boolean_t* /*unused*/) const noexcept
     {
-        return is_boolean() ? &m_value.boolean : nullptr;
+        return is_boolean() ? &m_data.m_value.boolean : nullptr;
     }
 
     /// get a pointer to the value (integer number)
     number_integer_t* get_impl_ptr(number_integer_t* /*unused*/) noexcept
     {
-        return is_number_integer() ? &m_value.number_integer : nullptr;
+        return is_number_integer() ? &m_data.m_value.number_integer : nullptr;
     }
 
     /// get a pointer to the value (integer number)
     constexpr const number_integer_t* get_impl_ptr(const number_integer_t* /*unused*/) const noexcept
     {
-        return is_number_integer() ? &m_value.number_integer : nullptr;
+        return is_number_integer() ? &m_data.m_value.number_integer : nullptr;
     }
 
     /// get a pointer to the value (unsigned number)
     number_unsigned_t* get_impl_ptr(number_unsigned_t* /*unused*/) noexcept
     {
-        return is_number_unsigned() ? &m_value.number_unsigned : nullptr;
+        return is_number_unsigned() ? &m_data.m_value.number_unsigned : nullptr;
     }
 
     /// get a pointer to the value (unsigned number)
     constexpr const number_unsigned_t* get_impl_ptr(const number_unsigned_t* /*unused*/) const noexcept
     {
-        return is_number_unsigned() ? &m_value.number_unsigned : nullptr;
+        return is_number_unsigned() ? &m_data.m_value.number_unsigned : nullptr;
     }
 
     /// get a pointer to the value (floating-point number)
     number_float_t* get_impl_ptr(number_float_t* /*unused*/) noexcept
     {
-        return is_number_float() ? &m_value.number_float : nullptr;
+        return is_number_float() ? &m_data.m_value.number_float : nullptr;
     }
 
     /// get a pointer to the value (floating-point number)
     constexpr const number_float_t* get_impl_ptr(const number_float_t* /*unused*/) const noexcept
     {
-        return is_number_float() ? &m_value.number_float : nullptr;
+        return is_number_float() ? &m_data.m_value.number_float : nullptr;
     }
 
     /// get a pointer to the value (binary)
     binary_t* get_impl_ptr(binary_t* /*unused*/) noexcept
     {
-        return is_binary() ? m_value.binary : nullptr;
+        return is_binary() ? m_data.m_value.binary : nullptr;
     }
 
     /// get a pointer to the value (binary)
     constexpr const binary_t* get_impl_ptr(const binary_t* /*unused*/) const noexcept
     {
-        return is_binary() ? m_value.binary : nullptr;
+        return is_binary() ? m_data.m_value.binary : nullptr;
     }
 
     /*!
@@ -21053,7 +21191,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 #if defined(JSON_HAS_CPP_17) && (defined(__GNUC__) || (defined(_MSC_VER) && _MSC_VER >= 1910 && _MSC_VER <= 1914))
                                                 detail::negation<std::is_same<ValueType, std::string_view>>,
 #endif
-#if defined(JSON_HAS_CPP_17)
+#if defined(JSON_HAS_CPP_17) && JSON_HAS_STATIC_RTTI
                                                 detail::negation<std::is_same<ValueType, std::any>>,
 #endif
                                                 detail::is_detected_lazy<detail::get_template_function, const basic_json_t&, ValueType>
@@ -21090,7 +21228,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @}
 
-
     ////////////////////
     // element access //
     ////////////////////
@@ -21108,7 +21245,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         {
             JSON_TRY
             {
-                return set_parent(m_value.array->at(idx));
+                return set_parent(m_data.m_value.array->at(idx));
             }
             JSON_CATCH (std::out_of_range&)
             {
@@ -21131,7 +21268,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         {
             JSON_TRY
             {
-                return m_value.array->at(idx);
+                return m_data.m_value.array->at(idx);
             }
             JSON_CATCH (std::out_of_range&)
             {
@@ -21155,8 +21292,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
         }
 
-        auto it = m_value.object->find(key);
-        if (it == m_value.object->end())
+        auto it = m_data.m_value.object->find(key);
+        if (it == m_data.m_value.object->end())
         {
             JSON_THROW(out_of_range::create(403, detail::concat("key '", key, "' not found"), this));
         }
@@ -21175,8 +21312,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
         }
 
-        auto it = m_value.object->find(std::forward<KeyType>(key));
-        if (it == m_value.object->end())
+        auto it = m_data.m_value.object->find(std::forward<KeyType>(key));
+        if (it == m_data.m_value.object->end())
         {
             JSON_THROW(out_of_range::create(403, detail::concat("key '", string_t(std::forward<KeyType>(key)), "' not found"), this));
         }
@@ -21193,8 +21330,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
         }
 
-        auto it = m_value.object->find(key);
-        if (it == m_value.object->end())
+        auto it = m_data.m_value.object->find(key);
+        if (it == m_data.m_value.object->end())
         {
             JSON_THROW(out_of_range::create(403, detail::concat("key '", key, "' not found"), this));
         }
@@ -21213,8 +21350,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this));
         }
 
-        auto it = m_value.object->find(std::forward<KeyType>(key));
-        if (it == m_value.object->end())
+        auto it = m_data.m_value.object->find(std::forward<KeyType>(key));
+        if (it == m_data.m_value.object->end())
         {
             JSON_THROW(out_of_range::create(403, detail::concat("key '", string_t(std::forward<KeyType>(key)), "' not found"), this));
         }
@@ -21228,8 +21365,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // implicitly convert null value to an empty array
         if (is_null())
         {
-            m_type = value_t::array;
-            m_value.array = create<array_t>();
+            m_data.m_type = value_t::array;
+            m_data.m_value.array = create<array_t>();
             assert_invariant();
         }
 
@@ -21237,17 +21374,17 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         if (JSON_HEDLEY_LIKELY(is_array()))
         {
             // fill up array with null values if given idx is outside range
-            if (idx >= m_value.array->size())
+            if (idx >= m_data.m_value.array->size())
             {
 #if JSON_DIAGNOSTICS
                 // remember array size & capacity before resizing
-                const auto old_size = m_value.array->size();
-                const auto old_capacity = m_value.array->capacity();
+                const auto old_size = m_data.m_value.array->size();
+                const auto old_capacity = m_data.m_value.array->capacity();
 #endif
-                m_value.array->resize(idx + 1);
+                m_data.m_value.array->resize(idx + 1);
 
 #if JSON_DIAGNOSTICS
-                if (JSON_HEDLEY_UNLIKELY(m_value.array->capacity() != old_capacity))
+                if (JSON_HEDLEY_UNLIKELY(m_data.m_value.array->capacity() != old_capacity))
                 {
                     // capacity has changed: update all parents
                     set_parents();
@@ -21261,7 +21398,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                 assert_invariant();
             }
 
-            return m_value.array->operator[](idx);
+            return m_data.m_value.array->operator[](idx);
         }
 
         JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a numeric argument with ", type_name()), this));
@@ -21274,7 +21411,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // const operator[] only works for arrays
         if (JSON_HEDLEY_LIKELY(is_array()))
         {
-            return m_value.array->operator[](idx);
+            return m_data.m_value.array->operator[](idx);
         }
 
         JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a numeric argument with ", type_name()), this));
@@ -21287,15 +21424,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // implicitly convert null value to an empty object
         if (is_null())
         {
-            m_type = value_t::object;
-            m_value.object = create<object_t>();
+            m_data.m_type = value_t::object;
+            m_data.m_value.object = create<object_t>();
             assert_invariant();
         }
 
         // operator[] only works for objects
         if (JSON_HEDLEY_LIKELY(is_object()))
         {
-            auto result = m_value.object->emplace(std::move(key), nullptr);
+            auto result = m_data.m_value.object->emplace(std::move(key), nullptr);
             return set_parent(result.first->second);
         }
 
@@ -21309,8 +21446,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // const operator[] only works for objects
         if (JSON_HEDLEY_LIKELY(is_object()))
         {
-            auto it = m_value.object->find(key);
-            JSON_ASSERT(it != m_value.object->end());
+            auto it = m_data.m_value.object->find(key);
+            JSON_ASSERT(it != m_data.m_value.object->end());
             return it->second;
         }
 
@@ -21340,15 +21477,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // implicitly convert null value to an empty object
         if (is_null())
         {
-            m_type = value_t::object;
-            m_value.object = create<object_t>();
+            m_data.m_type = value_t::object;
+            m_data.m_value.object = create<object_t>();
             assert_invariant();
         }
 
         // operator[] only works for objects
         if (JSON_HEDLEY_LIKELY(is_object()))
         {
-            auto result = m_value.object->emplace(std::forward<KeyType>(key), nullptr);
+            auto result = m_data.m_value.object->emplace(std::forward<KeyType>(key), nullptr);
             return set_parent(result.first->second);
         }
 
@@ -21364,8 +21501,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // const operator[] only works for objects
         if (JSON_HEDLEY_LIKELY(is_object()))
         {
-            auto it = m_value.object->find(std::forward<KeyType>(key));
-            JSON_ASSERT(it != m_value.object->end());
+            auto it = m_data.m_value.object->find(std::forward<KeyType>(key));
+            JSON_ASSERT(it != m_data.m_value.object->end());
             return it->second;
         }
 
@@ -21602,7 +21739,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
         IteratorType result = end();
 
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::boolean:
             case value_t::number_float:
@@ -21619,32 +21756,32 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                 if (is_string())
                 {
                     AllocatorType<string_t> alloc;
-                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_value.string);
-                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_value.string, 1);
-                    m_value.string = nullptr;
+                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_data.m_value.string);
+                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_data.m_value.string, 1);
+                    m_data.m_value.string = nullptr;
                 }
                 else if (is_binary())
                 {
                     AllocatorType<binary_t> alloc;
-                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_value.binary);
-                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_value.binary, 1);
-                    m_value.binary = nullptr;
+                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_data.m_value.binary);
+                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_data.m_value.binary, 1);
+                    m_data.m_value.binary = nullptr;
                 }
 
-                m_type = value_t::null;
+                m_data.m_type = value_t::null;
                 assert_invariant();
                 break;
             }
 
             case value_t::object:
             {
-                result.m_it.object_iterator = m_value.object->erase(pos.m_it.object_iterator);
+                result.m_it.object_iterator = m_data.m_value.object->erase(pos.m_it.object_iterator);
                 break;
             }
 
             case value_t::array:
             {
-                result.m_it.array_iterator = m_value.array->erase(pos.m_it.array_iterator);
+                result.m_it.array_iterator = m_data.m_value.array->erase(pos.m_it.array_iterator);
                 break;
             }
 
@@ -21672,7 +21809,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
         IteratorType result = end();
 
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::boolean:
             case value_t::number_float:
@@ -21690,33 +21827,33 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                 if (is_string())
                 {
                     AllocatorType<string_t> alloc;
-                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_value.string);
-                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_value.string, 1);
-                    m_value.string = nullptr;
+                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_data.m_value.string);
+                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_data.m_value.string, 1);
+                    m_data.m_value.string = nullptr;
                 }
                 else if (is_binary())
                 {
                     AllocatorType<binary_t> alloc;
-                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_value.binary);
-                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_value.binary, 1);
-                    m_value.binary = nullptr;
+                    std::allocator_traits<decltype(alloc)>::destroy(alloc, m_data.m_value.binary);
+                    std::allocator_traits<decltype(alloc)>::deallocate(alloc, m_data.m_value.binary, 1);
+                    m_data.m_value.binary = nullptr;
                 }
 
-                m_type = value_t::null;
+                m_data.m_type = value_t::null;
                 assert_invariant();
                 break;
             }
 
             case value_t::object:
             {
-                result.m_it.object_iterator = m_value.object->erase(first.m_it.object_iterator,
+                result.m_it.object_iterator = m_data.m_value.object->erase(first.m_it.object_iterator,
                                               last.m_it.object_iterator);
                 break;
             }
 
             case value_t::array:
             {
-                result.m_it.array_iterator = m_value.array->erase(first.m_it.array_iterator,
+                result.m_it.array_iterator = m_data.m_value.array->erase(first.m_it.array_iterator,
                                              last.m_it.array_iterator);
                 break;
             }
@@ -21741,7 +21878,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             JSON_THROW(type_error::create(307, detail::concat("cannot use erase() with ", type_name()), this));
         }
 
-        return m_value.object->erase(std::forward<KeyType>(key));
+        return m_data.m_value.object->erase(std::forward<KeyType>(key));
     }
 
     template < typename KeyType, detail::enable_if_t <
@@ -21754,10 +21891,10 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             JSON_THROW(type_error::create(307, detail::concat("cannot use erase() with ", type_name()), this));
         }
 
-        const auto it = m_value.object->find(std::forward<KeyType>(key));
-        if (it != m_value.object->end())
+        const auto it = m_data.m_value.object->find(std::forward<KeyType>(key));
+        if (it != m_data.m_value.object->end())
         {
-            m_value.object->erase(it);
+            m_data.m_value.object->erase(it);
             return 1;
         }
         return 0;
@@ -21795,7 +21932,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                 JSON_THROW(out_of_range::create(401, detail::concat("array index ", std::to_string(idx), " is out of range"), this));
             }
 
-            m_value.array->erase(m_value.array->begin() + static_cast<difference_type>(idx));
+            m_data.m_value.array->erase(m_data.m_value.array->begin() + static_cast<difference_type>(idx));
         }
         else
         {
@@ -21805,7 +21942,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @}
 
-
     ////////////
     // lookup //
     ////////////
@@ -21821,7 +21957,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
         if (is_object())
         {
-            result.m_it.object_iterator = m_value.object->find(key);
+            result.m_it.object_iterator = m_data.m_value.object->find(key);
         }
 
         return result;
@@ -21835,7 +21971,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
         if (is_object())
         {
-            result.m_it.object_iterator = m_value.object->find(key);
+            result.m_it.object_iterator = m_data.m_value.object->find(key);
         }
 
         return result;
@@ -21851,7 +21987,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
         if (is_object())
         {
-            result.m_it.object_iterator = m_value.object->find(std::forward<KeyType>(key));
+            result.m_it.object_iterator = m_data.m_value.object->find(std::forward<KeyType>(key));
         }
 
         return result;
@@ -21867,7 +22003,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
         if (is_object())
         {
-            result.m_it.object_iterator = m_value.object->find(std::forward<KeyType>(key));
+            result.m_it.object_iterator = m_data.m_value.object->find(std::forward<KeyType>(key));
         }
 
         return result;
@@ -21878,7 +22014,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     size_type count(const typename object_t::key_type& key) const
     {
         // return 0 for all nonobject types
-        return is_object() ? m_value.object->count(key) : 0;
+        return is_object() ? m_data.m_value.object->count(key) : 0;
     }
 
     /// @brief returns the number of occurrences of a key in a JSON object
@@ -21888,14 +22024,14 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     size_type count(KeyType && key) const
     {
         // return 0 for all nonobject types
-        return is_object() ? m_value.object->count(std::forward<KeyType>(key)) : 0;
+        return is_object() ? m_data.m_value.object->count(std::forward<KeyType>(key)) : 0;
     }
 
     /// @brief check the existence of an element in a JSON object
     /// @sa https://json.nlohmann.me/api/basic_json/contains/
     bool contains(const typename object_t::key_type& key) const
     {
-        return is_object() && m_value.object->find(key) != m_value.object->end();
+        return is_object() && m_data.m_value.object->find(key) != m_data.m_value.object->end();
     }
 
     /// @brief check the existence of an element in a JSON object
@@ -21904,7 +22040,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                  detail::is_usable_as_basic_json_key_type<basic_json_t, KeyType>::value, int> = 0>
     bool contains(KeyType && key) const
     {
-        return is_object() && m_value.object->find(std::forward<KeyType>(key)) != m_value.object->end();
+        return is_object() && m_data.m_value.object->find(std::forward<KeyType>(key)) != m_data.m_value.object->end();
     }
 
     /// @brief check the existence of an element in a JSON object given a JSON pointer
@@ -21923,7 +22059,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @}
 
-
     ///////////////
     // iterators //
     ///////////////
@@ -22062,7 +22197,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @}
 
-
     //////////////
     // capacity //
     //////////////
@@ -22074,7 +22208,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @sa https://json.nlohmann.me/api/basic_json/empty/
     bool empty() const noexcept
     {
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::null:
             {
@@ -22085,13 +22219,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             case value_t::array:
             {
                 // delegate call to array_t::empty()
-                return m_value.array->empty();
+                return m_data.m_value.array->empty();
             }
 
             case value_t::object:
             {
                 // delegate call to object_t::empty()
-                return m_value.object->empty();
+                return m_data.m_value.object->empty();
             }
 
             case value_t::string:
@@ -22113,7 +22247,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @sa https://json.nlohmann.me/api/basic_json/size/
     size_type size() const noexcept
     {
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::null:
             {
@@ -22124,13 +22258,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             case value_t::array:
             {
                 // delegate call to array_t::size()
-                return m_value.array->size();
+                return m_data.m_value.array->size();
             }
 
             case value_t::object:
             {
                 // delegate call to object_t::size()
-                return m_value.object->size();
+                return m_data.m_value.object->size();
             }
 
             case value_t::string:
@@ -22152,18 +22286,18 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @sa https://json.nlohmann.me/api/basic_json/max_size/
     size_type max_size() const noexcept
     {
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::array:
             {
                 // delegate call to array_t::max_size()
-                return m_value.array->max_size();
+                return m_data.m_value.array->max_size();
             }
 
             case value_t::object:
             {
                 // delegate call to object_t::max_size()
-                return m_value.object->max_size();
+                return m_data.m_value.object->max_size();
             }
 
             case value_t::null:
@@ -22184,7 +22318,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @}
 
-
     ///////////////
     // modifiers //
     ///////////////
@@ -22196,53 +22329,53 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     /// @sa https://json.nlohmann.me/api/basic_json/clear/
     void clear() noexcept
     {
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::number_integer:
             {
-                m_value.number_integer = 0;
+                m_data.m_value.number_integer = 0;
                 break;
             }
 
             case value_t::number_unsigned:
             {
-                m_value.number_unsigned = 0;
+                m_data.m_value.number_unsigned = 0;
                 break;
             }
 
             case value_t::number_float:
             {
-                m_value.number_float = 0.0;
+                m_data.m_value.number_float = 0.0;
                 break;
             }
 
             case value_t::boolean:
             {
-                m_value.boolean = false;
+                m_data.m_value.boolean = false;
                 break;
             }
 
             case value_t::string:
             {
-                m_value.string->clear();
+                m_data.m_value.string->clear();
                 break;
             }
 
             case value_t::binary:
             {
-                m_value.binary->clear();
+                m_data.m_value.binary->clear();
                 break;
             }
 
             case value_t::array:
             {
-                m_value.array->clear();
+                m_data.m_value.array->clear();
                 break;
             }
 
             case value_t::object:
             {
-                m_value.object->clear();
+                m_data.m_value.object->clear();
                 break;
             }
 
@@ -22266,15 +22399,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // transform null object into an array
         if (is_null())
         {
-            m_type = value_t::array;
-            m_value = value_t::array;
+            m_data.m_type = value_t::array;
+            m_data.m_value = value_t::array;
             assert_invariant();
         }
 
         // add element to array (move semantics)
-        const auto old_capacity = m_value.array->capacity();
-        m_value.array->push_back(std::move(val));
-        set_parent(m_value.array->back(), old_capacity);
+        const auto old_capacity = m_data.m_value.array->capacity();
+        m_data.m_value.array->push_back(std::move(val));
+        set_parent(m_data.m_value.array->back(), old_capacity);
         // if val is moved from, basic_json move constructor marks it null, so we do not call the destructor
     }
 
@@ -22299,15 +22432,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // transform null object into an array
         if (is_null())
         {
-            m_type = value_t::array;
-            m_value = value_t::array;
+            m_data.m_type = value_t::array;
+            m_data.m_value = value_t::array;
             assert_invariant();
         }
 
         // add element to array
-        const auto old_capacity = m_value.array->capacity();
-        m_value.array->push_back(val);
-        set_parent(m_value.array->back(), old_capacity);
+        const auto old_capacity = m_data.m_value.array->capacity();
+        m_data.m_value.array->push_back(val);
+        set_parent(m_data.m_value.array->back(), old_capacity);
     }
 
     /// @brief add an object to an array
@@ -22331,13 +22464,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // transform null object into an object
         if (is_null())
         {
-            m_type = value_t::object;
-            m_value = value_t::object;
+            m_data.m_type = value_t::object;
+            m_data.m_value = value_t::object;
             assert_invariant();
         }
 
         // add element to object
-        auto res = m_value.object->insert(val);
+        auto res = m_data.m_value.object->insert(val);
         set_parent(res.first->second);
     }
 
@@ -22387,15 +22520,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // transform null object into an array
         if (is_null())
         {
-            m_type = value_t::array;
-            m_value = value_t::array;
+            m_data.m_type = value_t::array;
+            m_data.m_value = value_t::array;
             assert_invariant();
         }
 
         // add element to array (perfect forwarding)
-        const auto old_capacity = m_value.array->capacity();
-        m_value.array->emplace_back(std::forward<Args>(args)...);
-        return set_parent(m_value.array->back(), old_capacity);
+        const auto old_capacity = m_data.m_value.array->capacity();
+        m_data.m_value.array->emplace_back(std::forward<Args>(args)...);
+        return set_parent(m_data.m_value.array->back(), old_capacity);
     }
 
     /// @brief add an object to an object if key does not exist
@@ -22412,13 +22545,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // transform null object into an object
         if (is_null())
         {
-            m_type = value_t::object;
-            m_value = value_t::object;
+            m_data.m_type = value_t::object;
+            m_data.m_value = value_t::object;
             assert_invariant();
         }
 
         // add element to array (perfect forwarding)
-        auto res = m_value.object->emplace(std::forward<Args>(args)...);
+        auto res = m_data.m_value.object->emplace(std::forward<Args>(args)...);
         set_parent(res.first->second);
 
         // create result iterator and set iterator to the result of emplace
@@ -22436,14 +22569,14 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     iterator insert_iterator(const_iterator pos, Args&& ... args)
     {
         iterator result(this);
-        JSON_ASSERT(m_value.array != nullptr);
+        JSON_ASSERT(m_data.m_value.array != nullptr);
 
-        auto insert_pos = std::distance(m_value.array->begin(), pos.m_it.array_iterator);
-        m_value.array->insert(pos.m_it.array_iterator, std::forward<Args>(args)...);
-        result.m_it.array_iterator = m_value.array->begin() + insert_pos;
+        auto insert_pos = std::distance(m_data.m_value.array->begin(), pos.m_it.array_iterator);
+        m_data.m_value.array->insert(pos.m_it.array_iterator, std::forward<Args>(args)...);
+        result.m_it.array_iterator = m_data.m_value.array->begin() + insert_pos;
 
         // This could have been written as:
-        // result.m_it.array_iterator = m_value.array->insert(pos.m_it.array_iterator, cnt, val);
+        // result.m_it.array_iterator = m_data.m_value.array->insert(pos.m_it.array_iterator, cnt, val);
         // but the return value of insert is missing in GCC 4.8, so it is written this way instead.
 
         set_parents();
@@ -22570,7 +22703,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             JSON_THROW(invalid_iterator::create(202, "iterators first and last must point to objects", this));
         }
 
-        m_value.object->insert(first.m_it.object_iterator, last.m_it.object_iterator);
+        m_data.m_value.object->insert(first.m_it.object_iterator, last.m_it.object_iterator);
     }
 
     /// @brief updates a JSON object from another object, overwriting existing keys
@@ -22587,8 +22720,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         // implicitly convert null value to an empty object
         if (is_null())
         {
-            m_type = value_t::object;
-            m_value.object = create<object_t>();
+            m_data.m_type = value_t::object;
+            m_data.m_value.object = create<object_t>();
             assert_invariant();
         }
 
@@ -22613,16 +22746,16 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         {
             if (merge_objects && it.value().is_object())
             {
-                auto it2 = m_value.object->find(it.key());
-                if (it2 != m_value.object->end())
+                auto it2 = m_data.m_value.object->find(it.key());
+                if (it2 != m_data.m_value.object->end())
                 {
                     it2->second.update(it.value(), true);
                     continue;
                 }
             }
-            m_value.object->operator[](it.key()) = it.value();
+            m_data.m_value.object->operator[](it.key()) = it.value();
 #if JSON_DIAGNOSTICS
-            m_value.object->operator[](it.key()).m_parent = this;
+            m_data.m_value.object->operator[](it.key()).m_parent = this;
 #endif
         }
     }
@@ -22632,12 +22765,12 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     void swap(reference other) noexcept (
         std::is_nothrow_move_constructible<value_t>::value&&
         std::is_nothrow_move_assignable<value_t>::value&&
-        std::is_nothrow_move_constructible<json_value>::value&&
+        std::is_nothrow_move_constructible<json_value>::value&& // NOLINT(cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
         std::is_nothrow_move_assignable<json_value>::value
     )
     {
-        std::swap(m_type, other.m_type);
-        std::swap(m_value, other.m_value);
+        std::swap(m_data.m_type, other.m_data.m_type);
+        std::swap(m_data.m_value, other.m_data.m_value);
 
         set_parents();
         other.set_parents();
@@ -22649,7 +22782,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     friend void swap(reference left, reference right) noexcept (
         std::is_nothrow_move_constructible<value_t>::value&&
         std::is_nothrow_move_assignable<value_t>::value&&
-        std::is_nothrow_move_constructible<json_value>::value&&
+        std::is_nothrow_move_constructible<json_value>::value&& // NOLINT(cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
         std::is_nothrow_move_assignable<json_value>::value
     )
     {
@@ -22658,13 +22791,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @brief exchanges the values
     /// @sa https://json.nlohmann.me/api/basic_json/swap/
-    void swap(array_t& other) // NOLINT(bugprone-exception-escape)
+    void swap(array_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
     {
         // swap only works for arrays
         if (JSON_HEDLEY_LIKELY(is_array()))
         {
             using std::swap;
-            swap(*(m_value.array), other);
+            swap(*(m_data.m_value.array), other);
         }
         else
         {
@@ -22674,13 +22807,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @brief exchanges the values
     /// @sa https://json.nlohmann.me/api/basic_json/swap/
-    void swap(object_t& other) // NOLINT(bugprone-exception-escape)
+    void swap(object_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
     {
         // swap only works for objects
         if (JSON_HEDLEY_LIKELY(is_object()))
         {
             using std::swap;
-            swap(*(m_value.object), other);
+            swap(*(m_data.m_value.object), other);
         }
         else
         {
@@ -22690,13 +22823,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @brief exchanges the values
     /// @sa https://json.nlohmann.me/api/basic_json/swap/
-    void swap(string_t& other) // NOLINT(bugprone-exception-escape)
+    void swap(string_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
     {
         // swap only works for strings
         if (JSON_HEDLEY_LIKELY(is_string()))
         {
             using std::swap;
-            swap(*(m_value.string), other);
+            swap(*(m_data.m_value.string), other);
         }
         else
         {
@@ -22706,13 +22839,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 
     /// @brief exchanges the values
     /// @sa https://json.nlohmann.me/api/basic_json/swap/
-    void swap(binary_t& other) // NOLINT(bugprone-exception-escape)
+    void swap(binary_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
     {
         // swap only works for strings
         if (JSON_HEDLEY_LIKELY(is_binary()))
         {
             using std::swap;
-            swap(*(m_value.binary), other);
+            swap(*(m_data.m_value.binary), other);
         }
         else
         {
@@ -22728,7 +22861,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         if (JSON_HEDLEY_LIKELY(is_binary()))
         {
             using std::swap;
-            swap(*(m_value.binary), other);
+            swap(*(m_data.m_value.binary), other);
         }
         else
         {
@@ -22756,31 +22889,31 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         switch (lhs_type)                                                                                \
         {                                                                                                \
             case value_t::array:                                                                         \
-                return (*lhs.m_value.array) op (*rhs.m_value.array);                                     \
+                return (*lhs.m_data.m_value.array) op (*rhs.m_data.m_value.array);                                     \
                 \
             case value_t::object:                                                                        \
-                return (*lhs.m_value.object) op (*rhs.m_value.object);                                   \
+                return (*lhs.m_data.m_value.object) op (*rhs.m_data.m_value.object);                                   \
                 \
             case value_t::null:                                                                          \
                 return (null_result);                                                                    \
                 \
             case value_t::string:                                                                        \
-                return (*lhs.m_value.string) op (*rhs.m_value.string);                                   \
+                return (*lhs.m_data.m_value.string) op (*rhs.m_data.m_value.string);                                   \
                 \
             case value_t::boolean:                                                                       \
-                return (lhs.m_value.boolean) op (rhs.m_value.boolean);                                   \
+                return (lhs.m_data.m_value.boolean) op (rhs.m_data.m_value.boolean);                                   \
                 \
             case value_t::number_integer:                                                                \
-                return (lhs.m_value.number_integer) op (rhs.m_value.number_integer);                     \
+                return (lhs.m_data.m_value.number_integer) op (rhs.m_data.m_value.number_integer);                     \
                 \
             case value_t::number_unsigned:                                                               \
-                return (lhs.m_value.number_unsigned) op (rhs.m_value.number_unsigned);                   \
+                return (lhs.m_data.m_value.number_unsigned) op (rhs.m_data.m_value.number_unsigned);                   \
                 \
             case value_t::number_float:                                                                  \
-                return (lhs.m_value.number_float) op (rhs.m_value.number_float);                         \
+                return (lhs.m_data.m_value.number_float) op (rhs.m_data.m_value.number_float);                         \
                 \
             case value_t::binary:                                                                        \
-                return (*lhs.m_value.binary) op (*rhs.m_value.binary);                                   \
+                return (*lhs.m_data.m_value.binary) op (*rhs.m_data.m_value.binary);                                   \
                 \
             case value_t::discarded:                                                                     \
             default:                                                                                     \
@@ -22789,27 +22922,27 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     }                                                                                                    \
     else if (lhs_type == value_t::number_integer && rhs_type == value_t::number_float)                   \
     {                                                                                                    \
-        return static_cast<number_float_t>(lhs.m_value.number_integer) op rhs.m_value.number_float;      \
+        return static_cast<number_float_t>(lhs.m_data.m_value.number_integer) op rhs.m_data.m_value.number_float;      \
     }                                                                                                    \
     else if (lhs_type == value_t::number_float && rhs_type == value_t::number_integer)                   \
     {                                                                                                    \
-        return lhs.m_value.number_float op static_cast<number_float_t>(rhs.m_value.number_integer);      \
+        return lhs.m_data.m_value.number_float op static_cast<number_float_t>(rhs.m_data.m_value.number_integer);      \
     }                                                                                                    \
     else if (lhs_type == value_t::number_unsigned && rhs_type == value_t::number_float)                  \
     {                                                                                                    \
-        return static_cast<number_float_t>(lhs.m_value.number_unsigned) op rhs.m_value.number_float;     \
+        return static_cast<number_float_t>(lhs.m_data.m_value.number_unsigned) op rhs.m_data.m_value.number_float;     \
     }                                                                                                    \
     else if (lhs_type == value_t::number_float && rhs_type == value_t::number_unsigned)                  \
     {                                                                                                    \
-        return lhs.m_value.number_float op static_cast<number_float_t>(rhs.m_value.number_unsigned);     \
+        return lhs.m_data.m_value.number_float op static_cast<number_float_t>(rhs.m_data.m_value.number_unsigned);     \
     }                                                                                                    \
     else if (lhs_type == value_t::number_unsigned && rhs_type == value_t::number_integer)                \
     {                                                                                                    \
-        return static_cast<number_integer_t>(lhs.m_value.number_unsigned) op rhs.m_value.number_integer; \
+        return static_cast<number_integer_t>(lhs.m_data.m_value.number_unsigned) op rhs.m_data.m_value.number_integer; \
     }                                                                                                    \
     else if (lhs_type == value_t::number_integer && rhs_type == value_t::number_unsigned)                \
     {                                                                                                    \
-        return lhs.m_value.number_integer op static_cast<number_integer_t>(rhs.m_value.number_unsigned); \
+        return lhs.m_data.m_value.number_integer op static_cast<number_integer_t>(rhs.m_data.m_value.number_unsigned); \
     }                                                                                                    \
     else if(compares_unordered(lhs, rhs))\
     {\
@@ -22826,8 +22959,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     // an operation is computed as an odd number of inverses of others
     static bool compares_unordered(const_reference lhs, const_reference rhs, bool inverse = false) noexcept
     {
-        if ((lhs.is_number_float() && std::isnan(lhs.m_value.number_float) && rhs.is_number())
-                || (rhs.is_number_float() && std::isnan(rhs.m_value.number_float) && lhs.is_number()))
+        if ((lhs.is_number_float() && std::isnan(lhs.m_data.m_value.number_float) && rhs.is_number())
+                || (rhs.is_number_float() && std::isnan(rhs.m_data.m_value.number_float) && lhs.is_number()))
         {
             return true;
         }
@@ -23171,7 +23304,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
 #endif  // JSON_NO_IO
     /// @}
 
-
     /////////////////////
     // deserialization //
     /////////////////////
@@ -23328,7 +23460,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
     JSON_HEDLEY_RETURNS_NON_NULL
     const char* type_name() const noexcept
     {
-        switch (m_type)
+        switch (m_data.m_type)
         {
             case value_t::null:
                 return "null";
@@ -23352,17 +23484,43 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         }
     }
 
-
   JSON_PRIVATE_UNLESS_TESTED:
     //////////////////////
     // member variables //
     //////////////////////
 
-    /// the type of the current element
-    value_t m_type = value_t::null;
+    struct data
+    {
+        /// the type of the current element
+        value_t m_type = value_t::null;
 
-    /// the value of the current element
-    json_value m_value = {};
+        /// the value of the current element
+        json_value m_value = {};
+
+        data(const value_t v)
+            : m_type(v), m_value(v)
+        {
+        }
+
+        data(size_type cnt, const basic_json& val)
+            : m_type(value_t::array)
+        {
+            m_value.array = create<array_t>(cnt, val);
+        }
+
+        data() noexcept = default;
+        data(data&&) noexcept = default;
+        data(const data&) noexcept = delete;
+        data& operator=(data&&) noexcept = delete;
+        data& operator=(const data&) noexcept = delete;
+
+        ~data() noexcept
+        {
+            m_value.destroy(m_type);
+        }
+    };
+
+    data m_data = {};
 
 #if JSON_DIAGNOSTICS
     /// a pointer to a parent value (for debugging purposes)
@@ -23543,7 +23701,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         return from_cbor(ptr, ptr + len, strict, allow_exceptions, tag_handler);
     }
 
-
     JSON_HEDLEY_WARN_UNUSED_RESULT
     JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_cbor(ptr, ptr + len))
     static basic_json from_cbor(detail::span_input_adapter&& i,
@@ -23667,7 +23824,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         return res ? result : basic_json(value_t::discarded);
     }
 
-
     /// @brief create a JSON value from an input in BJData format
     /// @sa https://json.nlohmann.me/api/basic_json/from_bjdata/
     template<typename InputType>
@@ -23890,7 +24046,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             }
 
             // make sure the top element of the pointer exists
-            json_pointer top_pointer = ptr.top();
+            json_pointer const top_pointer = ptr.top();
             if (top_pointer != ptr)
             {
                 result.at(top_pointer);
@@ -23902,7 +24058,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
             // parent must exist when performing patch add per RFC6902 specs
             basic_json& parent = result.at(ptr);
 
-            switch (parent.m_type)
+            switch (parent.m_data.m_type)
             {
                 case value_t::null:
                 case value_t::object:
@@ -23948,7 +24104,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
         };
 
         // wrapper for "remove" operation; remove value at ptr
-        const auto operation_remove = [this, &result](json_pointer & ptr)
+        const auto operation_remove = [this, & result](json_pointer & ptr)
         {
             // get reference to parent of JSON pointer ptr
             const auto last_path = ptr.back();
@@ -23991,13 +24147,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                                           bool string_type) -> basic_json &
             {
                 // find value
-                auto it = val.m_value.object->find(member);
+                auto it = val.m_data.m_value.object->find(member);
 
                 // context-sensitive error message
-                const auto error_msg = (op == "op") ? "operation" : detail::concat("operation '", op, '\'');
+                const auto error_msg = (op == "op") ? "operation" : detail::concat("operation '", op, '\''); // NOLINT(bugprone-unused-local-non-trivial-variable)
 
                 // check if desired value is present
-                if (JSON_HEDLEY_UNLIKELY(it == val.m_value.object->end()))
+                if (JSON_HEDLEY_UNLIKELY(it == val.m_data.m_value.object->end()))
                 {
                     // NOLINTNEXTLINE(performance-inefficient-string-concatenation)
                     JSON_THROW(parse_error::create(105, 0, detail::concat(error_msg, " must have member '", member, "'"), &val));
@@ -24052,7 +24208,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                     json_pointer from_ptr(from_path);
 
                     // the "from" location must exist - use at()
-                    basic_json v = result.at(from_ptr);
+                    basic_json const v = result.at(from_ptr);
 
                     // The move operation is functionally identical to a
                     // "remove" operation on the "from" location, followed
@@ -24069,7 +24225,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec
                     const json_pointer from_ptr(from_path);
 
                     // the "from" location must exist - use at()
-                    basic_json v = result.at(from_ptr);
+                    basic_json const v = result.at(from_ptr);
 
                     // The copy is functionally identical to an "add"
                     // operation at the target location using the value
@@ -24311,7 +24467,11 @@ inline namespace json_literals
 /// @brief user-defined string literal for JSON values
 /// @sa https://json.nlohmann.me/api/basic_json/operator_literal_json/
 JSON_HEDLEY_NON_NULL(1)
-inline nlohmann::json operator "" _json(const char* s, std::size_t n)
+#if !defined(JSON_HEDLEY_GCC_VERSION) || JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0)
+    inline nlohmann::json operator ""_json(const char* s, std::size_t n)
+#else
+    inline nlohmann::json operator "" _json(const char* s, std::size_t n)
+#endif
 {
     return nlohmann::json::parse(s, s + n);
 }
@@ -24319,7 +24479,11 @@ inline nlohmann::json operator "" _json(const char* s, std::size_t n)
 /// @brief user-defined string literal for JSON pointer
 /// @sa https://json.nlohmann.me/api/basic_json/operator_literal_json_pointer/
 JSON_HEDLEY_NON_NULL(1)
-inline nlohmann::json::json_pointer operator "" _json_pointer(const char* s, std::size_t n)
+#if !defined(JSON_HEDLEY_GCC_VERSION) || JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0)
+    inline nlohmann::json::json_pointer operator ""_json_pointer(const char* s, std::size_t n)
+#else
+    inline nlohmann::json::json_pointer operator "" _json_pointer(const char* s, std::size_t n)
+#endif
 {
     return nlohmann::json::json_pointer(std::string(s, n));
 }
@@ -24338,7 +24502,7 @@ namespace std // NOLINT(cert-dcl58-cpp)
 /// @brief hash value for JSON objects
 /// @sa https://json.nlohmann.me/api/basic_json/std_hash/
 NLOHMANN_BASIC_JSON_TPL_DECLARATION
-struct hash<nlohmann::NLOHMANN_BASIC_JSON_TPL>
+struct hash<nlohmann::NLOHMANN_BASIC_JSON_TPL> // NOLINT(cert-dcl58-cpp)
 {
     std::size_t operator()(const nlohmann::NLOHMANN_BASIC_JSON_TPL& j) const
     {
@@ -24371,8 +24535,8 @@ struct less< ::nlohmann::detail::value_t> // do not remove the space after '<',
 /// @brief exchanges the values of two JSON objects
 /// @sa https://json.nlohmann.me/api/basic_json/std_swap/
 NLOHMANN_BASIC_JSON_TPL_DECLARATION
-inline void swap(nlohmann::NLOHMANN_BASIC_JSON_TPL& j1, nlohmann::NLOHMANN_BASIC_JSON_TPL& j2) noexcept(  // NOLINT(readability-inconsistent-declaration-parameter-name)
-    is_nothrow_move_constructible<nlohmann::NLOHMANN_BASIC_JSON_TPL>::value&&                          // NOLINT(misc-redundant-expression)
+inline void swap(nlohmann::NLOHMANN_BASIC_JSON_TPL& j1, nlohmann::NLOHMANN_BASIC_JSON_TPL& j2) noexcept(  // NOLINT(readability-inconsistent-declaration-parameter-name, cert-dcl58-cpp)
+    is_nothrow_move_constructible<nlohmann::NLOHMANN_BASIC_JSON_TPL>::value&&                          // NOLINT(misc-redundant-expression,cppcoreguidelines-noexcept-swap,performance-noexcept-swap)
     is_nothrow_move_assignable<nlohmann::NLOHMANN_BASIC_JSON_TPL>::value)
 {
     j1.swap(j2);
@@ -24383,17 +24547,22 @@ inline void swap(nlohmann::NLOHMANN_BASIC_JSON_TPL& j1, nlohmann::NLOHMANN_BASIC
 }  // namespace std
 
 #if JSON_USE_GLOBAL_UDLS
-    using nlohmann::literals::json_literals::operator "" _json; // NOLINT(misc-unused-using-decls,google-global-names-in-headers)
-    using nlohmann::literals::json_literals::operator "" _json_pointer; //NOLINT(misc-unused-using-decls,google-global-names-in-headers)
+    #if !defined(JSON_HEDLEY_GCC_VERSION) || JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0)
+        using nlohmann::literals::json_literals::operator ""_json; // NOLINT(misc-unused-using-decls,google-global-names-in-headers)
+        using nlohmann::literals::json_literals::operator ""_json_pointer; //NOLINT(misc-unused-using-decls,google-global-names-in-headers)
+    #else
+        using nlohmann::literals::json_literals::operator "" _json; // NOLINT(misc-unused-using-decls,google-global-names-in-headers)
+        using nlohmann::literals::json_literals::operator "" _json_pointer; //NOLINT(misc-unused-using-decls,google-global-names-in-headers)
+    #endif
 #endif
 
 // #include <nlohmann/detail/macro_unscope.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
@@ -24428,16 +24597,17 @@ inline void swap(nlohmann::NLOHMANN_BASIC_JSON_TPL& j1, nlohmann::NLOHMANN_BASIC
     #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM
     #undef JSON_HAS_THREE_WAY_COMPARISON
     #undef JSON_HAS_RANGES
+    #undef JSON_HAS_STATIC_RTTI
     #undef JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON
 #endif
 
 // #include <nlohmann/thirdparty/hedley/hedley_undef.hpp>
 //     __ _____ _____ _____
 //  __|  |   __|     |   | |  JSON for Modern C++
-// |  |  |__   |  |  | | | |  version 3.11.2
+// |  |  |__   |  |  | | | |  version 3.11.3
 // |_____|_____|_____|_|___|  https://github.com/nlohmann/json
 //
-// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann <https://nlohmann.me>
+// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann <https://nlohmann.me>
 // SPDX-License-Identifier: MIT
 
 
diff --git a/common/log.h b/common/log.h
index e4e1b9f4f..e4edcac7d 100644
--- a/common/log.h
+++ b/common/log.h
@@ -234,7 +234,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
 // INTERNAL, DO NOT USE
 //  USE LOG() INSTEAD
 //
-#ifndef _MSC_VER
+#if !defined(_MSC_VER) or defined(__INTEL_LLVM_COMPILER)
     #define LOG_IMPL(str, ...)                                                                                      \
     do {                                                                                                            \
         if (LOG_TARGET != nullptr)                                                                                  \
@@ -257,7 +257,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
 // INTERNAL, DO NOT USE
 //  USE LOG_TEE() INSTEAD
 //
-#ifndef _MSC_VER
+#if !defined(_MSC_VER) or defined(__INTEL_LLVM_COMPILER)
     #define LOG_TEE_IMPL(str, ...)                                                                                                      \
     do {                                                                                                                                \
         if (LOG_TARGET != nullptr)                                                                                                      \
@@ -297,7 +297,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
 #ifndef _MSC_VER
     #define LOG(...) LOG_IMPL(__VA_ARGS__, "")
 #else
-    #define LOG(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "")
+    #define LOG(str, ...) LOG_IMPL("%s" str, "", ##__VA_ARGS__, "")
 #endif
 
 // Main TEE macro.
@@ -311,7 +311,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
 #ifndef _MSC_VER
     #define LOG_TEE(...) LOG_TEE_IMPL(__VA_ARGS__, "")
 #else
-    #define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "")
+    #define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", ##__VA_ARGS__, "")
 #endif
 
 // LOG macro variants with auto endline.
@@ -319,8 +319,8 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
     #define LOGLN(...) LOG_IMPL(__VA_ARGS__, "\n")
     #define LOG_TEELN(...) LOG_TEE_IMPL(__VA_ARGS__, "\n")
 #else
-    #define LOGLN(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "\n")
-    #define LOG_TEELN(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "\n")
+    #define LOGLN(str, ...) LOG_IMPL("%s" str, "", ##__VA_ARGS__, "\n")
+    #define LOG_TEELN(str, ...) LOG_TEE_IMPL("%s" str, "", ##__VA_ARGS__, "\n")
 #endif
 
 // INTERNAL, DO NOT USE
@@ -566,6 +566,7 @@ inline void log_print_usage()
     printf("  --log-new             Create a separate new log file on start. "
                                    "Each log file will have unique name: \"<name>.<ID>.log\"\n");
     printf("  --log-append          Don't truncate the old log file.\n");
+    printf("\n");
 }
 
 #define log_dump_cmdline(argc, argv) log_dump_cmdline_impl(argc, argv)
diff --git a/common/ngram-cache.cpp b/common/ngram-cache.cpp
new file mode 100644
index 000000000..3ca112ef1
--- /dev/null
+++ b/common/ngram-cache.cpp
@@ -0,0 +1,282 @@
+#include "ngram-cache.h"
+#include "common.h"
+#include "log.h"
+
+#include <cstdint>
+#include <fstream>
+
+void llama_ngram_cache_update(llama_ngram_cache & ngram_cache, int ngram_min, int ngram_max,
+                              std::vector<llama_token> & inp, int nnew, bool print_progress) {
+    const int64_t t_start_ms = ggml_time_ms();
+    const int64_t inp_size = inp.size();
+
+    const int64_t n_todo = inp_size * (ngram_max - ngram_min + 1);
+    int64_t n_done = 0;
+
+    for (int64_t ngram_size = ngram_min; ngram_size <= ngram_max; ++ngram_size) {
+        const int64_t i_start = std::max(inp_size - nnew, ngram_size);
+        for (int64_t i = i_start; i < inp_size; ++i) {
+            const int64_t ngram_start = i - ngram_size;
+            llama_ngram ngram(&inp[ngram_start], ngram_size);
+            const llama_token token = inp[i];
+
+            llama_ngram_cache::iterator part_it = ngram_cache.find(ngram);
+            if (part_it == ngram_cache.end()) {
+                llama_ngram_cache_part part;
+                part.emplace(token, 1);
+                ngram_cache.emplace(ngram, part);
+            } else {
+                llama_ngram_cache_part::iterator token_count_it = part_it->second.find(token);
+                if (token_count_it == part_it->second.end()) {
+                    part_it->second.emplace(token, 1);
+                } else {
+                    token_count_it->second++;
+                }
+            }
+            ++n_done;
+
+            if (print_progress && n_done % 10000000 == 0) {
+                const int64_t t_now_ms = ggml_time_ms();
+                const int64_t eta_ms   = (inp_size*(ngram_max-ngram_min+1) - n_done) * (t_now_ms - t_start_ms) / n_done;
+                const int64_t eta_min  = eta_ms / (60*1000);
+                const int64_t eta_s    = (eta_ms - 60*1000*eta_min) / 1000;
+
+                fprintf(stderr, "%s: %" PRId64 "/%" PRId64 " done, ETA: %02" PRId64 ":%02" PRId64 "\n", __func__, n_done, n_todo, eta_min, eta_s);
+            }
+        }
+    }
+}
+
+// Helper function to get a token from the combined, speculative sequence of inp and draft.
+static llama_token get_token(const std::vector<llama_token> & inp, const std::vector<llama_token> & draft, const size_t i) {
+    return i < inp.size() ? inp[i] : draft[1 + i - inp.size()];
+}
+
+// If sample size or percentage are below these thresholds the draft is aborted early:
+constexpr int    draft_min_sample_size_lax[LLAMA_NGRAM_MAX] = { 2,  2,  1,  1};
+constexpr int        draft_min_percent_lax[LLAMA_NGRAM_MAX] = {66, 50, 50, 50};
+constexpr int draft_min_sample_size_strict[LLAMA_NGRAM_MAX] = { 4,  3,  2,  2};
+constexpr int     draft_min_percent_strict[LLAMA_NGRAM_MAX] = {75, 66, 66, 66};
+
+// Helper function that tries to draft a token from only the static ngram cache:
+static llama_token try_draft(llama_ngram_cache & nc_static, const llama_ngram ngram_static) {
+    llama_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
+    if (part_static_it == nc_static.end()) {
+        return -1;
+    }
+    const llama_ngram_cache_part part_static = part_static_it->second;
+
+    int max_count_static  = 0;
+    int sum_count_static  = 0;
+    llama_token max_token = -1;
+
+    for (std::pair<llama_token, int> token_count_static : part_static) {
+        const llama_token token = token_count_static.first;
+        const int32_t count_static  = token_count_static.second;
+
+        if (count_static > max_count_static) {
+            max_token        = token;
+            max_count_static = count_static;
+        }
+        sum_count_static += count_static;
+    }
+
+    if (sum_count_static < draft_min_sample_size_lax[LLAMA_NGRAM_STATIC-1]) {
+        return -1;
+    }
+    if (100*max_count_static < draft_min_percent_lax[LLAMA_NGRAM_STATIC-1]*sum_count_static) {
+        return -1;
+    }
+    return max_token;
+}
+
+// Try to draft a token from primary cache (context/dynamic), validate with static cache:
+static llama_token try_draft(
+    llama_ngram_cache & nc_primary, const std::vector<llama_ngram> & ngrams_primary, llama_ngram_cache_part & part_static,
+    const int * min_sample_size, const int * min_percent) {
+
+    llama_token drafted_token = -1;
+
+    for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == -1; --i) {
+        const llama_ngram ngram_primary = ngrams_primary[i];
+
+        llama_ngram_cache::iterator part_primary_it = nc_primary.find(ngram_primary);
+        if (part_primary_it == nc_primary.end()) {
+            continue;
+        }
+        const llama_ngram_cache_part part_primary = part_primary_it->second;
+
+        int max_count_primary = 0;
+        int max_count_static  = 0;
+        int sum_count_primary = 0;
+        llama_token max_token = -1;
+
+        for (std::pair<llama_token, int> token_count_primary : part_primary) {
+            const llama_token token = token_count_primary.first;
+
+            llama_ngram_cache_part::iterator token_count_static_it = part_static.find(token);
+
+            const int32_t count_primary = token_count_primary.second;
+            const int32_t count_static  = token_count_static_it != part_static.end() ? 100*token_count_static_it->second : 1;
+
+            if (count_primary*count_static > max_count_primary*max_count_static) {
+                max_token         = token;
+                max_count_primary = count_primary;
+                max_count_static  = count_static;
+            }
+            sum_count_primary += count_primary;
+        }
+
+        if (sum_count_primary < min_sample_size[i]) {
+            continue;
+        }
+        if (100*max_count_primary < min_percent[i]*sum_count_primary) {
+            continue;;
+        }
+        drafted_token = max_token;
+    }
+
+    return drafted_token;
+}
+
+void llama_ngram_cache_draft(
+    std::vector<llama_token> & inp, std::vector<llama_token> & draft, int n_draft, int ngram_min, int ngram_max,
+    llama_ngram_cache & nc_context, llama_ngram_cache & nc_dynamic, llama_ngram_cache & nc_static
+) {
+    GGML_ASSERT(draft.size() == 1);
+    const int inp_size = inp.size();
+
+    if (inp_size < LLAMA_NGRAM_STATIC) {
+        return;
+    }
+
+    while ((int) draft.size()-1 < n_draft) {
+        llama_token drafted_token = -1;
+
+        const int ngram_start_static = inp_size-LLAMA_NGRAM_STATIC + draft.size()-1;
+        llama_ngram ngram_static;
+        for (int j = ngram_start_static; j < ngram_start_static + LLAMA_NGRAM_STATIC; ++j) {
+            ngram_static.tokens[j-ngram_start_static] = get_token(inp, draft, j);
+        }
+        llama_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
+        llama_ngram_cache_part part_static;
+        if (part_static_it != nc_static.end()) {
+            part_static = part_static_it->second;
+        }
+
+        // cd = context + dynamic
+        std::vector<llama_ngram> ngrams_cd;
+        for (int ngram_size_cd = ngram_min; ngram_size_cd <= ngram_max; ++ngram_size_cd) {
+            const int ngram_start_cd = inp_size-ngram_size_cd + draft.size()-1;
+            llama_ngram ngram_cd;
+            for (int j = ngram_start_cd; j < ngram_start_cd + ngram_size_cd; ++j) {
+                ngram_cd.tokens[j-ngram_start_cd] = get_token(inp, draft, j);
+            }
+            ngrams_cd.push_back(ngram_cd);
+        }
+        if (drafted_token == -1) {
+            drafted_token = try_draft(nc_context, ngrams_cd, part_static, draft_min_sample_size_lax, draft_min_percent_lax);
+        }
+        if (drafted_token == -1) {
+            drafted_token = try_draft(nc_dynamic, ngrams_cd, part_static, draft_min_sample_size_strict, draft_min_percent_strict);
+        }
+        if (drafted_token == -1) {
+            drafted_token = try_draft(nc_static, ngram_static);
+        }
+
+        if (drafted_token == -1) {
+            break;
+        }
+
+        LOG(" - draft candidate: token=%d\n", drafted_token);
+        draft.push_back(drafted_token);
+    }
+}
+
+void llama_ngram_cache_save(llama_ngram_cache & ngram_cache, std::string & filename) {
+    std::ofstream file_out(filename, std::ios::binary);
+    for (std::pair<llama_ngram, llama_ngram_cache_part> item : ngram_cache) {
+        const llama_ngram      ngram        = item.first;
+        llama_ngram_cache_part token_counts = item.second;
+        GGML_ASSERT(!token_counts.empty());
+        const int32_t ntokens = token_counts.size();
+        GGML_ASSERT(ntokens > 0);
+
+        file_out.write(reinterpret_cast<const char *>(&ngram),   sizeof(llama_ngram));
+        file_out.write(reinterpret_cast<const char *>(&ntokens), sizeof(int32_t));
+        for (std::pair<llama_token, int32_t> item2 : token_counts) {
+            const llama_token token = item2.first;
+            const int32_t     count = item2.second;
+            GGML_ASSERT(count > 0);
+
+            file_out.write(reinterpret_cast<const char *>(&token), sizeof(llama_token));
+            file_out.write(reinterpret_cast<const char *>(&count), sizeof(int32_t));
+        }
+    }
+
+}
+
+llama_ngram_cache llama_ngram_cache_load(std::string & filename) {
+    std::ifstream hashmap_file(filename, std::ios::binary);
+    if (!hashmap_file) {
+        throw std::ifstream::failure("Unable to open file " + filename);
+    }
+    llama_ngram_cache ngram_cache;
+
+    llama_ngram ngram;
+    int32_t     ntokens;
+    llama_token token;
+    int32_t     count;
+
+    char * ngramc   = reinterpret_cast<char*>(&ngram);
+    char * ntokensc = reinterpret_cast<char*>(&ntokens);
+    char * tokenc   = reinterpret_cast<char*>(&token);
+    char * countc   = reinterpret_cast<char*>(&count);
+    while(hashmap_file.read(ngramc, sizeof(llama_ngram))) {
+        GGML_ASSERT(!hashmap_file.eof());
+        GGML_ASSERT(hashmap_file.read(ntokensc, sizeof(int32_t)));
+        GGML_ASSERT(ntokens > 0);
+        llama_ngram_cache_part token_counts;
+
+        for (int i = 0; i < ntokens; ++i) {
+            GGML_ASSERT(!hashmap_file.eof());
+            GGML_ASSERT(hashmap_file.read(tokenc, sizeof(llama_token)));
+            GGML_ASSERT(!hashmap_file.eof());
+            GGML_ASSERT(hashmap_file.read(countc, sizeof(int32_t)));
+            GGML_ASSERT(count > 0);
+            token_counts.emplace(token, count);
+        }
+
+        ngram_cache.emplace(ngram, token_counts);
+    }
+    GGML_ASSERT(hashmap_file.eof());
+
+    return ngram_cache;
+}
+
+void llama_ngram_cache_merge(llama_ngram_cache & ngram_cache_target, llama_ngram_cache & ngram_cache_add) {
+    for (std::pair<llama_ngram, llama_ngram_cache_part> ngram_part : ngram_cache_add) {
+        const llama_ngram      ngram = ngram_part.first;
+        llama_ngram_cache_part  part = ngram_part.second;
+
+        llama_ngram_cache::iterator part_merged_it = ngram_cache_target.find(ngram);
+        if (part_merged_it == ngram_cache_target.end()) {
+            ngram_cache_target.emplace(ngram, part);
+            continue;
+        }
+
+        for (std::pair<llama_token, int32_t> token_count : part) {
+            const llama_token token = token_count.first;
+            const int32_t     count = token_count.second;
+            GGML_ASSERT(count > 0);
+
+            llama_ngram_cache_part::iterator token_count_merged_it = part_merged_it->second.find(token);
+            if (token_count_merged_it == part_merged_it->second.end()) {
+                part_merged_it->second.emplace(token, count);
+                continue;
+            }
+
+            token_count_merged_it->second += count;
+        }
+    }
+}
diff --git a/common/ngram-cache.h b/common/ngram-cache.h
new file mode 100644
index 000000000..e4fa4cbd1
--- /dev/null
+++ b/common/ngram-cache.h
@@ -0,0 +1,94 @@
+#pragma once
+
+#include "llama.h"
+
+#include <unordered_map>
+#include <string>
+#include <vector>
+
+#define LLAMA_NGRAM_MIN    1
+#define LLAMA_NGRAM_MAX    4
+#define LLAMA_NGRAM_STATIC 2
+
+// Data structures to map n-grams to empirical token probabilities:
+
+struct llama_ngram {
+    llama_token tokens[LLAMA_NGRAM_MAX];
+
+    llama_ngram() {
+        for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
+            tokens[i] = -1;
+        }
+    }
+
+    llama_ngram(const llama_token * input, const int ngram_size) {
+        for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
+            tokens[i] = i < ngram_size ? input[i] : -1;
+        }
+    }
+
+    bool operator==(const llama_ngram & other) const {
+        for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
+            if (tokens[i] != other.tokens[i]) {
+                return false;
+            }
+        }
+        return true;
+    }
+};
+
+struct llama_ngram_hash_function {
+    size_t operator()(const llama_ngram & ngram) const {
+        size_t hash = 0;
+        for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
+            hash ^= std::hash<llama_token>{}(ngram.tokens[i]);
+        }
+        return hash;
+    }
+};
+
+// token -> number of times token has been seen
+typedef std::unordered_map<llama_token, int32_t> llama_ngram_cache_part;
+
+// n-gram -> empirical distribution of following tokens
+typedef std::unordered_map<llama_ngram, llama_ngram_cache_part, llama_ngram_hash_function> llama_ngram_cache;
+
+
+// Update an ngram cache with tokens.
+// ngram_cache:         the cache to modify.
+// ngram_min/ngram_max: the min/max size of the ngrams to extract from inp_data.
+// inp_data:            the token sequence with which to update ngram_cache.
+// nnew:                how many new tokens have been appended to inp_data since the last call to this function.
+// print_progress:      whether to print progress to stderr.
+//
+// In order to get correct results inp_data can ONLY BE APPENDED TO.
+// Changes in the middle need a complete rebuild.
+void llama_ngram_cache_update(
+    llama_ngram_cache & ngram_cache, int ngram_min, int ngram_max, std::vector<llama_token> & inp_data, int nnew, bool print_progress);
+
+// Try to draft tokens from ngram caches.
+// inp:                the tokens generated so far.
+// draft:              the token sequence to draft. Expected to initially contain the previously sampled token.
+// n_draft:            maximum number of tokens to add to draft.
+// ngram_min/gram_max: the min/max size of the ngrams in nc_context and nc_dynamic.
+// nc_context:         ngram cache based on current context.
+// nc_dynamic:         ngram cache based on previous user generations.
+// nc_static:          ngram cache generated from a large text corpus, used for validation.
+void llama_ngram_cache_draft(
+    std::vector<llama_token> & inp, std::vector<llama_token> & draft, int n_draft, int ngram_min, int ngram_max,
+    llama_ngram_cache & nc_context, llama_ngram_cache & nc_dynamic, llama_ngram_cache & nc_static);
+
+// Save an ngram cache to a file.
+// ngram_cache: the ngram cache to save.
+// filename:    the path under which to save the ngram cache.
+void llama_ngram_cache_save(llama_ngram_cache & ngram_cache, std::string & filename);
+
+// Load an ngram cache saved with llama_ngram_cache_save.
+// filename: the path from which to load the ngram cache.
+// returns:  an ngram cache containing the information saved to filename.
+llama_ngram_cache llama_ngram_cache_load(std::string & filename);
+
+// Merge two ngram caches.
+// ngram_cache_target: the ngram cache to which to add the information from ngram_cache_add.
+// ngram_cache_add:    the ngram cache to add to ngram_cache_target.
+void llama_ngram_cache_merge(llama_ngram_cache & ngram_cache_target, llama_ngram_cache & ngram_cache_add);
diff --git a/common/sampling.cpp b/common/sampling.cpp
index 8e45909f1..45d68b26c 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -13,6 +13,14 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_
         // will be empty (default) if there are parse errors
         if (result->parsed_grammar.rules.empty()) {
             fprintf(stderr, "%s: failed to parse grammar\n", __func__);
+            delete result;
+            return nullptr;
+        }
+
+        // Ensure that there is a "root" node.
+        if (result->parsed_grammar.symbol_ids.find("root") == result->parsed_grammar.symbol_ids.end()) {
+            fprintf(stderr, "%s: grammar does not contain a 'root' symbol\n", __func__);
+            delete result;
             return nullptr;
         }
 
@@ -102,15 +110,10 @@ std::string llama_sampling_print(const llama_sampling_params & params) {
 std::string llama_sampling_order_print(const llama_sampling_params & params) {
     std::string result = "CFG -> Penalties ";
     if (params.mirostat == 0) {
-        for (auto s : params.samplers_sequence) {
-            switch (s) {
-                case 'k': result += "-> top_k "; break;
-                case 'f': result += "-> tfs_z "; break;
-                case 'y': result += "-> typical_p "; break;
-                case 'p': result += "-> top_p "; break;
-                case 'm': result += "-> min_p "; break;
-                case 't': result += "-> temp "; break;
-                default : break;
+        for (auto sampler_type : params.samplers_sequence) {
+            const auto sampler_type_name = sampler_type_to_name_string(sampler_type);
+            if (!sampler_type_name.empty()) {
+                result += "-> " + sampler_type_name + " ";
             }
         }
     } else {
@@ -125,25 +128,33 @@ static void sampler_queue(
                    struct llama_context * ctx_main,
             const llama_sampling_params & params,
                  llama_token_data_array & cur_p,
-                                 size_t & min_keep) {
-    const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));
-
+                                 size_t   min_keep) {
     const float         temp              = params.temp;
-    const int32_t       top_k             = params.top_k <= 0 ? n_vocab : params.top_k;
+    const float         dynatemp_range    = params.dynatemp_range;
+    const float         dynatemp_exponent = params.dynatemp_exponent;
+    const int32_t       top_k             = params.top_k;
     const float         top_p             = params.top_p;
     const float         min_p             = params.min_p;
     const float         tfs_z             = params.tfs_z;
     const float         typical_p         = params.typical_p;
-    const std::string & samplers_sequence = params.samplers_sequence;
+    const std::vector<llama_sampler_type> & samplers_sequence = params.samplers_sequence;
 
-    for (auto s : samplers_sequence) {
-        switch (s){
-            case 'k': llama_sample_top_k    (ctx_main, &cur_p, top_k,     min_keep); break;
-            case 'f': llama_sample_tail_free(ctx_main, &cur_p, tfs_z,     min_keep); break;
-            case 'y': llama_sample_typical  (ctx_main, &cur_p, typical_p, min_keep); break;
-            case 'p': llama_sample_top_p    (ctx_main, &cur_p, top_p,     min_keep); break;
-            case 'm': llama_sample_min_p    (ctx_main, &cur_p, min_p,     min_keep); break;
-            case 't': llama_sample_temp     (ctx_main, &cur_p, temp); break;
+    for (auto sampler_type : samplers_sequence) {
+        switch (sampler_type) {
+            case llama_sampler_type::TOP_K    : llama_sample_top_k    (ctx_main, &cur_p, top_k,     min_keep); break;
+            case llama_sampler_type::TFS_Z    : llama_sample_tail_free(ctx_main, &cur_p, tfs_z,     min_keep); break;
+            case llama_sampler_type::TYPICAL_P: llama_sample_typical  (ctx_main, &cur_p, typical_p, min_keep); break;
+            case llama_sampler_type::TOP_P    : llama_sample_top_p    (ctx_main, &cur_p, top_p,     min_keep); break;
+            case llama_sampler_type::MIN_P    : llama_sample_min_p    (ctx_main, &cur_p, min_p,     min_keep); break;
+            case llama_sampler_type::TEMPERATURE:
+                if (dynatemp_range > 0) {
+                    float dynatemp_min = std::max(0.0f, temp - dynatemp_range);
+                    float dynatemp_max = std::max(0.0f, temp + dynatemp_range);
+                    llama_sample_entropy(ctx_main, &cur_p, dynatemp_min, dynatemp_max, dynatemp_exponent);
+                } else {
+                    llama_sample_temp(ctx_main, &cur_p, temp);
+                }
+                break;
             default : break;
         }
     }
@@ -157,76 +168,20 @@ static llama_token llama_sampling_sample_impl(
                   bool is_resampling) {  // Add a parameter to indicate if we are resampling
     const llama_sampling_params & params = ctx_sampling->params;
 
-    const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));
-
     const float   temp            = params.temp;
-    const int32_t penalty_last_n  = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;
-    const float   penalty_repeat  = params.penalty_repeat;
-    const float   penalty_freq    = params.penalty_freq;
-    const float   penalty_present = params.penalty_present;
     const int     mirostat        = params.mirostat;
     const float   mirostat_tau    = params.mirostat_tau;
     const float   mirostat_eta    = params.mirostat_eta;
-    const bool    penalize_nl     = params.penalize_nl;
-
-    auto & prev = ctx_sampling->prev;
-    auto & cur  = ctx_sampling->cur;
 
+    std::vector<float> original_logits;
+    auto cur_p = llama_sampling_prepare(ctx_sampling, ctx_main, ctx_cfg, idx, !is_resampling, &original_logits);
+    if (!is_resampling) {
+        GGML_ASSERT(!original_logits.empty());
+    }
     llama_token id = 0;
-
     // Get a pointer to the logits
     float * logits = llama_get_logits_ith(ctx_main, idx);
 
-    // Declare original_logits at the beginning of the function scope
-    std::vector<float> original_logits;
-
-    if (!is_resampling) {
-        // Only make a copy of the original logits if we are not in the resampling phase, not sure if I actually have to do this.
-        original_logits = std::vector<float>(logits, logits + llama_n_vocab(llama_get_model(ctx_main)));
-    }
-
-    // apply params.logit_bias map
-    for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
-        logits[it->first] += it->second;
-    }
-
-    cur.clear();
-
-    for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-        cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
-    }
-
-    llama_token_data_array cur_p = { cur.data(), cur.size(), false };
-
-    if (ctx_cfg) {
-        llama_sample_classifier_free_guidance(ctx_main, &cur_p, ctx_cfg, params.cfg_scale);
-    }
-
-    // apply penalties
-    const auto& penalty_tokens = params.use_penalty_prompt_tokens ? params.penalty_prompt_tokens : prev;
-    const int penalty_tokens_used_size = std::min((int)penalty_tokens.size(), penalty_last_n);
-    if (penalty_tokens_used_size) {
-        const float nl_logit = logits[llama_token_nl(llama_get_model(ctx_main))];
-
-        llama_sample_repetition_penalties(ctx_main, &cur_p,
-                penalty_tokens.data() + penalty_tokens.size() - penalty_tokens_used_size,
-                penalty_tokens_used_size, penalty_repeat, penalty_freq, penalty_present);
-
-        if (!penalize_nl) {
-            for (size_t idx = 0; idx < cur_p.size; idx++) {
-                if (cur_p.data[idx].id == llama_token_nl(llama_get_model(ctx_main))) {
-                    cur_p.data[idx].logit = nl_logit;
-                    break;
-                }
-            }
-        }
-    }
-
-    // If we are in the resampling phase, apply grammar checks before sampling logic
-    if (is_resampling && ctx_sampling->grammar != NULL) {
-        llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar);
-    }
-
     if (temp < 0.0) {
         // greedy sampling, with probs
         llama_sample_softmax(ctx_main, &cur_p);
@@ -244,7 +199,7 @@ static llama_token llama_sampling_sample_impl(
             id = llama_sample_token_mirostat_v2(ctx_main, &cur_p, mirostat_tau, mirostat_eta, &ctx_sampling->mirostat_mu);
         } else {
             // temperature sampling
-            size_t min_keep = std::max(1, params.n_probs);
+            size_t min_keep = std::max(1, params.min_keep);
 
             sampler_queue(ctx_main, params, cur_p, min_keep);
 
@@ -261,7 +216,7 @@ static llama_token llama_sampling_sample_impl(
             //    }
             //}
 
-            LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx_main, id).c_str());
+            //LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx_main, id).c_str());
         }
     }
 
@@ -290,6 +245,81 @@ static llama_token llama_sampling_sample_impl(
     return id;
 }
 
+static llama_token_data_array llama_sampling_prepare_impl(
+                  struct llama_sampling_context * ctx_sampling,
+                  struct llama_context * ctx_main,
+                  struct llama_context * ctx_cfg,
+                  const int idx,
+                  bool apply_grammar,
+                  std::vector<float> * original_logits) {
+    const llama_sampling_params & params = ctx_sampling->params;
+
+    const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));
+
+    const int32_t penalty_last_n  = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;
+    const float   penalty_repeat  = params.penalty_repeat;
+    const float   penalty_freq    = params.penalty_freq;
+    const float   penalty_present = params.penalty_present;
+
+    const bool    penalize_nl     = params.penalize_nl;
+
+    auto & prev = ctx_sampling->prev;
+    auto & cur  = ctx_sampling->cur;
+
+    // Get a pointer to the logits
+    float * logits = llama_get_logits_ith(ctx_main, idx);
+
+    if (apply_grammar && original_logits != NULL) {
+        // Only make a copy of the original logits if we are not applying grammar checks, not sure if I actually have to do this.
+        *original_logits = {logits, logits + llama_n_vocab(llama_get_model(ctx_main))};
+    }
+
+    // apply params.logit_bias map
+    for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
+        logits[it->first] += it->second;
+    }
+
+    if (ctx_cfg) {
+        float * logits_guidance = llama_get_logits_ith(ctx_cfg, idx);
+        llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale);
+    }
+
+    cur.clear();
+
+    for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+        cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
+    }
+
+    llama_token_data_array cur_p = { cur.data(), cur.size(), false };
+
+    // apply penalties
+    const auto& penalty_tokens = params.use_penalty_prompt_tokens ? params.penalty_prompt_tokens : prev;
+    const int penalty_tokens_used_size = std::min((int)penalty_tokens.size(), penalty_last_n);
+    if (penalty_tokens_used_size) {
+        const float nl_logit = logits[llama_token_nl(llama_get_model(ctx_main))];
+
+        llama_sample_repetition_penalties(ctx_main, &cur_p,
+                penalty_tokens.data() + penalty_tokens.size() - penalty_tokens_used_size,
+                penalty_tokens_used_size, penalty_repeat, penalty_freq, penalty_present);
+
+        if (!penalize_nl) {
+            for (size_t idx = 0; idx < cur_p.size; idx++) {
+                if (cur_p.data[idx].id == llama_token_nl(llama_get_model(ctx_main))) {
+                    cur_p.data[idx].logit = nl_logit;
+                    break;
+                }
+            }
+        }
+    }
+
+    // apply grammar checks before sampling logic
+    if (apply_grammar && ctx_sampling->grammar != NULL) {
+        llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar);
+    }
+
+    return cur_p;
+}
+
 llama_token llama_sampling_sample(
                   struct llama_sampling_context * ctx_sampling,
                   struct llama_context * ctx_main,
@@ -299,6 +329,16 @@ llama_token llama_sampling_sample(
     return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, false);
 }
 
+llama_token_data_array llama_sampling_prepare(
+                  struct llama_sampling_context * ctx_sampling,
+                  struct llama_context * ctx_main,
+                  struct llama_context * ctx_cfg,
+                  const int idx,
+                  bool apply_grammar,
+                  std::vector<float> * original_logits) {
+    return llama_sampling_prepare_impl(ctx_sampling,ctx_main, ctx_cfg, idx, apply_grammar, original_logits);
+}
+
 void llama_sampling_accept(
         struct llama_sampling_context * ctx_sampling,
         struct llama_context * ctx_main,
diff --git a/common/sampling.h b/common/sampling.h
index f16ef97e3..56ed991b8 100644
--- a/common/sampling.h
+++ b/common/sampling.h
@@ -8,25 +8,46 @@
 #include <vector>
 #include <unordered_map>
 
+// sampler types
+enum class llama_sampler_type : char {
+    TOP_K       = 'k',
+    TOP_P       = 'p',
+    MIN_P       = 'm',
+    TFS_Z       = 'f',
+    TYPICAL_P   = 'y',
+    TEMPERATURE = 't'
+};
+
 // sampling parameters
 typedef struct llama_sampling_params {
     int32_t     n_prev                = 64;       // number of previous tokens to remember
     int32_t     n_probs               = 0;        // if greater than 0, output the probabilities of top n_probs tokens.
+    int32_t     min_keep              = 0;        // 0 = disabled, otherwise samplers should return at least min_keep tokens
     int32_t     top_k                 = 40;       // <= 0 to use vocab size
     float       top_p                 = 0.95f;    // 1.0 = disabled
     float       min_p                 = 0.05f;    // 0.0 = disabled
     float       tfs_z                 = 1.00f;    // 1.0 = disabled
     float       typical_p             = 1.00f;    // 1.0 = disabled
-    float       temp                  = 0.80f;    // 1.0 = disabled
+    float       temp                  = 0.80f;    // <= 0.0 to sample greedily, 0.0 to not output probabilities
+    float       dynatemp_range        = 0.00f;    // 0.0 = disabled
+    float       dynatemp_exponent     = 1.00f;    // controls how entropy maps to temperature in dynamic temperature sampler
     int32_t     penalty_last_n        = 64;       // last n tokens to penalize (0 = disable penalty, -1 = context size)
-    float       penalty_repeat        = 1.10f;    // 1.0 = disabled
+    float       penalty_repeat        = 1.00f;    // 1.0 = disabled
     float       penalty_freq          = 0.00f;    // 0.0 = disabled
     float       penalty_present       = 0.00f;    // 0.0 = disabled
     int32_t     mirostat              = 0;        // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
     float       mirostat_tau          = 5.00f;    // target entropy
     float       mirostat_eta          = 0.10f;    // learning rate
-    bool        penalize_nl           = true;     // consider newlines as a repeatable token
-    std::string samplers_sequence     = "kfypmt"; // top_k, tail_free, typical_p, top_p, min_p, temp
+    bool        penalize_nl           = false;     // consider newlines as a repeatable token
+
+    std::vector<llama_sampler_type> samplers_sequence = {
+        llama_sampler_type::TOP_K,
+        llama_sampler_type::TFS_Z,
+        llama_sampler_type::TYPICAL_P,
+        llama_sampler_type::TOP_P,
+        llama_sampler_type::MIN_P,
+        llama_sampler_type::TEMPERATURE
+    };
 
     std::string grammar;  // optional BNF-like grammar to constrain sampling
 
@@ -110,6 +131,15 @@ llama_token llama_sampling_sample(
         struct llama_context * ctx_cfg,
         int idx = 0);
 
+// Prepares and adjusts the set of token candidates for sampling based on penalties, biases, and sampling parameters.
+llama_token_data_array llama_sampling_prepare(
+        struct llama_sampling_context * ctx_sampling,
+        struct llama_context * ctx_main,
+        struct llama_context * ctx_cfg,
+        int idx = 0,
+        bool apply_grammar = true,
+        std::vector<float> * original_logits = nullptr);
+
 void llama_sampling_accept(
         struct llama_sampling_context * ctx_sampling,
         struct llama_context * ctx_main,
diff --git a/common/train.cpp b/common/train.cpp
index e6f2f7a2f..0dbfd24df 100644
--- a/common/train.cpp
+++ b/common/train.cpp
@@ -31,7 +31,7 @@ struct train_state  * init_train_state() {
 
     state->opt = new struct ggml_opt_context;
     state->opt->ctx = NULL;
-    state->opt->params = ggml_opt_default_params(GGML_OPT_ADAM);
+    state->opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
     state->opt->params.graph_size = LLAMA_TRAIN_MAX_NODES;
     state->opt->loss_after = 0.0f;
 
@@ -556,7 +556,7 @@ void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_g
     std::string opt_type;
     GGUF_GET_KEY(fctx, opt_type, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_OPTIMIZER_TYPE);
     if (opt_type == LLM_KV_OPTIMIZER_TYPE_ADAM) {
-        opt->params.type = GGML_OPT_ADAM;
+        opt->params.type = GGML_OPT_TYPE_ADAM;
 
         GGUF_GET_KEY(fctx, opt->adam.fx_best,          gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS);
         GGUF_GET_KEY(fctx, opt->adam.fx_prev,          gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS);
@@ -568,7 +568,7 @@ void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_g
         copy_tensor_by_name(opt->adam.v,  f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS);
         copy_tensor_by_name(opt->adam.pf, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES);
     } else if (opt_type == LLM_KV_OPTIMIZER_TYPE_LBFGS) {
-        opt->params.type = GGML_OPT_LBFGS;
+        opt->params.type = GGML_OPT_TYPE_LBFGS;
 
         GGUF_GET_KEY(fctx, opt->params.lbfgs.m,         gguf_get_val_u32, GGUF_TYPE_UINT32,  true, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT);
         GGUF_GET_KEY(fctx, opt->lbfgs.fx_best,          gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS);
@@ -603,7 +603,7 @@ void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context *
     gguf_set_val_bool(fctx, LLM_KV_OPTIMIZER_JUST_INITIALIZED, opt->just_initialized);
 
     switch (opt->params.type) {
-        case GGML_OPT_ADAM:
+        case GGML_OPT_TYPE_ADAM:
             {
                 gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM);
                 gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS,            opt->adam.fx_best);
@@ -622,7 +622,7 @@ void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context *
                     gguf_add_tensor(fctx, opt->adam.pf);
                 }
             } break;
-        case GGML_OPT_LBFGS:
+        case GGML_OPT_TYPE_LBFGS:
             {
                 gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS);
                 gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, opt->params.lbfgs.m);
@@ -1363,12 +1363,12 @@ bool consume_common_train_arg(
                 *invalid_param = true;
                 return true;
             }
-#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
-            params->n_gpu_layers = std::stoi(argv[i]);
-#else
-            fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n");
-            fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
-#endif
+            if (llama_supports_gpu_offload()) {
+                params->n_gpu_layers = std::stoi(argv[i]);
+            } else {
+                fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n");
+                fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
+            }
     } else if (arg == "-h" || arg == "--help") {
         params->print_usage = true;
         return true;
diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py
index 203eaf64b..7e601170e 100755
--- a/convert-hf-to-gguf.py
+++ b/convert-hf-to-gguf.py
@@ -8,9 +8,10 @@ import json
 import os
 import re
 import sys
+from abc import ABC, abstractmethod
 from enum import IntEnum
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast, Optional
+from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterator, Sequence, TypeVar, cast
 
 import numpy as np
 import torch
@@ -22,6 +23,8 @@ if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf
 
+from convert import LlamaHfVocab, permute
+
 
 ###### MODEL DEFINITIONS ######
 
@@ -34,7 +37,12 @@ class SentencePieceTokenTypes(IntEnum):
     BYTE = 6
 
 
-class Model:
+AnyModel = TypeVar("AnyModel", bound="type[Model]")
+
+
+class Model(ABC):
+    _model_classes: dict[str, type[Model]] = {}
+
     def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool):
         self.dir_model = dir_model
         self.ftype = ftype
@@ -45,8 +53,21 @@ class Model:
         self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin")
         self.part_names = self._get_part_names()
         self.hparams = Model.load_hparams(self.dir_model)
-        self.model_arch = self._get_model_architecture()
         self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=False)
+        self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
+
+    @property
+    @abstractmethod
+    def model_arch(self) -> gguf.MODEL_ARCH:
+        pass
+
+    def find_hparam(self, keys: Sequence[str], optional: bool = False) -> Any:
+        key = next((k for k in keys if k in self.hparams), None)
+        if key is not None:
+            return self.hparams[key]
+        if optional:
+            return None
+        raise KeyError(f"could not find any of: {keys}")
 
     def set_vocab(self):
         self._set_vocab_gpt2()
@@ -68,28 +89,46 @@ class Model:
 
     def set_gguf_parameters(self):
         self.gguf_writer.add_name(self.dir_model.name)
-        self.gguf_writer.add_block_count(self.hparams.get(
-            "n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")),
-        ))
-        if (n_ctx := self.hparams.get("max_position_embeddings")) is not None:
+        self.gguf_writer.add_block_count(self.block_count)
+
+        if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None:
             self.gguf_writer.add_context_length(n_ctx)
-        if (n_embd := self.hparams.get("hidden_size")) is not None:
-            self.gguf_writer.add_embedding_length(n_embd)
-        if (n_ff := self.hparams.get("intermediate_size")) is not None:
+            print(f"gguf: context length = {n_ctx}")
+
+        n_embd = self.find_hparam(["hidden_size", "n_embd"])
+        self.gguf_writer.add_embedding_length(n_embd)
+        print(f"gguf: embedding length = {n_embd}")
+
+        if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None:
             self.gguf_writer.add_feed_forward_length(n_ff)
-        if (n_head := self.hparams.get("num_attention_heads")) is not None:
-            self.gguf_writer.add_head_count(n_head)
+            print(f"gguf: feed forward length = {n_ff}")
+
+        n_head = self.find_hparam(["num_attention_heads", "n_head"])
+        self.gguf_writer.add_head_count(n_head)
+        print(f"gguf: head count = {n_head}")
+
         if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None:
             self.gguf_writer.add_head_count_kv(n_head_kv)
+            print(f"gguf: key-value head count = {n_head_kv}")
 
-        if (n_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
-            self.gguf_writer.add_layer_norm_rms_eps(n_rms_eps)
+        if (rope_theta := self.hparams.get("rope_theta")) is not None:
+            self.gguf_writer.add_rope_freq_base(rope_theta)
+            print(f"gguf: rope theta = {rope_theta}")
+        if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
+            self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
+            print(f"gguf: rms norm epsilon = {f_rms_eps}")
+        if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None:
+            self.gguf_writer.add_layer_norm_eps(f_norm_eps)
+            print(f"gguf: layer norm epsilon = {f_norm_eps}")
         if (n_experts := self.hparams.get("num_local_experts")) is not None:
             self.gguf_writer.add_expert_count(n_experts)
+            print(f"gguf: expert count = {n_experts}")
         if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
             self.gguf_writer.add_expert_used_count(n_experts_used)
+            print(f"gguf: experts used count = {n_experts_used}")
 
-        self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
+        self.gguf_writer.add_file_type(self.ftype)
+        print(f"gguf: file type = {self.ftype}")
 
     def write_tensors(self):
         block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
@@ -158,37 +197,22 @@ class Model:
         with open(dir_model / "config.json", "r", encoding="utf-8") as f:
             return json.load(f)
 
-    @staticmethod
-    def from_model_architecture(model_architecture):
-        if model_architecture == "GPTNeoXForCausalLM":
-            return GPTNeoXModel
-        if model_architecture == "BloomForCausalLM":
-            return BloomModel
-        if model_architecture == "MPTForCausalLM":
-            return MPTModel
-        if model_architecture in ("BaichuanForCausalLM", "BaiChuanForCausalLM"):
-            return BaichuanModel
-        if model_architecture in ("FalconForCausalLM", "RWForCausalLM"):
-            return FalconModel
-        if model_architecture == "GPTBigCodeForCausalLM":
-            return StarCoderModel
-        if model_architecture == "GPTRefactForCausalLM":
-            return RefactModel
-        if model_architecture == "PersimmonForCausalLM":
-            return PersimmonModel
-        if model_architecture in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
-            return StableLMModel
-        if model_architecture == "QWenLMHeadModel":
-            return QwenModel
-        if model_architecture == "MixtralForCausalLM":
-            return MixtralModel
-        if model_architecture == "GPT2LMHeadModel":
-            return GPT2Model
-        if model_architecture == "PhiForCausalLM":
-            return Phi2Model
-        if model_architecture == "PlamoForCausalLM":
-            return PlamoModel
-        return Model
+    @classmethod
+    def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]:
+        assert names
+
+        def func(modelcls: type[Model]):
+            for name in names:
+                cls._model_classes[name] = modelcls
+            return modelcls
+        return func
+
+    @classmethod
+    def from_model_architecture(cls, arch):
+        try:
+            return cls._model_classes[arch]
+        except KeyError:
+            raise NotImplementedError(f'Architecture {arch!r} not supported!') from None
 
     def _is_model_safetensors(self) -> bool:
         return Model.count_model_parts(self.dir_model, ".safetensors") > 0
@@ -203,43 +227,10 @@ class Model:
             return ("pytorch_model.bin",)
         return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1))
 
-    def _get_model_architecture(self) -> gguf.MODEL_ARCH:
-        arch = self.hparams["architectures"][0]
-        if arch == "GPTNeoXForCausalLM":
-            return gguf.MODEL_ARCH.GPTNEOX
-        if arch == "BloomForCausalLM":
-            return gguf.MODEL_ARCH.BLOOM
-        if arch == "MPTForCausalLM":
-            return gguf.MODEL_ARCH.MPT
-        if arch in ("BaichuanForCausalLM", "BaiChuanForCausalLM"):
-            return gguf.MODEL_ARCH.BAICHUAN
-        if arch in ("FalconForCausalLM", "RWForCausalLM"):
-            return gguf.MODEL_ARCH.FALCON
-        if arch == "GPTBigCodeForCausalLM":
-            return gguf.MODEL_ARCH.STARCODER
-        if arch == "GPTRefactForCausalLM":
-            return gguf.MODEL_ARCH.REFACT
-        if arch == "PersimmonForCausalLM":
-            return gguf.MODEL_ARCH.PERSIMMON
-        if arch in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
-            return gguf.MODEL_ARCH.STABLELM
-        if arch == "QWenLMHeadModel":
-            return gguf.MODEL_ARCH.QWEN
-        if arch == "MixtralForCausalLM":
-            return gguf.MODEL_ARCH.LLAMA
-        if arch == "GPT2LMHeadModel":
-            return gguf.MODEL_ARCH.GPT2
-        if arch == "PhiForCausalLM":
-            return gguf.MODEL_ARCH.PHI2
-        if arch == "PlamoForCausalLM":
-            return gguf.MODEL_ARCH.PLAMO
-
-        raise NotImplementedError(f'Architecture "{arch}" not supported!')
-
     def _set_vocab_gpt2(self):
         dir_model = self.dir_model
         hparams = self.hparams
-        tokens: list[bytearray] = []
+        tokens: list[str] = []
         toktypes: list[int] = []
 
         from transformers import AutoTokenizer
@@ -252,8 +243,7 @@ class Model:
 
         for i in range(vocab_size):
             if i not in reverse_vocab:
-                pad_token = f"[PAD{i}]".encode('utf-8')
-                tokens.append(bytearray(pad_token))
+                tokens.append(f"[PAD{i}]")
                 toktypes.append(gguf.TokenType.USER_DEFINED)
             elif reverse_vocab[i] in added_vocab:
                 tokens.append(reverse_vocab[i])
@@ -272,6 +262,57 @@ class Model:
         special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
         special_vocab.add_to_gguf(self.gguf_writer)
 
+    def _set_vocab_qwen(self):
+        dir_model = self.dir_model
+        hparams = self.hparams
+        tokens: list[str] = []
+        toktypes: list[int] = []
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
+        vocab_size = hparams["vocab_size"]
+        assert max(tokenizer.get_vocab().values()) < vocab_size
+
+        merges = []
+        vocab = {}
+        mergeable_ranks = tokenizer.mergeable_ranks
+        for token, rank in mergeable_ranks.items():
+            vocab[QwenModel.token_bytes_to_string(token)] = rank
+            if len(token) == 1:
+                continue
+            merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
+            assert len(merged) == 2
+            merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
+
+        # for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined
+        added_vocab = tokenizer.special_tokens
+        reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in (vocab | added_vocab).items()}
+
+        for i in range(vocab_size):
+            if i not in reverse_vocab:
+                tokens.append(f"[PAD{i}]")
+                toktypes.append(gguf.TokenType.USER_DEFINED)
+            elif reverse_vocab[i] in added_vocab:
+                tokens.append(reverse_vocab[i])
+                toktypes.append(gguf.TokenType.CONTROL)
+            else:
+                tokens.append(reverse_vocab[i])
+                toktypes.append(gguf.TokenType.NORMAL)
+
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
+        special_vocab.merges = merges
+        # only add special tokens when they were not already loaded from config.json
+        if len(special_vocab.special_token_ids) == 0:
+            special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"])
+            special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"])
+        # this one is usually not in config.json anyway
+        special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])
+        special_vocab.add_to_gguf(self.gguf_writer)
+
     def _set_vocab_sentencepiece(self):
         from sentencepiece import SentencePieceProcessor
 
@@ -282,13 +323,12 @@ class Model:
         toktypes: list[int] = []
 
         if not tokenizer_path.is_file():
-            print(f'Error: Missing {tokenizer_path}', file=sys.stderr)
-            sys.exit(1)
+            raise FileNotFoundError(f"File not found: {tokenizer_path}")
 
         tokenizer = SentencePieceProcessor(str(tokenizer_path))
         vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
 
-        for token_id in range(vocab_size):
+        for token_id in range(tokenizer.vocab_size()):
             piece = tokenizer.id_to_piece(token_id)
             text = piece.encode("utf-8")
             score = tokenizer.get_score(token_id)
@@ -313,9 +353,34 @@ class Model:
                 added_tokens_json = json.load(f)
 
                 for key in added_tokens_json:
-                    tokens.append(key.encode("utf-8"))
-                    scores.append(-1000.0)
-                    toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
+                    key = key.encode("utf-8")
+                    if key not in tokens:
+                        tokens.append(key)
+                        scores.append(-1000.0)
+                        toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
+
+        assert len(tokens) == vocab_size
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def _set_vocab_llama_hf(self):
+        vocab = LlamaHfVocab(self.dir_model)
+        tokens = []
+        scores = []
+        toktypes = []
+
+        for text, score, toktype in vocab.all_tokens():
+            tokens.append(text)
+            scores.append(score)
+            toktypes.append(toktype)
+
+        assert len(tokens) == vocab.vocab_size
 
         self.gguf_writer.add_tokenizer_model("llama")
         self.gguf_writer.add_token_list(tokens)
@@ -326,7 +391,10 @@ class Model:
         special_vocab.add_to_gguf(self.gguf_writer)
 
 
+@Model.register("GPTNeoXForCausalLM")
 class GPTNeoXModel(Model):
+    model_arch = gguf.MODEL_ARCH.GPTNEOX
+
     def set_gguf_parameters(self):
         block_count = self.hparams["num_hidden_layers"]
 
@@ -343,7 +411,10 @@ class GPTNeoXModel(Model):
         self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
 
 
+@Model.register("BloomForCausalLM")
 class BloomModel(Model):
+    model_arch = gguf.MODEL_ARCH.BLOOM
+
     def set_gguf_parameters(self):
         self.gguf_writer.add_name("Bloom")
         n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
@@ -435,7 +506,21 @@ class BloomModel(Model):
                 print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}")
 
 
+@Model.register("MPTForCausalLM")
 class MPTModel(Model):
+    model_arch = gguf.MODEL_ARCH.MPT
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_gpt2()
+        except Exception:
+            # Fallback for SEA-LION model
+            self._set_vocab_sentencepiece()
+            self.gguf_writer.add_add_bos_token(False)
+            self.gguf_writer.add_pad_token_id(3)
+            self.gguf_writer.add_eos_token_id(1)
+            self.gguf_writer.add_unk_token_id(0)
+
     def set_gguf_parameters(self):
         block_count = self.hparams["n_layers"]
         self.gguf_writer.add_name(self.dir_model.name)
@@ -449,7 +534,10 @@ class MPTModel(Model):
         self.gguf_writer.add_layer_norm_eps(1e-5)
         if self.hparams["attn_config"]["clip_qkv"] is not None:
             self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"])
-        self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"])
+        if self.hparams["attn_config"]["alibi"]:
+            self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"])
+        else:
+            self.gguf_writer.add_max_alibi_bias(0.0)
 
     def write_tensors(self):
         block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers"))
@@ -470,7 +558,8 @@ class MPTModel(Model):
             # map tensor names
             if "scales" in name:
                 new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias", ".scales"))
-                new_name = new_name.replace("scales", "act.scales")
+                if new_name is not None:
+                    new_name = new_name.replace("scales", "act.scales")
             else:
                 new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
             if new_name is None:
@@ -496,13 +585,93 @@ class MPTModel(Model):
 
             self.gguf_writer.add_tensor(new_name, data)
 
-            # note: MPT output is tied to (same as) wte in original model;
-            # for easier implementation in llama.cpp it's duplicated in GGUF, though :/
-            if new_name == "token_embd.weight":
-                self.gguf_writer.add_tensor("output.weight", data)
+
+@Model.register("OrionForCausalLM")
+class OrionModel(Model):
+    model_arch = gguf.MODEL_ARCH.ORION
+
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+    def set_gguf_parameters(self):
+        block_count = self.hparams["num_hidden_layers"]
+        head_count = self.hparams["num_attention_heads"]
+        head_count_kv = self.hparams.get("num_key_value_heads", head_count)
+        hf_repo = self.hparams.get("_name_or_path", "")
+
+        ctx_length = 0
+        if "max_sequence_length" in self.hparams:
+            ctx_length = self.hparams["max_sequence_length"]
+        elif "max_position_embeddings" in self.hparams:
+            ctx_length = self.hparams["max_position_embeddings"]
+        elif "model_max_length" in self.hparams:
+            ctx_length = self.hparams["model_max_length"]
+        else:
+            print("gguf: can not find ctx length parameter.")
+            sys.exit()
+
+        self.gguf_writer.add_file_type(self.ftype)
+        self.gguf_writer.add_name(self.dir_model.name)
+        self.gguf_writer.add_source_hf_repo(hf_repo)
+        self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
+        self.gguf_writer.add_context_length(ctx_length)
+        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
+        self.gguf_writer.add_head_count(head_count)
+        self.gguf_writer.add_head_count_kv(head_count_kv)
+        # note: config provides rms norm but it is actually layer norm
+        # ref:  https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/276a17221ce42beb45f66fac657a41540e71f4f5/modeling_orion.py#L570-L571
+        self.gguf_writer.add_layer_norm_eps(self.hparams["rms_norm_eps"])
+
+    def write_tensors(self):
+        # Collect tensors from generator object
+        model_kv = dict(self.get_tensors())
+        block_count = self.hparams["num_hidden_layers"]
+        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+
+        for name, data_torch in model_kv.items():
+            # we don't need these
+            if name.endswith(".rotary_emb.inv_freq"):
+                continue
+
+            old_dtype = data_torch.dtype
+
+            # convert any unsupported data types to float32
+            if data_torch.dtype not in (torch.float16, torch.float32):
+                data_torch = data_torch.to(torch.float32)
+
+            data = data_torch.squeeze().numpy()
+
+            # map tensor names
+            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+            if new_name is None:
+                print(f"Can not map tensor {name!r}")
+                sys.exit()
+
+            n_dims = len(data.shape)
+            data_dtype = data.dtype
+
+            # if f32 desired, convert any float16 to float32
+            if self.ftype == 0 and data_dtype == np.float16:
+                data = data.astype(np.float32)
+
+            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
+            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
+                data = data.astype(np.float32)
+
+            # if f16 desired, convert any float32 2-dim weight tensors to float16
+            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+                data = data.astype(np.float16)
+
+            print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
+            self.gguf_writer.add_tensor(new_name, data)
 
 
+@Model.register("BaichuanForCausalLM", "BaiChuanForCausalLM")
 class BaichuanModel(Model):
+    model_arch = gguf.MODEL_ARCH.BAICHUAN
+
     def set_vocab(self):
         self._set_vocab_sentencepiece()
 
@@ -617,7 +786,152 @@ class BaichuanModel(Model):
         return weights[r * n_part:r * n_part + r, ...]
 
 
+@Model.register("XverseForCausalLM")
+class XverseModel(Model):
+    model_arch = gguf.MODEL_ARCH.XVERSE
+
+    def set_vocab(self):
+        assert (self.dir_model / "tokenizer.json").is_file()
+        dir_model = self.dir_model
+        hparams = self.hparams
+
+        tokens: list[bytearray] = []
+        toktypes: list[int] = []
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(dir_model)
+        vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
+        assert max(tokenizer.vocab.values()) < vocab_size
+
+        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
+        added_vocab = tokenizer.get_added_vocab()
+
+        for token_id in range(vocab_size):
+            token_text = reverse_vocab[token_id].encode('utf-8')
+            # replace "\x00" to string with length > 0
+            if token_text == b"\x00":
+                toktype = gguf.TokenType.BYTE  # special
+                token_text = f"<{token_text}>".encode('utf-8')
+            elif re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
+                toktype = gguf.TokenType.BYTE  # special
+            elif reverse_vocab[token_id] in added_vocab:
+                if tokenizer.added_tokens_decoder[token_id].special:
+                    toktype = gguf.TokenType.CONTROL
+                else:
+                    toktype = gguf.TokenType.USER_DEFINED
+            else:
+                toktype = gguf.TokenType.NORMAL
+
+            tokens.append(token_text)
+            toktypes.append(toktype)
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        block_count = self.hparams["num_hidden_layers"]
+        head_count = self.hparams["num_attention_heads"]
+        head_count_kv = self.hparams.get("num_key_value_heads", head_count)
+        hf_repo = self.hparams.get("_name_or_path", "")
+
+        ctx_length = 0
+        if "max_sequence_length" in self.hparams:
+            ctx_length = self.hparams["max_sequence_length"]
+        elif "max_position_embeddings" in self.hparams:
+            ctx_length = self.hparams["max_position_embeddings"]
+        elif "model_max_length" in self.hparams:
+            ctx_length = self.hparams["model_max_length"]
+        else:
+            print("gguf: can not find ctx length parameter.")
+            sys.exit()
+
+        self.gguf_writer.add_name(self.dir_model.name)
+        self.gguf_writer.add_source_hf_repo(hf_repo)
+        self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
+        self.gguf_writer.add_context_length(ctx_length)
+        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
+        self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count(head_count)
+        self.gguf_writer.add_head_count_kv(head_count_kv)
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
+
+        if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
+            if self.hparams["rope_scaling"].get("type") == "linear":
+                self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+                self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
+
+    def write_tensors(self):
+        # Collect tensors from generator object
+        model_kv = dict(self.get_tensors())
+        block_count = self.hparams["num_hidden_layers"]
+        head_count = self.hparams["num_attention_heads"]
+        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+        head_count_kv = self.hparams.get("num_key_value_heads", head_count)
+
+        for name, data_torch in model_kv.items():
+            # we don't need these
+            if name.endswith(".rotary_emb.inv_freq"):
+                continue
+
+            old_dtype = data_torch.dtype
+
+            # convert any unsupported data types to float32
+            if data_torch.dtype not in (torch.float16, torch.float32):
+                data_torch = data_torch.to(torch.float32)
+
+            # HF models permute some of the tensors, so we need to undo that
+            if name.endswith(("q_proj.weight")):
+                data_torch = self._reverse_hf_permute(data_torch, head_count, head_count)
+            if name.endswith(("k_proj.weight")):
+                data_torch = self._reverse_hf_permute(data_torch, head_count, head_count_kv)
+
+            data = data_torch.squeeze().numpy()
+
+            # map tensor names
+            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+            if new_name is None:
+                print(f"Can not map tensor {name!r}")
+                sys.exit()
+
+            n_dims = len(data.shape)
+            data_dtype = data.dtype
+
+            # if f32 desired, convert any float16 to float32
+            if self.ftype == 0 and data_dtype == np.float16:
+                data = data.astype(np.float32)
+
+            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
+            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
+                data = data.astype(np.float32)
+
+            # if f16 desired, convert any float32 2-dim weight tensors to float16
+            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+                data = data.astype(np.float16)
+
+            print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
+            self.gguf_writer.add_tensor(new_name, data)
+
+    def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
+        if n_kv_head is not None and n_head != n_kv_head:
+            n_head //= n_kv_head
+
+        return (
+            weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+            .swapaxes(1, 2)
+            .reshape(weights.shape)
+        )
+
+
+@Model.register("FalconForCausalLM", "RWForCausalLM")
 class FalconModel(Model):
+    model_arch = gguf.MODEL_ARCH.FALCON
+
     def set_gguf_parameters(self):
         block_count = self.hparams.get("num_hidden_layers")
         if block_count is None:
@@ -710,7 +1024,10 @@ class FalconModel(Model):
             self.gguf_writer.add_tensor(new_name, data)
 
 
+@Model.register("GPTBigCodeForCausalLM")
 class StarCoderModel(Model):
+    model_arch = gguf.MODEL_ARCH.STARCODER
+
     def set_gguf_parameters(self):
         block_count = self.hparams["n_layer"]
 
@@ -725,7 +1042,10 @@ class StarCoderModel(Model):
         self.gguf_writer.add_file_type(self.ftype)
 
 
+@Model.register("GPTRefactForCausalLM")
 class RefactModel(Model):
+    model_arch = gguf.MODEL_ARCH.REFACT
+
     def set_gguf_parameters(self):
         hidden_dim = self.hparams["n_embd"]
         inner_dim = 4 * hidden_dim
@@ -809,7 +1129,10 @@ class RefactModel(Model):
             self.gguf_writer.add_tensor(new_name, data)
 
 
+@Model.register("PersimmonForCausalLM")
 class PersimmonModel(Model):
+    model_arch = gguf.MODEL_ARCH.PERSIMMON
+
     def set_gguf_parameters(self):
         block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers"))
         head_count = self.hparams["num_attention_heads"]
@@ -817,15 +1140,21 @@ class PersimmonModel(Model):
         hidden_size = self.hparams["hidden_size"]
 
         self.gguf_writer.add_name('persimmon-8b-chat')
+        self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
         self.gguf_writer.add_embedding_length(hidden_size)
         self.gguf_writer.add_block_count(block_count)
         self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
-        self.gguf_writer.add_rope_dimension_count(hidden_size // head_count)
+
+        # NOTE: not sure about this change - why does the model not have a rope dimension count when it is smaller
+        #       than the head size?
+        #       ref: https://github.com/ggerganov/llama.cpp/pull/4889
+        # self.gguf_writer.add_rope_dimension_count(hidden_size // head_count)
+        self.gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2)
+
         self.gguf_writer.add_head_count(head_count)
         self.gguf_writer.add_head_count_kv(head_count_kv)
         self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"])
         self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
 
     def set_vocab(self):
         self._set_vocab_sentencepiece()
@@ -851,7 +1180,17 @@ class PersimmonModel(Model):
             self.gguf_writer.add_tensor(new_name, data)
 
 
+@Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM")
 class StableLMModel(Model):
+    model_arch = gguf.MODEL_ARCH.STABLELM
+
+    def set_vocab(self):
+        if (self.dir_model / "tokenizer.json").is_file():
+            self._set_vocab_gpt2()
+        else:
+            # StableLM 2 1.6B uses a vocab in a similar format to Qwen's vocab
+            self._set_vocab_qwen()
+
     def set_gguf_parameters(self):
         hparams = self.hparams
         block_count = hparams["num_hidden_layers"]
@@ -861,18 +1200,314 @@ class StableLMModel(Model):
         self.gguf_writer.add_embedding_length(hparams["hidden_size"])
         self.gguf_writer.add_block_count(block_count)
         self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
-        self.gguf_writer.add_rope_dimension_count(int(hparams["rope_pct"] * (hparams["hidden_size"] // hparams["num_attention_heads"])))
+        rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"])
+        self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
         self.gguf_writer.add_head_count(hparams["num_attention_heads"])
         self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
-        self.gguf_writer.add_layer_norm_eps(1e-5)
+        self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"]))
 
 
-class MixtralModel(Model):
+@Model.register("LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM")
+class LlamaModel(Model):
+    model_arch = gguf.MODEL_ARCH.LLAMA
+
+    def set_vocab(self):
+        try:
+            self. _set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_llama_hf()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+        self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
+
+    # Same as super class, but permuting q_proj, k_proj
+    def write_tensors(self):
+        block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
+        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+        n_head = self.hparams.get("num_attention_heads")
+        n_kv_head = self.hparams.get("num_key_value_heads")
+        n_experts = self.hparams.get("num_local_experts")
+        experts = dict()
+        for name, data_torch in self.get_tensors():
+            # we don't need these
+            if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
+                continue
+
+            old_dtype = data_torch.dtype
+
+            # convert any unsupported data types to float32
+            if data_torch.dtype not in (torch.float16, torch.float32):
+                data_torch = data_torch.to(torch.float32)
+
+            data = data_torch.numpy()
+
+            if name.endswith("q_proj.weight"):
+                data = permute(data, n_head, n_head)
+            if name.endswith("k_proj.weight"):
+                data = permute(data, n_head, n_kv_head)
+
+            data = data.squeeze()
+
+            # process the experts separately
+            if name.find("block_sparse_moe.experts") != -1:
+                experts[name] = data
+                if len(experts) >= n_experts:
+                    # merge the experts into a single 3d tensor
+                    for bid in range(block_count):
+                        for wid in range(1, 4):
+                            full = True
+                            for xid in range(n_experts):
+                                ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.w{wid}.weight"
+                                if ename not in experts:
+                                    full = False
+                                    break
+                            if not full:
+                                continue
+
+                            datas = []
+                            for xid in range(n_experts):
+                                ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.w{wid}.weight"
+                                datas.append(experts[ename])
+                                del experts[ename]
+
+                            data = np.stack(datas, axis=0)
+                            data_dtype = data.dtype
+
+                            if self.ftype == 0 and data_dtype == np.float16:
+                                data = data.astype(np.float32)
+
+                            if self.ftype == 1 and data_dtype == np.float32:
+                                data = data.astype(np.float16)
+
+                            merged_name = f"layers.{bid}.feed_forward.experts.w{wid}.weight"
+
+                            new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias"))
+                            if new_name is None:
+                                print(f"Can not map tensor {name!r}")
+                                sys.exit()
+
+                            print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}")
+
+                            self.gguf_writer.add_tensor(new_name, data)
+                continue
+
+            # map tensor names
+            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+            if new_name is None:
+                print(f"Can not map tensor {name!r}")
+                sys.exit()
+
+            n_dims = len(data.shape)
+            data_dtype = data.dtype
+
+            # if f32 desired, convert any float16 to float32
+            if self.ftype == 0 and data_dtype == np.float16:
+                data = data.astype(np.float32)
+
+            # 1d tensors need to be converted to float32
+            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
+                data = data.astype(np.float32)
+
+            # if f16 desired, convert any float32 2-dim weight tensors to float16
+            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+                data = data.astype(np.float16)
+
+            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
+
+            self.gguf_writer.add_tensor(new_name, data)
+
+        if len(experts) > 0:
+            raise ValueError(f"Unprocessed experts: {experts.keys()}")
+
+
+@Model.register("GrokForCausalLM")
+class GrokModel(Model):
+    model_arch = gguf.MODEL_ARCH.GROK
+
     def set_vocab(self):
         self._set_vocab_sentencepiece()
 
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
 
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_name("Grok")
+
+    def write_tensors(self):
+        block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
+        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+        n_experts = self.hparams.get("num_local_experts")
+        experts = dict()
+        for name, data_torch in self.get_tensors():
+            # we don't need these
+            if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
+                continue
+
+            old_dtype = data_torch.dtype
+
+            # convert any unsupported data types to float32
+            if data_torch.dtype not in (torch.float16, torch.float32):
+                data_torch = data_torch.to(torch.float32)
+
+            data = data_torch.squeeze().numpy()
+
+            # process the experts separately
+            if name.find(".moe.") != -1:
+                experts[name] = data
+                if len(experts) >= n_experts:
+                    # merge the experts into a single 3d tensor
+                    for bid in range(block_count):
+                        for wid in ["linear", "linear_1", "linear_v"]:
+                            full = True
+                            for xid in range(n_experts):
+                                ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid}.weight"
+                                if ename not in experts:
+                                    full = False
+                                    break
+                            if not full:
+                                continue
+
+                            datas = []
+                            for xid in range(n_experts):
+                                ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid}.weight"
+                                datas.append(experts[ename])
+                                del experts[ename]
+
+                            data = np.stack(datas, axis=0)
+                            data_dtype = data.dtype
+
+                            if self.ftype == 0 and data_dtype == np.float16:
+                                data = data.astype(np.float32)
+
+                            if self.ftype == 1 and data_dtype == np.float32:
+                                data = data.astype(np.float16)
+
+                            merged_name = f"transformer.decoder_layer.{bid}.moe.{wid}.weight"
+
+                            new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias"))
+                            if new_name is None:
+                                print(f"Can not map tensor {name!r}")
+                                sys.exit()
+
+                            print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}")
+
+                            self.gguf_writer.add_tensor(new_name, data)
+                continue
+
+            # map tensor names
+            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+            if new_name is None:
+                print(f"Can not map tensor {name!r}")
+                sys.exit()
+
+            n_dims = len(data.shape)
+            data_dtype = data.dtype
+
+            # if f32 desired, convert any float16 to float32
+            if self.ftype == 0 and data_dtype == np.float16:
+                data = data.astype(np.float32)
+
+            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
+            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
+                data = data.astype(np.float32)
+
+            # if f16 desired, convert any float32 2-dim weight tensors to float16
+            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+                data = data.astype(np.float16)
+
+            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
+
+            self.gguf_writer.add_tensor(new_name, data)
+
+
+@Model.register("MiniCPMForCausalLM")
+class MiniCPMModel(Model):
+    model_arch = gguf.MODEL_ARCH.MINICPM
+
+    def set_gguf_parameters(self):
+        block_count = self.hparams["num_hidden_layers"]
+        self.gguf_writer.add_name("MiniCPM")
+        self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
+        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
+        self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def set_vocab(self):
+        self._set_vocab_llama_hf()
+
+    def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
+        if n_kv_head is not None and n_head != n_kv_head:
+            n_head //= n_kv_head
+
+        return (
+            weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+            .swapaxes(1, 2)
+            .reshape(weights.shape)
+        )
+
+    def write_tensors(self):
+        block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
+        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+        n_head = self.hparams.get("num_attention_heads")
+        n_kv_head = self.hparams.get("num_key_value_heads")
+        for name, data_torch in self.get_tensors():
+            # we don't need these
+            if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
+                continue
+
+            old_dtype = data_torch.dtype
+
+            # convert any unsupported data types to float32
+            if data_torch.dtype not in (torch.float16, torch.float32):
+                data_torch = data_torch.to(torch.float32)
+
+            # HF models permute some of the tensors, so we need to undo that
+            if name.endswith(("q_proj.weight")):
+                data_torch = self._reverse_hf_permute(data_torch, n_head, n_head)
+            if name.endswith(("k_proj.weight")):
+                data_torch = self._reverse_hf_permute(data_torch, n_head, n_kv_head)
+
+            data = data_torch.squeeze().numpy()
+
+            # map tensor names
+            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+            if new_name is None:
+                print(f"Can not map tensor {name!r}")
+                sys.exit()
+
+            n_dims = len(data.shape)
+            data_dtype = data.dtype
+
+            # if f32 desired, convert any float16 to float32
+            if self.ftype == 0 and data_dtype == np.float16:
+                data = data.astype(np.float32)
+
+            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
+            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
+                data = data.astype(np.float32)
+
+            # if f16 desired, convert any float32 2-dim weight tensors to float16
+            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+                data = data.astype(np.float16)
+
+            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
+
+            self.gguf_writer.add_tensor(new_name, data)
+
+
+@Model.register("QWenLMHeadModel")
 class QwenModel(Model):
+    model_arch = gguf.MODEL_ARCH.QWEN
+
     @staticmethod
     def token_bytes_to_string(b):
         from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
@@ -880,7 +1515,7 @@ class QwenModel(Model):
         return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
 
     @staticmethod
-    def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: Optional[int] = None) -> list[bytes]:
+    def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]:
         parts = [bytes([b]) for b in token]
         while True:
             min_idx = None
@@ -897,52 +1532,7 @@ class QwenModel(Model):
         return parts
 
     def set_vocab(self):
-        dir_model = self.dir_model
-        hparams = self.hparams
-        tokens: list[bytearray] = []
-        toktypes: list[int] = []
-
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
-        vocab_size = hparams["vocab_size"]
-        assert max(tokenizer.get_vocab().values()) < vocab_size
-
-        merges = []
-        vocab = {}
-        mergeable_ranks = tokenizer.mergeable_ranks
-        for token, rank in mergeable_ranks.items():
-            vocab[self.token_bytes_to_string(token)] = rank
-            if len(token) == 1:
-                continue
-            merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
-            assert len(merged) == 2
-            merges.append(' '.join(map(self.token_bytes_to_string, merged)))
-
-        reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in vocab.items()}
-        added_vocab = tokenizer.special_tokens
-
-        for i in range(vocab_size):
-            if i not in reverse_vocab:
-                pad_token = f"[PAD{i}]".encode("utf-8")
-                tokens.append(bytearray(pad_token))
-                toktypes.append(gguf.TokenType.USER_DEFINED)
-            elif reverse_vocab[i] in added_vocab:
-                tokens.append(reverse_vocab[i])
-                toktypes.append(gguf.TokenType.CONTROL)
-            else:
-                tokens.append(reverse_vocab[i])
-                toktypes.append(gguf.TokenType.NORMAL)
-
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
-        special_vocab.merges = merges
-        special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"])
-        special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"])
-        special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])
-        special_vocab.add_to_gguf(self.gguf_writer)
+        self._set_vocab_qwen()
 
     def set_gguf_parameters(self):
         self.gguf_writer.add_name("Qwen")
@@ -997,7 +1587,15 @@ class QwenModel(Model):
             self.gguf_writer.add_tensor(new_name, data)
 
 
+@Model.register("Qwen2ForCausalLM")
+class Qwen2Model(Model):
+    model_arch = gguf.MODEL_ARCH.QWEN2
+
+
+@Model.register("GPT2LMHeadModel")
 class GPT2Model(Model):
+    model_arch = gguf.MODEL_ARCH.GPT2
+
     def set_gguf_parameters(self):
         self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_block_count(self.hparams["n_layer"])
@@ -1014,7 +1612,7 @@ class GPT2Model(Model):
 
         for name, data_torch in self.get_tensors():
             # we don't need these
-            if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq", ".attn.bias")):
+            if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq", ".attn.bias", ".attn.masked_bias")):
                 continue
 
             if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")):
@@ -1059,24 +1657,35 @@ class GPT2Model(Model):
                 self.gguf_writer.add_tensor("output.weight", data)
 
 
+@Model.register("PhiForCausalLM")
 class Phi2Model(Model):
+    model_arch = gguf.MODEL_ARCH.PHI2
+
     def set_gguf_parameters(self):
-        block_count = self.hparams["n_layer"]
+        block_count = self.find_hparam(["num_hidden_layers", "n_layer"])
+
+        rot_pct = self.find_hparam(["partial_rotary_factor"])
+        n_embd = self.find_hparam(["hidden_size", "n_embd"])
+        n_head = self.find_hparam(["num_attention_heads", "n_head"])
 
         self.gguf_writer.add_name("Phi2")
-        self.gguf_writer.add_context_length(self.hparams["n_positions"])
-        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
-        self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
+        self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"]))
+
+        self.gguf_writer.add_embedding_length(n_embd)
+        self.gguf_writer.add_feed_forward_length(4 * n_embd)
         self.gguf_writer.add_block_count(block_count)
-        self.gguf_writer.add_head_count(self.hparams["n_head"])
-        self.gguf_writer.add_head_count_kv(self.hparams["n_head"])
-        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
-        self.gguf_writer.add_rope_dimension_count(self.hparams["rotary_dim"])
+        self.gguf_writer.add_head_count(n_head)
+        self.gguf_writer.add_head_count_kv(n_head)
+        self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_epsilon", "layer_norm_eps"]))
+        self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
         self.gguf_writer.add_file_type(self.ftype)
         self.gguf_writer.add_add_bos_token(False)
 
 
+@Model.register("PlamoForCausalLM")
 class PlamoModel(Model):
+    model_arch = gguf.MODEL_ARCH.PLAMO
+
     def set_vocab(self):
         self._set_vocab_sentencepiece()
 
@@ -1155,6 +1764,594 @@ class PlamoModel(Model):
             self.gguf_writer.add_tensor(new_name, data)
 
 
+@Model.register("CodeShellForCausalLM")
+class CodeShellModel(Model):
+    model_arch = gguf.MODEL_ARCH.CODESHELL
+
+    def set_gguf_parameters(self):
+        block_count = self.hparams["n_layer"]
+
+        self.gguf_writer.add_name("CodeShell")
+        self.gguf_writer.add_context_length(self.hparams["n_positions"])
+        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
+        self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_head_count(self.hparams["n_head"])
+        self.gguf_writer.add_head_count_kv(self.hparams["num_query_groups"])
+        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_file_type(self.ftype)
+        self.gguf_writer.add_rope_freq_base(10000.0)
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+        self.gguf_writer.add_rope_scaling_factor(1.0)
+
+    def write_tensors(self):
+        block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
+        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+        tensors = dict(self.get_tensors())
+        has_lm_head = "lm_head.weight" in tensors.keys() or "output.weight" in tensors.keys()
+        for name, data_torch in tensors.items():
+            # we don't need these
+            if name.endswith((".attn.rotary_emb.inv_freq")):
+                continue
+
+            old_dtype = data_torch.dtype
+
+            # convert any unsupported data types to float32
+            if data_torch.dtype not in (torch.float16, torch.float32):
+                data_torch = data_torch.to(torch.float32)
+
+            data = data_torch.squeeze().numpy()
+
+            # map tensor names
+            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+            if new_name is None:
+                print(f"Can not map tensor {name!r}")
+                sys.exit()
+
+            n_dims = len(data.shape)
+            data_dtype = data.dtype
+
+            # if f32 desired, convert any float16 to float32
+            if self.ftype == 0 and data_dtype == np.float16:
+                data = data.astype(np.float32)
+
+            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
+            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
+                data = data.astype(np.float32)
+
+            # if f16 desired, convert any float32 2-dim weight tensors to float16
+            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+                data = data.astype(np.float16)
+
+            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
+
+            self.gguf_writer.add_tensor(new_name, data)
+
+            if not has_lm_head and name == "transformer.wte.weight":
+                self.gguf_writer.add_tensor("output.weight", data)
+                print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}")
+
+
+@Model.register("InternLM2ForCausalLM")
+class InternLM2Model(Model):
+    model_arch = gguf.MODEL_ARCH.INTERNLM2
+
+    def set_vocab(self):
+        # (TODO): Is there a better way?
+        # Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character
+        # \x00 specially and convert it into an emoji character to prevent it from being mistakenly
+        # recognized as an empty string in C++.
+        from sentencepiece import SentencePieceProcessor
+        from sentencepiece import sentencepiece_model_pb2 as model
+
+        tokenizer_path = self.dir_model / 'tokenizer.model'
+
+        tokens: list[bytes] = []
+        scores: list[float] = []
+        toktypes: list[int] = []
+
+        if not tokenizer_path.is_file():
+            print(f'Error: Missing {tokenizer_path}', file=sys.stderr)
+            sys.exit(1)
+
+        sentencepiece_model = model.ModelProto()
+        sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
+        add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
+
+        tokenizer = SentencePieceProcessor(str(tokenizer_path))
+        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
+
+        for token_id in range(vocab_size):
+            piece = tokenizer.id_to_piece(token_id)
+            text = piece.encode("utf-8")
+            score = tokenizer.get_score(token_id)
+            if text == b"\x00":
+                # (TODO): fixme
+                # Hack here and replace the \x00 characters.
+                print(f"InternLM2 convert token '{text}' to '🐉'!")
+                text = "🐉"
+
+            toktype = SentencePieceTokenTypes.NORMAL
+            if tokenizer.is_unknown(token_id):
+                toktype = SentencePieceTokenTypes.UNKNOWN
+            elif tokenizer.is_control(token_id):
+                toktype = SentencePieceTokenTypes.CONTROL
+            elif tokenizer.is_unused(token_id):
+                toktype = SentencePieceTokenTypes.UNUSED
+            elif tokenizer.is_byte(token_id):
+                toktype = SentencePieceTokenTypes.BYTE
+
+            tokens.append(text)
+            scores.append(score)
+            toktypes.append(toktype)
+
+        added_tokens_file = self.dir_model / 'added_tokens.json'
+        if added_tokens_file.is_file():
+            with open(added_tokens_file, "r", encoding="utf-8") as f:
+                added_tokens_json = json.load(f)
+
+                for key in added_tokens_json:
+                    tokens.append(key.encode("utf-8"))
+                    scores.append(-1000.0)
+                    toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+        self.gguf_writer.add_add_space_prefix(add_prefix)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        old_eos = special_vocab.special_token_ids["eos"]
+        if "chat" in os.path.basename(self.dir_model.absolute()):
+            # For the chat model, we replace the eos with '<|im_end|>'.
+            special_vocab.special_token_ids["eos"] = self._try_get_sft_eos(tokenizer)
+            print(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \
+in chat mode so that the conversation can end normally.")
+
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def _try_get_sft_eos(self, tokenizer):
+        unused_145_list = tokenizer.encode('[UNUSED_TOKEN_145]')
+        im_end_list = tokenizer.encode('<|im_end|>')
+        assert (len(unused_145_list) == 1) ^ (len(im_end_list) == 1)
+        if len(unused_145_list) == 1:
+            eos_token = unused_145_list[0]
+        if len(im_end_list) == 1:
+            eos_token = im_end_list[0]
+        return eos_token
+
+    def _hf_permute_qk(self, weights, n_head: int, n_head_kv: int):
+        if n_head_kv is not None and n_head != n_head_kv:
+            n_head = n_head_kv
+        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+                .swapaxes(1, 2)
+                .reshape(weights.shape))
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_name("InternLM2")
+        self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
+        self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
+        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
+        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
+        self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"])
+        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
+        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
+
+    def post_write_tensors(self, tensor_map, name, data_torch):
+        old_dtype = data_torch.dtype
+
+        # convert any unsupported data types to float32
+        if data_torch.dtype not in (torch.float16, torch.float32):
+            data_torch = data_torch.to(torch.float32)
+
+        data = data_torch.squeeze().numpy()
+
+        # map tensor names
+        new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+        if new_name is None:
+            print(f"Can not map tensor {name!r}")
+            sys.exit()
+
+        n_dims = len(data.shape)
+        data_dtype = data.dtype
+
+        # if f32 desired, convert any float16 to float32
+        if self.ftype == 0 and data_dtype == np.float16:
+            data = data.astype(np.float32)
+
+        # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
+        if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
+            data = data.astype(np.float32)
+
+        # if f16 desired, convert any float32 2-dim weight tensors to float16
+        if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+            data = data.astype(np.float16)
+
+        print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
+        self.gguf_writer.add_tensor(new_name, data)
+
+    def write_tensors(self):
+        from einops import rearrange
+
+        num_heads = self.hparams.get("num_attention_heads")
+        num_kv_heads = self.hparams.get("num_key_value_heads")
+        hidden_size = self.hparams.get("hidden_size")
+        q_per_kv = num_heads // num_kv_heads
+        head_dim = hidden_size // num_heads
+        num_groups = num_heads // q_per_kv
+
+        block_count = self.hparams["num_hidden_layers"]
+        model_kv = dict(self.get_tensors())
+        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+        qkv_pattern = r"model\.layers\.(\d+)\.attention\.wqkv"
+        for name, data_torch in model_kv.items():
+            # we don't need these
+            if name.endswith(".rotary_emb.inv_freq"):
+                continue
+
+            if re.match(qkv_pattern, name):
+                bid = re.findall(qkv_pattern, name)[0]
+                qkv = data_torch
+                qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim)
+                q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :]
+                # The model weights of q and k equire additional reshape.
+                q = self._hf_permute_qk(rearrange(q, " o g n i ->  o (g n i)").T, num_heads, num_heads)
+                k = self._hf_permute_qk(rearrange(k, " o g n i ->  o (g n i)").T, num_heads, num_kv_heads)
+                v = rearrange(v, " o g n i ->  o (g n i)").T
+                self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wq.weight", q)
+                self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wk.weight", k)
+                self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wv.weight", v)
+            else:
+                self.post_write_tensors(tensor_map, name, data_torch)
+
+
+@Model.register("BertModel", "CamembertModel")
+class BertModel(Model):
+    model_arch = gguf.MODEL_ARCH.BERT
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.vocab_size = None
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_causal_attention(False)
+
+        # get pooling path
+        pooling_path = None
+        module_path = self.dir_model / "modules.json"
+        if module_path.is_file():
+            with open(module_path, encoding="utf-8") as f:
+                modules = json.load(f)
+            for mod in modules:
+                if mod["type"] == "sentence_transformers.models.Pooling":
+                    pooling_path = mod["path"]
+                    break
+
+        # get pooling type
+        if pooling_path is not None:
+            with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f:
+                pooling = json.load(f)
+            if pooling["pooling_mode_mean_tokens"]:
+                pooling_type = gguf.PoolingType.MEAN
+            elif pooling["pooling_mode_cls_token"]:
+                pooling_type = gguf.PoolingType.CLS
+            else:
+                raise NotImplementedError("Only MEAN and CLS pooling types supported")
+            self.gguf_writer.add_pooling_type(pooling_type)
+
+    def set_vocab(self):
+        # use huggingface vocab to get all tokens
+        vocab = LlamaHfVocab(self.dir_model, ignore_nonllama=True)
+        tokens, scores, toktypes = zip(*vocab.all_tokens())
+        assert len(tokens) == vocab.vocab_size
+        self.vocab_size = vocab.vocab_size
+
+        # we need this to validate the size of the token_type embeddings
+        # though currently we are passing all zeros to the token_type embeddings
+        n_token_types = len(set(toktypes))
+        self.gguf_writer.add_token_type_count(n_token_types)
+
+        # convert to phantom space vocab
+        def phantom(tok, typ):
+            if tok.startswith(b"[") and tok.endswith(b"]"):
+                return tok
+            if tok.startswith(b"##"):
+                return tok[2:]
+            return b"\xe2\x96\x81" + tok
+        tokens = tuple(phantom(t, y) for t, y in zip(tokens, toktypes))
+
+        # set up bos and eos tokens (cls and sep)
+        self.gguf_writer.add_bos_token_id(vocab.tokenizer.cls_token_id)
+        self.gguf_writer.add_eos_token_id(vocab.tokenizer.sep_token_id)
+
+        # add vocab to gguf
+        self.gguf_writer.add_tokenizer_model("bert")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        # handle special tokens
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def write_tensors(self):
+        tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+        tensors = dict(self.get_tensors())
+        for name, data_torch in tensors.items():
+            # we are only using BERT for embeddings so we don't need the pooling layer
+            if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"):
+                continue  # we don't need these
+
+            # map tensor names
+            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+            if new_name is None:
+                print(f"Can not map tensor {name!r}")
+                sys.exit()
+
+            data = data_torch.squeeze().numpy()
+            n_dims = len(data.shape)
+            new_dtype: type[np.floating[Any]]
+
+            if (
+                self.ftype == 1 and name.endswith(".weight") and n_dims == 2
+                and name != "embeddings.token_type_embeddings.weight"  # not used with get_rows, must be F32
+            ):
+                # if f16 desired, convert any float32 2-dim weight tensors to float16
+                new_dtype = np.float16
+            else:
+                # if f32 desired, convert any float16 to float32
+                new_dtype = np.float32
+
+            print(f"{new_name}, n_dims = {n_dims}, {data_torch.dtype} --> {new_dtype}")
+
+            if data.dtype != new_dtype:
+                data = data.astype(new_dtype)
+
+            self.gguf_writer.add_tensor(new_name, data)
+
+
+@Model.register("NomicBertModel")
+class NomicBertModel(BertModel):
+    model_arch = gguf.MODEL_ARCH.NOMIC_BERT
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # the HF config claims n_ctx=8192, but it uses RoPE scaling
+        self.hparams["n_ctx"] = 2048
+
+        # SwigLU activation
+        assert self.hparams["activation_function"] == "swiglu"
+        # this doesn't do anything in the HF version
+        assert self.hparams["causal"] is False
+        # no bias tensors
+        assert self.hparams["qkv_proj_bias"] is False
+        assert self.hparams["mlp_fc1_bias"] is False
+        assert self.hparams["mlp_fc2_bias"] is False
+        # norm at end of layer
+        assert self.hparams["prenorm"] is False
+        # standard RoPE
+        assert self.hparams["rotary_emb_fraction"] == 1.0
+        assert self.hparams["rotary_emb_interleaved"] is False
+        assert self.hparams["rotary_emb_scale_base"] is None
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
+
+    def get_tensors(self):
+        assert self.vocab_size is not None
+        for name, data in super().get_tensors():
+            # Nomic Embed's token embeddings tensor is padded, but llama.cpp wants tensor sizes to match exactly.
+            if name == 'embeddings.word_embeddings.weight' and data.shape[1] != self.vocab_size:
+                rounded_vocab_size = (self.vocab_size + 63) // 64 * 64
+                assert data.shape == (rounded_vocab_size, self.hparams["n_embd"])
+                data = data[:self.vocab_size, :]
+            yield name, data
+
+
+@Model.register("GemmaForCausalLM")
+class GemmaModel(Model):
+    model_arch = gguf.MODEL_ARCH.GEMMA
+
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+    def set_gguf_parameters(self):
+        hparams = self.hparams
+        block_count = hparams["num_hidden_layers"]
+
+        self.gguf_writer.add_name(self.dir_model.name)
+        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
+        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
+        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
+        self.gguf_writer.add_key_length(hparams["head_dim"])
+        self.gguf_writer.add_value_length(hparams["head_dim"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def write_tensors(self):
+        block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
+        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+
+        for name, data_torch in self.get_tensors():
+            old_dtype = data_torch.dtype
+
+            # convert any unsupported data types to float32
+            if data_torch.dtype not in (torch.float16, torch.float32):
+                data_torch = data_torch.to(torch.float32)
+
+            # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
+            if name.endswith("norm.weight"):
+                data_torch = data_torch + 1
+            data = data_torch.squeeze().numpy()
+
+            # map tensor names
+            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+            if new_name is None:
+                print(f"Can not map tensor {name!r}")
+                sys.exit()
+
+            n_dims = len(data.shape)
+            data_dtype = data.dtype
+
+            data = data.astype(np.float32)
+
+            # if f16 desired, convert any float32 2-dim weight tensors to float16
+            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+                data = data.astype(np.float16)
+
+            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
+
+            self.gguf_writer.add_tensor(new_name, data)
+
+
+@Model.register("Starcoder2ForCausalLM")
+class StarCoder2Model(Model):
+    model_arch = gguf.MODEL_ARCH.STARCODER2
+
+
+@Model.register("MambaForCausalLM", "MambaLMHeadModel")
+class MambaModel(Model):
+    model_arch = gguf.MODEL_ARCH.MAMBA
+
+    def set_vocab(self):
+        vocab_size = self.hparams["vocab_size"]
+        # Round vocab size to next multiple of 8
+        pad_vocab = self.hparams.get("pad_vocab_size_multiple", 8)
+        # pad using ceiling division
+        # ref: https://stackoverflow.com/a/17511341/22827863
+        vocab_size = -(vocab_size // -pad_vocab) * pad_vocab
+        self.hparams["vocab_size"] = vocab_size
+
+        if (self.dir_model / "tokenizer.json").is_file():
+            self._set_vocab_gpt2()
+        else:
+            # Use the GPT-NeoX tokenizer when no tokenizer files are present
+            tokenizer_path = Path(sys.path[0]) / "models" / "ggml-vocab-gpt-neox.gguf"
+            print(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'")
+            neox_reader = gguf.GGUFReader(tokenizer_path, "r")
+
+            field = neox_reader.get_field(gguf.Keys.Tokenizer.MODEL)
+            self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]))
+            field = neox_reader.get_field(gguf.Keys.Tokenizer.LIST)
+            self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])
+            field = neox_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
+            self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
+            field = neox_reader.get_field(gguf.Keys.Tokenizer.MERGES)
+            self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])
+            field = neox_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)
+            self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0])
+            field = neox_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)
+            self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0])
+            field = neox_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)
+            self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0])
+
+    def set_gguf_parameters(self):
+        d_model = self.find_hparam(["hidden_size", "d_model"])
+        d_conv  = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4
+        d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model
+        d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16
+        # ceiling division
+        # ref: https://stackoverflow.com/a/17511341/22827863
+        # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58
+        dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16)
+        rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
+
+        # Fail early for models which don't have a block expansion factor of 2
+        assert d_inner == 2 * d_model
+
+        self.gguf_writer.add_name(self.dir_model.name)
+        self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default
+        self.gguf_writer.add_embedding_length(d_model)
+        self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading
+        self.gguf_writer.add_head_count(0) # unused, but seemingly required when loading
+        self.gguf_writer.add_block_count(self.hparams["n_layer"])
+        self.gguf_writer.add_ssm_conv_kernel(d_conv)
+        self.gguf_writer.add_ssm_inner_size(d_inner)
+        self.gguf_writer.add_ssm_state_size(d_state)
+        self.gguf_writer.add_ssm_time_step_rank(dt_rank)
+        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def write_tensors(self):
+        block_count = self.hparams["n_layer"]
+        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+
+        tok_embd = None
+        tok_embd_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.TOKEN_EMBD] + ".weight"
+        output_name   = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.OUTPUT]     + ".weight"
+
+        for name, data_torch in self.get_tensors():
+            old_dtype = data_torch.dtype
+
+            # convert any unsupported data types to float32
+            if data_torch.dtype not in (torch.float16, torch.float32):
+                data_torch = data_torch.to(torch.float32)
+
+            # map tensor names
+            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+            if new_name is None:
+                print(f"Can not map tensor {name!r}")
+                sys.exit()
+
+            if name.endswith(".A_log"):
+                print("A_log --> A ==> " + new_name)
+                data_torch = -torch.exp(data_torch)
+
+            # assuming token_embd.weight is seen before output.weight
+            if tok_embd is not None and new_name == output_name:
+                if torch.equal(tok_embd, data_torch):
+                    print(f"{output_name} is equivalent to {tok_embd_name}, omitting")
+                    continue
+            if new_name == tok_embd_name:
+                tok_embd = data_torch
+
+            data = data_torch.squeeze().numpy()
+
+            n_dims = len(data.shape)
+            data_dtype = data.dtype
+
+            # if f32 desired, convert any float16 to float32
+            if self.ftype == 0 and data_dtype == np.float16:
+                data = data.astype(np.float32)
+
+            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
+            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
+                data = data.astype(np.float32)
+
+            # if f16 desired, convert big float32 2-dim weight tensors to float16
+            if self.ftype == 1 and data_dtype == np.float32 and new_name.removesuffix(".weight").endswith((".ssm_in", ".ssm_out", "token_embd", "output")) and n_dims == 2:
+                data = data.astype(np.float16)
+
+            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
+
+            self.gguf_writer.add_tensor(new_name, data)
+
+
+@Model.register("CohereForCausalLM")
+class CommandR2Model(Model):
+    model_arch = gguf.MODEL_ARCH.COMMAND_R
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # max_position_embeddings = 8192 in config.json but model was actually
+        # trained on 128k context length
+        self.hparams["max_position_embeddings"] = self.hparams["model_max_length"]
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_logit_scale(self.hparams["logit_scale"])
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+
+
 ###### CONVERSION LOGIC ######
 
 
@@ -1192,7 +2389,7 @@ def main() -> None:
 
     if args.awq_path:
         sys.path.insert(1, str(Path(__file__).parent / 'awq-py'))
-        from awq.apply_awq import add_scale_weights
+        from awq.apply_awq import add_scale_weights  # type: ignore[import-not-found]
         tmp_model_path = args.model / "weighted_model"
         dir_model = tmp_model_path
         if tmp_model_path.is_dir():
diff --git a/convert-llama-ggml-to-gguf.py b/convert-llama-ggml-to-gguf.py
index e359330af..cd9644fcb 100755
--- a/convert-llama-ggml-to-gguf.py
+++ b/convert-llama-ggml-to-gguf.py
@@ -2,6 +2,7 @@
 from __future__ import annotations
 
 import argparse
+import os
 import struct
 import sys
 from enum import IntEnum
@@ -9,7 +10,6 @@ from pathlib import Path
 
 import numpy as np
 
-import os
 if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf
@@ -371,15 +371,11 @@ def handle_metadata(cfg, hp):
         params = convert.Params.loadOriginalParamsJson(fakemodel, orig_config_path)
     else:
         raise ValueError('Unable to load metadata')
-    vocab = convert.load_vocab(
-        cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir,
-        cfg.vocabtype)
-    # FIXME: Respect cfg.vocab_dir?
-    svocab = gguf.SpecialVocab(cfg.model_metadata_dir,
-                               load_merges = cfg.vocabtype == 'bpe',
-                               n_vocab = vocab.vocab_size)
+    vocab_path = Path(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir)
+    vocab_factory = convert.VocabFactory(vocab_path)
+    vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype.split(","), cfg.model_metadata_dir)
     convert.check_vocab_size(params, vocab)
-    return (params, vocab, svocab)
+    return params, vocab, special_vocab
 
 
 def handle_args():
@@ -402,8 +398,8 @@ def handle_args():
                         help ='Load HuggingFace/.pth vocab and metadata from the specified directory')
     parser.add_argument("--vocab-dir", type=Path,
                         help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir")
-    parser.add_argument("--vocabtype", choices=["spm", "bpe"], default="spm",
-                        help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)")
+    parser.add_argument("--vocabtype", default="spm,hfft",
+                        help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm,hfft)")
     return parser.parse_args()
 
 
diff --git a/convert-lora-to-ggml.py b/convert-lora-to-ggml.py
index 35ce152f4..9a9936dec 100755
--- a/convert-lora-to-ggml.py
+++ b/convert-lora-to-ggml.py
@@ -5,17 +5,16 @@ import json
 import os
 import struct
 import sys
+from pathlib import Path
 from typing import Any, BinaryIO, Sequence
 
 import numpy as np
 import torch
 
-from pathlib import Path
 if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
 import gguf
 
-
 NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1}
 
 
@@ -60,7 +59,14 @@ if __name__ == '__main__':
     input_model = os.path.join(sys.argv[1], "adapter_model.bin")
     output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin")
 
-    model = torch.load(input_model, map_location="cpu")
+    if os.path.exists(input_model):
+        model = torch.load(input_model, map_location="cpu")
+    else:
+        input_model = os.path.join(sys.argv[1], "adapter_model.safetensors")
+        # lazy import load_file only if lora is in safetensors format.
+        from safetensors.torch import load_file
+        model = load_file(input_model, device="cpu")
+
     arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama"
 
     if arch_name not in gguf.MODEL_ARCH_NAMES.values():
diff --git a/convert-persimmon-to-gguf.py b/convert-persimmon-to-gguf.py
index 1ba5864dc..ccb99279e 100755
--- a/convert-persimmon-to-gguf.py
+++ b/convert-persimmon-to-gguf.py
@@ -1,11 +1,13 @@
 #!/usr/bin/env python3
-import torch
-import os
-from pprint import pprint
-import sys
 import argparse
+import os
+import sys
 from pathlib import Path
+from pprint import pprint
+
+import torch
 from sentencepiece import SentencePieceProcessor
+
 if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf
@@ -69,7 +71,7 @@ def main():
     persimmon_model = torch.load(args.ckpt_path)
     hparams = persimmon_model['args']
     pprint(hparams)
-    tensors = {}
+    tensors: dict[str, torch.Tensor] = {}
     _flatten_dict(persimmon_model['model'], tensors, None)
 
     arch = gguf.MODEL_ARCH.PERSIMMON
@@ -86,7 +88,8 @@ def main():
     gguf_writer.add_embedding_length(hidden_size)
     gguf_writer.add_block_count(block_count)
     gguf_writer.add_feed_forward_length(hparams.ffn_hidden_size)
-    gguf_writer.add_rope_dimension_count(hidden_size // head_count)
+    # ref: https://github.com/ggerganov/llama.cpp/pull/4889/commits/eea19039fc52ea2dbd1aab45b59ab4e3e29a3443
+    gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2)
     gguf_writer.add_head_count(head_count)
     gguf_writer.add_head_count_kv(head_count_kv)
     gguf_writer.add_rope_freq_base(hparams.rotary_emb_base)
@@ -103,12 +106,12 @@ def main():
     tensor_map = gguf.get_tensor_name_map(arch, block_count)
     print(tensor_map)
     for name in tensors.keys():
-        data = tensors[name]
+        data_torch = tensors[name]
         if name.endswith(".self_attention.rotary_emb.inv_freq"):
             continue
-        old_dtype = data.dtype
+        old_dtype = data_torch.dtype
         # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?)
-        data = data.to(torch.float32).squeeze().numpy()
+        data = data_torch.to(torch.float32).squeeze().numpy()
         new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
         if new_name is None:
             print("Can not map tensor '" + name + "'")
diff --git a/convert.py b/convert.py
index 70fe55e43..d212776ff 100755
--- a/convert.py
+++ b/convert.py
@@ -16,69 +16,42 @@ import re
 import signal
 import struct
 import sys
+import textwrap
 import time
-import warnings
 import zipfile
-from abc import ABCMeta, abstractmethod
-from argparse import ArgumentParser
+from abc import ABC, abstractmethod
 from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
 from dataclasses import dataclass
 from pathlib import Path
-from typing import (
-    IO,
-    TYPE_CHECKING,
-    Any,
-    Callable,
-    Iterable,
-    Literal,
-    Optional,
-    Tuple,
-    TypeVar,
-)
+from typing import TYPE_CHECKING, Any, Callable, ClassVar, IO, Iterable, Literal, Protocol, TypeVar, runtime_checkable
 
 import numpy as np
 from sentencepiece import SentencePieceProcessor
 
-try:
-    from transformers import AutoTokenizer
-except ModuleNotFoundError as e:
-    warnings.warn(f"Could not import AutoTokenizer from transformers: {e}")
+if 'NO_LOCAL_GGUF' not in os.environ:
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
+import gguf
 
-# If NO_LOCAL_GGUF is not set, try to import gguf from the local gguf-py directory
-if "NO_LOCAL_GGUF" not in os.environ:
-    # Use absolute path to the gguf-py directory
-    gguf_py_dir = str(Path(__file__).resolve().parent / "gguf-py")
-    print(gguf_py_dir)  # NOTE: Remove this once path is verified after changes are completed
-    if gguf_py_dir not in sys.path:
-        sys.path.insert(1, gguf_py_dir)
+if TYPE_CHECKING:
+    from typing import TypeAlias
 
-# Import gguf module
-try:
-    import gguf
-except ModuleNotFoundError as e:
-    print(f"Could not import gguf: {e}")
-    sys.exit(1)
-
-if TYPE_CHECKING:  # NOTE: This isn't necessary.
-    from typing import TypeAlias  # This can technically be omitted.
-
-if hasattr(faulthandler, "register") and hasattr(signal, "SIGUSR1"):
+if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'):
     faulthandler.register(signal.SIGUSR1)
 
-# NOTE: n-dimensional arrays should be directly referenced
-NDArray: TypeAlias = "np.ndarray[Any, Any]"
+NDArray: TypeAlias = 'np.ndarray[Any, Any]'
 
-# Why is this here? LLAMA and GPT are technically the only compatible ARCHs.
 ARCH = gguf.MODEL_ARCH.LLAMA
 
 DEFAULT_CONCURRENCY = 8
 
+ADDED_TOKENS_FILE = 'added_tokens.json'
+FAST_TOKENIZER_FILE = 'tokenizer.json'
+
 #
 # data types
 #
 
 
-# TODO: Clean up and refactor data types
 @dataclass(frozen=True)
 class DataType:
     name: str
@@ -183,86 +156,69 @@ GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = {
 
 @dataclass
 class Params:
-    n_vocab: int
-    n_embd: int
-    n_layer: int
-    n_ctx: int
-    n_ff: int
-    n_head: int
-    n_head_kv: int
-    f_norm_eps: Optional[float] = None
-    n_experts: Optional[int] = None
-    n_experts_used: Optional[int] = None
+    n_vocab:        int
+    n_embd:         int
+    n_layer:        int
+    n_ctx:          int
+    n_ff:           int
+    n_head:         int
+    n_head_kv:      int
+    n_experts:      int | None = None
+    n_experts_used: int | None = None
+    f_norm_eps:     float | None = None
 
-    rope_scaling_type: Optional[gguf.RopeScalingType] = None
-    f_rope_freq_base: Optional[float] = None
-    f_rope_scale: Optional[float] = None
-    n_orig_ctx: Optional[int] = None
-    rope_finetuned: Optional[bool] = None
+    rope_scaling_type: gguf.RopeScalingType | None = None
+    f_rope_freq_base: float | None = None
+    f_rope_scale: float | None = None
+    n_orig_ctx: int | None = None
+    rope_finetuned: bool | None = None
 
-    ftype: Optional[GGMLFileType] = None
+    ftype: GGMLFileType | None = None
 
     # path to the directory containing the model files
-    path_model: Optional[Path] = None
+    path_model: Path | None = None
 
     @staticmethod
-    def guessed(model: LazyModel) -> "Params":
+    def guessed(model: LazyModel) -> Params:
         # try transformer naming first
-        n_vocab, n_embd = (
-            model["model.embed_tokens.weight"].shape
-            if "model.embed_tokens.weight" in model
-            else model["tok_embeddings.weight"].shape
-        )
+        n_vocab, n_embd = model["model.embed_tokens.weight"].shape if "model.embed_tokens.weight" in model else model["tok_embeddings.weight"].shape
 
         # try transformer naming first
         if "model.layers.0.self_attn.q_proj.weight" in model:
-            n_layer = next(
-                i
-                for i in itertools.count()
-                if f"model.layers.{i}.self_attn.q_proj.weight" not in model
-            )
-        elif (
-            "model.layers.0.self_attn.W_pack.weight" in model
-        ):  # next: try baichuan naming
-            n_layer = next(
-                i
-                for i in itertools.count()
-                if f"model.layers.{i}.self_attn.W_pack.weight" not in model
-            )
+            n_layer = next(i for i in itertools.count() if f"model.layers.{i}.self_attn.q_proj.weight" not in model)
+        elif "model.layers.0.self_attn.W_pack.weight" in model:   # next: try baichuan naming
+            n_layer = next(i for i in itertools.count() if f"model.layers.{i}.self_attn.W_pack.weight" not in model)
         else:
-            n_layer = next(
-                i
-                for i in itertools.count()
-                if f"layers.{i}.attention.wq.weight" not in model
-            )
+            n_layer = next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model)
 
         if n_layer < 1:
-            raise Exception(
-                "failed to guess 'n_layer'. This model is unknown or unsupported.\n"
-                "Suggestion: provide 'config.json' of the model in the same directory containing model files."
-            )
+            msg = """\
+                failed to guess 'n_layer'. This model is unknown or unsupported.
+                Suggestion: provide 'config.json' of the model in the same directory containing model files."""
+            raise KeyError(textwrap.dedent(msg))
 
-        n_head = n_embd // 128  # guessed
-        n_mult = 256  # guessed
+        n_head = n_embd // 128 # guessed
+        n_mult = 256           # guessed
 
         # TODO: verify this
         n_ff = int(2 * (4 * n_embd) / 3)
         n_ff = n_mult * ((n_ff + n_mult - 1) // n_mult)
 
         return Params(
-            n_vocab=n_vocab,
-            n_embd=n_embd,
-            n_layer=n_layer,
-            n_ctx=-1,
-            n_ff=n_ff,
-            n_head=n_head,
-            n_head_kv=n_head,
-            f_norm_eps=1e-5,
+            n_vocab    = n_vocab,
+            n_embd     = n_embd,
+            n_layer    = n_layer,
+            n_ctx      = -1,
+            n_ff       = n_ff,
+            n_head     = n_head,
+            n_head_kv  = n_head,
+            f_norm_eps = 1e-5,
         )
 
     @staticmethod
-    def load_transformers_config(model: LazyModel, config_path: Path) -> "Params":
-        config = json.load(open(config_path))
+    def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params:
+        with open(config_path) as f:
+            config = json.load(f)
 
         rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None
         rope_scaling = config.get("rope_scaling")
@@ -274,22 +230,22 @@ class Params:
                 rope_scaling_type = gguf.RopeScalingType.LINEAR
             elif typ == "yarn":
                 rope_scaling_type = gguf.RopeScalingType.YARN
-                n_orig_ctx = rope_scaling["original_max_position_embeddings"]
-                rope_finetuned = rope_scaling["finetuned"]
+                n_orig_ctx = rope_scaling['original_max_position_embeddings']
+                rope_finetuned = rope_scaling['finetuned']
             else:
-                raise NotImplementedError(f"Unknown rope scaling type: {typ}")
+                raise NotImplementedError(f'Unknown rope scaling type: {typ}')
 
         if "max_sequence_length" in config:
             n_ctx = config["max_sequence_length"]
         elif "max_position_embeddings" in config:
             n_ctx = config["max_position_embeddings"]
         else:
-            raise Exception(
-                "failed to guess 'n_ctx'. This model is unknown or unsupported.\n"
-                "Suggestion: provide 'config.json' of the model in the same directory containing model files."
-            )
+            msg = """\
+                failed to guess 'n_ctx'. This model is unknown or unsupported.
+                Suggestion: provide 'config.json' of the model in the same directory containing model files."""
+            raise KeyError(textwrap.dedent(msg))
 
-        n_experts = None
+        n_experts      = None
         n_experts_used = None
 
         if "num_local_experts" in config:
@@ -297,30 +253,31 @@ class Params:
             n_experts_used = config["num_experts_per_tok"]
 
         return Params(
-            n_vocab=config["vocab_size"],
-            n_embd=config["hidden_size"],
-            n_layer=config["num_hidden_layers"],
-            n_ctx=n_ctx,
-            n_ff=config["intermediate_size"],
-            n_head=(n_head := config["num_attention_heads"]),
-            n_head_kv=config.get("num_key_value_heads", n_head),
-            n_experts=n_experts,
-            n_experts_used=n_experts_used,
-            f_norm_eps=config["rms_norm_eps"],
-            f_rope_freq_base=config.get("rope_theta"),
-            rope_scaling_type=rope_scaling_type,
-            f_rope_scale=f_rope_scale,
-            n_orig_ctx=n_orig_ctx,
-            rope_finetuned=rope_finetuned,
+            n_vocab           = config["vocab_size"],
+            n_embd            = config["hidden_size"],
+            n_layer           = config["num_hidden_layers"],
+            n_ctx             = n_ctx,
+            n_ff              = config["intermediate_size"],
+            n_head            = (n_head := config["num_attention_heads"]),
+            n_head_kv         = config.get("num_key_value_heads", n_head),
+            n_experts         = n_experts,
+            n_experts_used    = n_experts_used,
+            f_norm_eps        = config["rms_norm_eps"],
+            f_rope_freq_base  = config.get("rope_theta"),
+            rope_scaling_type = rope_scaling_type,
+            f_rope_scale      = f_rope_scale,
+            n_orig_ctx        = n_orig_ctx,
+            rope_finetuned    = rope_finetuned,
         )
 
     # LLaMA v2 70B params.json
     # {"dim": 8192, "multiple_of": 4096, "ffn_dim_multiplier": 1.3, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-05, "vocab_size": -1}
     @staticmethod
-    def load_torch_params(model: LazyModel, config_path: Path) -> "Params":
-        config = json.load(open(config_path))
+    def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params:
+        with open(config_path) as f:
+            config = json.load(f)
 
-        n_experts = None
+        n_experts      = None
         n_experts_used = None
         f_rope_freq_base = None
 
@@ -343,89 +300,132 @@ class Params:
 
         if config.get("moe"):
             n_ff = model["layers.0.feed_forward.experts.0.w1.weight"].shape[0]
-            n_experts = config["moe"]["num_experts"]
+            n_experts      = config["moe"]["num_experts"]
             n_experts_used = config["moe"]["num_experts_per_tok"]
             f_rope_freq_base = 1e6
 
         return Params(
-            n_vocab=config.get("vocab_size", model["tok_embeddings.weight"].shape[0]),
-            n_embd=config["dim"],
-            n_layer=config["n_layers"],
-            n_ctx=n_ctx,
-            n_ff=n_ff,
-            n_head=(n_head := config["n_heads"]),
-            n_head_kv=config.get("n_kv_heads", n_head),
-            n_experts=n_experts,
-            n_experts_used=n_experts_used,
-            f_norm_eps=config["norm_eps"],
-            f_rope_freq_base=config.get("rope_theta", f_rope_freq_base),
+            n_vocab          = model["tok_embeddings.weight"].shape[0],
+            n_embd           = config["dim"],
+            n_layer          = config["n_layers"],
+            n_ctx            = n_ctx,
+            n_ff             = n_ff,
+            n_head           = (n_head := config["n_heads"]),
+            n_head_kv        = config.get("n_kv_heads", n_head),
+            n_experts        = n_experts,
+            n_experts_used   = n_experts_used,
+            f_norm_eps       = config["norm_eps"],
+            f_rope_freq_base = config.get("rope_theta", f_rope_freq_base),
         )
 
     @staticmethod
-    def load(model_plus: ModelPlus) -> "Params":
-        hf_config_path = model_plus.paths[0].parent / "config.json"
+    def load(model_plus: ModelPlus) -> Params:
+        hf_config_path   = model_plus.paths[0].parent / "config.json"
         orig_config_path = model_plus.paths[0].parent / "params.json"
 
         if hf_config_path.exists():
-            params = Params.load_transformers_config(model_plus.model, hf_config_path)
+            params = Params.loadHFTransformerJson(model_plus.model, hf_config_path)
         elif orig_config_path.exists():
-            params = Params.load_torch_params(model_plus.model, orig_config_path)
-        elif model_plus.format != "none":
+            params = Params.loadOriginalParamsJson(model_plus.model, orig_config_path)
+        elif model_plus.format != 'none':
             params = Params.guessed(model_plus.model)
         else:
-            raise ValueError("Cannot guess params when model format is none")
+            raise ValueError('Cannot guess params when model format is none')
 
         params.path_model = model_plus.paths[0].parent
 
         return params
 
 
-class BpeVocab:  # GPT
-    def __init__(
-        self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]
-    ) -> None:
-        self.bpe_tokenizer = json.loads(
-            open(str(fname_tokenizer), encoding="utf-8").read()
-        )
-        added_tokens: dict[str, int]
-        if fname_added_tokens is not None:
-            # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
-            added_tokens = json.load(open(fname_added_tokens, encoding="utf-8"))
-        else:
-            # Fall back to trying to find the added tokens in tokenizer.json
-            tokenizer_json_file = fname_tokenizer.parent / "tokenizer.json"
-            if not tokenizer_json_file.is_file():
-                added_tokens = {}
-            else:
-                tokenizer_json = json.load(open(tokenizer_json_file, encoding="utf-8"))
-                added_tokens = dict(
-                    (item["content"], item["id"])
-                    for item in tokenizer_json.get("added_tokens", [])
-                    # Added tokens here can be duplicates of the main vocabulary.
-                    if item["content"] not in self.bpe_tokenizer
-                )
+#
+# vocab
+#
 
-        vocab_size: int = len(self.bpe_tokenizer)
+@runtime_checkable
+class BaseVocab(Protocol):
+    tokenizer_model: ClassVar[str]
+    name: ClassVar[str]
+
+
+class NoVocab(BaseVocab):
+    tokenizer_model = "no_vocab"
+    name = "no_vocab"
+
+    def __repr__(self) -> str:
+        return "<NoVocab for a model without integrated vocabulary>"
+
+
+@runtime_checkable
+class Vocab(BaseVocab, Protocol):
+    vocab_size: int
+    added_tokens_dict: dict[str, int]
+    added_tokens_list: list[str]
+    fname_tokenizer: Path
+
+    def __init__(self, base_path: Path): ...
+    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ...
+
+
+class BpeVocab(Vocab):
+    tokenizer_model = "gpt2"
+    name = "bpe"
+
+    def __init__(self, base_path: Path):
+        added_tokens: dict[str, int] = {}
+
+        if (fname_tokenizer := base_path / 'vocab.json').exists():
+            # "slow" tokenizer
+            with open(fname_tokenizer, encoding="utf-8") as f:
+                self.vocab = json.load(f)
+
+            try:
+                # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
+                with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f:
+                    added_tokens = json.load(f)
+            except FileNotFoundError:
+                pass
+        else:
+            # "fast" tokenizer
+            fname_tokenizer = base_path / FAST_TOKENIZER_FILE
+
+            # if this fails, FileNotFoundError propagates to caller
+            with open(fname_tokenizer, encoding="utf-8") as f:
+                tokenizer_json = json.load(f)
+
+            tokenizer_model: dict[str, Any] = tokenizer_json['model']
+            if (
+                tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False)
+                or tokenizer_json['decoder']['type'] != 'ByteLevel'
+            ):
+                raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer')
+
+            self.vocab = tokenizer_model["vocab"]
+
+            if (added := tokenizer_json.get('added_tokens')) is not None:
+                # Added tokens here can be duplicates of the main vocabulary.
+                added_tokens = {item['content']: item['id']
+                                for item in added
+                                if item['content'] not in self.vocab}
+
+        vocab_size   = len(self.vocab)
         expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
-        actual_ids = sorted(added_tokens.values())
+        actual_ids   = sorted(added_tokens.values())
         if expected_ids != actual_ids:
             expected_end_id = vocab_size + len(actual_ids) - 1
-            raise Exception(
-                f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range {vocab_size} - {expected_end_id}; got {actual_ids}"
-            )
+            raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range "
+                             f"{vocab_size} - {expected_end_id}; got {actual_ids}")
 
         items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
-        self.added_tokens_list = [text for (text, idx) in items]
-        self.vocab_size_base: int = vocab_size
-        self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_list)
-        self.fname_tokenizer = fname_tokenizer
-        self.fname_added_tokens = fname_added_tokens
+        self.added_tokens_dict    = added_tokens
+        self.added_tokens_list    = [text for (text, idx) in items]
+        self.vocab_size_base      = vocab_size
+        self.vocab_size           = self.vocab_size_base + len(self.added_tokens_list)
+        self.fname_tokenizer      = fname_tokenizer
 
     def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
-        tokenizer = self.bpe_tokenizer
-        reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.items()}
+        reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()}
 
-        for i, _ in enumerate(tokenizer):
+        for i, _ in enumerate(self.vocab):
             yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL
 
     def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
@@ -441,42 +441,45 @@ class BpeVocab:  # GPT
         return f"<BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
 
 
-class SentencePieceVocab:  # LlaMa
-    def __init__(
-        self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]
-    ) -> None:
+class SentencePieceVocab(Vocab):
+    tokenizer_model = "llama"
+    name = "spm"
+
+    def __init__(self, base_path: Path):
+        added_tokens: dict[str, int] = {}
+        if (fname_tokenizer := base_path / 'tokenizer.model').exists():
+            # normal location
+            try:
+                with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f:
+                    added_tokens = json.load(f)
+            except FileNotFoundError:
+                pass
+        elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists():
+            # not found in alternate location either
+            raise FileNotFoundError('Cannot find tokenizer.model')
+
         self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
-        added_tokens: dict[str, int]
-        if fname_added_tokens is not None:
-            added_tokens = json.load(open(fname_added_tokens, encoding="utf-8"))
-        else:
-            added_tokens = {}
+        vocab_size = self.sentencepiece_tokenizer.vocab_size()
 
-        vocab_size: int = self.sentencepiece_tokenizer.vocab_size()
-
-        new_tokens = {
-            id: piece for piece, id in added_tokens.items() if id >= vocab_size
-        }
+        new_tokens       = {id: piece for piece, id in added_tokens.items() if id >= vocab_size}
         expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens)))
-        actual_new_ids = sorted(new_tokens.keys())
+        actual_new_ids   = sorted(new_tokens.keys())
 
         if expected_new_ids != actual_new_ids:
-            raise ValueError(
-                f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}"
-            )
+            raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}")
 
         # Token pieces that were added to the base vocabulary.
-        self.added_tokens_list = [new_tokens[id] for id in actual_new_ids]
-        self.vocab_size_base = vocab_size
-        self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
-        self.fname_tokenizer = fname_tokenizer
-        self.fname_added_tokens = fname_added_tokens
+        self.added_tokens_dict  = added_tokens
+        self.added_tokens_list  = [new_tokens[id] for id in actual_new_ids]
+        self.vocab_size_base    = vocab_size
+        self.vocab_size         = self.vocab_size_base + len(self.added_tokens_list)
+        self.fname_tokenizer    = fname_tokenizer
 
     def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
         tokenizer = self.sentencepiece_tokenizer
         for i in range(tokenizer.vocab_size()):
             piece = tokenizer.id_to_piece(i)
-            text: bytes = piece.encode("utf-8")
+            text         = piece.encode("utf-8")
             score: float = tokenizer.get_score(i)
 
             toktype = gguf.TokenType.NORMAL
@@ -509,25 +512,47 @@ class SentencePieceVocab:  # LlaMa
         return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
 
 
-class HfVocab:
-    def __init__(
-        self,
-        fname_tokenizer: Path,
-        fname_added_tokens: Optional[Path] = None,
-    ) -> None:
-        print("fname_tokenizer:", fname_tokenizer)
+class LlamaHfVocab(Vocab):
+    tokenizer_model = "llama"
+    name = "hfft"
+
+    def __init__(self, base_path: Path, ignore_nonllama: bool = False):
+        fname_tokenizer = base_path / FAST_TOKENIZER_FILE
+        # if this fails, FileNotFoundError propagates to caller
+        with open(fname_tokenizer, encoding='utf-8') as f:
+            tokenizer_json = json.load(f)
+
+        # pre-check so we know if we need transformers
+        tokenizer_model: dict[str, Any] = tokenizer_json['model']
+        if ignore_nonllama:
+            pass  # workaround incorrect use of this class for WordPiece
+        elif (
+            tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False)
+            or tokenizer_json['decoder']['type'] != 'Sequence'
+        ):
+            raise FileNotFoundError('Cannot find Llama BPE tokenizer')
+
+        try:
+            from transformers import AutoTokenizer
+        except ImportError as e:
+            raise ImportError(
+                "To use LlamaHfVocab, please install the `transformers` package. "
+                "You can install it with `pip install transformers`."
+            ) from e
+
         # Allow the tokenizer to default to slow or fast versions.
         # Explicitly set tokenizer to use local paths.
         self.tokenizer = AutoTokenizer.from_pretrained(
-            fname_tokenizer,
-            cache_dir=fname_tokenizer,
+            base_path,
+            cache_dir=base_path,
             local_files_only=True,
         )
+        assert self.tokenizer.is_fast  # assume tokenizer.json is used
 
         # Initialize lists and dictionaries for added tokens
         self.added_tokens_list = []
         self.added_tokens_dict = dict()
-        self.added_tokens_ids = set()
+        self.added_tokens_ids  = set()
 
         # Process added tokens
         for tok, tokidx in sorted(
@@ -548,12 +573,11 @@ class HfVocab:
 
         # Set vocabulary sizes
         self.vocab_size_base = self.tokenizer.vocab_size
-        self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
+        self.vocab_size      = self.vocab_size_base + len(self.added_tokens_list)
 
         self.fname_tokenizer = fname_tokenizer
-        self.fname_added_tokens = fname_added_tokens
 
-    def hf_tokens(self) -> Iterable[Tuple[bytes, float, gguf.TokenType]]:
+    def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
         reverse_vocab = {
             id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items()
         }
@@ -568,14 +592,16 @@ class HfVocab:
 
             # Yield token text, score, and type
             yield token_text, self.get_token_score(token_id), self.get_token_type(
-                token_id, self.special_ids  # Reuse already stored special IDs
+                token_id, token_text, self.special_ids  # Reuse already stored special IDs
             )
 
-    def get_token_type(self, token_id: int, special_ids: set) -> gguf.TokenType:
+    def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType:
+        # Special case for byte tokens
+        if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
+            return gguf.TokenType.BYTE
+
         # Determine token type based on whether it's a special token
-        return (
-            gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL
-        )
+        return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL
 
     def get_token_score(self, token_id: int) -> float:
         # Placeholder for actual logic to determine the token's score
@@ -585,9 +611,8 @@ class HfVocab:
     def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
         for text in self.added_tokens_list:
             if text in self.specials:
-                toktype = self.get_token_type(self.specials[text], self.special_ids)
+                toktype = self.get_token_type(self.specials[text], b'', self.special_ids)
                 score = self.get_token_score(self.specials[text])
-
             else:
                 toktype = gguf.TokenType.USER_DEFINED
                 score = -1000.0
@@ -602,10 +627,7 @@ class HfVocab:
         yield from self.added_tokens()
 
     def __repr__(self) -> str:
-        return f"<HfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
-
-
-Vocab: TypeAlias = "BpeVocab | SentencePieceVocab | HfVocab"
+        return f"<LlamaHfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
 
 
 #
@@ -623,7 +645,7 @@ def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray:
             .reshape(weights.shape))
 
 
-class Tensor(metaclass=ABCMeta):
+class Tensor(ABC):
     data_type: DataType
 
     @abstractmethod
@@ -645,7 +667,7 @@ def bf16_to_fp32(bf16_arr: np.ndarray[Any, np.dtype[np.uint16]]) -> NDArray:
 
 
 class UnquantizedTensor(Tensor):
-    def __init__(self, ndarray: NDArray) -> None:
+    def __init__(self, ndarray: NDArray):
         assert isinstance(ndarray, np.ndarray)
         self.ndarray = ndarray
         self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype]
@@ -724,7 +746,7 @@ class ModelPlus:
     model: LazyModel
     paths: list[Path]  # Where this was read from.
     format: Literal['ggml', 'torch', 'safetensors', 'none']
-    vocab: Vocab | None  # For GGML models (which have vocab built in), the vocab.
+    vocab: BaseVocab | None  # For GGML models (which have vocab built in), the vocab.
 
 
 def merge_sharded(models: list[LazyModel]) -> LazyModel:
@@ -733,7 +755,7 @@ def merge_sharded(models: list[LazyModel]) -> LazyModel:
     names = {name: None for model in models for name in model}
 
     def convert(name: str) -> LazyTensor:
-        lazy_tensors: list[LazyTensor] = [model[name] for model in models]
+        lazy_tensors = [model[name] for model in models]
         if len(lazy_tensors) == 1:
             # only one file; don't go through this procedure since there might
             # be quantized tensors
@@ -754,7 +776,7 @@ def merge_sharded(models: list[LazyModel]) -> LazyModel:
 
         def load() -> UnquantizedTensor:
             ndarrays = [load_unquantized(tensor) for tensor in lazy_tensors]
-            concatenated: NDArray = np.concatenate(ndarrays, axis=axis)
+            concatenated = np.concatenate(ndarrays, axis=axis)
             return UnquantizedTensor(concatenated)
         description = 'concatenated[[' + '] | ['.join(lt.description for lt in lazy_tensors) + ']]'
         return LazyTensor(load, concatenated_shape, lazy_tensors[0].data_type, description)
@@ -781,7 +803,7 @@ def merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus:
     else:
         model = merge_sharded([mp.model for mp in models_plus])
 
-    return ModelPlus(model, paths, format, vocab)
+    return ModelPlus(model, paths, format, vocab)  # pytype: disable=wrong-arg-types
 
 
 def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_head_kv: int) -> LazyTensor:
@@ -806,6 +828,15 @@ def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor:
     return LazyTensor(load, s, lazy_tensor.data_type, 'part ' + lazy_tensor.description)
 
 
+def pack_experts_lazy(lazy_tensors: list[LazyTensor]) -> LazyTensor:
+    def load() -> Tensor:
+        tensors = [lazy_tensor.load() for lazy_tensor in lazy_tensors]
+        return UnquantizedTensor(np.array([tensor.ndarray for tensor in tensors]))
+    s = lazy_tensors[0].shape.copy()
+    s.insert(0, len(lazy_tensors))
+    return LazyTensor(load, s, lazy_tensors[0].data_type, 'pack_experts ' + ' | '.join(lt.description for lt in lazy_tensors))
+
+
 # Functionality that simulates `torch.load` but where individual tensors are
 # only loaded into memory on demand, not all at once.
 # PyTorch can't do this natively as of time of writing:
@@ -842,10 +873,10 @@ class LazyUnpickler(pickle.Unpickler):
 
         def load(offset: int, elm_count: int) -> NDArray:
             dtype = data_type.dtype
-            fp = self.zip_file.open(info)
-            fp.seek(offset * dtype.itemsize)
-            size = elm_count * dtype.itemsize
-            data = fp.read(size)
+            with self.zip_file.open(info) as fp:
+                fp.seek(offset * dtype.itemsize)
+                size = elm_count * dtype.itemsize
+                data = fp.read(size)
             assert len(data) == size
             return np.frombuffer(data, dtype)
         description = f'storage data_type={data_type} path-in-zip={filename} path={self.zip_file.filename}'
@@ -866,20 +897,16 @@ class LazyUnpickler(pickle.Unpickler):
     def rebuild_from_type_v2(func, new_type, args, state):
         return func(*args)
 
-    CLASSES: dict[tuple[str, str], Any] = {
+    CLASSES = {
         # getattr used here as a workaround for mypy not being smart enough to determine
         # the staticmethods have a __func__ attribute.
-        ("torch._tensor", "_rebuild_from_type_v2"): getattr(
-            rebuild_from_type_v2, "__func__"
-        ),
-        ("torch._utils", "_rebuild_tensor_v2"): getattr(
-            lazy_rebuild_tensor_v2, "__func__"
-        ),
-        ("torch", "BFloat16Storage"): LazyStorageKind(DT_BF16),
-        ("torch", "HalfStorage"): LazyStorageKind(DT_F16),
-        ("torch", "FloatStorage"): LazyStorageKind(DT_F32),
-        ("torch", "IntStorage"): LazyStorageKind(DT_I32),
-        ("torch", "Tensor"): LazyTensor,
+        ('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'),
+        ('torch._utils', '_rebuild_tensor_v2'): getattr(lazy_rebuild_tensor_v2, '__func__'),
+        ('torch', 'BFloat16Storage'): LazyStorageKind(DT_BF16),
+        ('torch', 'HalfStorage'): LazyStorageKind(DT_F16),
+        ('torch', 'FloatStorage'): LazyStorageKind(DT_F32),
+        ('torch', 'IntStorage'): LazyStorageKind(DT_I32),
+        ('torch', 'Tensor'): LazyTensor,
     }
 
     def find_class(self, module: str, name: str) -> Any:
@@ -929,7 +956,7 @@ def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus:
 def must_read(fp: IO[bytes], length: int) -> bytes:
     ret = fp.read(length)
     if len(ret) < length:
-        raise Exception("unexpectedly reached end of file")
+        raise EOFError("unexpectedly reached end of file")
     return ret
 
 
@@ -966,7 +993,7 @@ def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], conc
         executor_class = ProcessPoolExecutor
     else:
         executor_class = ThreadPoolExecutor
-    with executor_class(max_workers = max_workers) as executor:
+    with executor_class(max_workers=max_workers) as executor:
         futures: list[concurrent.futures.Future[Out]] = []
         done = False
         for _ in range(concurrency):
@@ -987,12 +1014,15 @@ def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], conc
             yield result
 
 
-def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> None:
+def check_vocab_size(params: Params, vocab: BaseVocab, pad_vocab: bool = False) -> None:
     # Handle special case where the model's vocab size is not set
     if params.n_vocab == -1:
         raise ValueError(
-            f"The model's vocab size is set to -1 in params.json. Please update it manually. Maybe {vocab.vocab_size}?"
+            "The model's vocab size is set to -1 in params.json. Please update it manually."
+            + (f" Maybe {vocab.vocab_size}?" if isinstance(vocab, Vocab) else ""),
         )
+    if not isinstance(vocab, Vocab):
+        return  # model has no vocab
 
     # Check for a vocab size mismatch
     if params.n_vocab == vocab.vocab_size:
@@ -1006,6 +1036,7 @@ def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> N
         )
         for i in range(1, pad_count + 1):
             vocab.added_tokens_dict[f"<dummy{i:05}>"] = -1
+            vocab.added_tokens_list.append(f"<dummy{i:05}>")
         vocab.vocab_size = params.n_vocab
         return
 
@@ -1015,16 +1046,12 @@ def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> N
     if vocab.vocab_size < params.n_vocab:
         msg += " Add the --pad-vocab option and try again."
 
-    raise Exception(msg)
+    raise ValueError(msg)
 
 
 class OutputFile:
-    def __init__(
-        self, fname_out: Path, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE
-    ) -> None:
-        self.gguf = gguf.GGUFWriter(
-            fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess
-        )
+    def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE):
+        self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess)
 
     def add_meta_arch(self, params: Params) -> None:
         name = "LLaMA"
@@ -1035,19 +1062,15 @@ class OutputFile:
         elif params.path_model is not None:
             name = params.path_model.name
 
-        self.gguf.add_name(name)
-        self.gguf.add_context_length(params.n_ctx)
-        self.gguf.add_embedding_length(params.n_embd)
-        self.gguf.add_block_count(params.n_layer)
-        self.gguf.add_feed_forward_length(params.n_ff)
+        self.gguf.add_name                (name)
+        self.gguf.add_vocab_size          (params.n_vocab)
+        self.gguf.add_context_length      (params.n_ctx)
+        self.gguf.add_embedding_length    (params.n_embd)
+        self.gguf.add_block_count         (params.n_layer)
+        self.gguf.add_feed_forward_length (params.n_ff)
         self.gguf.add_rope_dimension_count(params.n_embd // params.n_head)
-        self.gguf.add_head_count(params.n_head)
-        self.gguf.add_head_count_kv(params.n_head_kv)
-
-        if params.f_norm_eps is None:
-            raise ValueError("f_norm_eps is None")
-
-        self.gguf.add_layer_norm_rms_eps(params.f_norm_eps)
+        self.gguf.add_head_count          (params.n_head)
+        self.gguf.add_head_count_kv       (params.n_head_kv)
 
         if params.n_experts:
             self.gguf.add_expert_count(params.n_experts)
@@ -1055,6 +1078,11 @@ class OutputFile:
         if params.n_experts_used:
             self.gguf.add_expert_used_count(params.n_experts_used)
 
+        if params.f_norm_eps:
+            self.gguf.add_layer_norm_rms_eps(params.f_norm_eps)
+        else:
+            raise ValueError('f_norm_eps is None')
+
         if params.f_rope_freq_base is not None:
             self.gguf.add_rope_freq_base(params.f_rope_freq_base)
 
@@ -1072,21 +1100,7 @@ class OutputFile:
         if params.ftype is not None:
             self.gguf.add_file_type(params.ftype)
 
-    def handle_tokenizer_model(self, vocab: Vocab) -> str:
-        # Map the vocab types to the supported tokenizer models
-        tokenizer_model = {
-            SentencePieceVocab: "llama",
-            HfVocab: "llama",
-            BpeVocab: "gpt2",
-        }.get(type(vocab))
-
-        # Block if vocab type is not predefined
-        if tokenizer_model is None:
-            raise ValueError("Unknown vocab type: Not supported")
-
-        return tokenizer_model
-
-    def extract_vocabulary_from_model(self, vocab: Vocab) -> Tuple[list, list, list]:
+    def extract_vocabulary_from_model(self, vocab: Vocab) -> tuple[list[bytes], list[float], list[gguf.TokenType]]:
         tokens = []
         scores = []
         toktypes = []
@@ -1097,14 +1111,13 @@ class OutputFile:
             scores.append(score)
             toktypes.append(toktype)
 
+        assert len(tokens) == vocab.vocab_size
+
         return tokens, scores, toktypes
 
     def add_meta_vocab(self, vocab: Vocab) -> None:
-        # Handle the tokenizer model
-        tokenizer_model = self.handle_tokenizer_model(vocab)
-
         # Ensure that tokenizer_model is added to the GGUF model
-        self.gguf.add_tokenizer_model(tokenizer_model)
+        self.gguf.add_tokenizer_model(vocab.tokenizer_model)
 
         # Extract model vocabulary for model conversion
         tokens, scores, toktypes = self.extract_vocabulary_from_model(vocab)
@@ -1119,14 +1132,10 @@ class OutputFile:
 
     def add_tensor_info(self, name: str, tensor: LazyTensor) -> None:
         n_elements = int(np.prod(tensor.shape))
-        raw_dtype = getattr(tensor.data_type, "ggml_type", None)
-        data_type = (
-            getattr(tensor.data_type, "quantized_type", None) or tensor.data_type.dtype
-        )
+        raw_dtype = getattr(tensor.data_type, 'ggml_type', None)
+        data_type = getattr(tensor.data_type, 'quantized_type', None) or tensor.data_type.dtype
         data_nbytes = tensor.data_type.elements_to_bytes(n_elements)
-        self.gguf.add_tensor_info(
-            name, tensor.shape, data_type, data_nbytes, raw_dtype=raw_dtype
-        )
+        self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes, raw_dtype=raw_dtype)
 
     def write_meta(self) -> None:
         self.gguf.write_header_to_file()
@@ -1135,17 +1144,33 @@ class OutputFile:
     def write_tensor_info(self) -> None:
         self.gguf.write_ti_data_to_file()
 
+    def write_tensor_data(self, ftype: GGMLFileType, model: LazyModel, concurrency: int) -> None:
+        ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency=concurrency)
+        if ftype == GGMLFileType.MostlyQ8_0:
+            ndarrays = bounded_parallel_map(
+                OutputFile.maybe_do_quantize, ndarrays_inner, concurrency=concurrency, max_workers=concurrency,
+                use_processpool_executor=True,
+            )
+        else:
+            ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)
+
+        start = time.time()
+        for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
+            elapsed = time.time() - start
+            size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
+            padi = len(str(len(model)))
+            print(
+                f"[{i + 1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}"
+            )
+            self.gguf.write_tensor_data(ndarray)
+
     def close(self) -> None:
         self.gguf.close()
 
     @staticmethod
     def write_vocab_only(
-        fname_out: Path,
-        params: Params,
-        vocab: Vocab,
-        svocab: gguf.SpecialVocab,
-        endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE,
-        pad_vocab: bool = False,
+        fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab,
+        endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False,
     ) -> None:
         check_vocab_size(params, vocab, pad_vocab=pad_vocab)
 
@@ -1175,14 +1200,8 @@ class OutputFile:
 
     @staticmethod
     def write_all(
-        fname_out: Path,
-        ftype: GGMLFileType,
-        params: Params,
-        model: LazyModel,
-        vocab: Vocab,
-        svocab: gguf.SpecialVocab,
-        concurrency: int = DEFAULT_CONCURRENCY,
-        endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE,
+        fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: BaseVocab, svocab: gguf.SpecialVocab,
+        concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE,
         pad_vocab: bool = False,
     ) -> None:
         check_vocab_size(params, vocab, pad_vocab=pad_vocab)
@@ -1191,8 +1210,11 @@ class OutputFile:
 
         # meta data
         of.add_meta_arch(params)
-        of.add_meta_vocab(vocab)
-        of.add_meta_special_vocab(svocab)
+        if isinstance(vocab, Vocab):
+            of.add_meta_vocab(vocab)
+            of.add_meta_special_vocab(svocab)
+        else:  # NoVocab
+            of.gguf.add_tokenizer_model(vocab.tokenizer_model)
 
         # tensor info
         for name, lazy_tensor in model.items():
@@ -1202,31 +1224,7 @@ class OutputFile:
         of.write_tensor_info()
 
         # tensor data
-        ndarrays_inner = bounded_parallel_map(
-            OutputFile.do_item, model.items(), concurrency=concurrency
-        )
-        if ftype == GGMLFileType.MostlyQ8_0:
-            ndarrays = bounded_parallel_map(
-                OutputFile.maybe_do_quantize,
-                ndarrays_inner,
-                concurrency=concurrency,
-                max_workers=concurrency,
-                use_processpool_executor=True,
-            )
-        else:
-            ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)
-
-        start = time.time()
-        for i, ((name, lazy_tensor), ndarray) in enumerate(
-            zip(model.items(), ndarrays)
-        ):
-            elapsed = time.time() - start
-            size = " x ".join(f"{dim:6d}" for dim in lazy_tensor.shape)
-            padi = len(str(len(model)))
-            print(
-                f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}"
-            )
-            of.gguf.write_tensor_data(ndarray)
+        of.write_tensor_data(ftype, model, concurrency)
 
         of.close()
 
@@ -1234,16 +1232,16 @@ class OutputFile:
 def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType:
     wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) + ".weight"].data_type
 
-    if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32):
+    if output_type_str == "f32" or (output_type_str is None and wq_type in (DT_F32, DT_BF16)):
         return GGMLFileType.AllF32
-    if output_type_str == "f16" or (output_type_str is None and wq_type in (DT_F16, DT_BF16)):
+    if output_type_str == "f16" or (output_type_str is None and wq_type == DT_F16):
         return GGMLFileType.MostlyF16
     if output_type_str == "q8_0":
         return GGMLFileType.MostlyQ8_0
 
     name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()}
 
-    raise Exception(f"Unexpected combination of types: {name_to_type}")
+    raise ValueError(f"Unexpected combination of types: {name_to_type}")
 
 
 def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel:
@@ -1251,12 +1249,28 @@ def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyM
             for (name, tensor) in model.items()}
 
 
-def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
+def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) -> LazyModel:
     tmap = gguf.TensorNameMap(ARCH, params.n_layer)
-    should_skip: set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, []))
+    should_skip = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, []))
 
     tmp = model
 
+    # merge experts into one tensor
+    if params.n_experts and params.n_experts > 0:
+        for i_l in range(params.n_layer):
+            for w in range(1, 4):
+                experts = []
+                for e in range(params.n_experts):
+                    if f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight" in model:
+                        experts.append(model[f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight"])
+                        del tmp[f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight"]
+                    elif f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight" in model:
+                        experts.append(model[f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight"])
+                        del tmp[f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight"]
+                    else:
+                        raise ValueError(f"Expert tensor not found: layers.{i_l}.feed_forward.experts.{e}.w{w}.weight")
+                tmp[f"layers.{i_l}.feed_forward.experts.w{w}.weight"] = pack_experts_lazy(experts)
+
     # HF models permut or pack some of the tensors, so we need to undo that
     for i in itertools.count():
         if f"model.layers.{i}.self_attn.q_proj.weight" in model:
@@ -1277,7 +1291,10 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
     for name, lazy_tensor in model.items():
         tensor_type, name_new = tmap.get_type_and_name(name, try_suffixes = (".weight", ".bias")) or (None, None)
         if name_new is None:
-            raise Exception(f"Unexpected tensor name: {name}")
+            if skip_unknown:
+                print(f"Unexpected tensor name: {name} - skipping")
+                continue
+            raise ValueError(f"Unexpected tensor name: {name}. Use --skip-unknown to ignore it (e.g. LLaVA)")
 
         if tensor_type in should_skip:
             print(f"skipping tensor {name_new}")
@@ -1294,7 +1311,7 @@ def nth_multifile_path(path: Path, n: int) -> Path | None:
     the nth path in the model.
     '''
     # Support the following patterns:
-    patterns: list[tuple[str, str]] = [
+    patterns = [
         # - x.00.pth, x.01.pth, etc.
         (r'\.[0-9]{2}\.pth$', f'.{n:02}.pth'),
         # - x-00001-of-00002.bin, x-00002-of-00002.bin, etc.
@@ -1340,9 +1357,9 @@ def load_some_model(path: Path) -> ModelPlus:
             globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt", "pytorch_model.bin"]
             files = [file for glob in globs for file in path.glob(glob)]
         if not files:
-            raise Exception(f"Can't find model in directory {path}")
+            raise FileNotFoundError(f"Can't find model in directory {path}")
         if len(files) > 1:
-            raise Exception(f"Found multiple models in {path}, not sure which to pick: {files}")
+            raise ValueError(f"Found multiple models in {path}, not sure which to pick: {files}")
         path = files[0]
 
     paths = find_multifile_paths(path)
@@ -1356,46 +1373,14 @@ def load_some_model(path: Path) -> ModelPlus:
 
 
 class VocabFactory:
+    _VOCAB_CLASSES: list[type[Vocab]] = [SentencePieceVocab, BpeVocab, LlamaHfVocab]
+
     def __init__(self, path: Path):
         self.path = path
-        self.files = {
-            "tokenizer.model": None,
-            "vocab.json": None,
-            "tokenizer.json": None,
-        }
-        self._detect_files()
 
-    def _detect_files(self):
-        for file in self.files.keys():
-            file_path = self.path / file
-            parent_file_path = self.path.parent / file
-            if file_path.exists():
-                self.files[file] = file_path
-            elif parent_file_path.exists():
-                self.files[file] = parent_file_path
-
-    def _select_file(self, vocabtype: Optional[str]) -> Path:
-        if vocabtype in ["spm", "bpe"]:
-            # For SentencePiece and BPE, return specific files as before
-            file_key = "tokenizer.model" if vocabtype == "spm" else "vocab.json"
-            if self.files[file_key]:
-                return self.files[file_key]
-            else:
-                raise FileNotFoundError(f"{vocabtype} {file_key} not found.")
-        elif vocabtype == "hfft":
-            # For Hugging Face Fast Tokenizer, return the directory path instead of a specific file
-            return self.path
-        else:
-            raise ValueError(f"Unsupported vocabulary type {vocabtype}")
-
-    def _create_special_vocab(
-        self,
-        vocab: Vocab,
-        vocabtype: str,
-        model_parent_path: Path,
-    ) -> gguf.SpecialVocab:
-        load_merges = vocabtype == "bpe"
-        n_vocab = vocab.vocab_size if hasattr(vocab, "vocab_size") else None
+    def _create_special_vocab(self, vocab: BaseVocab, model_parent_path: Path) -> gguf.SpecialVocab:
+        load_merges = vocab.name == "bpe"
+        n_vocab = vocab.vocab_size if isinstance(vocab, Vocab) else None
         return gguf.SpecialVocab(
             model_parent_path,
             load_merges=load_merges,
@@ -1403,47 +1388,52 @@ class VocabFactory:
             n_vocab=n_vocab,
         )
 
-    def load_vocab(
-        self, vocabtype: str, model_parent_path: Path
-    ) -> Tuple[Vocab, gguf.SpecialVocab]:
-        path = self._select_file(vocabtype)
-        print(f"Loading vocab file '{path}', type '{vocabtype}'")
+    def _create_vocab_by_path(self, vocab_types: list[str]) -> Vocab:
+        vocab_classes: dict[str, type[Vocab]] = {cls.name: cls for cls in self._VOCAB_CLASSES}
+        selected_vocabs: dict[str, type[Vocab]] = {}
+        for vtype in vocab_types:
+            try:
+                selected_vocabs[vtype] = vocab_classes[vtype]
+            except KeyError:
+                raise ValueError(f"Unsupported vocabulary type {vtype}") from None
 
-        added_tokens_path = path.parent / "added_tokens.json"
-        if vocabtype == "bpe":
-            vocab = BpeVocab(
-                path, added_tokens_path if added_tokens_path.exists() else None
-            )
-        elif vocabtype == "spm":
-            vocab = SentencePieceVocab(
-                path, added_tokens_path if added_tokens_path.exists() else None
-            )
-        elif vocabtype == "hfft":
-            vocab = HfVocab(
-                path, added_tokens_path if added_tokens_path.exists() else None
-            )
+        for vtype, cls in selected_vocabs.items():
+            try:
+                vocab = cls(self.path)
+                break
+            except FileNotFoundError:
+                pass  # ignore unavailable tokenizers
         else:
-            raise ValueError(f"Unsupported vocabulary type {vocabtype}")
+            raise FileNotFoundError(f"Could not find a tokenizer matching any of {vocab_types}")
+
+        print(f"Loaded vocab file {vocab.fname_tokenizer!r}, type {vocab.name!r}")
+        return vocab
+
+    def load_vocab(self, vocab_types: list[str] | None, model_parent_path: Path) -> tuple[BaseVocab, gguf.SpecialVocab]:
+        vocab: BaseVocab
+        if vocab_types is None:
+            vocab = NoVocab()
+        else:
+            vocab = self._create_vocab_by_path(vocab_types)
+        # FIXME: Respect --vocab-dir?
         special_vocab = self._create_special_vocab(
             vocab,
-            vocabtype,
             model_parent_path,
         )
         return vocab, special_vocab
 
 
-def default_output_file(model_paths: list[Path], file_type: GGMLFileType) -> Path:
+def default_outfile(model_paths: list[Path], file_type: GGMLFileType) -> Path:
     namestr = {
-        GGMLFileType.AllF32: "f32",
+        GGMLFileType.AllF32:    "f32",
         GGMLFileType.MostlyF16: "f16",
-        GGMLFileType.MostlyQ8_0: "q8_0",
+        GGMLFileType.MostlyQ8_0:"q8_0",
     }[file_type]
     ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf"
     if ret in model_paths:
         sys.stderr.write(
             f"Error: Default output path ({ret}) would overwrite the input. "
-            "Please explicitly specify a path using --outfile.\n"
-        )
+            "Please explicitly specify a path using --outfile.\n")
         sys.exit(1)
     return ret
 
@@ -1453,120 +1443,33 @@ def do_dump_model(model_plus: ModelPlus) -> None:
     print(f"model_plus.format = {model_plus.format!r}")
     print(f"model_plus.vocab = {model_plus.vocab!r}")
     for name, lazy_tensor in model_plus.model.items():
-        print(
-            f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}"
-        )
+        print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}")
 
 
-def get_argument_parser() -> ArgumentParser:
+def main(args_in: list[str] | None = None) -> None:
     output_choices = ["f32", "f16"]
     if np.uint32(1) == np.uint32(1).newbyteorder("<"):
         # We currently only support Q8_0 output on little endian systems.
         output_choices.append("q8_0")
+    parser = argparse.ArgumentParser(description="Convert a LLaMA model to a GGML compatible file")
+    parser.add_argument("--dump",         action="store_true",    help="don't convert, just show what's in the model")
+    parser.add_argument("--dump-single",  action="store_true",    help="don't convert, just show what's in a single model file")
+    parser.add_argument("--vocab-only",   action="store_true",    help="extract only the vocab")
+    parser.add_argument("--no-vocab",     action="store_true",    help="store model without the vocab")
+    parser.add_argument("--outtype",      choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
+    parser.add_argument("--vocab-dir",    type=Path,              help="directory containing tokenizer.model, if separate from model file")
+    parser.add_argument("--vocab-type",                           help="vocab types to try in order, choose from 'spm', 'bpe', 'hfft' (default: spm,hfft)", default="spm,hfft")
+    parser.add_argument("--outfile",      type=Path,              help="path to write to; default: based on input")
+    parser.add_argument("model",          type=Path,              help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
+    parser.add_argument("--ctx",          type=int,               help="model training context (default: based on input)")
+    parser.add_argument("--concurrency",  type=int,               help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default=DEFAULT_CONCURRENCY)
+    parser.add_argument("--big-endian",   action="store_true",    help="model is executed on big endian machine")
+    parser.add_argument("--pad-vocab",    action="store_true",    help="add pad tokens when model vocab expects more than tokenizer metadata provides")
+    parser.add_argument("--skip-unknown", action="store_true",    help="skip unknown tensor names instead of failing")
 
-    parser = argparse.ArgumentParser(
-        description="Convert a LLaMa model to a GGML compatible file"
-    )
-
-    parser.add_argument(
-        "model",
-        type=Path,
-        help="Directory containing the model file or the model file itself (*.pth, *.pt, *.bin)",
-    )
-
-    parser.add_argument(
-        "--awq-path",
-        type=Path,
-        help="Path to the Activation-aware Weight Quantization cache file",
-        default=None,
-    )
-
-    parser.add_argument(
-        "--dump",
-        action="store_true",
-        help="Display the model content without converting it",
-    )
-
-    parser.add_argument(
-        "--dump-single",
-        action="store_true",
-        help="Display the content of a single model file without conversion",
-    )
-
-    parser.add_argument(
-        "--vocab-only",
-        action="store_true",
-        help="Extract and output only the vocabulary",
-    )
-
-    parser.add_argument(
-        "--outtype",
-        choices=output_choices,
-        help="Output format - note: q8_0 may be very slow (default: f16 or f32 based on input)",
-    )
-
-    parser.add_argument(
-        "--vocab-dir",
-        type=Path,
-        help="Directory containing the tokenizer.model, if separate from the model file",
-    )
-
-    parser.add_argument(
-        "--vocab-type",
-        choices=["spm", "bpe", "hfft"],  # hfft: Hugging Face Fast Tokenizer
-        default="spm",
-        help="The vocabulary format used to define the tokenizer model (default: spm)",
-    )
-
-    parser.add_argument(
-        "--pad-vocab",
-        action="store_true",
-        help="Add padding tokens when the model's vocabulary size exceeds the tokenizer metadata",
-    )
-
-    parser.add_argument(
-        "--outfile",
-        type=Path,
-        help="Specify the path for the output file (default is based on input)",
-    )
-
-    parser.add_argument(
-        "--ctx", type=int, help="Model training context (default is based on input)"
-    )
-
-    parser.add_argument(
-        "--concurrency",
-        type=int,
-        help=f"Concurrency used for conversion (default: {DEFAULT_CONCURRENCY})",
-        default=DEFAULT_CONCURRENCY,
-    )
-
-    parser.add_argument(
-        "--big-endian",
-        action="store_true",
-        help="Indicate that the model is executed on a big-endian machine",
-    )
-
-    return parser
-
-
-def main(argv: Optional[list[str]] = None) -> None:
-    parser = get_argument_parser()
-    args = parser.parse_args(argv)
-
-    if args.awq_path:
-        sys.path.insert(1, str(Path(__file__).resolve().parent / "awq-py"))
-        from awq.apply_awq import add_scale_weights
-
-        tmp_model_path = args.model / "weighted_model"
-        if tmp_model_path.is_dir():
-            print(f"{tmp_model_path} exists as a weighted model.")
-        else:
-            tmp_model_path.mkdir(parents=True, exist_ok=True)
-            print("Saving new weighted model ...")
-            add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path))
-            print(f"Saved weighted model at {tmp_model_path}.")
-        args.model = tmp_model_path
+    args = parser.parse_args(args_in)
+    if args.no_vocab and args.vocab_only:
+        raise ValueError("--vocab-only does not make sense with --no-vocab")
 
     if args.dump_single:
         model_plus = lazy_load_file(args.model)
@@ -1576,14 +1479,11 @@ def main(argv: Optional[list[str]] = None) -> None:
     if not args.vocab_only:
         model_plus = load_some_model(args.model)
     else:
-        model_plus = ModelPlus(
-            model={}, paths=[args.model / "dummy"], format="none", vocab=None
-        )
+        model_plus = ModelPlus(model = {}, paths = [args.model / 'dummy'], format = 'none', vocab = None)
 
     if args.dump:
         do_dump_model(model_plus)
         return
-
     endianess = gguf.GGUFEndian.LITTLE
     if args.big_endian:
         endianess = gguf.GGUFEndian.BIG
@@ -1591,12 +1491,12 @@ def main(argv: Optional[list[str]] = None) -> None:
     params = Params.load(model_plus)
     if params.n_ctx == -1:
         if args.ctx is None:
-            raise Exception(
-                "The model doesn't have a context size, and you didn't specify one with --ctx\n"
-                "Please specify one with --ctx:\n"
-                " - LLaMA v1: --ctx 2048\n"
-                " - LLaMA v2: --ctx 4096\n"
-            )
+            msg = """\
+                The model doesn't have a context size, and you didn't specify one with --ctx
+                Please specify one with --ctx:
+                 - LLaMA v1: --ctx 2048
+                 - LLaMA v2: --ctx 4096"""
+            parser.error(textwrap.dedent(msg))
         params.n_ctx = args.ctx
 
     if args.outtype:
@@ -1611,48 +1511,38 @@ def main(argv: Optional[list[str]] = None) -> None:
     model_parent_path = model_plus.paths[0].parent
     vocab_path = Path(args.vocab_dir or args.model or model_parent_path)
     vocab_factory = VocabFactory(vocab_path)
-    vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type, model_parent_path)
+    vocab_types = None if args.no_vocab else args.vocab_type.split(",")
+    vocab, special_vocab = vocab_factory.load_vocab(vocab_types, model_parent_path)
 
     if args.vocab_only:
+        assert isinstance(vocab, Vocab)
         if not args.outfile:
             raise ValueError("need --outfile if using --vocab-only")
         outfile = args.outfile
-        OutputFile.write_vocab_only(
-            outfile,
-            params,
-            vocab,
-            special_vocab,
-            endianess=endianess,
-            pad_vocab=args.pad_vocab,
-        )
+        OutputFile.write_vocab_only(outfile, params, vocab, special_vocab,
+                                    endianess=endianess, pad_vocab=args.pad_vocab)
         print(f"Wrote {outfile}")
         return
 
-    if model_plus.vocab is not None and args.vocab_dir is None:
+    if model_plus.vocab is not None and args.vocab_dir is None and not args.no_vocab:
         vocab = model_plus.vocab
 
-    model = model_plus.model
-    model = convert_model_names(model, params)
-    ftype = pick_output_type(model, args.outtype)
-    model = convert_to_output_type(model, ftype)
-    outfile = args.outfile or default_output_file(model_plus.paths, ftype)
+    print(f"Vocab info: {vocab}")
+    print(f"Special vocab info: {special_vocab}")
+
+    model   = model_plus.model
+    model   = convert_model_names(model, params, args.skip_unknown)
+    ftype   = pick_output_type(model, args.outtype)
+    model   = convert_to_output_type(model, ftype)
+    outfile = args.outfile or default_outfile(model_plus.paths, ftype)
 
     params.ftype = ftype
     print(f"Writing {outfile}, format {ftype}")
 
-    OutputFile.write_all(
-        outfile,
-        ftype,
-        params,
-        model,
-        vocab,
-        special_vocab,
-        concurrency=args.concurrency,
-        endianess=endianess,
-        pad_vocab=args.pad_vocab,
-    )
+    OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab,
+                         concurrency=args.concurrency, endianess=endianess, pad_vocab=args.pad_vocab)
     print(f"Wrote {outfile}")
 
 
-if __name__ == "__main__":
-    main(sys.argv[1:])  # Exclude the first element (script name) from sys.argv
+if __name__ == '__main__':
+    main()
diff --git a/docs/token_generation_performance_tips.md b/docs/token_generation_performance_tips.md
index d7e863dff..3c4343147 100644
--- a/docs/token_generation_performance_tips.md
+++ b/docs/token_generation_performance_tips.md
@@ -1,7 +1,7 @@
 # Token generation performance troubleshooting
 
-## Verifying that the model is running on the GPU with cuBLAS
-Make sure you compiled llama with the correct env variables according to [this guide](../README.md#cublas), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example:
+## Verifying that the model is running on the GPU with CUDA
+Make sure you compiled llama with the correct env variables according to [this guide](../README.md#CUDA), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example:
 ```shell
 ./main -m "path/to/model.gguf" -ngl 200000 -p "Please sir, may I have some "
 ```
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 0c71cbdf7..76496bf06 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -20,25 +20,30 @@ else()
     add_subdirectory(convert-llama2c-to-ggml)
     add_subdirectory(embedding)
     add_subdirectory(finetune)
+    add_subdirectory(gritlm)
+    add_subdirectory(gguf-split)
     add_subdirectory(infill)
     add_subdirectory(llama-bench)
     add_subdirectory(llava)
+    if (LLAMA_SYCL)
+        add_subdirectory(sycl)
+    endif()
     add_subdirectory(main)
     add_subdirectory(tokenize)
     add_subdirectory(parallel)
     add_subdirectory(perplexity)
     add_subdirectory(quantize)
     add_subdirectory(quantize-stats)
+    add_subdirectory(retrieval)
     add_subdirectory(save-load-state)
     add_subdirectory(simple)
     add_subdirectory(passkey)
     add_subdirectory(speculative)
     add_subdirectory(lookahead)
     add_subdirectory(lookup)
+    add_subdirectory(gguf)
     add_subdirectory(train-text-from-scratch)
-    if (LLAMA_METAL)
-        add_subdirectory(metal)
-    endif()
+    add_subdirectory(imatrix)
     if (LLAMA_BUILD_SERVER)
         add_subdirectory(server)
     endif()
diff --git a/examples/baby-llama/baby-llama.cpp b/examples/baby-llama/baby-llama.cpp
index e7d2ad592..bf0125e75 100644
--- a/examples/baby-llama/baby-llama.cpp
+++ b/examples/baby-llama/baby-llama.cpp
@@ -1533,27 +1533,28 @@ int main(int argc, char ** argv) {
 
         int n_past = 0;
 
-        ggml_cgraph gf = {};
+        struct ggml_cgraph * gf = NULL;
+        gf = ggml_new_graph_custom(ctx0, LLAMA_TRAIN_MAX_NODES, true);
 
         get_example_targets_batch(ctx0, 64*ex+0,  tokens_input, targets);
 
-        struct ggml_tensor * logits = forward_batch(&model, &kv_self, ctx0, &gf, tokens_input, n_tokens, n_past, n_batch);
+        struct ggml_tensor * logits = forward_batch(&model, &kv_self, ctx0, gf, tokens_input, n_tokens, n_past, n_batch);
         // struct ggml_tensor * e = cross_entropy_loss(ctx0, targets, logits);
         struct ggml_tensor * e = square_error_loss(ctx0, targets, logits);
 
-        ggml_build_forward_expand(&gf, e);
-        ggml_graph_compute_helper(work_buffer, &gf, /*n_threads*/ 1);
+        ggml_build_forward_expand(gf, e);
+        ggml_graph_compute_helper(work_buffer, gf, /*n_threads*/ 1);
 
         float error_before_opt = ggml_get_f32_1d(e, 0);
 
-        struct ggml_opt_params opt_params_lbfgs = ggml_opt_default_params(GGML_OPT_LBFGS);
+        struct ggml_opt_params opt_params_lbfgs = ggml_opt_default_params(GGML_OPT_TYPE_LBFGS);
         opt_params_lbfgs.print_forward_graph = false;
         opt_params_lbfgs.print_backward_graph = false;
         opt_params_lbfgs.lbfgs.n_iter = 16;
         ggml_opt(ctx0, opt_params_lbfgs, e);
         //
-        ggml_build_forward_expand(&gf, e);
-        ggml_graph_compute_helper(work_buffer, &gf, /*n_threads*/ 1);
+        ggml_build_forward_expand(gf, e);
+        ggml_graph_compute_helper(work_buffer, gf, /*n_threads*/ 1);
 
         float error_after_opt = ggml_get_f32_1d(e, 0);
 
@@ -1600,13 +1601,14 @@ int main(int argc, char ** argv) {
             };
             struct ggml_context * ctx0 = ggml_init(params);
 
-            ggml_cgraph gf = {};
+            struct ggml_cgraph * gf = NULL;
+            gf = ggml_new_graph_custom(ctx0, LLAMA_TRAIN_MAX_NODES, true);
 
             int n_past = 0;
-            struct ggml_tensor * logits = forward(&model, &kv_self, ctx0, &gf, tokens_input, sample_ctx, n_past);
+            struct ggml_tensor * logits = forward(&model, &kv_self, ctx0, gf, tokens_input, sample_ctx, n_past);
 
-            ggml_build_forward_expand(&gf, logits);
-            ggml_graph_compute_helper(work_buffer, &gf, /*n_threads*/ 1);
+            ggml_build_forward_expand(gf, logits);
+            ggml_graph_compute_helper(work_buffer, gf, /*n_threads*/ 1);
 
             struct ggml_tensor * best_samples = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, sample_ctx);
             struct ggml_tensor * probs        = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_vocab, sample_ctx);
diff --git a/examples/batched-bench/README.md b/examples/batched-bench/README.md
index 34b343f66..bf951baf7 100644
--- a/examples/batched-bench/README.md
+++ b/examples/batched-bench/README.md
@@ -10,16 +10,16 @@ There are 2 modes of operation:
 - `prompt is shared` - there is a common prompt of size `PP` used by all batches (i.e. `N_KV = PP + B*TG`)
 
 ```bash
-./batched-bench MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] <PP> <TG> <PL>
+./batched-bench MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [IS_PP_SHARED] [NGL] [MMQ] <PP> <TG> <PL>
 
 # LLaMA 7B, F16, N_KV_MAX = 16384 (8GB), prompt not shared
-./batched-bench ./models/llama-7b/ggml-model-f16.gguf 16384 0 99
+./batched-bench ./models/llama-7b/ggml-model-f16.gguf 16384 2048 512 0 99
 
 # LLaMA 7B, Q8_0, N_KV_MAX = 16384 (8GB), prompt is shared
-./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 16384 1 99
+./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 16384 2048 512 1 99
 
 # custom set of batches
-./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32
+./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 2048 512 512 0 999 0 128,256,512 128,256 1,2,4,8,16,32
 ```
 
 ## Sample results
diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp
index 57596ed98..1e34de620 100644
--- a/examples/batched-bench/batched-bench.cpp
+++ b/examples/batched-bench/batched-bench.cpp
@@ -32,16 +32,17 @@ int main(int argc, char ** argv) {
     gpt_params params;
 
     if (argc == 1 || argv[1][0] == '-') {
-        printf("usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] <PP> <TG> <PL>\n" , argv[0]);
+        printf("usage: %s MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [IS_PP_SHARED] [NGL] <PP> <TG> <PL>\n" , argv[0]);
         printf("  <PP>, <TG> and PL are comma-separated lists of numbers without spaces\n\n");
-        printf("  example: %s ggml-model-f16.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]);
+        printf("  example: %s ggml-model-f16.gguf 2048 2048 512 0 999 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]);
         return 1 ;
     }
 
     int n_kv_max     = 2048;
+    int n_batch      = 2048;
+    int n_ubatch     = 512;
     int is_pp_shared = 0;
     int n_gpu_layers = 0;
-    int mmq          = 0;
 
     std::vector<int> n_pp = { 128, 256, 512, 1024, 2048, 3584, 7680, };
     std::vector<int> n_tg = { 128, 256, };
@@ -57,38 +58,46 @@ int main(int argc, char ** argv) {
     }
 
     if (argc >= 4) {
-        is_pp_shared = std::atoi(argv[3]);
+        n_batch = std::atoi(argv[3]);
     }
 
     if (argc >= 5) {
-        n_gpu_layers = std::atoi(argv[4]);
+        n_ubatch = std::atoi(argv[4]);
     }
 
     if (argc >= 6) {
-        mmq = std::atoi(argv[5]);
+        is_pp_shared = std::atoi(argv[5]);
     }
 
     if (argc >= 7) {
-        n_pp = parse_list(argv[6]);
+        n_gpu_layers = std::atoi(argv[6]);
     }
 
     if (argc >= 8) {
-        n_tg = parse_list(argv[7]);
+        n_pp = parse_list(argv[7]);
     }
 
     if (argc >= 9) {
-        n_pl = parse_list(argv[8]);
+        n_tg = parse_list(argv[8]);
+    }
+
+    if (argc >= 10) {
+        n_pl = parse_list(argv[9]);
     }
 
     // init LLM
 
-    llama_backend_init(params.numa);
+    llama_backend_init();
+    llama_numa_init(params.numa);
 
     // initialize the model
 
     llama_model_params model_params = llama_model_default_params();
 
+    const std::vector<float> t_split(llama_max_devices(), 0.0f);
+
     model_params.n_gpu_layers = n_gpu_layers;
+    model_params.tensor_split = t_split.data();
 
     llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
 
@@ -101,12 +110,15 @@ int main(int argc, char ** argv) {
 
     ctx_params.seed      = 1234;
     ctx_params.n_ctx     = n_kv_max;
-    ctx_params.n_batch   = 512;
-    ctx_params.mul_mat_q = mmq;
+    ctx_params.n_batch   = n_batch;
+    ctx_params.n_ubatch  = n_ubatch;
 
     ctx_params.n_threads       = params.n_threads;
     ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
 
+    // ensure enough sequences are available
+    ctx_params.n_seq_max = *std::max_element(n_pl.begin(), n_pl.end());
+
     llama_context * ctx = llama_new_context_with_model(model, ctx_params);
 
     if (ctx == NULL) {
@@ -137,6 +149,8 @@ int main(int argc, char ** argv) {
                 LOG_TEE("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret);
                 return false;
             }
+
+            llama_synchronize(ctx);
         }
 
         return true;
@@ -155,7 +169,7 @@ int main(int argc, char ** argv) {
     }
 
     LOG_TEE("\n");
-    LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d, n_threads = %d, n_threads_batch = %d\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq, ctx_params.n_threads, ctx_params.n_threads_batch);
+    LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, n_batch, n_ubatch, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
     LOG_TEE("\n");
 
     LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP",     "TG",     "B",    "N_KV",     "T_PP s",   "S_PP t/s", "T_TG s",   "S_TG t/s", "T s",      "S t/s");
@@ -176,10 +190,10 @@ int main(int argc, char ** argv) {
 
                 llama_batch_clear(batch);
 
-                const int n_tokens = is_pp_shared ? pp : pl*pp;
-
-                for (int i = 0; i < n_tokens; ++i) {
-                    llama_batch_add(batch, 0, i, { 0 }, false);
+                for (int i = 0; i < pp; ++i) {
+                    for (int j = 0; j < (is_pp_shared ? 1 : pl); ++j) {
+                        llama_batch_add(batch, 0, i, { j }, false);
+                    }
                 }
                 batch.logits[batch.n_tokens - 1] = true;
 
@@ -194,7 +208,7 @@ int main(int argc, char ** argv) {
 
                 if (is_pp_shared) {
                     for (int32_t i = 1; i < pl; ++i) {
-                        llama_kv_cache_seq_cp(ctx, 0, i, 0, pp);
+                        llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
                     }
                 }
 
diff --git a/examples/batched.swift/Sources/main.swift b/examples/batched.swift/Sources/main.swift
index 4d0005349..d75c503d5 100644
--- a/examples/batched.swift/Sources/main.swift
+++ b/examples/batched.swift/Sources/main.swift
@@ -17,7 +17,7 @@ let n_parallel: Int = arguments.count > 3 && Int(arguments[3]) != nil ? Int(argu
 let n_len: Int = 32
 
 // init LLM
-llama_backend_init(false)
+llama_backend_init()
 defer {
     llama_backend_free()
 }
diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index b1775e0b0..7aaf63ceb 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -48,9 +48,12 @@ int main(int argc, char ** argv) {
         params.prompt = "Hello my name is";
     }
 
+    process_escapes(params.prompt);
+
     // init LLM
 
-    llama_backend_init(params.numa);
+    llama_backend_init();
+    llama_numa_init(params.numa);
 
     // initialize the model
 
@@ -77,8 +80,9 @@ int main(int argc, char ** argv) {
     llama_context_params ctx_params = llama_context_default_params();
 
     ctx_params.seed  = 1234;
-    ctx_params.n_ctx = n_kv_req;
+    ctx_params.n_ctx   = n_kv_req;
     ctx_params.n_batch = std::max(n_len, n_parallel);
+    ctx_params.n_seq_max       = n_parallel;
     ctx_params.n_threads       = params.n_threads;
     ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
 
@@ -91,7 +95,7 @@ int main(int argc, char ** argv) {
 
     const int n_ctx    = llama_n_ctx(ctx);
 
-    LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_batch = %d, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req);
+    LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req);
 
     // make sure the KV cache is big enough to hold all the prompt and generated tokens
     if (n_kv_req > n_ctx) {
@@ -131,7 +135,7 @@ int main(int argc, char ** argv) {
     // assign the system KV cache to all parallel sequences
     // this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
     for (int32_t i = 1; i < n_parallel; ++i) {
-        llama_kv_cache_seq_cp(ctx, 0, i, 0, batch.n_tokens);
+        llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
     }
 
     if (n_parallel > 1) {
diff --git a/examples/beam-search/beam-search.cpp b/examples/beam-search/beam-search.cpp
index 679b382e1..866c6d7a6 100644
--- a/examples/beam-search/beam-search.cpp
+++ b/examples/beam-search/beam-search.cpp
@@ -119,7 +119,8 @@ int main(int argc, char ** argv)
     // Init LLM :
     //---------------------------------
 
-    llama_backend_init(params.numa);
+    llama_backend_init();
+    llama_numa_init(params.numa);
 
     llama_model * model;
     llama_context * ctx;
diff --git a/examples/benchmark/benchmark-matmult.cpp b/examples/benchmark/benchmark-matmult.cpp
index 434e1d6bd..47cb16c69 100644
--- a/examples/benchmark/benchmark-matmult.cpp
+++ b/examples/benchmark/benchmark-matmult.cpp
@@ -189,12 +189,10 @@ int main(int argc, char ** argv)  {
 
     int32_t nelements = sizex*sizey;
 
-    std::vector<int64_t> hist_cur(1 << 4, 0);
-
     // Set up a the benchmark matrices
     // printf("Creating new tensor q11 & Running quantize\n");
     struct ggml_tensor * q11 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey);
-    ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements, hist_cur.data());
+    ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements/m11->ne[0], m11->ne[0], nullptr);
 
     // Set up a the compute graph
     // printf("Creating new tensor q31\n");
@@ -207,7 +205,7 @@ int main(int argc, char ** argv)  {
     // Set up a second graph computation to make sure we override the CPU cache lines
     // printf("Creating new tensor q12 & Running quantize\n");
     struct ggml_tensor * q12 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey);
-    ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements, hist_cur.data());
+    ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements/m12->ne[0], m12->ne[0], nullptr);
 
     // printf("Creating new tensor q32\n");
     struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2);
diff --git a/examples/convert-llama2c-to-ggml/README.md b/examples/convert-llama2c-to-ggml/README.md
index 0f37d295b..6da2d7e18 100644
--- a/examples/convert-llama2c-to-ggml/README.md
+++ b/examples/convert-llama2c-to-ggml/README.md
@@ -21,6 +21,8 @@ An example command using a model from [karpathy/tinyllamas](https://huggingface.
 
 `$ ./convert-llama2c-to-ggml --copy-vocab-from-model llama-2-7b-chat.gguf.q2_K.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.gguf.bin`
 
+Note: The vocabulary for `stories260K.bin` should be its own tokenizer `tok512.bin` found in [karpathy/tinyllamas/stories260K](https://huggingface.co/karpathy/tinyllamas/tree/main/stories260K).
+
 Now you can use the model with a command like:
 
 `$ ./main -m stories42M.gguf.bin -p "One day, Lily met a Shoggoth" -n 500 -c 256`
diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
index 4d41e1779..746c3fbef 100644
--- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
+++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
@@ -1,6 +1,7 @@
 #include "ggml.h"
 #include "llama.h"
 #include "common.h"
+#include "log.h"
 
 #include <unordered_map>
 #include <vector>
@@ -78,111 +79,101 @@ typedef struct {
 
 struct TransformerWeights {
     // token embedding table
-    float* token_embedding_table;    // (vocab_size, dim)
+    std::vector<float> token_embedding_table;    // (vocab_size, dim)
     // weights for rmsnorms
-    float* rms_att_weight; // (layer, dim) rmsnorm weights
-    float* rms_ffn_weight; // (layer, dim)
+    std::vector<float> rms_att_weight; // (layer, dim) rmsnorm weights
+    std::vector<float> rms_ffn_weight; // (layer, dim)
     // weights for matmuls
-    float* wq; // (layer, dim, dim)
-    float* wk; // (layer, dim, dim)
-    float* wv; // (layer, dim, dim)
-    float* wo; // (layer, dim, dim)
+    std::vector<float> wq; // (layer, dim, dim)
+    std::vector<float> wk; // (layer, dim, dim)
+    std::vector<float> wv; // (layer, dim, dim)
+    std::vector<float> wo; // (layer, dim, dim)
     // weights for ffn
-    float* w1; // (layer, hidden_dim, dim)
-    float* w2; // (layer, dim, hidden_dim)
-    float* w3; // (layer, hidden_dim, dim)
+    std::vector<float> w1; // (layer, hidden_dim, dim)
+    std::vector<float> w2; // (layer, dim, hidden_dim)
+    std::vector<float> w3; // (layer, hidden_dim, dim)
     // final rmsnorm
-    float* rms_final_weight; // (dim,)
+    std::vector<float> rms_final_weight; // (dim,)
     // freq_cis for RoPE relatively positional embeddings
-    // float* freq_cis_real; // (seq_len, dim/2)
-    // float* freq_cis_imag; // (seq_len, dim/2)
+    // std::vector<float> freq_cis_real; // (seq_len, dim/2)
+    // std::vector<float> freq_cis_imag; // (seq_len, dim/2)
     // (optional) classifier weights for the logits, on the last layer
-    float* wcls;
-
-    ~TransformerWeights() {
-        delete[] token_embedding_table;
-        delete[] rms_att_weight;
-        delete[] rms_ffn_weight;
-        delete[] wq;
-        delete[] wk;
-        delete[] wv;
-        delete[] wo;
-        delete[] w1;
-        delete[] w2;
-        delete[] w3;
-        delete[] rms_final_weight;
-        delete[] wcls;
-    }
+    std::vector<float> wcls;
 };
 
-static void malloc_weights(TransformerWeights* w, Config* p, bool shared_weights) {
-    // we calloc instead of malloc to keep valgrind happy
-    w->token_embedding_table = new float[p->vocab_size * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->token_embedding_table\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
+static void alloc_weights(TransformerWeights * w, const Config * p, bool shared_weights) {
+    const int n_multiqueries = p->n_kv_heads <= 0 || p->n_kv_heads >= p->n_heads ? 1 : p->n_heads / p->n_kv_heads;
+    try {
+        w->token_embedding_table.resize(p->vocab_size * p->dim);
+        LOG("%s: Allocating [%d] x [%d] = [%d] float space for w->token_embedding_table\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
 
-    w->rms_att_weight = new float[p->n_layers * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->rms_att_weight\n",__func__,p->n_layers, p->dim, p->n_layers * p->dim);
+        w->rms_att_weight.resize(p->n_layers * p->dim);
+        LOG("%s: Allocating [%d] x [%d] = [%d] float space for w->rms_att_weight\n",__func__,p->n_layers, p->dim, p->n_layers * p->dim);
 
-    w->rms_ffn_weight = new float[p->n_layers * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->rms_ffn_weight\n",__func__,p->n_layers , p->dim, p->n_layers * p->dim);
+        w->rms_ffn_weight.resize(p->n_layers * p->dim);
+        LOG("%s: Allocating [%d] x [%d] = [%d] float space for w->rms_ffn_weight\n",__func__,p->n_layers , p->dim, p->n_layers * p->dim);
 
-    w->wq = new float[p->n_layers * p->dim * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wq\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
+        w->wq.resize(p->n_layers * p->dim * p->dim);
+        LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wq\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
 
-    w->wk = new float[p->n_layers * p->dim * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wk\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
+        w->wk.resize(p->n_layers * p->dim * p->dim / n_multiqueries);
+        LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wk\n",__func__,p->n_layers, p->dim, p->dim / n_multiqueries, p->n_layers * p->dim * p->dim / n_multiqueries);
 
-    w->wv = new float[p->n_layers * p->dim * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wv\n",__func__, p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
+        w->wv.resize(p->n_layers * p->dim * p->dim / n_multiqueries);
+        LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wv\n",__func__, p->n_layers, p->dim, p->dim / n_multiqueries, p->n_layers * p->dim * p->dim / n_multiqueries);
 
-    w->wo = new float[p->n_layers * p->dim * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wo\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
+        w->wo.resize(p->n_layers * p->dim * p->dim);
+        LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wo\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
 
-    w->w1 = new float[p->n_layers * p->hidden_dim * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w1\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);
+        w->w1.resize(p->n_layers * p->hidden_dim * p->dim);
+        LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->w1\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);
 
-    w->w2 = new float[p->n_layers * p->hidden_dim * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w2\n",__func__,p->n_layers, p->dim, p->hidden_dim, p->n_layers * p->hidden_dim * p->dim);
+        w->w2.resize(p->n_layers * p->hidden_dim * p->dim);
+        LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->w2\n",__func__,p->n_layers, p->dim, p->hidden_dim, p->n_layers * p->hidden_dim * p->dim);
 
-    w->w3 = new float[p->n_layers * p->hidden_dim * p->dim]();
-    printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w3\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);
+        w->w3.resize(p->n_layers * p->hidden_dim * p->dim);
+        LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->w3\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);
 
-    w->rms_final_weight = new float[p->dim]();
-    printf("[%s:AK] Allocating [%d] float space for w->rms_final_weight\n",__func__,p->dim);
+        w->rms_final_weight.resize(p->dim);
+        LOG("%s: Allocating [%d] float space for w->rms_final_weight\n",__func__,p->dim);
 
-    if (shared_weights) {
-        w->wcls = NULL;
-    } else {
-        w->wcls = new float[p->vocab_size * p->dim]();
-        printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->wcls\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
+        if (shared_weights) {
+            w->wcls = {};
+        } else {
+            w->wcls.resize(p->vocab_size * p->dim);
+            LOG("%s: Allocating [%d] x [%d] = [%d] float space for w->wcls\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
+        }
+    }
+    catch (std::length_error &) {
+        die("Invalid configuration. Failed to allocate memory for weights");
     }
 }
 
-static int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f, bool shared_weights) {
-    if (fread(w->token_embedding_table, sizeof(float), p->vocab_size * p->dim, f) != static_cast<size_t>(p->vocab_size * p->dim)) return 1;
-    if (fread(w->rms_att_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim)) return 1;
-    if (fread(w->wq, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
-    if (fread(w->wk, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
-    if (fread(w->wv, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
-    if (fread(w->wo, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
-    if (fread(w->rms_ffn_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim)) return 1;
-    if (fread(w->w1, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->hidden_dim)) return 1;
-    if (fread(w->w2, sizeof(float), p->n_layers * p->hidden_dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->hidden_dim * p->dim)) return 1;
-    if (fread(w->w3, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->hidden_dim)) return 1;
-    if (fread(w->rms_final_weight, sizeof(float), p->dim, f) != static_cast<size_t>(p->dim)) return 1;
+static int checkpoint_init_weights(TransformerWeights * w, const Config * p, FILE * f, bool shared_weights) {
+    if (fread(w->token_embedding_table.data(), sizeof(float), w->token_embedding_table.size(), f) != w->token_embedding_table.size()) return 1;
+    if (fread(w->rms_att_weight.data(), sizeof(float), w->rms_att_weight.size(), f) != w->rms_att_weight.size()) return 1;
+    if (fread(w->wq.data(), sizeof(float), w->wq.size(), f) != w->wq.size()) return 1;
+    if (fread(w->wk.data(), sizeof(float), w->wk.size(), f) != w->wk.size()) return 1;
+    if (fread(w->wv.data(), sizeof(float), w->wv.size(), f) != w->wv.size()) return 1;
+    if (fread(w->wo.data(), sizeof(float), w->wo.size(), f) != w->wo.size()) return 1;
+    if (fread(w->rms_ffn_weight.data(), sizeof(float), w->rms_ffn_weight.size(), f) != w->rms_ffn_weight.size()) return 1;
+    if (fread(w->w1.data(), sizeof(float), w->w1.size(), f) != w->w1.size()) return 1;
+    if (fread(w->w2.data(), sizeof(float), w->w2.size(), f) != w->w2.size()) return 1;
+    if (fread(w->w3.data(), sizeof(float), w->w3.size(), f) != w->w3.size()) return 1;
+    if (fread(w->rms_final_weight.data(), sizeof(float), w->rms_final_weight.size(), f) != w->rms_final_weight.size()) return 1;
 
     // Skip freq_cis_real & freq_cis_imag
     int head_size = p->dim / p->n_heads;
     fseek(f, p->seq_len * head_size * sizeof(float), SEEK_CUR);
 
-    if (!shared_weights && fread(w->wcls, sizeof(float), p->vocab_size * p->dim, f) != static_cast<size_t>(p->vocab_size * p->dim)) return 1;
+    if (!shared_weights && fread(w->wcls.data(), sizeof(float), w->wcls.size(), f) != w->wcls.size()) return 1;
 
     // Check we didn't forget to read anything
     auto curr = ftell(f);
     fseek(f, 0, SEEK_END);
     auto end = ftell(f);
     if (curr != end) {
-        printf("Error: failed to read the checkpoint file to the end (curr = %ld, end =  %ld)\n", curr, end);
+        LOG("%s: Error: failed to read the checkpoint file to the end (curr = %ld, end =  %ld)\n", __func__, curr, end);
         return 1;
     }
 
@@ -190,20 +181,20 @@ static int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f, bo
 }
 
 static void print_sample_weights(TransformerWeights *w){
-    printf("----- Quick print of first of the weight vales of all the variables\n");
-    printf("%f\n", w->token_embedding_table[0]);
-    printf("%f\n", w->rms_att_weight[0]);
-    printf("%f\n", w->rms_ffn_weight[0]);
+    LOG("----- Quick print of first of the weight vales of all the variables\n");
+    LOG("%f\n", w->token_embedding_table[0]);
+    LOG("%f\n", w->rms_att_weight[0]);
+    LOG("%f\n", w->rms_ffn_weight[0]);
 
-    printf("%f\n", w->wq[0]);
-    printf("%f\n", w->wk[0]);
-    printf("%f\n", w->wv[0]);
-    printf("%f\n", w->wo[0]);
-    printf("%f\n", w->w1[0]);
-    printf("%f\n", w->w2[0]);
-    printf("%f\n", w->w3[0]);
-    printf("%f\n", w->rms_att_weight[0]);
-    if (w->wcls) printf("%f\n", w->wcls[0]);
+    LOG("%f\n", w->wq[0]);
+    LOG("%f\n", w->wk[0]);
+    LOG("%f\n", w->wv[0]);
+    LOG("%f\n", w->wo[0]);
+    LOG("%f\n", w->w1[0]);
+    LOG("%f\n", w->w2[0]);
+    LOG("%f\n", w->w3[0]);
+    LOG("%f\n", w->rms_att_weight[0]);
+    if (!w->wcls.empty()) LOG("%f\n", w->wcls[0]);
 }
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////
 
@@ -225,14 +216,16 @@ struct llama_vocab {
 };
 
 struct my_llama_hparams {
-    uint32_t n_vocab = 32000;
-    uint32_t n_ctx   = 512;   // this is provided as user input?
-    uint32_t n_embd  = 4096;
-    uint32_t n_ff    = 11008;
-    uint32_t n_mult  = 4;
-    uint32_t n_head  = 32;
-    uint32_t n_layer = 32;
-    uint32_t n_rot   = 64;
+    uint32_t n_vocab   = 32000;
+    uint32_t n_ctx     = 512;   // this is provided as user input?
+    uint32_t n_embd    = 4096;
+    uint32_t n_ff      = 11008;
+    uint32_t n_mult    = 4;
+    uint32_t n_head    = 32;
+    uint32_t n_head_kv = 32;
+    uint32_t n_layer   = 32;
+    uint32_t n_rot     = 64;
+
     bool operator!=(const my_llama_hparams& other) const {
         return memcmp(this, &other, sizeof(my_llama_hparams));
     }
@@ -325,14 +318,30 @@ struct train_params {
 };
 
 static void print_params(struct my_llama_hparams * params) {
-    printf("%s: n_vocab: %d\n", __func__, params->n_vocab);
-    printf("%s: n_ctx:   %d\n", __func__, params->n_ctx);
-    printf("%s: n_embd:  %d\n", __func__, params->n_embd);
-    printf("%s: n_mult:  %d\n", __func__, params->n_mult);
-    printf("%s: n_head:  %d\n", __func__, params->n_head);
-    printf("%s: n_ff:    %d\n", __func__, params->n_ff);
-    printf("%s: n_layer: %d\n", __func__, params->n_layer);
-    printf("%s: n_rot:   %d\n", __func__, params->n_rot);
+    LOG("%s: n_vocab:   %u\n", __func__, params->n_vocab);
+    LOG("%s: n_ctx:     %u\n", __func__, params->n_ctx);
+    LOG("%s: n_embd:    %u\n", __func__, params->n_embd);
+    LOG("%s: n_mult:    %u\n", __func__, params->n_mult);
+    LOG("%s: n_head:    %u\n", __func__, params->n_head);
+    LOG("%s: n_head_kv: %u\n", __func__, params->n_head_kv);
+    LOG("%s: n_ff:      %u\n", __func__, params->n_ff);
+    LOG("%s: n_layer:   %u\n", __func__, params->n_layer);
+    LOG("%s: n_rot:     %u\n", __func__, params->n_rot);
+}
+
+static void print_tensor_info(const struct ggml_context * ctx) {
+    for (auto t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+        LOG("%s: Allocating ", __func__);
+        int64_t total = 1;
+        int i = 0;
+        for (; i < ggml_n_dims(t); ++i) {
+            if (i > 0) LOG("x ");
+            LOG("[%" PRId64 "] ", t->ne[i]);
+            total *= t->ne[i];
+        }
+        if (i > 1) LOG("= [%" PRId64 "] ", total);
+        LOG("float space for %s\n", ggml_get_name(t));
+    }
 }
 
 static void init_model(struct my_llama_model * model) {
@@ -342,6 +351,8 @@ static void init_model(struct my_llama_model * model) {
     const uint32_t n_layer = hparams.n_layer;
     const uint32_t n_vocab = hparams.n_vocab;
 
+    const uint32_t n_multiqueries = hparams.n_head_kv <= 0 || hparams.n_head_kv >= hparams.n_head ? 1 : hparams.n_head / hparams.n_head_kv;
+
     const uint32_t n_ff = hparams.n_ff;
     struct ggml_context * ctx = model->ctx;
 
@@ -350,25 +361,8 @@ static void init_model(struct my_llama_model * model) {
     model->train_tokens = 0;
 
     model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
-    printf("[%s:GG] Allocating [%d] x [%d] = [%d] float space for model->tok_embeddings\n",__func__,n_embd , n_vocab, n_embd * n_vocab);
-
     model->norm           = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-    printf("[%s:GG] Allocating [%d] float space for model->norm\n",__func__,n_embd);
-
     model->output         = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
-    printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for model->output\n",__func__,n_embd, n_vocab, n_embd * n_vocab);
-
-    // printing the per-layer allocations here so we dont print in the for loop.
-    printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wq for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
-    printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wk for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
-    printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wv for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
-    printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wo for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
-
-    printf("[%s:GG] Allocating [%d] float space for layer.ffn_norm for [%d] layers\n",__func__,n_embd, n_layer);
-
-    printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.w1 for [%d] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer);
-    printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.w2 for [%d] layers\n",__func__, n_embd, n_ff, n_ff * n_embd, n_layer);
-    printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.w3 for [%d] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer);
 
     ggml_set_name(model->tok_embeddings, "tok_embeddings.weight");
     ggml_set_name(model->norm,           "norm.weight");
@@ -383,8 +377,8 @@ static void init_model(struct my_llama_model * model) {
         layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
 
         layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
-        layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
-        layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
+        layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd / n_multiqueries);
+        layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd / n_multiqueries);
         layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
 
         layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
@@ -406,6 +400,8 @@ static void init_model(struct my_llama_model * model) {
         ggml_format_name(layer.w2, "%s.feed_forward.w2.weight", layers_i.c_str());
         ggml_format_name(layer.w3, "%s.feed_forward.w3.weight", layers_i.c_str());
     }
+
+    print_tensor_info(ctx);
 }
 
 static float get_f32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) {
@@ -421,9 +417,9 @@ static int32_t get_i32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) {
 static void print_row(struct ggml_tensor * probs, int i) {
     for (int k = 0; k < probs->ne[0]; ++k) {
         float p = get_f32_2d(probs, k, i);
-        printf(" %f", p);
+        LOG(" %f", p);
     }
-    printf("\n");
+    LOG("\n");
 }
 
 static void print_matrix(struct ggml_tensor * probs) {
@@ -431,33 +427,12 @@ static void print_matrix(struct ggml_tensor * probs) {
     for (int i = 0; i < probs->ne[1]; ++i) {
         for (int k = 0; k < probs->ne[0]; ++k) {
             float p = get_f32_2d(probs, k, i);
-            printf(" %.2f", p);
+            LOG(" %.2f", p);
         }
-        printf("\n");
+        LOG("\n");
     }
 }
 
-#ifdef __GNUC__
-#ifdef __MINGW32__
-__attribute__((format(gnu_printf, 1, 2)))
-#else
-__attribute__((format(printf, 1, 2)))
-#endif
-#endif
-static std::string format(const char * fmt, ...) {
-    va_list ap, ap2;
-    va_start(ap, fmt);
-    va_copy(ap2, ap);
-    int size = vsnprintf(NULL, 0, fmt, ap);
-    GGML_ASSERT(size >= 0 && size < INT_MAX);
-    std::vector<char> buf(size + 1);
-    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
-    GGML_ASSERT(size2 == size);
-    va_end(ap2);
-    va_end(ap);
-    return std::string(buf.data(), size);
-}
-
 struct llama_file {
     // use FILE * so we don't have to re-open the file to mmap
     FILE * fp;
@@ -549,8 +524,9 @@ static std::string llama_escape_whitespaces(const std::string & text) {
     return out.str();
 }
 
-static void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) {
+static void load_vocab(const char * filename, const Config * config, struct llama_vocab * vocab) {
     if (is_ggml_file(filename)) {
+        LOG("%s: Loading vocabulary from gguf file %s\n", __func__, filename);
         struct ggml_context * ctx_data = NULL;
 
         struct gguf_init_params params = {
@@ -578,6 +554,9 @@ static void load_vocab(const char *filename, Config *config, struct llama_vocab
         const int * toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
 
         const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);
+        if (n_vocab != static_cast<uint32_t>(config->vocab_size)) {
+            die_fmt("vocab size mismatch: (gguf) %u != (llama2c) %d", n_vocab, config->vocab_size);
+        }
 
         vocab->id_to_token.resize(n_vocab);
 
@@ -595,7 +574,7 @@ static void load_vocab(const char *filename, Config *config, struct llama_vocab
         gguf_free(ctx);
     } else {
         // assume llama2.c vocabulary
-        printf("Assuming llama2.c vocabulary since %s is not a gguf file\n", filename);
+        LOG("%s: Assuming llama2.c vocabulary since %s is not a gguf file\n", __func__, filename);
         llama_file file(filename, "rb");
         if (!file.fp) {
             die_fmt("%s: %s", strerror(errno), filename);
@@ -638,38 +617,15 @@ static void load_vocab(const char *filename, Config *config, struct llama_vocab
 }
 
 static void convert_weights_ak_to_gg(struct ggml_tensor * gg_weights, const float * karpathy_weights) {
-    int ct;
-    switch (ggml_n_dims(gg_weights)) {
-        case 1:
-            ct = 0;
-            for (int i0 = 0; i0 < gg_weights->ne[0]; i0++){
-                float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0]);
-                *ptr = karpathy_weights[ct];
-                ct++;
-            }
-            break;
-        case 2:
-            ct = 0;
-            for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) {
-                for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) {
-                    float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0] + i1*gg_weights->nb[1]);
-                    *ptr = karpathy_weights[ct];
-                    ct++;
-                }
-            }
-            break;
-        case 3:
-            ct = 0;
-            for (int i2 = 0; i2 < gg_weights->ne[2]; i2++) {
-                for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) {
-                    for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) {
-                        float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0] + i1*gg_weights->nb[1] + i2*gg_weights->nb[2]);
-                        *ptr = karpathy_weights[ct];
-                        ct++;
-                    }
-                }
-            }
-            break;
+    int size = 1;
+    for (int dim = 0; dim < ggml_n_dims(gg_weights); ++dim) {
+        size *= gg_weights->ne[dim];
+    }
+    for (int ct = 0; ct < size; ++ct) {
+        int64_t i0 = 0; int64_t i1 = 0;
+        int64_t i2 = 0; int64_t i3 = 0;
+        ggml_unravel_index(gg_weights, ct, &i0, &i1, &i2, &i3);
+        ggml_set_f32_nd(gg_weights, i0, i1, i2, i3, karpathy_weights[ct]);
     }
 }
 
@@ -679,16 +635,18 @@ static void save_as_llama_model(
     // convert AK weights into GG weights one by one.
     // w->token_embedding_table -> model->tok_embeddings
     // float*                   -> struct ggml_tensor
-    convert_weights_ak_to_gg(model->tok_embeddings, w->token_embedding_table);
-    convert_weights_ak_to_gg(model->output, w->wcls ? w->wcls : w->token_embedding_table);
+    convert_weights_ak_to_gg(model->tok_embeddings, w->token_embedding_table.data());
+    convert_weights_ak_to_gg(model->output, !w->wcls.empty() ? w->wcls.data() : w->token_embedding_table.data());
 
-    convert_weights_ak_to_gg(model->norm, w->rms_final_weight);
+    convert_weights_ak_to_gg(model->norm, w->rms_final_weight.data());
     //print_row(model->norm, 0);
 
     // for rms-att-weight
     int row_length = model->hparams.n_embd;
     int n_ff = model->hparams.n_ff;
 
+    const uint32_t n_multiqueries = model->hparams.n_head_kv <= 0 || model->hparams.n_head_kv >= model->hparams.n_head ? 1 : model->hparams.n_head / model->hparams.n_head_kv;
+
     for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
         auto & layer = model->layers[i];
         // 1d
@@ -697,9 +655,10 @@ static void save_as_llama_model(
 
         // from 3d matrix layer x dim x dim to 2d matrix dim x dim
         convert_weights_ak_to_gg(layer.wq            , &w->wq[i*row_length*row_length]);
-        convert_weights_ak_to_gg(layer.wk            , &w->wk[i*row_length*row_length]);
-        convert_weights_ak_to_gg(layer.wv            , &w->wv[i*row_length*row_length]);
         convert_weights_ak_to_gg(layer.wo            , &w->wo[i*row_length*row_length]);
+        // from 3d matrix layer x dim x dim to 2d matrix dim x dim / n_multiqueries
+        convert_weights_ak_to_gg(layer.wk            , &w->wk[i*row_length*row_length/n_multiqueries]);
+        convert_weights_ak_to_gg(layer.wv            , &w->wv[i*row_length*row_length/n_multiqueries]);
 
         convert_weights_ak_to_gg(layer.w1            , &w->w1[i*row_length*n_ff]);
         convert_weights_ak_to_gg(layer.w2            , &w->w2[i*n_ff*row_length]);
@@ -736,8 +695,8 @@ static void save_as_llama_model(
     gguf_set_val_u32(ctx, KV_EMBEDDING_LENGTH, model->hparams.n_embd);
     gguf_set_val_u32(ctx, KV_FEED_FORWARD_LENGTH, model->hparams.n_ff);
     gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head);
-    // n_head_kv is optional, default to n_head
-    // gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT_KV, ...);
+    gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head);
+    gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT_KV, model->hparams.n_head_kv);
     gguf_set_val_u32(ctx, KV_BLOCK_COUNT, model->hparams.n_layer);
     gguf_set_val_u32(ctx, KV_ROPE_DIMENSION_COUNT, model->hparams.n_rot);
     gguf_set_val_f32(ctx, KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f);
@@ -789,12 +748,12 @@ static void save_as_llama_model(
 
 static struct train_params get_default_train_params() {
     struct train_params params;
-    params.fn_vocab_model    = "models/7B/ggml-model-f16.gguf";
+    params.fn_vocab_model          = "models/7B/ggml-model-f16.gguf";
     params.fn_llama2c_output_model = "ak_llama_model.bin";
-    params.fn_train_data     = "shakespeare.txt";
-    params.fn_checkpoint_in  = "checkpoint.bin";
-    params.fn_checkpoint_out = "checkpoint.bin";
-    params.fn_model_out      = "ggml-checkpoint-f32.bin";
+    params.fn_train_data           = "shakespeare.txt";
+    params.fn_checkpoint_in        = "checkpoint.bin";
+    params.fn_checkpoint_out       = "checkpoint.bin";
+    params.fn_model_out            = "ggml-checkpoint-f32.bin";
 
     params.seed       =   -1;
 
@@ -829,8 +788,8 @@ static struct train_params get_default_train_params() {
     params.adam_alpha        = 1e-3f;
     params.adam_decay        = 1e-3f;
 
-    params.mem_model_gb   = 2;
-    params.mem_compute_gb = 24;
+    params.mem_model_gb    = 2;
+    params.mem_compute_gb  = 24;
     params.mem_compute0_gb = 8;
     params.mem_compute1_gb = 2;
 
@@ -916,19 +875,30 @@ int main(int argc, char ** argv) {
     if (!params_parse(argc, argv, &params)) {
         return 1;
     }
+    log_set_target(stdout);
     Config config;
     TransformerWeights weights = {};
     {
-        FILE *file = fopen(params.fn_llama2c_model, "rb");
-        if (!file) { printf("Unable to open the checkpoint file %s!\n", params.fn_llama2c_model); return 1; }
+        LOG("%s: Loading llama2c model from %s\n", __func__, params.fn_llama2c_model);
+        FILE * file = fopen(params.fn_llama2c_model, "rb");
+        if (!file) {
+            LOG("%s: Unable to open the checkpoint file %s!\n", __func__, params.fn_llama2c_model);
+            return 1;
+        }
         // read in the config header
-        if(fread(&config, sizeof(Config), 1, file) != 1) { return 1; }
+        if (fread(&config, sizeof(Config), 1, file) != 1) {
+            LOG("%s: Unable to read llama2c config from %s!\n",__func__,params.fn_llama2c_model);
+            return 1;
+        }
         auto shared_weights = config.vocab_size > 0;
         config.vocab_size = abs(config.vocab_size);
 
         // read in the Transformer weights
-        malloc_weights(&weights, &config, shared_weights);
-        if(checkpoint_init_weights(&weights, &config, file, shared_weights)) { return 1; }
+        alloc_weights(&weights, &config, shared_weights);
+        if (checkpoint_init_weights(&weights, &config, file, shared_weights)) {
+            LOG("%s: Unable to initialize transformer weights from %s!",__func__,params.fn_llama2c_model);
+            return 1;
+        }
         fclose(file);
     }
 
@@ -936,15 +906,18 @@ int main(int argc, char ** argv) {
     load_vocab(params.fn_vocab_model, &config, &vocab);
 
     struct my_llama_model model;
-    model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx);
-    model.hparams.n_ctx   = params.n_ctx;
-    model.hparams.n_embd  = config.dim; //params.n_embd;
-    model.hparams.n_ff    = config.hidden_dim;
-    model.hparams.n_mult  = 32;//params.n_mult;
-    model.hparams.n_head  = config.n_heads; //params.n_head;
-    model.hparams.n_layer = config.n_layers; //params.n_layer;
-    model.hparams.n_rot   = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head);
+    model.hparams.n_vocab   = config.vocab_size; //llama_n_vocab(lctx);
+    model.hparams.n_ctx     = params.n_ctx;
+    model.hparams.n_embd    = config.dim; //params.n_embd;
+    model.hparams.n_ff      = config.hidden_dim;
+    model.hparams.n_mult    = 32;//params.n_mult;
+    model.hparams.n_head    = config.n_heads; //params.n_head;
+    model.hparams.n_head_kv = config.n_kv_heads;
+    model.hparams.n_layer   = config.n_layers; //params.n_layer;
+    model.hparams.n_rot     = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head);
+
     print_params(&model.hparams);
+
     struct ggml_init_params lcparams;
     lcparams.mem_size   = 1024ll*1024ll*1024ll*((size_t) params.mem_model_gb);
     lcparams.mem_buffer = NULL;
@@ -956,7 +929,7 @@ int main(int argc, char ** argv) {
     model.name = basename(params.fn_llama2c_model);
     save_as_llama_model(&vocab, &model, &weights, params.fn_llama2c_output_model);
 
-    printf("Saving llama.c model file %s in ggml format at %s\n", params.fn_llama2c_model, params.fn_llama2c_output_model);
+    LOG("%s: Saving llama.c model file %s in ggml format at %s\n", __func__, params.fn_llama2c_model, params.fn_llama2c_output_model);
 
     ggml_free(model.ctx);
     return 0;
diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp
index 3295cd240..536657526 100644
--- a/examples/embedding/embedding.cpp
+++ b/examples/embedding/embedding.cpp
@@ -7,6 +7,52 @@
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
+static std::vector<std::string> split_lines(const std::string & s) {
+    std::string line;
+    std::vector<std::string> lines;
+    std::stringstream ss(s);
+    while (std::getline(ss, line)) {
+        lines.push_back(line);
+    }
+    return lines;
+}
+
+static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, int seq_id) {
+    for (size_t i = 0; i < tokens.size(); i++) {
+        llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
+    }
+}
+
+static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) {
+    // clear previous kv_cache values (irrelevant for embeddings)
+    llama_kv_cache_clear(ctx);
+
+    // run model
+    fprintf(stderr, "%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
+    if (llama_decode(ctx, batch) < 0) {
+        fprintf(stderr, "%s : failed to decode\n", __func__);
+    }
+
+    for (int i = 0; i < batch.n_tokens; i++) {
+        if (!batch.logits[i]) {
+            continue;
+        }
+
+        // try to get sequence embeddings - supported only when pooling_type is not NONE
+        const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
+        if (embd == NULL) {
+            embd = llama_get_embeddings_ith(ctx, i);
+            if (embd == NULL) {
+                fprintf(stderr, "%s: failed to get embeddings for token %d\n", __func__, i);
+                continue;
+            }
+        }
+
+        float * out = output + batch.seq_id[i][0] * n_embd;
+        llama_embd_normalize(embd, out, n_embd);
+    }
+}
+
 int main(int argc, char ** argv) {
     gpt_params params;
 
@@ -15,6 +61,8 @@ int main(int argc, char ** argv) {
     }
 
     params.embedding = true;
+    // For non-causal models, batch size must be equal to ubatch size
+    params.n_ubatch = params.n_batch;
 
     print_build_info();
 
@@ -29,7 +77,8 @@ int main(int argc, char ** argv) {
         params.prompt = gpt_random_prompt(rng);
     }
 
-    llama_backend_init(params.numa);
+    llama_backend_init();
+    llama_numa_init(params.numa);
 
     llama_model * model;
     llama_context * ctx;
@@ -55,49 +104,107 @@ int main(int argc, char ** argv) {
         fprintf(stderr, "%s\n", get_system_info(params).c_str());
     }
 
-    int n_past = 0;
+    // split the prompt into lines
+    std::vector<std::string> prompts = split_lines(params.prompt);
 
-    // tokenize the prompt
-    auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
+    // max batch size
+    const uint64_t n_batch = params.n_batch;
+    GGML_ASSERT(params.n_batch >= params.n_ctx);
 
-    if (params.verbose_prompt) {
-        fprintf(stderr, "\n");
-        fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
-        fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
-        for (int i = 0; i < (int) embd_inp.size(); i++) {
-            fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
-        }
-        fprintf(stderr, "\n");
-    }
-
-    if (embd_inp.size() > (size_t)n_ctx) {
-        fprintf(stderr, "%s: error: prompt is longer than the context window (%zu tokens, n_ctx = %d)\n",
-                __func__, embd_inp.size(), n_ctx);
-        return 1;
-    }
-
-    while (!embd_inp.empty()) {
-        int n_tokens = std::min(params.n_batch, (int) embd_inp.size());
-        if (llama_decode(ctx, llama_batch_get_one(embd_inp.data(), n_tokens, n_past, 0))) {
-            fprintf(stderr, "%s : failed to eval\n", __func__);
+    // tokenize the prompts and trim
+    std::vector<std::vector<int32_t>> inputs;
+    for (const auto & prompt : prompts) {
+        auto inp = ::llama_tokenize(ctx, prompt, true, false);
+        if (inp.size() > n_batch) {
+            fprintf(stderr, "%s: error: number of tokens in input line (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
+                    __func__, (long long int) inp.size(), (long long int) n_batch);
             return 1;
         }
-        n_past += n_tokens;
-        embd_inp.erase(embd_inp.begin(), embd_inp.begin() + n_tokens);
+        inputs.push_back(inp);
     }
 
+    // add eos if not present
+    for (auto & inp : inputs) {
+        if (inp.empty() || inp.back() != llama_token_eos(model)) {
+            inp.push_back(llama_token_eos(model));
+        }
+    }
+
+    // tokenization stats
+    if (params.verbose_prompt) {
+        for (int i = 0; i < (int) inputs.size(); i++) {
+            fprintf(stderr, "%s: prompt %d: '%s'\n", __func__, i, prompts[i].c_str());
+            fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, inputs[i].size());
+            for (int j = 0; j < (int) inputs[i].size(); j++) {
+                fprintf(stderr, "%6d -> '%s'\n", inputs[i][j], llama_token_to_piece(ctx, inputs[i][j]).c_str());
+            }
+            fprintf(stderr, "\n\n");
+        }
+    }
+
+    // initialize batch
+    const int n_prompts = prompts.size();
+    struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
+
+    // allocate output
     const int n_embd = llama_n_embd(model);
-    const auto * embeddings = llama_get_embeddings(ctx);
+    std::vector<float> embeddings(n_prompts * n_embd, 0);
+    float * emb = embeddings.data();
 
-    for (int i = 0; i < n_embd; i++) {
-        printf("%f ", embeddings[i]);
+    // break into batches
+    int p = 0; // number of prompts processed already
+    int s = 0; // number of prompts in current batch
+    for (int k = 0; k < n_prompts; k++) {
+        // clamp to n_batch tokens
+        auto & inp = inputs[k];
+
+        const uint64_t n_toks = inp.size();
+
+        // encode if at capacity
+        if (batch.n_tokens + n_toks > n_batch) {
+            float * out = emb + p * n_embd;
+            batch_decode(ctx, batch, out, s, n_embd);
+            llama_batch_clear(batch);
+            p += s;
+            s = 0;
+        }
+
+        // add to batch
+        batch_add_seq(batch, inp, s);
+        s += 1;
     }
-    printf("\n");
 
+    // final batch
+    float * out = emb + p * n_embd;
+    batch_decode(ctx, batch, out, s, n_embd);
+
+    // print the first part of the embeddings or for a single prompt, the full embedding
+    fprintf(stdout, "\n");
+    for (int j = 0; j < n_prompts; j++) {
+        fprintf(stdout, "embedding %d: ", j);
+        for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd) : n_embd); i++) {
+            fprintf(stdout, "%9.6f ", emb[j * n_embd + i]);
+        }
+        fprintf(stdout, "\n");
+    }
+
+    // print cosine similarity matrix
+    if (n_prompts > 1) {
+        fprintf(stdout, "\n");
+        printf("cosine similarity matrix:\n\n");
+        for (int i = 0; i < n_prompts; i++) {
+            for (int j = 0; j < n_prompts; j++) {
+                float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
+                fprintf(stdout, "%6.2f ", sim);
+            }
+            fprintf(stdout, "\n");
+        }
+    }
+
+    // clean up
     llama_print_timings(ctx);
     llama_free(ctx);
     llama_free_model(model);
-
     llama_backend_free();
 
     return 0;
diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp
index 58fbe204d..08413f57e 100644
--- a/examples/export-lora/export-lora.cpp
+++ b/examples/export-lora/export-lora.cpp
@@ -7,8 +7,6 @@
 #include <string>
 #include <thread>
 
-static const size_t tensor_alignment = 32;
-
 struct lora_info {
     std::string filename;
     float scale;
@@ -245,9 +243,8 @@ static struct lora_data * load_lora(struct lora_info * info) {
     params_ggml.no_alloc   = true;
     result->ctx = ggml_init(params_ggml);
 
-    uint32_t LLAMA_FILE_MAGIC_LORA = 0x67676C61; // 'ggla'
     uint32_t magic   = file.read_u32();
-    if (magic != LLAMA_FILE_MAGIC_LORA) {
+    if (magic != LLAMA_FILE_MAGIC_GGLA) {
         die_fmt("unexpected lora header file magic in '%s'", info->filename.c_str());
     }
     uint32_t version = file.read_u32();
@@ -338,24 +335,14 @@ static bool apply_lora(struct ggml_tensor * tensor, struct lora_data * lora, int
     params.mem_buffer = NULL;
     params.no_alloc   = true;
     struct ggml_context * ctx = NULL;
-    struct ggml_allocr * alloc = NULL;
-    struct ggml_cgraph * gf = NULL;
+    struct ggml_gallocr * alloc = NULL;
+    struct ggml_cgraph  * gf = NULL;
 
     ctx   = ggml_init(params);
-    alloc = ggml_allocr_new_measure(tensor_alignment);
+    alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
     gf    = build_graph_lora(ctx, tensor, lora_a, lora_b, scaling);
-    size_t alloc_size = ggml_allocr_alloc_graph(alloc, gf);
-    ggml_allocr_free(alloc);
-    ggml_free(ctx);
 
-    static std::vector<uint8_t> data_compute;
-    data_compute.resize(alloc_size + tensor_alignment);
-
-    ctx   = ggml_init(params);
-    alloc = ggml_allocr_new(data_compute.data(), data_compute.size(), tensor_alignment);
-    gf    = build_graph_lora(ctx, tensor, lora_a, lora_b, scaling);
-    ggml_allocr_alloc_graph(alloc, gf);
-    ggml_allocr_free(alloc);
+    ggml_gallocr_alloc_graph(alloc, gf);
 
     struct ggml_cplan cplan = ggml_graph_plan(gf, n_threads);
     static std::vector<uint8_t> data_work;
@@ -364,6 +351,7 @@ static bool apply_lora(struct ggml_tensor * tensor, struct lora_data * lora, int
 
     ggml_graph_compute(gf, &cplan);
 
+    ggml_gallocr_free(alloc);
     ggml_free(ctx);
     return true;
 }
diff --git a/examples/finetune/README.md b/examples/finetune/README.md
index a884706c5..2fafd505e 100644
--- a/examples/finetune/README.md
+++ b/examples/finetune/README.md
@@ -80,9 +80,9 @@ The LORA rank can be configured for each model tensor type separately with these
   --rank-wk N                LORA rank for wk tensor (default 4)
   --rank-wv N                LORA rank for wv tensor (default 4)
   --rank-wo N                LORA rank for wo tensor (default 4)
-  --rank-w1 N                LORA rank for w1 tensor (default 4)
-  --rank-w2 N                LORA rank for w2 tensor (default 4)
-  --rank-w3 N                LORA rank for w3 tensor (default 4)
+  --rank-ffn_gate N          LORA rank for ffn_gate tensor (default 4)
+  --rank-ffn_down N          LORA rank for ffn_down tensor (default 4)
+  --rank-ffn_up N            LORA rank for ffn_up tensor (default 4)
 ```
 
 The LORA rank of 'norm' tensors should always be 1.
diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp
index eaca42fc1..3da5317b3 100644
--- a/examples/finetune/finetune.cpp
+++ b/examples/finetune/finetune.cpp
@@ -1,5 +1,6 @@
 #include "ggml.h"
 #include "ggml-alloc.h"
+#include "ggml-backend.h"
 #include "llama.h"
 #include "common.h"
 #include "train.h"
@@ -13,8 +14,6 @@
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
-static const size_t tensor_alignment = 32;
-
 struct my_llama_hparams {
     uint32_t n_vocab    = 32000;
     uint32_t n_ctx      = 512;
@@ -61,9 +60,9 @@ struct my_llama_layer {
     struct ggml_tensor * ffn_norm;
 
     // ff
-    struct ggml_tensor * w1;
-    struct ggml_tensor * w2;
-    struct ggml_tensor * w3;
+    struct ggml_tensor * ffn_gate; // w1
+    struct ggml_tensor * ffn_down; // w2
+    struct ggml_tensor * ffn_up;   // w3
 };
 
 struct my_llama_model {
@@ -86,9 +85,9 @@ struct my_llama_lora_hparams {
     uint32_t n_rank_wv = 4;
     uint32_t n_rank_wo = 4;
     uint32_t n_rank_ffn_norm = 1;
-    uint32_t n_rank_w1 = 4;
-    uint32_t n_rank_w2 = 4;
-    uint32_t n_rank_w3 = 4;
+    uint32_t n_rank_ffn_gate = 4;
+    uint32_t n_rank_ffn_down = 4;
+    uint32_t n_rank_ffn_up = 4;
     uint32_t n_rank_tok_embeddings = 4;
     uint32_t n_rank_norm = 1;
     uint32_t n_rank_output = 4;
@@ -118,17 +117,17 @@ struct my_llama_lora_layer {
     struct ggml_tensor * ffn_norm_b;
 
     // ff
-    struct ggml_tensor * w1_a;
-    struct ggml_tensor * w1_b;
-    struct ggml_tensor * w2_a;
-    struct ggml_tensor * w2_b;
-    struct ggml_tensor * w3_a;
-    struct ggml_tensor * w3_b;
+    struct ggml_tensor * ffn_gate_a;
+    struct ggml_tensor * ffn_gate_b;
+    struct ggml_tensor * ffn_down_a;
+    struct ggml_tensor * ffn_down_b;
+    struct ggml_tensor * ffn_up_a;
+    struct ggml_tensor * ffn_up_b;
 };
 
 struct my_llama_lora {
     struct ggml_context * ctx = NULL;
-    std::vector<uint8_t> data;
+    ggml_backend_buffer_t data;
 
     my_llama_lora_hparams hparams;
 
@@ -209,9 +208,9 @@ static void print_lora_params(struct my_llama_lora_hparams * params) {
     printf("%s: n_rank_wv             : %u\n", __func__, params->n_rank_wv);
     printf("%s: n_rank_wo             : %u\n", __func__, params->n_rank_wo);
     printf("%s: n_rank_ffn_norm       : %u\n", __func__, params->n_rank_ffn_norm);
-    printf("%s: n_rank_w1             : %u\n", __func__, params->n_rank_w1);
-    printf("%s: n_rank_w2             : %u\n", __func__, params->n_rank_w2);
-    printf("%s: n_rank_w3             : %u\n", __func__, params->n_rank_w3);
+    printf("%s: n_rank_ffn_gate       : %u\n", __func__, params->n_rank_ffn_gate);
+    printf("%s: n_rank_ffn_down       : %u\n", __func__, params->n_rank_ffn_down);
+    printf("%s: n_rank_ffn_up         : %u\n", __func__, params->n_rank_ffn_up);
     printf("%s: n_rank_tok_embeddings : %u\n", __func__, params->n_rank_tok_embeddings);
     printf("%s: n_rank_norm           : %u\n", __func__, params->n_rank_norm);
     printf("%s: n_rank_output         : %u\n", __func__, params->n_rank_output);
@@ -320,9 +319,9 @@ static void init_model(struct llama_model * input, struct my_llama_model * model
         layer.wv             = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_V, i));
         layer.wo             = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_OUT, i));
         layer.ffn_norm       = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_NORM, i));
-        layer.w1             = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_GATE, i));
-        layer.w2             = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_DOWN, i));
-        layer.w3             = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_UP, i));
+        layer.ffn_gate       = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_GATE, i));
+        layer.ffn_down       = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_DOWN, i));
+        layer.ffn_up         = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_UP, i));
 
         assert_shape_1d(layer.attention_norm, hparams.n_embd);
         assert_shape_2d(layer.wq,             hparams.n_embd, hparams.n_embd);
@@ -330,9 +329,9 @@ static void init_model(struct llama_model * input, struct my_llama_model * model
         assert_shape_2d(layer.wv,             hparams.n_embd, hparams.n_embd_gqa());
         assert_shape_2d(layer.wo,             hparams.n_embd, hparams.n_embd);
         assert_shape_1d(layer.ffn_norm,       hparams.n_embd);
-        assert_shape_2d(layer.w1,             hparams.n_embd, hparams.n_ff);
-        assert_shape_2d(layer.w2,             hparams.n_ff,   hparams.n_embd);
-        assert_shape_2d(layer.w3,             hparams.n_embd, hparams.n_ff);
+        assert_shape_2d(layer.ffn_gate,       hparams.n_embd, hparams.n_ff);
+        assert_shape_2d(layer.ffn_down,       hparams.n_ff,   hparams.n_embd);
+        assert_shape_2d(layer.ffn_up,         hparams.n_embd, hparams.n_ff);
     }
 }
 
@@ -363,69 +362,12 @@ static void set_param_lora(struct my_llama_lora * lora) {
         ggml_set_param(ctx, layer.wo_b);
         ggml_set_param(ctx, layer.ffn_norm_a);
         ggml_set_param(ctx, layer.ffn_norm_b);
-        ggml_set_param(ctx, layer.w1_a);
-        ggml_set_param(ctx, layer.w1_b);
-        ggml_set_param(ctx, layer.w2_a);
-        ggml_set_param(ctx, layer.w2_b);
-        ggml_set_param(ctx, layer.w3_a);
-        ggml_set_param(ctx, layer.w3_b);
-    }
-}
-
-static void alloc_lora(struct ggml_allocr * alloc, struct my_llama_lora * lora) {
-    ggml_allocr_alloc(alloc, lora->tok_embeddings_a);
-    ggml_allocr_alloc(alloc, lora->tok_embeddings_b);
-    ggml_allocr_alloc(alloc, lora->norm_a);
-    ggml_allocr_alloc(alloc, lora->norm_b);
-    ggml_allocr_alloc(alloc, lora->output_a);
-    ggml_allocr_alloc(alloc, lora->output_b);
-    for (uint32_t i = 0; i < lora->layers.size(); ++i) {
-        auto & layer = lora->layers[i];
-        ggml_allocr_alloc(alloc, layer.attention_norm_a);
-        ggml_allocr_alloc(alloc, layer.attention_norm_b);
-        ggml_allocr_alloc(alloc, layer.wq_a);
-        ggml_allocr_alloc(alloc, layer.wq_b);
-        ggml_allocr_alloc(alloc, layer.wk_a);
-        ggml_allocr_alloc(alloc, layer.wk_b);
-        ggml_allocr_alloc(alloc, layer.wv_a);
-        ggml_allocr_alloc(alloc, layer.wv_b);
-        ggml_allocr_alloc(alloc, layer.wo_a);
-        ggml_allocr_alloc(alloc, layer.wo_b);
-        ggml_allocr_alloc(alloc, layer.ffn_norm_a);
-        ggml_allocr_alloc(alloc, layer.ffn_norm_b);
-        ggml_allocr_alloc(alloc, layer.w1_a);
-        ggml_allocr_alloc(alloc, layer.w1_b);
-        ggml_allocr_alloc(alloc, layer.w2_a);
-        ggml_allocr_alloc(alloc, layer.w2_b);
-        ggml_allocr_alloc(alloc, layer.w3_a);
-        ggml_allocr_alloc(alloc, layer.w3_b);
-    }
-    ggml_allocr_alloc(alloc, lora->tok_embeddings_a->grad);
-    ggml_allocr_alloc(alloc, lora->tok_embeddings_b->grad);
-    ggml_allocr_alloc(alloc, lora->norm_a->grad);
-    ggml_allocr_alloc(alloc, lora->norm_b->grad);
-    ggml_allocr_alloc(alloc, lora->output_a->grad);
-    ggml_allocr_alloc(alloc, lora->output_b->grad);
-    for (uint32_t i = 0; i < lora->layers.size(); ++i) {
-        auto & layer = lora->layers[i];
-        ggml_allocr_alloc(alloc, layer.attention_norm_a->grad);
-        ggml_allocr_alloc(alloc, layer.attention_norm_b->grad);
-        ggml_allocr_alloc(alloc, layer.wq_a->grad);
-        ggml_allocr_alloc(alloc, layer.wq_b->grad);
-        ggml_allocr_alloc(alloc, layer.wk_a->grad);
-        ggml_allocr_alloc(alloc, layer.wk_b->grad);
-        ggml_allocr_alloc(alloc, layer.wv_a->grad);
-        ggml_allocr_alloc(alloc, layer.wv_b->grad);
-        ggml_allocr_alloc(alloc, layer.wo_a->grad);
-        ggml_allocr_alloc(alloc, layer.wo_b->grad);
-        ggml_allocr_alloc(alloc, layer.ffn_norm_a->grad);
-        ggml_allocr_alloc(alloc, layer.ffn_norm_b->grad);
-        ggml_allocr_alloc(alloc, layer.w1_a->grad);
-        ggml_allocr_alloc(alloc, layer.w1_b->grad);
-        ggml_allocr_alloc(alloc, layer.w2_a->grad);
-        ggml_allocr_alloc(alloc, layer.w2_b->grad);
-        ggml_allocr_alloc(alloc, layer.w3_a->grad);
-        ggml_allocr_alloc(alloc, layer.w3_b->grad);
+        ggml_set_param(ctx, layer.ffn_gate_a);
+        ggml_set_param(ctx, layer.ffn_gate_b);
+        ggml_set_param(ctx, layer.ffn_down_a);
+        ggml_set_param(ctx, layer.ffn_down_b);
+        ggml_set_param(ctx, layer.ffn_up_a);
+        ggml_set_param(ctx, layer.ffn_up_b);
     }
 }
 
@@ -493,12 +435,12 @@ static void init_lora(const struct my_llama_model * model, struct my_llama_lora
         layer.ffn_norm_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_norm, n_embd);
         layer.ffn_norm_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_norm, 1);
 
-        layer.w1_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w1, n_embd);
-        layer.w1_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w1, n_ff);
-        layer.w2_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w2, n_ff);
-        layer.w2_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w2, n_embd);
-        layer.w3_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w3, n_embd);
-        layer.w3_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_w3, n_ff);
+        layer.ffn_gate_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_gate, n_embd);
+        layer.ffn_gate_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_gate, n_ff);
+        layer.ffn_down_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_down, n_ff);
+        layer.ffn_down_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_down, n_embd);
+        layer.ffn_up_a   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_up,   n_embd);
+        layer.ffn_up_b   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_up,   n_ff);
 
         ggml_set_name(layer.attention_norm_a, tni(LLM_TENSOR_ATTN_NORM, ".weight.lora_a", i));
         ggml_set_name(layer.attention_norm_b, tni(LLM_TENSOR_ATTN_NORM, ".weight.lora_b", i));
@@ -512,28 +454,18 @@ static void init_lora(const struct my_llama_model * model, struct my_llama_lora
         ggml_set_name(layer.wo_b,             tni(LLM_TENSOR_ATTN_OUT,  ".weight.lora_b", i));
         ggml_set_name(layer.ffn_norm_a,       tni(LLM_TENSOR_FFN_NORM,  ".weight.lora_a", i));
         ggml_set_name(layer.ffn_norm_b,       tni(LLM_TENSOR_FFN_NORM,  ".weight.lora_b", i));
-        ggml_set_name(layer.w1_a,             tni(LLM_TENSOR_FFN_GATE,  ".weight.lora_a", i));
-        ggml_set_name(layer.w1_b,             tni(LLM_TENSOR_FFN_GATE,  ".weight.lora_b", i));
-        ggml_set_name(layer.w2_a,             tni(LLM_TENSOR_FFN_DOWN,  ".weight.lora_a", i));
-        ggml_set_name(layer.w2_b,             tni(LLM_TENSOR_FFN_DOWN,  ".weight.lora_b", i));
-        ggml_set_name(layer.w3_a,             tni(LLM_TENSOR_FFN_UP,    ".weight.lora_a", i));
-        ggml_set_name(layer.w3_b,             tni(LLM_TENSOR_FFN_UP,    ".weight.lora_b", i));
+        ggml_set_name(layer.ffn_gate_a,       tni(LLM_TENSOR_FFN_GATE,  ".weight.lora_a", i));
+        ggml_set_name(layer.ffn_gate_b,       tni(LLM_TENSOR_FFN_GATE,  ".weight.lora_b", i));
+        ggml_set_name(layer.ffn_down_a,       tni(LLM_TENSOR_FFN_DOWN,  ".weight.lora_a", i));
+        ggml_set_name(layer.ffn_down_b,       tni(LLM_TENSOR_FFN_DOWN,  ".weight.lora_b", i));
+        ggml_set_name(layer.ffn_up_a,         tni(LLM_TENSOR_FFN_UP,    ".weight.lora_a", i));
+        ggml_set_name(layer.ffn_up_b,         tni(LLM_TENSOR_FFN_UP,    ".weight.lora_b", i));
     }
 
     set_param_lora(lora);
 
-    // measure data size
-    size_t size = 0;
-    for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
-        size += GGML_PAD(ggml_nbytes(t), tensor_alignment);
-    }
-
-    // allocate data
-    struct ggml_allocr * alloc = NULL;
-    lora->data.resize(size + tensor_alignment);
-    alloc = ggml_allocr_new(lora->data.data(), lora->data.size(), tensor_alignment);
-    alloc_lora(alloc, lora);
-    ggml_allocr_free(alloc);
+    // allocate data for lora tensors
+    lora->data = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_cpu_buffer_type());
 }
 
 static void randomize_lora(struct my_llama_lora * lora, int seed, float mean, float std, float min, float max) {
@@ -565,12 +497,12 @@ static void randomize_lora(struct my_llama_lora * lora, int seed, float mean, fl
         randomize_tensor_normal(layer.ffn_norm_a, rnd);
         ggml_set_zero(layer.ffn_norm_b);
 
-        randomize_tensor_normal(layer.w1_a, rnd);
-        ggml_set_zero(layer.w1_b);
-        randomize_tensor_normal(layer.w2_a, rnd);
-        ggml_set_zero(layer.w2_b);
-        randomize_tensor_normal(layer.w3_a, rnd);
-        ggml_set_zero(layer.w3_b);
+        randomize_tensor_normal(layer.ffn_gate_a, rnd);
+        ggml_set_zero(layer.ffn_gate_b);
+        randomize_tensor_normal(layer.ffn_down_a, rnd);
+        ggml_set_zero(layer.ffn_down_b);
+        randomize_tensor_normal(layer.ffn_up_a, rnd);
+        ggml_set_zero(layer.ffn_up_b);
     }
 
     free_random_normal_distribution(rnd);
@@ -579,7 +511,7 @@ static void randomize_lora(struct my_llama_lora * lora, int seed, float mean, fl
 static struct ggml_tensor * llama_build_lora_finetune_graphs(
         struct my_llama_model * model,
         struct my_llama_lora  * lora,
-        struct ggml_allocr    * alloc,
+        ggml_gallocr_t          alloc,
         struct ggml_context   * ctx,
         struct ggml_cgraph    * gf,
         struct ggml_cgraph    * gb,
@@ -590,7 +522,8 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
         const  int              n_tokens,
         const  int              n_batch,
         const  bool             enable_flash_attn,
-        const  bool             enable_checkpointing) {
+        const  bool             enable_checkpointing,
+        const  bool             measure_only) {
 
     ggml_set_scratch(ctx, { 0, 0, nullptr, });
     const int n_past = 0;
@@ -622,13 +555,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
 
     // KQ_pos - contains the positions
     struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, N);
-    ggml_allocr_alloc(alloc, KQ_pos);
-    if (!ggml_allocr_is_measure(alloc)) {
-        int * data = (int *) KQ_pos->data;
-        for (int i = 0; i < N; ++i) {
-            data[i] = n_past + i;
-        }
-    }
+    ggml_set_input(KQ_pos);
 
     // rope has so much parameters that we make a custom function for it
     auto rope = [ctx, KQ_pos, n_rot, n_ctx, rope_freq_base, rope_freq_scale]
@@ -683,13 +610,13 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
 
         struct ggml_tensor * attention_norm = add_to_f32(ctx, layer.attention_norm, ggml_mul_mat(ctx, llayer.attention_norm_a, llayer.attention_norm_b));
         struct ggml_tensor * ffn_norm = add_to_f32(ctx, layer.ffn_norm, ggml_mul_mat(ctx, llayer.ffn_norm_a, llayer.ffn_norm_b));
-        struct ggml_tensor * wq = add_to_f32(ctx, layer.wq, ggml_mul_mat(ctx, llayer.wq_a, llayer.wq_b));
-        struct ggml_tensor * wk = add_to_f32(ctx, layer.wk, ggml_mul_mat(ctx, llayer.wk_a, llayer.wk_b));
-        struct ggml_tensor * wv = add_to_f32(ctx, layer.wv, ggml_mul_mat(ctx, llayer.wv_a, llayer.wv_b));
-        struct ggml_tensor * wo = add_to_f32(ctx, layer.wo, ggml_mul_mat(ctx, llayer.wo_a, llayer.wo_b));
-        struct ggml_tensor * w1 = add_to_f32(ctx, layer.w1, ggml_mul_mat(ctx, llayer.w1_a, llayer.w1_b));
-        struct ggml_tensor * w2 = add_to_f32(ctx, layer.w2, ggml_mul_mat(ctx, llayer.w2_a, llayer.w2_b));
-        struct ggml_tensor * w3 = add_to_f32(ctx, layer.w3, ggml_mul_mat(ctx, llayer.w3_a, llayer.w3_b));
+        struct ggml_tensor * wq       = add_to_f32(ctx, layer.wq, ggml_mul_mat(ctx, llayer.wq_a, llayer.wq_b));
+        struct ggml_tensor * wk       = add_to_f32(ctx, layer.wk, ggml_mul_mat(ctx, llayer.wk_a, llayer.wk_b));
+        struct ggml_tensor * wv       = add_to_f32(ctx, layer.wv, ggml_mul_mat(ctx, llayer.wv_a, llayer.wv_b));
+        struct ggml_tensor * wo       = add_to_f32(ctx, layer.wo, ggml_mul_mat(ctx, llayer.wo_a, llayer.wo_b));
+        struct ggml_tensor * ffn_gate = add_to_f32(ctx, layer.ffn_gate, ggml_mul_mat(ctx, llayer.ffn_gate_a, llayer.ffn_gate_b));
+        struct ggml_tensor * ffn_down = add_to_f32(ctx, layer.ffn_down, ggml_mul_mat(ctx, llayer.ffn_down_a, llayer.ffn_down_b));
+        struct ggml_tensor * ffn_up   = add_to_f32(ctx, layer.ffn_up, ggml_mul_mat(ctx, llayer.ffn_up_a, llayer.ffn_up_b));
 
         struct ggml_tensor * t02 = ggml_rms_norm     (ctx, cur, rms_norm_eps);                       set_name(t02, "t02");     assert_shape_2d(t02, n_embd, N*n_batch);
         struct ggml_tensor * t03 = ggml_repeat       (ctx, attention_norm, t02);                     set_name(t03, "t03");     assert_shape_2d(t03, n_embd, N*n_batch);
@@ -732,11 +659,11 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
         struct ggml_tensor * t22 = ggml_rms_norm     (ctx, t21, rms_norm_eps);                       set_name(t22, "t22");     assert_shape_2d(t22, n_embd, N*n_batch);
         struct ggml_tensor * t23 = ggml_repeat       (ctx, ffn_norm, t22);                           set_name(t23, "t23");     assert_shape_2d(t23, n_embd, N*n_batch);
         struct ggml_tensor * t24 = ggml_mul          (ctx, t23, t22);                                set_name(t24, "t24");     assert_shape_2d(t24, n_embd, N*n_batch);
-        struct ggml_tensor * t25 = ggml_mul_mat      (ctx, w3, t24);                                 set_name(t25, "t25");     assert_shape_2d(t25, n_ff, N*n_batch);
-        struct ggml_tensor * t26 = ggml_mul_mat      (ctx, w1, t24);                                 set_name(t26, "t26");     assert_shape_2d(t26, n_ff, N*n_batch);
+        struct ggml_tensor * t25 = ggml_mul_mat      (ctx, ffn_up, t24);                             set_name(t25, "t25");     assert_shape_2d(t25, n_ff, N*n_batch);
+        struct ggml_tensor * t26 = ggml_mul_mat      (ctx, ffn_gate, t24);                           set_name(t26, "t26");     assert_shape_2d(t26, n_ff, N*n_batch);
         struct ggml_tensor * t27 = ggml_silu         (ctx, t26);                                     set_name(t27, "t27");     assert_shape_2d(t27, n_ff, N*n_batch);
         struct ggml_tensor * t28 = ggml_mul          (ctx, t27, t25);                                set_name(t28, "t28");     assert_shape_2d(t28, n_ff, N*n_batch);
-        struct ggml_tensor * t29 = ggml_mul_mat      (ctx, w2, t28);                                 set_name(t29, "t29");     assert_shape_2d(t29, n_embd, N*n_batch);
+        struct ggml_tensor * t29 = ggml_mul_mat      (ctx, ffn_down, t28);                           set_name(t29, "t29");     assert_shape_2d(t29, n_embd, N*n_batch);
         struct ggml_tensor * t30 = ggml_add          (ctx, t29, t21);                                set_name(t30, "t30");     assert_shape_2d(t30, n_embd, N*n_batch);
         cur = t30;
         if (enable_checkpointing) {
@@ -780,7 +707,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
     // input gradient
     ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, 1.0f));
     GGML_ASSERT(t36->grad->data == NULL && t36->grad->view_src == NULL);
-    ggml_allocr_alloc(alloc, t36->grad);
+    ggml_set_input(t36->grad);
     // KQ_pos
     ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, 1.0f));
 
@@ -796,20 +723,32 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
         ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wk, 1.0f));
         ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wv, 1.0f));
         ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wo, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w1, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w2, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.w3, 1.0f));
+        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_gate, 1.0f));
+        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_down, 1.0f));
+        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_up, 1.0f));
     }
 
     // allocating checkpoints in one block to reduce memory fragmentation
     // note: they will be freed in reverse order
     for (unsigned int i = 0; i < checkpoints.size(); ++i) {
         if (checkpoints[i]->data == NULL && checkpoints[i]->view_src == NULL) {
-            ggml_allocr_alloc(alloc, checkpoints[i]);
+            ggml_set_input(checkpoints[i]);
         }
     }
 
-    ggml_allocr_alloc_graph(alloc, gb);
+    if (measure_only) {
+        ggml_gallocr_reserve(alloc, gb);
+    } else {
+        ggml_gallocr_alloc_graph(alloc, gb);
+
+        // set KQ_pos
+        {
+            int * data = (int *) KQ_pos->data;
+            for (int i = 0; i < N; ++i) {
+                data[i] = n_past + i;
+            }
+        }
+    }
 
     // remove the additional nodes and leafs
     for (int i = n_leafs_before; i < gb->n_leafs; ++i) {
@@ -859,9 +798,9 @@ static void load_llama_lora_gguf(struct gguf_context * fctx, struct ggml_context
     GGUF_GET_KEY(fctx, lora->hparams.n_rank_wv,             gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_V);
     GGUF_GET_KEY(fctx, lora->hparams.n_rank_wo,             gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_OUT);
     GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_norm,       gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_NORM);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_w1,             gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_GATE);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_w2,             gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_w3,             gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_UP);
+    GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_gate,       gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_GATE);
+    GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_down,       gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN);
+    GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_up,         gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_UP);
 
     init_lora(model, lora);
 
@@ -886,12 +825,12 @@ static void load_llama_lora_gguf(struct gguf_context * fctx, struct ggml_context
         copy_tensor_by_name(layer.wo_b,             f_ggml_ctx, ggml_get_name(layer.wo_b));
         copy_tensor_by_name(layer.ffn_norm_a,       f_ggml_ctx, ggml_get_name(layer.ffn_norm_a));
         copy_tensor_by_name(layer.ffn_norm_b,       f_ggml_ctx, ggml_get_name(layer.ffn_norm_b));
-        copy_tensor_by_name(layer.w1_a,             f_ggml_ctx, ggml_get_name(layer.w1_a));
-        copy_tensor_by_name(layer.w1_b,             f_ggml_ctx, ggml_get_name(layer.w1_b));
-        copy_tensor_by_name(layer.w2_a,             f_ggml_ctx, ggml_get_name(layer.w2_a));
-        copy_tensor_by_name(layer.w2_b,             f_ggml_ctx, ggml_get_name(layer.w2_b));
-        copy_tensor_by_name(layer.w3_a,             f_ggml_ctx, ggml_get_name(layer.w3_a));
-        copy_tensor_by_name(layer.w3_b,             f_ggml_ctx, ggml_get_name(layer.w3_b));
+        copy_tensor_by_name(layer.ffn_gate_a,       f_ggml_ctx, ggml_get_name(layer.ffn_gate_a));
+        copy_tensor_by_name(layer.ffn_gate_b,       f_ggml_ctx, ggml_get_name(layer.ffn_gate_b));
+        copy_tensor_by_name(layer.ffn_down_a,       f_ggml_ctx, ggml_get_name(layer.ffn_down_a));
+        copy_tensor_by_name(layer.ffn_down_b,       f_ggml_ctx, ggml_get_name(layer.ffn_down_b));
+        copy_tensor_by_name(layer.ffn_up_a,         f_ggml_ctx, ggml_get_name(layer.ffn_up_a));
+        copy_tensor_by_name(layer.ffn_up_b,         f_ggml_ctx, ggml_get_name(layer.ffn_up_b));
     }
 }
 
@@ -929,9 +868,9 @@ static void save_llama_lora_gguf(struct gguf_context * fctx, struct my_llama_mod
     gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_V,       lora->hparams.n_rank_wv);
     gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_OUT,     lora->hparams.n_rank_wo);
     gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_NORM,     lora->hparams.n_rank_ffn_norm);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_GATE,     lora->hparams.n_rank_w1);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN,     lora->hparams.n_rank_w2);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_UP,       lora->hparams.n_rank_w3);
+    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_GATE,     lora->hparams.n_rank_ffn_gate);
+    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN,     lora->hparams.n_rank_ffn_down);
+    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_UP,       lora->hparams.n_rank_ffn_up);
 
     gguf_add_tensor(fctx, lora->tok_embeddings_a);
     gguf_add_tensor(fctx, lora->tok_embeddings_b);
@@ -955,12 +894,12 @@ static void save_llama_lora_gguf(struct gguf_context * fctx, struct my_llama_mod
         gguf_add_tensor(fctx, layer.wo_b);
         gguf_add_tensor(fctx, layer.ffn_norm_a);
         gguf_add_tensor(fctx, layer.ffn_norm_b);
-        gguf_add_tensor(fctx, layer.w1_a);
-        gguf_add_tensor(fctx, layer.w1_b);
-        gguf_add_tensor(fctx, layer.w2_a);
-        gguf_add_tensor(fctx, layer.w2_b);
-        gguf_add_tensor(fctx, layer.w3_a);
-        gguf_add_tensor(fctx, layer.w3_b);
+        gguf_add_tensor(fctx, layer.ffn_gate_a);
+        gguf_add_tensor(fctx, layer.ffn_gate_b);
+        gguf_add_tensor(fctx, layer.ffn_down_a);
+        gguf_add_tensor(fctx, layer.ffn_down_b);
+        gguf_add_tensor(fctx, layer.ffn_up_a);
+        gguf_add_tensor(fctx, layer.ffn_up_b);
     }
 }
 
@@ -1138,9 +1077,8 @@ static void save_as_llama_lora(const char * filename, struct my_llama_lora * lor
         return tn_buf.data();
     };
 
-    uint32_t LLAMA_FILE_MAGIC_LORA = 0x67676C61; // 'ggla'
     // write_magic
-    file.write_u32(LLAMA_FILE_MAGIC_LORA);   // magic
+    file.write_u32(LLAMA_FILE_MAGIC_GGLA);   // magic
     file.write_u32(1); // version
     // write_hparams
     file.write_u32(lora->hparams.lora_r);
@@ -1166,12 +1104,12 @@ static void save_as_llama_lora(const char * filename, struct my_llama_lora * lor
         write_tensor(&file, layer.wo_b,             tni(LLM_TENSOR_ATTN_OUT,  i, ".weight.loraB"));
         write_tensor(&file, layer.ffn_norm_a,       tni(LLM_TENSOR_FFN_NORM,  i, ".weight.loraA"));
         write_tensor(&file, layer.ffn_norm_b,       tni(LLM_TENSOR_FFN_NORM,  i, ".weight.loraB"));
-        write_tensor(&file, layer.w1_a,             tni(LLM_TENSOR_FFN_GATE,  i, ".weight.loraA"));
-        write_tensor(&file, layer.w1_b,             tni(LLM_TENSOR_FFN_GATE,  i, ".weight.loraB"));
-        write_tensor(&file, layer.w2_a,             tni(LLM_TENSOR_FFN_DOWN,  i, ".weight.loraA"));
-        write_tensor(&file, layer.w2_b,             tni(LLM_TENSOR_FFN_DOWN,  i, ".weight.loraB"));
-        write_tensor(&file, layer.w3_a,             tni(LLM_TENSOR_FFN_UP,    i, ".weight.loraA"));
-        write_tensor(&file, layer.w3_b,             tni(LLM_TENSOR_FFN_UP,    i, ".weight.loraB"));
+        write_tensor(&file, layer.ffn_gate_a,       tni(LLM_TENSOR_FFN_GATE,  i, ".weight.loraA"));
+        write_tensor(&file, layer.ffn_gate_b,       tni(LLM_TENSOR_FFN_GATE,  i, ".weight.loraB"));
+        write_tensor(&file, layer.ffn_down_a,       tni(LLM_TENSOR_FFN_DOWN,  i, ".weight.loraA"));
+        write_tensor(&file, layer.ffn_down_b,       tni(LLM_TENSOR_FFN_DOWN,  i, ".weight.loraB"));
+        write_tensor(&file, layer.ffn_up_a,         tni(LLM_TENSOR_FFN_UP,    i, ".weight.loraA"));
+        write_tensor(&file, layer.ffn_up_b,         tni(LLM_TENSOR_FFN_UP,    i, ".weight.loraB"));
     }
 }
 
@@ -1201,9 +1139,9 @@ struct train_params {
     uint32_t n_rank_wv;
     uint32_t n_rank_wo;
     uint32_t n_rank_ffn_norm;
-    uint32_t n_rank_w1;
-    uint32_t n_rank_w2;
-    uint32_t n_rank_w3;
+    uint32_t n_rank_ffn_gate;
+    uint32_t n_rank_ffn_down;
+    uint32_t n_rank_ffn_up;
     uint32_t n_rank_tok_embeddings;
     uint32_t n_rank_norm;
     uint32_t n_rank_output;
@@ -1214,9 +1152,9 @@ struct train_params {
     bool custom_n_rank_wv;
     bool custom_n_rank_wo;
     bool custom_n_rank_ffn_norm;
-    bool custom_n_rank_w1;
-    bool custom_n_rank_w2;
-    bool custom_n_rank_w3;
+    bool custom_n_rank_ffn_gate;
+    bool custom_n_rank_ffn_down;
+    bool custom_n_rank_ffn_up;
     bool custom_n_rank_tok_embeddings;
     bool custom_n_rank_norm;
     bool custom_n_rank_output;
@@ -1248,9 +1186,9 @@ static struct train_params get_default_train_params() {
     params.n_rank_wv             = 4;
     params.n_rank_wo             = 4;
     params.n_rank_ffn_norm       = 1;
-    params.n_rank_w1             = 4;
-    params.n_rank_w2             = 4;
-    params.n_rank_w3             = 4;
+    params.n_rank_ffn_gate       = 4;
+    params.n_rank_ffn_down       = 4;
+    params.n_rank_ffn_up         = 4;
     params.n_rank_tok_embeddings = 4;
     params.n_rank_norm           = 1;
     params.n_rank_output         = 4;
@@ -1261,9 +1199,9 @@ static struct train_params get_default_train_params() {
     params.custom_n_rank_wv             = false;
     params.custom_n_rank_wo             = false;
     params.custom_n_rank_ffn_norm       = false;
-    params.custom_n_rank_w1             = false;
-    params.custom_n_rank_w2             = false;
-    params.custom_n_rank_w3             = false;
+    params.custom_n_rank_ffn_gate       = false;
+    params.custom_n_rank_ffn_down       = false;
+    params.custom_n_rank_ffn_up         = false;
     params.custom_n_rank_tok_embeddings = false;
     params.custom_n_rank_norm           = false;
     params.custom_n_rank_output         = false;
@@ -1294,9 +1232,9 @@ static void train_print_usage(int argc, char ** argv, const struct train_params
     fprintf(stderr, "  --rank-wk N                LORA rank for wk tensor, overrides default rank.\n");
     fprintf(stderr, "  --rank-wv N                LORA rank for wv tensor, overrides default rank.\n");
     fprintf(stderr, "  --rank-wo N                LORA rank for wo tensor, overrides default rank.\n");
-    fprintf(stderr, "  --rank-w1 N                LORA rank for w1 tensor, overrides default rank.\n");
-    fprintf(stderr, "  --rank-w2 N                LORA rank for w2 tensor, overrides default rank.\n");
-    fprintf(stderr, "  --rank-w3 N                LORA rank for w3 tensor, overrides default rank.\n");
+    fprintf(stderr, "  --rank-ffn_gate N          LORA rank for ffn_gate tensor, overrides default rank.\n");
+    fprintf(stderr, "  --rank-ffn_down N          LORA rank for ffn_down tensor, overrides default rank.\n");
+    fprintf(stderr, "  --rank-ffn_up N            LORA rank for ffn_up tensor, overrides default rank.\n");
 
     print_common_train_usage(argc, argv, &params->common);
 }
@@ -1431,27 +1369,27 @@ static bool train_params_parse(int argc, char ** argv, struct train_params * par
             }
             params->n_rank_wo = std::stoi(argv[i]);
             params->custom_n_rank_wo = true;
-        } else if (arg == "--rank-w1") {
+        } else if (arg == "--rank-ffn_gate") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params->n_rank_w1 = std::stoi(argv[i]);
-            params->custom_n_rank_w1 = true;
-        } else if (arg == "--rank-w2") {
+            params->n_rank_ffn_gate = std::stoi(argv[i]);
+            params->custom_n_rank_ffn_gate = true;
+        } else if (arg == "--rank-ffn_down") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params->n_rank_w2 = std::stoi(argv[i]);
-            params->custom_n_rank_w2 = true;
-        } else if (arg == "--rank-w3") {
+            params->n_rank_ffn_down = std::stoi(argv[i]);
+            params->custom_n_rank_ffn_down = true;
+        } else if (arg == "--rank-ffn_up") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params->n_rank_w3 = std::stoi(argv[i]);
-            params->custom_n_rank_w3 = true;
+            params->n_rank_ffn_up = std::stoi(argv[i]);
+            params->custom_n_rank_ffn_up = true;
         } else {
             fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
             train_print_usage(argc, argv, &default_params);
@@ -1514,12 +1452,12 @@ static int64_t get_parameter_count(struct my_llama_lora* lora) {
         nx += ggml_nelements(layer.wo_b);
         nx += ggml_nelements(layer.ffn_norm_a);
         nx += ggml_nelements(layer.ffn_norm_b);
-        nx += ggml_nelements(layer.w1_a);
-        nx += ggml_nelements(layer.w1_b);
-        nx += ggml_nelements(layer.w2_a);
-        nx += ggml_nelements(layer.w2_b);
-        nx += ggml_nelements(layer.w3_a);
-        nx += ggml_nelements(layer.w3_b);
+        nx += ggml_nelements(layer.ffn_gate_a);
+        nx += ggml_nelements(layer.ffn_gate_b);
+        nx += ggml_nelements(layer.ffn_down_a);
+        nx += ggml_nelements(layer.ffn_down_b);
+        nx += ggml_nelements(layer.ffn_up_a);
+        nx += ggml_nelements(layer.ffn_up_b);
     }
     return nx;
 }
@@ -1573,9 +1511,9 @@ int main(int argc, char ** argv) {
     uint32_t n_rank_wv                 = params.custom_n_rank_wv             ? params.n_rank_wv             : params.lora_r;
     uint32_t n_rank_wo                 = params.custom_n_rank_wo             ? params.n_rank_wo             : params.lora_r;
     uint32_t n_rank_ffn_norm           = params.custom_n_rank_ffn_norm       ? params.n_rank_ffn_norm       : 1;
-    uint32_t n_rank_w1                 = params.custom_n_rank_w1             ? params.n_rank_w1             : params.lora_r;
-    uint32_t n_rank_w2                 = params.custom_n_rank_w2             ? params.n_rank_w2             : params.lora_r;
-    uint32_t n_rank_w3                 = params.custom_n_rank_w3             ? params.n_rank_w3             : params.lora_r;
+    uint32_t n_rank_ffn_gate           = params.custom_n_rank_ffn_gate       ? params.n_rank_ffn_gate       : params.lora_r;
+    uint32_t n_rank_ffn_down           = params.custom_n_rank_ffn_down       ? params.n_rank_ffn_down       : params.lora_r;
+    uint32_t n_rank_ffn_up             = params.custom_n_rank_ffn_up         ? params.n_rank_ffn_up         : params.lora_r;
     uint32_t n_rank_tok_embeddings     = params.custom_n_rank_tok_embeddings ? params.n_rank_tok_embeddings : params.lora_r;
     uint32_t n_rank_norm               = params.custom_n_rank_norm           ? params.n_rank_norm           : 1;
     uint32_t n_rank_output             = params.custom_n_rank_output         ? params.n_rank_output         : params.lora_r;
@@ -1585,15 +1523,15 @@ int main(int argc, char ** argv) {
     lora.hparams.n_rank_wv             = n_rank_wv;
     lora.hparams.n_rank_wo             = n_rank_wo;
     lora.hparams.n_rank_ffn_norm       = n_rank_ffn_norm;
-    lora.hparams.n_rank_w1             = n_rank_w1;
-    lora.hparams.n_rank_w2             = n_rank_w2;
-    lora.hparams.n_rank_w3             = n_rank_w3;
+    lora.hparams.n_rank_ffn_gate       = n_rank_ffn_gate;
+    lora.hparams.n_rank_ffn_down       = n_rank_ffn_down;
+    lora.hparams.n_rank_ffn_up         = n_rank_ffn_up;
     lora.hparams.n_rank_tok_embeddings = n_rank_tok_embeddings;
     lora.hparams.n_rank_norm           = n_rank_norm;
     lora.hparams.n_rank_output         = n_rank_output;
 
     // set opt params from command line
-    opt->params = ggml_opt_default_params(GGML_OPT_ADAM);
+    opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
     opt->params.print_forward_graph     = false;
     opt->params.print_backward_graph    = false;
     opt->params.graph_size              = LLAMA_TRAIN_MAX_NODES;
@@ -1628,9 +1566,9 @@ int main(int argc, char ** argv) {
         || (lora.hparams.n_rank_wv             != n_rank_wv)
         || (lora.hparams.n_rank_wo             != n_rank_wo)
         || (lora.hparams.n_rank_ffn_norm       != n_rank_ffn_norm)
-        || (lora.hparams.n_rank_w1             != n_rank_w1)
-        || (lora.hparams.n_rank_w2             != n_rank_w2)
-        || (lora.hparams.n_rank_w3             != n_rank_w3)
+        || (lora.hparams.n_rank_ffn_gate       != n_rank_ffn_gate)
+        || (lora.hparams.n_rank_ffn_down       != n_rank_ffn_down)
+        || (lora.hparams.n_rank_ffn_up         != n_rank_ffn_up)
         || (lora.hparams.n_rank_tok_embeddings != n_rank_tok_embeddings)
         || (lora.hparams.n_rank_norm           != n_rank_norm)
         || (lora.hparams.n_rank_output         != n_rank_output)
@@ -1664,7 +1602,7 @@ int main(int argc, char ** argv) {
     printf("%s: seen train_samples     %llu\n", __func__, (long long unsigned) train->train_samples);
     printf("%s: seen train_tokens      %llu\n", __func__, (long long unsigned) train->train_tokens);
     printf("%s: completed train_epochs %llu\n", __func__, (long long unsigned) train->train_epochs);
-    printf("%s: lora_size = %zu bytes (%.1f MB)\n", __func__, (ggml_used_mem(lora.ctx) + lora.data.size()), (float) (ggml_used_mem(lora.ctx) + lora.data.size()) / (1024.0f*1024.0f));
+    printf("%s: lora_size = %zu bytes (%.1f MB)\n", __func__, (ggml_used_mem(lora.ctx) + ggml_backend_buffer_get_size(lora.data)), (float) (ggml_used_mem(lora.ctx) + ggml_backend_buffer_get_size(lora.data)) / (1024.0f*1024.0f));
 
     if (params.only_write_lora) {
         save_train_files_data save_data;
@@ -1691,10 +1629,6 @@ int main(int argc, char ** argv) {
     int n_vocab  = model.hparams.n_vocab;
     int n_batch  = params.common.n_batch;
 
-
-    std::vector<uint8_t> mem_input_data;
-    std::vector<uint8_t> mem_compute_data;
-
     // context for input tensors without their data
     struct ggml_init_params ctx_input_params = {
         ggml_tensor_overhead() * 2, // mem_size
@@ -1707,17 +1641,11 @@ int main(int argc, char ** argv) {
     struct ggml_tensor * tokens_input  = ggml_new_tensor_2d(ctx_input, GGML_TYPE_I32, n_tokens, n_batch);
     struct ggml_tensor * target_probs  = ggml_new_tensor_3d(ctx_input, GGML_TYPE_F32, n_vocab,  n_tokens, n_batch);
 
-    // measure required memory for input tensors
-    size_t max_input_size = GGML_PAD(ggml_nbytes(tokens_input), tensor_alignment) +
-                            GGML_PAD(ggml_nbytes(target_probs), tensor_alignment) +
-                            tensor_alignment;
-    printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f));
-
     // allocate input tensors
-    mem_input_data.resize(max_input_size);
-    ggml_allocr_t alloc_inps = ggml_allocr_new(mem_input_data.data(), mem_input_data.size(), tensor_alignment);
-    ggml_allocr_alloc(alloc_inps, tokens_input);
-    ggml_allocr_alloc(alloc_inps, target_probs);
+    // measure required memory for input tensors
+    ggml_backend_buffer_t input_data = ggml_backend_alloc_ctx_tensors_from_buft(ctx_input, ggml_backend_cpu_buffer_type());
+    size_t max_input_size = ggml_backend_buffer_get_size(input_data);
+    printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f));
 
     // context for compute tensors without their data
     const size_t estimated_compute_size_wo_data = (
@@ -1744,7 +1672,7 @@ int main(int argc, char ** argv) {
     // find best evaluation order
     for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) {
         ctx_compute = ggml_init(ctx_compute_params);
-        ggml_allocr_t alloc = ggml_allocr_new_measure(tensor_alignment);
+        ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
         gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
         gf->order = (enum ggml_cgraph_eval_order) order;
         gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
@@ -1757,14 +1685,15 @@ int main(int argc, char ** argv) {
             &logits, tokens_input, target_probs,
             n_tokens, n_batch,
             params.common.use_flash,
-            params.common.use_checkpointing
+            params.common.use_checkpointing,
+            true
         );
-        size_t max_compute_size = ggml_allocr_max_size(alloc) + tensor_alignment;
+        size_t max_compute_size = ggml_gallocr_get_buffer_size(alloc, 0); // FIXME: this will still allocate the buffer
         if (max_compute_size < best_compute_size) {
             best_compute_size = max_compute_size;
             best_order = gf->order;
         }
-        ggml_allocr_free(alloc);
+        ggml_gallocr_free(alloc);
         ggml_free(ctx_compute);
     }
     size_t max_compute_size = best_compute_size;
@@ -1775,9 +1704,8 @@ int main(int argc, char ** argv) {
         "invalid");
 
     // allocate compute tensors
-    mem_compute_data.resize(max_compute_size);
     ctx_compute = ggml_init(ctx_compute_params);
-    ggml_allocr_t alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment);
+    ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
     gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
     gf->order = best_order;
     gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
@@ -1790,17 +1718,17 @@ int main(int argc, char ** argv) {
         &logits, tokens_input, target_probs,
         n_tokens, n_batch,
         params.common.use_flash,
-        params.common.use_checkpointing
+        params.common.use_checkpointing,
+        false
     );
-    ggml_allocr_free(alloc);
-    ggml_allocr_free(alloc_inps);
-
 
     // tokenize data
     std::vector<llama_token> train_tokens;
     std::vector<size_t> train_samples_begin;
     std::vector<size_t> train_samples_size;
-    printf("%s: tokenize training data\n", __func__);
+    printf("%s: tokenize training data from %s\n", __func__, params.common.fn_train_data);
+    printf("%s: sample-start: %s\n", __func__, params.common.sample_start.c_str());
+    printf("%s: include-sample-start: %s\n", __func__, params.common.include_sample_start ? "true" : "false");
     tokenize_file(lctx,
             params.common.fn_train_data,
             params.common.sample_start,
@@ -1907,6 +1835,8 @@ int main(int argc, char ** argv) {
     ggml_free(ctx_work);
     ggml_free(ctx_compute);
     ggml_free(ctx_input);
+    ggml_gallocr_free(alloc);
+
 
     int64_t t1 = ggml_time_ms();
     printf("%s: total training time: ", __func__);
diff --git a/examples/gbnf-validator/CMakeLists.txt b/examples/gbnf-validator/CMakeLists.txt
new file mode 100644
index 000000000..166e3ad2a
--- /dev/null
+++ b/examples/gbnf-validator/CMakeLists.txt
@@ -0,0 +1,5 @@
+set(TARGET gbnf-validator)
+add_executable(${TARGET} gbnf-validator.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common grammar-parser llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/gbnf-validator/gbnf-validator.cpp b/examples/gbnf-validator/gbnf-validator.cpp
new file mode 100644
index 000000000..e4c0c1689
--- /dev/null
+++ b/examples/gbnf-validator/gbnf-validator.cpp
@@ -0,0 +1,132 @@
+#define LLAMA_API_INTERNAL
+
+#include "grammar-parser.h"
+#include "ggml.h"
+#include "llama.h"
+#include "unicode.h"
+
+#include <cstdio>
+#include <cstdlib>
+#include <string>
+#include <vector>
+
+static bool llama_sample_grammar_string(struct llama_grammar * grammar, const std::string & input_str, size_t & error_pos, std::string & error_msg) {
+    auto decoded = decode_utf8(input_str, {});
+    const auto & code_points = decoded.first;
+
+    size_t pos = 0;
+    for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
+        auto prev_stacks = grammar->stacks;
+        grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it);
+        if (grammar->stacks.empty()) {
+            error_pos = pos;
+            error_msg = "Unexpected character '" + unicode_cpt_to_utf8(*it) + "'";
+            grammar->stacks = prev_stacks;
+            return false;
+        }
+        ++pos;
+    }
+
+    for (const auto & stack : grammar->stacks) {
+        if (stack.empty()) {
+            return true;
+        }
+    }
+
+    error_pos = pos;
+    error_msg = "Unexpected end of input";
+    return false;
+}
+
+static void print_error_message(const std::string & input_str, size_t error_pos, const std::string & error_msg) {
+    fprintf(stdout, "Input string is invalid according to the grammar.\n");
+    fprintf(stdout, "Error: %s at position %zu\n", error_msg.c_str(), error_pos);
+    fprintf(stdout, "\n");
+    fprintf(stdout, "Input string:\n");
+    fprintf(stdout, "%s", input_str.substr(0, error_pos).c_str());
+    if (error_pos < input_str.size()) {
+        fprintf(stdout, "\033[1;31m%c", input_str[error_pos]);
+        if (error_pos+1 < input_str.size()) {
+            fprintf(stdout, "\033[0;31m%s", input_str.substr(error_pos+1).c_str());
+        }
+        fprintf(stdout, "\033[0m\n");
+    }
+}
+
+int main(int argc, char** argv) {
+    if (argc != 3) {
+        fprintf(stdout, "Usage: %s <grammar_filename> <input_filename>\n", argv[0]);
+        return 1;
+    }
+
+    const std::string grammar_filename = argv[1];
+    const std::string input_filename = argv[2];
+
+    // Read the GBNF grammar file
+    FILE* grammar_file = fopen(grammar_filename.c_str(), "r");
+    if (!grammar_file) {
+        fprintf(stdout, "Failed to open grammar file: %s\n", grammar_filename.c_str());
+        return 1;
+    }
+
+    fseek(grammar_file, 0, SEEK_END);
+    size_t grammar_size = ftell(grammar_file);
+    fseek(grammar_file, 0, SEEK_SET);
+
+    std::string grammar_str(grammar_size, ' ');
+    fread(&grammar_str[0], 1, grammar_size, grammar_file);
+    fclose(grammar_file);
+
+    // Parse the GBNF grammar
+    auto parsed_grammar = grammar_parser::parse(grammar_str.c_str());
+
+    // will be empty (default) if there are parse errors
+    if (parsed_grammar.rules.empty()) {
+        fprintf(stdout, "%s: failed to parse grammar\n", __func__);
+        return 1;
+    }
+
+    // Ensure that there is a "root" node.
+    if (parsed_grammar.symbol_ids.find("root") == parsed_grammar.symbol_ids.end()) {
+        fprintf(stdout, "%s: grammar does not contain a 'root' symbol\n", __func__);
+        return 1;
+    }
+
+    std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
+
+    // Create the LLAMA grammar
+    auto grammar = llama_grammar_init(
+            grammar_rules.data(),
+            grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
+
+    // Read the input file
+    FILE* input_file = fopen(input_filename.c_str(), "r");
+    if (!input_file) {
+        fprintf(stdout, "Failed to open input file: %s\n", input_filename.c_str());
+        return 1;
+    }
+
+    fseek(input_file, 0, SEEK_END);
+    size_t input_size = ftell(input_file);
+    fseek(input_file, 0, SEEK_SET);
+
+    std::string input_str(input_size, ' ');
+    fread(&input_str[0], 1, input_size, input_file);
+    fclose(input_file);
+
+    // Validate the input string against the grammar
+    size_t error_pos;
+    std::string error_msg;
+    bool is_valid = llama_sample_grammar_string(grammar, input_str, error_pos, error_msg);
+
+    if (is_valid) {
+        fprintf(stdout, "Input string is valid according to the grammar.\n");
+    } else {
+        print_error_message(input_str, error_pos, error_msg);
+    }
+
+    // Clean up
+    llama_grammar_free(grammar);
+
+    return 0;
+}
diff --git a/examples/gguf-split/CMakeLists.txt b/examples/gguf-split/CMakeLists.txt
new file mode 100644
index 000000000..828e62435
--- /dev/null
+++ b/examples/gguf-split/CMakeLists.txt
@@ -0,0 +1,5 @@
+set(TARGET gguf-split)
+add_executable(${TARGET} gguf-split.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/gguf-split/README.md b/examples/gguf-split/README.md
new file mode 100644
index 000000000..ddb1f7649
--- /dev/null
+++ b/examples/gguf-split/README.md
@@ -0,0 +1,9 @@
+## GGUF split Example
+
+CLI to split / merge GGUF files.
+
+**Command line options:**
+
+- `--split`: split GGUF to multiple GGUF, default operation.
+- `--split-max-tensors`: maximum tensors in each split: default(128)
+- `--merge`: merge multiple GGUF to a single GGUF.
diff --git a/examples/gguf-split/gguf-split.cpp b/examples/gguf-split/gguf-split.cpp
new file mode 100644
index 000000000..24acbf02a
--- /dev/null
+++ b/examples/gguf-split/gguf-split.cpp
@@ -0,0 +1,553 @@
+#include "llama.h"
+#include "common.h"
+
+#include <algorithm>
+#include <cmath>
+#include <cstdlib>
+#include <fstream>
+#include <string>
+#include <vector>
+
+#include <stdio.h>
+#include <string.h>
+#include <climits>
+#include <stdexcept>
+
+#if defined(_WIN32)
+    #include <windows.h>
+    #ifndef PATH_MAX
+        #define PATH_MAX MAX_PATH
+    #endif
+    #include <io.h>
+#endif
+
+enum split_operation : uint8_t {
+    SPLIT_OP_SPLIT,
+    SPLIT_OP_MERGE,
+};
+
+struct split_params {
+    split_operation operation = SPLIT_OP_SPLIT;
+    size_t n_bytes_split = 0;
+    int n_split_tensors = 128;
+    std::string input;
+    std::string output;
+    bool dry_run = false;
+};
+
+static void split_print_usage(const char * executable) {
+    const split_params default_params;
+    printf("\n");
+    printf("usage: %s [options] GGUF_IN GGUF_OUT\n", executable);
+    printf("\n");
+    printf("Apply a GGUF operation on IN to OUT.");
+    printf("\n");
+    printf("options:\n");
+    printf("  -h, --help              show this help message and exit\n");
+    printf("  --version               show version and build info\n");
+    printf("  --split                 split GGUF to multiple GGUF (enabled by default)\n");
+    printf("  --merge                 merge multiple GGUF to a single GGUF\n");
+    printf("  --split-max-tensors     max tensors in each split (default: %d)\n", default_params.n_split_tensors);
+    printf("  --split-max-size N(M|G) max size per split\n");
+    printf("  --dry-run               only print out a split plan and exit, without writing any new files\n");
+    printf("\n");
+}
+
+// return convert string, for example "128M" or "4G" to number of bytes
+static size_t split_str_to_n_bytes(std::string str) {
+    size_t n_bytes = 0;
+    int n;
+    if (str.back() == 'M') {
+        sscanf(str.c_str(), "%d", &n);
+        n_bytes = n * 1024 * 1024; // megabytes
+    } else if (str.back() == 'G') {
+        sscanf(str.c_str(), "%d", &n);
+        n_bytes = n * 1024 * 1024 * 1024; // gigabytes
+    } else {
+        throw std::invalid_argument("error: supported units are M (megabytes) or G (gigabytes), but got: " + std::string(1, str.back()));
+    }
+    if (n <= 0) {
+        throw std::invalid_argument("error: size must be a positive value");
+    }
+    return n_bytes;
+}
+
+static void split_params_parse_ex(int argc, const char ** argv, split_params & params) {
+    std::string arg;
+    const std::string arg_prefix = "--";
+    bool invalid_param = false;
+
+    int arg_idx = 1;
+    for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) {
+        arg = argv[arg_idx];
+        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
+            std::replace(arg.begin(), arg.end(), '_', '-');
+        }
+
+        bool arg_found = false;
+        bool is_op_set = false;
+        bool is_mode_set = false;
+        if (arg == "-h" || arg == "--help") {
+            split_print_usage(argv[0]);
+            exit(0);
+        }
+        if (arg == "--version") {
+            fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
+            fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
+            exit(0);
+        }
+        if (arg == "--dry-run") {
+            arg_found = true;
+            params.dry_run = true;
+        }
+
+        if (is_op_set) {
+            throw std::invalid_argument("error: either --split or --merge can be specified, but not both");
+        }
+        if (arg == "--merge") {
+            arg_found = true;
+            is_op_set = true;
+            params.operation = SPLIT_OP_MERGE;
+        }
+        if (arg == "--split") {
+            arg_found = true;
+            is_op_set = true;
+            params.operation = SPLIT_OP_SPLIT;
+        }
+
+        if (is_mode_set) {
+            throw std::invalid_argument("error: either --split-max-tensors or --split-max-size can be specified, but not both");
+        }
+        if (arg == "--split-max-tensors") {
+            if (++arg_idx >= argc) {
+                invalid_param = true;
+                break;
+            }
+            arg_found = true;
+            is_mode_set = true;
+            params.n_split_tensors = atoi(argv[arg_idx]);
+        }
+        if (arg == "--split-max-size") {
+            if (++arg_idx >= argc) {
+                invalid_param = true;
+                break;
+            }
+            arg_found = true;
+            is_mode_set = true;
+            params.n_bytes_split = split_str_to_n_bytes(argv[arg_idx]);
+        }
+
+        if (!arg_found) {
+            throw std::invalid_argument("error: unknown argument: " + arg);
+        }
+    }
+
+    if (invalid_param) {
+        throw std::invalid_argument("error: invalid parameter for argument: " + arg);
+    }
+
+    if (argc - arg_idx < 2) {
+        throw std::invalid_argument("error: bad arguments");
+    }
+
+    params.input = argv[arg_idx++];
+    params.output = argv[arg_idx++];
+}
+
+static bool split_params_parse(int argc, const char ** argv, split_params & params) {
+    bool result = true;
+    try {
+        split_params_parse_ex(argc, argv, params);
+    }
+    catch (const std::invalid_argument & ex) {
+        fprintf(stderr, "%s\n", ex.what());
+        split_print_usage(argv[0]);
+        exit(EXIT_FAILURE);
+    }
+    return result;
+}
+
+static void zeros(std::ofstream & file, size_t n) {
+    char zero = 0;
+    for (size_t i = 0; i < n; ++i) {
+        file.write(&zero, 1);
+    }
+}
+
+struct split_strategy {
+    const split_params params;
+    std::ifstream & f_input;
+    struct gguf_context * ctx_gguf;
+    struct ggml_context * ctx_meta = NULL;
+    const int n_tensors;
+
+    // one ctx_out per one output file
+    std::vector<struct gguf_context *> ctx_outs;
+
+    // temporary buffer for reading in tensor data
+    std::vector<uint8_t> read_buf;
+
+    split_strategy(const split_params & params,
+            std::ifstream & f_input,
+            struct gguf_context * ctx_gguf,
+            struct ggml_context * ctx_meta) :
+        params(params),
+        f_input(f_input),
+        ctx_gguf(ctx_gguf),
+        ctx_meta(ctx_meta),
+        n_tensors(gguf_get_n_tensors(ctx_gguf)) {
+
+        // because we need to know list of tensors for each file in advance, we will build all the ctx_out for all output splits
+        int i_split = -1;
+        struct gguf_context * ctx_out = NULL;
+        auto new_ctx_out = [&]() {
+            i_split++;
+            if (ctx_out != NULL) {
+                if (gguf_get_n_tensors(ctx_out) == 0) {
+                    fprintf(stderr, "error: one of splits have 0 tensors. Maybe size or tensors limit is too small\n");
+                    exit(EXIT_FAILURE);
+                }
+                ctx_outs.push_back(ctx_out);
+            }
+            ctx_out = gguf_init_empty();
+            // Save all metadata in first split only
+            if (i_split == 0) {
+                gguf_set_kv(ctx_out, ctx_gguf);
+            }
+            gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_NO, i_split);
+            gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_COUNT, 0); // placeholder
+            gguf_set_val_i32(ctx_out, LLM_KV_SPLIT_TENSORS_COUNT, n_tensors);
+        };
+
+        // initialize ctx_out for the first split
+        new_ctx_out();
+
+        // process tensors one by one
+        size_t curr_tensors_size = 0; // current size by counting only tensors size (without metadata)
+        for (int i = 0; i < n_tensors; ++i) {
+            struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
+            // calculate the "imaginary" size = the current size + next tensor size
+            size_t n_bytes = GGML_PAD(ggml_nbytes(t), GGUF_DEFAULT_ALIGNMENT);
+            size_t next_tensors_size = curr_tensors_size + n_bytes;
+            if (should_split(i, next_tensors_size)) {
+                new_ctx_out();
+                curr_tensors_size = n_bytes;
+            } else {
+                curr_tensors_size = next_tensors_size;
+            }
+            gguf_add_tensor(ctx_out, t);
+        }
+
+        // push the last ctx_out
+        ctx_outs.push_back(ctx_out);
+
+        // set the correct n_split for all ctx_out
+        for (auto & ctx : ctx_outs) {
+            gguf_set_val_u16(ctx, LLM_KV_SPLIT_COUNT, ctx_outs.size());
+        }
+    }
+
+    ~split_strategy() {
+        for (auto & ctx_out : ctx_outs) {
+            gguf_free(ctx_out);
+        }
+    }
+
+    bool should_split(int i_tensor, size_t next_size) {
+        if (params.n_bytes_split > 0) {
+            // split by max size per file
+            return next_size > params.n_bytes_split;
+        } else {
+            // split by number of tensors per file
+            return i_tensor > 0 && i_tensor < n_tensors && i_tensor % params.n_split_tensors == 0;
+        }
+    }
+
+    void print_info() {
+        printf("n_split: %ld\n", ctx_outs.size());
+        int i_split = 0;
+        for (auto & ctx_out : ctx_outs) {
+            // re-calculate the real gguf size for each split (= metadata size + total size of all tensors)
+            size_t total_size = gguf_get_meta_size(ctx_out);
+            for (int i = 0; i < gguf_get_n_tensors(ctx_out); ++i) {
+                struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_out, i));
+                total_size += ggml_nbytes(t);
+            }
+            total_size = total_size / 1024 / 1024; // convert to megabytes
+            printf("split %05d: n_tensors = %d, total_size = %ldM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size);
+            i_split++;
+        }
+    }
+
+    void write() {
+        int i_split = 0;
+        int n_split = ctx_outs.size();
+        for (auto & ctx_out : ctx_outs) {
+            // construct file path
+            char split_path[PATH_MAX] = {0};
+            llama_split_path(split_path, sizeof(split_path), params.output.c_str(), i_split, n_split);
+
+            // open the output file
+            printf("Writing file %s ... ", split_path);
+            fflush(stdout);
+            std::ofstream fout = std::ofstream(split_path, std::ios::binary);
+            fout.exceptions(std::ofstream::failbit); // fail fast on write errors
+
+            // write metadata
+            std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
+            gguf_get_meta_data(ctx_out, data.data());
+            fout.write((const char *)data.data(), data.size());
+
+            // write tensors
+            for (int i = 0; i < gguf_get_n_tensors(ctx_out); ++i) {
+                // read tensor meta and prepare buffer
+                const char * t_name = gguf_get_tensor_name(ctx_out, i);
+                struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
+                auto n_bytes = ggml_nbytes(t);
+                read_buf.resize(n_bytes);
+
+                // calculate offset
+                auto i_tensor_in = gguf_find_tensor(ctx_gguf, t_name); // idx of tensor in the input file
+                auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor_in);
+
+                // copy tensor from input to output file
+                copy_file_to_file(f_input, fout, offset, n_bytes);
+                zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes);
+            }
+
+            printf("done\n");
+            // close the file
+            fout.close();
+            i_split++;
+        }
+    }
+
+    void copy_file_to_file(std::ifstream & f_in, std::ofstream & f_out, const size_t in_offset, const size_t len) {
+        // TODO: detect OS and use copy_file_range() here for better performance
+        if (read_buf.size() < len) {
+            read_buf.resize(len);
+        }
+        f_in.seekg(in_offset);
+        f_in.read((char *)read_buf.data(), len);
+        f_out.write((const char *)read_buf.data(), len);
+    }
+};
+
+static void gguf_split(const split_params & split_params) {
+    struct ggml_context * ctx_meta = NULL;
+
+    struct gguf_init_params params = {
+        /*.no_alloc = */ true,
+        /*.ctx      = */ &ctx_meta,
+    };
+
+    std::ifstream f_input(split_params.input.c_str(), std::ios::binary);
+    if (!f_input.is_open()) {
+        fprintf(stderr, "%s:  failed to open input GGUF from %s\n", __func__, split_params.input.c_str());
+        exit(EXIT_FAILURE);
+    }
+
+    auto * ctx_gguf = gguf_init_from_file(split_params.input.c_str(), params);
+    if (!ctx_gguf) {
+        fprintf(stderr, "%s:  failed to load input GGUF from %s\n", __func__, split_params.input.c_str());
+        exit(EXIT_FAILURE);
+    }
+
+    // prepare the strategy
+    split_strategy strategy(split_params, f_input, ctx_gguf, ctx_meta);
+    int n_split = strategy.ctx_outs.size();
+    strategy.print_info();
+
+    if (!split_params.dry_run) {
+        // write all output splits
+        strategy.write();
+    }
+
+    // done, clean up
+    gguf_free(ctx_gguf);
+    f_input.close();
+
+    fprintf(stderr, "%s: %d gguf split written with a total of %d tensors.\n",
+            __func__, n_split, strategy.n_tensors);
+}
+
+static void gguf_merge(const split_params & split_params) {
+    fprintf(stderr, "%s: %s -> %s\n",
+            __func__, split_params.input.c_str(),
+            split_params.output.c_str());
+    int n_split = 1;
+    int total_tensors = 0;
+
+    auto * ctx_out = gguf_init_empty();
+    std::ofstream fout(split_params.output.c_str(), std::ios::binary);
+    fout.exceptions(std::ofstream::failbit); // fail fast on write errors
+
+    std::vector<uint8_t> read_data;
+    std::vector<ggml_context *> ctx_metas;
+    std::vector<gguf_context *> ctx_ggufs;
+
+    char split_path[PATH_MAX] = {0};
+    strncpy(split_path, split_params.input.c_str(), sizeof(split_path) - 1);
+    char split_prefix[PATH_MAX] = {0};
+
+    // First pass to find KV and tensors metadata
+    for (int i_split = 0; i_split < n_split; i_split++) {
+        struct ggml_context * ctx_meta = NULL;
+
+        struct gguf_init_params params = {
+            /*.no_alloc = */ true,
+            /*.ctx      = */ &ctx_meta,
+        };
+
+        if (i_split > 0) {
+            llama_split_path(split_path, sizeof(split_path), split_prefix, i_split, n_split);
+        }
+        fprintf(stderr, "%s: reading metadata %s ...", __func__, split_path);
+
+        auto * ctx_gguf = gguf_init_from_file(split_path, params);
+        if (!ctx_gguf) {
+            fprintf(stderr, "\n%s:  failed to load input GGUF from %s\n", __func__, split_params.input.c_str());
+            exit(EXIT_FAILURE);
+        }
+        ctx_ggufs.push_back(ctx_gguf);
+        ctx_metas.push_back(ctx_meta);
+
+        if (i_split == 0) {
+            auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT);
+            if (key_n_split < 0) {
+                fprintf(stderr,
+                        "\n%s: input file does not contain %s metadata\n",
+                        __func__,
+                        LLM_KV_SPLIT_COUNT);
+                gguf_free(ctx_gguf);
+                ggml_free(ctx_meta);
+                gguf_free(ctx_out);
+                fout.close();
+                exit(EXIT_FAILURE);
+            }
+
+            n_split = gguf_get_val_u16(ctx_gguf, key_n_split);
+            if (n_split < 1) {
+                fprintf(stderr,
+                        "\n%s: input file does not contain a valid split count %d\n",
+                        __func__,
+                        n_split);
+                gguf_free(ctx_gguf);
+                ggml_free(ctx_meta);
+                gguf_free(ctx_out);
+                fout.close();
+                exit(EXIT_FAILURE);
+            }
+
+            // Verify the file naming and extract split_prefix
+            if (!llama_split_prefix(split_prefix, sizeof (split_prefix), split_path, i_split, n_split)) {
+                fprintf(stderr, "\n%s: unexpected input file name: %s"
+                                " i_split=%d"
+                                " n_split=%d\n", __func__,
+                        split_path, i_split, n_split);
+                gguf_free(ctx_gguf);
+                ggml_free(ctx_meta);
+                gguf_free(ctx_out);
+                fout.close();
+                exit(EXIT_FAILURE);
+            }
+
+            // Do not trigger merge if we try to merge again the output
+            gguf_set_val_u16(ctx_gguf, LLM_KV_SPLIT_COUNT, 0);
+
+            // Set metadata from the first split
+            gguf_set_kv(ctx_out, ctx_gguf);
+        }
+
+        auto n_tensors = gguf_get_n_tensors(ctx_gguf);
+        for (int i_tensor = 0; i_tensor < n_tensors; i_tensor++) {
+            const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor);
+            struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
+            gguf_add_tensor(ctx_out, t);
+        }
+        total_tensors += n_tensors;
+
+        fprintf(stderr, "\033[3Ddone\n");
+    }
+
+    // placeholder for the meta data
+    {
+        auto meta_size = gguf_get_meta_size(ctx_out);
+        ::zeros(fout, meta_size);
+    }
+
+    // Write tensors data
+    for (int i_split = 0; i_split < n_split; i_split++) {
+        llama_split_path(split_path, sizeof(split_path), split_prefix, i_split, n_split);
+        std::ifstream f_input(split_path, std::ios::binary);
+        if (!f_input.is_open()) {
+            fprintf(stderr, "%s:  failed to open input GGUF from %s\n", __func__, split_path);
+            for (uint32_t i = 0; i < ctx_ggufs.size(); i++) {
+                gguf_free(ctx_ggufs[i]);
+                ggml_free(ctx_metas[i]);
+            }
+            gguf_free(ctx_out);
+            fout.close();
+            exit(EXIT_FAILURE);
+        }
+        fprintf(stderr, "%s: writing tensors %s ...", __func__, split_path);
+
+        auto * ctx_gguf = ctx_ggufs[i_split];
+        auto * ctx_meta = ctx_metas[i_split];
+
+        auto n_tensors = gguf_get_n_tensors(ctx_gguf);
+        for (int i_tensor = 0; i_tensor < n_tensors; i_tensor++) {
+            const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor);
+            struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
+
+            auto n_bytes = ggml_nbytes(t);
+
+            if (read_data.size() < n_bytes) {
+                read_data.resize(n_bytes);
+            }
+
+            auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor);
+            f_input.seekg(offset);
+            f_input.read((char *)read_data.data(), n_bytes);
+
+            // write tensor data + padding
+            fout.write((const char *)read_data.data(), n_bytes);
+            zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes);
+        }
+
+        gguf_free(ctx_gguf);
+        ggml_free(ctx_meta);
+        f_input.close();
+        fprintf(stderr, "\033[3Ddone\n");
+    }
+
+    {
+        // go back to beginning of file and write the updated metadata
+        fout.seekp(0);
+        std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
+        gguf_get_meta_data(ctx_out, data.data());
+        fout.write((const char *)data.data(), data.size());
+
+        fout.close();
+        gguf_free(ctx_out);
+    }
+
+    fprintf(stderr, "%s: %s merged from %d split with %d tensors.\n",
+            __func__, split_params.output.c_str(), n_split, total_tensors);
+}
+
+int main(int argc, const char ** argv) {
+    split_params params;
+    split_params_parse(argc, argv, params);
+
+    switch (params.operation) {
+        case SPLIT_OP_SPLIT: gguf_split(params);
+            break;
+        case SPLIT_OP_MERGE: gguf_merge(params);
+            break;
+        default: split_print_usage(argv[0]);
+            exit(EXIT_FAILURE);
+    }
+
+    return 0;
+}
diff --git a/examples/gguf/gguf.cpp b/examples/gguf/gguf.cpp
index e67be4fb2..5444503a5 100644
--- a/examples/gguf/gguf.cpp
+++ b/examples/gguf/gguf.cpp
@@ -211,6 +211,7 @@ static bool gguf_ex_read_1(const std::string & fname) {
                 for (int j = 0; j < ggml_nelements(cur); ++j) {
                     if (data[j] != 100 + i) {
                         fprintf(stderr, "%s: tensor[%d]: data[%d] = %f\n", __func__, i, j, data[j]);
+                        gguf_free(ctx);
                         return false;
                     }
                 }
diff --git a/examples/gritlm/CMakeLists.txt b/examples/gritlm/CMakeLists.txt
new file mode 100644
index 000000000..ac4a5ae79
--- /dev/null
+++ b/examples/gritlm/CMakeLists.txt
@@ -0,0 +1,5 @@
+set(TARGET gritlm)
+add_executable(${TARGET} gritlm.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/gritlm/README.md b/examples/gritlm/README.md
new file mode 100644
index 000000000..64cc19204
--- /dev/null
+++ b/examples/gritlm/README.md
@@ -0,0 +1,62 @@
+## Generative Representational Instruction Tuning (GRIT) Example
+[gritlm] a model which can generate embeddings as well as "normal" text
+generation depending on the instructions in the prompt.
+
+* Paper: https://arxiv.org/pdf/2402.09906.pdf
+
+### Retrieval-Augmented Generation (RAG) use case
+One use case for `gritlm` is to use it with RAG. If we recall how RAG works is
+that we take documents that we want to use as context, to ground the large
+language model (LLM), and we create token embeddings for them. We then store
+these token embeddings in a vector database.
+
+When we perform a query, prompt the LLM, we will first create token embeddings
+for the query and then search the vector database to retrieve the most
+similar vectors, and return those documents so they can be passed to the LLM as
+context. Then the query and the context will be passed to the LLM which will
+have to _again_ create token embeddings for the query. But because gritlm is used
+the first query can be cached and the second query tokenization generation does
+not have to be performed at all.
+
+### Running the example
+Download a Grit model:
+```console
+$ scripts/hf.sh --repo cohesionet/GritLM-7B_gguf --file gritlm-7b_q4_1.gguf
+```
+
+Run the example using the downloaded model:
+```console
+$ ./gritlm -m gritlm-7b_q4_1.gguf
+
+Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "A purely peer-to-peer version of electronic cash w" is: 0.605
+Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "All text-based language problems can be reduced to" is: 0.103
+Cosine similarity between "Generative Representational Instruction Tuning" and "A purely peer-to-peer version of electronic cash w" is: 0.112
+Cosine similarity between "Generative Representational Instruction Tuning" and "All text-based language problems can be reduced to" is: 0.547
+
+Oh, brave adventurer, who dared to climb
+The lofty peak of Mt. Fuji in the night,
+When shadows lurk and ghosts do roam,
+And darkness reigns, a fearsome sight.
+
+Thou didst set out, with heart aglow,
+To conquer this mountain, so high,
+And reach the summit, where the stars do glow,
+And the moon shines bright, up in the sky.
+
+Through the mist and fog, thou didst press on,
+With steadfast courage, and a steadfast will,
+Through the darkness, thou didst not be gone,
+But didst climb on, with a steadfast skill.
+
+At last, thou didst reach the summit's crest,
+And gazed upon the world below,
+And saw the beauty of the night's best,
+And felt the peace, that only nature knows.
+
+Oh, brave adventurer, who dared to climb
+The lofty peak of Mt. Fuji in the night,
+Thou art a hero, in the eyes of all,
+For thou didst conquer this mountain, so bright.
+```
+
+[gritlm]: https://github.com/ContextualAI/gritlm
diff --git a/examples/gritlm/gritlm.cpp b/examples/gritlm/gritlm.cpp
new file mode 100644
index 000000000..52fd719b3
--- /dev/null
+++ b/examples/gritlm/gritlm.cpp
@@ -0,0 +1,215 @@
+#include "common.h"
+#include "llama.h"
+
+#include <string>
+#include <vector>
+
+// #define GRIT_DEBUG
+
+static std::vector<std::vector<float>> encode(llama_context * ctx, const std::vector<std::string> & sentences, const std::string & instruction) {
+    std::vector<std::vector<float>> result;
+
+    const llama_model * mdl = llama_get_model(ctx);
+
+    llama_batch batch = llama_batch_init(llama_n_batch(ctx), 0, 1);
+
+    for (uint64_t i = 0; i < sentences.size(); i++) {
+        llama_batch_clear(batch);
+
+        const std::string input_string = instruction + sentences[i];
+
+        std::vector<llama_token> inputs = llama_tokenize(mdl, input_string, true, false);
+
+        const int32_t n_toks = inputs.size();
+
+        // GritLM seems to have EOS = ""
+        // https://github.com/ContextualAI/gritlm/blob/92025b16534712b31b3c4aaaf069350e222bd5f8/gritlm/gritlm.py#L18
+        // inputs.push_back(llama_token_eos(mdl));
+
+        // we want to ignore instruction tokens for mean pooling
+        const int32_t n_inst = llama_tokenize(mdl, instruction, true, false).size();
+
+#ifdef GRIT_DEBUG
+        // debug tokens - should be matching as referenced in the GritLM sample
+        std::for_each(inputs.begin(), inputs.end(), [&ctx](llama_token t) {
+            std::printf("[%u:%s]", t, llama_token_to_piece(ctx, t).c_str());
+        });
+        std::printf("\n");
+#endif
+
+        // add input to batch (this increments n_tokens)
+        for (int32_t j = 0; j < n_toks; j++) {
+            llama_batch_add(batch, inputs[j], j, { 0 }, j >= n_inst);
+        }
+
+        // clear previous kv_cache values (irrelevant for embeddings)
+        llama_kv_cache_clear(ctx);
+        llama_set_causal_attn(ctx, false);
+
+        // run model
+        llama_decode(ctx, batch);
+
+        // get embedding dimensions
+        uint64_t n_embd = llama_n_embd(mdl);
+
+        // allocate embedding output
+        std::vector<float> emb_unorm(n_embd, 0.0f);
+
+        // sum up all token embeddings
+        for (int32_t k = n_inst; k < n_toks; k++) {
+            float * emb = llama_get_embeddings_ith(ctx, k);
+            for (uint64_t j = 0; j < n_embd; j++) {
+                emb_unorm[j] += emb[j];
+            }
+        }
+
+        // divide by number of tokens (mean pooling)
+        {
+            const uint64_t n_sent = n_toks - n_inst;
+
+            for (uint64_t j = 0; j < n_embd; j++) {
+                emb_unorm[j] /= n_sent;
+            }
+        }
+
+        std::vector<float> emb_norm(emb_unorm.size());
+        llama_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd);
+        result.push_back(emb_norm);
+
+#ifdef GRIT_DEBUG
+        // print out emb_norm
+        std::printf("embedding %ld: ", i);
+        for (uint64_t j = 0; j < n_embd; j++) {
+            std::printf("%.5f ", emb_norm[j]);
+        }
+        std::printf("\n\n");
+#endif
+    }
+
+    llama_batch_free(batch);
+
+    return result;
+}
+
+static std::string generate(llama_context * ctx, const std::string & prompt, bool stream) {
+    std::string result;
+
+    const llama_model * mdl = llama_get_model(ctx);
+    llama_token eos_token = llama_token_eos(mdl);
+
+    llama_kv_cache_clear(ctx);
+    llama_set_causal_attn(ctx, true);
+    llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1);
+
+    std::vector<llama_token> inputs = llama_tokenize(mdl, prompt, false, true);
+    int32_t i_current_token = 0;
+
+    while (true) {
+        llama_batch_clear(bat);
+        auto n_inputs = (int32_t)inputs.size();
+        for (int32_t i = 0; i < n_inputs; i++) {
+            llama_batch_add(bat, inputs[i], i_current_token++, { 0 }, i == n_inputs - 1);
+        }
+        inputs.clear();
+
+        llama_decode(ctx, bat);
+        auto logits = llama_get_logits_ith(ctx, bat.n_tokens - 1);
+
+        auto candidates = std::vector<llama_token_data>(llama_n_vocab(mdl));
+        auto n_candidates = (int32_t)candidates.size();
+        for (int32_t token = 0; token < n_candidates; token++) {
+            candidates[token] = llama_token_data{ token, logits[token], 0.0f };
+        }
+        auto candidates_p = llama_token_data_array{ candidates.data(), candidates.size(), false };
+
+        llama_token token = llama_sample_token_greedy(ctx, &candidates_p);
+        if (token == eos_token) {
+            break;
+        }
+
+        std::string piece = llama_token_to_piece(ctx, token);
+        if (stream) {
+            std::printf("%s", piece.c_str());
+            std::fflush(stdout);
+        }
+
+        inputs.push_back(token);
+
+        result += piece;
+    }
+
+    if (stream) {
+        std::printf("\n");
+    }
+
+    llama_batch_free(bat);
+
+    return result;
+}
+
+static std::string gritlm_instruction(const std::string & instruction) {
+    return !instruction.empty() ? "<|user|>\n" + instruction + "\n<|embed|>\n" : "<|embed|>\n";
+}
+
+int main(int argc, char * argv[]) {
+    gpt_params params;
+    if (!gpt_params_parse(argc, argv, params)) {
+        return 1;
+    }
+
+    llama_model_params mparams = llama_model_params_from_gpt_params(params);
+    llama_context_params cparams = llama_context_params_from_gpt_params(params);
+
+    llama_backend_init();
+
+    llama_model * mdl = llama_load_model_from_file(params.model.c_str(), mparams);
+
+    // create new context - set to embedding mode
+    cparams.embeddings = true;
+    llama_context * ctx = llama_new_context_with_model(mdl, cparams);
+
+    // ### Embedding/Representation ###
+    // samples taken from: https://github.com/ContextualAI/gritlm#basic
+    {
+        const std::string instruction = "Given a scientific paper title, retrieve the paper's abstract";
+
+        const std::vector<std::string> queries = {
+            "Bitcoin: A Peer-to-Peer Electronic Cash System",
+            "Generative Representational Instruction Tuning",
+        };
+
+        const std::vector<std::string> documents = {
+            "A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers. The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone.",
+            "All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational instruction tuning (GRIT) whereby a large language model is trained to handle both generative and embedding tasks by distinguishing between them through instructions. Compared to other open models, our resulting GritLM 7B sets a new state of the art on the Massive Text Embedding Benchmark (MTEB) and outperforms all models up to its size on a range of generative tasks. By scaling up further, GritLM 8X7B outperforms all open generative language models that we tried while still being among the best embedding models. Notably, we find that GRIT matches training on only generative or embedding data, thus we can unify both at no performance loss. Among other benefits, the unification via GRIT speeds up Retrieval-Augmented Generation (RAG) by > 60% for long documents, by no longer requiring separate retrieval and generation models. Models, code, etc. are freely available at https://github.com/ContextualAI/gritlm.",
+        };
+
+        // No need to add instruction for retrieval documents
+        const std::vector<std::vector<float>> d_rep = encode(ctx, documents, gritlm_instruction(""));
+        const std::vector<std::vector<float>> q_rep = encode(ctx, queries,   gritlm_instruction(instruction));
+
+        const int n_embd = llama_n_embd(mdl);
+
+        const float cosine_sim_q0_d0 = llama_embd_similarity_cos(q_rep[0].data(), d_rep[0].data(), n_embd);
+        const float cosine_sim_q0_d1 = llama_embd_similarity_cos(q_rep[0].data(), d_rep[1].data(), n_embd);
+        const float cosine_sim_q1_d0 = llama_embd_similarity_cos(q_rep[1].data(), d_rep[0].data(), n_embd);
+        const float cosine_sim_q1_d1 = llama_embd_similarity_cos(q_rep[1].data(), d_rep[1].data(), n_embd);
+
+        std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[0].c_str(), cosine_sim_q0_d0);
+        std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[1].c_str(), cosine_sim_q0_d1);
+        std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[1].c_str(), documents[0].c_str(), cosine_sim_q1_d0);
+        std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[1].c_str(), documents[1].c_str(), cosine_sim_q1_d1);
+    }
+
+    // ### Generation ###
+    // GritLM models are not finetuned with system prompts, as you can just include system-like instructions together with your user instruction
+    {
+        const std::string prompt = "<|user|>\nPlease write me a poem about my recent hike of Mt. Fuji at midnight in the style of Shakespeare.\n<|assistant|>\n";
+        std::string response = generate(ctx, prompt, true);
+    }
+
+    llama_free(ctx);
+    llama_free_model(mdl);
+    llama_backend_free();
+
+    return 0;
+}
diff --git a/examples/imatrix/CMakeLists.txt b/examples/imatrix/CMakeLists.txt
new file mode 100644
index 000000000..d688a1620
--- /dev/null
+++ b/examples/imatrix/CMakeLists.txt
@@ -0,0 +1,5 @@
+set(TARGET imatrix)
+add_executable(${TARGET} imatrix.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/imatrix/README.md b/examples/imatrix/README.md
new file mode 100644
index 000000000..458c01b87
--- /dev/null
+++ b/examples/imatrix/README.md
@@ -0,0 +1,32 @@
+# llama.cpp/examples/imatrix
+
+Compute an importance matrix for a model and given text dataset. Can be used during quantization to enchance the quality of the quantum models.
+More information is available here: https://github.com/ggerganov/llama.cpp/pull/4861
+
+## Usage
+
+```
+./imatrix -m <some_fp_model> -f <some_training_data> [-o <output_file>] [--verbosity <verbosity_level>]
+        [-ofreq num_chunks] [-ow <0 or 1>] [other common params]
+```
+
+Here `-m` with a model name and `-f` with a file containing training data (such as e.g. `wiki.train.raw`) are mandatory.
+The parameters in square brackets are optional and have the following meaning:
+* `-o` (or `--output-file`) specifies the name of the file where the computed data will be stored. If missing `imatrix.dat` is used.
+* `--verbosity` specifies the verbosity level. If set to `0`, no output other than the perplexity of the processed chunks will be generated. If set to `1`, each time the results are saved a message is written to `stderr`. If `>=2`, a message is output each time data is collected for any tensor. Default verbosity level is `1`.
+* `-ofreq` (or `--output-frequency`) specifies how often the so far computed result is saved to disk. Default is 10 (i.e., every 10 chunks)
+* `-ow` (or `--output-weight`) specifies if data will be collected for the `output.weight` tensor. My experience is that it is better to not utilize the importance matrix when quantizing `output.weight`, so this is set to `false` by default.
+
+For faster computation, make sure to use GPU offloading via the `-ngl` argument
+
+## Example
+
+```bash
+LLAMA_CUDA=1 make -j
+
+# generate importance matrix (imatrix.dat)
+./imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99
+
+# use the imatrix to perform a Q4_K_M quantization
+./quantize --imatrix imatrix.dat ggml-model-f16.gguf ./ggml-model-q4_k_m.gguf q4_k_m
+```
diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp
new file mode 100644
index 000000000..d8cb0a642
--- /dev/null
+++ b/examples/imatrix/imatrix.cpp
@@ -0,0 +1,647 @@
+#include "common.h"
+#include "llama.h"
+
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <ctime>
+#include <sstream>
+#include <thread>
+#include <mutex>
+#include <vector>
+#include <fstream>
+#include <unordered_map>
+#include <algorithm>
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+struct Stats {
+    std::vector<float> values;
+    int ncall = 0;
+};
+
+struct StatParams {
+    std::string ofile = "imatrix.dat";
+    int         n_output_frequency = 10;
+    int         verbosity = 1;
+    int         keep_every = 0;
+    bool        collect_output_weight = false;
+};
+
+class IMatrixCollector {
+public:
+    IMatrixCollector() = default;
+    void set_parameters(StatParams&& params) { m_params = std::move(params); }
+    bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
+    void save_imatrix() const;
+    bool load_imatrix(const char * file_name, bool add);
+    static bool load_imatrix(const char * file_name, std::unordered_map<std::string, Stats>& imatrix);
+private:
+    std::unordered_map<std::string, Stats> m_stats;
+    StatParams                             m_params;
+    std::mutex                             m_mutex;
+    int                                    m_last_call = 0;
+    std::vector<float>                     m_src1_data;
+    std::vector<int>                       m_ids; // the expert ids from ggml_mul_mat_id
+                                                  //
+    void save_imatrix(const char * file_name) const;
+    void keep_imatrix(int ncall) const;
+};
+
+// remove any prefix and suffixes from the name
+// CUDA0#blk.0.attn_k.weight#0 => blk.0.attn_k.weight
+static std::string filter_tensor_name(const char * name) {
+    std::string wname;
+    const char * p = strchr(name, '#');
+    if (p != NULL) {
+        p = p + 1;
+        const char * q = strchr(p, '#');
+        if (q != NULL) {
+            wname = std::string(p, q - p);
+        } else {
+            wname = p;
+        }
+    } else {
+        wname = name;
+    }
+    return wname;
+}
+
+bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) {
+    GGML_UNUSED(user_data);
+
+    const struct ggml_tensor * src0 = t->src[0];
+    const struct ggml_tensor * src1 = t->src[1];
+    std::string wname = filter_tensor_name(src0->name);
+
+    // when ask is true, the scheduler wants to know if we are interested in data from this tensor
+    // if we return true, a follow-up call will be made with ask=false in which we can do the actual collection
+    if (ask) {
+        if (t->op == GGML_OP_MUL_MAT_ID) return true; // collect all indirect matrix multiplications
+        if (t->op != GGML_OP_MUL_MAT) return false;
+        if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return false;
+        if (!(wname.substr(0, 4) == "blk." || (m_params.collect_output_weight && wname == "output.weight"))) return false;
+        return true;
+    }
+
+    std::lock_guard<std::mutex> lock(m_mutex);
+
+    // copy the data from the GPU memory if needed
+    const bool is_host = ggml_backend_buffer_is_host(src1->buffer);
+
+    if (!is_host) {
+        m_src1_data.resize(ggml_nelements(src1));
+        ggml_backend_tensor_get(src1, m_src1_data.data(), 0, ggml_nbytes(src1));
+    }
+
+    const float * data = is_host ? (const float *) src1->data : m_src1_data.data();
+
+    // this has been adapted to the new format of storing merged experts in a single 3d tensor
+    // ref: https://github.com/ggerganov/llama.cpp/pull/6387
+    if (t->op == GGML_OP_MUL_MAT_ID) {
+        const int idx  = ((int32_t *) t->op_params)[0];
+        const ggml_tensor * ids = t->src[2];
+        const int n_as = src0->ne[2];
+
+        // the top-k selected expert ids are stored in the ids tensor
+        // for simplicity, always copy ids to host, because it is small
+        // take into account that ids is not contiguous!
+        GGML_ASSERT(ids->ne[1] == src1->ne[1]);
+        GGML_ASSERT(n_as*ggml_nrows(ids)*sizeof(int) == GGML_PAD(ggml_nbytes(ids), n_as*sizeof(int)));
+        m_ids.resize(ggml_nbytes(ids)/sizeof(int));
+        ggml_backend_tensor_get(ids, m_ids.data(), 0, ggml_nbytes(ids));
+
+        auto & e = m_stats[wname];
+
+        ++e.ncall;
+        // NOTE: since we select top-k experts, the number of calls for the expert tensors will be k times larger
+        //       using the following line, we can correct for that if needed by replacing the line above with:
+        //if (idx == t->src[0]->ne[0] - 1) ++e.ncall;
+
+        // loop over all possible experts, regardless if they are used or not in the batch
+        for (int ex = 0; ex < n_as; ++ex) {
+            size_t e_start = ex*src1->ne[0];
+            if (e.values.empty()) {
+                e.values.resize(src1->ne[0]*n_as, 0);
+            }
+            else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
+                fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as);
+                exit(1); //GGML_ASSERT(false);
+            }
+            if (m_params.verbosity > 1) {
+                printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
+            }
+            for (int row = 0; row < (int)src1->ne[1]; ++row) {
+                const int excur = m_ids[row*n_as + idx];
+                GGML_ASSERT(excur >= 0 && excur < n_as); // sanity check
+                if (excur != ex) continue;
+                const float * x = data + row * src1->ne[0];
+                for (int j = 0; j < (int)src1->ne[0]; ++j) {
+                    e.values[e_start + j] += x[j]*x[j];
+                }
+            }
+            if (e.ncall > m_last_call) {
+                m_last_call = e.ncall;
+                if (m_last_call % m_params.n_output_frequency == 0) {
+                    save_imatrix();
+                }
+                if (m_params.keep_every > 0 && m_last_call%m_params.keep_every == 0) {
+                    keep_imatrix(m_last_call);
+                }
+            }
+        }
+    } else {
+        auto& e = m_stats[wname];
+        if (e.values.empty()) {
+            e.values.resize(src1->ne[0], 0);
+        }
+        else if (e.values.size() != (size_t)src1->ne[0]) {
+            fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
+            exit(1); //GGML_ASSERT(false);
+        }
+        ++e.ncall;
+        if (m_params.verbosity > 1) {
+            printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
+        }
+        for (int row = 0; row < (int)src1->ne[1]; ++row) {
+            const float * x = data + row * src1->ne[0];
+            for (int j = 0; j < (int)src1->ne[0]; ++j) {
+                e.values[j] += x[j]*x[j];
+            }
+        }
+        if (e.ncall > m_last_call) {
+            m_last_call = e.ncall;
+            if (m_last_call % m_params.n_output_frequency == 0) {
+                save_imatrix();
+            }
+            if (m_params.keep_every > 0 && m_last_call%m_params.keep_every == 0) {
+                keep_imatrix(m_last_call);
+            }
+        }
+    }
+
+    return true;
+}
+
+void IMatrixCollector::save_imatrix() const {
+    save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str());
+}
+
+void IMatrixCollector::keep_imatrix(int ncall) const {
+    auto file_name = m_params.ofile;
+    if (file_name.empty()) file_name = "imatrix.dat";
+    file_name += ".at_";
+    file_name += std::to_string(ncall);
+    save_imatrix(file_name.c_str());
+}
+
+void IMatrixCollector::save_imatrix(const char * fname) const {
+    std::ofstream out(fname, std::ios::binary);
+    int n_entries = m_stats.size();
+    out.write((const char*)&n_entries, sizeof(n_entries));
+    for (auto& p : m_stats) {
+        int len = p.first.size();
+        out.write((const char*)&len, sizeof(len));
+        out.write(p.first.c_str(), len);
+        out.write((const char*)&p.second.ncall, sizeof(p.second.ncall));
+        int nval = p.second.values.size();
+        out.write((const char*)&nval, sizeof(nval));
+        if (nval > 0) out.write((const char*)p.second.values.data(), nval*sizeof(float));
+    }
+    if (m_params.verbosity > 0) {
+        fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n",__func__,m_last_call,fname);
+    }
+}
+
+bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_map<std::string, Stats>& imatrix_data) {
+    std::ifstream in(imatrix_file, std::ios::binary);
+    if (!in) {
+        printf("%s: failed to open %s\n",__func__,imatrix_file);
+        return false;
+    }
+    int n_entries;
+    in.read((char*)&n_entries, sizeof(n_entries));
+    if (in.fail() || n_entries < 1) {
+        printf("%s: no data in file %s\n", __func__, imatrix_file);
+        return false;
+    }
+    for (int i = 0; i < n_entries; ++i) {
+        int len; in.read((char *)&len, sizeof(len));
+        std::vector<char> name_as_vec(len+1);
+        in.read((char *)name_as_vec.data(), len);
+        if (in.fail()) {
+            printf("%s: failed reading name for entry %d from %s\n",__func__,i+1,imatrix_file);
+            return false;
+        }
+        name_as_vec[len] = 0;
+        std::string name{name_as_vec.data()};
+        auto& e = imatrix_data[std::move(name)];
+        int ncall;
+        in.read((char*)&ncall, sizeof(ncall));
+        int nval;
+        in.read((char *)&nval, sizeof(nval));
+        if (in.fail() || nval < 1) {
+            printf("%s: failed reading number of values for entry %d\n",__func__,i);
+            imatrix_data = {};
+            return false;
+        }
+        e.values.resize(nval);
+        in.read((char*)e.values.data(), nval*sizeof(float));
+        if (in.fail()) {
+            printf("%s: failed reading data for entry %d\n",__func__,i);
+            imatrix_data = {};
+            return false;
+        }
+        e.ncall = ncall;
+    }
+    return true;
+}
+
+bool IMatrixCollector::load_imatrix(const char * file_name, bool add) {
+    if (!add) {
+        m_stats.clear();
+    }
+    return load_imatrix(file_name, m_stats);
+}
+
+static IMatrixCollector g_collector;
+
+static bool ik_collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) {
+    return g_collector.collect_imatrix(t, ask, user_data);
+}
+
+
+struct results_log_softmax {
+    double log_softmax;
+    float  logit;
+    float  prob;
+};
+
+static std::vector<float> softmax(const std::vector<float>& logits) {
+    std::vector<float> probs(logits.size());
+    float max_logit = logits[0];
+    for (float v : logits) {
+        max_logit = std::max(max_logit, v);
+    }
+    double sum_exp = 0.0;
+    for (size_t i = 0; i < logits.size(); i++) {
+        // Subtract the maximum logit value from the current logit value for numerical stability
+        const float logit = logits[i] - max_logit;
+        const float exp_logit = expf(logit);
+        sum_exp += exp_logit;
+        probs[i] = exp_logit;
+    }
+    for (size_t i = 0; i < probs.size(); i++) {
+        probs[i] /= sum_exp;
+    }
+    return probs;
+}
+
+static results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) {
+    float max_logit = logits[0];
+    for (int i = 1; i < n_vocab; ++i) {
+        max_logit = std::max(max_logit, logits[i]);
+    }
+    double sum_exp = 0.0;
+    for (int i = 0; i < n_vocab; ++i) {
+        sum_exp += expf(logits[i] - max_logit);
+    }
+    return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp};
+}
+
+static void process_logits(
+    int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread> & workers,
+    double & nll, double & nll2, float * logit_history, float * prob_history
+) {
+    std::mutex mutex;
+    int counter = 0;
+    auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () {
+        double local_nll  = 0;
+        double local_nll2 = 0;
+        while (true) {
+            std::unique_lock<std::mutex> lock(mutex);
+            int i = counter++;
+            if (i >= n_token) {
+                nll += local_nll; nll2 += local_nll2;
+                break;
+            }
+            lock.unlock();
+            const results_log_softmax results = log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]);
+            const double v = -results.log_softmax;
+            local_nll += v;
+            local_nll2 += v*v;
+
+            logit_history[i] = results.logit;
+            prob_history[i]  = results.prob;
+        }
+    };
+    for (auto & w : workers) {
+        w = std::thread(compute);
+    }
+    compute();
+    for (auto & w : workers) {
+        w.join();
+    }
+}
+
+static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl, int from_chunk) {
+
+    const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
+    const int n_ctx = llama_n_ctx(ctx);
+
+    auto tim1 = std::chrono::high_resolution_clock::now();
+    fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
+
+    std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
+
+    auto tim2 = std::chrono::high_resolution_clock::now();
+    fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
+
+    if (from_chunk > 0) {
+        if (size_t((from_chunk + 2)*n_ctx) >= tokens.size()) {
+            fprintf(stderr, "%s: there will be not enough tokens left after removing %d chunks\n", __func__, from_chunk);
+            return false;
+        }
+        fprintf(stderr, "%s: removing initial %d chunks (%d tokens)\n", __func__, from_chunk, from_chunk*n_ctx);
+        tokens.erase(tokens.begin(), tokens.begin() + from_chunk*n_ctx);
+    }
+
+    if (int(tokens.size()) < 2*n_ctx) {
+        fprintf(stderr, "%s: you need at least %d tokens for a context of %d tokens\n",__func__,2*n_ctx,
+                n_ctx);
+        fprintf(stderr, "%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size());
+        return false;
+    }
+
+    std::vector<float> logit_history;
+    std::vector<float> prob_history;
+
+    if (compute_ppl) {
+        logit_history.resize(tokens.size());
+        prob_history.resize(tokens.size());
+    }
+
+    const int n_chunk_max = tokens.size() / n_ctx;
+
+    const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
+    const int n_vocab = llama_n_vocab(llama_get_model(ctx));
+    const int n_batch = params.n_batch;
+
+    int count = 0;
+    double nll = 0.0;
+    double nll2 = 0.0;
+
+    fprintf(stderr, "%s: computing over %d chunks with batch_size %d\n", __func__, n_chunk, n_batch);
+
+    std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
+
+    const int num_batches = (n_ctx + n_batch - 1) / n_batch;
+
+    std::vector<float> logits;
+    if (compute_ppl && num_batches > 1) {
+        logits.reserve((size_t)n_ctx * n_vocab);
+    }
+
+    for (int i = 0; i < n_chunk; ++i) {
+        const int start =     i * n_ctx;
+        const int end   = start + n_ctx;
+
+        std::vector<float> logits;
+
+        const auto t_start = std::chrono::high_resolution_clock::now();
+
+        // clear the KV cache
+        llama_kv_cache_clear(ctx);
+
+        for (int j = 0; j < num_batches; ++j) {
+            const int batch_start = start + j * n_batch;
+            const int batch_size  = std::min(end - batch_start, n_batch);
+
+            // save original token and restore it after eval
+            const auto token_org = tokens[batch_start];
+
+            // add BOS token for the first batch of each chunk
+            if (add_bos && j == 0) {
+                tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
+            }
+
+            // TODO: use batch.logits to save computations instead of relying on logits_all == true
+            if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
+                fprintf(stderr, "%s : failed to eval\n", __func__);
+                return false;
+            }
+
+            // restore the original token in case it was set to BOS
+            tokens[batch_start] = token_org;
+
+            if (compute_ppl && num_batches > 1) {
+                const auto * batch_logits = llama_get_logits(ctx);
+                logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
+            }
+        }
+
+        const auto t_end = std::chrono::high_resolution_clock::now();
+
+        if (i == 0) {
+            const float t_total = std::chrono::duration<float>(t_end - t_start).count();
+            fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total);
+            int total_seconds = (int)(t_total * n_chunk);
+            if (total_seconds >= 60*60) {
+                fprintf(stderr, "%d hours ", total_seconds / (60*60));
+                total_seconds = total_seconds % (60*60);
+            }
+            fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
+        }
+
+        if (compute_ppl) {
+            const int first = n_ctx/2;
+            const auto all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
+            process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
+                    workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
+            count += n_ctx - first - 1;
+
+            printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
+            fflush(stdout);
+
+            logits.clear();
+        }
+    }
+    printf("\n");
+
+    if (compute_ppl) {
+        nll2 /= count;
+        nll /= count;
+        const double ppl = exp(nll);
+        nll2 -= nll * nll;
+        if (nll2 > 0) {
+            nll2 = sqrt(nll2/(count-1));
+            printf("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl);
+        } else {
+            printf("Unexpected negative standard deviation of log(prob)\n");
+        }
+    }
+
+    return true;
+}
+
+int main(int argc, char ** argv) {
+
+    StatParams sparams;
+    std::string prev_result_file;
+    std::string combine_files;
+    bool compute_ppl = true;
+    int  from_chunk  = 0;
+    std::vector<char*> args;
+    args.push_back(argv[0]);
+    int iarg = 1;
+    for (; iarg < argc-1; ++iarg) {
+        std::string arg{argv[iarg]};
+        if (arg == "-o" || arg == "--output-file") {
+            sparams.ofile = argv[++iarg];
+        }
+        else if (arg == "-ofreq" || arg == "--output-frequency") {
+            sparams.n_output_frequency = std::stoi(argv[++iarg]);
+        }
+        else if (arg == "-ow" || arg == "--output-weight") {
+            sparams.collect_output_weight = std::stoi(argv[++iarg]);
+        }
+        else if (arg == "--verbosity") {
+            sparams.verbosity = std::stoi(argv[++iarg]);
+        } else if (arg == "--no-ppl") {
+            compute_ppl = false;
+        } else if (arg == "--keep-imatrix") {
+            sparams.keep_every = std::stoi(argv[++iarg]);
+        } else if (arg == "--continue-from") {
+            prev_result_file = argv[++iarg];
+        } else if (arg == "--combine") {
+            combine_files = argv[++iarg];
+        }
+        else if (arg == "--from-chunk") {
+            from_chunk = std::stoi(argv[++iarg]);
+        } else {
+            args.push_back(argv[iarg]);
+        }
+    }
+    if (iarg < argc) {
+        std::string arg{argv[iarg]};
+        if (arg == "--no-ppl") {
+            compute_ppl = false;
+        } else {
+            args.push_back(argv[iarg]);
+        }
+    }
+
+    g_collector.set_parameters(std::move(sparams));
+
+    if (!combine_files.empty()) {
+        std::vector<std::string> files;
+        size_t pos = 0;
+        while (true) {
+            auto new_pos = combine_files.find(',', pos);
+            if (new_pos != std::string::npos) {
+                files.emplace_back(combine_files.substr(pos, new_pos - pos));
+                pos = new_pos + 1;
+            } else {
+                files.emplace_back(combine_files.substr(pos));
+                break;
+            }
+        }
+        if (files.size() < 2) {
+            fprintf(stderr, "You must provide at least two comma separated files to use --combine\n");
+            return 1;
+        }
+        printf("Combining the following %d files\n", int(files.size()));
+        for (auto& file : files) {
+            printf("    %s\n", file.c_str());
+            if (!g_collector.load_imatrix(file.c_str(), true)) {
+                fprintf(stderr, "Failed to load %s\n", file.c_str());
+                return 1;
+            }
+        }
+        g_collector.save_imatrix();
+        return 0;
+    }
+
+    if (!prev_result_file.empty()) {
+        if (!g_collector.load_imatrix(prev_result_file.c_str(), false)) {
+            fprintf(stderr, "=============== Failed to load %s\n", prev_result_file.c_str());
+            return 1;
+        }
+    }
+
+    gpt_params params;
+    params.n_batch = 512;
+    if (!gpt_params_parse(args.size(), args.data(), params)) {
+        return 1;
+    }
+
+    params.logits_all = true;
+    params.n_batch = std::min(params.n_batch, params.n_ctx);
+
+    print_build_info();
+
+    if (params.seed == LLAMA_DEFAULT_SEED) {
+        params.seed = time(NULL);
+    }
+
+    fprintf(stderr, "%s: seed  = %u\n", __func__, params.seed);
+
+    std::mt19937 rng(params.seed);
+    if (params.random_prompt) {
+        params.prompt = gpt_random_prompt(rng);
+    }
+
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    llama_model_params mparams = llama_model_params_from_gpt_params(params);
+
+    llama_model * model = llama_load_model_from_file(params.model.c_str(), mparams);
+    if (model == NULL) {
+        fprintf(stderr, "%s: error: unable to load model\n", __func__);
+        return 1;
+    }
+
+    llama_context_params cparams = llama_context_params_from_gpt_params(params);
+
+    // pass the callback to the backend scheduler
+    // it will be executed for each node during the graph computation
+    cparams.cb_eval = ik_collect_imatrix;
+    cparams.cb_eval_user_data = NULL;
+
+    llama_context * ctx = llama_new_context_with_model(model, cparams);
+    if (ctx == NULL) {
+        fprintf(stderr, "%s: error: unable to create context\n", __func__);
+        return 1;
+    }
+
+    const int n_ctx_train = llama_n_ctx_train(model);
+    if (params.n_ctx > n_ctx_train) {
+        fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
+                __func__, n_ctx_train, params.n_ctx);
+    }
+
+    // print system information
+    {
+        fprintf(stderr, "\n");
+        fprintf(stderr, "%s\n", get_system_info(params).c_str());
+    }
+
+    bool OK = compute_imatrix(ctx, params, compute_ppl, from_chunk);
+    if (!OK) {
+        return 1;
+    }
+
+    g_collector.save_imatrix();
+
+    llama_print_timings(ctx);
+
+    llama_free(ctx);
+    llama_free_model(model);
+
+    llama_backend_free();
+
+    return 0;
+}
diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp
index 4a7827876..91c39c5ae 100644
--- a/examples/infill/infill.cpp
+++ b/examples/infill/infill.cpp
@@ -202,7 +202,8 @@ int main(int argc, char ** argv) {
     std::mt19937 rng(params.seed);
 
     LOG("%s: llama backend init\n", __func__);
-    llama_backend_init(params.numa);
+    llama_backend_init();
+    llama_numa_init(params.numa);
 
     llama_model * model;
     llama_context * ctx;
@@ -241,7 +242,7 @@ int main(int argc, char ** argv) {
     LOG("add_bos: %d\n", add_bos);
 
     bool suff_rm_leading_spc = params.escape;
-    if (suff_rm_leading_spc && params.input_suffix.find_first_of(" ") == 0 && params.input_suffix.size() > 1) {
+    if (suff_rm_leading_spc && params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) {
         params.input_suffix.erase(0, 1);
         suff_rm_leading_spc = false;
     }
@@ -377,10 +378,10 @@ int main(int argc, char ** argv) {
     if (params.interactive) {
         const char *control_message;
         if (params.multiline_input) {
-            control_message = " - To return control to LLaMa, end your input with '\\'.\n"
+            control_message = " - To return control to LLaMA, end your input with '\\'.\n"
                               " - To return control without starting a new line, end your input with '/'.\n";
         } else {
-            control_message = " - Press Return to return control to LLaMa.\n"
+            control_message = " - Press Return to return control to LLaMA.\n"
                               " - To return control without starting a new line, end your input with '/'.\n"
                               " - If you want to submit another line, end your input with '\\'.\n";
         }
@@ -446,8 +447,8 @@ int main(int argc, char ** argv) {
                 LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
                     n_past, n_left, n_ctx, params.n_keep, n_discard);
 
-                llama_kv_cache_seq_rm   (ctx, 0, params.n_keep + 1            , params.n_keep + n_discard + 1);
-                llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
+                llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1            , params.n_keep + n_discard + 1);
+                llama_kv_cache_seq_add(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
 
                 n_past -= n_discard;
 
diff --git a/examples/json-schema-pydantic-example.py b/examples/json-schema-pydantic-example.py
new file mode 100644
index 000000000..69ebfd409
--- /dev/null
+++ b/examples/json-schema-pydantic-example.py
@@ -0,0 +1,74 @@
+# Usage:
+#! ./server -m some-model.gguf &
+#! pip install pydantic
+#! python json-schema-pydantic-example.py
+
+from pydantic import BaseModel, TypeAdapter
+from annotated_types import MinLen
+from typing import Annotated, List, Optional
+import json, requests
+
+if True:
+
+    def create_completion(*, response_model=None, endpoint="http://localhost:8080/v1/chat/completions", messages, **kwargs):
+        '''
+        Creates a chat completion using an OpenAI-compatible endpoint w/ JSON schema support
+        (llama.cpp server, llama-cpp-python, Anyscale / Together...)
+
+        The response_model param takes a type (+ supports Pydantic) and behaves just as w/ Instructor (see below)
+        '''
+        if response_model:
+            type_adapter = TypeAdapter(response_model)
+            schema = type_adapter.json_schema()
+            messages = [{
+                "role": "system",
+                "content": f"You respond in JSON format with the following schema: {json.dumps(schema, indent=2)}"
+            }] + messages
+            response_format={"type": "json_object", "schema": schema}
+
+        data = requests.post(endpoint, headers={"Content-Type": "application/json"},
+                             json=dict(messages=messages, response_format=response_format, **kwargs)).json()
+        if 'error' in data:
+            raise Exception(data['error']['message'])
+
+        content = data["choices"][0]["message"]["content"]
+        return type_adapter.validate_json(content) if type_adapter else content
+
+else:
+
+    # This alternative branch uses Instructor + OpenAI client lib.
+    # Instructor support streamed iterable responses, retry & more.
+    # (see https://python.useinstructor.com/)
+    #! pip install instructor openai
+    import instructor, openai
+    client = instructor.patch(
+        openai.OpenAI(api_key="123", base_url="http://localhost:8080"),
+        mode=instructor.Mode.JSON_SCHEMA)
+    create_completion = client.chat.completions.create
+
+
+if __name__ == '__main__':
+
+    class QAPair(BaseModel):
+        question: str
+        concise_answer: str
+        justification: str
+
+    class PyramidalSummary(BaseModel):
+        title: str
+        summary: str
+        question_answers: Annotated[List[QAPair], MinLen(2)]
+        sub_sections: Optional[Annotated[List['PyramidalSummary'], MinLen(2)]]
+
+    print("# Summary\n", create_completion(
+        model="...",
+        response_model=PyramidalSummary,
+        messages=[{
+            "role": "user",
+            "content": f"""
+                You are a highly efficient corporate document summarizer.
+                Create a pyramidal summary of an imaginary internal document about our company processes
+                (starting high-level, going down to each sub sections).
+                Keep questions short, and answers even shorter (trivia / quizz style).
+            """
+        }]))
diff --git a/examples/json-schema-to-grammar.py b/examples/json-schema-to-grammar.py
index 2a4cb65bc..91dd734cc 100755
--- a/examples/json-schema-to-grammar.py
+++ b/examples/json-schema-to-grammar.py
@@ -1,8 +1,10 @@
 #!/usr/bin/env python3
 import argparse
+import itertools
 import json
 import re
 import sys
+from typing import Any, Dict, List, Set, Tuple, Union
 
 # whitespace is constrained to a single space char to prevent model "running away" in
 # whitespace. Also maybe improves generation quality?
@@ -12,26 +14,54 @@ PRIMITIVE_RULES = {
     'boolean': '("true" | "false") space',
     'number': '("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space',
     'integer': '("-"? ([0-9] | [1-9] [0-9]*)) space',
+    'value'  : 'object | array | string | number | boolean',
+    'object' : '"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space',
+    'array'  : '"[" space ( value ("," space value)* )? "]" space',
+    'uuid'   : '"\\"" ' + ' "-" '.join('[0-9a-fA-F]' * n for n in [8, 4, 4, 4, 12]) + ' "\\"" space',
     'string': r''' "\"" (
         [^"\\] |
         "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
-      )* "\"" space ''',
+      )* "\"" space''',
     'null': '"null" space',
 }
+OBJECT_RULE_NAMES = ['object', 'array', 'string', 'number', 'boolean', 'null', 'value']
+
+# TODO: support "uri", "email" string formats
+DATE_RULES = {
+    'date'   : '[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )',
+    'time'   : '([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )',
+    'date-time': 'date "T" time',
+    'date-string': '"\\"" date "\\"" space',
+    'time-string': '"\\"" time "\\"" space',
+    'date-time-string': '"\\"" date-time "\\"" space',
+}
+
+RESERVED_NAMES = set(["root", *PRIMITIVE_RULES.keys(), *DATE_RULES.keys()])
 
 INVALID_RULE_CHARS_RE = re.compile(r'[^a-zA-Z0-9-]+')
 GRAMMAR_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"]')
-GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"'}
+GRAMMAR_RANGE_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"\]\-\\]')
+GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']': '\\]'}
 
+NON_LITERAL_SET = set('|.()[]{}*+?')
+ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = set('[]()|{}*+?')
+
+DATE_PATTERN = '[0-9]{4}-(0[1-9]|1[0-2])-([0-2][0-9]|3[0-1])'
+TIME_PATTERN = '([01][0-9]|2[0-3])(:[0-5][0-9]){2}(\\.[0-9]{1,3})?(Z|[+-](([01][0-9]|2[0-3]):[0-5][0-9]))' # Cap millisecond precision w/ 3 digits
 
 class SchemaConverter:
-    def __init__(self, prop_order):
+    def __init__(self, *, prop_order, allow_fetch, dotall, raw_pattern):
         self._prop_order = prop_order
+        self._allow_fetch = allow_fetch
+        self._dotall = dotall
+        self._raw_pattern = raw_pattern
         self._rules = {'space': SPACE_RULE}
+        self._refs = {}
+        self._refs_being_resolved = set()
 
     def _format_literal(self, literal):
         escaped = GRAMMAR_LITERAL_ESCAPE_RE.sub(
-            lambda m: GRAMMAR_LITERAL_ESCAPES.get(m.group(0)), json.dumps(literal)
+            lambda m: GRAMMAR_LITERAL_ESCAPES.get(m.group(0)), literal
         )
         return f'"{escaped}"'
 
@@ -41,64 +71,420 @@ class SchemaConverter:
             key = esc_name
         else:
             i = 0
-            while f'{esc_name}{i}' in self._rules:
+            while f'{esc_name}{i}' in self._rules and self._rules[f'{esc_name}{i}'] != rule:
                 i += 1
             key = f'{esc_name}{i}'
         self._rules[key] = rule
         return key
 
+    def resolve_refs(self, schema: dict, url: str):
+        '''
+            Resolves all $ref fields in the given schema, fetching any remote schemas,
+            replacing $ref with absolute reference URL and populating self._refs with the
+            respective referenced (sub)schema dictionaries.
+        '''
+        def visit(n: dict):
+            if isinstance(n, list):
+                return [visit(x) for x in n]
+            elif isinstance(n, dict):
+                ref = n.get('$ref')
+                if ref is not None and ref not in self._refs:
+                    if ref.startswith('https://'):
+                        assert self._allow_fetch, 'Fetching remote schemas is not allowed (use --allow-fetch for force)'
+                        import requests
+
+                        frag_split = ref.split('#')
+                        base_url = frag_split[0]
+
+                        target = self._refs.get(base_url)
+                        if target is None:
+                            target = self.resolve_refs(requests.get(ref).json(), base_url)
+                            self._refs[base_url] = target
+
+                        if len(frag_split) == 1 or frag_split[-1] == '':
+                            return target
+                    elif ref.startswith('#/'):
+                        target = schema
+                        ref = f'{url}{ref}'
+                        n['$ref'] = ref
+                    else:
+                        raise ValueError(f'Unsupported ref {ref}')
+
+                    for sel in ref.split('#')[-1].split('/')[1:]:
+                        assert target is not None and sel in target, f'Error resolving ref {ref}: {sel} not in {target}'
+                        target = target[sel]
+
+                    self._refs[ref] = target
+                else:
+                    for v in n.values():
+                        visit(v)
+
+            return n
+        return visit(schema)
+
+    def _generate_union_rule(self, name, alt_schemas):
+        return ' | '.join((
+            self.visit(alt_schema, f'{name}{"-" if name else "alternative-"}{i}')
+            for i, alt_schema in enumerate(alt_schemas)
+        ))
+
+    def _visit_pattern(self, pattern, name):
+        '''
+            Transforms a regular expression pattern into a GBNF rule.
+
+            Input: https://json-schema.org/understanding-json-schema/reference/regular_expressions
+            Output: https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md
+
+            Unsupported features: negative/positive lookaheads, greedy/non-greedy modifiers.
+
+            Mostly a 1:1 translation, except for {x} / {x,} / {x,y} quantifiers for which
+            we define sub-rules to keep the output lean.
+        '''
+
+        assert pattern.startswith('^') and pattern.endswith('$'), 'Pattern must start with "^" and end with "$"'
+        pattern = pattern[1:-1]
+        sub_rule_ids = {}
+
+        i = 0
+        length = len(pattern)
+
+        def to_rule(s: Tuple[str, bool]) -> str:
+            (txt, is_literal) = s
+            return "\"" + txt + "\"" if is_literal else txt
+
+        def transform() -> Tuple[str, bool]:
+            '''
+                Parse a unit at index i (advancing it), and return its string representation + whether it's a literal.
+            '''
+            nonlocal i
+            nonlocal pattern
+            nonlocal sub_rule_ids
+
+            start = i
+            # For each component of this sequence, store its string representation and whether it's a literal.
+            # We only need a flat structure here to apply repetition operators to the last item, and
+            # to merge literals at the and (we're parsing grouped ( sequences ) recursively and don't treat '|' specially
+            # (GBNF's syntax is luckily very close to regular expressions!)
+            seq: list[Tuple[str, bool]] = []
+
+            def get_dot():
+                if self._dotall:
+                    rule = '[\\U00000000-\\U0010FFFF]'
+                else:
+                    # Accept any character... except \n and \r line break chars (\x0A and \xOD)
+                    rule = '[\\U00000000-\\x09\\x0B\\x0C\\x0E-\\U0010FFFF]'
+                return self._add_rule(f'dot', rule)
+
+            def join_seq():
+                nonlocal seq
+                ret = []
+                for is_literal, g in itertools.groupby(seq, lambda x: x[1]):
+                    if is_literal:
+                        ret.append((''.join(x[0] for x in g), True))
+                    else:
+                        ret.extend(g)
+                if len(ret) == 1:
+                    return ret[0]
+                return (' '.join(to_rule(x) for x in seq), False)
+
+            while i < length:
+                c = pattern[i]
+                if c == '.':
+                    seq.append((get_dot(), False))
+                    i += 1
+                elif c == '(':
+                    i += 1
+                    if i < length:
+                        assert pattern[i] != '?', f'Unsupported pattern syntax "{pattern[i]}" at index {i} of /{pattern}/'
+                    seq.append((f'({to_rule(transform())})', False))
+                elif c == ')':
+                    i += 1
+                    assert start > 0 and pattern[start-1] == '(', f'Unbalanced parentheses; start = {start}, i = {i}, pattern = {pattern}'
+                    return join_seq()
+                elif c == '[':
+                    square_brackets = c
+                    i += 1
+                    while i < length and pattern[i] != ']':
+                        if pattern[i] == '\\':
+                            square_brackets += pattern[i:i+2]
+                            i += 2
+                        else:
+                            square_brackets += pattern[i]
+                            i += 1
+                    assert i < length, f'Unbalanced square brackets; start = {start}, i = {i}, pattern = {pattern}'
+                    square_brackets += ']'
+                    i += 1
+                    seq.append((square_brackets, False))
+                elif c == '|':
+                    seq.append(('|', False))
+                    i += 1
+                elif c in ('*', '+', '?'):
+                    seq[-1] = (to_rule(seq[-1]) + c, False)
+                    i += 1
+                elif c == '{':
+                    curly_brackets = c
+                    i += 1
+                    while i < length and pattern[i] != '}':
+                        curly_brackets += pattern[i]
+                        i += 1
+                    assert i < length, f'Unbalanced curly brackets; start = {start}, i = {i}, pattern = {pattern}'
+                    curly_brackets += '}'
+                    i += 1
+                    nums = [s.strip() for s in curly_brackets[1:-1].split(',')]
+                    min_times = 0
+                    max_times = None
+                    try:
+                        if len(nums) == 1:
+                            min_times = int(nums[0])
+                            max_times = min_times
+                        else:
+                            assert len(nums) == 2
+                            min_times = int(nums[0]) if nums[0] else 0
+                            max_times = int(nums[1]) if nums[1] else None
+                    except ValueError:
+                        raise ValueError(f'Invalid quantifier {curly_brackets} in /{pattern}/')
+
+                    (sub, sub_is_literal) = seq[-1]
+
+                    if min_times == 0 and max_times is None:
+                        seq[-1] = (f'{sub}*', False)
+                    elif min_times == 0 and max_times == 1:
+                        seq[-1] = (f'{sub}?', False)
+                    elif min_times == 1 and max_times is None:
+                        seq[-1] = (f'{sub}+', False)
+                    else:
+                        if not sub_is_literal:
+                            id = sub_rule_ids.get(sub)
+                            if id is None:
+                                id = self._add_rule(f'{name}-{len(sub_rule_ids) + 1}', sub)
+                                sub_rule_ids[sub] = id
+                            sub = id
+
+                        seq[-1] = (
+                            ' '.join(
+                                ([f'"{sub[1:-1] * min_times}"'] if sub_is_literal else [sub] * min_times) +
+                                ([f'{sub}?'] * (max_times - min_times) if max_times is not None else [f'{sub}*'])),
+                            False
+                        )
+                else:
+                    literal = ''
+                    while i < length:
+                        if pattern[i] == '\\' and i < length - 1:
+                            next = pattern[i + 1]
+                            if next in ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS:
+                                i += 1
+                                literal += pattern[i]
+                                i += 1
+                            else:
+                                literal += pattern[i:i+2]
+                                i += 2
+                        elif pattern[i] == '"' and not self._raw_pattern:
+                            literal += '\\"'
+                            i += 1
+                        elif pattern[i] not in NON_LITERAL_SET and \
+                                (i == length - 1 or literal == '' or pattern[i+1] == '.' or pattern[i+1] not in NON_LITERAL_SET):
+                            literal += pattern[i]
+                            i += 1
+                        else:
+                            break
+                    if literal:
+                        seq.append((literal, True))
+
+            return join_seq()
+
+        return self._add_rule(
+            name,
+            to_rule(transform()) if self._raw_pattern \
+                else "\"\\\"\" " + to_rule(transform()) + " \"\\\"\" space")
+
+
+    def _resolve_ref(self, ref):
+        ref_name = ref.split('/')[-1]
+        if ref_name not in self._rules and ref not in self._refs_being_resolved:
+            self._refs_being_resolved.add(ref)
+            resolved = self._refs[ref]
+            ref_name = self.visit(resolved, ref_name)
+            self._refs_being_resolved.remove(ref)
+        return ref_name
+
+    def _generate_constant_rule(self, value):
+        return self._format_literal(json.dumps(value))
+
     def visit(self, schema, name):
         schema_type = schema.get('type')
-        rule_name = name or 'root'
+        schema_format = schema.get('format')
+        rule_name = name + '-' if name in RESERVED_NAMES else name or 'root'
 
-        if 'oneOf' in schema or 'anyOf' in schema:
-            rule = ' | '.join((
-                self.visit(alt_schema, f'{name}{"-" if name else ""}{i}')
-                for i, alt_schema in enumerate(schema.get('oneOf') or schema['anyOf'])
-            ))
-            return self._add_rule(rule_name, rule)
+        if (ref := schema.get('$ref')) is not None:
+            return self._add_rule(rule_name, self._resolve_ref(ref))
+
+        elif 'oneOf' in schema or 'anyOf' in schema:
+            return self._add_rule(rule_name, self._generate_union_rule(name, schema.get('oneOf') or schema['anyOf']))
+
+        elif isinstance(schema_type, list):
+            return self._add_rule(rule_name, self._generate_union_rule(name, [{'type': t} for t in schema_type]))
 
         elif 'const' in schema:
-            return self._add_rule(rule_name, self._format_literal(schema['const']))
+            return self._add_rule(rule_name, self._generate_constant_rule(schema['const']))
 
         elif 'enum' in schema:
-            rule = ' | '.join((self._format_literal(v) for v in schema['enum']))
+            rule = ' | '.join((self._generate_constant_rule(v) for v in schema['enum']))
             return self._add_rule(rule_name, rule)
 
-        elif schema_type == 'object' and 'properties' in schema:
-            # TODO: `required` keyword
-            prop_order = self._prop_order
-            prop_pairs = sorted(
-                schema['properties'].items(),
-                # sort by position in prop_order (if specified) then by key
-                key=lambda kv: (prop_order.get(kv[0], len(prop_order)), kv[0]),
+        elif schema_type in (None, 'object') and \
+             ('properties' in schema or \
+              ('additionalProperties' in schema and schema['additionalProperties'] is not True)):
+            required = set(schema.get('required', []))
+            properties = list(schema.get('properties', {}).items())
+            return self._add_rule(rule_name, self._build_object_rule(properties, required, name, schema.get('additionalProperties')))
+
+        elif schema_type in (None, 'object') and 'allOf' in schema:
+            required = set()
+            properties = []
+            hybrid_name = name
+            def add_component(comp_schema, is_required):
+                if (ref := comp_schema.get('$ref')) is not None:
+                    comp_schema = self._refs[ref]
+
+                if 'properties' in comp_schema:
+                    for prop_name, prop_schema in comp_schema['properties'].items():
+                        properties.append((prop_name, prop_schema))
+                        if is_required:
+                            required.add(prop_name)
+
+            for t in schema['allOf']:
+                if 'anyOf' in t:
+                    for tt in t['anyOf']:
+                        add_component(tt, is_required=False)
+                else:
+                    add_component(t, is_required=True)
+
+            return self._add_rule(rule_name, self._build_object_rule(properties, required, hybrid_name, additional_properties=[]))
+
+        elif schema_type in (None, 'array') and ('items' in schema or 'prefixItems' in schema):
+            items = schema.get('items') or schema['prefixItems']
+            if isinstance(items, list):
+                return self._add_rule(
+                    rule_name,
+                    '"[" space ' +
+                    ' "," space '.join(
+                        self.visit(item, f'{name}{"-" if name else ""}tuple-{i}')
+                        for i, item in enumerate(items)) +
+                    ' "]" space')
+            else:
+                item_rule_name = self.visit(items, f'{name}{"-" if name else ""}item')
+                list_item_operator = f'( "," space {item_rule_name} )'
+                successive_items = ""
+                min_items = schema.get("minItems", 0)
+                max_items = schema.get("maxItems")
+                if min_items > 0:
+                    successive_items = list_item_operator * (min_items - 1)
+                    min_items -= 1
+                if max_items is not None and max_items > min_items:
+                    successive_items += (list_item_operator + "?") * (max_items - min_items - 1)
+                else:
+                    successive_items += list_item_operator + "*"
+                if min_items == 0:
+                    rule = f'"[" space ( {item_rule_name} {successive_items} )? "]" space'
+                else:
+                    rule = f'"[" space {item_rule_name} {successive_items} "]" space'
+                return self._add_rule(rule_name, rule)
+
+        elif schema_type in (None, 'string') and 'pattern' in schema:
+            return self._visit_pattern(schema['pattern'], rule_name)
+
+        elif schema_type in (None, 'string') and re.match(r'^uuid[1-5]?$', schema_format or ''):
+            return self._add_rule(
+                'root' if rule_name == 'root' else schema_format,
+                PRIMITIVE_RULES['uuid']
             )
 
-            rule = '"{" space'
-            for i, (prop_name, prop_schema) in enumerate(prop_pairs):
-                prop_rule_name = self.visit(prop_schema, f'{name}{"-" if name else ""}{prop_name}')
-                if i > 0:
-                    rule += ' "," space'
-                rule += fr' {self._format_literal(prop_name)} space ":" space {prop_rule_name}'
-            rule += ' "}" space'
+        elif schema_type in (None, 'string') and schema_format in DATE_RULES:
+            for t, r in DATE_RULES.items():
+                self._add_rule(t, r)
+            return schema_format + '-string'
 
-            return self._add_rule(rule_name, rule)
-
-        elif schema_type == 'array' and 'items' in schema:
-            # TODO `prefixItems` keyword
-            item_rule_name = self.visit(schema['items'], f'{name}{"-" if name else ""}item')
-            rule = f'"[" space ({item_rule_name} ("," space {item_rule_name})*)? "]" space'
-            return self._add_rule(rule_name, rule)
+        elif (schema_type == 'object') or (len(schema) == 0):
+            for n in OBJECT_RULE_NAMES:
+                self._add_rule(n, PRIMITIVE_RULES[n])
+            return self._add_rule(rule_name, 'object')
 
         else:
             assert schema_type in PRIMITIVE_RULES, f'Unrecognized schema: {schema}'
+            # TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero
             return self._add_rule(
                 'root' if rule_name == 'root' else schema_type,
                 PRIMITIVE_RULES[schema_type]
             )
 
+    def _build_object_rule(self, properties: List[Tuple[str, Any]], required: Set[str], name: str, additional_properties: Union[bool, Any]):
+        prop_order = self._prop_order
+        # sort by position in prop_order (if specified) then by original order
+        sorted_props = [kv[0] for _, kv in sorted(enumerate(properties), key=lambda ikv: (prop_order.get(ikv[1][0], len(prop_order)), ikv[0]))]
+
+        prop_kv_rule_names = {}
+        for prop_name, prop_schema in properties:
+            prop_rule_name = self.visit(prop_schema, f'{name}{"-" if name else ""}{prop_name}')
+            prop_kv_rule_names[prop_name] = self._add_rule(
+                f'{name}{"-" if name else ""}{prop_name}-kv',
+                fr'{self._format_literal(json.dumps(prop_name))} space ":" space {prop_rule_name}'
+            )
+        required_props = [k for k in sorted_props if k in required]
+        optional_props = [k for k in sorted_props if k not in required]
+
+        if additional_properties == True or isinstance(additional_properties, dict):
+            sub_name = f'{name}{"-" if name else ""}additional'
+            value_rule = self.visit({} if additional_properties == True else additional_properties, f'{sub_name}-value')
+            prop_kv_rule_names["*"] = self._add_rule(
+                f'{sub_name}-kv',
+                self._add_rule('string', PRIMITIVE_RULES['string']) + f' ":" space {value_rule}'
+            )
+            optional_props.append("*")
+
+        rule = '"{" space '
+        rule += ' "," space '.join(prop_kv_rule_names[k] for k in required_props)
+
+        if optional_props:
+            rule += ' ('
+            if required_props:
+                rule += ' "," space ( '
+
+            def get_recursive_refs(ks, first_is_optional):
+                [k, *rest] = ks
+                kv_rule_name = prop_kv_rule_names[k]
+                if k == '*':
+                    res = self._add_rule(
+                        f'{name}{"-" if name else ""}additional-kvs',
+                        f'{kv_rule_name} ( "," space ' + kv_rule_name + ' )*'
+                    )
+                elif first_is_optional:
+                    res = f'( "," space {kv_rule_name} )?'
+                else:
+                    res = kv_rule_name
+                if len(rest) > 0:
+                    res += ' ' + self._add_rule(
+                        f'{name}{"-" if name else ""}{k}-rest',
+                        get_recursive_refs(rest, first_is_optional=True)
+                    )
+                return res
+
+            rule += ' | '.join(
+                get_recursive_refs(optional_props[i:], first_is_optional=False)
+                for i in range(len(optional_props))
+            )
+            if required_props:
+                rule += ' )'
+            rule += ' )?'
+
+        rule += ' "}" space'
+
+        return rule
+
     def format_grammar(self):
-        return '\n'.join((f'{name} ::= {rule}' for name, rule in self._rules.items()))
+        return '\n'.join(
+            f'{name} ::= {rule}'
+            for name, rule in sorted(self._rules.items(), key=lambda kv: kv[0])
+        )
 
 
 def main(args_in = None):
@@ -115,16 +501,47 @@ def main(args_in = None):
         type=lambda s: s.split(','),
         help='''
             comma-separated property names defining the order of precedence for object properties;
-            properties not specified here are given lower precedence than those that are, and are
-            sorted alphabetically
+            properties not specified here are given lower precedence than those that are, and
+            are kept in their original order from the schema. Required properties are always
+            given precedence over optional properties.
         '''
     )
+    parser.add_argument(
+        '--allow-fetch',
+        action='store_true',
+        default=False,
+        help='Whether to allow fetching referenced schemas over HTTPS')
+    parser.add_argument(
+        '--dotall',
+        action='store_true',
+        default=False,
+        help='Whether to treat dot (".") as matching all chars including line breaks in regular expression patterns')
+    parser.add_argument(
+        '--raw-pattern',
+        action='store_true',
+        default=False,
+        help='Treats string patterns as raw patterns w/o quotes (or quote escapes)')
+
     parser.add_argument('schema', help='file containing JSON schema ("-" for stdin)')
     args = parser.parse_args(args_in)
 
-    schema = json.load(sys.stdin if args.schema == '-' else open(args.schema))
-    prop_order = {name: idx for idx, name in enumerate(args.prop_order)}
-    converter = SchemaConverter(prop_order)
+    if args.schema.startswith('https://'):
+        url = args.schema
+        import requests
+        schema = requests.get(url).json()
+    elif args.schema == '-':
+        url = 'stdin'
+        schema = json.load(sys.stdin)
+    else:
+        url = f'file://{args.schema}'
+        with open(args.schema) as f:
+            schema = json.load(f)
+    converter = SchemaConverter(
+        prop_order={name: idx for idx, name in enumerate(args.prop_order)},
+        allow_fetch=args.allow_fetch,
+        dotall=args.dotall,
+        raw_pattern=args.raw_pattern)
+    schema = converter.resolve_refs(schema, url)
     converter.visit(schema, '')
     print(converter.format_grammar())
 
diff --git a/examples/llama-bench/README.md b/examples/llama-bench/README.md
index d02824bfa..10f37b441 100644
--- a/examples/llama-bench/README.md
+++ b/examples/llama-bench/README.md
@@ -23,19 +23,22 @@ usage: ./llama-bench [options]
 
 options:
   -h, --help
-  -m, --model <filename>            (default: models/7B/ggml-model-q4_0.gguf)
-  -p, --n-prompt <n>                (default: 512)
-  -n, --n-gen <n>                   (default: 128)
-  -b, --batch-size <n>              (default: 512)
-  --memory-f32 <0|1>                (default: 0)
-  -t, --threads <n>                 (default: 16)
-  -ngl N, --n-gpu-layers <n>        (default: 99)
-  -mg i, --main-gpu <i>             (default: 0)
-  -mmq, --mul-mat-q <0|1>           (default: 1)
-  -ts, --tensor_split <ts0/ts1/..>
-  -r, --repetitions <n>             (default: 5)
-  -o, --output <csv|json|md|sql>    (default: md)
-  -v, --verbose                     (default: 0)
+  -m, --model <filename>              (default: models/7B/ggml-model-q4_0.gguf)
+  -p, --n-prompt <n>                  (default: 512)
+  -n, --n-gen <n>                     (default: 128)
+  -b, --batch-size <n>                (default: 512)
+  -ctk <t>, --cache-type-k <t>        (default: f16)
+  -ctv <t>, --cache-type-v <t>        (default: f16)
+  -t, --threads <n>                   (default: 112)
+  -ngl, --n-gpu-layers <n>            (default: 99)
+  -sm, --split-mode <none|layer|row>  (default: layer)
+  -mg, --main-gpu <i>                 (default: 0)
+  -nkvo, --no-kv-offload <0|1>        (default: 0)
+  -mmp, --mmap <0|1>                  (default: 1)
+  -ts, --tensor_split <ts0/ts1/..>    (default: 0)
+  -r, --repetitions <n>               (default: 5)
+  -o, --output <csv|json|md|sql>      (default: md)
+  -v, --verbose                       (default: 0)
 
 Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.
 ```
@@ -51,6 +54,10 @@ Each test is repeated the number of times given by `-r`, and the results are ave
 
 For a description of the other options, see the [main example](../main/README.md).
 
+Note:
+
+- When using SYCL backend, there would be hang issue in some cases. Please set `--mmp 0`.
+
 ## Examples
 
 ### Text generation with different models
diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp
index 7f7186cde..27e113203 100644
--- a/examples/llama-bench/llama-bench.cpp
+++ b/examples/llama-bench/llama-bench.cpp
@@ -8,6 +8,7 @@
 #include <cstdio>
 #include <cstring>
 #include <ctime>
+#include <cstdlib>
 #include <iterator>
 #include <map>
 #include <numeric>
@@ -20,6 +21,7 @@
 #include "llama.h"
 #include "common.h"
 #include "ggml-cuda.h"
+#include "ggml-sycl.h"
 
 // utils
 static uint64_t get_time_ns() {
@@ -102,6 +104,7 @@ static std::string get_cpu_info() {
                 }
             }
         }
+        fclose(f);
     }
 #endif
     // TODO: other platforms
@@ -110,11 +113,22 @@ static std::string get_cpu_info() {
 
 static std::string get_gpu_info() {
     std::string id;
-#ifdef GGML_USE_CUBLAS
-    int count = ggml_cuda_get_device_count();
+#ifdef GGML_USE_CUDA
+    int count = ggml_backend_cuda_get_device_count();
     for (int i = 0; i < count; i++) {
         char buf[128];
-        ggml_cuda_get_device_description(i, buf, sizeof(buf));
+        ggml_backend_cuda_get_device_description(i, buf, sizeof(buf));
+        id += buf;
+        if (i < count - 1) {
+            id += "/";
+        }
+    }
+#endif
+#ifdef GGML_USE_SYCL
+    int count = ggml_backend_sycl_get_device_count();
+    for (int i = 0; i < count; i++) {
+        char buf[128];
+        ggml_sycl_get_device_description(i, buf, sizeof(buf));
         id += buf;
         if (i < count - 1) {
             id += "/";
@@ -128,19 +142,41 @@ static std::string get_gpu_info() {
 // command line params
 enum output_formats {CSV, JSON, MARKDOWN, SQL};
 
+static const char * output_format_str(output_formats format) {
+    switch (format) {
+        case CSV:      return "csv";
+        case JSON:     return "json";
+        case MARKDOWN: return "md";
+        case SQL:      return "sql";
+        default: GGML_ASSERT(!"invalid output format");
+    }
+}
+
+static const char * split_mode_str(llama_split_mode mode) {
+    switch (mode) {
+        case LLAMA_SPLIT_MODE_NONE:  return "none";
+        case LLAMA_SPLIT_MODE_LAYER: return "layer";
+        case LLAMA_SPLIT_MODE_ROW:   return "row";
+        default: GGML_ASSERT(!"invalid split mode");
+    }
+}
+
 struct cmd_params {
     std::vector<std::string> model;
     std::vector<int> n_prompt;
     std::vector<int> n_gen;
     std::vector<int> n_batch;
+    std::vector<int> n_ubatch;
     std::vector<ggml_type> type_k;
     std::vector<ggml_type> type_v;
     std::vector<int> n_threads;
     std::vector<int> n_gpu_layers;
+    std::vector<llama_split_mode> split_mode;
     std::vector<int> main_gpu;
     std::vector<bool> no_kv_offload;
-    std::vector<bool> mul_mat_q;
-    std::vector<std::array<float, LLAMA_MAX_DEVICES>> tensor_split;
+    std::vector<std::vector<float>> tensor_split;
+    std::vector<bool> use_mmap;
+    std::vector<bool> embeddings;
     int reps;
     bool verbose;
     output_formats output_format;
@@ -150,15 +186,18 @@ static const cmd_params cmd_params_defaults = {
     /* model         */ {"models/7B/ggml-model-q4_0.gguf"},
     /* n_prompt      */ {512},
     /* n_gen         */ {128},
-    /* n_batch       */ {512},
+    /* n_batch       */ {2048},
+    /* n_ubatch      */ {512},
     /* type_k        */ {GGML_TYPE_F16},
     /* type_v        */ {GGML_TYPE_F16},
     /* n_threads     */ {get_num_physical_cores()},
     /* n_gpu_layers  */ {99},
+    /* split_mode    */ {LLAMA_SPLIT_MODE_LAYER},
     /* main_gpu      */ {0},
     /* no_kv_offload */ {false},
-    /* mul_mat_q     */ {true},
-    /* tensor_split  */ {{}},
+    /* tensor_split  */ {std::vector<float>(llama_max_devices(), 0.0f)},
+    /* use_mmap      */ {true},
+    /* embeddings    */ {false},
     /* reps          */ 5,
     /* verbose       */ false,
     /* output_format */ MARKDOWN
@@ -169,21 +208,24 @@ static void print_usage(int /* argc */, char ** argv) {
     printf("\n");
     printf("options:\n");
     printf("  -h, --help\n");
-    printf("  -m, --model <filename>            (default: %s)\n", join(cmd_params_defaults.model, ",").c_str());
-    printf("  -p, --n-prompt <n>                (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str());
-    printf("  -n, --n-gen <n>                   (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str());
-    printf("  -b, --batch-size <n>              (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str());
-    printf("  -ctk <t>, --cache-type-k <t>      (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str());
-    printf("  -ctv <t>, --cache-type-v <t>      (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
-    printf("  -t, --threads <n>                 (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
-    printf("  -ngl, --n-gpu-layers <n>          (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
-    printf("  -mg, --main-gpu <i>               (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
-    printf("  -nkvo, --no-kv-offload <0|1>      (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
-    printf("  -mmq, --mul-mat-q <0|1>           (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str());
-    printf("  -ts, --tensor_split <ts0/ts1/..>               \n");
-    printf("  -r, --repetitions <n>             (default: %d)\n", cmd_params_defaults.reps);
-    printf("  -o, --output <csv|json|md|sql>    (default: %s)\n", cmd_params_defaults.output_format == CSV ? "csv" : cmd_params_defaults.output_format == JSON ? "json" : cmd_params_defaults.output_format == MARKDOWN ? "md" : "sql");
-    printf("  -v, --verbose                     (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
+    printf("  -m, --model <filename>              (default: %s)\n", join(cmd_params_defaults.model, ",").c_str());
+    printf("  -p, --n-prompt <n>                  (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str());
+    printf("  -n, --n-gen <n>                     (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str());
+    printf("  -b, --batch-size <n>                (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str());
+    printf("  -ub N, --ubatch-size <n>            (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str());
+    printf("  -ctk <t>, --cache-type-k <t>        (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str());
+    printf("  -ctv <t>, --cache-type-v <t>        (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
+    printf("  -t, --threads <n>                   (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
+    printf("  -ngl, --n-gpu-layers <n>            (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
+    printf("  -sm, --split-mode <none|layer|row>  (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str());
+    printf("  -mg, --main-gpu <i>                 (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
+    printf("  -nkvo, --no-kv-offload <0|1>        (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
+    printf("  -mmp, --mmap <0|1>                  (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str());
+    printf("  -embd, --embeddings <0|1>           (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str());
+    printf("  -ts, --tensor-split <ts0/ts1/..>    (default: 0)\n");
+    printf("  -r, --repetitions <n>               (default: %d)\n", cmd_params_defaults.reps);
+    printf("  -o, --output <csv|json|md|sql>      (default: %s)\n", output_format_str(cmd_params_defaults.output_format));
+    printf("  -v, --verbose                       (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
     printf("\n");
     printf("Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n");
 }
@@ -207,6 +249,9 @@ static ggml_type ggml_type_from_name(const std::string & s) {
     if (s == "q5_1") {
         return GGML_TYPE_Q5_1;
     }
+    if (s == "iq4_nl") {
+        return GGML_TYPE_IQ4_NL;
+    }
 
     return GGML_TYPE_COUNT;
 }
@@ -260,6 +305,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
             }
             auto p = split<int>(argv[i], split_delim);
             params.n_batch.insert(params.n_batch.end(), p.begin(), p.end());
+        } else if (arg == "-ub" || arg == "--ubatch-size") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            auto p = split<int>(argv[i], split_delim);
+            params.n_ubatch.insert(params.n_ubatch.end(), p.begin(), p.end());
         } else if (arg == "-ctk" || arg == "--cache-type-k") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -306,6 +358,28 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
             }
             auto p = split<int>(argv[i], split_delim);
             params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end());
+        } else if (arg == "-sm" || arg == "--split-mode") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            auto p = split<std::string>(argv[i], split_delim);
+            std::vector<llama_split_mode> modes;
+            for (const auto & m : p) {
+                llama_split_mode mode;
+                if (m == "none") {
+                    mode = LLAMA_SPLIT_MODE_NONE;
+                } else if (m == "layer") {
+                    mode = LLAMA_SPLIT_MODE_LAYER;
+                } else if (m == "row") {
+                    mode = LLAMA_SPLIT_MODE_ROW;
+                } else {
+                    invalid_param = true;
+                    break;
+                }
+                modes.push_back(mode);
+            }
+            params.split_mode.insert(params.split_mode.end(), modes.begin(), modes.end());
         } else if (arg == "-mg" || arg == "--main-gpu") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -319,13 +393,20 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
             }
             auto p = split<bool>(argv[i], split_delim);
             params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end());
-        } else if (arg == "-mmq" || arg == "--mul-mat-q") {
+        } else if (arg == "-mmp" || arg == "--mmap") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             auto p = split<bool>(argv[i], split_delim);
-            params.mul_mat_q.insert(params.mul_mat_q.end(), p.begin(), p.end());
+            params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end());
+        } else if (arg == "-embd" || arg == "--embeddings") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            auto p = split<bool>(argv[i], split_delim);
+            params.embeddings.insert(params.embeddings.end(), p.begin(), p.end());
         } else if (arg == "-ts" || arg == "--tensor-split") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -336,10 +417,10 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                 const std::regex regex{R"([;/]+)"};
                 std::sregex_token_iterator it{ts.begin(), ts.end(), regex, -1};
                 std::vector<std::string> split_arg{it, {}};
-                GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
+                GGML_ASSERT(split_arg.size() <= llama_max_devices());
 
-                std::array<float, LLAMA_MAX_DEVICES> tensor_split;
-                for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
+                std::vector<float> tensor_split(llama_max_devices());
+                for (size_t i = 0; i < llama_max_devices(); ++i) {
                     if (i < split_arg.size()) {
                         tensor_split[i] = std::stof(split_arg[i]);
                     } else {
@@ -389,13 +470,16 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
     if (params.n_prompt.empty())     { params.n_prompt = cmd_params_defaults.n_prompt; }
     if (params.n_gen.empty())        { params.n_gen = cmd_params_defaults.n_gen; }
     if (params.n_batch.empty())      { params.n_batch = cmd_params_defaults.n_batch; }
+    if (params.n_ubatch.empty())     { params.n_ubatch = cmd_params_defaults.n_ubatch; }
     if (params.type_k.empty())       { params.type_k = cmd_params_defaults.type_k; }
     if (params.type_v.empty())       { params.type_v = cmd_params_defaults.type_v; }
     if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; }
+    if (params.split_mode.empty())   { params.split_mode = cmd_params_defaults.split_mode; }
     if (params.main_gpu.empty())     { params.main_gpu = cmd_params_defaults.main_gpu; }
     if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; }
-    if (params.mul_mat_q.empty())    { params.mul_mat_q = cmd_params_defaults.mul_mat_q; }
     if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; }
+    if (params.use_mmap.empty())     { params.use_mmap = cmd_params_defaults.use_mmap; }
+    if (params.embeddings.empty())   { params.embeddings = cmd_params_defaults.embeddings; }
     if (params.n_threads.empty())    { params.n_threads = cmd_params_defaults.n_threads; }
 
     return params;
@@ -406,21 +490,26 @@ struct cmd_params_instance {
     int n_prompt;
     int n_gen;
     int n_batch;
+    int n_ubatch;
     ggml_type type_k;
     ggml_type type_v;
     int n_threads;
     int n_gpu_layers;
+    llama_split_mode split_mode;
     int main_gpu;
     bool no_kv_offload;
-    bool mul_mat_q;
-    std::array<float, LLAMA_MAX_DEVICES> tensor_split;
+    std::vector<float> tensor_split;
+    bool use_mmap;
+    bool embeddings;
 
     llama_model_params to_llama_mparams() const {
         llama_model_params mparams = llama_model_default_params();
 
         mparams.n_gpu_layers = n_gpu_layers;
+        mparams.split_mode = split_mode;
         mparams.main_gpu = main_gpu;
         mparams.tensor_split = tensor_split.data();
+        mparams.use_mmap = use_mmap;
 
         return mparams;
     }
@@ -428,7 +517,9 @@ struct cmd_params_instance {
     bool equal_mparams(const cmd_params_instance & other) const {
         return model == other.model &&
                n_gpu_layers == other.n_gpu_layers &&
+               split_mode == other.split_mode &&
                main_gpu == other.main_gpu &&
+               use_mmap == other.use_mmap &&
                tensor_split == other.tensor_split;
     }
 
@@ -437,60 +528,31 @@ struct cmd_params_instance {
 
         cparams.n_ctx = n_prompt + n_gen;
         cparams.n_batch = n_batch;
+        cparams.n_ubatch = n_ubatch;
         cparams.type_k = type_k;
         cparams.type_v = type_v;
-        cparams.mul_mat_q = mul_mat_q;
         cparams.offload_kqv = !no_kv_offload;
+        cparams.embeddings = embeddings;
 
         return cparams;
     }
 };
 
-static std::vector<cmd_params_instance> get_cmd_params_instances_int(const cmd_params & params, int n_gen, int n_prompt) {
-    std::vector<cmd_params_instance> instances;
-
-    for (const auto & m : params.model)
-    for (const auto & nl : params.n_gpu_layers)
-    for (const auto & mg : params.main_gpu)
-    for (const auto & ts : params.tensor_split)
-    for (const auto & nb : params.n_batch)
-    for (const auto & tk : params.type_k)
-    for (const auto & tv : params.type_v)
-    for (const auto & mmq : params.mul_mat_q)
-    for (const auto & nkvo : params.no_kv_offload)
-    for (const auto & nt : params.n_threads) {
-        cmd_params_instance instance = {
-            /* .model        = */ m,
-            /* .n_prompt     = */ n_prompt,
-            /* .n_gen        = */ n_gen,
-            /* .n_batch      = */ nb,
-            /* .type_k       = */ tk,
-            /* .type_v       = */ tv,
-            /* .n_threads    = */ nt,
-            /* .n_gpu_layers = */ nl,
-            /* .main_gpu     = */ mg,
-            /* .no_kv_offload= */ nkvo,
-            /* .mul_mat_q    = */ mmq,
-            /* .tensor_split = */ ts,
-        };
-        instances.push_back(instance);
-    }
-    return instances;
-}
-
 static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_params & params) {
     std::vector<cmd_params_instance> instances;
 
-#if 1
     // this ordering minimizes the number of times that each model needs to be reloaded
     for (const auto & m : params.model)
     for (const auto & nl : params.n_gpu_layers)
+    for (const auto & sm : params.split_mode)
     for (const auto & mg : params.main_gpu)
     for (const auto & ts : params.tensor_split)
+    for (const auto & mmp : params.use_mmap)
+    for (const auto & embd : params.embeddings)
     for (const auto & nb : params.n_batch)
+    for (const auto & nub : params.n_ubatch)
     for (const auto & tk : params.type_k)
     for (const auto & tv : params.type_v)
-    for (const auto & mmq : params.mul_mat_q)
     for (const auto & nkvo : params.no_kv_offload)
     for (const auto & nt : params.n_threads) {
         for (const auto & n_prompt : params.n_prompt) {
@@ -502,14 +564,17 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .n_prompt     = */ n_prompt,
                 /* .n_gen        = */ 0,
                 /* .n_batch      = */ nb,
+                /* .n_ubatch     = */ nub,
                 /* .type_k       = */ tk,
                 /* .type_v       = */ tv,
                 /* .n_threads    = */ nt,
                 /* .n_gpu_layers = */ nl,
+                /* .split_mode   = */ sm,
                 /* .main_gpu     = */ mg,
                 /* .no_kv_offload= */ nkvo,
-                /* .mul_mat_q    = */ mmq,
                 /* .tensor_split = */ ts,
+                /* .use_mmap     = */ mmp,
+                /* .embeddings   = */ embd,
             };
             instances.push_back(instance);
         }
@@ -523,36 +588,21 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .n_prompt     = */ 0,
                 /* .n_gen        = */ n_gen,
                 /* .n_batch      = */ nb,
+                /* .n_ubatch     = */ nub,
                 /* .type_k       = */ tk,
                 /* .type_v       = */ tv,
                 /* .n_threads    = */ nt,
                 /* .n_gpu_layers = */ nl,
+                /* .split_mode   = */ sm,
                 /* .main_gpu     = */ mg,
                 /* .no_kv_offload= */ nkvo,
-                /* .mul_mat_q    = */ mmq,
                 /* .tensor_split = */ ts,
+                /* .use_mmap     = */ mmp,
+                /* .embeddings   = */ embd,
             };
             instances.push_back(instance);
         }
     }
-#else
-    // this ordering separates the prompt and generation tests
-    for (const auto & n_prompt : params.n_prompt) {
-        if (n_prompt == 0) {
-            continue;
-        }
-        auto instances_prompt = get_cmd_params_instances_int(params, 0, n_prompt);
-        instances.insert(instances.end(), instances_prompt.begin(), instances_prompt.end());
-    }
-
-    for (const auto & n_gen : params.n_gen) {
-        if (n_gen == 0) {
-            continue;
-        }
-        auto instances_gen = get_cmd_params_instances_int(params, n_gen, 0);
-        instances.insert(instances.end(), instances_gen.begin(), instances_gen.end());
-    }
-#endif
 
     return instances;
 }
@@ -562,7 +612,10 @@ struct test {
     static const int build_number;
     static const bool cuda;
     static const bool opencl;
+    static const bool vulkan;
+    static const bool kompute;
     static const bool metal;
+    static const bool sycl;
     static const bool gpu_blas;
     static const bool blas;
     static const std::string cpu_info;
@@ -572,14 +625,17 @@ struct test {
     uint64_t model_size;
     uint64_t model_n_params;
     int n_batch;
+    int n_ubatch;
     int n_threads;
     ggml_type type_k;
     ggml_type type_v;
     int n_gpu_layers;
+    llama_split_mode split_mode;
     int main_gpu;
     bool no_kv_offload;
-    bool mul_mat_q;
-    std::array<float, LLAMA_MAX_DEVICES> tensor_split;
+    std::vector<float> tensor_split;
+    bool use_mmap;
+    bool embeddings;
     int n_prompt;
     int n_gen;
     std::string test_time;
@@ -593,14 +649,17 @@ struct test {
         model_size = llama_model_size(lmodel);
         model_n_params = llama_model_n_params(lmodel);
         n_batch = inst.n_batch;
+        n_ubatch = inst.n_ubatch;
         n_threads = inst.n_threads;
         type_k = inst.type_k;
         type_v = inst.type_v;
         n_gpu_layers = inst.n_gpu_layers;
+        split_mode = inst.split_mode;
         main_gpu = inst.main_gpu;
         no_kv_offload = inst.no_kv_offload;
-        mul_mat_q = inst.mul_mat_q;
         tensor_split = inst.tensor_split;
+        use_mmap = inst.use_mmap;
+        embeddings = inst.embeddings;
         n_prompt = inst.n_prompt;
         n_gen = inst.n_gen;
         // RFC 3339 date-time format
@@ -641,27 +700,39 @@ struct test {
         if (opencl) {
             return "OpenCL";
         }
+        if (vulkan) {
+            return "Vulkan";
+        }
+        if (kompute) {
+            return "Kompute";
+        }
         if (metal) {
             return "Metal";
         }
+        if (sycl) {
+            return GGML_SYCL_NAME;
+        }
         if (gpu_blas) {
             return "GPU BLAS";
         }
         if (blas) {
             return "BLAS";
         }
+
         return "CPU";
     }
 
     static const std::vector<std::string> & get_fields() {
         static const std::vector<std::string> fields = {
             "build_commit", "build_number",
-            "cuda", "opencl", "metal", "gpu_blas", "blas",
+            "cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas",
             "cpu_info", "gpu_info",
             "model_filename", "model_type", "model_size", "model_n_params",
-            "n_batch", "n_threads", "type_k", "type_v",
-            "n_gpu_layers", "main_gpu", "no_kv_offload",
-            "mul_mat_q", "tensor_split",
+            "n_batch", "n_ubatch",
+            "n_threads", "type_k", "type_v",
+            "n_gpu_layers", "split_mode",
+            "main_gpu", "no_kv_offload",
+            "tensor_split", "use_mmap", "embeddings",
             "n_prompt", "n_gen", "test_time",
             "avg_ns", "stddev_ns",
             "avg_ts", "stddev_ts"
@@ -672,15 +743,17 @@ struct test {
     enum field_type {STRING, BOOL, INT, FLOAT};
 
     static field_type get_field_type(const std::string & field) {
-        if (field == "build_number" || field == "n_batch" || field == "n_threads" ||
+        if (field == "build_number" || field == "n_batch" || field == "n_ubatch" ||
+            field == "n_threads" ||
             field == "model_size" || field == "model_n_params" ||
             field == "n_gpu_layers" || field == "main_gpu" ||
             field == "n_prompt" || field == "n_gen" ||
             field == "avg_ns" || field == "stddev_ns") {
             return INT;
         }
-        if (field == "cuda" || field == "opencl" || field == "metal" || field == "gpu_blas" || field == "blas" ||
-            field == "f16_kv" || field == "no_kv_offload" || field == "mul_mat_q") {
+        if (field == "cuda" || field == "opencl"  || field == "vulkan" || field == "kompute" || field == "metal" ||
+            field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" ||
+            field == "use_mmap" || field == "embeddings") {
             return BOOL;
         }
         if (field == "avg_ts" || field == "stddev_ts") {
@@ -692,7 +765,7 @@ struct test {
     std::vector<std::string> get_values() const {
         std::string tensor_split_str;
         int max_nonzero = 0;
-        for (int i = 0; i < LLAMA_MAX_DEVICES; i++) {
+        for (size_t i = 0; i < llama_max_devices(); i++) {
             if (tensor_split[i] > 0) {
                 max_nonzero = i;
             }
@@ -707,12 +780,15 @@ struct test {
         }
         std::vector<std::string> values = {
             build_commit, std::to_string(build_number),
-            std::to_string(cuda), std::to_string(opencl), std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas),
+            std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(vulkan),
+            std::to_string(metal), std::to_string(sycl), std::to_string(gpu_blas), std::to_string(blas),
             cpu_info, gpu_info,
             model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
-            std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
-            std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(no_kv_offload),
-            std::to_string(mul_mat_q), tensor_split_str,
+            std::to_string(n_batch), std::to_string(n_ubatch),
+            std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
+            std::to_string(n_gpu_layers), split_mode_str(split_mode),
+            std::to_string(main_gpu), std::to_string(no_kv_offload),
+            tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings),
             std::to_string(n_prompt), std::to_string(n_gen), test_time,
             std::to_string(avg_ns()), std::to_string(stdev_ns()),
             std::to_string(avg_ts()), std::to_string(stdev_ts())
@@ -732,11 +808,14 @@ struct test {
 
 const std::string test::build_commit = LLAMA_COMMIT;
 const int         test::build_number = LLAMA_BUILD_NUMBER;
-const bool        test::cuda         = !!ggml_cpu_has_cublas();
+const bool        test::cuda         = !!ggml_cpu_has_cuda();
 const bool        test::opencl       = !!ggml_cpu_has_clblast();
+const bool        test::vulkan       = !!ggml_cpu_has_vulkan();
+const bool        test::kompute      = !!ggml_cpu_has_kompute();
 const bool        test::metal        = !!ggml_cpu_has_metal();
 const bool        test::gpu_blas     = !!ggml_cpu_has_gpublas();
 const bool        test::blas         = !!ggml_cpu_has_blas();
+const bool        test::sycl         = !!ggml_cpu_has_sycl();
 const std::string test::cpu_info     = get_cpu_info();
 const std::string test::gpu_info     = get_gpu_info();
 
@@ -867,15 +946,21 @@ struct markdown_printer : public printer {
         if (field == "n_gpu_layers") {
             return "ngl";
         }
+        if (field == "split_mode") {
+            return "sm";
+        }
         if (field == "n_threads") {
             return "threads";
         }
-        if (field == "mul_mat_q") {
-            return "mmq";
-        }
         if (field == "no_kv_offload") {
             return "nkvo";
         }
+        if (field == "use_mmap") {
+            return "mmap";
+        }
+        if (field == "embeddings") {
+            return "embd";
+        }
         if (field == "tensor_split") {
             return "ts";
         }
@@ -884,40 +969,49 @@ struct markdown_printer : public printer {
 
     void print_header(const cmd_params & params) override {
         // select fields to print
-        fields.push_back("model");
-        fields.push_back("size");
-        fields.push_back("params");
-        fields.push_back("backend");
+        fields.emplace_back("model");
+        fields.emplace_back("size");
+        fields.emplace_back("params");
+        fields.emplace_back("backend");
         bool is_cpu_backend = test::get_backend() == "CPU" || test::get_backend() == "BLAS";
         if (!is_cpu_backend) {
-            fields.push_back("n_gpu_layers");
+            fields.emplace_back("n_gpu_layers");
         }
         if (params.n_threads.size() > 1 || params.n_threads != cmd_params_defaults.n_threads || is_cpu_backend) {
-            fields.push_back("n_threads");
+            fields.emplace_back("n_threads");
         }
         if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) {
-            fields.push_back("n_batch");
+            fields.emplace_back("n_batch");
+        }
+        if (params.n_ubatch.size() > 1 || params.n_ubatch != cmd_params_defaults.n_ubatch) {
+            fields.emplace_back("n_ubatch");
         }
         if (params.type_k.size() > 1 || params.type_k != cmd_params_defaults.type_k) {
-            fields.push_back("type_k");
+            fields.emplace_back("type_k");
         }
         if (params.type_v.size() > 1 || params.type_v != cmd_params_defaults.type_v) {
-            fields.push_back("type_v");
+            fields.emplace_back("type_v");
         }
         if (params.main_gpu.size() > 1 || params.main_gpu != cmd_params_defaults.main_gpu) {
-            fields.push_back("main_gpu");
+            fields.emplace_back("main_gpu");
         }
-        if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) {
-            fields.push_back("mul_mat_q");
+        if (params.split_mode.size() > 1 || params.split_mode != cmd_params_defaults.split_mode) {
+            fields.emplace_back("split_mode");
         }
         if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) {
-            fields.push_back("no_kv_offload");
+            fields.emplace_back("no_kv_offload");
         }
         if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) {
-            fields.push_back("tensor_split");
+            fields.emplace_back("tensor_split");
         }
-        fields.push_back("test");
-        fields.push_back("t/s");
+        if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) {
+            fields.emplace_back("use_mmap");
+        }
+        if (params.embeddings.size() > 1 || params.embeddings != cmd_params_defaults.embeddings) {
+            fields.emplace_back("embeddings");
+        }
+        fields.emplace_back("test");
+        fields.emplace_back("t/s");
 
         fprintf(fout, "|");
         for (const auto & field : fields) {
@@ -1031,25 +1125,40 @@ struct sql_printer : public printer {
 };
 
 static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) {
-    std::vector<llama_token> tokens(n_batch, llama_token_bos(llama_get_model(ctx)));
-    int n_processed = 0;
-
     llama_set_n_threads(ctx, n_threads, n_threads);
 
+    const llama_model * model = llama_get_model(ctx);
+    const int32_t n_vocab = llama_n_vocab(model);
+
+    std::vector<llama_token> tokens(n_batch);
+
+    int n_processed = 0;
+
     while (n_processed < n_prompt) {
         int n_tokens = std::min(n_prompt - n_processed, n_batch);
+        tokens[0] = n_processed == 0 && llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab;
+        for (int i = 1; i < n_tokens; i++) {
+            tokens[i] = std::rand() % n_vocab;
+        }
         llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0));
         n_processed += n_tokens;
     }
+
+    llama_synchronize(ctx);
 }
 
 static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) {
-    llama_token token = llama_token_bos(llama_get_model(ctx));
-
     llama_set_n_threads(ctx, n_threads, n_threads);
 
+    const llama_model * model = llama_get_model(ctx);
+    const int32_t n_vocab = llama_n_vocab(model);
+
+    llama_token token = llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab;
+
     for (int i = 0; i < n_gen; i++) {
         llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0));
+        llama_synchronize(ctx);
+        token = std::rand() % n_vocab;
     }
 }
 
@@ -1081,8 +1190,7 @@ int main(int argc, char ** argv) {
     if (!params.verbose) {
         llama_log_set(llama_null_log_callback, NULL);
     }
-    bool numa = false;
-    llama_backend_init(numa);
+    llama_backend_init();
 
     // initialize printer
     std::unique_ptr<printer> p;
@@ -1139,7 +1247,8 @@ int main(int argc, char ** argv) {
 
         // warmup run
         if (t.n_prompt > 0) {
-            test_prompt(ctx, std::min(2, t.n_batch), 0, t.n_batch, t.n_threads);
+            //test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads);
+            test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads);
         }
         if (t.n_gen > 0) {
             test_gen(ctx, 1, 0, t.n_threads);
@@ -1155,6 +1264,7 @@ int main(int argc, char ** argv) {
             if (t.n_gen > 0) {
                 test_gen(ctx, t.n_gen, t.n_prompt, t.n_threads);
             }
+
             uint64_t t_ns = get_time_ns() - t_start;
             t.samples_ns.push_back(t_ns);
         }
diff --git a/examples/llama.android/.gitignore b/examples/llama.android/.gitignore
new file mode 100644
index 000000000..347e252ef
--- /dev/null
+++ b/examples/llama.android/.gitignore
@@ -0,0 +1,33 @@
+# Gradle files
+.gradle/
+build/
+
+# Local configuration file (sdk path, etc)
+local.properties
+
+# Log/OS Files
+*.log
+
+# Android Studio generated files and folders
+captures/
+.externalNativeBuild/
+.cxx/
+*.apk
+output.json
+
+# IntelliJ
+*.iml
+.idea/
+misc.xml
+deploymentTargetDropDown.xml
+render.experimental.xml
+
+# Keystore files
+*.jks
+*.keystore
+
+# Google Services (e.g. APIs or Firebase)
+google-services.json
+
+# Android Profiling
+*.hprof
diff --git a/examples/llama.android/README.md b/examples/llama.android/README.md
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/llama.android/app/.gitignore b/examples/llama.android/app/.gitignore
new file mode 100644
index 000000000..796b96d1c
--- /dev/null
+++ b/examples/llama.android/app/.gitignore
@@ -0,0 +1 @@
+/build
diff --git a/examples/llama.android/app/build.gradle.kts b/examples/llama.android/app/build.gradle.kts
new file mode 100644
index 000000000..d42140efe
--- /dev/null
+++ b/examples/llama.android/app/build.gradle.kts
@@ -0,0 +1,88 @@
+plugins {
+    id("com.android.application")
+    id("org.jetbrains.kotlin.android")
+}
+
+android {
+    namespace = "com.example.llama"
+    compileSdk = 34
+
+    ndkVersion = "26.1.10909125"
+
+    defaultConfig {
+        applicationId = "com.example.llama"
+        minSdk = 33
+        targetSdk = 34
+        versionCode = 1
+        versionName = "1.0"
+
+        testInstrumentationRunner = "androidx.test.runner.AndroidJUnitRunner"
+        vectorDrawables {
+            useSupportLibrary = true
+        }
+        ndk {
+            // Add NDK properties if wanted, e.g.
+            // abiFilters += listOf("arm64-v8a")
+        }
+        externalNativeBuild {
+            cmake {
+                arguments += "-DCMAKE_BUILD_TYPE=Release"
+                cppFlags += listOf()
+                arguments += listOf()
+            }
+        }
+    }
+
+    buildTypes {
+        release {
+            isMinifyEnabled = false
+            proguardFiles(
+                getDefaultProguardFile("proguard-android-optimize.txt"),
+                "proguard-rules.pro"
+            )
+        }
+    }
+    compileOptions {
+        sourceCompatibility = JavaVersion.VERSION_1_8
+        targetCompatibility = JavaVersion.VERSION_1_8
+    }
+    kotlinOptions {
+        jvmTarget = "1.8"
+    }
+    buildFeatures {
+        compose = true
+    }
+    composeOptions {
+        kotlinCompilerExtensionVersion = "1.5.1"
+    }
+    packaging {
+        resources {
+            excludes += "/META-INF/{AL2.0,LGPL2.1}"
+        }
+    }
+    externalNativeBuild {
+        cmake {
+            path = file("src/main/cpp/CMakeLists.txt")
+            version = "3.22.1"
+        }
+    }
+}
+
+dependencies {
+
+    implementation("androidx.core:core-ktx:1.12.0")
+    implementation("androidx.lifecycle:lifecycle-runtime-ktx:2.6.2")
+    implementation("androidx.activity:activity-compose:1.8.2")
+    implementation(platform("androidx.compose:compose-bom:2023.08.00"))
+    implementation("androidx.compose.ui:ui")
+    implementation("androidx.compose.ui:ui-graphics")
+    implementation("androidx.compose.ui:ui-tooling-preview")
+    implementation("androidx.compose.material3:material3")
+    testImplementation("junit:junit:4.13.2")
+    androidTestImplementation("androidx.test.ext:junit:1.1.5")
+    androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1")
+    androidTestImplementation(platform("androidx.compose:compose-bom:2023.08.00"))
+    androidTestImplementation("androidx.compose.ui:ui-test-junit4")
+    debugImplementation("androidx.compose.ui:ui-tooling")
+    debugImplementation("androidx.compose.ui:ui-test-manifest")
+}
diff --git a/examples/llama.android/app/proguard-rules.pro b/examples/llama.android/app/proguard-rules.pro
new file mode 100644
index 000000000..f1b424510
--- /dev/null
+++ b/examples/llama.android/app/proguard-rules.pro
@@ -0,0 +1,21 @@
+# Add project specific ProGuard rules here.
+# You can control the set of applied configuration files using the
+# proguardFiles setting in build.gradle.
+#
+# For more details, see
+#   http://developer.android.com/guide/developing/tools/proguard.html
+
+# If your project uses WebView with JS, uncomment the following
+# and specify the fully qualified class name to the JavaScript interface
+# class:
+#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
+#   public *;
+#}
+
+# Uncomment this to preserve the line number information for
+# debugging stack traces.
+#-keepattributes SourceFile,LineNumberTable
+
+# If you keep the line number information, uncomment this to
+# hide the original source file name.
+#-renamesourcefileattribute SourceFile
diff --git a/examples/llama.android/app/src/main/AndroidManifest.xml b/examples/llama.android/app/src/main/AndroidManifest.xml
new file mode 100644
index 000000000..41a358a29
--- /dev/null
+++ b/examples/llama.android/app/src/main/AndroidManifest.xml
@@ -0,0 +1,30 @@
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:tools="http://schemas.android.com/tools">
+
+    <uses-permission android:name="android.permission.INTERNET" />
+
+    <application
+        android:allowBackup="true"
+        android:dataExtractionRules="@xml/data_extraction_rules"
+        android:fullBackupContent="@xml/backup_rules"
+        android:icon="@mipmap/ic_launcher"
+        android:label="@string/app_name"
+        android:roundIcon="@mipmap/ic_launcher_round"
+        android:supportsRtl="true"
+        android:theme="@style/Theme.LlamaAndroid"
+        >
+
+        <activity
+            android:name=".MainActivity"
+            android:exported="true"
+            android:theme="@style/Theme.LlamaAndroid">
+            <intent-filter>
+                <action android:name="android.intent.action.MAIN" />
+
+                <category android:name="android.intent.category.LAUNCHER" />
+            </intent-filter>
+        </activity>
+    </application>
+
+</manifest>
diff --git a/examples/llama.android/app/src/main/cpp/CMakeLists.txt b/examples/llama.android/app/src/main/cpp/CMakeLists.txt
new file mode 100644
index 000000000..85139329a
--- /dev/null
+++ b/examples/llama.android/app/src/main/cpp/CMakeLists.txt
@@ -0,0 +1,50 @@
+
+# For more information about using CMake with Android Studio, read the
+# documentation: https://d.android.com/studio/projects/add-native-code.html.
+# For more examples on how to use CMake, see https://github.com/android/ndk-samples.
+
+# Sets the minimum CMake version required for this project.
+cmake_minimum_required(VERSION 3.22.1)
+
+# Declares the project name. The project name can be accessed via ${ PROJECT_NAME},
+# Since this is the top level CMakeLists.txt, the project name is also accessible
+# with ${CMAKE_PROJECT_NAME} (both CMake variables are in-sync within the top level
+# build script scope).
+project("llama-android")
+
+include(FetchContent)
+FetchContent_Declare(
+        llama
+        GIT_REPOSITORY https://github.com/ggerganov/llama.cpp
+        GIT_TAG        master
+)
+
+# Also provides "common"
+FetchContent_MakeAvailable(llama)
+
+# Creates and names a library, sets it as either STATIC
+# or SHARED, and provides the relative paths to its source code.
+# You can define multiple libraries, and CMake builds them for you.
+# Gradle automatically packages shared libraries with your APK.
+#
+# In this top level CMakeLists.txt, ${CMAKE_PROJECT_NAME} is used to define
+# the target library name; in the sub-module's CMakeLists.txt, ${PROJECT_NAME}
+# is preferred for the same purpose.
+#
+# In order to load a library into your app from Java/Kotlin, you must call
+# System.loadLibrary() and pass the name of the library defined here;
+# for GameActivity/NativeActivity derived applications, the same library name must be
+# used in the AndroidManifest.xml file.
+add_library(${CMAKE_PROJECT_NAME} SHARED
+    # List C/C++ source files with relative paths to this CMakeLists.txt.
+    llama-android.cpp)
+
+# Specifies libraries CMake should link to your target library. You
+# can link libraries from various origins, such as libraries defined in this
+# build script, prebuilt third-party libraries, or Android system libraries.
+target_link_libraries(${CMAKE_PROJECT_NAME}
+    # List libraries link to the target library
+    llama
+    common
+    android
+    log)
diff --git a/examples/llama.android/app/src/main/cpp/llama-android.cpp b/examples/llama.android/app/src/main/cpp/llama-android.cpp
new file mode 100644
index 000000000..ce8ab3b70
--- /dev/null
+++ b/examples/llama.android/app/src/main/cpp/llama-android.cpp
@@ -0,0 +1,443 @@
+#include <android/log.h>
+#include <jni.h>
+#include <iomanip>
+#include <math.h>
+#include <string>
+#include <unistd.h>
+#include "llama.h"
+#include "common/common.h"
+
+// Write C++ code here.
+//
+// Do not forget to dynamically load the C++ library into your application.
+//
+// For instance,
+//
+// In MainActivity.java:
+//    static {
+//       System.loadLibrary("llama-android");
+//    }
+//
+// Or, in MainActivity.kt:
+//    companion object {
+//      init {
+//         System.loadLibrary("llama-android")
+//      }
+//    }
+
+#define TAG "llama-android.cpp"
+#define LOGi(...) __android_log_print(ANDROID_LOG_INFO, TAG, __VA_ARGS__)
+#define LOGe(...) __android_log_print(ANDROID_LOG_ERROR, TAG, __VA_ARGS__)
+
+jclass la_int_var;
+jmethodID la_int_var_value;
+jmethodID la_int_var_inc;
+
+std::string cached_token_chars;
+
+bool is_valid_utf8(const char * string) {
+    if (!string) {
+        return true;
+    }
+
+    const unsigned char * bytes = (const unsigned char *)string;
+    int num;
+
+    while (*bytes != 0x00) {
+        if ((*bytes & 0x80) == 0x00) {
+            // U+0000 to U+007F
+            num = 1;
+        } else if ((*bytes & 0xE0) == 0xC0) {
+            // U+0080 to U+07FF
+            num = 2;
+        } else if ((*bytes & 0xF0) == 0xE0) {
+            // U+0800 to U+FFFF
+            num = 3;
+        } else if ((*bytes & 0xF8) == 0xF0) {
+            // U+10000 to U+10FFFF
+            num = 4;
+        } else {
+            return false;
+        }
+
+        bytes += 1;
+        for (int i = 1; i < num; ++i) {
+            if ((*bytes & 0xC0) != 0x80) {
+                return false;
+            }
+            bytes += 1;
+        }
+    }
+
+    return true;
+}
+
+static void log_callback(ggml_log_level level, const char * fmt, void * data) {
+    if (level == GGML_LOG_LEVEL_ERROR)     __android_log_print(ANDROID_LOG_ERROR, TAG, fmt, data);
+    else if (level == GGML_LOG_LEVEL_INFO) __android_log_print(ANDROID_LOG_INFO, TAG, fmt, data);
+    else if (level == GGML_LOG_LEVEL_WARN) __android_log_print(ANDROID_LOG_WARN, TAG, fmt, data);
+    else __android_log_print(ANDROID_LOG_DEFAULT, TAG, fmt, data);
+}
+
+extern "C"
+JNIEXPORT jlong JNICALL
+Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) {
+    llama_model_params model_params = llama_model_default_params();
+
+    auto path_to_model = env->GetStringUTFChars(filename, 0);
+    LOGi("Loading model from %s", path_to_model);
+
+    auto model = llama_load_model_from_file(path_to_model, model_params);
+    env->ReleaseStringUTFChars(filename, path_to_model);
+
+    if (!model) {
+        LOGe("load_model() failed");
+        env->ThrowNew(env->FindClass("java/lang/IllegalStateException"), "load_model() failed");
+        return 0;
+    }
+
+    return reinterpret_cast<jlong>(model);
+}
+
+extern "C"
+JNIEXPORT void JNICALL
+Java_com_example_llama_Llm_free_1model(JNIEnv *, jobject, jlong model) {
+    llama_free_model(reinterpret_cast<llama_model *>(model));
+}
+
+extern "C"
+JNIEXPORT jlong JNICALL
+Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) {
+    auto model = reinterpret_cast<llama_model *>(jmodel);
+
+    if (!model) {
+        LOGe("new_context(): model cannot be null");
+        env->ThrowNew(env->FindClass("java/lang/IllegalArgumentException"), "Model cannot be null");
+        return 0;
+    }
+
+    int n_threads = std::max(1, std::min(8, (int) sysconf(_SC_NPROCESSORS_ONLN) - 2));
+    LOGi("Using %d threads", n_threads);
+
+    llama_context_params ctx_params = llama_context_default_params();
+    ctx_params.seed  = 1234;
+    ctx_params.n_ctx = 2048;
+    ctx_params.n_threads       = n_threads;
+    ctx_params.n_threads_batch = n_threads;
+
+    llama_context * context = llama_new_context_with_model(model, ctx_params);
+
+    if (!context) {
+        LOGe("llama_new_context_with_model() returned null)");
+        env->ThrowNew(env->FindClass("java/lang/IllegalStateException"),
+                      "llama_new_context_with_model() returned null)");
+        return 0;
+    }
+
+    return reinterpret_cast<jlong>(context);
+}
+
+extern "C"
+JNIEXPORT void JNICALL
+Java_com_example_llama_Llm_free_1context(JNIEnv *, jobject, jlong context) {
+    llama_free(reinterpret_cast<llama_context *>(context));
+}
+
+extern "C"
+JNIEXPORT void JNICALL
+Java_com_example_llama_Llm_backend_1free(JNIEnv *, jobject) {
+    llama_backend_free();
+}
+
+extern "C"
+JNIEXPORT void JNICALL
+Java_com_example_llama_Llm_log_1to_1android(JNIEnv *, jobject) {
+    llama_log_set(log_callback, NULL);
+}
+
+extern "C"
+JNIEXPORT jstring JNICALL
+Java_com_example_llama_Llm_bench_1model(
+        JNIEnv *env,
+        jobject,
+        jlong context_pointer,
+        jlong model_pointer,
+        jlong batch_pointer,
+        jint pp,
+        jint tg,
+        jint pl,
+        jint nr
+        ) {
+    auto pp_avg = 0.0;
+    auto tg_avg = 0.0;
+    auto pp_std = 0.0;
+    auto tg_std = 0.0;
+
+    const auto context = reinterpret_cast<llama_context *>(context_pointer);
+    const auto model = reinterpret_cast<llama_model *>(model_pointer);
+    const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
+
+    const int n_ctx = llama_n_ctx(context);
+
+    LOGi("n_ctx = %d", n_ctx);
+
+    int i, j;
+    int nri;
+    for (nri = 0; nri < nr; nri++) {
+        LOGi("Benchmark prompt processing (pp)");
+
+        llama_batch_clear(*batch);
+
+        const int n_tokens = pp;
+        for (i = 0; i < n_tokens; i++) {
+            llama_batch_add(*batch, 0, i, { 0 }, false);
+        }
+
+        batch->logits[batch->n_tokens - 1] = true;
+        llama_kv_cache_clear(context);
+
+        const auto t_pp_start = ggml_time_us();
+        if (llama_decode(context, *batch) != 0) {
+            LOGi("llama_decode() failed during prompt processing");
+        }
+        const auto t_pp_end = ggml_time_us();
+
+        // bench text generation
+
+        LOGi("Benchmark text generation (tg)");
+
+        llama_kv_cache_clear(context);
+        const auto t_tg_start = ggml_time_us();
+        for (i = 0; i < tg; i++) {
+
+            llama_batch_clear(*batch);
+            for (j = 0; j < pl; j++) {
+                llama_batch_add(*batch, 0, i, { j }, true);
+            }
+
+            LOGi("llama_decode() text generation: %d", i);
+            if (llama_decode(context, *batch) != 0) {
+                LOGi("llama_decode() failed during text generation");
+            }
+        }
+
+        const auto t_tg_end = ggml_time_us();
+
+        llama_kv_cache_clear(context);
+
+        const auto t_pp = double(t_pp_end - t_pp_start) / 1000000.0;
+        const auto t_tg = double(t_tg_end - t_tg_start) / 1000000.0;
+
+        const auto speed_pp = double(pp) / t_pp;
+        const auto speed_tg = double(pl * tg) / t_tg;
+
+        pp_avg += speed_pp;
+        tg_avg += speed_tg;
+
+        pp_std += speed_pp * speed_pp;
+        tg_std += speed_tg * speed_tg;
+
+        LOGi("pp %f t/s, tg %f t/s", speed_pp, speed_tg);
+    }
+
+    pp_avg /= double(nr);
+    tg_avg /= double(nr);
+
+    if (nr > 1) {
+        pp_std = sqrt(pp_std / double(nr - 1) - pp_avg * pp_avg * double(nr) / double(nr - 1));
+        tg_std = sqrt(tg_std / double(nr - 1) - tg_avg * tg_avg * double(nr) / double(nr - 1));
+    } else {
+        pp_std = 0;
+        tg_std = 0;
+    }
+
+    char model_desc[128];
+    llama_model_desc(model, model_desc, sizeof(model_desc));
+
+    const auto model_size     = double(llama_model_size(model)) / 1024.0 / 1024.0 / 1024.0;
+    const auto model_n_params = double(llama_model_n_params(model)) / 1e9;
+
+    const auto backend    = "(Android)"; // TODO: What should this be?
+
+    std::stringstream result;
+    result << std::setprecision(2);
+    result << "| model | size | params | backend | test | t/s |\n";
+    result << "| --- | --- | --- | --- | --- | --- |\n";
+    result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | " << backend << " | pp " << pp << " | " << pp_avg << " ± " << pp_std << " |\n";
+    result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | " << backend << " | tg " << tg << " | " << tg_avg << " ± " << tg_std << " |\n";
+
+    return env->NewStringUTF(result.str().c_str());
+}
+
+extern "C"
+JNIEXPORT void JNICALL
+Java_com_example_llama_Llm_free_1batch(JNIEnv *, jobject, jlong batch_pointer) {
+    llama_batch_free(*reinterpret_cast<llama_batch *>(batch_pointer));
+}
+
+extern "C"
+JNIEXPORT jlong JNICALL
+Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) {
+
+    // Source: Copy of llama.cpp:llama_batch_init but heap-allocated.
+
+    llama_batch *batch = new llama_batch {
+        0,
+        nullptr,
+        nullptr,
+        nullptr,
+        nullptr,
+        nullptr,
+        nullptr,
+        0,
+        0,
+        0,
+    };
+
+    if (embd) {
+        batch->embd = (float *) malloc(sizeof(float) * n_tokens * embd);
+    } else {
+        batch->token = (llama_token *) malloc(sizeof(llama_token) * n_tokens);
+    }
+
+    batch->pos      = (llama_pos *)     malloc(sizeof(llama_pos)      * n_tokens);
+    batch->n_seq_id = (int32_t *)       malloc(sizeof(int32_t)        * n_tokens);
+    batch->seq_id   = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * n_tokens);
+    for (int i = 0; i < n_tokens; ++i) {
+        batch->seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max);
+    }
+    batch->logits   = (int8_t *)        malloc(sizeof(int8_t)         * n_tokens);
+
+    return reinterpret_cast<jlong>(batch);
+}
+
+extern "C"
+JNIEXPORT void JNICALL
+Java_com_example_llama_Llm_backend_1init(JNIEnv *, jobject) {
+    llama_backend_init();
+}
+
+extern "C"
+JNIEXPORT jstring JNICALL
+Java_com_example_llama_Llm_system_1info(JNIEnv *env, jobject) {
+    return env->NewStringUTF(llama_print_system_info());
+}
+
+extern "C"
+JNIEXPORT jint JNICALL
+Java_com_example_llama_Llm_completion_1init(
+        JNIEnv *env,
+        jobject,
+        jlong context_pointer,
+        jlong batch_pointer,
+        jstring jtext,
+        jint n_len
+    ) {
+
+    cached_token_chars.clear();
+
+    const auto text = env->GetStringUTFChars(jtext, 0);
+    const auto context = reinterpret_cast<llama_context *>(context_pointer);
+    const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
+
+    const auto tokens_list = llama_tokenize(context, text, 1);
+
+    auto n_ctx = llama_n_ctx(context);
+    auto n_kv_req = tokens_list.size() + (n_len - tokens_list.size());
+
+    LOGi("n_len = %d, n_ctx = %d, n_kv_req = %d", n_len, n_ctx, n_kv_req);
+
+    if (n_kv_req > n_ctx) {
+        LOGe("error: n_kv_req > n_ctx, the required KV cache size is not big enough");
+    }
+
+    for (auto id : tokens_list) {
+        LOGi("%s", llama_token_to_piece(context, id).c_str());
+    }
+
+    llama_batch_clear(*batch);
+
+    // evaluate the initial prompt
+    for (auto i = 0; i < tokens_list.size(); i++) {
+        llama_batch_add(*batch, tokens_list[i], i, { 0 }, false);
+    }
+
+    // llama_decode will output logits only for the last token of the prompt
+    batch->logits[batch->n_tokens - 1] = true;
+
+    if (llama_decode(context, *batch) != 0) {
+        LOGe("llama_decode() failed");
+    }
+
+    env->ReleaseStringUTFChars(jtext, text);
+
+    return batch->n_tokens;
+}
+
+extern "C"
+JNIEXPORT jstring JNICALL
+Java_com_example_llama_Llm_completion_1loop(
+        JNIEnv * env,
+        jobject,
+        jlong context_pointer,
+        jlong batch_pointer,
+        jint n_len,
+        jobject intvar_ncur
+) {
+    const auto context = reinterpret_cast<llama_context *>(context_pointer);
+    const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
+    const auto model = llama_get_model(context);
+
+    if (!la_int_var) la_int_var = env->GetObjectClass(intvar_ncur);
+    if (!la_int_var_value) la_int_var_value = env->GetMethodID(la_int_var, "getValue", "()I");
+    if (!la_int_var_inc) la_int_var_inc = env->GetMethodID(la_int_var, "inc", "()V");
+
+    auto n_vocab = llama_n_vocab(model);
+    auto logits = llama_get_logits_ith(context, batch->n_tokens - 1);
+
+    std::vector<llama_token_data> candidates;
+    candidates.reserve(n_vocab);
+
+    for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+        candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
+    }
+
+    llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+
+    // sample the most likely token
+    const auto new_token_id = llama_sample_token_greedy(context, &candidates_p);
+
+    const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value);
+    if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
+        return env->NewStringUTF("");
+    }
+
+    auto new_token_chars = llama_token_to_piece(context, new_token_id);
+    cached_token_chars += new_token_chars;
+
+    jstring new_token = nullptr;
+    if (is_valid_utf8(cached_token_chars.c_str())) {
+        new_token = env->NewStringUTF(cached_token_chars.c_str());
+        LOGi("cached: %s, new_token_chars: `%s`, id: %d", cached_token_chars.c_str(), new_token_chars.c_str(), new_token_id);
+        cached_token_chars.clear();
+    } else {
+        new_token = env->NewStringUTF("");
+    }
+
+    llama_batch_clear(*batch);
+    llama_batch_add(*batch, new_token_id, n_cur, { 0 }, true);
+
+    env->CallVoidMethod(intvar_ncur, la_int_var_inc);
+
+    if (llama_decode(context, *batch) != 0) {
+        LOGe("llama_decode() returned null");
+    }
+
+    return new_token;
+}
+
+extern "C"
+JNIEXPORT void JNICALL
+Java_com_example_llama_Llm_kv_1cache_1clear(JNIEnv *, jobject, jlong context) {
+    llama_kv_cache_clear(reinterpret_cast<llama_context *>(context));
+}
diff --git a/examples/llama.android/app/src/main/java/com/example/llama/Downloadable.kt b/examples/llama.android/app/src/main/java/com/example/llama/Downloadable.kt
new file mode 100644
index 000000000..78c231ae5
--- /dev/null
+++ b/examples/llama.android/app/src/main/java/com/example/llama/Downloadable.kt
@@ -0,0 +1,119 @@
+package com.example.llama
+
+import android.app.DownloadManager
+import android.net.Uri
+import android.util.Log
+import androidx.compose.material3.Button
+import androidx.compose.material3.Text
+import androidx.compose.runtime.Composable
+import androidx.compose.runtime.getValue
+import androidx.compose.runtime.mutableDoubleStateOf
+import androidx.compose.runtime.mutableStateOf
+import androidx.compose.runtime.remember
+import androidx.compose.runtime.rememberCoroutineScope
+import androidx.compose.runtime.setValue
+import androidx.core.database.getLongOrNull
+import androidx.core.net.toUri
+import kotlinx.coroutines.delay
+import kotlinx.coroutines.launch
+import java.io.File
+
+data class Downloadable(val name: String, val source: Uri, val destination: File) {
+    companion object {
+        @JvmStatic
+        private val tag: String? = this::class.qualifiedName
+
+        sealed interface State
+        data object Ready: State
+        data class Downloading(val id: Long): State
+        data class Downloaded(val downloadable: Downloadable): State
+        data class Error(val message: String): State
+
+        @JvmStatic
+        @Composable
+        fun Button(viewModel: MainViewModel, dm: DownloadManager, item: Downloadable) {
+            var status: State by remember {
+                mutableStateOf(
+                    if (item.destination.exists()) Downloaded(item)
+                    else Ready
+                )
+            }
+            var progress by remember { mutableDoubleStateOf(0.0) }
+
+            val coroutineScope = rememberCoroutineScope()
+
+            suspend fun waitForDownload(result: Downloading, item: Downloadable): State {
+                while (true) {
+                    val cursor = dm.query(DownloadManager.Query().setFilterById(result.id))
+
+                    if (cursor == null) {
+                        Log.e(tag, "dm.query() returned null")
+                        return Error("dm.query() returned null")
+                    }
+
+                    if (!cursor.moveToFirst() || cursor.count < 1) {
+                        cursor.close()
+                        Log.i(tag, "cursor.moveToFirst() returned false or cursor.count < 1, download canceled?")
+                        return Ready
+                    }
+
+                    val pix = cursor.getColumnIndex(DownloadManager.COLUMN_BYTES_DOWNLOADED_SO_FAR)
+                    val tix = cursor.getColumnIndex(DownloadManager.COLUMN_TOTAL_SIZE_BYTES)
+                    val sofar = cursor.getLongOrNull(pix) ?: 0
+                    val total = cursor.getLongOrNull(tix) ?: 1
+                    cursor.close()
+
+                    if (sofar == total) {
+                        return Downloaded(item)
+                    }
+
+                    progress = (sofar * 1.0) / total
+
+                    delay(1000L)
+                }
+            }
+
+            fun onClick() {
+                when (val s = status) {
+                    is Downloaded -> {
+                        viewModel.load(item.destination.path)
+                    }
+
+                    is Downloading -> {
+                        coroutineScope.launch {
+                            status = waitForDownload(s, item)
+                        }
+                    }
+
+                    else -> {
+                        item.destination.delete()
+
+                        val request = DownloadManager.Request(item.source).apply {
+                            setTitle("Downloading model")
+                            setDescription("Downloading model: ${item.name}")
+                            setAllowedNetworkTypes(DownloadManager.Request.NETWORK_WIFI)
+                            setDestinationUri(item.destination.toUri())
+                        }
+
+                        viewModel.log("Saving ${item.name} to ${item.destination.path}")
+                        Log.i(tag, "Saving ${item.name} to ${item.destination.path}")
+
+                        val id = dm.enqueue(request)
+                        status = Downloading(id)
+                        onClick()
+                    }
+                }
+            }
+
+            Button(onClick = { onClick() }, enabled = status !is Downloading) {
+                when (status) {
+                    is Downloading -> Text(text = "Downloading ${(progress * 100).toInt()}%")
+                    is Downloaded -> Text("Load ${item.name}")
+                    is Ready -> Text("Download ${item.name}")
+                    is Error -> Text("Download ${item.name}")
+                }
+            }
+        }
+
+    }
+}
diff --git a/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt b/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt
new file mode 100644
index 000000000..d86afee37
--- /dev/null
+++ b/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt
@@ -0,0 +1,172 @@
+package com.example.llama
+
+import android.util.Log
+import kotlinx.coroutines.CoroutineDispatcher
+import kotlinx.coroutines.asCoroutineDispatcher
+import kotlinx.coroutines.flow.Flow
+import kotlinx.coroutines.flow.flow
+import kotlinx.coroutines.flow.flowOn
+import kotlinx.coroutines.withContext
+import java.util.concurrent.Executors
+import kotlin.concurrent.thread
+
+class Llm {
+    private val tag: String? = this::class.simpleName
+
+    private val threadLocalState: ThreadLocal<State> = ThreadLocal.withInitial { State.Idle }
+
+    private val runLoop: CoroutineDispatcher = Executors.newSingleThreadExecutor {
+        thread(start = false, name = "Llm-RunLoop") {
+            Log.d(tag, "Dedicated thread for native code: ${Thread.currentThread().name}")
+
+            // No-op if called more than once.
+            System.loadLibrary("llama-android")
+
+            // Set llama log handler to Android
+            log_to_android()
+            backend_init(false)
+
+            Log.d(tag, system_info())
+
+            it.run()
+        }.apply {
+            uncaughtExceptionHandler = Thread.UncaughtExceptionHandler { _, exception: Throwable ->
+                Log.e(tag, "Unhandled exception", exception)
+            }
+        }
+    }.asCoroutineDispatcher()
+
+    private val nlen: Int = 64
+
+    private external fun log_to_android()
+    private external fun load_model(filename: String): Long
+    private external fun free_model(model: Long)
+    private external fun new_context(model: Long): Long
+    private external fun free_context(context: Long)
+    private external fun backend_init(numa: Boolean)
+    private external fun backend_free()
+    private external fun free_batch(batch: Long)
+    private external fun new_batch(nTokens: Int, embd: Int, nSeqMax: Int): Long
+    private external fun bench_model(
+        context: Long,
+        model: Long,
+        batch: Long,
+        pp: Int,
+        tg: Int,
+        pl: Int,
+        nr: Int
+    ): String
+
+    private external fun system_info(): String
+
+    private external fun completion_init(
+        context: Long,
+        batch: Long,
+        text: String,
+        nLen: Int
+    ): Int
+
+    private external fun completion_loop(
+        context: Long,
+        batch: Long,
+        nLen: Int,
+        ncur: IntVar
+    ): String?
+
+    private external fun kv_cache_clear(context: Long)
+
+    suspend fun bench(pp: Int, tg: Int, pl: Int, nr: Int = 1): String {
+        return withContext(runLoop) {
+            when (val state = threadLocalState.get()) {
+                is State.Loaded -> {
+                    Log.d(tag, "bench(): $state")
+                    bench_model(state.context, state.model, state.batch, pp, tg, pl, nr)
+                }
+
+                else -> throw IllegalStateException("No model loaded")
+            }
+        }
+    }
+
+    suspend fun load(pathToModel: String) {
+        withContext(runLoop) {
+            when (threadLocalState.get()) {
+                is State.Idle -> {
+                    val model = load_model(pathToModel)
+                    if (model == 0L)  throw IllegalStateException("load_model() failed")
+
+                    val context = new_context(model)
+                    if (context == 0L) throw IllegalStateException("new_context() failed")
+
+                    val batch = new_batch(512, 0, 1)
+                    if (batch == 0L) throw IllegalStateException("new_batch() failed")
+
+                    Log.i(tag, "Loaded model $pathToModel")
+                    threadLocalState.set(State.Loaded(model, context, batch))
+                }
+                else -> throw IllegalStateException("Model already loaded")
+            }
+        }
+    }
+
+    fun send(message: String): Flow<String> = flow {
+        when (val state = threadLocalState.get()) {
+            is State.Loaded -> {
+                val ncur = IntVar(completion_init(state.context, state.batch, message, nlen))
+                while (ncur.value <= nlen) {
+                    val str = completion_loop(state.context, state.batch, nlen, ncur)
+                    if (str == null) {
+                        break
+                    }
+                    emit(str)
+                }
+                kv_cache_clear(state.context)
+            }
+            else -> {}
+        }
+    }.flowOn(runLoop)
+
+    /**
+     * Unloads the model and frees resources.
+     *
+     * This is a no-op if there's no model loaded.
+     */
+    suspend fun unload() {
+        withContext(runLoop) {
+            when (val state = threadLocalState.get()) {
+                is State.Loaded -> {
+                    free_context(state.context)
+                    free_model(state.model)
+                    free_batch(state.batch)
+
+                    threadLocalState.set(State.Idle)
+                }
+                else -> {}
+            }
+        }
+    }
+
+    companion object {
+        private class IntVar(value: Int) {
+            @Volatile
+            var value: Int = value
+                private set
+
+            fun inc() {
+                synchronized(this) {
+                    value += 1
+                }
+            }
+        }
+
+        private sealed interface State {
+            data object Idle: State
+            data class Loaded(val model: Long, val context: Long, val batch: Long): State
+        }
+
+        // Enforce only one instance of Llm.
+        private val _instance: Llm = Llm()
+
+        fun instance(): Llm = _instance
+    }
+}
diff --git a/examples/llama.android/app/src/main/java/com/example/llama/MainActivity.kt b/examples/llama.android/app/src/main/java/com/example/llama/MainActivity.kt
new file mode 100644
index 000000000..9da04f7d3
--- /dev/null
+++ b/examples/llama.android/app/src/main/java/com/example/llama/MainActivity.kt
@@ -0,0 +1,154 @@
+package com.example.llama
+
+import android.app.ActivityManager
+import android.app.DownloadManager
+import android.content.ClipData
+import android.content.ClipboardManager
+import android.net.Uri
+import android.os.Bundle
+import android.os.StrictMode
+import android.os.StrictMode.VmPolicy
+import android.text.format.Formatter
+import androidx.activity.ComponentActivity
+import androidx.activity.compose.setContent
+import androidx.activity.viewModels
+import androidx.compose.foundation.layout.Box
+import androidx.compose.foundation.layout.Column
+import androidx.compose.foundation.layout.Row
+import androidx.compose.foundation.layout.fillMaxSize
+import androidx.compose.foundation.layout.padding
+import androidx.compose.foundation.lazy.LazyColumn
+import androidx.compose.foundation.lazy.items
+import androidx.compose.foundation.lazy.rememberLazyListState
+import androidx.compose.material3.Button
+import androidx.compose.material3.LocalContentColor
+import androidx.compose.material3.MaterialTheme
+import androidx.compose.material3.OutlinedTextField
+import androidx.compose.material3.Surface
+import androidx.compose.material3.Text
+import androidx.compose.runtime.Composable
+import androidx.compose.ui.Modifier
+import androidx.compose.ui.unit.dp
+import androidx.core.content.getSystemService
+import com.example.llama.ui.theme.LlamaAndroidTheme
+import java.io.File
+
+class MainActivity(
+    activityManager: ActivityManager? = null,
+    downloadManager: DownloadManager? = null,
+    clipboardManager: ClipboardManager? = null,
+): ComponentActivity() {
+    private val tag: String? = this::class.simpleName
+
+    private val activityManager by lazy { activityManager ?: getSystemService<ActivityManager>()!! }
+    private val downloadManager by lazy { downloadManager ?: getSystemService<DownloadManager>()!! }
+    private val clipboardManager by lazy { clipboardManager ?: getSystemService<ClipboardManager>()!! }
+
+    private val viewModel: MainViewModel by viewModels()
+
+    // Get a MemoryInfo object for the device's current memory status.
+    private fun availableMemory(): ActivityManager.MemoryInfo {
+        return ActivityManager.MemoryInfo().also { memoryInfo ->
+            activityManager.getMemoryInfo(memoryInfo)
+        }
+    }
+
+    override fun onCreate(savedInstanceState: Bundle?) {
+        super.onCreate(savedInstanceState)
+
+        StrictMode.setVmPolicy(
+            VmPolicy.Builder(StrictMode.getVmPolicy())
+                .detectLeakedClosableObjects()
+                .build()
+        )
+
+        val free = Formatter.formatFileSize(this, availableMemory().availMem)
+        val total = Formatter.formatFileSize(this, availableMemory().totalMem)
+
+        viewModel.log("Current memory: $free / $total")
+        viewModel.log("Downloads directory: ${getExternalFilesDir(null)}")
+
+        val extFilesDir = getExternalFilesDir(null)
+
+        val models = listOf(
+            Downloadable(
+                "Phi-2 7B (Q4_0, 1.6 GiB)",
+                Uri.parse("https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true"),
+                File(extFilesDir, "phi-2-q4_0.gguf"),
+            ),
+            Downloadable(
+                "TinyLlama 1.1B (f16, 2.2 GiB)",
+                Uri.parse("https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true"),
+                File(extFilesDir, "tinyllama-1.1-f16.gguf"),
+            ),
+            Downloadable(
+                "Phi 2 DPO (Q3_K_M, 1.48 GiB)",
+                Uri.parse("https://huggingface.co/TheBloke/phi-2-dpo-GGUF/resolve/main/phi-2-dpo.Q3_K_M.gguf?download=true"),
+                File(extFilesDir, "phi-2-dpo.Q3_K_M.gguf")
+            ),
+        )
+
+        setContent {
+            LlamaAndroidTheme {
+                // A surface container using the 'background' color from the theme
+                Surface(
+                    modifier = Modifier.fillMaxSize(),
+                    color = MaterialTheme.colorScheme.background
+                ) {
+                    MainCompose(
+                        viewModel,
+                        clipboardManager,
+                        downloadManager,
+                        models,
+                    )
+                }
+
+            }
+        }
+    }
+}
+
+@Composable
+fun MainCompose(
+    viewModel: MainViewModel,
+    clipboard: ClipboardManager,
+    dm: DownloadManager,
+    models: List<Downloadable>
+) {
+    Column {
+        val scrollState = rememberLazyListState()
+
+        Box(modifier = Modifier.weight(1f)) {
+            LazyColumn(state = scrollState) {
+                items(viewModel.messages) {
+                    Text(
+                        it,
+                        style = MaterialTheme.typography.bodyLarge.copy(color = LocalContentColor.current),
+                        modifier = Modifier.padding(16.dp)
+                    )
+                }
+            }
+        }
+        OutlinedTextField(
+            value = viewModel.message,
+            onValueChange = { viewModel.updateMessage(it) },
+            label = { Text("Message") },
+        )
+        Row {
+            Button({ viewModel.send() }) { Text("Send") }
+            Button({ viewModel.bench(8, 4, 1) }) { Text("Bench") }
+            Button({ viewModel.clear() }) { Text("Clear") }
+            Button({
+                viewModel.messages.joinToString("\n").let {
+                    clipboard.setPrimaryClip(ClipData.newPlainText("", it))
+                }
+            }) { Text("Copy") }
+        }
+
+        Column {
+            for (model in models) {
+                Downloadable.Button(viewModel, dm, model)
+            }
+        }
+    }
+}
diff --git a/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt b/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt
new file mode 100644
index 000000000..be95e2221
--- /dev/null
+++ b/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt
@@ -0,0 +1,104 @@
+package com.example.llama
+
+import android.util.Log
+import androidx.compose.runtime.getValue
+import androidx.compose.runtime.mutableStateOf
+import androidx.compose.runtime.setValue
+import androidx.lifecycle.ViewModel
+import androidx.lifecycle.viewModelScope
+import kotlinx.coroutines.flow.catch
+import kotlinx.coroutines.launch
+
+class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() {
+    companion object {
+        @JvmStatic
+        private val NanosPerSecond = 1_000_000_000.0
+    }
+
+    private val tag: String? = this::class.simpleName
+
+    var messages by mutableStateOf(listOf("Initializing..."))
+        private set
+
+    var message by mutableStateOf("")
+        private set
+
+    override fun onCleared() {
+        super.onCleared()
+
+        viewModelScope.launch {
+            try {
+                llm.unload()
+            } catch (exc: IllegalStateException) {
+                messages += exc.message!!
+            }
+        }
+    }
+
+    fun send() {
+        val text = message
+        message = ""
+
+        // Add to messages console.
+        messages += text
+        messages += ""
+
+        viewModelScope.launch {
+            llm.send(text)
+                .catch {
+                    Log.e(tag, "send() failed", it)
+                    messages += it.message!!
+                }
+                .collect { messages = messages.dropLast(1) + (messages.last() + it) }
+        }
+    }
+
+    fun bench(pp: Int, tg: Int, pl: Int, nr: Int = 1) {
+        viewModelScope.launch {
+            try {
+                val start = System.nanoTime()
+                val warmupResult = llm.bench(pp, tg, pl, nr)
+                val end = System.nanoTime()
+
+                messages += warmupResult
+
+                val warmup = (end - start).toDouble() / NanosPerSecond
+                messages += "Warm up time: $warmup seconds, please wait..."
+
+                if (warmup > 5.0) {
+                    messages += "Warm up took too long, aborting benchmark"
+                    return@launch
+                }
+
+                messages += llm.bench(512, 128, 1, 3)
+            } catch (exc: IllegalStateException) {
+                Log.e(tag, "bench() failed", exc)
+                messages += exc.message!!
+            }
+        }
+    }
+
+    fun load(pathToModel: String) {
+        viewModelScope.launch {
+            try {
+                llm.load(pathToModel)
+                messages += "Loaded $pathToModel"
+            } catch (exc: IllegalStateException) {
+                Log.e(tag, "load() failed", exc)
+                messages += exc.message!!
+            }
+        }
+    }
+
+    fun updateMessage(newMessage: String) {
+        message = newMessage
+    }
+
+    fun clear() {
+        messages = listOf()
+    }
+
+    fun log(message: String) {
+        messages += message
+    }
+}
diff --git a/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Color.kt b/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Color.kt
new file mode 100644
index 000000000..40c30e8d9
--- /dev/null
+++ b/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Color.kt
@@ -0,0 +1,11 @@
+package com.example.llama.ui.theme
+
+import androidx.compose.ui.graphics.Color
+
+val Purple80 = Color(0xFFD0BCFF)
+val PurpleGrey80 = Color(0xFFCCC2DC)
+val Pink80 = Color(0xFFEFB8C8)
+
+val Purple40 = Color(0xFF6650a4)
+val PurpleGrey40 = Color(0xFF625b71)
+val Pink40 = Color(0xFF7D5260)
diff --git a/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Theme.kt b/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Theme.kt
new file mode 100644
index 000000000..e742220a8
--- /dev/null
+++ b/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Theme.kt
@@ -0,0 +1,70 @@
+package com.example.llama.ui.theme
+
+import android.app.Activity
+import android.os.Build
+import androidx.compose.foundation.isSystemInDarkTheme
+import androidx.compose.material3.MaterialTheme
+import androidx.compose.material3.darkColorScheme
+import androidx.compose.material3.dynamicDarkColorScheme
+import androidx.compose.material3.dynamicLightColorScheme
+import androidx.compose.material3.lightColorScheme
+import androidx.compose.runtime.Composable
+import androidx.compose.runtime.SideEffect
+import androidx.compose.ui.graphics.toArgb
+import androidx.compose.ui.platform.LocalContext
+import androidx.compose.ui.platform.LocalView
+import androidx.core.view.WindowCompat
+
+private val DarkColorScheme = darkColorScheme(
+    primary = Purple80,
+    secondary = PurpleGrey80,
+    tertiary = Pink80
+)
+
+private val LightColorScheme = lightColorScheme(
+    primary = Purple40,
+    secondary = PurpleGrey40,
+    tertiary = Pink40
+
+    /* Other default colors to override
+    background = Color(0xFFFFFBFE),
+    surface = Color(0xFFFFFBFE),
+    onPrimary = Color.White,
+    onSecondary = Color.White,
+    onTertiary = Color.White,
+    onBackground = Color(0xFF1C1B1F),
+    onSurface = Color(0xFF1C1B1F),
+    */
+)
+
+@Composable
+fun LlamaAndroidTheme(
+    darkTheme: Boolean = isSystemInDarkTheme(),
+    // Dynamic color is available on Android 12+
+    dynamicColor: Boolean = true,
+    content: @Composable () -> Unit
+) {
+    val colorScheme = when {
+        dynamicColor && Build.VERSION.SDK_INT >= Build.VERSION_CODES.S -> {
+            val context = LocalContext.current
+            if (darkTheme) dynamicDarkColorScheme(context) else dynamicLightColorScheme(context)
+        }
+
+        darkTheme -> DarkColorScheme
+        else -> LightColorScheme
+    }
+    val view = LocalView.current
+    if (!view.isInEditMode) {
+        SideEffect {
+            val window = (view.context as Activity).window
+            window.statusBarColor = colorScheme.primary.toArgb()
+            WindowCompat.getInsetsController(window, view).isAppearanceLightStatusBars = darkTheme
+        }
+    }
+
+    MaterialTheme(
+        colorScheme = colorScheme,
+        typography = Typography,
+        content = content
+    )
+}
diff --git a/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Type.kt b/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Type.kt
new file mode 100644
index 000000000..0b87946ca
--- /dev/null
+++ b/examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Type.kt
@@ -0,0 +1,34 @@
+package com.example.llama.ui.theme
+
+import androidx.compose.material3.Typography
+import androidx.compose.ui.text.TextStyle
+import androidx.compose.ui.text.font.FontFamily
+import androidx.compose.ui.text.font.FontWeight
+import androidx.compose.ui.unit.sp
+
+// Set of Material typography styles to start with
+val Typography = Typography(
+    bodyLarge = TextStyle(
+        fontFamily = FontFamily.Default,
+        fontWeight = FontWeight.Normal,
+        fontSize = 16.sp,
+        lineHeight = 24.sp,
+        letterSpacing = 0.5.sp
+    )
+    /* Other default text styles to override
+    titleLarge = TextStyle(
+        fontFamily = FontFamily.Default,
+        fontWeight = FontWeight.Normal,
+        fontSize = 22.sp,
+        lineHeight = 28.sp,
+        letterSpacing = 0.sp
+    ),
+    labelSmall = TextStyle(
+        fontFamily = FontFamily.Default,
+        fontWeight = FontWeight.Medium,
+        fontSize = 11.sp,
+        lineHeight = 16.sp,
+        letterSpacing = 0.5.sp
+    )
+    */
+)
diff --git a/examples/llama.android/app/src/main/res/drawable/ic_launcher_background.xml b/examples/llama.android/app/src/main/res/drawable/ic_launcher_background.xml
new file mode 100644
index 000000000..07d5da9cb
--- /dev/null
+++ b/examples/llama.android/app/src/main/res/drawable/ic_launcher_background.xml
@@ -0,0 +1,170 @@
+<?xml version="1.0" encoding="utf-8"?>
+<vector xmlns:android="http://schemas.android.com/apk/res/android"
+    android:width="108dp"
+    android:height="108dp"
+    android:viewportWidth="108"
+    android:viewportHeight="108">
+    <path
+        android:fillColor="#3DDC84"
+        android:pathData="M0,0h108v108h-108z" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M9,0L9,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,0L19,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M29,0L29,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M39,0L39,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M49,0L49,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M59,0L59,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M69,0L69,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M79,0L79,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M89,0L89,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M99,0L99,108"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,9L108,9"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,19L108,19"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,29L108,29"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,39L108,39"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,49L108,49"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,59L108,59"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,69L108,69"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,79L108,79"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,89L108,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M0,99L108,99"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,29L89,29"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,39L89,39"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,49L89,49"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,59L89,59"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,69L89,69"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M19,79L89,79"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M29,19L29,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M39,19L39,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M49,19L49,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M59,19L59,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M69,19L69,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+    <path
+        android:fillColor="#00000000"
+        android:pathData="M79,19L79,89"
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
+</vector>
diff --git a/examples/llama.android/app/src/main/res/drawable/ic_launcher_foreground.xml b/examples/llama.android/app/src/main/res/drawable/ic_launcher_foreground.xml
new file mode 100644
index 000000000..7706ab9e6
--- /dev/null
+++ b/examples/llama.android/app/src/main/res/drawable/ic_launcher_foreground.xml
@@ -0,0 +1,30 @@
+<vector xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:aapt="http://schemas.android.com/aapt"
+    android:width="108dp"
+    android:height="108dp"
+    android:viewportWidth="108"
+    android:viewportHeight="108">
+    <path android:pathData="M31,63.928c0,0 6.4,-11 12.1,-13.1c7.2,-2.6 26,-1.4 26,-1.4l38.1,38.1L107,108.928l-32,-1L31,63.928z">
+        <aapt:attr name="android:fillColor">
+            <gradient
+                android:endX="85.84757"
+                android:endY="92.4963"
+                android:startX="42.9492"
+                android:startY="49.59793"
+                android:type="linear">
+                <item
+                    android:color="#44000000"
+                    android:offset="0.0" />
+                <item
+                    android:color="#00000000"
+                    android:offset="1.0" />
+            </gradient>
+        </aapt:attr>
+    </path>
+    <path
+        android:fillColor="#FFFFFF"
+        android:fillType="nonZero"
+        android:pathData="M65.3,45.828l3.8,-6.6c0.2,-0.4 0.1,-0.9 -0.3,-1.1c-0.4,-0.2 -0.9,-0.1 -1.1,0.3l-3.9,6.7c-6.3,-2.8 -13.4,-2.8 -19.7,0l-3.9,-6.7c-0.2,-0.4 -0.7,-0.5 -1.1,-0.3C38.8,38.328 38.7,38.828 38.9,39.228l3.8,6.6C36.2,49.428 31.7,56.028 31,63.928h46C76.3,56.028 71.8,49.428 65.3,45.828zM43.4,57.328c-0.8,0 -1.5,-0.5 -1.8,-1.2c-0.3,-0.7 -0.1,-1.5 0.4,-2.1c0.5,-0.5 1.4,-0.7 2.1,-0.4c0.7,0.3 1.2,1 1.2,1.8C45.3,56.528 44.5,57.328 43.4,57.328L43.4,57.328zM64.6,57.328c-0.8,0 -1.5,-0.5 -1.8,-1.2s-0.1,-1.5 0.4,-2.1c0.5,-0.5 1.4,-0.7 2.1,-0.4c0.7,0.3 1.2,1 1.2,1.8C66.5,56.528 65.6,57.328 64.6,57.328L64.6,57.328z"
+        android:strokeWidth="1"
+        android:strokeColor="#00000000" />
+</vector>
diff --git a/examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher.xml b/examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher.xml
new file mode 100644
index 000000000..b3e26b4c6
--- /dev/null
+++ b/examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="utf-8"?>
+<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
+    <background android:drawable="@drawable/ic_launcher_background" />
+    <foreground android:drawable="@drawable/ic_launcher_foreground" />
+    <monochrome android:drawable="@drawable/ic_launcher_foreground" />
+</adaptive-icon>
diff --git a/examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher_round.xml b/examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher_round.xml
new file mode 100644
index 000000000..b3e26b4c6
--- /dev/null
+++ b/examples/llama.android/app/src/main/res/mipmap-anydpi/ic_launcher_round.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="utf-8"?>
+<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
+    <background android:drawable="@drawable/ic_launcher_background" />
+    <foreground android:drawable="@drawable/ic_launcher_foreground" />
+    <monochrome android:drawable="@drawable/ic_launcher_foreground" />
+</adaptive-icon>
diff --git a/examples/llama.android/app/src/main/res/mipmap-hdpi/ic_launcher.webp b/examples/llama.android/app/src/main/res/mipmap-hdpi/ic_launcher.webp
new file mode 100644
index 000000000..c209e78ec
Binary files /dev/null and b/examples/llama.android/app/src/main/res/mipmap-hdpi/ic_launcher.webp differ
diff --git a/examples/llama.android/app/src/main/res/mipmap-hdpi/ic_launcher_round.webp b/examples/llama.android/app/src/main/res/mipmap-hdpi/ic_launcher_round.webp
new file mode 100644
index 000000000..b2dfe3d1b
Binary files /dev/null and b/examples/llama.android/app/src/main/res/mipmap-hdpi/ic_launcher_round.webp differ
diff --git a/examples/llama.android/app/src/main/res/mipmap-mdpi/ic_launcher.webp b/examples/llama.android/app/src/main/res/mipmap-mdpi/ic_launcher.webp
new file mode 100644
index 000000000..4f0f1d64e
Binary files /dev/null and b/examples/llama.android/app/src/main/res/mipmap-mdpi/ic_launcher.webp differ
diff --git a/examples/llama.android/app/src/main/res/mipmap-mdpi/ic_launcher_round.webp b/examples/llama.android/app/src/main/res/mipmap-mdpi/ic_launcher_round.webp
new file mode 100644
index 000000000..62b611da0
Binary files /dev/null and b/examples/llama.android/app/src/main/res/mipmap-mdpi/ic_launcher_round.webp differ
diff --git a/examples/llama.android/app/src/main/res/mipmap-xhdpi/ic_launcher.webp b/examples/llama.android/app/src/main/res/mipmap-xhdpi/ic_launcher.webp
new file mode 100644
index 000000000..948a3070f
Binary files /dev/null and b/examples/llama.android/app/src/main/res/mipmap-xhdpi/ic_launcher.webp differ
diff --git a/examples/llama.android/app/src/main/res/mipmap-xhdpi/ic_launcher_round.webp b/examples/llama.android/app/src/main/res/mipmap-xhdpi/ic_launcher_round.webp
new file mode 100644
index 000000000..1b9a6956b
Binary files /dev/null and b/examples/llama.android/app/src/main/res/mipmap-xhdpi/ic_launcher_round.webp differ
diff --git a/examples/llama.android/app/src/main/res/mipmap-xxhdpi/ic_launcher.webp b/examples/llama.android/app/src/main/res/mipmap-xxhdpi/ic_launcher.webp
new file mode 100644
index 000000000..28d4b77f9
Binary files /dev/null and b/examples/llama.android/app/src/main/res/mipmap-xxhdpi/ic_launcher.webp differ
diff --git a/examples/llama.android/app/src/main/res/mipmap-xxhdpi/ic_launcher_round.webp b/examples/llama.android/app/src/main/res/mipmap-xxhdpi/ic_launcher_round.webp
new file mode 100644
index 000000000..9287f5083
Binary files /dev/null and b/examples/llama.android/app/src/main/res/mipmap-xxhdpi/ic_launcher_round.webp differ
diff --git a/examples/llama.android/app/src/main/res/mipmap-xxxhdpi/ic_launcher.webp b/examples/llama.android/app/src/main/res/mipmap-xxxhdpi/ic_launcher.webp
new file mode 100644
index 000000000..aa7d6427e
Binary files /dev/null and b/examples/llama.android/app/src/main/res/mipmap-xxxhdpi/ic_launcher.webp differ
diff --git a/examples/llama.android/app/src/main/res/mipmap-xxxhdpi/ic_launcher_round.webp b/examples/llama.android/app/src/main/res/mipmap-xxxhdpi/ic_launcher_round.webp
new file mode 100644
index 000000000..9126ae37c
Binary files /dev/null and b/examples/llama.android/app/src/main/res/mipmap-xxxhdpi/ic_launcher_round.webp differ
diff --git a/examples/llama.android/app/src/main/res/values/colors.xml b/examples/llama.android/app/src/main/res/values/colors.xml
new file mode 100644
index 000000000..ca1931bca
--- /dev/null
+++ b/examples/llama.android/app/src/main/res/values/colors.xml
@@ -0,0 +1,10 @@
+<?xml version="1.0" encoding="utf-8"?>
+<resources>
+    <color name="purple_200">#FFBB86FC</color>
+    <color name="purple_500">#FF6200EE</color>
+    <color name="purple_700">#FF3700B3</color>
+    <color name="teal_200">#FF03DAC5</color>
+    <color name="teal_700">#FF018786</color>
+    <color name="black">#FF000000</color>
+    <color name="white">#FFFFFFFF</color>
+</resources>
diff --git a/examples/llama.android/app/src/main/res/values/strings.xml b/examples/llama.android/app/src/main/res/values/strings.xml
new file mode 100644
index 000000000..7a9d314e2
--- /dev/null
+++ b/examples/llama.android/app/src/main/res/values/strings.xml
@@ -0,0 +1,3 @@
+<resources>
+    <string name="app_name">LlamaAndroid</string>
+</resources>
diff --git a/examples/llama.android/app/src/main/res/values/themes.xml b/examples/llama.android/app/src/main/res/values/themes.xml
new file mode 100644
index 000000000..8a24fda56
--- /dev/null
+++ b/examples/llama.android/app/src/main/res/values/themes.xml
@@ -0,0 +1,5 @@
+<?xml version="1.0" encoding="utf-8"?>
+<resources>
+
+    <style name="Theme.LlamaAndroid" parent="android:Theme.Material.Light.NoActionBar" />
+</resources>
diff --git a/examples/llama.android/app/src/main/res/xml/backup_rules.xml b/examples/llama.android/app/src/main/res/xml/backup_rules.xml
new file mode 100644
index 000000000..148c18b65
--- /dev/null
+++ b/examples/llama.android/app/src/main/res/xml/backup_rules.xml
@@ -0,0 +1,13 @@
+<?xml version="1.0" encoding="utf-8"?><!--
+   Sample backup rules file; uncomment and customize as necessary.
+   See https://developer.android.com/guide/topics/data/autobackup
+   for details.
+   Note: This file is ignored for devices older that API 31
+   See https://developer.android.com/about/versions/12/backup-restore
+-->
+<full-backup-content>
+    <!--
+   <include domain="sharedpref" path="."/>
+   <exclude domain="sharedpref" path="device.xml"/>
+-->
+</full-backup-content>
diff --git a/examples/llama.android/app/src/main/res/xml/data_extraction_rules.xml b/examples/llama.android/app/src/main/res/xml/data_extraction_rules.xml
new file mode 100644
index 000000000..0c4f95cab
--- /dev/null
+++ b/examples/llama.android/app/src/main/res/xml/data_extraction_rules.xml
@@ -0,0 +1,19 @@
+<?xml version="1.0" encoding="utf-8"?><!--
+   Sample data extraction rules file; uncomment and customize as necessary.
+   See https://developer.android.com/about/versions/12/backup-restore#xml-changes
+   for details.
+-->
+<data-extraction-rules>
+    <cloud-backup>
+        <!-- TODO: Use <include> and <exclude> to control what is backed up.
+        <include .../>
+        <exclude .../>
+        -->
+    </cloud-backup>
+    <!--
+    <device-transfer>
+        <include .../>
+        <exclude .../>
+    </device-transfer>
+    -->
+</data-extraction-rules>
diff --git a/examples/llama.android/build.gradle.kts b/examples/llama.android/build.gradle.kts
new file mode 100644
index 000000000..50ebc8211
--- /dev/null
+++ b/examples/llama.android/build.gradle.kts
@@ -0,0 +1,5 @@
+// Top-level build file where you can add configuration options common to all sub-projects/modules.
+plugins {
+    id("com.android.application") version "8.2.0" apply false
+    id("org.jetbrains.kotlin.android") version "1.9.0" apply false
+}
diff --git a/examples/llama.android/gradle.properties b/examples/llama.android/gradle.properties
new file mode 100644
index 000000000..2cbd6d19d
--- /dev/null
+++ b/examples/llama.android/gradle.properties
@@ -0,0 +1,23 @@
+# Project-wide Gradle settings.
+# IDE (e.g. Android Studio) users:
+# Gradle settings configured through the IDE *will override*
+# any settings specified in this file.
+# For more details on how to configure your build environment visit
+# http://www.gradle.org/docs/current/userguide/build_environment.html
+# Specifies the JVM arguments used for the daemon process.
+# The setting is particularly useful for tweaking memory settings.
+org.gradle.jvmargs=-Xmx2048m -Dfile.encoding=UTF-8
+# When configured, Gradle will run in incubating parallel mode.
+# This option should only be used with decoupled projects. More details, visit
+# http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects
+# org.gradle.parallel=true
+# AndroidX package structure to make it clearer which packages are bundled with the
+# Android operating system, and which are packaged with your app's APK
+# https://developer.android.com/topic/libraries/support-library/androidx-rn
+android.useAndroidX=true
+# Kotlin code style for this project: "official" or "obsolete":
+kotlin.code.style=official
+# Enables namespacing of each library's R class so that its R class includes only the
+# resources declared in the library itself and none from the library's dependencies,
+# thereby reducing the size of the R class for that library
+android.nonTransitiveRClass=true
diff --git a/examples/llama.android/gradle/wrapper/gradle-wrapper.jar b/examples/llama.android/gradle/wrapper/gradle-wrapper.jar
new file mode 100644
index 000000000..e708b1c02
Binary files /dev/null and b/examples/llama.android/gradle/wrapper/gradle-wrapper.jar differ
diff --git a/examples/llama.android/gradle/wrapper/gradle-wrapper.properties b/examples/llama.android/gradle/wrapper/gradle-wrapper.properties
new file mode 100644
index 000000000..a3958c140
--- /dev/null
+++ b/examples/llama.android/gradle/wrapper/gradle-wrapper.properties
@@ -0,0 +1,6 @@
+#Thu Dec 21 14:31:09 AEDT 2023
+distributionBase=GRADLE_USER_HOME
+distributionPath=wrapper/dists
+distributionUrl=https\://services.gradle.org/distributions/gradle-8.2-bin.zip
+zipStoreBase=GRADLE_USER_HOME
+zipStorePath=wrapper/dists
diff --git a/examples/llama.android/gradlew b/examples/llama.android/gradlew
new file mode 100755
index 000000000..4f906e0c8
--- /dev/null
+++ b/examples/llama.android/gradlew
@@ -0,0 +1,185 @@
+#!/usr/bin/env sh
+
+#
+# Copyright 2015 the original author or authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+##############################################################################
+##
+##  Gradle start up script for UN*X
+##
+##############################################################################
+
+# Attempt to set APP_HOME
+# Resolve links: $0 may be a link
+PRG="$0"
+# Need this for relative symlinks.
+while [ -h "$PRG" ] ; do
+    ls=`ls -ld "$PRG"`
+    link=`expr "$ls" : '.*-> \(.*\)$'`
+    if expr "$link" : '/.*' > /dev/null; then
+        PRG="$link"
+    else
+        PRG=`dirname "$PRG"`"/$link"
+    fi
+done
+SAVED="`pwd`"
+cd "`dirname \"$PRG\"`/" >/dev/null
+APP_HOME="`pwd -P`"
+cd "$SAVED" >/dev/null
+
+APP_NAME="Gradle"
+APP_BASE_NAME=`basename "$0"`
+
+# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+DEFAULT_JVM_OPTS='"-Xmx64m" "-Xms64m"'
+
+# Use the maximum available, or set MAX_FD != -1 to use that value.
+MAX_FD="maximum"
+
+warn () {
+    echo "$*"
+}
+
+die () {
+    echo
+    echo "$*"
+    echo
+    exit 1
+}
+
+# OS specific support (must be 'true' or 'false').
+cygwin=false
+msys=false
+darwin=false
+nonstop=false
+case "`uname`" in
+  CYGWIN* )
+    cygwin=true
+    ;;
+  Darwin* )
+    darwin=true
+    ;;
+  MINGW* )
+    msys=true
+    ;;
+  NONSTOP* )
+    nonstop=true
+    ;;
+esac
+
+CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
+
+
+# Determine the Java command to use to start the JVM.
+if [ -n "$JAVA_HOME" ] ; then
+    if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
+        # IBM's JDK on AIX uses strange locations for the executables
+        JAVACMD="$JAVA_HOME/jre/sh/java"
+    else
+        JAVACMD="$JAVA_HOME/bin/java"
+    fi
+    if [ ! -x "$JAVACMD" ] ; then
+        die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+    fi
+else
+    JAVACMD="java"
+    which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+fi
+
+# Increase the maximum file descriptors if we can.
+if [ "$cygwin" = "false" -a "$darwin" = "false" -a "$nonstop" = "false" ] ; then
+    MAX_FD_LIMIT=`ulimit -H -n`
+    if [ $? -eq 0 ] ; then
+        if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then
+            MAX_FD="$MAX_FD_LIMIT"
+        fi
+        ulimit -n $MAX_FD
+        if [ $? -ne 0 ] ; then
+            warn "Could not set maximum file descriptor limit: $MAX_FD"
+        fi
+    else
+        warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT"
+    fi
+fi
+
+# For Darwin, add options to specify how the application appears in the dock
+if $darwin; then
+    GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\""
+fi
+
+# For Cygwin or MSYS, switch paths to Windows format before running java
+if [ "$cygwin" = "true" -o "$msys" = "true" ] ; then
+    APP_HOME=`cygpath --path --mixed "$APP_HOME"`
+    CLASSPATH=`cygpath --path --mixed "$CLASSPATH"`
+
+    JAVACMD=`cygpath --unix "$JAVACMD"`
+
+    # We build the pattern for arguments to be converted via cygpath
+    ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`
+    SEP=""
+    for dir in $ROOTDIRSRAW ; do
+        ROOTDIRS="$ROOTDIRS$SEP$dir"
+        SEP="|"
+    done
+    OURCYGPATTERN="(^($ROOTDIRS))"
+    # Add a user-defined pattern to the cygpath arguments
+    if [ "$GRADLE_CYGPATTERN" != "" ] ; then
+        OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)"
+    fi
+    # Now convert the arguments - kludge to limit ourselves to /bin/sh
+    i=0
+    for arg in "$@" ; do
+        CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -`
+        CHECK2=`echo "$arg"|egrep -c "^-"`                                 ### Determine if an option
+
+        if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then                    ### Added a condition
+            eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"`
+        else
+            eval `echo args$i`="\"$arg\""
+        fi
+        i=`expr $i + 1`
+    done
+    case $i in
+        0) set -- ;;
+        1) set -- "$args0" ;;
+        2) set -- "$args0" "$args1" ;;
+        3) set -- "$args0" "$args1" "$args2" ;;
+        4) set -- "$args0" "$args1" "$args2" "$args3" ;;
+        5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;;
+        6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;;
+        7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;;
+        8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;;
+        9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;;
+    esac
+fi
+
+# Escape application args
+save () {
+    for i do printf %s\\n "$i" | sed "s/'/'\\\\''/g;1s/^/'/;\$s/\$/' \\\\/" ; done
+    echo " "
+}
+APP_ARGS=`save "$@"`
+
+# Collect all arguments for the java command, following the shell quoting and substitution rules
+eval set -- $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS "\"-Dorg.gradle.appname=$APP_BASE_NAME\"" -classpath "\"$CLASSPATH\"" org.gradle.wrapper.GradleWrapperMain "$APP_ARGS"
+
+exec "$JAVACMD" "$@"
diff --git a/examples/llama.android/settings.gradle.kts b/examples/llama.android/settings.gradle.kts
new file mode 100644
index 000000000..2ba32c4fa
--- /dev/null
+++ b/examples/llama.android/settings.gradle.kts
@@ -0,0 +1,17 @@
+pluginManagement {
+    repositories {
+        google()
+        mavenCentral()
+        gradlePluginPortal()
+    }
+}
+dependencyResolutionManagement {
+    repositoriesMode.set(RepositoriesMode.FAIL_ON_PROJECT_REPOS)
+    repositories {
+        google()
+        mavenCentral()
+    }
+}
+
+rootProject.name = "LlamaAndroid"
+include(":app")
diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
index fc79fd346..c249291ae 100644
--- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
+++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
@@ -51,7 +51,7 @@ actor LlamaContext {
     }
 
     static func create_context(path: String) throws -> LlamaContext {
-        llama_backend_init(false)
+        llama_backend_init()
         var model_params = llama_model_default_params()
 
 #if targetEnvironment(simulator)
@@ -221,6 +221,7 @@ actor LlamaContext {
             if llama_decode(context, batch) != 0 {
                 print("llama_decode() failed during prompt")
             }
+            llama_synchronize(context)
 
             let t_pp_end = ggml_time_us()
 
@@ -240,6 +241,7 @@ actor LlamaContext {
                 if llama_decode(context, batch) != 0 {
                     print("llama_decode() failed during text generation")
                 }
+                llama_synchronize(context)
             }
 
             let t_tg_end = ggml_time_us()
diff --git a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
index a8848a49f..3950b9e9d 100644
--- a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
+++ b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj
@@ -8,6 +8,7 @@
 
 /* Begin PBXBuildFile section */
 		549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 549479CA2AC9E16000E0F78B /* Metal.framework */; };
+		79E1D9CD2B4CD16E005F8E46 /* InputButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = 79E1D9CC2B4CD16E005F8E46 /* InputButton.swift */; };
 		7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = 7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */; };
 		8A1C83772AC328BD0096AF73 /* llama_swiftuiApp.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */; };
 		8A1C83792AC328BD0096AF73 /* ContentView.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A1C83782AC328BD0096AF73 /* ContentView.swift */; };
@@ -22,6 +23,7 @@
 
 /* Begin PBXFileReference section */
 		549479CA2AC9E16000E0F78B /* Metal.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Metal.framework; path = System/Library/Frameworks/Metal.framework; sourceTree = SDKROOT; };
+		79E1D9CC2B4CD16E005F8E46 /* InputButton.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = InputButton.swift; sourceTree = "<group>"; };
 		7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = DownloadButton.swift; sourceTree = "<group>"; };
 		8A1C83732AC328BD0096AF73 /* llama.swiftui.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = llama.swiftui.app; sourceTree = BUILT_PRODUCTS_DIR; };
 		8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = llama_swiftuiApp.swift; sourceTree = "<group>"; };
@@ -119,6 +121,7 @@
 				7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */,
 				8A1C83782AC328BD0096AF73 /* ContentView.swift */,
 				F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */,
+				79E1D9CC2B4CD16E005F8E46 /* InputButton.swift */,
 			);
 			path = UI;
 			sourceTree = "<group>";
@@ -213,6 +216,7 @@
 				8A1C83792AC328BD0096AF73 /* ContentView.swift in Sources */,
 				8A1C83772AC328BD0096AF73 /* llama_swiftuiApp.swift in Sources */,
 				7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */,
+				79E1D9CD2B4CD16E005F8E46 /* InputButton.swift in Sources */,
 			);
 			runOnlyForDeploymentPostprocessing = 0;
 		};
@@ -345,7 +349,7 @@
 				CLANG_ENABLE_MODULES = YES;
 				CODE_SIGN_STYLE = Automatic;
 				CURRENT_PROJECT_VERSION = 1;
-				DEVELOPMENT_TEAM = STLSG3FG8Q;
+				DEVELOPMENT_TEAM = K5UQJPP73A;
 				ENABLE_PREVIEWS = YES;
 				GENERATE_INFOPLIST_FILE = YES;
 				INFOPLIST_KEY_UIApplicationSceneManifest_Generation = YES;
@@ -377,7 +381,7 @@
 				CLANG_ENABLE_MODULES = YES;
 				CODE_SIGN_STYLE = Automatic;
 				CURRENT_PROJECT_VERSION = 1;
-				DEVELOPMENT_TEAM = STLSG3FG8Q;
+				DEVELOPMENT_TEAM = K5UQJPP73A;
 				ENABLE_PREVIEWS = YES;
 				GENERATE_INFOPLIST_FILE = YES;
 				INFOPLIST_KEY_UIApplicationSceneManifest_Generation = YES;
diff --git a/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift b/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift
index 17cb5b9dd..5bde18917 100644
--- a/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift
+++ b/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift
@@ -1,9 +1,19 @@
 import Foundation
 
+struct Model: Identifiable {
+    var id = UUID()
+    var name: String
+    var url: String
+    var filename: String
+    var status: String?
+}
+
 @MainActor
 class LlamaState: ObservableObject {
     @Published var messageLog = ""
     @Published var cacheCleared = false
+    @Published var downloadedModels: [Model] = []
+    @Published var undownloadedModels: [Model] = []
     let NS_PER_S = 1_000_000_000.0
 
     private var llamaContext: LlamaContext?
@@ -13,23 +23,102 @@ class LlamaState: ObservableObject {
     }
 
     init() {
+        loadModelsFromDisk()
+        loadDefaultModels()
+    }
+
+    private func loadModelsFromDisk() {
+        do {
+            let documentsURL = getDocumentsDirectory()
+            let modelURLs = try FileManager.default.contentsOfDirectory(at: documentsURL, includingPropertiesForKeys: nil, options: [.skipsHiddenFiles, .skipsSubdirectoryDescendants])
+            for modelURL in modelURLs {
+                let modelName = modelURL.deletingPathExtension().lastPathComponent
+                downloadedModels.append(Model(name: modelName, url: "", filename: modelURL.lastPathComponent, status: "downloaded"))
+            }
+        } catch {
+            print("Error loading models from disk: \(error)")
+        }
+    }
+
+    private func loadDefaultModels() {
         do {
             try loadModel(modelUrl: defaultModelUrl)
         } catch {
             messageLog += "Error!\n"
         }
+
+        for model in defaultModels {
+            let fileURL = getDocumentsDirectory().appendingPathComponent(model.filename)
+            if FileManager.default.fileExists(atPath: fileURL.path) {
+
+            } else {
+                var undownloadedModel = model
+                undownloadedModel.status = "download"
+                undownloadedModels.append(undownloadedModel)
+            }
+        }
     }
 
+    func getDocumentsDirectory() -> URL {
+        let paths = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)
+        return paths[0]
+    }
+    private let defaultModels: [Model] = [
+        Model(name: "TinyLlama-1.1B (Q4_0, 0.6 GiB)",url: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_0.gguf?download=true",filename: "tinyllama-1.1b-1t-openorca.Q4_0.gguf", status: "download"),
+        Model(
+            name: "TinyLlama-1.1B Chat (Q8_0, 1.1 GiB)",
+            url: "https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/tinyllama-1.1b-chat-v1.0.Q8_0.gguf?download=true",
+            filename: "tinyllama-1.1b-chat-v1.0.Q8_0.gguf", status: "download"
+        ),
+
+        Model(
+            name: "TinyLlama-1.1B (F16, 2.2 GiB)",
+            url: "https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true",
+            filename: "tinyllama-1.1b-f16.gguf", status: "download"
+        ),
+
+        Model(
+            name: "Phi-2.7B (Q4_0, 1.6 GiB)",
+            url: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true",
+            filename: "phi-2-q4_0.gguf", status: "download"
+        ),
+
+        Model(
+            name: "Phi-2.7B (Q8_0, 2.8 GiB)",
+            url: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q8_0.gguf?download=true",
+            filename: "phi-2-q8_0.gguf", status: "download"
+        ),
+
+        Model(
+            name: "Mistral-7B-v0.1 (Q4_0, 3.8 GiB)",
+            url: "https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/resolve/main/mistral-7b-v0.1.Q4_0.gguf?download=true",
+            filename: "mistral-7b-v0.1.Q4_0.gguf", status: "download"
+        ),
+        Model(
+            name: "OpenHermes-2.5-Mistral-7B (Q3_K_M, 3.52 GiB)",
+            url: "https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/resolve/main/openhermes-2.5-mistral-7b.Q3_K_M.gguf?download=true",
+            filename: "openhermes-2.5-mistral-7b.Q3_K_M.gguf", status: "download"
+        )
+    ]
     func loadModel(modelUrl: URL?) throws {
         if let modelUrl {
             messageLog += "Loading model...\n"
             llamaContext = try LlamaContext.create_context(path: modelUrl.path())
             messageLog += "Loaded model \(modelUrl.lastPathComponent)\n"
+
+            // Assuming that the model is successfully loaded, update the downloaded models
+            updateDownloadedModels(modelName: modelUrl.lastPathComponent, status: "downloaded")
         } else {
             messageLog += "Load a model from the list below\n"
         }
     }
 
+
+    private func updateDownloadedModels(modelName: String, status: String) {
+        undownloadedModels.removeAll { $0.name == modelName }
+    }
+
+
     func complete(text: String) async {
         guard let llamaContext else {
             return
diff --git a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift
index 7c81ea256..30c2dc431 100644
--- a/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift
+++ b/examples/llama.swiftui/llama.swiftui/UI/ContentView.swift
@@ -2,115 +2,57 @@ import SwiftUI
 
 struct ContentView: View {
     @StateObject var llamaState = LlamaState()
-
     @State private var multiLineText = ""
-
-    private static func cleanupModelCaches() {
-        // Delete all models (*.gguf)
-        let fileManager = FileManager.default
-        let documentsUrl =  FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)[0]
-        do {
-            let fileURLs = try fileManager.contentsOfDirectory(at: documentsUrl, includingPropertiesForKeys: nil)
-            for fileURL in fileURLs {
-                if fileURL.pathExtension == "gguf" {
-                    try fileManager.removeItem(at: fileURL)
-                }
-            }
-        } catch {
-            print("Error while enumerating files \(documentsUrl.path): \(error.localizedDescription)")
-        }
-    }
+    @State private var showingHelp = false    // To track if Help Sheet should be shown
 
     var body: some View {
-        VStack {
-            ScrollView(.vertical, showsIndicators: true) {
-                Text(llamaState.messageLog)
-                .font(.system(size: 12))
-                .frame(maxWidth: .infinity, alignment: .leading)
+        NavigationView {
+            VStack {
+                ScrollView(.vertical, showsIndicators: true) {
+                    Text(llamaState.messageLog)
+                        .font(.system(size: 12))
+                        .frame(maxWidth: .infinity, alignment: .leading)
+                        .padding()
+                        .onTapGesture {
+                            UIApplication.shared.sendAction(#selector(UIResponder.resignFirstResponder), to: nil, from: nil, for: nil)
+                        }
+                }
+
+                TextEditor(text: $multiLineText)
+                    .frame(height: 80)
+                    .padding()
+                    .border(Color.gray, width: 0.5)
+
+                HStack {
+                    Button("Send") {
+                        sendText()
+                    }
+
+                    Button("Bench") {
+                        bench()
+                    }
+
+                    Button("Clear") {
+                        clear()
+                    }
+
+                    Button("Copy") {
+                        UIPasteboard.general.string = llamaState.messageLog
+                    }
+                }
+                .buttonStyle(.bordered)
                 .padding()
-                .onTapGesture {
-                    UIApplication.shared.sendAction(#selector(UIResponder.resignFirstResponder), to: nil, from: nil, for: nil)
-                }
-            }
 
-            TextEditor(text: $multiLineText)
-                .frame(height: 80)
+                NavigationLink(destination: DrawerView(llamaState: llamaState)) {
+                    Text("View Models")
+                }
                 .padding()
-                .border(Color.gray, width: 0.5)
 
-            HStack {
-                Button("Send") {
-                    sendText()
-                }
-
-                Button("Bench") {
-                    bench()
-                }
-
-                Button("Clear") {
-                    clear()
-                }
-
-                Button("Copy") {
-                    UIPasteboard.general.string = llamaState.messageLog
-                }
-            }.buttonStyle(.bordered)
-
-            VStack(alignment: .leading) {
-                DownloadButton(
-                    llamaState: llamaState,
-                    modelName: "TinyLlama-1.1B (Q4_0, 0.6 GiB)",
-                    modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_0.gguf?download=true",
-                    filename: "tinyllama-1.1b-1t-openorca.Q4_0.gguf"
-                )
-
-                DownloadButton(
-                    llamaState: llamaState,
-                    modelName: "TinyLlama-1.1B (Q8_0, 1.1 GiB)",
-                    modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q8_0.gguf?download=true",
-                    filename: "tinyllama-1.1b-1t-openorca.Q8_0.gguf"
-                )
-
-                DownloadButton(
-                    llamaState: llamaState,
-                    modelName: "TinyLlama-1.1B (F16, 2.2 GiB)",
-                    modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true",
-                    filename: "tinyllama-1.1b-f16.gguf"
-                )
-
-                DownloadButton(
-                    llamaState: llamaState,
-                    modelName: "Phi-2.7B (Q4_0, 1.6 GiB)",
-                    modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true",
-                    filename: "phi-2-q4_0.gguf"
-                )
-
-                DownloadButton(
-                    llamaState: llamaState,
-                    modelName: "Phi-2.7B (Q8_0, 2.8 GiB)",
-                    modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q8_0.gguf?download=true",
-                    filename: "phi-2-q8_0.gguf"
-                )
-
-                DownloadButton(
-                    llamaState: llamaState,
-                    modelName: "Mistral-7B-v0.1 (Q4_0, 3.8 GiB)",
-                    modelUrl: "https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/resolve/main/mistral-7b-v0.1.Q4_0.gguf?download=true",
-                    filename: "mistral-7b-v0.1.Q4_0.gguf"
-                )
-
-                Button("Clear downloaded models") {
-                    ContentView.cleanupModelCaches()
-                    llamaState.cacheCleared = true
-                }
-
-                LoadCustomButton(llamaState: llamaState)
             }
-            .padding(.top, 4)
-            .font(.system(size: 12))
-            .frame(maxWidth: .infinity, alignment: .leading)
+            .padding()
+            .navigationBarTitle("Model Settings", displayMode: .inline)
+
         }
-        .padding()
     }
 
     func sendText() {
@@ -131,8 +73,73 @@ struct ContentView: View {
             await llamaState.clear()
         }
     }
+    struct DrawerView: View {
+
+        @ObservedObject var llamaState: LlamaState
+        @State private var showingHelp = false
+        func delete(at offsets: IndexSet) {
+            offsets.forEach { offset in
+                let model = llamaState.downloadedModels[offset]
+                let fileURL = getDocumentsDirectory().appendingPathComponent(model.filename)
+                do {
+                    try FileManager.default.removeItem(at: fileURL)
+                } catch {
+                    print("Error deleting file: \(error)")
+                }
+            }
+
+            // Remove models from downloadedModels array
+            llamaState.downloadedModels.remove(atOffsets: offsets)
+        }
+
+        func getDocumentsDirectory() -> URL {
+            let paths = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)
+            return paths[0]
+        }
+        var body: some View {
+            List {
+                Section(header: Text("Download Models From Hugging Face")) {
+                    HStack {
+                        InputButton(llamaState: llamaState)
+                    }
+                }
+                Section(header: Text("Downloaded Models")) {
+                    ForEach(llamaState.downloadedModels) { model in
+                        DownloadButton(llamaState: llamaState, modelName: model.name, modelUrl: model.url, filename: model.filename)
+                    }
+                    .onDelete(perform: delete)
+                }
+                Section(header: Text("Default Models")) {
+                    ForEach(llamaState.undownloadedModels) { model in
+                        DownloadButton(llamaState: llamaState, modelName: model.name, modelUrl: model.url, filename: model.filename)
+                    }
+                }
+
+            }
+            .listStyle(GroupedListStyle())
+            .navigationBarTitle("Model Settings", displayMode: .inline).toolbar {
+                ToolbarItem(placement: .navigationBarTrailing) {
+                    Button("Help") {
+                        showingHelp = true
+                    }
+                }
+            }.sheet(isPresented: $showingHelp) {    // Sheet for help modal
+                VStack(alignment: .leading) {
+                    VStack(alignment: .leading) {
+                        Text("1. Make sure the model is in GGUF Format")
+                               .padding()
+                        Text("2. Copy the download link of the quantized model")
+                               .padding()
+                    }
+                    Spacer()
+                   }
+            }
+        }
+    }
 }
 
-//#Preview {
-//    ContentView()
-//}
+struct ContentView_Previews: PreviewProvider {
+    static var previews: some View {
+        ContentView()
+    }
+}
diff --git a/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift b/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift
index c9f322ca1..4584d6eaa 100644
--- a/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift
+++ b/examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift
@@ -53,6 +53,8 @@ struct DownloadButton: View {
 
                     llamaState.cacheCleared = false
 
+                    let model = Model(name: modelName, url: modelUrl, filename: filename, status: "downloaded")
+                    llamaState.downloadedModels.append(model)
                     status = "downloaded"
                 }
             } catch let err {
diff --git a/examples/llama.swiftui/llama.swiftui/UI/InputButton.swift b/examples/llama.swiftui/llama.swiftui/UI/InputButton.swift
new file mode 100644
index 000000000..c5ffbad4e
--- /dev/null
+++ b/examples/llama.swiftui/llama.swiftui/UI/InputButton.swift
@@ -0,0 +1,131 @@
+import SwiftUI
+
+struct InputButton: View {
+    @ObservedObject var llamaState: LlamaState
+    @State private var inputLink: String = ""
+    @State private var status: String = "download"
+    @State private var filename: String = ""
+
+    @State private var downloadTask: URLSessionDownloadTask?
+    @State private var progress = 0.0
+    @State private var observation: NSKeyValueObservation?
+
+    private static func extractModelInfo(from link: String) -> (modelName: String, filename: String)? {
+        guard let url = URL(string: link),
+              let lastPathComponent = url.lastPathComponent.components(separatedBy: ".").first,
+              let modelName = lastPathComponent.components(separatedBy: "-").dropLast().joined(separator: "-").removingPercentEncoding,
+              let filename = lastPathComponent.removingPercentEncoding else {
+            return nil
+        }
+
+        return (modelName, filename)
+    }
+
+    private static func getFileURL(filename: String) -> URL {
+        FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)[0].appendingPathComponent(filename)
+    }
+
+    private func download() {
+        guard let extractedInfo = InputButton.extractModelInfo(from: inputLink) else {
+            // Handle invalid link or extraction failure
+            return
+        }
+
+        let (modelName, filename) = extractedInfo
+        self.filename = filename  // Set the state variable
+
+        status = "downloading"
+        print("Downloading model \(modelName) from \(inputLink)")
+        guard let url = URL(string: inputLink) else { return }
+        let fileURL = InputButton.getFileURL(filename: filename)
+
+        downloadTask = URLSession.shared.downloadTask(with: url) { temporaryURL, response, error in
+            if let error = error {
+                print("Error: \(error.localizedDescription)")
+                return
+            }
+
+            guard let response = response as? HTTPURLResponse, (200...299).contains(response.statusCode) else {
+                print("Server error!")
+                return
+            }
+
+            do {
+                if let temporaryURL = temporaryURL {
+                    try FileManager.default.copyItem(at: temporaryURL, to: fileURL)
+                    print("Writing to \(filename) completed")
+
+                    llamaState.cacheCleared = false
+
+                    let model = Model(name: modelName, url: self.inputLink, filename: filename, status: "downloaded")
+                    llamaState.downloadedModels.append(model)
+                    status = "downloaded"
+                }
+            } catch let err {
+                print("Error: \(err.localizedDescription)")
+            }
+        }
+
+        observation = downloadTask?.progress.observe(\.fractionCompleted) { progress, _ in
+            self.progress = progress.fractionCompleted
+        }
+
+        downloadTask?.resume()
+    }
+
+    var body: some View {
+        VStack {
+            HStack {
+                TextField("Paste Quantized Download Link", text: $inputLink)
+                    .textFieldStyle(RoundedBorderTextFieldStyle())
+
+                Button(action: {
+                    downloadTask?.cancel()
+                    status = "download"
+                }) {
+                    Text("Cancel")
+                }
+            }
+
+            if status == "download" {
+                Button(action: download) {
+                    Text("Download Custom Model")
+                }
+            } else if status == "downloading" {
+                Button(action: {
+                    downloadTask?.cancel()
+                    status = "download"
+                }) {
+                    Text("Downloading \(Int(progress * 100))%")
+                }
+            } else if status == "downloaded" {
+                Button(action: {
+                    let fileURL = InputButton.getFileURL(filename: self.filename)
+                    if !FileManager.default.fileExists(atPath: fileURL.path) {
+                        download()
+                        return
+                    }
+                    do {
+                        try llamaState.loadModel(modelUrl: fileURL)
+                    } catch let err {
+                        print("Error: \(err.localizedDescription)")
+                    }
+                }) {
+                    Text("Load Custom Model")
+                }
+            } else {
+                Text("Unknown status")
+            }
+        }
+        .onDisappear() {
+            downloadTask?.cancel()
+        }
+        .onChange(of: llamaState.cacheCleared) { newValue in
+            if newValue {
+                downloadTask?.cancel()
+                let fileURL = InputButton.getFileURL(filename: self.filename)
+                status = FileManager.default.fileExists(atPath: fileURL.path) ? "downloaded" : "download"
+            }
+        }
+    }
+}
diff --git a/examples/llama.vim b/examples/llama.vim
index f03fadfb7..1b5ad6ba0 100644
--- a/examples/llama.vim
+++ b/examples/llama.vim
@@ -6,7 +6,7 @@
 " Similarly, you could add an insert mode keybind with
 " inoremap <C-B> <Cmd>call llama#doLlamaGen()<CR>
 "
-" g:llama_api_url and g:llama_overrides can be configured in your .vimrc
+" g:llama_api_url, g:llama_api_key and g:llama_overrides can be configured in your .vimrc
 " let g:llama_api_url = "192.168.1.10:8080"
 " llama_overrides can also be set through buffer/window scopes. For instance
 " autocmd filetype python let b:llama_overrides = {"temp": 0.2}
@@ -82,6 +82,9 @@ func llama#doLlamaGen()
    endif
    let l:querydata.prompt = join(l:buflines, "\n")
    let l:curlcommand = copy(s:curlcommand)
+   if exists("g:llama_api_key")
+       call extend(l:curlcommand, ['--header', 'Authorization: Bearer ' .. g:llama_api_key])
+   endif
    let l:curlcommand[2] = json_encode(l:querydata)
    let b:job = job_start(l:curlcommand, {"callback": function("s:callbackHandler", [l:cbuffer])})
 endfunction
diff --git a/examples/llava/MobileVLM-README.md b/examples/llava/MobileVLM-README.md
new file mode 100644
index 000000000..96b048525
--- /dev/null
+++ b/examples/llava/MobileVLM-README.md
@@ -0,0 +1,377 @@
+# MobileVLM
+
+Currently this implementation supports [MobileVLM-1.7B](https://huggingface.co/mtgv/MobileVLM-1.7B) / [MobileVLM_V2-1.7B](https://huggingface.co/mtgv/MobileVLM_V2-1.7B) variants.
+
+for more information, please go to [Meituan-AutoML/MobileVLM](https://github.com/Meituan-AutoML/MobileVLM)
+
+The implementation is based on llava, and is compatible with llava and mobileVLM. The usage is basically same as llava.
+
+Notice: The overall process of model inference for both **MobileVLM** and **MobileVLM_V2** models is the same, but the process of model conversion is a little different. Therefore, using **MobileVLM-1.7B** as an example, the different conversion step will be shown.
+
+## Usage
+Build with cmake or run `make llava-cli` to build it.
+
+After building, run: `./llava-cli` to see the usage. For example:
+
+```sh
+./llava-cli -m MobileVLM-1.7B/ggml-model-q4_k.gguf \
+    --mmproj MobileVLM-1.7B/mmproj-model-f16.gguf \
+    --image path/to/an/image.jpg \
+    -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWho is the author of this book? Answer the question using a single word or phrase. ASSISTANT:"
+```
+
+## Model conversion
+
+- Clone `mobileVLM-1.7B` and `clip-vit-large-patch14-336` locally:
+
+```sh
+git clone https://huggingface.co/mtgv/MobileVLM-1.7B
+
+git clone https://huggingface.co/openai/clip-vit-large-patch14-336
+```
+
+2. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
+
+```sh
+python ./examples/llava/llava-surgery.py -m path/to/MobileVLM-1.7B
+```
+
+3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` (for **V2** please use `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF:
+
+```sh
+python ./examples/llava/convert-image-encoder-to-gguf \
+    -m path/to/clip-vit-large-patch14-336 \
+    --llava-projector path/to/MobileVLM-1.7B/llava.projector \
+    --output-dir path/to/MobileVLM-1.7B \
+    --projector-type ldp
+```
+
+```sh
+python ./examples/llava/convert-image-encoder-to-gguf \
+    -m path/to/clip-vit-large-patch14-336 \
+    --llava-projector path/to/MobileVLM-1.7B_V2/llava.projector \
+    --output-dir path/to/MobileVLM-1.7B_V2 \
+    --projector-type ldpv2
+```
+
+4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:
+
+```sh
+python ./convert.py path/to/MobileVLM-1.7B
+```
+
+5. Use `quantize` to convert LLaMA part's DataType from `fp16` to `q4_k`
+```sh
+./quantize path/to/MobileVLM-1.7B/ggml-model-f16.gguf path/to/MobileVLM-1.7B/ggml-model-q4_k.gguf q4_k_s
+```
+
+Now both the LLaMA part and the image encoder is in the `MobileVLM-1.7B` directory.
+
+## Android compile and run
+### compile
+refer to `examples/llava/android/build_64.sh`
+```sh
+mkdir examples/llava/android/build_64
+cd examples/llava/android/build_64
+../build_64.sh
+```
+### run on Android
+refer to `android/adb_run.sh`, modify resources' `name` and `path`
+
+## Some result on Android with `Snapdragon 888` chip
+### case 1
+**input**
+```sh
+/data/local/tmp/llava-cli \
+    -m /data/local/tmp/ggml-model-q4_k.gguf \
+    --mmproj /data/local/tmp/mmproj-model-f16.gguf \
+    -t 4 \
+    --image /data/local/tmp/demo.jpg \
+    -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWho is the author of this book? \nAnswer the question using a single word or phrase. ASSISTANT:"
+```
+**output**
+```sh
+encode_image_with_clip: image encoded in 21148.71 ms by CLIP (  146.87 ms per image patch)
+ Susan Wise Bauer
+llama_print_timings:        load time =   23574.72 ms
+llama_print_timings:      sample time =       1.24 ms /     6 runs   (    0.21 ms per token,  4850.44 tokens per second)
+llama_print_timings: prompt eval time =   12460.15 ms /   246 tokens (   50.65 ms per token,    19.74 tokens per second)
+llama_print_timings:        eval time =     424.86 ms /     6 runs   (   70.81 ms per token,    14.12 tokens per second)
+llama_print_timings:       total time =   34731.93 ms
+```
+### case 2
+**input**
+```sh
+/data/local/tmp/llava-cli \
+    -m /data/local/tmp/ggml-model-q4_k.gguf \
+    --mmproj /data/local/tmp/mmproj-model-f16.gguf \
+    -t 4 \
+    --image /data/local/tmp/cat.jpeg \
+    -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat is in the image? ASSISTANT:"
+```
+**output**
+```sh
+encode_image_with_clip: image encoded in 21149.51 ms by CLIP (  146.87 ms per image patch)
+ The image depicts a cat sitting in the grass near some tall green plants.
+llama_print_timings:        load time =   23257.32 ms
+llama_print_timings:      sample time =       5.25 ms /    18 runs   (    0.29 ms per token,  3430.53 tokens per second)
+llama_print_timings: prompt eval time =   11900.73 ms /   232 tokens (   51.30 ms per token,    19.49 tokens per second)
+llama_print_timings:        eval time =    1279.03 ms /    18 runs   (   71.06 ms per token,    14.07 tokens per second)
+llama_print_timings:       total time =   34570.79 ms
+```
+
+
+## Some result on Android with `Snapdragon 778G` chip
+### MobileVLM-1.7B case
+#### llava-cli release-b2005
+**input**
+```sh
+/data/local/tmp/llava-cli \
+    -m /data/local/tmp/ggml-model-q4_k.gguf \
+    --mmproj /data/local/tmp/mmproj-model-f16.gguf \
+    -t 4 \
+    --image /data/local/tmp/many_llamas.jpeg \
+    -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat's that? ASSISTANT:"
+```
+**output**
+```sh
+encode_image_with_clip: image encoded in 18728.52 ms by CLIP (  130.06 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that? ASSISTANT:
+
+ A group of llamas are standing in a green pasture.
+
+llama_print_timings:        load time =   20357.33 ms
+llama_print_timings:      sample time =       2.96 ms /    14 runs   (    0.21 ms per token,  4734.53 tokens per second)
+llama_print_timings: prompt eval time =    8119.49 ms /   191 tokens (   42.51 ms per token,    23.52 tokens per second)
+llama_print_timings:        eval time =    1005.75 ms /    14 runs   (   71.84 ms per token,    13.92 tokens per second)
+llama_print_timings:       total time =   28038.34 ms /   205 tokens
+```
+#### llava-cli latest-version
+**input**
+
+Just the same as above.
+
+**output**(seems to be much slower)
+```sh
+encode_image_with_clip: image embedding created: 144 tokens
+
+encode_image_with_clip: image encoded in 288268.88 ms by CLIP ( 2001.87 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that? ASSISTANT:
+
+ It is a group of sheep standing together in a grass field.
+
+llama_print_timings:        load time =  818120.91 ms
+llama_print_timings:      sample time =       3.44 ms /    14 runs   (    0.25 ms per token,  4067.40 tokens per second)
+llama_print_timings: prompt eval time =  529274.69 ms /   191 tokens ( 2771.07 ms per token,     0.36 tokens per second)
+llama_print_timings:        eval time =   43894.02 ms /    13 runs   ( 3376.46 ms per token,     0.30 tokens per second)
+llama_print_timings:       total time =  865441.76 ms /   204 tokens
+```
+### MobileVLM_V2-1.7B case
+#### llava-cli release-2005b
+**input**
+
+Just the same as above.
+
+**output**
+```sh
+encode_image_with_clip: image encoded in 20609.61 ms by CLIP (  143.12 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that? ASSISTANT:
+
+ This image captures a lively scene of 20 llamas in motion on an expansive, grassy field. The llama is scattered across the landscape with some standing and others sitting down as if taking rest or observing their surroundings from different vantage points within this verdant setting.
+
+The background offers glimpses into a picturesque town nestled amidst hills under an overcast sky, adding depth to the scene while also emphasizing that distance between these llama and human-made structures like houses or roads in which they roam freely without any barriers around them. The image is framed by text at both right angles on white backgrounds against a contrasting blue backdrop with green foliage, further drawing attention to the llamas amidst their natural habitat while also inviting viewers into this picturesque landscape within town limits of Alta Llama
+
+llama_print_timings:        load time =   22406.77 ms
+llama_print_timings:      sample time =      49.26 ms /   186 runs   (    0.26 ms per token,  3776.27 tokens per second)
+llama_print_timings: prompt eval time =    9044.54 ms /   191 tokens (   47.35 ms per token,    21.12 tokens per second)
+llama_print_timings:        eval time =   14497.49 ms /   186 runs   (   77.94 ms per token,    12.83 tokens per second)
+llama_print_timings:       total time =   44411.01 ms /   377 tokens
+```
+
+## Orin compile and run
+### compile
+```sh
+make LLAMA_CUDA=1 CUDA_DOCKER_ARCH=sm_87 LLAMA_CUDA_F16=1 -j 32
+```
+### run on Orin
+### case 1
+**input**
+```sh
+./llava-cli \
+    -m /data/local/tmp/ggml-model-q4_k.gguf \
+    --mmproj /data/local/tmp/mmproj-model-f16.gguf \
+    --image /data/local/tmp/demo.jpeg \
+    -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWho is the author of this book? \nAnswer the question using a single word or phrase. ASSISTANT:" \
+    --n-gpu-layers 999
+```
+**output**
+```sh
+
+encode_image_with_clip: image encoded in   296.62 ms by CLIP (    2.06 ms per image patch)
+
+ Susan Wise Bauer
+
+llama_print_timings:        load time =    1067.64 ms
+llama_print_timings:      sample time =       1.53 ms /     6 runs   (    0.25 ms per token,  3934.43 tokens per second)
+llama_print_timings: prompt eval time =     306.84 ms /   246 tokens (    1.25 ms per token,   801.72 tokens per second)
+llama_print_timings:        eval time =      91.50 ms /     6 runs   (   15.25 ms per token,    65.58 tokens per second)
+llama_print_timings:       total time =    1352.63 ms /   252 tokens
+```
+
+### case 2
+**input**
+```sh
+./llava-cli \
+    -m /data/local/tmp/ggml-model-q4_k.gguf \
+    --mmproj /data/local/tmp/mmproj-model-f16.gguf \
+    -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat is in the image? ASSISTANT:" \
+    --n-gpu-layers 999
+
+```
+**output**
+```sh
+encode_image_with_clip: image encoded in   302.15 ms by CLIP (    2.10 ms per image patch)
+
+ The image features a cat lying in the grass.
+
+llama_print_timings:        load time =    1057.07 ms
+llama_print_timings:      sample time =       3.27 ms /    11 runs   (    0.30 ms per token,  3360.83 tokens per second)
+llama_print_timings: prompt eval time =     213.60 ms /   232 tokens (    0.92 ms per token,  1086.14 tokens per second)
+llama_print_timings:        eval time =     166.65 ms /    11 runs   (   15.15 ms per token,    66.01 tokens per second)
+llama_print_timings:       total time =    1365.47 ms /   243 tokens
+```
+
+## Running on Intel(R) Core(TM) i7-10750H
+### Operating system
+Ubuntu22.04
+### compile
+```sh
+make -j32
+```
+### MobileVLM-1.7B case
+**input**
+```sh
+-m /path/to/ggml-model-q4_k.gguf \
+    --mmproj /path/to/mmproj-model-f16.gguf \
+    --image /path/to/many_llamas.jpeg
+    -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat's that? ASSISTANT:" \
+```
+**output**
+```sh
+encode_image_with_clip: image embedding created: 144 tokens
+
+encode_image_with_clip: image encoded in  2730.94 ms by CLIP (   18.96 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that?ASSISTANT:
+
+ A group of llamas are walking together in a field.
+
+llama_print_timings:        load time =    5506.60 ms
+llama_print_timings:      sample time =       0.44 ms /    13 runs   (    0.03 ms per token, 29545.45 tokens per second)
+llama_print_timings: prompt eval time =    2031.58 ms /   190 tokens (   10.69 ms per token,    93.52 tokens per second)
+llama_print_timings:        eval time =     438.92 ms /    12 runs   (   36.58 ms per token,    27.34 tokens per second)
+llama_print_timings:       total time =    5990.25 ms /   202 tokens
+```
+
+### MobileVLM_V2-1.7B case
+**input**
+
+Just the same as above.
+
+**ouput**
+```sh
+encode_image_with_clip: image embedding created: 144 tokens
+
+encode_image_with_clip: image encoded in  3223.89 ms by CLIP (   22.39 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that?ASSISTANT:
+
+ The image captures a tranquil scene in a park, where a group of approximately 20 llamas are gathered. The llamas, a mix of white and black, are standing in a line, their black and white patterns contrasting with the lush green grass of the park. The lamas are arranged in a line, suggesting a social order.
+
+The park itself is lush and green, with trees dotting the landscape in the background. A sign reading "Llamas Tico  Ana" is also visible in the image, possibly indicating the location or the breed of the llamas. The image seems to be taken from a distance, providing a wide view of the scene and the surrounding environment.
+
+The llamas' positions relative to each other, the sign, and the trees create a harmonious composition. The image does not contain any discernible text. The overall scene is one of peace and natural beauty, with the llamas in their natural habitat, surrounded by the vibrant colors and lush greenery of the park.
+
+llama_print_timings:        load time =    6642.61 ms
+llama_print_timings:      sample time =       8.15 ms /   223 runs   (    0.04 ms per token, 27358.61 tokens per second)
+llama_print_timings: prompt eval time =    2475.07 ms /   190 tokens (   13.03 ms per token,    76.77 tokens per second)
+llama_print_timings:        eval time =    8760.60 ms /   222 runs   (   39.46 ms per token,    25.34 tokens per second)
+llama_print_timings:       total time =   15513.95 ms /   412 tokens
+```
+
+## Run on Intel(R) Core(TM) Ultra7 115H
+### operation system
+Windows11
+### comiple
+```sh
+make -j32
+```
+### MobileVLM-1.7B case
+**input**
+```sh
+-m /path/to/ggml-model-q4_k.gguf \
+    --mmproj /path/to/tmp/mmproj-model-f16.gguf \
+    -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat's that? ASSISTANT:" \
+```
+**output**
+```sh
+encode_image_with_clip: image encoded in  4902.81 ms by CLIP (   34.05 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that? ASSISTANT:
+
+ The image features a group of brown and white llamas standing in a grassy field.
+
+llama_print_timings:        load time =    7441.06 ms
+llama_print_timings:      sample time =       0.72 ms /    19 runs   (    0.04 ms per token, 26279.39 tokens per second)
+llama_print_timings: prompt eval time =    2090.71 ms /   191 tokens (   10.95 ms per token,    91.36 tokens per second)
+llama_print_timings:        eval time =     512.35 ms /    18 runs   (   28.46 ms per token,    35.13 tokens per second)
+llama_print_timings:       total time =    7987.23 ms /   209 tokens
+```
+
+### MobileVLM_V2-1.7B case
+**input**
+
+Just the same as above.
+
+**output**
+```sh
+encode_image_with_clip: image encoded in  4682.44 ms by CLIP (   32.52 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that? ASSISTANT:
+
+ This image captures a lively scene of a group of 14 llamas in a grassy field. The llamas, with their distinctive black and white coats, are standing and walking in a line, seemingly engaged in a social activity. One
+ of them, possibly the first in the line, has its back turned, perhaps observing something in the distance.
+
+The llama in the front of the line stands out due to its black and white coloring, which is quite unusual for llama patterns. The llama in the front also seems to be more aware of its surroundings, as it faces the camera, giving a sense of engagement with the viewer.
+
+The image is taken from the side of the llama, providing a clear view of the llama in the front and its companions. The lameness in the llama in
+ front is not visible, indicating that it might not be the main focus of the photo.
+
+The background of the image features a grassy field, with a fence and a tree visible in the distance. The tree appears to be bare, suggesting that it might be during a time of year when most trees are dormant or have shed their leaves.
+
+
+llama_print_timings:        load time =    7015.35 ms
+llama_print_timings:      sample time =      10.61 ms /   256 runs   (    0.04 ms per token, 24119.09 tokens per second)
+llama_print_timings: prompt eval time =    2052.45 ms /   191 tokens (   10.75 ms per token,    93.06 tokens per second)
+llama_print_timings:        eval time =    7259.43 ms /   255 runs   (   28.47 ms per token,    35.13 tokens per second)
+llama_print_timings:       total time =   14371.19 ms /   446 tokens
+```
+
+## TODO
+
+- [x] Support non-CPU backend for the new operators, such as `depthwise`, `hardswish`, `hardsigmoid`
+- [ ] Optimize LDP projector performance
+
+      - Optimize the structure definition to avoid unnecessary memory rearrangements, to reduce the use of `ggml_permute_cpy`;
+      - Optimize operator implementation (ARM CPU/NVIDIA GPU): such as depthwise conv, hardswish, hardsigmoid, etc.
+- [x] run MobileVLM on `Jetson Orin`
+- [ ] Support more model variants, such as `MobileVLM-3B`.
+
+
+## contributor
+```sh
+zhangjidong05, yangyang260, huyiming03, chenxiaotao03, ZiangWu-77
+```
diff --git a/examples/llava/README.md b/examples/llava/README.md
index 323c5fdd0..67cb0f22b 100644
--- a/examples/llava/README.md
+++ b/examples/llava/README.md
@@ -1,10 +1,12 @@
 # LLaVA
 
-Currently this implementation supports [llava-v1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) variants.
+Currently this implementation supports [llava-v1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) variants,
+as well as llava-1.6 [llava-v1.6](https://huggingface.co/collections/liuhaotian/llava-16-65b9e40155f60fd046a5ccf2) variants.
 
 The pre-converted [7b](https://huggingface.co/mys/ggml_llava-v1.5-7b)
 and [13b](https://huggingface.co/mys/ggml_llava-v1.5-13b)
 models are available.
+For llava-1.6 a variety of prepared gguf models are available as well [7b-34b](https://huggingface.co/cmp-nct/llava-1.6-gguf)
 
 After API is confirmed, more models will be supported / uploaded.
 
@@ -14,14 +16,15 @@ Build with cmake or run `make llava-cli` to build it.
 After building, run: `./llava-cli` to see the usage. For example:
 
 ```sh
-./llava-cli -m llava-v1.5-7b/ggml-model-q5_k.gguf --mmproj llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg
+./llava-cli -m ../llava-v1.5-7b/ggml-model-f16.gguf --mmproj ../llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg
 ```
 
 **note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so.
+**note**: For GPU offloading ensure to use the `-ngl` flag just like usual
 
-## Model conversion
+## LLaVA 1.5
 
-- Clone `llava-v15-7b`` and `clip-vit-large-patch14-336`` locally:
+- Clone a LLaVA and a CLIP model ([available options](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)). For example:
 
 ```sh
 git clone https://huggingface.co/liuhaotian/llava-v1.5-7b
@@ -29,28 +32,108 @@ git clone https://huggingface.co/liuhaotian/llava-v1.5-7b
 git clone https://huggingface.co/openai/clip-vit-large-patch14-336
 ```
 
-2. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
+2. Install the required Python packages:
+
+```sh
+pip install -r examples/llava/requirements.txt
+```
+
+3. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
 
 ```sh
 python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b
 ```
 
-3. Use `convert-image-encoder-to-gguf.py` to convert the LLaVA image encoder to GGUF:
+4. Use `convert-image-encoder-to-gguf.py` to convert the LLaVA image encoder to GGUF:
 
 ```sh
-python ./examples/llava/convert-image-encoder-to-gguf -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
+python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
 ```
 
-4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:
+5. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:
 
 ```sh
-python ./convert.py ../llava-v1.5-7b
+python ./convert.py ../llava-v1.5-7b --skip-unknown
 ```
 
 Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` directory.
 
+## LLaVA 1.6 gguf conversion
+1) First clone a LLaVA 1.6 model:
+```console
+git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b
+```
+
+2) Install the required Python packages:
+
+```sh
+pip install -r examples/llava/requirements.txt
+```
+
+3) Use `llava-surgery-v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
+```console
+python examples/llava/llava-surgery-v2.py -C -m ../llava-v1.6-vicuna-7b/
+```
+- you will find a llava.projector and a llava.clip file in your model directory
+
+4) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory:
+```console
+mkdir vit
+cp ../llava-v1.6-vicuna-7b/llava.clip vit/pytorch_model.bin
+cp ../llava-v1.6-vicuna-7b/llava.projector vit/
+curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.json -o vit/config.json
+```
+
+5) Create the visual gguf model:
+```console
+python ./examples/llava/convert-image-encoder-to-gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision
+```
+- This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP
+
+6) Then convert the model to gguf format:
+```console
+python ./convert.py ../llava-v1.6-vicuna-7b/ --skip-unknown
+```
+
+7) And finally we can run the llava-cli using the 1.6 model version:
+```console
+./llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096
+```
+
+**note** llava-1.6 needs more context than llava-1.5, at least 3000 is needed (just run it at -c 4096)
+**note** llava-1.6 greatly benefits from batched prompt processing (defaults work)
+
+## llava-cli templating and llava-1.6 prompting
+
+llava-1.5 models all use the same vicuna prompt, here you can just add your image question like `-p "Provide a full description."`
+For llava-1.5 models which are not vicuna (mistral and Yi) you need to adapt system prompt as well as user prompt, for this purpose llava-cli has a basic templating system:
+
+**For Mistral and using llava-cli binary:**
+Add this: `-p "<image>\nUSER:\nProvide a full description.\nASSISTANT:\n"`
+The mistral template for llava-1.6 seems to be no system print and a USER/ASSISTANT role
+
+**For the 34B this should work:**
+Add this: `-e -p <|im_start|>system\nAnswer the questions.<|im_end|><|im_start|>user\n<image>\nProvide a full description.<|im_end|><|im_start|>assistant\n`
+
+
+## How to know if you are running in llava-1.5 or llava-1.6 mode
+
+When running llava-cli you will see a visual information right before the prompt is being processed:
+
+**Llava-1.5:**
+`encode_image_with_clip: image embedding created: 576 tokens`
+
+**Llava-1.6 (anything above 576):**
+`encode_image_with_clip: image embedding created: 2880 tokens`
+
+
+Alternatively just pay notice to how many "tokens" have been used for your prompt, it will also show 1000+ tokens for llava-1.6
+
+
+
+
 ## TODO
 
-- [ ] Support non-CPU backend for the image encoding part.
+- [x] Support non-CPU backend for the image encoding part.
 - [ ] Support different sampling methods.
 - [ ] Support more model variants.
diff --git a/examples/llava/android/adb_run.sh b/examples/llava/android/adb_run.sh
new file mode 100755
index 000000000..f73623ae3
--- /dev/null
+++ b/examples/llava/android/adb_run.sh
@@ -0,0 +1,53 @@
+#!/bin/bash
+
+model_dir="/Users/cxt/model/llm/mobileVLM/MobileVLM-1.7B_processed"
+projector_name="mmproj-model-f16.gguf"
+llama_name="ggml-model-q4_k.gguf"
+img_dir="/Users/cxt/model/llm"
+img_name="demo.jpg"
+prompt="A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWho is the author of this book? \nAnswer the question using a single word or phrase. ASSISTANT:"
+# img_name="cat.jpeg"
+# prompt="A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat is in the image? ASSISTANT:"
+
+program_dir="build_64/bin"
+binName="llava-cli"
+n_threads=4
+
+
+deviceDir="/data/local/tmp"
+saveDir="output"
+if [ ! -d ${saveDir} ]; then
+    mkdir ${saveDir}
+fi
+
+
+function android_run() {
+    # # copy resource into device
+    # adb push ${model_dir}/${projector_name} ${deviceDir}/${projector_name}
+    # adb push ${model_dir}/${llama_name} ${deviceDir}/${llama_name}
+    adb push ${img_dir}/${img_name} ${deviceDir}/${img_name}
+    # copy program into device
+    adb push ${program_dir}/${binName} ${deviceDir}/${binName}
+    adb shell "chmod 0777 ${deviceDir}/${binName}"
+
+    # run
+    adb shell "echo cd ${deviceDir} ${deviceDir}/${binName} \
+                                                 -m ${deviceDir}/${llama_name} \
+                                                 --mmproj ${deviceDir}/${projector_name} \
+                                                 -t ${n_threads} \
+                                                 --image ${deviceDir}/${img_name} \
+                                                 -p \"${prompt}\" \
+                                                 > ${deviceDir}/${modelName}_${projector_name}_${n_threads}_${img_name}.txt"
+    adb shell "cd ${deviceDir}; pwd; ${deviceDir}/${binName} \
+                                                 -m ${deviceDir}/${llama_name} \
+                                                 --mmproj ${deviceDir}/${projector_name} \
+                                                 -t ${n_threads} \
+                                                 --image ${deviceDir}/${img_name} \
+                                                 -p \"${prompt}\" \
+                                                 >> ${deviceDir}/${modelName}_${projector_name}_${n_threads}_${img_name}.txt 2>&1"
+    adb pull ${deviceDir}/${modelName}_${projector_name}_${n_threads}_${img_name}.txt ${saveDir}
+}
+
+android_run
+
+echo "android_run is Done!"
diff --git a/examples/llava/android/build_64.sh b/examples/llava/android/build_64.sh
new file mode 100755
index 000000000..71b6fd3f7
--- /dev/null
+++ b/examples/llava/android/build_64.sh
@@ -0,0 +1,8 @@
+#!/bin/bash
+cmake ../../../../ \
+-DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \
+-DCMAKE_BUILD_TYPE=Release \
+-DANDROID_ABI="arm64-v8a" \
+-DANDROID_PLATFORM=android-23 $1
+
+make -j4
diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index cfb79e789..5954bf6cd 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -1,6 +1,22 @@
 // NOTE: This is modified from clip.cpp only for LLaVA,
 // so there might be still unnecessary artifacts hanging around
 // I'll gradually clean and extend it
+// Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
+#include "clip.h"
+#include "ggml.h"
+#include "ggml-alloc.h"
+#include "ggml-backend.h"
+
+#ifdef GGML_USE_CUDA
+#include "ggml-cuda.h"
+#endif
+
+#ifdef GGML_USE_METAL
+#include "ggml-metal.h"
+#endif
+
+#define STB_IMAGE_IMPLEMENTATION
+#include "stb_image.h"
 
 #include <cassert>
 #include <cmath>
@@ -12,22 +28,28 @@
 #include <regex>
 #include <stdexcept>
 #include <vector>
+#include <sstream>
+#include <cinttypes>
+#include <limits>
 
-#include "clip.h"
-#include "ggml.h"
-#include "ggml-alloc.h"
-#include "ggml-backend.h"
+//#define CLIP_DEBUG_FUNCTIONS
 
-#ifdef GGML_USE_CUBLAS
-#include "ggml-cuda.h"
-#endif
+// RGB uint8 image
+struct clip_image_u8 {
+    int nx;
+    int ny;
 
-#ifdef GGML_USE_METAL
-#include "ggml-metal.h"
-#endif
+    std::vector<uint8_t> buf;
+};
 
-#define STB_IMAGE_IMPLEMENTATION
-#include "stb_image.h"
+// RGB float32 image (NHWC)
+// Memory layout: RGBRGBRGB...
+struct clip_image_f32 {
+    int nx;
+    int ny;
+
+    std::vector<float> buf;
+};
 
 static std::string format(const char * fmt, ...) {
     va_list ap;
@@ -48,47 +70,73 @@ static std::string format(const char * fmt, ...) {
 // key constants
 //
 
-#define KEY_FTYPE "general.file_type"
-#define KEY_NAME "general.name"
-#define KEY_DESCRIPTION "general.description"
-#define KEY_HAS_TEXT_ENC "clip.has_text_encoder"
-#define KEY_HAS_VIS_ENC "clip.has_vision_encoder"
+#define KEY_FTYPE          "general.file_type"
+#define KEY_NAME           "general.name"
+#define KEY_DESCRIPTION    "general.description"
+#define KEY_HAS_TEXT_ENC   "clip.has_text_encoder"
+#define KEY_HAS_VIS_ENC    "clip.has_vision_encoder"
 #define KEY_HAS_LLAVA_PROJ "clip.has_llava_projector"
-#define KEY_USE_GELU "clip.use_gelu"
-#define KEY_N_EMBD "clip.%s.embedding_length"
-#define KEY_N_FF "clip.%s.feed_forward_length"
-#define KEY_N_BLOCK "clip.%s.block_count"
-#define KEY_N_HEAD "clip.%s.attention.head_count"
+#define KEY_USE_GELU       "clip.use_gelu"
+#define KEY_N_EMBD         "clip.%s.embedding_length"
+#define KEY_N_FF           "clip.%s.feed_forward_length"
+#define KEY_N_BLOCK        "clip.%s.block_count"
+#define KEY_N_HEAD         "clip.%s.attention.head_count"
 #define KEY_LAYER_NORM_EPS "clip.%s.attention.layer_norm_epsilon"
-#define KEY_PROJ_DIM "clip.%s.projection_dim"
-#define KEY_TOKENS "tokenizer.ggml.tokens"
-#define KEY_N_POSITIONS "clip.text.context_length"
-#define KEY_IMAGE_SIZE "clip.vision.image_size"
-#define KEY_PATCH_SIZE "clip.vision.patch_size"
-#define KEY_IMAGE_MEAN "clip.vision.image_mean"
-#define KEY_IMAGE_STD "clip.vision.image_std"
+#define KEY_PROJ_DIM       "clip.%s.projection_dim"
+#define KEY_TOKENS         "tokenizer.ggml.tokens"
+#define KEY_N_POSITIONS    "clip.text.context_length"
+#define KEY_IMAGE_SIZE     "clip.vision.image_size"
+#define KEY_PATCH_SIZE     "clip.vision.patch_size"
+#define KEY_IMAGE_MEAN     "clip.vision.image_mean"
+#define KEY_IMAGE_STD      "clip.vision.image_std"
+#define KEY_PROJ_TYPE      "clip.projector_type"
+
+#define KEY_MM_PATCH_MERGE_TYPE   "clip.vision.mm_patch_merge_type"
+#define KEY_IMAGE_GRID_PINPOINTS  "clip.vision.image_grid_pinpoints"
+#define KEY_IMAGE_CROP_RESOLUTION "clip.vision.image_crop_resolution"
+
 
 //
 // tensor name constants
 //
 
-#define TN_TOKEN_EMBD "%s.token_embd.weight"
-#define TN_POS_EMBD "%s.position_embd.weight"
-#define TN_CLASS_EMBD "v.class_embd"
-#define TN_PATCH_EMBD "v.patch_embd.weight"
-#define TN_ATTN_K "%s.blk.%d.attn_k.%s"
-#define TN_ATTN_Q "%s.blk.%d.attn_q.%s"
-#define TN_ATTN_V "%s.blk.%d.attn_v.%s"
-#define TN_ATTN_OUTPUT "%s.blk.%d.attn_out.%s"
-#define TN_FFN_DOWN "%s.blk.%d.ffn_down.%s"
-#define TN_FFN_UP "%s.blk.%d.ffn_up.%s"
-#define TN_LN_1 "%s.blk.%d.ln1.%s"
-#define TN_LN_2 "%s.blk.%d.ln2.%s"
-#define TN_LN_PRE "%s.pre_ln.%s"
-#define TN_LN_POST "%s.post_ln.%s"
-#define TN_TEXT_PROJ "text_projection.weight"
-#define TN_VIS_PROJ "visual_projection.weight"
-#define TN_LLAVA_PROJ "mm.%d.%s"
+#define TN_TOKEN_EMBD      "%s.token_embd.weight"
+#define TN_POS_EMBD        "%s.position_embd.weight"
+#define TN_CLASS_EMBD      "v.class_embd"
+#define TN_PATCH_EMBD      "v.patch_embd.weight"
+#define TN_ATTN_K          "%s.blk.%d.attn_k.%s"
+#define TN_ATTN_Q          "%s.blk.%d.attn_q.%s"
+#define TN_ATTN_V          "%s.blk.%d.attn_v.%s"
+#define TN_ATTN_OUTPUT     "%s.blk.%d.attn_out.%s"
+#define TN_FFN_DOWN        "%s.blk.%d.ffn_down.%s"
+#define TN_FFN_UP          "%s.blk.%d.ffn_up.%s"
+#define TN_LN_1            "%s.blk.%d.ln1.%s"
+#define TN_LN_2            "%s.blk.%d.ln2.%s"
+#define TN_LN_PRE          "%s.pre_ln.%s"
+#define TN_LN_POST         "%s.post_ln.%s"
+#define TN_TEXT_PROJ       "text_projection.weight"
+#define TN_VIS_PROJ        "visual_projection.weight"
+#define TN_LLAVA_PROJ      "mm.%d.%s"
+#define TN_MVLM_PROJ_MLP   "mm.model.mlp.%d.%s"
+#define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s"
+#define TN_MVLM_PROJ_PEG   "mm.model.peg.%d.%s"
+#define TN_IMAGE_NEWLINE   "model.image_newline"
+
+
+enum projector_type {
+    PROJECTOR_TYPE_MLP,
+    PROJECTOR_TYPE_MLP_NORM,
+    PROJECTOR_TYPE_LDP,
+    PROJECTOR_TYPE_LDPV2,
+    PROJECTOR_TYPE_UNKNOWN,
+};
+
+static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
+    { PROJECTOR_TYPE_MLP, "mlp" },
+    { PROJECTOR_TYPE_LDP, "ldp" },
+    { PROJECTOR_TYPE_LDPV2, "ldpv2"},
+};
+
 
 //
 // utilities to get data from a gguf file
@@ -126,51 +174,223 @@ static struct ggml_tensor * get_tensor(struct ggml_context * ctx, const std::str
 }
 
 static std::string get_ftype(int ftype) {
-    switch (ftype) {
-    case 0:
-        return "f32";
-    case 1:
-        return "f16";
-    case 2:
-        return "q4_0";
-    case 3:
-        return "q4_1";
-    case 6:
-        return "q5_0";
-    case 7:
-        return "q5_1";
-    case 8:
-        return "q8_0";
-    default:
-        throw std::runtime_error(format("%s: Unrecognized file type: %d\n", __func__, ftype));
+    return ggml_type_name(static_cast<ggml_type>(ftype));
+}
+
+static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) {
+    switch (type) {
+        case GGUF_TYPE_UINT8:   return std::to_string(((const uint8_t  *)data)[i]);
+        case GGUF_TYPE_INT8:    return std::to_string(((const int8_t   *)data)[i]);
+        case GGUF_TYPE_UINT16:  return std::to_string(((const uint16_t *)data)[i]);
+        case GGUF_TYPE_INT16:   return std::to_string(((const int16_t  *)data)[i]);
+        case GGUF_TYPE_UINT32:  return std::to_string(((const uint32_t *)data)[i]);
+        case GGUF_TYPE_INT32:   return std::to_string(((const int32_t  *)data)[i]);
+        case GGUF_TYPE_UINT64:  return std::to_string(((const uint64_t *)data)[i]);
+        case GGUF_TYPE_INT64:   return std::to_string(((const int64_t  *)data)[i]);
+        case GGUF_TYPE_FLOAT32: return std::to_string(((const float    *)data)[i]);
+        case GGUF_TYPE_FLOAT64: return std::to_string(((const double   *)data)[i]);
+        case GGUF_TYPE_BOOL:    return ((const bool *)data)[i] ? "true" : "false";
+        default:                return format("unknown type %d", type);
     }
 }
 
-//
-// image data
-//
+static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
+    std::string result;
+    for (size_t pos = 0; ; pos += search.length()) {
+        auto new_pos = s.find(search, pos);
+        if (new_pos == std::string::npos) {
+            result += s.substr(pos, s.size() - pos);
+            break;
+        }
+        result += s.substr(pos, new_pos - pos) + replace;
+        pos = new_pos;
+    }
+    s = std::move(result);
+}
 
-// RGB uint8 image
-struct clip_image_u8 {
-    int nx;
-    int ny;
+static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
+    const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i);
 
-    std::vector<uint8_t> buf;
-};
+    switch (type) {
+        case GGUF_TYPE_STRING:
+            return gguf_get_val_str(ctx_gguf, i);
+        case GGUF_TYPE_ARRAY:
+            {
+                const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i);
+                int arr_n = gguf_get_arr_n(ctx_gguf, i);
+                const void * data = gguf_get_arr_data(ctx_gguf, i);
+                std::stringstream ss;
+                ss << "[";
+                for (int j = 0; j < arr_n; j++) {
+                    if (arr_type == GGUF_TYPE_STRING) {
+                        std::string val = gguf_get_arr_str(ctx_gguf, i, j);
+                        // escape quotes
+                        replace_all(val, "\\", "\\\\");
+                        replace_all(val, "\"", "\\\"");
+                        ss << '"' << val << '"';
+                    } else if (arr_type == GGUF_TYPE_ARRAY) {
+                        ss << "???";
+                    } else {
+                        ss << gguf_data_to_str(arr_type, data, j);
+                    }
+                    if (j < arr_n - 1) {
+                        ss << ", ";
+                    }
+                }
+                ss << "]";
+                return ss.str();
+            }
+        default:
+            return gguf_data_to_str(type, gguf_get_val_data(ctx_gguf, i), 0);
+    }
+}
 
-// RGB float32 image (NHWC)
-// Memory layout: RGBRGBRGB...
-struct clip_image_f32 {
-    int nx;
-    int ny;
+static void print_tensor_info(const ggml_tensor * tensor, const char * prefix = "") {
+    size_t tensor_size = ggml_nbytes(tensor);
+    printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n",
+            prefix, ggml_n_dims(tensor), tensor->name, tensor_size,
+            tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], ggml_type_name(tensor->type));
+}
+
+static projector_type clip_projector_type_from_string(const std::string & name) {
+    for (const auto & kv : PROJECTOR_TYPE_NAMES) { // NOLINT
+        if (kv.second == name) {
+            return kv.first;
+        }
+    }
+    return PROJECTOR_TYPE_UNKNOWN;
+}
+
+#ifdef CLIP_DEBUG_FUNCTIONS
+static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::string& filename) {
+    std::ofstream file(filename, std::ios::binary);
+    if (!file.is_open()) {
+        std::cerr << "Failed to open file for writing: " << filename << std::endl;
+        return;
+    }
+
+    // PPM header: P6 format, width, height, and max color value
+    file << "P6\n" << img.nx << " " << img.ny << "\n255\n";
+
+    // Write pixel data
+    for (size_t i = 0; i < img.buf.size(); i += 3) {
+        // PPM expects binary data in RGB format, which matches our image buffer
+        file.write(reinterpret_cast<const char*>(&img.buf[i]), 3);
+    }
+
+    file.close();
+}
+
+static void clip_image_save_to_bmp(const clip_image_u8& img, const std::string& filename) {
+    std::ofstream file(filename, std::ios::binary);
+    if (!file.is_open()) {
+        std::cerr << "Failed to open file for writing: " << filename << std::endl;
+        return;
+    }
+
+    int fileSize = 54 + 3 * img.nx * img.ny; // File header + info header + pixel data
+    int bytesPerPixel = 3;
+    int widthInBytes = img.nx * bytesPerPixel;
+    int paddingAmount = (4 - (widthInBytes % 4)) % 4;
+    int stride = widthInBytes + paddingAmount;
+
+    // Bitmap file header
+    unsigned char fileHeader[14] = {
+        'B','M',     // Signature
+        0,0,0,0,    // Image file size in bytes
+        0,0,0,0,    // Reserved
+        54,0,0,0    // Start of pixel array
+    };
+
+    // Total file size
+    fileSize = 54 + (stride * img.ny);
+    fileHeader[2] = (unsigned char)(fileSize);
+    fileHeader[3] = (unsigned char)(fileSize >> 8);
+    fileHeader[4] = (unsigned char)(fileSize >> 16);
+    fileHeader[5] = (unsigned char)(fileSize >> 24);
+
+    // Bitmap information header (BITMAPINFOHEADER)
+    unsigned char infoHeader[40] = {
+        40,0,0,0,   // Size of this header (40 bytes)
+        0,0,0,0,    // Image width
+        0,0,0,0,    // Image height
+        1,0,        // Number of color planes
+        24,0,       // Bits per pixel
+        0,0,0,0,    // No compression
+        0,0,0,0,    // Image size (can be 0 for no compression)
+        0,0,0,0,    // X pixels per meter (not specified)
+        0,0,0,0,    // Y pixels per meter (not specified)
+        0,0,0,0,    // Total colors (color table not used)
+        0,0,0,0     // Important colors (all are important)
+    };
+
+    // Width and height in the information header
+    infoHeader[4] = (unsigned char)(img.nx);
+    infoHeader[5] = (unsigned char)(img.nx >> 8);
+    infoHeader[6] = (unsigned char)(img.nx >> 16);
+    infoHeader[7] = (unsigned char)(img.nx >> 24);
+    infoHeader[8] = (unsigned char)(img.ny);
+    infoHeader[9] = (unsigned char)(img.ny >> 8);
+    infoHeader[10] = (unsigned char)(img.ny >> 16);
+    infoHeader[11] = (unsigned char)(img.ny >> 24);
+
+    // Write file headers
+    file.write(reinterpret_cast<char*>(fileHeader), sizeof(fileHeader));
+    file.write(reinterpret_cast<char*>(infoHeader), sizeof(infoHeader));
+
+    // Pixel data
+    std::vector<unsigned char> padding(3, 0); // Max padding size to be added to each row
+    for (int y = img.ny - 1; y >= 0; --y) { // BMP files are stored bottom-to-top
+        for (int x = 0; x < img.nx; ++x) {
+            // Each pixel
+            size_t pixelIndex = (y * img.nx + x) * 3;
+            unsigned char pixel[3] = {
+                img.buf[pixelIndex + 2], // BMP stores pixels in BGR format
+                img.buf[pixelIndex + 1],
+                img.buf[pixelIndex]
+            };
+            file.write(reinterpret_cast<char*>(pixel), 3);
+        }
+        // Write padding for the row
+        file.write(reinterpret_cast<char*>(padding.data()), paddingAmount);
+    }
+
+    file.close();
+}
+
+// debug function to convert f32 to u8
+static void clip_image_convert_f32_to_u8(const clip_image_f32& src, clip_image_u8& dst) {
+    dst.nx = src.nx;
+    dst.ny = src.ny;
+    dst.buf.resize(3 * src.nx * src.ny);
+    for (size_t i = 0; i < src.buf.size(); ++i) {
+        dst.buf[i] = static_cast<uint8_t>(std::min(std::max(int(src.buf[i] * 255.0f), 0), 255));
+    }
+}
+#endif
 
-    std::vector<float> buf;
-};
 
 //
 // clip layers
 //
 
+struct clip_hparams {
+    int32_t image_size;
+    int32_t patch_size;
+    int32_t hidden_size;
+    int32_t n_intermediate;
+    int32_t projection_dim;
+    int32_t n_head;
+    int32_t n_layer;
+
+    float eps;
+
+    char mm_patch_merge_type[32] = "flat"; // spatial_unpad or flat (default)
+
+    int32_t image_grid_pinpoints[32];
+    int32_t image_crop_resolution;
+};
+
 struct clip_layer {
     // attention
     struct ggml_tensor * k_w;
@@ -200,7 +420,7 @@ struct clip_layer {
 };
 
 struct clip_vision_model {
-    struct clip_vision_hparams hparams;
+    struct clip_hparams hparams;
 
     // embeddings
     struct ggml_tensor * class_embedding;
@@ -218,10 +438,54 @@ struct clip_vision_model {
     struct ggml_tensor * projection;
 
     // LLaVA projection
-    struct ggml_tensor * mm_0_w;
-    struct ggml_tensor * mm_0_b;
-    struct ggml_tensor * mm_2_w;
-    struct ggml_tensor * mm_2_b;
+    struct ggml_tensor * mm_0_w = NULL;
+    struct ggml_tensor * mm_0_b = NULL;
+    struct ggml_tensor * mm_2_w = NULL;
+    struct ggml_tensor * mm_2_b = NULL;
+
+    struct ggml_tensor * image_newline = NULL;
+
+    // Yi type models with mlp+normalization projection
+    struct ggml_tensor * mm_1_w = NULL; // Yi type models have 0, 1, 3, 4
+    struct ggml_tensor * mm_1_b = NULL;
+    struct ggml_tensor * mm_3_w = NULL;
+    struct ggml_tensor * mm_3_b = NULL;
+    struct ggml_tensor * mm_4_w = NULL;
+    struct ggml_tensor * mm_4_b = NULL;
+
+    // MobileVLM projection
+    struct ggml_tensor * mm_model_mlp_1_w;
+    struct ggml_tensor * mm_model_mlp_1_b;
+    struct ggml_tensor * mm_model_mlp_3_w;
+    struct ggml_tensor * mm_model_mlp_3_b;
+    struct ggml_tensor * mm_model_block_1_block_0_0_w;
+    struct ggml_tensor * mm_model_block_1_block_0_1_w;
+    struct ggml_tensor * mm_model_block_1_block_0_1_b;
+    struct ggml_tensor * mm_model_block_1_block_1_fc1_w;
+    struct ggml_tensor * mm_model_block_1_block_1_fc1_b;
+    struct ggml_tensor * mm_model_block_1_block_1_fc2_w;
+    struct ggml_tensor * mm_model_block_1_block_1_fc2_b;
+    struct ggml_tensor * mm_model_block_1_block_2_0_w;
+    struct ggml_tensor * mm_model_block_1_block_2_1_w;
+    struct ggml_tensor * mm_model_block_1_block_2_1_b;
+    struct ggml_tensor * mm_model_block_2_block_0_0_w;
+    struct ggml_tensor * mm_model_block_2_block_0_1_w;
+    struct ggml_tensor * mm_model_block_2_block_0_1_b;
+    struct ggml_tensor * mm_model_block_2_block_1_fc1_w;
+    struct ggml_tensor * mm_model_block_2_block_1_fc1_b;
+    struct ggml_tensor * mm_model_block_2_block_1_fc2_w;
+    struct ggml_tensor * mm_model_block_2_block_1_fc2_b;
+    struct ggml_tensor * mm_model_block_2_block_2_0_w;
+    struct ggml_tensor * mm_model_block_2_block_2_1_w;
+    struct ggml_tensor * mm_model_block_2_block_2_1_b;
+
+    // MobileVLM_V2 projection
+    struct ggml_tensor * mm_model_mlp_0_w;
+    struct ggml_tensor * mm_model_mlp_0_b;
+    struct ggml_tensor * mm_model_mlp_2_w;
+    struct ggml_tensor * mm_model_mlp_2_b;
+    struct ggml_tensor * mm_model_peg_0_w;
+    struct ggml_tensor * mm_model_peg_0_b;
 };
 
 struct clip_ctx {
@@ -230,6 +494,7 @@ struct clip_ctx {
     bool has_llava_projector = false;
 
     struct clip_vision_model vision_model;
+    projector_type proj_type = PROJECTOR_TYPE_MLP;
 
     float image_mean[3];
     float image_std[3];
@@ -242,10 +507,10 @@ struct clip_ctx {
     std::vector<uint8_t> buf_compute_meta;
 
     // memory buffers to evaluate the model
-    ggml_backend_buffer_t params_buffer = NULL;
-    ggml_backend_buffer_t compute_buffer = NULL;
-    ggml_backend_t backend = NULL;
-    ggml_allocr * compute_alloc = NULL;
+    ggml_backend_buffer_t params_buffer  = NULL;
+
+    ggml_backend_t backend       = NULL;
+    ggml_gallocr_t compute_alloc = NULL;
 };
 
 static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs) {
@@ -257,18 +522,19 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     const auto & model = ctx->vision_model;
     const auto & hparams = model.hparams;
 
-    const int image_size = hparams.image_size;
-    const int patch_size = hparams.patch_size;
-    const int num_patches = ((image_size / patch_size) * (image_size / patch_size));
-    const int num_positions = num_patches + 1;
-    const int hidden_size = hparams.hidden_size;
-    const int n_head = hparams.n_head;
-    const int d_head = hidden_size / n_head;
-    const int n_layer = hparams.n_layer;
-    //const int n_intermediate = hparams.n_intermediate;
-    //const int projection_dim = hparams.projection_dim;
-    const float eps = hparams.eps;
-    int batch_size = imgs->size;
+    const int image_size           = hparams.image_size;
+    const int patch_size           = hparams.patch_size;
+    const int num_patches          = ((image_size / patch_size) * (image_size / patch_size));
+    const int num_patches_per_side = image_size / patch_size; GGML_UNUSED(num_patches_per_side);
+    const int num_positions        = num_patches + 1;
+    const int hidden_size          = hparams.hidden_size;
+    const int n_head               = hparams.n_head;
+    const int d_head               = hidden_size / n_head;
+    const int n_layer              = hparams.n_layer;
+    const float eps                = hparams.eps;
+
+    const int batch_size = imgs->size;
+
     if (ctx->has_llava_projector) {
         GGML_ASSERT(batch_size == 1);
     }
@@ -283,31 +549,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     struct ggml_cgraph * gf = ggml_new_graph(ctx0);
 
     struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size, image_size, 3, batch_size);
-    ggml_allocr_alloc(ctx->compute_alloc, inp_raw);
-
-    if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
-        float * data = (float *)malloc(ggml_nbytes(inp_raw));
-
-        for (size_t i = 0; i < imgs->size; i++) {
-            const int nx = imgs->data[i].nx;
-            const int ny = imgs->data[i].ny;
-            GGML_ASSERT(nx == image_size && ny == image_size);
-
-            const int n = nx * ny;
-
-            for (int b = 0; b < batch_size; b++) {
-                for (int k = 0; k < 3; k++) {
-                    for (int y = 0; y < ny; y++) {
-                        for (int x = 0; x < nx; x++) {
-                            data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].buf[3 * (y * nx + x) + k];
-                        }
-                    }
-                }
-            }
-        }
-        ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw));
-        free(data);
-    }
+    ggml_set_name(inp_raw, "inp_raw");
+    ggml_set_input(inp_raw);
 
     struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
 
@@ -316,13 +559,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
 
     // concat class_embeddings and patch_embeddings
     struct ggml_tensor * embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size);
-    ggml_allocr_alloc(ctx->compute_alloc, embeddings);
-    if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
-        void* zero_mem = malloc(ggml_nbytes(embeddings));
-        memset(zero_mem, 0, ggml_nbytes(embeddings));
-        ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings));
-        free(zero_mem);
-    }
+    ggml_set_name(embeddings, "embeddings");
+    ggml_set_input(embeddings);
 
     embeddings = ggml_acc(ctx0, embeddings, model.class_embedding,
             embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0);
@@ -331,15 +569,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]);
 
     struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions);
-    ggml_allocr_alloc(ctx->compute_alloc, positions);
-    if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
-        int* positions_data = (int*)malloc(ggml_nbytes(positions));
-        for (int i = 0; i < num_positions; i++) {
-            positions_data[i] = i;
-        }
-        ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
-        free(positions_data);
-    }
+    ggml_set_name(positions, "positions");
+    ggml_set_input(positions);
 
     embeddings =
         ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions));
@@ -347,6 +578,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     // pre-layernorm
     {
         embeddings = ggml_norm(ctx0, embeddings, eps);
+        ggml_set_name(embeddings, "pre_ln");
 
         embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.pre_ln_w), model.pre_ln_b);
     }
@@ -394,9 +626,9 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             KQ = ggml_soft_max_inplace(ctx0, KQ);
             struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ);
             KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_positions, n_head, batch_size);
-            KQV = ggml_cont(ctx0, ggml_permute(ctx0, KQV, 0, 2, 1, 3));
+            KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
 
-            cur = ggml_cpy(ctx0, KQV, ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size));
+            cur = ggml_cont_3d(ctx0, KQV, hidden_size, num_positions, batch_size);
         }
 
         // attention output
@@ -437,26 +669,182 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
         embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]);
 
         struct ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_patches);
-        ggml_allocr_alloc(ctx->compute_alloc, patches);
-        if (!ggml_allocr_is_measure(ctx->compute_alloc)) {
-            int* patches_data = (int*)malloc(ggml_nbytes(patches));
-            for (int i = 0; i < num_patches; i++) {
-                patches_data[i] = i + 1;
-            }
-            ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
-            free(patches_data);
-        }
+        ggml_set_name(patches, "patches");
+        ggml_set_input(patches);
 
+        // shape [1, 576, 1024]
+        // ne is whcn, ne = [1024, 576, 1, 1]
         embeddings = ggml_get_rows(ctx0, embeddings, patches);
 
-        // mm projection 0
-        embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
-        embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
+        // print_tensor_info(embeddings, "embeddings");
 
-        embeddings = ggml_gelu(ctx0, embeddings);
+        // llava projector
+        if (ctx->proj_type == PROJECTOR_TYPE_MLP) {
+            embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
+            embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
 
-        embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings);
-        embeddings = ggml_add(ctx0, embeddings, model.mm_2_b);
+            embeddings = ggml_gelu(ctx0, embeddings);
+            embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings);
+            embeddings = ggml_add(ctx0, embeddings, model.mm_2_b);
+
+        } else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) {
+            embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
+            embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
+            // ggml_tensor_printf(embeddings, "mm_0_w",0,true,false);
+            // First LayerNorm
+            embeddings = ggml_norm(ctx0, embeddings, eps);
+            embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_1_w),
+                                model.mm_1_b);
+
+            // GELU activation
+            embeddings = ggml_gelu(ctx0, embeddings);
+
+            // Second linear layer
+            embeddings = ggml_mul_mat(ctx0, model.mm_3_w, embeddings);
+            embeddings = ggml_add(ctx0, embeddings, model.mm_3_b);
+
+            // Second LayerNorm
+            embeddings = ggml_norm(ctx0, embeddings, eps);
+            embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_4_w),
+                                model.mm_4_b);
+        }
+        else if (ctx->proj_type == PROJECTOR_TYPE_LDP) {
+            // MobileVLM projector
+            int n_patch = 24;
+            struct ggml_tensor * mlp_1 = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w, embeddings);
+            mlp_1 = ggml_add(ctx0, mlp_1, model.mm_model_mlp_1_b);
+            mlp_1 = ggml_gelu(ctx0, mlp_1);
+            struct ggml_tensor * mlp_3 = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, mlp_1);
+            mlp_3 = ggml_add(ctx0, mlp_3, model.mm_model_mlp_3_b);
+            // mlp_3 shape = [1, 576, 2048], ne = [2048, 576, 1, 1]
+
+            // block 1
+            struct ggml_tensor * block_1 = nullptr;
+            {
+                // transpose from [1, 576, 2048] --> [1, 2048, 576] --> [1, 2048, 24, 24]
+                mlp_3 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_3, 1, 0, 2, 3));
+                mlp_3 = ggml_reshape_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]);
+                // stride = 1, padding = 1, bias is nullptr
+                block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1);
+
+                // layer norm
+                // // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3));
+                // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1]
+                block_1 = ggml_norm(ctx0, block_1, eps);
+                block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_0_1_w), model.mm_model_block_1_block_0_1_b);
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3));
+
+                // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
+                // hardswish
+                struct ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1);
+
+                block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0);
+                // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1]
+                // pointwise conv
+                block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]);
+                block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc1_w, block_1);
+                block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc1_b);
+                block_1 = ggml_relu(ctx0, block_1);
+                block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc2_w, block_1);
+                block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc2_b);
+                block_1 = ggml_hardsigmoid(ctx0, block_1);
+                // block_1_hw shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1], block_1 shape = [1, 2048], ne = [2048, 1, 1, 1]
+                block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]);
+                block_1 = ggml_mul(ctx0, block_1_hw, block_1);
+
+                int w = block_1->ne[0], h = block_1->ne[1];
+                block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]);
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3));
+
+                // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1]
+                block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_2_0_w, block_1);
+                block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]);
+
+                // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1]
+                block_1 = ggml_norm(ctx0, block_1, eps);
+                block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_2_1_w), model.mm_model_block_1_block_2_1_b);
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3));
+                // block1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
+                // residual
+                block_1 = ggml_add(ctx0, mlp_3, block_1);
+            }
+
+            // block_2
+            {
+                // stride = 2
+                block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1);
+
+                // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1]
+                // layer norm
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3));
+                // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1]
+                block_1 = ggml_norm(ctx0, block_1, eps);
+                block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_0_1_w), model.mm_model_block_2_block_0_1_b);
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3));
+                // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1]
+                // hardswish
+                struct ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1);
+
+                // not sure the parameters is right for globalAvgPooling
+                block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0);
+                // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1]
+                // pointwise conv
+                block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]);
+                block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc1_w, block_1);
+                block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc1_b);
+                block_1 = ggml_relu(ctx0, block_1);
+                block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc2_w, block_1);
+                block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc2_b);
+                block_1 = ggml_hardsigmoid(ctx0, block_1);
+
+                // block_1_hw shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1], block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1]
+                block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]);
+                block_1 = ggml_mul(ctx0, block_1_hw, block_1);
+
+                int w = block_1->ne[0], h = block_1->ne[1];
+                block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]);
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3));
+                // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1]
+                block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_2_0_w, block_1);
+                block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]);
+
+
+                // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1]
+                block_1 = ggml_norm(ctx0, block_1, eps);
+                block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_2_1_w), model.mm_model_block_2_block_2_1_b);
+                block_1 = ggml_reshape_3d(ctx0, block_1, block_1->ne[0], block_1->ne[1] * block_1->ne[2], block_1->ne[3]);
+                // block_1 shape = [1, 144, 2048], ne = [2048, 144, 1]
+            }
+            embeddings = block_1;
+        }
+        else if (ctx->proj_type == PROJECTOR_TYPE_LDPV2)
+        {
+            int n_patch = 24;
+            struct ggml_tensor * mlp_0 = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings);
+            mlp_0 = ggml_add(ctx0, mlp_0, model.mm_model_mlp_0_b);
+            mlp_0 = ggml_gelu(ctx0, mlp_0);
+            struct ggml_tensor * mlp_2 = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, mlp_0);
+            mlp_2 = ggml_add(ctx0, mlp_2, model.mm_model_mlp_2_b);
+            // mlp_2 ne = [2048, 576, 1, 1]
+            // // AVG Pool Layer 2*2, strides = 2
+            mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 0, 2, 3));
+            // mlp_2 ne = [576, 2048, 1, 1]
+            mlp_2 = ggml_reshape_4d(ctx0, mlp_2, n_patch, n_patch, mlp_2->ne[1], mlp_2->ne[2]);
+            // mlp_2 ne [24, 24, 2048, 1]
+            mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0);
+            // weight ne = [3, 3, 2048, 1]
+            struct ggml_tensor * peg_0 = ggml_conv_depthwise_2d(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1);
+            peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3));
+            peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b);
+            mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3));
+            peg_0 = ggml_add(ctx0, peg_0, mlp_2);
+            peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]);
+            embeddings = peg_0;
+        }
+        else {
+            GGML_ASSERT(false);
+        }
     }
 
     // build the graph
@@ -502,38 +890,86 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         printf("\n");
     }
     const int n_tensors = gguf_get_n_tensors(ctx);
+
     // kv
-    if (verbosity >= 3) {
-        const int n_kv = gguf_get_n_kv(ctx);
+    const int n_kv = gguf_get_n_kv(ctx);
+    printf("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n",
+        __func__, n_kv, n_tensors, fname);
+    {
+        std::map<enum ggml_type, uint32_t> n_type;
 
-        for (int i = 0; i < n_kv; ++i) {
-            const char * key = gguf_get_key(ctx, i);
+        for (int i = 0; i < n_tensors; i++) {
+            enum ggml_type type = gguf_get_tensor_type(ctx, i);
 
-            printf("%s: kv[%d]: key = %s\n", __func__, i, key);
+            n_type[type]++;
+        }
+
+        printf("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
+        for (int i = 0; i < n_kv; i++) {
+            const char * name           = gguf_get_key(ctx, i);
+            const enum gguf_type type   = gguf_get_kv_type(ctx, i);
+            const std::string type_name =
+                type == GGUF_TYPE_ARRAY
+                ? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(ctx, i)), gguf_get_arr_n(ctx, i))
+                : gguf_type_name(type);
+
+            std::string value          = gguf_kv_to_str(ctx, i);
+            const size_t MAX_VALUE_LEN = 40;
+            if (value.size() > MAX_VALUE_LEN) {
+                value = format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str());
+            }
+            replace_all(value, "\n", "\\n");
+
+            printf("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
+        }
+
+        // print type counts
+        for (auto & kv : n_type) {
+            if (kv.second == 0) {
+                continue;
+            }
+
+            printf("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
         }
-        printf("\n");
     }
 
     // data
-    size_t buffer_size = 0;
+    size_t model_size = 0;
     {
         for (int i = 0; i < n_tensors; ++i) {
             const char * name = gguf_get_tensor_name(ctx, i);
             const size_t offset = gguf_get_tensor_offset(ctx, i);
+            enum ggml_type type = gguf_get_tensor_type(ctx, i);
             struct ggml_tensor * cur = ggml_get_tensor(meta, name);
             size_t tensor_size = ggml_nbytes(cur);
-            buffer_size += tensor_size;
+            model_size += tensor_size;
             if (verbosity >= 3) {
-                printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu\n", __func__, i,
-                       ggml_n_dims(cur), cur->name, tensor_size, offset);
+                printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
+                       __func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type));
             }
         }
     }
 
-    buffer_size += n_tensors * 128 /* CLIP PADDING */;
-
     clip_ctx * new_clip = new clip_ctx;
-#ifdef GGML_USE_CUBLAS
+
+    // update projector type
+    {
+        int idx = gguf_find_key(ctx, KEY_PROJ_TYPE);
+        if (idx != -1) {
+            const std::string proj_type = gguf_get_val_str(ctx, idx);
+            new_clip->proj_type = clip_projector_type_from_string(proj_type);
+        } else {
+            new_clip->proj_type = PROJECTOR_TYPE_MLP;
+        }
+
+        if (new_clip->proj_type == PROJECTOR_TYPE_MLP) {
+            if (gguf_find_tensor(ctx, format(TN_LLAVA_PROJ, 3, "weight").c_str()) != -1) {
+                new_clip->proj_type = PROJECTOR_TYPE_MLP_NORM;
+            }
+        }
+    }
+
+#ifdef GGML_USE_CUDA
     new_clip->backend = ggml_backend_cuda_init(0);
     printf("%s: CLIP using CUDA backend\n", __func__);
 #endif
@@ -543,6 +979,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
     printf("%s: CLIP using Metal backend\n", __func__);
 #endif
 
+
     if (!new_clip->backend) {
         new_clip->backend = ggml_backend_cpu_init();
         printf("%s: CLIP using CPU backend\n", __func__);
@@ -572,12 +1009,12 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             printf("%s: text_encoder:   %d\n", __func__, new_clip->has_text_encoder);
             printf("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
             printf("%s: llava_projector:  %d\n", __func__, new_clip->has_llava_projector);
-            printf("%s: model size:     %.2f MB\n", __func__, buffer_size / 1024.0 / 1024.0);
+            printf("%s: model size:     %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
             printf("%s: metadata size:  %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
         }
     }
 
-    printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, buffer_size / (1024.0 * 1024.0), n_tensors);
+    printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors);
 
     // load tensors
     {
@@ -592,6 +1029,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         if (!new_clip->ctx_data) {
             fprintf(stderr, "%s: ggml_init() failed\n", __func__);
             clip_free(new_clip);
+            gguf_free(ctx);
             return nullptr;
         }
 
@@ -599,6 +1037,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         if (!fin) {
             printf("cannot open model file for loading tensors\n");
             clip_free(new_clip);
+            gguf_free(ctx);
             return nullptr;
         }
 
@@ -611,17 +1050,16 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         }
 
         // alloc memory and offload data
-        new_clip->params_buffer = ggml_backend_alloc_buffer(new_clip->backend, buffer_size);
-        ggml_allocr* alloc = ggml_allocr_new_from_buffer(new_clip->params_buffer);
+        new_clip->params_buffer = ggml_backend_alloc_ctx_tensors(new_clip->ctx_data, new_clip->backend);
         for (int i = 0; i < n_tensors; ++i) {
             const char * name = gguf_get_tensor_name(ctx, i);
             struct ggml_tensor * cur = ggml_get_tensor(new_clip->ctx_data, name);
-            ggml_allocr_alloc(alloc, cur);
             const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i);
             fin.seekg(offset, std::ios::beg);
             if (!fin) {
                 printf("%s: failed to seek for tensor %s\n", __func__, name);
                 clip_free(new_clip);
+                gguf_free(ctx);
                 return nullptr;
             }
             int num_bytes = ggml_nbytes(cur);
@@ -635,7 +1073,6 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
                 ggml_backend_tensor_set(cur, read_buf.data(), 0, num_bytes);
             }
         }
-        ggml_allocr_free(alloc);
         fin.close();
     }
 
@@ -653,11 +1090,41 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         hparams.projection_dim = get_u32(ctx, format(KEY_PROJ_DIM, "vision"));
         hparams.eps            = get_f32(ctx, format(KEY_LAYER_NORM_EPS, "vision"));
 
+        try {
+            int idx = get_key_idx(ctx, KEY_IMAGE_GRID_PINPOINTS);
+            int n = gguf_get_arr_n(ctx, idx);
+            const int32_t * pinpoints = (const int32_t *)gguf_get_arr_data(ctx, idx);
+            for (int i = 0; i < 32 && i < n && pinpoints[i] != 0; ++i) {
+                hparams.image_grid_pinpoints[i] = pinpoints[i];
+            }
+            if (n < 32)
+                hparams.image_grid_pinpoints[n] = 0;
+        } catch (std::runtime_error & e) {
+            hparams.image_grid_pinpoints[0]=0;
+        }
+
+        try {
+            int idx = get_key_idx(ctx, KEY_MM_PATCH_MERGE_TYPE);
+            strcpy(hparams.mm_patch_merge_type, gguf_get_val_str(ctx, idx));
+        } catch (std::runtime_error & e) {
+            strcpy(hparams.mm_patch_merge_type, "flat");
+        }
+
+        try {
+            hparams.image_crop_resolution = get_u32(ctx, KEY_IMAGE_CROP_RESOLUTION); // llava-1.6
+        } catch(const std::exception& e) {
+            hparams.image_crop_resolution = hparams.image_size;
+        }
+
         int idx_mean = get_key_idx(ctx, KEY_IMAGE_MEAN);
         int idx_std  = get_key_idx(ctx, KEY_IMAGE_STD);
+
+        const float * mean_data = (const float *)gguf_get_arr_data(ctx, idx_mean);
+        const float * std_data  = (const float *)gguf_get_arr_data(ctx, idx_std);
+
         for (int i = 0; i < 3; ++i) {
-            new_clip->image_mean[i] = *((const float *)gguf_get_arr_data(ctx, idx_mean));
-            new_clip->image_std[i]  = *((const float *)gguf_get_arr_data(ctx, idx_std));
+            new_clip->image_mean[i] = mean_data[i];
+            new_clip->image_std[i]  = std_data[i];
         }
 
         if (verbosity >= 2) {
@@ -669,19 +1136,100 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             printf("v_projection_dim   %d\n", hparams.projection_dim);
             printf("v_n_head           %d\n", hparams.n_head);
             printf("v_n_layer          %d\n", hparams.n_layer);
+            printf("v_eps              %f\n", hparams.eps);
+            printf("v_image_mean       %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]);
+            printf("v_image_std        %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]);
+            printf("v_image_grid_pinpoints: ");
+            for (int i = 0; i < 32 && (hparams.image_grid_pinpoints[i] != 0); ++i) {
+                printf("%d ", hparams.image_grid_pinpoints[i]);
+            }
+            printf("\n");
+            printf("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type);
+
         }
 
-        vision_model.patch_embeddings    = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
-        vision_model.class_embedding     = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD);
-        vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
-        vision_model.pre_ln_w            = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
-        vision_model.pre_ln_b            = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
-        vision_model.mm_0_w              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight"));
-        vision_model.mm_0_b              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias"));
-        vision_model.mm_2_w              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight"));
-        vision_model.mm_2_b              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias"));
+        try {
+            vision_model.patch_embeddings    = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
+            vision_model.class_embedding     = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD);
+            vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
+            vision_model.pre_ln_w            = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
+            vision_model.pre_ln_b            = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
+        } catch(const std::exception& e) {
+            fprintf(stderr, "%s: failed to load vision model tensors\n", __func__);
+        }
+
+        // LLaVA projection
+        if (new_clip->proj_type == PROJECTOR_TYPE_MLP || new_clip->proj_type == PROJECTOR_TYPE_MLP_NORM) {
+            vision_model.mm_0_w              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight"));
+            vision_model.mm_0_b              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias"));
+            try {
+                // Yi-type llava
+                vision_model.mm_1_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "weight"));
+                vision_model.mm_1_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "bias"));
+            } catch (std::runtime_error & e) {  }
+            try {
+                // missing in Yi-type llava
+                vision_model.mm_2_w              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight"));
+                vision_model.mm_2_b              = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias"));
+            } catch (std::runtime_error & e) {  }
+            try {
+                // Yi-type llava
+                vision_model.mm_3_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "weight"));
+                vision_model.mm_3_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "bias"));
+            } catch (std::runtime_error & e) {  }
+            try {
+                // Yi-type llava
+                vision_model.mm_4_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "weight"));
+                vision_model.mm_4_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "bias"));
+            } catch (std::runtime_error & e) {  }
+            try {
+                vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE);
+                // fprintf(stderr, "%s: image_newline tensor (llava-1.6) found\n", __func__);
+            } catch (std::runtime_error & e) {  }
+        } else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) {
+            // MobileVLM projection
+            vision_model.mm_model_mlp_1_w               = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "weight"));
+            vision_model.mm_model_mlp_1_b               = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "bias"));
+            vision_model.mm_model_mlp_3_w               = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 3, "weight"));
+            vision_model.mm_model_mlp_3_b               = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 3, "bias"));
+            vision_model.mm_model_block_1_block_0_0_w   = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 0, "0.weight"));
+            vision_model.mm_model_block_1_block_0_1_w   = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 0, "1.weight"));
+            vision_model.mm_model_block_1_block_0_1_b   = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 0, "1.bias"));
+            vision_model.mm_model_block_1_block_1_fc1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc1.weight"));
+            vision_model.mm_model_block_1_block_1_fc1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc1.bias"));
+            vision_model.mm_model_block_1_block_1_fc2_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc2.weight"));
+            vision_model.mm_model_block_1_block_1_fc2_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 1, "fc2.bias"));
+            vision_model.mm_model_block_1_block_2_0_w   = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 2, "0.weight"));
+            vision_model.mm_model_block_1_block_2_1_w   = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 2, "1.weight"));
+            vision_model.mm_model_block_1_block_2_1_b   = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 1, 2, "1.bias"));
+            vision_model.mm_model_block_2_block_0_0_w   = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 0, "0.weight"));
+            vision_model.mm_model_block_2_block_0_1_w   = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 0, "1.weight"));
+            vision_model.mm_model_block_2_block_0_1_b   = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 0, "1.bias"));
+            vision_model.mm_model_block_2_block_1_fc1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc1.weight"));
+            vision_model.mm_model_block_2_block_1_fc1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc1.bias"));
+            vision_model.mm_model_block_2_block_1_fc2_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc2.weight"));
+            vision_model.mm_model_block_2_block_1_fc2_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 1, "fc2.bias"));
+            vision_model.mm_model_block_2_block_2_0_w   = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "0.weight"));
+            vision_model.mm_model_block_2_block_2_1_w   = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.weight"));
+            vision_model.mm_model_block_2_block_2_1_b   = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.bias"));
+        }
+        else if (new_clip->proj_type == PROJECTOR_TYPE_LDPV2)
+        {
+            // MobilVLM_V2 projection
+            vision_model.mm_model_mlp_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 0, "weight"));
+            vision_model.mm_model_mlp_0_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 0, "bias"));
+            vision_model.mm_model_mlp_2_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 2, "weight"));
+            vision_model.mm_model_mlp_2_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 2, "bias"));
+            vision_model.mm_model_peg_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "weight"));
+            vision_model.mm_model_peg_0_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "bias"));
+        }
+        else {
+            std::string proj_type = PROJECTOR_TYPE_NAMES[new_clip->proj_type];
+            throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
+        }
 
         vision_model.layers.resize(hparams.n_layer);
+
         for (int il = 0; il < hparams.n_layer; ++il) {
             auto & layer = vision_model.layers[il];
             layer.k_w    = get_tensor(new_clip->ctx_data, format(TN_ATTN_K,      "v", il, "weight"));
@@ -710,15 +1258,12 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
     // measure mem requirement and allocate
     {
         new_clip->buf_compute_meta.resize(GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead());
-        new_clip->compute_alloc = ggml_allocr_new_measure_from_backend(new_clip->backend);
+        new_clip->compute_alloc = ggml_gallocr_new(ggml_backend_get_default_buffer_type(new_clip->backend));
         clip_image_f32_batch batch;
         batch.size = 1;
         ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch);
-        size_t compute_memory_buffer_size = ggml_allocr_alloc_graph(new_clip->compute_alloc, gf);
-        ggml_allocr_free(new_clip->compute_alloc);
-        new_clip->compute_buffer = ggml_backend_alloc_buffer(new_clip->backend, compute_memory_buffer_size);
-        new_clip->compute_alloc = ggml_allocr_new_from_buffer(new_clip->compute_buffer);
-
+        ggml_gallocr_reserve(new_clip->compute_alloc, gf);
+        size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0);
         printf("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
     }
 
@@ -733,8 +1278,20 @@ struct clip_image_f32 * clip_image_f32_init() {
     return new clip_image_f32();
 }
 
-void clip_image_u8_free (struct clip_image_u8  * img) { delete img; }
+void clip_image_u8_free(struct clip_image_u8  * img) { delete img; }
 void clip_image_f32_free(struct clip_image_f32 * img) { delete img; }
+void clip_image_u8_batch_free(struct clip_image_u8_batch  * batch) {
+    if (batch->size > 0) {
+        delete[] batch->data;
+        batch->size = 0;
+    }
+}
+void clip_image_f32_batch_free(struct clip_image_f32_batch  * batch) {
+    if (batch->size > 0) {
+        delete[] batch->data;
+        batch->size = 0;
+    }
+}
 
 static void build_clip_img_from_data(const stbi_uc * data, int nx, int ny, clip_image_u8 * img) {
     img->nx = nx;
@@ -767,24 +1324,252 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length
     return true;
 }
 
-// normalize: x = (x - mean) / std
-// TODO: implement bicubic interpolation instead of linear.
-bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32 * res, const bool pad2square) {
+// Linear interpolation between two points
+inline float lerp(float s, float e, float t) {
+    return s + (e - s) * t;
+}
+// Bilinear resize function
+static void bilinear_resize(const clip_image_u8& src, clip_image_u8& dst, int target_width, int target_height) {
+    dst.nx = target_width;
+    dst.ny = target_height;
+    dst.buf.resize(3 * target_width * target_height);
+
+    float x_ratio = static_cast<float>(src.nx - 1) / target_width;
+    float y_ratio = static_cast<float>(src.ny - 1) / target_height;
+
+    for (int y = 0; y < target_height; y++) {
+        for (int x = 0; x < target_width; x++) {
+            float px = x_ratio * x;
+            float py = y_ratio * y;
+            int x_floor = static_cast<int>(px);
+            int y_floor = static_cast<int>(py);
+            float x_lerp = px - x_floor;
+            float y_lerp = py - y_floor;
+
+            for (int c = 0; c < 3; c++) {
+                float top = lerp(
+                    static_cast<float>(src.buf[3 * (y_floor * src.nx + x_floor) + c]),
+                    static_cast<float>(src.buf[3 * (y_floor * src.nx + (x_floor + 1)) + c]),
+                    x_lerp
+                );
+                float bottom = lerp(
+                    static_cast<float>(src.buf[3 * ((y_floor + 1) * src.nx + x_floor) + c]),
+                    static_cast<float>(src.buf[3 * ((y_floor + 1) * src.nx + (x_floor + 1)) + c]),
+                    x_lerp
+                );
+                dst.buf[3 * (y * target_width + x) + c] = static_cast<uint8_t>(lerp(top, bottom, y_lerp));
+            }
+        }
+    }
+}
+
+// Normalize image to float32 - careful with pytorch .to(model.device, dtype=torch.float16) - this sometimes reduces precision (32>16>32), sometimes not
+static void normalize_image_u8_to_f32(const clip_image_u8* src, clip_image_f32* dst, const float mean[3], const float std[3]) {
+    dst->nx = src->nx;
+    dst->ny = src->ny;
+    dst->buf.resize(src->buf.size());
+
+    for (size_t i = 0; i < src->buf.size(); ++i) {
+        int c = i % 3; // rgb
+        dst->buf[i] = (static_cast<float>(src->buf[i]) / 255.0f - mean[c]) / std[c];
+    }
+}
+
+inline float clip(float x, float lower, float upper) {
+    return std::max(lower, std::min(x, upper));
+}
+
+static bool bicubic_resize(const clip_image_u8 &img, clip_image_u8 &dst, int target_width, int target_height) {
+    const int nx = img.nx;
+    const int ny = img.ny;
+
+    dst.nx = target_width;
+    dst.ny = target_height;
+    dst.buf.resize(3 * target_width * target_height);
+
+    float Cc;
+    float C[5];
+    float d0, d2, d3, a0, a1, a2, a3;
+    int i, j, k, jj;
+    int x, y;
+    float dx, dy;
+    float tx, ty;
+
+    tx = (float)nx / (float)target_width;
+    ty = (float)ny / (float)target_height;
+
+    // Bicubic interpolation; adapted from ViT.cpp, inspired from :
+    //    -> https://github.com/yglukhov/bicubic-interpolation-image-processing/blob/master/libimage.c#L36
+    //    -> https://en.wikipedia.org/wiki/Bicubic_interpolation
+
+    for (i = 0; i < target_height; i++) {
+        for (j = 0; j < target_width; j++) {
+            x = (int)(tx * j);
+            y = (int)(ty * i);
+
+            dx = tx * j - x;
+            dy = ty * i - y;
+
+            for (k = 0; k < 3; k++) {
+                for (jj = 0; jj <= 3; jj++) {
+                    d0 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x - 1, 0, nx - 1)) * 3 + k] - img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k];
+                    d2 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x + 1, 0, nx - 1)) * 3 + k] - img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k];
+                    d3 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x + 2, 0, nx - 1)) * 3 + k] - img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k];
+                    a0 = img.buf[(clip(y - 1 + jj, 0, ny - 1) * nx + clip(x, 0, nx - 1)) * 3 + k];
+
+                    a1 = -1.0 / 3 * d0 + d2 - 1.0 / 6 * d3;
+                    a2 =  1.0 / 2 * d0 +      1.0 / 2 * d2;
+                    a3 = -1.0 / 6 * d0 -      1.0 / 2 * d2 + 1.0 / 6 * d3;
+
+                    C[jj] = a0 + a1 * dx + a2 * dx * dx + a3 * dx * dx * dx;
+
+                    d0 = C[0] - C[1];
+                    d2 = C[2] - C[1];
+                    d3 = C[3] - C[1];
+                    a0 = C[1];
+                    a1 = -1.0 / 3 * d0 + d2 - 1.0 / 6 * d3;
+                    a2 =  1.0 / 2 * d0 +      1.0 / 2 * d2;
+                    a3 = -1.0 / 6 * d0 -      1.0 / 2 * d2 + 1.0 / 6 * d3;
+                    Cc = a0 + a1 * dy + a2 * dy * dy + a3 * dy * dy * dy;
+
+                    const uint8_t Cc2 = std::min(std::max(std::round(Cc), 0.0f), 255.0f);
+                    dst.buf[(i * target_width + j) * 3 + k] = float(Cc2);
+                }
+            }
+        }
+    }
+
+    return true;
+}
+
+// llava-1.6 type of resize_and_pad (black)
+static void resize_and_pad_image(const clip_image_u8& image, clip_image_u8 &image_output, const std::pair<int, int>& target_resolution) {
+    int target_width = target_resolution.first;
+    int target_height = target_resolution.second;
+
+    float scale_w = static_cast<float>(target_width) / image.nx;
+    float scale_h = static_cast<float>(target_height) / image.ny;
+
+    int new_width, new_height;
+
+    if (scale_w < scale_h) {
+        new_width = target_width;
+        new_height = std::min(static_cast<int>(std::ceil(image.ny * scale_w)), target_height);
+    } else {
+        new_height = target_height;
+        new_width = std::min(static_cast<int>(std::ceil(image.nx * scale_h)), target_width);
+    }
+
+    clip_image_u8 resized_image;
+    // bilinear_resize(image, resized_image, new_width, new_height);
+    bicubic_resize(image, resized_image, new_width, new_height);
+
+    clip_image_u8 padded_image;
+    padded_image.nx = target_width;
+    padded_image.ny = target_height;
+    padded_image.buf.resize(3 * target_width * target_height, 0); // Initialize with black
+
+    // Calculate padding offsets
+    int pad_x = (target_width - new_width) / 2;
+    int pad_y = (target_height - new_height) / 2;
+
+    // Copy the resized image into the center of the padded buffer
+    for (int y = 0; y < new_height; ++y) {
+        for (int x = 0; x < new_width; ++x) {
+            for (int c = 0; c < 3; ++c) {
+                padded_image.buf[3 * ((y + pad_y) * target_width + (x + pad_x)) + c] = resized_image.buf[3 * (y * new_width + x) + c];
+            }
+        }
+    }
+    image_output = std::move(padded_image);
+}
+
+/**
+ * Selects the best resolution from a list of possible resolutions based on the original size.
+ *
+ * @param original_size The original size of the image in the format (width, height).
+ * @param possible_resolutions A list of possible resolutions in the format [(width1, height1), (width2, height2), ...].
+ * @return The best fit resolution in the format (width, height).
+ */
+static std::pair<int, int> select_best_resolution(const std::pair<int, int> & original_size, const std::vector<std::pair<int, int>> & possible_resolutions) {
+    int original_width = original_size.first;
+    int original_height = original_size.second;
+    std::pair<int, int> best_fit;
+    int max_effective_resolution = 0;
+    int min_wasted_resolution = std::numeric_limits<int>::max();
+
+    for (const auto& resolution : possible_resolutions) {
+        int width = resolution.first;
+        int height = resolution.second;
+        float scale = std::min(static_cast<float>(width) / original_width, static_cast<float>(height) / original_height);
+        int downscaled_width = static_cast<int>(original_width * scale);
+        int downscaled_height = static_cast<int>(original_height * scale);
+        int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
+        int wasted_resolution = (width * height) - effective_resolution;
+        // fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
+        if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
+            max_effective_resolution = effective_resolution;
+            min_wasted_resolution = wasted_resolution;
+            best_fit = resolution;
+        }
+    }
+
+    return best_fit;
+}
+
+static std::vector<clip_image_u8*> divide_to_patches_u8(const clip_image_u8 & image, int patch_size) {
+    std::vector<clip_image_u8*> patches;
+    int width = image.nx;
+    int height = image.ny;
+    for (int i = 0; i < height; i += patch_size) {
+        for (int j = 0; j < width; j += patch_size) {
+            clip_image_u8 *patch = clip_image_u8_init();
+            patch->nx = std::min(patch_size, width - j);
+            patch->ny = std::min(patch_size, height - i);
+            patch->buf.resize(3 * patch->nx * patch->ny);
+            for (int y = 0; y < patch->ny; ++y) {
+                for (int x = 0; x < patch->nx; ++x) {
+                    for (int c = 0; c < 3; ++c) {
+                        patch->buf[3 * (y * patch->nx + x) + c] = image.buf[3 * ((i + y) * width + (j + x)) + c];
+                    }
+                }
+            }
+            patches.push_back(patch);
+        }
+    }
+    return patches;
+}
+
+// returns the normalized float tensor for llava-1.5, for spatial_unpad with anyres processing for llava-1.6 it returns the normalized image patch tensors as a vector
+// res_imgs memory is being allocated here, previous allocations will be freed if found
+bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) {
+    bool pad_to_square = true;
     if (!ctx->has_vision_encoder) {
         printf("This gguf file seems to have no vision encoder\n");
         return false;
     }
+    auto & params = ctx->vision_model.hparams;
+    // The model config actually contains all we need to decide on how to preprocess, here we automatically switch to the new llava-1.6 preprocessing
+    if (strcmp(params.mm_patch_merge_type, "spatial_unpad") == 0) {
+        pad_to_square = false;
+    }
+    // free the previous res_imgs if any set
+    if (res_imgs->size > 0) {
+        clip_image_f32_batch_free(res_imgs);
+    }
+    res_imgs->data = nullptr;
+    res_imgs->size = 0;
 
     // the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104)
     // see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156
 
     clip_image_u8 * temp = clip_image_u8_init(); // we will keep the input image data here temporarily
-    if (pad2square && img->nx != img->ny) {
+    if (pad_to_square && img->nx != img->ny) {
         int longer_side = std::max(img->nx, img->ny);
         temp->nx = longer_side;
         temp->ny = longer_side;
         temp->buf.resize(3 * longer_side * longer_side);
-        const uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA
+        const uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA (this is the mean rgb color * 255)
 
         // fill with background color
         for (size_t i = 0; i < temp->buf.size(); i++) {
@@ -802,18 +1587,63 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
             }
         }
     } else {
-        temp->nx = img->nx;
-        temp->ny = img->ny;
-        temp->buf.resize(img->buf.size());
-        memcpy(temp->buf.data(), img->buf.data(), temp->buf.size());
+        if (params.image_grid_pinpoints[0] != 0) {
+            // "spatial_unpad" with "anyres" processing for llava-1.6
+            std::vector<std::pair<int, int>> possible_resolutions;
+            for (int i = 0; i < 32 && params.image_grid_pinpoints[i] != 0; i+=2) {
+                possible_resolutions.push_back({params.image_grid_pinpoints[i], params.image_grid_pinpoints[i+1]});
+            }
+            std::pair<int, int> best_resolution = select_best_resolution({img->nx, img->ny}, possible_resolutions);
+            // clip_image_save_to_bmp(*img, "input.bmp");
+            resize_and_pad_image(*img, *temp, best_resolution);  // we do not pad with mean-bg color anymore in llava-1.6
+            // clip_image_save_to_bmp(*temp, "resized.bmp");
+            // visually verify normalized image:
+            // normalize_image_u8_to_f32(*temp, *res, ctx->image_mean, ctx->image_std);
+            // {
+            //     clip_image_u8 * temp2 = clip_image_u8_init();
+            //     clip_image_convert_f32_to_u8(*res, *temp2);
+            //     clip_image_save_to_bmp(*temp2, "resized_normalized_f32.bmp");
+            //     clip_image_u8_free(temp2);
+            // }
+
+            std::vector<clip_image_u8 *> patches = divide_to_patches_u8(*temp, params.image_size); // prepare spatial sorted main patches of image_size each (336 in llava-1.6)
+
+            clip_image_u8 *image_original_resize = clip_image_u8_init();
+            // bilinear_resize(*img, *image_original_resize, params.image_size, params.image_size); // in python this is "shortest_edge", but all CLIP are square
+            bicubic_resize(*img, *image_original_resize, params.image_size, params.image_size); // in python this is "shortest_edge", but all CLIP are square
+            patches.insert(patches.begin(), image_original_resize);
+            // clip_image_f32_batch_init(patches.size());
+            res_imgs->size = patches.size();
+            res_imgs->data = new clip_image_f32[res_imgs->size];
+            int num=0;
+            for (auto& patch : patches) {
+                normalize_image_u8_to_f32(patch, &res_imgs->data[num], ctx->image_mean, ctx->image_std);
+                num++;
+            }
+
+            for (size_t i = 0; i < patches.size(); i++) {
+                // printf("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny);
+                clip_image_u8_free(patches[i]);
+            }
+
+            clip_image_u8_free(temp);
+
+            return true;
+        } else {
+            temp->nx = img->nx;
+            temp->ny = img->ny;
+            temp->buf.resize(img->buf.size());
+            memcpy(temp->buf.data(), img->buf.data(), temp->buf.size());
+        }
     }
 
     const int nx = temp->nx;
     const int ny = temp->ny;
+    // clip_image_save_to_bmp(*temp, "resized_vanilla.bmp");
 
     const int nx2 = ctx->vision_model.hparams.image_size;
     const int ny2 = ctx->vision_model.hparams.image_size;
-
+    clip_image_f32 * res = clip_image_f32_init();
     res->nx = nx2;
     res->ny = ny2;
     res->buf.resize(3 * nx2 * ny2);
@@ -867,16 +1697,72 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
     }
     clip_image_u8_free(temp);
 
+    // {
+    //     clip_image_u8 * temp2 = clip_image_u8_init();
+    //     clip_image_convert_f32_to_u8(*res, *temp2);
+    //     clip_image_save_to_bmp(*temp2, "resized_normalized_f32_vanilla.bmp");
+    //     clip_image_u8_free(temp2);
+    // }
+    // res_imgs.push_back(res);
+
+    res_imgs->size = 1;
+    res_imgs->data = new clip_image_f32[res_imgs->size];
+    res_imgs->data[0] = *res;
+    clip_image_f32_free(res);
+
     return true;
 }
 
+ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx) {
+    return ctx->vision_model.image_newline;
+}
+
 void clip_free(clip_ctx * ctx) {
     ggml_free(ctx->ctx_data);
     gguf_free(ctx->ctx_gguf);
 
+    ggml_backend_buffer_free(ctx->params_buffer);
+    ggml_backend_free(ctx->backend);
+    ggml_gallocr_free(ctx->compute_alloc);
     delete ctx;
 }
 
+size_t clip_embd_nbytes(const struct clip_ctx * ctx) {
+    return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float);
+}
+
+int32_t clip_image_size(const struct clip_ctx * ctx) {
+    return ctx->vision_model.hparams.image_size;
+}
+
+int32_t clip_patch_size(const struct clip_ctx * ctx) {
+    return ctx->vision_model.hparams.patch_size;
+}
+
+int32_t clip_hidden_size(const struct clip_ctx * ctx) {
+    return ctx->vision_model.hparams.hidden_size;
+}
+
+const char * clip_patch_merge_type(const struct clip_ctx * ctx) {
+    return ctx->vision_model.hparams.mm_patch_merge_type;
+}
+
+const int32_t * clip_image_grid(const struct clip_ctx * ctx) {
+    return ctx->vision_model.hparams.image_grid_pinpoints;
+}
+
+int clip_n_patches(const struct clip_ctx * ctx) {
+    const auto & params = ctx->vision_model.hparams;
+
+    int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
+
+    if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2) {
+        n_patches /= 4;
+    }
+
+    return n_patches;
+}
+
 bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
     if (!ctx->has_vision_encoder) {
         printf("This gguf file seems to have no vision encoder\n");
@@ -896,16 +1782,77 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     }
 
     int batch_size = imgs->size;
-    if(ctx->has_llava_projector) {
+    if (ctx->has_llava_projector) {
         GGML_ASSERT(batch_size == 1); // TODO: support multiple images
     }
 
-    // reset alloc buffer to clean the memory from previous invocations
-    ggml_allocr_reset(ctx->compute_alloc);
-
     // build the inference graph
     ggml_cgraph * gf = clip_image_build_graph(ctx, imgs);
-    ggml_allocr_alloc_graph(ctx->compute_alloc, gf);
+    ggml_gallocr_alloc_graph(ctx->compute_alloc, gf);
+
+    // set inputs
+    const auto & model = ctx->vision_model;
+    const auto & hparams = model.hparams;
+
+    const int image_size    = hparams.image_size;
+    const int patch_size    = hparams.patch_size;
+    const int num_patches   = ((image_size / patch_size) * (image_size / patch_size));
+    const int num_positions = num_patches + 1;
+
+    {
+        struct ggml_tensor * inp_raw = ggml_graph_get_tensor(gf, "inp_raw");
+        float * data = (float *)malloc(ggml_nbytes(inp_raw));
+
+        for (size_t i = 0; i < imgs->size; i++) {
+            const int nx = imgs->data[i].nx;
+            const int ny = imgs->data[i].ny;
+            GGML_ASSERT(nx == image_size && ny == image_size);
+
+            const int n = nx * ny;
+
+            for (int b = 0; b < batch_size; b++) {
+                for (int k = 0; k < 3; k++) {
+                    for (int y = 0; y < ny; y++) {
+                        for (int x = 0; x < nx; x++) {
+                            data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].buf[3 * (y * nx + x) + k];
+                        }
+                    }
+                }
+            }
+        }
+        ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw));
+        free(data);
+    }
+
+    {
+        struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings");
+
+        void* zero_mem = malloc(ggml_nbytes(embeddings));
+        memset(zero_mem, 0, ggml_nbytes(embeddings));
+        ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings));
+        free(zero_mem);
+    }
+
+    {
+        struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
+
+        int* positions_data = (int*)malloc(ggml_nbytes(positions));
+        for (int i = 0; i < num_positions; i++) {
+            positions_data[i] = i;
+        }
+        ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
+        free(positions_data);
+    }
+
+    {
+        struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
+        int* patches_data = (int*)malloc(ggml_nbytes(patches));
+        for (int i = 0; i < num_patches; i++) {
+            patches_data[i] = i + 1;
+        }
+        ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
+        free(patches_data);
+    }
 
     if (ggml_backend_is_cpu(ctx->backend)) {
         ggml_backend_cpu_set_n_threads(ctx->backend, n_threads);
@@ -924,33 +1871,15 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
 
     // copy the embeddings to the location passed by the user
     ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
+
     return true;
 }
 
 bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype) {
-
     ggml_type type = GGML_TYPE_Q4_1;
 
-    switch (itype) {
-        case 2:
-            type = GGML_TYPE_Q4_0;
-            break;
-        case 3:
-            type = GGML_TYPE_Q4_1;
-            break;
-        case 6:
-            type = GGML_TYPE_Q5_0;
-            break;
-        case 7:
-            type = GGML_TYPE_Q5_1;
-            break;
-        case 8:
-            type = GGML_TYPE_Q8_0;
-            break;
-        default:
-            fprintf(stderr, "%s: invalid quantization type %d\n", __func__, itype);
-            return false;
-    };
+    assert(itype < GGML_TYPE_COUNT);
+    type = static_cast<ggml_type>(itype);
 
     auto * ctx_clip = clip_model_load(fname_inp, 2);
 
@@ -982,10 +1911,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
         ".*weight",
     };
 
-    std::vector<uint8_t> read_data(512);
     std::vector<uint8_t> work(512);
     std::vector<float> conv_buf(512);
-    std::vector<int64_t> hist_all(1 << 4, 0);
     size_t total_size_org = 0;
     size_t total_size_new = 0;
 
@@ -1010,6 +1937,10 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
 
         if (quantize) {
             new_type = type;
+            if (new_type >= GGML_TYPE_Q2_K && name.find("embd") != std::string::npos) {
+                new_type = GGML_TYPE_Q8_0; // ggml_get_rows needs non K type
+                // fprintf(stderr, "%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type));
+            }
             const size_t n_elms = ggml_nelements(cur);
             float * f32_data;
 
@@ -1028,6 +1959,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
                 break;
             default:
                 printf("Please use an input file in f32 or f16\n");
+                gguf_free(ctx_out);
                 return false;
             }
 
@@ -1036,33 +1968,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
             }
             new_data = work.data();
 
-            std::vector<int64_t> hist_cur(1 << 4, 0);
-
-            switch (new_type) {
-                case GGML_TYPE_Q4_0: {
-                    new_size = ggml_quantize_q4_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
-                } break;
-                case GGML_TYPE_Q4_1: {
-                    new_size = ggml_quantize_q4_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
-                } break;
-                case GGML_TYPE_Q5_0: {
-                    new_size = ggml_quantize_q5_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
-                } break;
-                case GGML_TYPE_Q5_1: {
-                    new_size = ggml_quantize_q5_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
-                } break;
-                case GGML_TYPE_Q8_0: {
-                    new_size = ggml_quantize_q8_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
-                } break;
-                default: {
-                    fprintf(stderr, "%s: unsupported quantization type %d\n", __func__, new_type);
-                    return false;
-                }
-            }
-
-            for (size_t j = 0; j < hist_cur.size(); ++j) {
-                hist_all[j] += hist_cur[j];
-            }
+            new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, n_elms/cur->ne[0], cur->ne[0], nullptr);
         } else {
             new_type = cur->type;
             new_data = cur->data;
@@ -1097,32 +2003,25 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
     {
         printf("%s: original  size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
         printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
-
-        int64_t sum_all = 0;
-        for (size_t i = 0; i < hist_all.size(); ++i) {
-            sum_all += hist_all[i];
-        }
-
-        printf("%s: hist: ", __func__);
-        for (size_t i = 0; i < hist_all.size(); ++i) {
-            printf("%5.3f ", hist_all[i] / (float)sum_all);
-        }
-        printf("\n");
     }
 
     return true;
 }
 
 int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
-    return ctx->vision_model.mm_2_b->ne[0];
-}
+    if (ctx->proj_type == PROJECTOR_TYPE_LDP) {
+        return ctx->vision_model.mm_model_block_1_block_2_1_b->ne[0];
+    }
+    if (ctx->proj_type == PROJECTOR_TYPE_LDPV2) {
+        return ctx->vision_model.mm_model_peg_0_b->ne[0];
+    }
+    if (ctx->proj_type == PROJECTOR_TYPE_MLP) {
+        return ctx->vision_model.mm_2_b->ne[0];
+    }
+    if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) {
+        return ctx->vision_model.mm_3_b->ne[0];
+    }
 
-int clip_n_patches(const struct clip_ctx * ctx) {
-    auto & params = ctx->vision_model.hparams;
-
-    return (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
-}
-
-size_t clip_embd_nbytes(const struct clip_ctx * ctx) {
-    return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float);
+    std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type];
+    throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
 }
diff --git a/examples/llava/clip.h b/examples/llava/clip.h
index 458a256a1..45bdad689 100644
--- a/examples/llava/clip.h
+++ b/examples/llava/clip.h
@@ -24,25 +24,7 @@ struct clip_ctx;
 extern "C" {
 #endif
 
-struct clip_vision_hparams {
-    int32_t image_size;
-    int32_t patch_size;
-    int32_t hidden_size;
-    int32_t n_intermediate;
-    int32_t projection_dim;
-    int32_t n_head;
-    int32_t n_layer;
-    float eps;
-};
-
-CLIP_API struct clip_ctx * clip_model_load(const char * fname, int verbosity);
-
-CLIP_API void clip_free(struct clip_ctx * ctx);
-
-CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx);
-
-CLIP_API int clip_n_patches    (const struct clip_ctx * ctx);
-CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx);
+struct clip_ctx;
 
 struct clip_image_u8_batch {
     struct clip_image_u8 * data;
@@ -54,18 +36,43 @@ struct clip_image_f32_batch {
     size_t size;
 };
 
+CLIP_API struct clip_ctx * clip_model_load    (const char * fname, int verbosity);
+CLIP_API struct clip_ctx * clip_model_load_cpu(const char * fname, int verbosity);
+
+CLIP_API void clip_free(struct clip_ctx * ctx);
+
+CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx);
+
+CLIP_API int32_t clip_image_size (const struct clip_ctx * ctx);
+CLIP_API int32_t clip_patch_size (const struct clip_ctx * ctx);
+CLIP_API int32_t clip_hidden_size(const struct clip_ctx * ctx);
+
+// TODO: should be enum, not string
+CLIP_API const char * clip_patch_merge_type(const struct clip_ctx * ctx);
+
+CLIP_API const int32_t * clip_image_grid(const struct clip_ctx * ctx);
+
+CLIP_API int clip_n_patches    (const struct clip_ctx * ctx);
+CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx);
+
 CLIP_API struct clip_image_u8  * clip_image_u8_init ();
 CLIP_API struct clip_image_f32 * clip_image_f32_init();
 
-CLIP_API void clip_image_u8_free (struct clip_image_u8 * img);
+CLIP_API void clip_image_u8_free (struct clip_image_u8  * img);
 CLIP_API void clip_image_f32_free(struct clip_image_f32 * img);
+CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch  * batch);
+CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch * batch);
 
 CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img);
 
 /** interpret bytes as an image file with length bytes_length, and use the result to populate img */
 CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);
 
-CLIP_API bool clip_image_preprocess  (struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32 * res, bool pad2square);
+/** preprocess img and store the result in res_imgs, pad_to_square may be overriden to false depending on model configuration */
+CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs );
+
+CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx);
+
 CLIP_API bool clip_image_encode      (struct clip_ctx * ctx, int n_threads, struct clip_image_f32 * img, float * vec);
 CLIP_API bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct clip_image_f32_batch * imgs, float * vec);
 
diff --git a/examples/llava/convert-image-encoder-to-gguf.py b/examples/llava/convert-image-encoder-to-gguf.py
index 03688e0ea..b00bf7c6d 100644
--- a/examples/llava/convert-image-encoder-to-gguf.py
+++ b/examples/llava/convert-image-encoder-to-gguf.py
@@ -1,6 +1,7 @@
 import argparse
 import os
 import json
+import re
 
 import torch
 import numpy as np
@@ -38,9 +39,11 @@ def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: b
 def get_tensor_name(name: str) -> str:
     if "projection" in name:
         return name
-
     if "mm_projector" in name:
-        return name.replace("model.mm_projector", "mm")
+        name = name.replace("model.mm_projector", "mm")
+        name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1)
+        name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1)
+        return name
 
     return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln")
 
@@ -71,24 +74,26 @@ def bytes_to_unicode():
     return dict(zip(bs, cs))
 
 
-ap = argparse.ArgumentParser(prog="convert_hf_to_gguf.py")
+ap = argparse.ArgumentParser()
 ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
 ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
 ap.add_argument("--text-only", action="store_true", required=False,
                 help="Save a text-only model. It can't be used to encode images")
 ap.add_argument("--vision-only", action="store_true", required=False,
                 help="Save a vision-only model. It can't be used to encode texts")
-ap.add_argument("--clip_model_is_vision", action="store_true", required=False,
+ap.add_argument("--clip-model-is-vision", action="store_true", required=False,
                 help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
+ap.add_argument("--clip-model-is-openclip", action="store_true", required=False,
+                help="The clip model is from openclip (for ViT-SO400M type))")
 ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
-ap.add_argument("--image-mean", nargs=3, type=float, required=False, help="Override image mean values")
-ap.add_argument("--image-std", nargs=3, type=float, required=False, help="Override image std values")
+ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2"], default="mlp")
 ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
 # Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
+# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5
 default_image_mean = [0.48145466, 0.4578275, 0.40821073]
 default_image_std = [0.26862954, 0.26130258, 0.27577711]
-ap.add_argument('--image_mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
-ap.add_argument('--image_std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
+ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
+ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
 
 # with proper
 args = ap.parse_args()
@@ -104,7 +109,7 @@ if args.use_f32:
 # output in the same directory as the model if output_dir is None
 dir_model = args.model_dir
 
-if args.clip_model_is_vision:
+if args.clip_model_is_vision or not os.path.exists(dir_model + "/vocab.json") or args.clip_model_is_openclip:
     vocab = None
     tokens = None
 else:
@@ -132,7 +137,7 @@ ftype = 1
 if args.use_f32:
     ftype = 0
 
-if args.clip_model_is_vision:
+if args.clip_model_is_vision or args.clip_model_is_openclip:
     model = CLIPVisionModel.from_pretrained(dir_model)
     processor = None
 else:
@@ -174,6 +179,8 @@ elif args.vision_only and not has_llava_projector:
     fout.add_description("vision-only CLIP model")
 elif has_llava_projector:
     fout.add_description("image encoder for LLaVA")
+    # add projector type
+    fout.add_string("clip.projector_type", args.projector_type)
 else:
     fout.add_description("two-tower CLIP model")
 
@@ -199,6 +206,57 @@ if has_vision_encoder:
     fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), v_hparams["layer_norm_eps"])
     block_count = v_hparams["num_hidden_layers"] - 1 if has_llava_projector else v_hparams["num_hidden_layers"]
     fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), block_count)
+                            #     /**
+                            #      "image_grid_pinpoints": [
+                            #         [
+                            #         336,
+                            #         672
+                            #         ],
+                            #         [
+                            #         672,
+                            #         336
+                            #         ],
+                            #         [
+                            #         672,
+                            #         672
+                            #         ],
+                            #         [
+                            #         1008,
+                            #         336
+                            #         ],
+                            #         [
+                            #         336,
+                            #         1008
+                            #         ]
+                            #     ],
+                            #     Flattened:
+                            #     [
+                            #         336, 672,
+                            #         672, 336,
+                            #         672, 672,
+                            #         1008, 336,
+                            #         336, 1008
+                            #     ]
+                            #  *
+                            #  */
+    if "image_grid_pinpoints" in v_hparams:
+        # flatten it
+        image_grid_pinpoints = []
+        for pinpoint in v_hparams["image_grid_pinpoints"]:
+            for p in pinpoint:
+                image_grid_pinpoints.append(p)
+        fout.add_array("clip.vision.image_grid_pinpoints", image_grid_pinpoints)
+    if "image_crop_resolution" in v_hparams:
+        fout.add_uint32("clip.vision.image_crop_resolution", v_hparams["image_crop_resolution"])
+    if "image_aspect_ratio" in v_hparams:
+        fout.add_string("clip.vision.image_aspect_ratio", v_hparams["image_aspect_ratio"])
+    if "image_split_resolution" in v_hparams:
+        fout.add_uint32("clip.vision.image_split_resolution", v_hparams["image_split_resolution"])
+    if "mm_patch_merge_type" in v_hparams:
+        fout.add_string("clip.vision.mm_patch_merge_type", v_hparams["mm_patch_merge_type"])
+    if "mm_projector_type" in v_hparams:
+        fout.add_string("clip.vision.mm_projector_type", v_hparams["mm_projector_type"])
+
 
     if processor is not None:
         image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean
@@ -218,7 +276,8 @@ if has_llava_projector:
     projector = torch.load(args.llava_projector)
     for name, data in projector.items():
         name = get_tensor_name(name)
-        if data.ndim == 2:
+        # pw and dw conv ndim==4
+        if data.ndim == 2 or data.ndim == 4:
             data = data.squeeze().numpy().astype(np.float16)
         else:
             data = data.squeeze().numpy().astype(np.float32)
diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp
index d94795fe3..e29da6cb2 100644
--- a/examples/llava/llava-cli.cpp
+++ b/examples/llava/llava-cli.cpp
@@ -34,7 +34,7 @@ static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) {
 
 static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){
     std::string              str2     = str;
-    std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos);
+    std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos, true);
     eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
     return true;
 }
@@ -148,22 +148,58 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
     const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict;
     const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx_llava->ctx_llama));
 
-    // llava chat format is "<system_prompt>\nUSER:<image_embeddings>\n<textual_prompt>\nASSISTANT:"
-    eval_string(ctx_llava->ctx_llama, "A chat between a curious human and an artificial intelligence assistant.  The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:", params->n_batch, &n_past, add_bos);
+    std::string system_prompt, user_prompt;
+    size_t image_pos = prompt.find("<image>");
+    if (image_pos != std::string::npos) {
+        // new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
+        system_prompt = prompt.substr(0, image_pos);
+        user_prompt = prompt.substr(image_pos + std::string("<image>").length());
+        printf("system_prompt: %s\n", system_prompt.c_str());
+        if (params->verbose_prompt) {
+            auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
+            for (int i = 0; i < (int) tmp.size(); i++) {
+                printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+            }
+        }
+        printf("user_prompt: %s\n", user_prompt.c_str());
+        if (params->verbose_prompt) {
+            auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
+            for (int i = 0; i < (int) tmp.size(); i++) {
+                printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+            }
+        }
+    } else {
+        // llava-1.5 native mode
+        system_prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:";
+        user_prompt = prompt + "\nASSISTANT:";
+        if (params->verbose_prompt) {
+            auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
+            for (int i = 0; i < (int) tmp.size(); i++) {
+                printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+            }
+        }
+    }
+
+    eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, add_bos);
     llava_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past);
-    eval_string(ctx_llava->ctx_llama, (prompt + "\nASSISTANT:").c_str(), params->n_batch, &n_past, false);
+    eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, false);
 
     // generate the response
 
     fprintf(stderr, "\n");
 
     struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams);
-
+    std::string response = "";
     for (int i = 0; i < max_tgt_len; i++) {
         const char * tmp = sample(ctx_sampling, ctx_llava->ctx_llama, &n_past);
+        response += tmp;
         if (strcmp(tmp, "</s>") == 0) break;
-
+        if (strstr(tmp, "###")) break; // Yi-VL behavior
         printf("%s", tmp);
+        if (strstr(response.c_str(), "<|im_end|>")) break; // Yi-34B llava-1.6 - for some reason those decode not as the correct token (tokenizer works)
+        if (strstr(response.c_str(), "<|im_start|>")) break; // Yi-34B llava-1.6
+        if (strstr(response.c_str(), "USER:")) break; // mistral llava-1.6
+
         fflush(stdout);
     }
 
@@ -182,7 +218,8 @@ static struct llava_context * llava_init(gpt_params * params) {
 
     auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
 
-    llama_backend_init(params->numa);
+    llama_backend_init();
+    llama_numa_init(params->numa);
 
     llama_model_params model_params = llama_model_params_from_gpt_params(*params);
 
diff --git a/examples/llava/llava-surgery-v2.py b/examples/llava/llava-surgery-v2.py
new file mode 100644
index 000000000..eb56d6988
--- /dev/null
+++ b/examples/llava/llava-surgery-v2.py
@@ -0,0 +1,155 @@
+import argparse
+import glob
+import os
+import torch
+from safetensors.torch import load as safe_load, save as safe_save, safe_open, save_file
+
+# Function to determine if file is a SafeTensor file
+def is_safetensor_file(file_path):
+    return file_path.endswith('.safetensors')
+
+
+# Unified loading function
+def load_model(file_path):
+    if is_safetensor_file(file_path):
+        tensors = {}
+        with safe_open(file_path, framework="pt", device="cpu") as f:
+            for key in f.keys():
+                tensors[key] = f.get_tensor(key).clone()
+                # output shape
+                print(f"{key} : {tensors[key].shape}")
+        return tensors, 'safetensor'
+    else:
+        return torch.load(file_path, map_location=torch.device('cpu')), 'pytorch'
+
+
+# Unified saving function
+def save_model(model, file_path, file_type):
+    if file_type == 'safetensor':
+        # safe_save(model, file_path)
+        save_file(model, file_path)
+    else:
+        torch.save(model, file_path)
+
+
+# Adapted function to clean vision tower from checkpoint
+def clean_vision_tower_from_checkpoint(checkpoint_path):
+    checkpoint, file_type = load_model(checkpoint_path)
+    # file_type = 'pytorch'
+    model_path = os.path.dirname(checkpoint_path)
+    print(f"Searching for vision tower tensors in {checkpoint_path}")
+    clip_tensors = [k for k, v in checkpoint.items() if (k.startswith("model.vision_tower") or k.startswith("vit."))]
+
+    if len(clip_tensors) > 0:
+        print(f"Found {len(clip_tensors)} tensors to extract from {checkpoint_path}")
+        # Adapted for file type
+        clip_path = os.path.join(model_path, "llava.clip")
+
+        if os.path.exists(clip_path):
+            print(f"Loading existing llava.clip from {clip_path}")
+            existing_clip, _ = load_model(clip_path)
+        else:
+            print(f"Creating new llava.clip at {clip_path}")
+            existing_clip = {}
+        # Update existing_clip with new tensors, avoid duplicates
+        for name in clip_tensors:
+            simple_name = name[name.index('vision_model.'):] if 'vision_model.' in name else name
+            print(f"Adding {simple_name} to llava.clip")
+            if simple_name not in existing_clip:
+                existing_clip[simple_name] = checkpoint[name]
+
+        # Save the updated clip tensors back to llava.clip
+        save_model(existing_clip, clip_path, 'pytorch')
+
+        # Remove the tensors from the original checkpoint
+        for name in clip_tensors:
+            del checkpoint[name]
+
+        checkpoint_path = checkpoint_path
+        return True
+    return False
+
+def find_relevant_checkpoints(checkpoint_paths, newline_criteria, projector):
+    newline_checkpoint_path = None
+    projector_checkpoint_path = None
+
+    for path in checkpoint_paths:
+        checkpoint, _ = load_model(path)
+        if newline_criteria(checkpoint) and newline_checkpoint_path is None:
+            newline_checkpoint_path = path
+        if projector(checkpoint):
+            projector_checkpoint_path = path
+
+    return newline_checkpoint_path, projector_checkpoint_path
+
+def newline_criteria(checkpoint):
+    return any(k.startswith("model.image_newline") for k in checkpoint.keys())
+
+def proj_criteria(checkpoint):
+    return any(k.startswith("model.mm_projector") or k.startswith("vision_proj.") for k in checkpoint.keys())
+
+
+# Command-line interface setup
+ap = argparse.ArgumentParser()
+ap.add_argument("-m", "--model", required=True, help="Path to LLaVA v1.5+ model")
+ap.add_argument("-C", "--clean-vision-tower", action="store_true", help="Remove any vision tower from the model files")
+args = ap.parse_args()
+
+if args.clean_vision_tower:
+    # Generalized to handle both PyTorch and SafeTensors models
+    model_files = sorted(glob.glob(f"{args.model}/*"), key=os.path.getmtime, reverse=True)
+    # checkpoint_paths = [path for path in model_files if (path.endswith('.bin') and path.startswith('pytorch')) or (path.endswith('.safetensors') and path.startswith('model'))]
+    checkpoint_paths = [path for path in model_files if (path.endswith('.bin') and 'pytorch' in path.split('/')[-1].split('\\')[-1]) or (path.endswith('.safetensors') and 'model' in path.split('/')[-1].split('\\')[-1])]
+    for projector_checkpoint_path in checkpoint_paths:
+        print(f"Cleaning {projector_checkpoint_path}")
+        if not clean_vision_tower_from_checkpoint(projector_checkpoint_path):
+            print(f"No vision tower found in {projector_checkpoint_path}")
+            # we break once none is found, so far all models append them at the end
+            # break
+    print("Done! All vision tower tensors are removed from the model files and stored in llava.clip file.")
+
+# Now we look for the projector in the last checkpoint
+model_files = sorted(glob.glob(f"{args.model}/*"), key=os.path.getmtime, reverse=True)
+checkpoint_paths = [path for path in model_files if (path.endswith('.bin') and 'pytorch' in path.split('/')[-1].split('\\')[-1]) or (path.endswith('.safetensors') and 'model' in path.split('/')[-1].split('\\')[-1])]
+# last_checkpoint_path = checkpoint_paths[0]
+# first_checkpoint_path = checkpoint_paths[-1]
+newline_checkpoint_path, projector_checkpoint_path = find_relevant_checkpoints(checkpoint_paths, newline_criteria, proj_criteria)
+
+print(f"Taking projector from {projector_checkpoint_path}")
+first_mm_tensors = []
+first_checkpoint = None
+if newline_checkpoint_path is not None:
+    print(f"Taking newline from {newline_checkpoint_path}")
+    first_checkpoint, file_type = load_model(newline_checkpoint_path)
+    first_mm_tensors = [k for k, v in first_checkpoint.items() if k.startswith("model.image_newline")]
+
+# Load the checkpoint
+mm_tensors = []
+last_checkpoint = None
+if projector_checkpoint_path is not None:
+    last_checkpoint, file_type = load_model(projector_checkpoint_path)
+    mm_tensors = [k for k, v in last_checkpoint.items() if k.startswith("model.mm_projector") or k.startswith("vision_proj.")]
+
+if len(mm_tensors) == 0:
+    if last_checkpoint is not None:
+        for k, v in last_checkpoint.items():
+            print(k)
+    print(f"Found {len(mm_tensors)} tensors to extract out of {len(last_checkpoint)} tensors.")
+    print("No tensors found. Is this a LLaVA model?")
+    exit()
+
+print(f"Found {len(mm_tensors)} tensors to extract.")
+print(f"Found additional {len(first_mm_tensors)} tensors to extract.")
+# projector = {name: checkpoint.[name].float() for name in mm_tensors}
+projector = {}
+for name in mm_tensors:
+    projector[name] = last_checkpoint[name].float()
+for name in first_mm_tensors:
+    projector[name] = first_checkpoint[name].float()
+
+if len(projector) > 0:
+    save_model(projector, f"{args.model}/llava.projector", 'pytorch')
+
+print("Done!")
+print(f"Now you can convert {args.model} to a a regular LLaMA GGUF file.")
+print(f"Also, use {args.model}/llava.projector to prepare a llava-encoder.gguf file.")
diff --git a/examples/llava/llava-surgery.py b/examples/llava/llava-surgery.py
index 515f6b58d..4f2da3bee 100644
--- a/examples/llava/llava-surgery.py
+++ b/examples/llava/llava-surgery.py
@@ -19,19 +19,12 @@ mm_tensors = [k for k, v in checkpoint.items() if k.startswith("model.mm_project
 projector = {name: checkpoint[name].float() for name in mm_tensors}
 torch.save(projector, f"{args.model}/llava.projector")
 
-# remove these tensors from the checkpoint and save it again
-for name in mm_tensors:
-    del checkpoint[name]
-
 # BakLLaVA models contain CLIP tensors in it
 clip_tensors = [k for k, v in checkpoint.items() if k.startswith("model.vision_tower")]
 if len(clip_tensors) > 0:
     clip = {name.replace("vision_tower.vision_tower.", ""): checkpoint[name].float() for name in clip_tensors}
     torch.save(clip, f"{args.model}/llava.clip")
 
-    # remove these tensors
-    for name in clip_tensors:
-        del checkpoint[name]
 
     # added tokens should be removed to be able to convert Mistral models
     if os.path.exists(f"{args.model}/added_tokens.json"):
@@ -39,8 +32,7 @@ if len(clip_tensors) > 0:
             f.write("{}\n")
 
 
-torch.save(checkpoint, path)
 
 print("Done!")
-print(f"Now you can convert {args.model} to a a regular LLaMA GGUF file.")
+print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.")
 print(f"Also, use {args.model}/llava.projector to prepare a llava-encoder.gguf file.")
diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp
index d42e7582e..29764757a 100644
--- a/examples/llava/llava.cpp
+++ b/examples/llava/llava.cpp
@@ -2,32 +2,296 @@
 #include "common.h"
 #include "llama.h"
 #include "llava.h"
+#include "base64.hpp"
 
 #include <cstdio>
 #include <cstdlib>
 #include <vector>
+#include <numeric>
+
+// RGB uint8 image
+struct clip_image_u8 {
+    int nx;
+    int ny;
+
+    std::vector<uint8_t> buf;
+};
+
+// RGB float32 image (NHWC)
+// Memory layout: RGBRGBRGB...
+struct clip_image_f32 {
+    int nx;
+    int ny;
+
+    std::vector<float> buf;
+};
+
+struct clip_image_grid_shape {
+    int first;
+    int second;
+};
+
+/**
+ * Selects the best resolution from a list of possible resolutions based on the original size.
+ *
+ * @param original_size The original size of the image in the format (width, height).
+ * @param possible_resolutions A list of possible resolutions in the format [(width1, height1), (width2, height2), ...].
+ * @return The best fit resolution in the format (width, height).
+ */
+static std::pair<int, int> select_best_resolution(const std::pair<int, int>& original_size, const std::vector<std::pair<int, int>>& possible_resolutions) {
+    int original_width  = original_size.first;
+    int original_height = original_size.second;
+
+    std::pair<int, int> best_fit;
+    int max_effective_resolution = 0;
+    int min_wasted_resolution = std::numeric_limits<int>::max();
+
+    for (const auto& resolution : possible_resolutions) {
+        int width = resolution.first;
+        int height = resolution.second;
+        float scale = std::min(static_cast<float>(width) / original_width, static_cast<float>(height) / original_height);
+        int downscaled_width  = static_cast<int>(original_width * scale);
+        int downscaled_height = static_cast<int>(original_height * scale);
+        int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
+        int wasted_resolution = (width * height) - effective_resolution;
+        // fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
+        if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
+            max_effective_resolution = effective_resolution;
+            min_wasted_resolution = wasted_resolution;
+            best_fit = resolution;
+        }
+    }
+
+    return best_fit;
+}
+
+/**
+ * @brief Get the anyres image grid shape object
+ *
+ * @param image_size
+ * @param grid_pinpoints
+ * @param image_patch_size
+ * @return <int, int>
+ */
+static struct clip_image_grid_shape get_anyres_image_grid_shape(const std::pair<int, int> & image_size, const std::vector<std::pair<int, int>> & grid_pinpoints, int image_patch_size) {
+    /**
+        Conversion from gguf flat array to vector:
+        std::vector<std::pair<int, int>> possible_resolutions;
+        for (int i = 0; i < 32 && params.image_grid_pinpoints[i] != 0; i+=2) {
+            possible_resolutions.push_back({params.image_grid_pinpoints[i], params.image_grid_pinpoints[i+1]});
+        }
+     */
+    auto best_resolution = select_best_resolution(image_size, grid_pinpoints);
+    return {best_resolution.first / image_patch_size, best_resolution.second / image_patch_size};
+}
+
+// Take the image segments in a grid configuration and return the embeddings and the number of embeddings into preallocated memory (image_embd_out)
+static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *> & image_embd_v, struct clip_image_grid_shape grid_shape, float * image_embd_out, int * n_img_pos_out) {
+    struct {
+        struct ggml_tensor * newline;
+        struct ggml_context * ctx;
+    } model;
+
+    const int32_t image_size = clip_image_size(ctx_clip);
+    const int32_t patch_size = clip_patch_size(ctx_clip);
+
+    int32_t num_patches_per_side = image_size / patch_size; // 336 / 14 = 24 - used for embedding-patching boxes (24*24 = 576 patches)
+
+    int num_patches_width  = grid_shape.first;  // grid 1-4
+    int num_patches_height = grid_shape.second; // grid 1-4
+
+    const size_t num_images = num_patches_width * num_patches_height + 1;
+
+    // TODO: size calculation is not calculated - it's only tens of MB
+    size_t ctx_size = 0;
+
+    {
+        ctx_size += clip_embd_nbytes(ctx_clip) * num_images * 8; // image_features
+        ctx_size += 1024*1024 * ggml_type_size(GGML_TYPE_F32);
+    }
+
+    struct ggml_init_params params {
+        /*.mem_size   =*/ ctx_size,
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ false, // NOTE: this should be false when using the legacy API
+    };
+
+    // Python reference code for full unpad:
+    /*
+        base_image_feature = image_feature[0]
+        image_feature = image_feature[1:]
+        image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
+        image_feature = image_feature.flatten(1, 2).flatten(2, 3)
+        image_feature = unpad_image(image_feature, image_sizes[image_idx])
+        image_feature = torch.cat((
+            image_feature,
+            self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1)
+        ), dim=-1)
+        image_feature = image_feature.flatten(1, 2).transpose(0, 1)
+        image_feature = torch.cat((base_image_feature, image_feature), dim=0)
+    */
+    // We now have two options: unpad or no unpad. Unpad removes tokens for faster llm eval.
+    // In terms of result quality it appears to make no difference, so we'll start with the easier approach given 5D tensors are not supported in ggml yet.
+    // Without unpad we have to split the sub-image embeddings into patches of 24 features each and permute them.
+    // Once all images are processed to prepended the base_image_features without any changes.
+
+    // Pytorch reference simplified, modified for ggml compatibility - confirmed identical output in python (for a 2x2 grid image (676x676 scaling))
+    /*
+        image_feature = image_feature.view(2, 2, 24, 24, 4096)
+        image_feature = image_feature.permute(0, 2, 1, 3, 4).contiguous()
+        image_feature = image_feature.view(2, 24, 2, 24, 4096)
+        image_feature = image_feature.flatten(0, 3)
+
+        // Reshape to 4D tensor by merging the last two dimensions
+        image_feature = image_feature.view(2, 2, 24, 24*4096)
+        image_feature = image_feature.permute(0, 2, 1, 3).contiguous()
+        image_feature = image_feature.view(-1, 4096)
+    */
+
+    model.ctx = ggml_init(params);
+
+    ggml_tensor * newline_tmp = clip_get_newline_tensor(ctx_clip);
+    model.newline = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, newline_tmp->ne[0]);
+    if (newline_tmp->backend != GGML_BACKEND_TYPE_CPU) {
+        if (newline_tmp->buffer == NULL) {
+            printf("newline_tmp tensor buffer is NULL\n");
+        }
+        ggml_backend_tensor_get(newline_tmp, model.newline->data, 0, ggml_nbytes(newline_tmp));
+    } else {
+        model.newline->data = newline_tmp->data;
+        if (model.newline->data == NULL) {
+            printf("newline_tmp tensor data is NULL\n");
+        }
+    }
+
+    struct ggml_tensor * image_features = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F32, clip_n_mmproj_embd(ctx_clip), clip_n_patches(ctx_clip), num_images - 1); // example: 4096 x 576 x 4
+    // ggml_tensor_printf(image_features,"image_features",__LINE__,false,false);
+    // fill it with the image embeddings, ignoring the base
+    for (size_t i = 1; i < num_images; i++) {
+        size_t offset = (i-1) * clip_embd_nbytes(ctx_clip);
+        memcpy((uint8_t *)(image_features->data) + offset, image_embd_v[i], clip_embd_nbytes(ctx_clip));
+    }
+
+    struct ggml_cgraph  * gf = ggml_new_graph(model.ctx);
+    size_t size_ele = ggml_type_size(GGML_TYPE_F32);
+
+    struct ggml_tensor *image_features_patchview = ggml_view_4d(model.ctx, image_features,
+                                                                num_patches_per_side * clip_n_mmproj_embd(ctx_clip),
+                                                                num_patches_per_side,
+                                                                num_patches_width,
+                                                                num_patches_height,
+                                                                size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip),
+                                                                size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip) * num_patches_per_side,
+                                                                size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip) * num_patches_per_side * num_patches_width, 0);
+    // ggml_tensor_printf(image_features_patchview,"image_features_patchview",__LINE__,false,false);
+    struct ggml_tensor *permuted_cont = ggml_cont(model.ctx, ggml_permute(model.ctx, image_features_patchview, 0, 2, 1, 3));
+    /**
+     At the end of each row we have to add the row_end embeddings, which are the same as the newline embeddings
+         image_feature = torch.cat((
+        image_feature,
+        self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)
+    ), dim=-1)
+     *
+     */
+
+    // ggml_tensor_printf(permuted_cont,"permuted_cont",__LINE__,false,false);
+    struct ggml_tensor *flatten = ggml_view_2d(model.ctx, permuted_cont, clip_n_mmproj_embd(ctx_clip), num_patches_height * num_patches_width * num_patches_per_side * num_patches_per_side,  size_ele * clip_n_mmproj_embd(ctx_clip), 0);
+    // ggml_tensor_printf(flatten,"flatten",__LINE__,false,false);
+    ggml_build_forward_expand(gf, flatten);
+    ggml_graph_compute_with_ctx(model.ctx, gf, 1);
+    struct ggml_tensor* result = gf->nodes[gf->n_nodes - 1];
+
+    memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as global context
+    // append without newline tokens (default behavior in llava_arch when not using unpad ):
+    memcpy(image_embd_out + clip_n_patches(ctx_clip) * clip_n_mmproj_embd(ctx_clip), (float*)result->data, clip_embd_nbytes(ctx_clip) * (num_images-1)); // grid patches
+    *n_img_pos_out = static_cast<int>(result->ne[1]+clip_n_patches(ctx_clip));
+
+    // Debug: Test single segments
+    // Current findings: sending base image, sending a segment embedding all works similar to python
+    // However, permuted embeddings do not work yet (stride issue?)
+    // memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as context
+    // memcpy(image_embd_out, (float*)prepared_cont->data, clip_embd_nbytes(ctx_clip)); // main image as context
+    // *n_img_pos_out=576;
+
+    ggml_free(model.ctx);
+    return true;
+}
 
-#include "base64.hpp"
 
 static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) {
-    clip_image_f32 * img_res = clip_image_f32_init();
-    if (!clip_image_preprocess(ctx_clip, img, img_res, /*pad2square =*/ true)) {
+    // std::vector<clip_image_f32*> img_res_v; // format VectN x H x W x RGB (N x 336 x 336 x 3), so interleaved RGB - different to the python implementation which is N x 3 x 336 x 336
+    clip_image_f32_batch img_res_v;
+    img_res_v.size = 0;
+    img_res_v.data = nullptr;
+    if (!clip_image_preprocess(ctx_clip, img, &img_res_v)) {
         fprintf(stderr, "%s: unable to preprocess image\n", __func__);
-        clip_image_f32_free(img_res);
+        delete[] img_res_v.data;
         return false;
     }
 
-    *n_img_pos = clip_n_patches(ctx_clip);
-
     const int64_t t_img_enc_start_us = ggml_time_us();
-    bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd);
-    clip_image_f32_free(img_res);
-    if (!encoded) {
-        fprintf(stderr, "Unable to encode image\n");
 
-        return false;
+    const char * mm_patch_merge_type = clip_patch_merge_type(ctx_clip);
+
+    if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) {
+        // flat / default llava-1.5 type embedding
+        *n_img_pos = clip_n_patches(ctx_clip);
+        bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd); // image_embd shape is 576 x 4096
+        delete[] img_res_v.data;
+        if (!encoded) {
+            fprintf(stderr, "Unable to encode image\n");
+
+            return false;
+        }
+    } else {
+        // spatial_unpad llava-1.6 type embedding
+        // TODO: CLIP needs batching support - in HF the llm projection is separate after encoding, which might be a solution to quickly get batching working
+        std::vector<float *> image_embd_v;
+        image_embd_v.resize(img_res_v.size);
+        for (size_t i = 0; i < img_res_v.size; i++) {
+            image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip)); // 576 patches * 4096 embeddings * 4 bytes = 9437184
+            const bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]); // image data is in 3x336x336 format and will be converted to 336x336x3 inside
+            if (!encoded) {
+                fprintf(stderr, "Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
+                return false;
+            }
+        }
+        const int64_t t_img_enc_batch_us = ggml_time_us();
+        printf("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
+
+        const int32_t * image_grid = clip_image_grid(ctx_clip);
+
+        std::vector<std::pair<int, int>> grid_pinpoints;
+        for (int i = 0; i < 32 && image_grid[i] != 0; i += 2) {
+            grid_pinpoints.push_back({image_grid[i], image_grid[i+1]});
+        }
+
+        // free all img_res_v - not needed anymore
+        delete[] img_res_v.data;
+        img_res_v.size = 0;
+        img_res_v.data = nullptr;
+
+        const int32_t image_size = clip_image_size(ctx_clip);
+
+        struct clip_image_grid_shape grid_shape = get_anyres_image_grid_shape({img->nx,img->ny}, grid_pinpoints, image_size);
+
+        int n_img_pos_out;
+        clip_llava_handle_patches(ctx_clip, image_embd_v, grid_shape, image_embd, &n_img_pos_out);
+        *n_img_pos = n_img_pos_out;
+
+        for (size_t i = 0; i < image_embd_v.size(); i++) {
+            free(image_embd_v[i]);
+        }
+        image_embd_v.clear();
+
+        // debug image/segment/normalization content:
+        // clip_image_u8 * tmp = clip_image_u8_init();
+        // clip_image_convert_f32_to_u8(*image_feature, *tmp);
+        // clip_image_save_to_bmp(*tmp, "image_feature.bmp");
     }
 
+    printf("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);
+
     const int64_t t_img_enc_end_us = ggml_time_us();
     float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0;
 
@@ -47,11 +311,10 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx *
     return true;
 }
 
-static bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) {
-    float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip));
+bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) {
+    float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*6); // TODO: base on gridsize/llava model
     if (!image_embd) {
         fprintf(stderr, "Unable to allocate memory for image embeddings\n");
-        free(image_embd);
         return false;
     }
 
@@ -85,7 +348,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
     return true;
 }
 
-LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) {
+struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) {
     clip_image_u8 * img = clip_image_u8_init();
     if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) {
         clip_image_u8_free(img);
@@ -142,7 +405,7 @@ static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long
     return true;
 }
 
-LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path) {
+struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path) {
     unsigned char* image_bytes;
     long image_bytes_length;
     auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length);
@@ -151,13 +414,13 @@ LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct
         return NULL;
     }
 
-    auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, image_bytes, image_bytes_length);
+    llava_image_embed *embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, image_bytes, image_bytes_length);
     free(image_bytes);
 
     return embed;
 }
 
-LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed) {
+void llava_image_embed_free(struct llava_image_embed * embed) {
     free(embed->embed);
     free(embed);
 }
diff --git a/examples/llava/llava.h b/examples/llava/llava.h
index e08ce7883..19212f6e9 100644
--- a/examples/llava/llava.h
+++ b/examples/llava/llava.h
@@ -3,7 +3,6 @@
 
 #include "ggml.h"
 
-
 #ifdef LLAMA_SHARED
 #    if defined(_WIN32) && !defined(__MINGW32__)
 #        ifdef LLAMA_BUILD
@@ -30,7 +29,9 @@ struct llava_image_embed {
 };
 
 /** sanity check for clip <-> llava embed size match */
-LLAVA_API bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip);
+LLAVA_API bool llava_validate_embed_size(const struct llama_context * ctx_llama, const struct clip_ctx * ctx_clip);
+
+LLAVA_API bool llava_image_embed_make_with_clip_img(struct clip_ctx * ctx_clip, int n_threads, const struct clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out);
 
 /** build an image embed from image file bytes */
 LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length);
@@ -42,7 +43,6 @@ LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed);
 /** write the image represented by embed into the llama context with batch size n_batch, starting at context pos n_past. on completion, n_past points to the next position in the context after the image embed. */
 LLAVA_API bool llava_eval_image_embed(struct llama_context * ctx_llama, const struct llava_image_embed * embed, int n_batch, int * n_past);
 
-
 #ifdef __cplusplus
 }
 #endif
diff --git a/examples/llava/requirements.txt b/examples/llava/requirements.txt
new file mode 100644
index 000000000..f80f727a7
--- /dev/null
+++ b/examples/llava/requirements.txt
@@ -0,0 +1,3 @@
+-r ../../requirements/requirements-convert.txt
+pillow~=10.2.0
+torch~=2.1.1
diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp
index e55a15a1b..e2551e7a4 100644
--- a/examples/lookahead/lookahead.cpp
+++ b/examples/lookahead/lookahead.cpp
@@ -54,7 +54,8 @@ int main(int argc, char ** argv) {
 #endif // LOG_DISABLE_LOGS
 
     // init llama.cpp
-    llama_backend_init(params.numa);
+    llama_backend_init();
+    llama_numa_init(params.numa);
 
     llama_model * model = NULL;
     llama_context * ctx = NULL;
diff --git a/examples/lookup/CMakeLists.txt b/examples/lookup/CMakeLists.txt
index c060b8f56..b91633f63 100644
--- a/examples/lookup/CMakeLists.txt
+++ b/examples/lookup/CMakeLists.txt
@@ -3,3 +3,21 @@ add_executable(${TARGET} lookup.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
+
+set(TARGET lookup-create)
+add_executable(${TARGET} lookup-create.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
+
+set(TARGET lookup-merge)
+add_executable(${TARGET} lookup-merge.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
+
+set(TARGET lookup-stats)
+add_executable(${TARGET} lookup-stats.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/lookup/lookup-create.cpp b/examples/lookup/lookup-create.cpp
new file mode 100644
index 000000000..46a6bed07
--- /dev/null
+++ b/examples/lookup/lookup-create.cpp
@@ -0,0 +1,43 @@
+#include "ggml.h"
+#include "llama.h"
+#include "common.h"
+#include "ngram-cache.h"
+
+#include <cstdint>
+#include <fstream>
+#include <iostream>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+int main(int argc, char ** argv){
+    gpt_params params;
+
+    if (!gpt_params_parse(argc, argv, params)) {
+        return 1;
+    }
+    // init llama.cpp
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    llama_model * model = NULL;
+    llama_context * ctx = NULL;
+
+    // load the model
+    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    GGML_ASSERT(model != nullptr);
+
+    // tokenize the prompt
+    const bool add_bos = llama_should_add_bos_token(model);
+
+    std::vector<llama_token> inp;
+    inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
+    fprintf(stderr, "%s: tokenization done\n", __func__);
+
+
+    llama_ngram_cache ngram_cache;
+    llama_ngram_cache_update(ngram_cache, LLAMA_NGRAM_STATIC, LLAMA_NGRAM_STATIC, inp, inp.size(), true);
+    fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.lookup_cache_static.c_str());
+
+    llama_ngram_cache_save(ngram_cache, params.lookup_cache_static);
+}
diff --git a/examples/lookup/lookup-merge.cpp b/examples/lookup/lookup-merge.cpp
new file mode 100644
index 000000000..07c93eb8d
--- /dev/null
+++ b/examples/lookup/lookup-merge.cpp
@@ -0,0 +1,47 @@
+#include "ggml.h"
+#include "llama.h"
+#include "common.h"
+#include "ngram-cache.h"
+
+#include <cstdint>
+#include <cstdio>
+#include <fstream>
+#include <iostream>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+static void print_usage() {
+    fprintf(stderr, "Merges multiple lookup cache files into a single one.\n");
+    fprintf(stderr, "Usage: lookup-merge [--help] lookup_part_1.bin lookup_part_2.bin ... lookup_merged.bin\n");
+}
+
+int main(int argc, char ** argv){
+    if (argc < 3) {
+        print_usage();
+        exit(1);
+    }
+
+    std::vector<std::string> args;
+    args.resize(argc-1);
+    for (int i = 0; i < argc-1; ++i) {
+        args[i] = argv[i+1];
+        if (args[i] == "-h" || args[i] == "--help") {
+            print_usage();
+            exit(0);
+        }
+    }
+
+    fprintf(stderr, "lookup-merge: loading file %s\n", args[0].c_str());
+    llama_ngram_cache ngram_cache_merged = llama_ngram_cache_load(args[0]);
+
+    for (size_t i = 1; i < args.size()-1; ++i) {
+        fprintf(stderr, "lookup-merge: loading file %s\n", args[i].c_str());
+        llama_ngram_cache ngram_cache = llama_ngram_cache_load(args[i]);
+
+        llama_ngram_cache_merge(ngram_cache_merged, ngram_cache);
+    }
+
+    fprintf(stderr, "lookup-merge: saving file %s\n", args.back().c_str());
+    llama_ngram_cache_save(ngram_cache_merged, args.back());
+}
diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp
new file mode 100644
index 000000000..31f227773
--- /dev/null
+++ b/examples/lookup/lookup-stats.cpp
@@ -0,0 +1,163 @@
+#include "ggml.h"
+#include "common.h"
+#include "llama.h"
+#include "log.h"
+#include "ngram-cache.h"
+
+#include <cmath>
+#include <cstdint>
+#include <cstdio>
+#include <fstream>
+#include <string>
+#include <vector>
+#include <unordered_map>
+
+int main(int argc, char ** argv){
+    gpt_params params;
+
+    if (!gpt_params_parse(argc, argv, params)) {
+        return 1;
+    }
+
+    const int n_draft = params.n_draft;
+
+    // init llama.cpp
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    llama_model * model = NULL;
+    llama_context * ctx = NULL;
+
+    // load the model
+    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    llama_set_rng_seed(ctx, params.seed);
+    GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
+
+    // tokenize the prompt
+    const bool add_bos = llama_should_add_bos_token(model);
+    LOG("add_bos tgt: %d\n", add_bos);
+
+    std::vector<llama_token> inp;
+    inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
+
+    llama_ngram_cache ngram_cache_context;
+    llama_ngram_cache ngram_cache_dynamic;
+    llama_ngram_cache ngram_cache_static;
+    int64_t t_draft_flat_us = 0;
+    int64_t t_draft_us = 0;
+
+    {
+        const int64_t t_start_draft_us = ggml_time_us();
+
+        if (!params.lookup_cache_static.empty()) {
+            try {
+                ngram_cache_static = llama_ngram_cache_load(params.lookup_cache_static);
+            } catch (std::ifstream::failure const &) {
+                fprintf(stderr, "error: failed to open static lookup cache: %s", params.lookup_cache_static.c_str());
+                exit(1);
+            }
+        }
+
+        if (!params.lookup_cache_dynamic.empty()) {
+            try {
+                ngram_cache_dynamic = llama_ngram_cache_load(params.lookup_cache_dynamic);
+            } catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program
+        }
+
+        t_draft_flat_us += ggml_time_us() - t_start_draft_us;
+    }
+
+    const int n_input = inp.size();
+    const int n_ctx = params.n_ctx;
+
+    int n_drafted = 0;
+    int n_accept  = 0;
+
+    const int64_t t_start_ms = ggml_time_ms();
+
+    // Iterate over input tokens in chunks of size n_ctx.
+    // Each chunk is treated as if a sequential generation but with pre-determined tokens to ensure reproducibility.
+    for (int i_start = 0; i_start + n_ctx < n_input; i_start += n_ctx) {
+        const std::vector<llama_token> inp_slice(inp.begin() + i_start, inp.begin() + i_start + n_ctx);
+        std::vector<llama_token> pseudo_output;
+        pseudo_output.push_back(inp_slice[0]);
+
+        while ((int) pseudo_output.size() < n_ctx) {
+            // Simulate drafting and decoding from draft:
+            std::vector<llama_token> draft;
+            draft.push_back(pseudo_output.back());
+
+            {
+                const int64_t t_start_draft_us = ggml_time_us();
+                llama_ngram_cache_draft(pseudo_output, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static);
+                t_draft_us += ggml_time_us() - t_start_draft_us;
+            }
+
+            n_drafted += draft.size() - 1;
+
+            for (size_t j = 1; j < draft.size() && (int) pseudo_output.size() < n_ctx; ++j) {
+                const llama_token ground_truth = inp_slice[pseudo_output.size()];
+                const llama_token drafted = draft[j];
+
+                if (ground_truth != drafted) {
+                    break;
+                }
+
+                ++n_accept;
+                pseudo_output.push_back(ground_truth);
+
+                {
+                    const int64_t t_start_draft_us = ggml_time_us();
+                    llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false);
+                    t_draft_us += ggml_time_us() - t_start_draft_us;
+                }
+            }
+
+            // After each simulated batch decoding simulate the sampling of a single token:
+            if ((int) pseudo_output.size() < n_ctx) {
+                pseudo_output.push_back(inp_slice[pseudo_output.size()]);
+                {
+                    const int64_t t_start_draft_us = ggml_time_us();
+                    llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false);
+                    t_draft_us += ggml_time_us() - t_start_draft_us;
+                }
+            }
+
+            draft.erase(draft.begin());
+
+        }
+        if (i_start > 0 && i_start / 100000 != (i_start - n_ctx) / 100000) {
+            const int64_t t_now_ms = ggml_time_ms();
+            const int64_t eta_ms   = (n_input - i_start) * (t_now_ms - t_start_ms) / i_start;
+            const int64_t eta_min  = eta_ms / (60*1000);
+            const int64_t eta_s    = (eta_ms - 60*1000*eta_min) / 1000;
+
+            LOG_TEE("lookup-stats: %d/%d done, ETA: %02" PRId64 ":%02" PRId64 "\n", i_start, n_input, eta_min, eta_s);
+        }
+
+        // After each chunk, update the dynamic ngram cache with the context ngram cache:
+        llama_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
+        ngram_cache_context.clear();
+    }
+
+    LOG_TEE("\n");
+
+    LOG_TEE("\n");
+    LOG_TEE("n_draft      = %d\n", n_draft);
+    LOG_TEE("n_predict    = %d\n", n_input - n_input % n_ctx);
+    LOG_TEE("n_drafted    = %d\n", n_drafted);
+    LOG_TEE("t_draft_flat = %.2f ms\n", t_draft_flat_us*1e-3);
+    LOG_TEE("t_draft      = %.2f ms, %.2f us per token, %.2f tokens per second\n",
+            t_draft_us*1e-3, 1.0f*t_draft_us/n_drafted, n_drafted/(1e-6*t_draft_us));
+    LOG_TEE("n_accept     = %d\n", n_accept);
+    LOG_TEE("accept       = %.3f%%\n", 100.0f * n_accept / n_drafted);
+
+    llama_free(ctx);
+    llama_free_model(model);
+
+    llama_backend_free();
+
+    fprintf(stderr, "\n\n");
+
+    return 0;
+}
diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp
index d8de7dd38..2e8c35de3 100644
--- a/examples/lookup/lookup.cpp
+++ b/examples/lookup/lookup.cpp
@@ -1,10 +1,15 @@
-#include "common.h"
+#include "ggml.h"
 #include "llama.h"
+#include "common.h"
+#include "ngram-cache.h"
 
 #include <cmath>
+#include <cstdint>
 #include <cstdio>
+#include <fstream>
 #include <string>
 #include <vector>
+#include <unordered_map>
 
 int main(int argc, char ** argv){
     gpt_params params;
@@ -13,11 +18,7 @@ int main(int argc, char ** argv){
         return 1;
     }
 
-    // max/min n-grams size to search for in prompt
-    const int ngram_max = 4;
-    const int ngram_min = 1;
-
-    // length of the candidate / draft sequence, if match is found
+    // max. number of additional tokens to draft if match is found
     const int n_draft = params.n_draft;
 
     const bool dump_kv_cache = params.dump_kv_cache;
@@ -29,13 +30,16 @@ int main(int argc, char ** argv){
 #endif // LOG_DISABLE_LOGS
 
     // init llama.cpp
-    llama_backend_init(params.numa);
+    llama_backend_init();
+    llama_numa_init(params.numa);
 
     llama_model * model = NULL;
     llama_context * ctx = NULL;
 
     // load the model
     std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    llama_set_rng_seed(ctx, params.seed);
+    GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
 
     // tokenize the prompt
     const bool add_bos = llama_should_add_bos_token(model);
@@ -44,6 +48,35 @@ int main(int argc, char ** argv){
     std::vector<llama_token> inp;
     inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
 
+    llama_ngram_cache ngram_cache_context;
+    llama_ngram_cache ngram_cache_dynamic;
+    llama_ngram_cache ngram_cache_static;
+    int64_t t_draft_flat_us = 0;
+    int64_t t_draft_us = 0;
+
+    {
+        // Fill up context ngram cache with tokens from user input:
+        const int64_t t_start_draft_us = ggml_time_us();
+        llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, inp.size(), false);
+
+        if (!params.lookup_cache_static.empty()) {
+            try {
+                ngram_cache_static = llama_ngram_cache_load(params.lookup_cache_static);
+            } catch (std::ifstream::failure const &) {
+                fprintf(stderr, "error: failed to open static lookup cache: %s", params.lookup_cache_static.c_str());
+                exit(1);
+            }
+        }
+
+        if (!params.lookup_cache_dynamic.empty()) {
+            try {
+                ngram_cache_dynamic = llama_ngram_cache_load(params.lookup_cache_dynamic);
+            } catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program
+        }
+
+        t_draft_flat_us += ggml_time_us() - t_start_draft_us;
+    }
+
     const int max_context_size     = llama_n_ctx(ctx);
     const int max_tokens_list_size = max_context_size - 4;
 
@@ -124,6 +157,12 @@ int main(int argc, char ** argv){
                 ++n_past;
                 ++i_dft;
                 inp.push_back(id);
+                {
+                    // Update context ngram cache with the newly accepted token:
+                    const int64_t t_start_draft_us = ggml_time_us();
+                    llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false);
+                    t_draft_us += ggml_time_us() - t_start_draft_us;
+                }
 
                 if (params.use_color) {
                     // color accepted draft token
@@ -144,6 +183,12 @@ int main(int argc, char ** argv){
             draft.clear();
             draft.push_back(id);
             inp.push_back(id);
+            {
+                // Update context ngram cache with the newly accepted token:
+                const int64_t t_start_draft_us = ggml_time_us();
+                llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false);
+                t_draft_us += ggml_time_us() - t_start_draft_us;
+            }
             break;
         }
 
@@ -158,40 +203,19 @@ int main(int argc, char ** argv){
         llama_batch_clear(batch_tgt);
         llama_batch_add(batch_tgt, draft[0], n_past, { 0 }, true);
 
-        // generate n_pred tokens through prompt lookup
-        auto prompt_lookup = [&]() -> void {
-            int inp_size = inp.size();
-            for (int ngram_size = ngram_max ; ngram_size > ngram_min; --ngram_size){
-                const llama_token * ngram = &inp[inp_size - ngram_size];
+        // Draft already contains a single token sampled from the model:
+        GGML_ASSERT(draft.size() == 1);
+        GGML_ASSERT(draft[0] == inp.back());
+        const int64_t t_start_draft_us = ggml_time_us();
 
-                for (int i = 0; i <= (int) inp_size - (ngram_size * 2); ++i) {
-                    bool match = true;
-                    for (int j = 0; j < ngram_size; ++j) {
-                        if (inp[i + j] != ngram[j]) {
-                            match = false;
-                            break;
-                        }
-                    }
+        llama_ngram_cache_draft(inp, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static);
 
-                    if (match) {
-                        const int startIdx = i + ngram_size;
-                        const int endIdx = startIdx + n_draft;
-                        if (endIdx < inp_size) {
-                            for (int j = startIdx; j < endIdx; ++j) {
-                                LOG(" - draft candidate %d: %d\n", j, inp[j]);
-                                draft.push_back(inp[j]);
-                                llama_batch_add(batch_tgt, inp[j], n_past + (j - startIdx) + 1, { 0 }, true);
-                                ++n_drafted;
-                            }
-                            return;
-                        }
-                    }
-                }
-            }
-            return;
-        };
+        for (size_t i = 1; i < draft.size(); ++i) {
+            llama_batch_add(batch_tgt, draft[i], n_past + i, { 0 }, true);
+        }
 
-        prompt_lookup();
+        t_draft_us += ggml_time_us() - t_start_draft_us;
+        n_drafted += draft.size() - 1;
 
         llama_decode(ctx, batch_tgt);
         ++n_past;
@@ -201,17 +225,24 @@ int main(int argc, char ** argv){
 
     auto t_dec_end = ggml_time_us();
 
+    // Update dynamic ngram cache with context ngram cache and save it to disk:
+    llama_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
+    llama_ngram_cache_save(ngram_cache_dynamic, params.lookup_cache_dynamic);
+
     LOG_TEE("\n\n");
 
     LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input,   (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
     LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict  / ((t_dec_end - t_dec_start) / 1e6f));
 
     LOG_TEE("\n");
-    LOG_TEE("n_draft   = %d\n", n_draft);
-    LOG_TEE("n_predict = %d\n", n_predict);
-    LOG_TEE("n_drafted = %d\n", n_drafted);
-    LOG_TEE("n_accept  = %d\n", n_accept);
-    LOG_TEE("accept    = %.3f%%\n", 100.0f * n_accept / n_drafted);
+    LOG_TEE("n_draft      = %d\n", n_draft);
+    LOG_TEE("n_predict    = %d\n", n_predict);
+    LOG_TEE("n_drafted    = %d\n", n_drafted);
+    LOG_TEE("t_draft_flat = %.2f ms\n", t_draft_flat_us*1e-3);
+    LOG_TEE("t_draft      = %.2f ms, %.2f us per token, %.2f tokens per second\n",
+            t_draft_us*1e-3, 1.0f*t_draft_us/n_drafted, n_drafted/(1e-6*t_draft_us));
+    LOG_TEE("n_accept     = %d\n", n_accept);
+    LOG_TEE("accept       = %.3f%%\n", 100.0f * n_accept / n_drafted);
 
     LOG_TEE("\ntarget:\n");
     llama_print_timings(ctx);
diff --git a/examples/main-cmake-pkg/README.md b/examples/main-cmake-pkg/README.md
index 6d665f28f..f599fbaec 100644
--- a/examples/main-cmake-pkg/README.md
+++ b/examples/main-cmake-pkg/README.md
@@ -8,7 +8,7 @@ Because this example is "outside of the source tree", it is important to first b
 
 ### Considerations
 
-When hardware acceleration libraries are used (e.g. CUBlas, Metal, CLBlast, etc.), CMake must be able to locate the associated CMake package. In the example below, when building _main-cmake-pkg_ notice the `CMAKE_PREFIX_PATH` includes the Llama CMake package location _in addition to_ the CLBlast package—which was used when compiling _llama.cpp_.
+When hardware acceleration libraries are used (e.g. CUDA, Metal, CLBlast, etc.), CMake must be able to locate the associated CMake package. In the example below, when building _main-cmake-pkg_ notice the `CMAKE_PREFIX_PATH` includes the Llama CMake package location _in addition to_ the CLBlast package—which was used when compiling _llama.cpp_.
 
 ### Build llama.cpp and install to C:\LlamaCPP directory
 
diff --git a/examples/main/README.md b/examples/main/README.md
index c7997f665..bb696b562 100644
--- a/examples/main/README.md
+++ b/examples/main/README.md
@@ -67,6 +67,7 @@ main.exe -m models\7B\ggml-model.bin --ignore-eos -n -1 --random-prompt
 In this section, we cover the most commonly used options for running the `main` program with the LLaMA models:
 
 -   `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
+-   `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf).
 -   `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses.
 -   `-ins, --instruct`: Run the program in instruction mode, which is particularly useful when working with Alpaca models.
 -   `-n N, --n-predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text.
@@ -283,7 +284,11 @@ These options help improve the performance and memory usage of the LLaMA models.
 
 ### NUMA support
 
--   `--numa`: Attempt optimizations that help on some systems with non-uniform memory access. This currently consists of pinning an equal proportion of the threads to the cores on each NUMA node, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop_caches' as root.
+-   `--numa distribute`: Pin an equal proportion of the threads to the cores on each NUMA node. This will spread the load amongst all cores on the system, utilitizing all memory channels at the expense of potentially requiring memory to travel over the slow links between nodes.
+-   `--numa isolate`: Pin all threads to the NUMA node that the program starts on. This limits the number of cores and amount of memory that can be used, but guarantees all memory access remains local to the NUMA node.
+-   `--numa numactl`: Pin threads to the CPUMAP that is passed to the program by starting it with the numactl utility. This is the most flexible mode, and allow arbitraty core usage patterns, for example a map that uses all the cores on one NUMA nodes, and just enough cores on a second node to saturate the inter-node memory bus.
+
+ These flags attempt optimizations that help on some systems with non-uniform memory access. This currently consists of one of the above strategies, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop_caches' as root.
 
 ### Memory Float 32
 
@@ -291,7 +296,9 @@ These options help improve the performance and memory usage of the LLaMA models.
 
 ### Batch Size
 
--   `-b N, --batch-size N`: Set the batch size for prompt processing (default: 512). This large batch size benefits users who have BLAS installed and enabled it during the build. If you don't have BLAS enabled ("BLAS=0"), you can use a smaller number, such as 8, to see the prompt progress as it's evaluated in some situations.
+-   `-b N, --batch-size N`: Set the batch size for prompt processing (default: `2048`). This large batch size benefits users who have BLAS installed and enabled it during the build. If you don't have BLAS enabled ("BLAS=0"), you can use a smaller number, such as 8, to see the prompt progress as it's evaluated in some situations.
+
+- `-ub N`, `--ubatch-size N`: physical maximum batch size. This is for pipeline parallelization. Default: `512`.
 
 ### Prompt Caching
 
@@ -311,8 +318,8 @@ These options provide extra functionality and customization when running the LLa
 
 -   `-h, --help`: Display a help message showing all available options and their default values. This is particularly useful for checking the latest options and default values, as they can change frequently, and the information in this document may become outdated.
 -   `--verbose-prompt`: Print the prompt before generating text.
--   `-ngl N, --n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
--   `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.
--   `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS.
+-   `-ngl N, --n-gpu-layers N`: When compiled with GPU support, this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
+-   `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used.
+-   `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance.
 -   `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
 -   `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 5ea67051f..e2d07a631 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -39,6 +39,17 @@ static std::ostringstream       * g_output_ss;
 static std::vector<llama_token> * g_output_tokens;
 static bool is_interacting = false;
 
+static bool file_exists(const std::string &path) {
+    std::ifstream f(path.c_str());
+    return f.good();
+}
+
+static bool file_is_empty(const std::string &path) {
+    std::ifstream f;
+    f.exceptions(std::ifstream::failbit | std::ifstream::badbit);
+    f.open(path.c_str(), std::ios::in | std::ios::binary | std::ios::ate);
+    return f.tellg() == 0;
+}
 
 static void write_logfile(
     const llama_context * ctx, const gpt_params & params, const llama_model * model,
@@ -87,7 +98,7 @@ static void write_logfile(
 #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
 static void sigint_handler(int signo) {
     if (signo == SIGINT) {
-        if (!is_interacting) {
+        if (!is_interacting && g_params->interactive) {
             is_interacting = true;
         } else {
             console::cleanup();
@@ -174,7 +185,8 @@ int main(int argc, char ** argv) {
     }
 
     LOG("%s: llama backend init\n", __func__);
-    llama_backend_init(params.numa);
+    llama_backend_init();
+    llama_numa_init(params.numa);
 
     llama_model * model;
     llama_context * ctx;
@@ -215,12 +227,12 @@ int main(int argc, char ** argv) {
 
     if (!path_session.empty()) {
         LOG_TEE("%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str());
-
-        // fopen to check for existing session
-        FILE * fp = std::fopen(path_session.c_str(), "rb");
-        if (fp != NULL) {
-            std::fclose(fp);
-
+        if (!file_exists(path_session)) {
+            LOG_TEE("%s: session file does not exist, will create.\n", __func__);
+        } else if (file_is_empty(path_session)) {
+            LOG_TEE("%s: The session file is empty. A new session will be initialized.\n", __func__);
+        } else {
+            // The file exists and is not empty
             session_tokens.resize(n_ctx);
             size_t n_token_count_out = 0;
             if (!llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
@@ -229,10 +241,7 @@ int main(int argc, char ** argv) {
             }
             session_tokens.resize(n_token_count_out);
             llama_set_rng_seed(ctx, params.seed);
-
-            LOG_TEE("%s: loaded a session with prompt size of %d tokens\n", __func__, (int) session_tokens.size());
-        } else {
-            LOG_TEE("%s: session file does not exist, will create\n", __func__);
+            LOG_TEE("%s: loaded a session with prompt size of %d tokens\n", __func__, (int)session_tokens.size());
         }
     }
 
@@ -325,6 +334,8 @@ int main(int argc, char ** argv) {
     // number of tokens to keep when resetting context
     if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct || params.chatml) {
         params.n_keep = (int)embd_inp.size();
+    } else {
+        params.n_keep += add_bos; // always keep the BOS token
     }
 
     // prefix & suffix for instruct mode
@@ -344,12 +355,12 @@ int main(int argc, char ** argv) {
     // in instruct mode, we inject a prefix and a suffix to each input by the user
     if (params.instruct) {
         params.interactive_first = true;
-        params.antiprompt.push_back("### Instruction:\n\n");
+        params.antiprompt.emplace_back("### Instruction:\n\n");
     }
     // similar for chatml mode
     else if (params.chatml) {
         params.interactive_first = true;
-        params.antiprompt.push_back("<|im_start|>user\n");
+        params.antiprompt.emplace_back("<|im_start|>user\n");
     }
 
     // enable interactive mode if interactive start is specified
@@ -374,8 +385,8 @@ int main(int argc, char ** argv) {
             }
         }
 
-        if (params.n_keep > 0) {
-        LOG_TEE("%s: static prompt based on n_keep: '", __func__);
+        if (params.n_keep > add_bos) {
+            LOG_TEE("%s: static prompt based on n_keep: '", __func__);
             for (int i = 0; i < params.n_keep; i++) {
                 LOG_TEE("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str());
             }
@@ -384,7 +395,8 @@ int main(int argc, char ** argv) {
         LOG_TEE("\n");
     }
 
-    if (params.interactive) {
+    // ctrl+C handling
+    {
 #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
         struct sigaction sigint_action;
         sigint_action.sa_handler = sigint_handler;
@@ -397,7 +409,9 @@ int main(int argc, char ** argv) {
         };
         SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
 #endif
+    }
 
+    if (params.interactive) {
         LOG_TEE("%s: interactive mode on.\n", __func__);
 
         if (!params.antiprompt.empty()) {
@@ -477,6 +491,7 @@ int main(int argc, char ** argv) {
 
     bool is_antiprompt        = false;
     bool input_echo           = true;
+    bool display              = true;
     bool need_to_save_session = !path_session.empty() && n_matching_session_tokens < embd_inp.size();
 
     int n_past             = 0;
@@ -491,16 +506,25 @@ int main(int argc, char ** argv) {
 
     // the first thing we will do is to output the prompt, so set color accordingly
     console::set_display(console::prompt);
+    display = params.display_prompt;
 
     std::vector<llama_token> embd;
     std::vector<llama_token> embd_guidance;
 
+    // tokenized antiprompts
+    std::vector<std::vector<llama_token>> antiprompt_ids;
+
+    antiprompt_ids.reserve(params.antiprompt.size());
+    for (const std::string & antiprompt : params.antiprompt) {
+        antiprompt_ids.emplace_back(::llama_tokenize(ctx, antiprompt, false, true));
+    }
+
     struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);
 
     while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
         // predict
         if (!embd.empty()) {
-            // Note: n_ctx - 4 here is to match the logic for commandline prompt handling via
+            // Note: (n_ctx - 4) here is to match the logic for commandline prompt handling via
             // --prompt or --file which uses the same value.
             int max_embd_size = n_ctx - 4;
 
@@ -526,14 +550,14 @@ int main(int argc, char ** argv) {
                         break;
                     }
 
-                    const int n_left    = n_past - params.n_keep - 1;
+                    const int n_left    = n_past - params.n_keep;
                     const int n_discard = n_left/2;
 
                     LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
                             n_past, n_left, n_ctx, params.n_keep, n_discard);
 
-                    llama_kv_cache_seq_rm   (ctx, 0, params.n_keep + 1            , params.n_keep + n_discard + 1);
-                    llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
+                    llama_kv_cache_seq_rm (ctx, 0, params.n_keep            , params.n_keep + n_discard);
+                    llama_kv_cache_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard);
 
                     n_past -= n_discard;
 
@@ -560,9 +584,9 @@ int main(int argc, char ** argv) {
                     LOG("div:   [%6d, %6d] / %6d -> [%6d, %6d]\n", ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n, (ga_i + ib*bd)/ga_n, (ga_i + ib*bd + ga_w)/ga_n);
                     LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i + ib*bd + ga_w, n_past + ib*bd, dd, ga_i + ib*bd + ga_w + dd, n_past + ib*bd + dd);
 
-                    llama_kv_cache_seq_shift(ctx, 0, ga_i,                n_past,              ib*bd);
-                    llama_kv_cache_seq_div  (ctx, 0, ga_i + ib*bd,        ga_i + ib*bd + ga_w, ga_n);
-                    llama_kv_cache_seq_shift(ctx, 0, ga_i + ib*bd + ga_w, n_past + ib*bd,      dd);
+                    llama_kv_cache_seq_add(ctx, 0, ga_i,                n_past,              ib*bd);
+                    llama_kv_cache_seq_div(ctx, 0, ga_i + ib*bd,        ga_i + ib*bd + ga_w, ga_n);
+                    llama_kv_cache_seq_add(ctx, 0, ga_i + ib*bd + ga_w, n_past + ib*bd,      dd);
 
                     n_past -= bd;
 
@@ -650,6 +674,10 @@ int main(int argc, char ** argv) {
                 n_past += n_eval;
 
                 LOG("n_past = %d\n", n_past);
+                // Display total tokens alongside total time
+                if (params.n_print > 0 && n_past % params.n_print == 0) {
+                    LOG_TEE("\n\033[31mTokens consumed so far = %d / %d \033[0m\n", n_past, n_ctx);
+                }
             }
 
             if (!embd.empty() && !path_session.empty()) {
@@ -703,7 +731,7 @@ int main(int argc, char ** argv) {
         }
 
         // display text
-        if (input_echo) {
+        if (input_echo && display) {
             for (auto id : embd) {
                 const std::string token_str = llama_token_to_piece(ctx, id);
                 printf("%s", token_str.c_str());
@@ -720,6 +748,7 @@ int main(int argc, char ** argv) {
         // reset color to default if there is no pending user input
         if (input_echo && (int) embd_inp.size() == n_consumed) {
             console::set_display(console::reset);
+            display = true;
         }
 
         // if not currently processing queued inputs;
@@ -748,6 +777,18 @@ int main(int argc, char ** argv) {
                     }
                 }
 
+                // check for reverse prompt using special tokens
+                llama_token last_token = llama_sampling_last(ctx_sampling);
+                for (std::vector<llama_token> ids : antiprompt_ids) {
+                    if (ids.size() == 1 && last_token == ids[0]) {
+                        if (params.interactive) {
+                            is_interacting = true;
+                        }
+                        is_antiprompt = true;
+                        break;
+                    }
+                }
+
                 if (is_antiprompt) {
                     LOG("found antiprompt: %s\n", last_output.c_str());
                 }
@@ -792,6 +833,7 @@ int main(int argc, char ** argv) {
 
                 // color user input only
                 console::set_display(console::user_input);
+                display = params.display_prompt;
 
                 std::string line;
                 bool another_line = true;
@@ -802,6 +844,7 @@ int main(int argc, char ** argv) {
 
                 // done taking input, reset color
                 console::set_display(console::reset);
+                display = true;
 
                 // Add tokens to embd only if the input buffer is non-empty
                 // Entering a empty line lets the user pass control back
@@ -835,6 +878,7 @@ int main(int argc, char ** argv) {
                     const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true);
                     const auto line_inp = ::llama_tokenize(ctx, buffer,              false, false);
                     const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true);
+
                     LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str());
 
                     embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end());
diff --git a/examples/metal/CMakeLists.txt b/examples/metal/CMakeLists.txt
deleted file mode 100644
index f16d49165..000000000
--- a/examples/metal/CMakeLists.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-set(TEST_TARGET metal)
-add_executable(${TEST_TARGET} metal.cpp)
-install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TEST_TARGET} PRIVATE ggml)
diff --git a/examples/metal/metal.cpp b/examples/metal/metal.cpp
deleted file mode 100644
index 16c1146f9..000000000
--- a/examples/metal/metal.cpp
+++ /dev/null
@@ -1,103 +0,0 @@
-// Evaluate a statically exported ggml computation graph with Metal
-//
-// - First, export a LLaMA graph:
-//
-//  $ ./bin/main -m ../models/7B/ggml-model-q4_0.gguf --export
-//
-// - Run this tool to evaluate the exported graph:
-//
-//  $ ./bin/metal llama.ggml
-//
-// The purpose of this tool is mostly for debugging and demonstration purposes.
-// The main limitation of exporting computation graphs is that their sizes are static which often
-// can be a problem for real-world applications.
-//
-
-#include "ggml.h"
-#include "ggml-metal.h"
-
-#include <cstdio>
-#include <cstring>
-#include <cstdlib>
-
-int main(int argc, char ** argv) {
-    ggml_time_init();
-
-    if (argc != 2) {
-        fprintf(stderr, "Usage: %s llama.ggml\n", argv[0]);
-        return -1;
-    }
-
-    const char * fname_cgraph = argv[1];
-
-    // load the compute graph
-    struct ggml_context * ctx_data = NULL;
-    struct ggml_context * ctx_eval = NULL;
-
-    struct ggml_cgraph * gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval);
-
-    // this allocates all Metal resources and memory buffers
-    auto * ctx_metal = ggml_metal_init(1);
-
-    const size_t max_size_data = ggml_get_max_tensor_size(ctx_data);
-    const size_t max_size_eval = ggml_get_max_tensor_size(ctx_eval);
-    ggml_metal_add_buffer(ctx_metal, "data", ggml_get_mem_buffer(ctx_data), ggml_get_mem_size(ctx_data), max_size_data);
-    ggml_metal_add_buffer(ctx_metal, "eval", ggml_get_mem_buffer(ctx_eval), ggml_get_mem_size(ctx_eval), max_size_eval);
-
-    // main
-    {
-        struct ggml_tensor * input = ggml_graph_get_tensor(gf, "embd");
-        *(int32_t *) input->data = 1; // BOS
-
-        ggml_metal_set_tensor(ctx_metal, input);
-
-        // warmup
-        ggml_metal_graph_compute(ctx_metal, gf);
-
-        const int n_iter = 16;
-
-        const int64_t t0 = ggml_time_us();
-
-        // the actual inference happens here
-        for (int i = 0; i < n_iter; ++i) {
-            ggml_metal_graph_compute(ctx_metal, gf);
-        }
-
-        const int64_t t1 = ggml_time_us();
-
-        printf("time: %.2f ms, %.2f ms/tok\n", (t1 - t0) / 1000.0, (t1 - t0) / 1000.0 / n_iter);
-    }
-
-    // debug output
-    {
-        struct ggml_tensor * logits = gf->nodes[gf->n_nodes - 1];
-        ggml_metal_get_tensor(ctx_metal, logits);
-
-        float * ptr = (float *) ggml_get_data(logits);
-
-        printf("logits: ");
-        for (int i = 0; i < 10; i++) {
-            printf("%8.4f ", ptr[i]);
-        }
-        printf("\n");
-        int imax = 0;
-        double sum = 0.0;
-        double vmax = -1e9;
-        for (int i = 0; i < 32000; i++) {
-            sum += (double) ptr[i];
-            if (ptr[i] > vmax) {
-                vmax = ptr[i];
-                imax = i;
-            }
-        }
-        printf("sum: %f, imax = %d, vmax = %f\n", sum, imax, vmax);
-    }
-
-    ggml_metal_free(ctx_metal);
-
-    ggml_free(ctx_data);
-    ggml_free(ctx_eval);
-
-    return 0;
-}
-
diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp
index d2e074d9e..f66c91013 100644
--- a/examples/parallel/parallel.cpp
+++ b/examples/parallel/parallel.cpp
@@ -107,6 +107,9 @@ int main(int argc, char ** argv) {
     // number of simultaneous "clients" to simulate
     const int32_t n_clients = params.n_parallel;
 
+    // dedicate one sequence to the system prompt
+    params.n_parallel += 1;
+
     // requests to simulate
     const int32_t n_seq = params.n_sequences;
 
@@ -122,13 +125,13 @@ int main(int argc, char ** argv) {
 #endif // LOG_DISABLE_LOGS
 
     // init llama.cpp
-    llama_backend_init(params.numa);
+    llama_backend_init();
+    llama_numa_init(params.numa);
 
     llama_model * model = NULL;
     llama_context * ctx = NULL;
 
     // load the target model
-    params.logits_all = true;
     std::tie(model, ctx) = llama_init_from_gpt_params(params);
 
     // load the prompts from an external file if there are any
@@ -195,8 +198,8 @@ int main(int argc, char ** argv) {
         }
 
         // assign the system KV cache to all parallel sequences
-        for (int32_t i = 1; i < n_clients; ++i) {
-            llama_kv_cache_seq_cp(ctx, 0, i, 0, n_tokens_system);
+        for (int32_t i = 1; i <= n_clients; ++i) {
+            llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
         }
 
         LOG_TEE("\n");
@@ -220,15 +223,17 @@ int main(int argc, char ** argv) {
 
             client.i_batch = batch.n_tokens;
 
-            llama_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id }, true);
+            llama_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id + 1 }, true);
 
             client.n_decoded += 1;
         }
 
         if (batch.n_tokens == 0) {
             // all sequences have ended - clear the entire KV cache
-            for (int i = 0; i < n_clients; ++i) {
-                llama_kv_cache_seq_rm(ctx, i, n_tokens_system, -1);
+            for (int i = 1; i <= n_clients; ++i) {
+                llama_kv_cache_seq_rm(ctx, i, -1, -1);
+                // but keep the system prompt
+                llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
             }
 
             LOG_TEE("%s: clearing the KV cache\n", __func__);
@@ -254,7 +259,7 @@ int main(int argc, char ** argv) {
                     tokens_prompt = ::llama_tokenize(ctx, client.prompt, false);
 
                     for (size_t i = 0; i < tokens_prompt.size(); ++i) {
-                        llama_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id }, false);
+                        llama_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id + 1 }, false);
                     }
 
                     // extract the logits only for the last token
@@ -365,7 +370,8 @@ int main(int argc, char ** argv) {
                     }
 
                     // delete only the generated part of the sequence, i.e. keep the system prompt in the cache
-                    llama_kv_cache_seq_rm(ctx, client.id, n_tokens_system, -1);
+                    llama_kv_cache_seq_rm(ctx, client.id + 1, -1, -1);
+                    llama_kv_cache_seq_cp(ctx, 0, client.id + 1, -1, -1);
 
                     const auto t_main_end = ggml_time_us();
 
diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp
index 5c0022832..2cbc9e1fa 100644
--- a/examples/passkey/passkey.cpp
+++ b/examples/passkey/passkey.cpp
@@ -71,7 +71,8 @@ int main(int argc, char ** argv) {
 
     // init LLM
 
-    llama_backend_init(params.numa);
+    llama_backend_init();
+    llama_numa_init(params.numa);
 
     // initialize the model
 
@@ -125,7 +126,7 @@ int main(int argc, char ** argv) {
     const int n_batch     = ctx_params.n_batch;
     const int n_batch_grp = ctx_params.n_batch/n_grp;
 
-    LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d, n_grp = %d, n_batch = %d\n", __func__, n_len, n_ctx, n_kv_req, n_grp, n_batch);
+    LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d, n_grp = %d, n_batch = %d, n_junk = %d, i_pos = %d\n", __func__, n_len, n_ctx, n_kv_req, n_grp, n_batch, n_junk, i_pos);
 
     // print the prompt token-by-token
 
@@ -145,10 +146,11 @@ int main(int argc, char ** argv) {
             const int ib = i/n_batch - 1;
             const int bd = n_batch_grp*(n_grp - 1);
 
-            llama_kv_cache_seq_shift(ctx, 0, n_past - n_batch,         n_past,         ib*bd);
-            llama_kv_cache_seq_div  (ctx, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp);
+            llama_kv_cache_seq_add (ctx, 0, n_past - n_batch,         n_past,         ib*bd);
+            llama_kv_cache_seq_div (ctx, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp);
+            llama_kv_cache_update  (ctx);
 
-            n_past -= bd;
+            n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1;
         }
 
         llama_batch_clear(batch);
@@ -178,10 +180,12 @@ int main(int argc, char ** argv) {
 
         LOG_TEE("%s: shifting KV cache with %d\n", __func__, n_discard);
 
-        llama_kv_cache_seq_rm   (ctx, 0, n_keep            , n_keep + n_discard);
-        llama_kv_cache_seq_shift(ctx, 0, n_keep + n_discard, n_ctx,  -n_discard);
+        llama_kv_cache_seq_rm (ctx, 0, n_keep            , n_keep + n_discard);
+        llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx,  -n_discard);
+      //llama_kv_cache_defrag (ctx);
+        llama_kv_cache_update (ctx);
 
-        n_past -= n_discard;
+        n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1;
 
         llama_batch_clear(batch);
 
@@ -207,10 +211,12 @@ int main(int argc, char ** argv) {
         if (n_discard > 0) {
             LOG_TEE("%s: shifting KV cache with %d to free space for the answer\n", __func__, n_discard);
 
-            llama_kv_cache_seq_rm   (ctx, 0, n_keep            , n_keep + n_discard);
-            llama_kv_cache_seq_shift(ctx, 0, n_keep + n_discard, n_ctx,  -n_discard);
+            llama_kv_cache_seq_rm (ctx, 0, n_keep            , n_keep + n_discard);
+            llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx,  -n_discard);
+          //llama_kv_cache_defrag (ctx);
+            llama_kv_cache_update (ctx);
 
-            n_past -= n_discard;
+            n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1;
         }
     }
 
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index 9a77beca6..c70385c62 100644
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -8,7 +8,11 @@
 #include <sstream>
 #include <thread>
 #include <mutex>
+#include <atomic>
 #include <vector>
+#include <array>
+#include <fstream>
+#include <sstream>
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
@@ -108,6 +112,43 @@ static results_log_softmax log_softmax(int n_vocab, const float * logits, int to
     return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp};
 }
 
+static inline int nearest_int(float fval) {
+    //assert(fval <= 4194303.f);
+    float val = fval + 12582912.f;
+    int i; memcpy(&i, &val, sizeof(int));
+    return (i & 0x007fffff) - 0x00400000;
+}
+
+static double log_softmax(int n_vocab, const float * logits, uint16_t * log_prob, int tok) {
+    float max_logit = logits[0];
+    float min_logit = logits[0];
+    for (int i = 1; i < n_vocab; ++i) {
+        max_logit = std::max(max_logit, logits[i]);
+        min_logit = std::min(min_logit, logits[i]);
+    }
+    min_logit = std::max(min_logit, max_logit - 16);
+    double sum_exp = 0.0;
+    for (int i = 0; i < n_vocab; ++i) {
+        sum_exp += expf(logits[i] - max_logit);
+    }
+    const float log_sum_exp = log(sum_exp);
+    const float min_log_prob = min_logit - max_logit - log_sum_exp;
+    const float scale = (max_logit - min_logit)/65535.f;
+    float * d = (float *)log_prob;
+    d[0] = scale;
+    d[1] = min_log_prob;
+    log_prob += 4;
+    if (scale) {
+        const float inv_scale = 1/scale;
+        for (int i = 0; i < n_vocab; ++i) {
+            log_prob[i] = logits[i] > min_logit ? nearest_int(inv_scale*(logits[i] - min_logit)) : 0;
+        }
+    } else {
+        std::memset(log_prob, 0, n_vocab*sizeof(uint16_t));
+    }
+    return max_logit + log_sum_exp - logits[tok];
+}
+
 static void process_logits(
     int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread> & workers,
     double & nll, double & nll2, float * logit_history, float * prob_history
@@ -143,8 +184,132 @@ static void process_logits(
     }
 }
 
+static void process_logits(std::ostream& out, int n_vocab, const float * logits, const int * tokens, int n_token,
+        std::vector<std::thread> & workers, std::vector<uint16_t> & log_probs, double & nll, double & nll2) {
+    std::mutex mutex;
+    const int nv = 2*((n_vocab + 1)/2) + 4;
+    int counter = 0;
+    auto compute = [&mutex, &counter, &log_probs, &nll, &nll2, n_vocab, logits, tokens, n_token, nv] () {
+        double local_nll  = 0;
+        double local_nll2 = 0;
+        while (true) {
+            std::unique_lock<std::mutex> lock(mutex);
+            int i = counter++;
+            if (i >= n_token) {
+                nll += local_nll; nll2 += local_nll2;
+                break;
+            }
+            lock.unlock();
+            const double v = log_softmax(n_vocab, logits + i*n_vocab, log_probs.data() + i*nv, tokens[i+1]);
+            local_nll += v;
+            local_nll2 += v*v;
+        }
+    };
+    for (auto & w : workers) {
+        w = std::thread(compute);
+    }
+    compute();
+    for (auto & w : workers) {
+        w.join();
+    }
+    out.write((const char *)log_probs.data(), n_token*nv*sizeof(uint16_t));
+}
+
+struct kl_divergence_result {
+    double sum_nll  = 0;
+    double sum_nll2 = 0;
+    double sum_kld  = 0;
+    double sum_kld2 = 0;
+    double sum_nll_diff  = 0;
+    double sum_nll_diff2 = 0;
+    size_t n_same_top = 0;
+    size_t count = 0;
+};
+
+static double log_softmax(int n_vocab, const float * logits, const uint16_t * base_log_prob, int tok, kl_divergence_result & kld) {
+    float max_logit = logits[0];
+    int imax = 0;
+    for (int i = 1; i < n_vocab; ++i) {
+        if (logits[i] > max_logit) {
+            max_logit = logits[i];
+            imax = i;
+        }
+    }
+    double sum_exp = 0.0;
+    for (int i = 0; i < n_vocab; ++i) {
+        sum_exp += expf(logits[i] - max_logit);
+    }
+    const float log_sum_exp = log(sum_exp);
+    const float * d = (const float *)base_log_prob;
+    const float scale = d[0];
+    const float min_log_prob = d[1];
+    base_log_prob += 4;
+    float nll = max_logit + log_sum_exp - logits[tok];
+    kld.sum_nll  += nll;
+    kld.sum_nll2 += nll*nll;
+    nll += (scale*base_log_prob[tok] + min_log_prob);
+    kld.sum_nll_diff  += nll;
+    kld.sum_nll_diff2 += nll*nll;
+    max_logit += log_sum_exp;
+    double sum = 0;
+    int imax_base = -1;
+    float p_log_base_max = 0;
+    for (int i = 0; i < n_vocab; ++i) {
+        const float p_log_base = scale*base_log_prob[i] + min_log_prob;
+        if (i == 0 || p_log_base > p_log_base_max) {
+            p_log_base_max = p_log_base;
+            imax_base = i;
+        }
+        if (p_log_base > -16.f) {
+            const float p_base = expf(p_log_base);
+            sum += p_base * (p_log_base - logits[i] + max_logit);
+        }
+    }
+    kld.sum_kld  += sum;
+    kld.sum_kld2 += sum*sum;
+    ++kld.count;
+    if (imax == imax_base) ++kld.n_same_top;
+    return sum;
+}
+
+static void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token,
+        std::vector<std::thread> & workers, const std::vector<uint16_t> & base_log_probs, kl_divergence_result & kld,
+        float * kld_values) {
+    std::mutex mutex;
+    const int nv = 2*((n_vocab + 1)/2) + 4;
+    int counter = 0;
+    auto compute = [&mutex, &counter, &base_log_probs, &kld, n_vocab, logits, tokens, n_token, nv, kld_values] () {
+        kl_divergence_result local_kld;
+        while (true) {
+            std::unique_lock<std::mutex> lock(mutex);
+            int i = counter++;
+            if (i >= n_token) {
+                kld.sum_nll  += local_kld.sum_nll;
+                kld.sum_nll2 += local_kld.sum_nll2;
+                kld.sum_kld  += local_kld.sum_kld;
+                kld.sum_kld2 += local_kld.sum_kld2;
+                kld.sum_nll_diff  += local_kld.sum_nll_diff;
+                kld.sum_nll_diff2 += local_kld.sum_nll_diff2;
+                kld.n_same_top += local_kld.n_same_top;
+                kld.count += local_kld.count;
+                break;
+            }
+            lock.unlock();
+            double v = log_softmax(n_vocab, logits + i*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld);
+            kld_values[i] = (float)v;
+        }
+    };
+    for (auto & w : workers) {
+        w = std::thread(compute);
+    }
+    compute();
+    for (auto & w : workers) {
+        w.join();
+    }
+}
+
 static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & params) {
-    // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
+    // Download: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
     // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
     // Output: `perplexity: 13.5106 [114/114]`
     // BOS tokens will be added for each chunk before eval
@@ -215,6 +380,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
             const int batch_size  = std::min(end - batch_start, n_batch);
 
             //fprintf(stderr, "    Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch);
+            // TODO: use llama_batch.logits instead of relying on logits_all == true
             if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
                 //fprintf(stderr, "%s : failed to eval\n", __func__);
                 return {tokens, -1, logit_history, prob_history};
@@ -277,18 +443,29 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
     return {tokens, std::exp(nll / count), logit_history, prob_history};
 }
 
-static results_perplexity perplexity(llama_context * ctx, const gpt_params & params) {
+static results_perplexity perplexity(llama_context * ctx, const gpt_params & params, const int32_t n_ctx) {
     if (params.ppl_stride > 0) {
         return perplexity_v2(ctx, params);
     }
 
-    // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
+    // Download: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
     // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
     // Output: `perplexity: 13.5106 [114/114]`
     // BOS tokens will be added for each chunk before eval
 
     const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
-    const int n_ctx = llama_n_ctx(ctx);
+
+    std::ofstream logits_stream;
+    if (!params.logits_file.empty()) {
+        logits_stream.open(params.logits_file.c_str(), std::ios::binary);
+        if (!logits_stream.is_open()) {
+            fprintf(stderr, "%s: failed to open %s for writing\n", __func__, params.logits_file.c_str());
+            return {};
+        }
+        fprintf(stderr, "%s: saving all logits to %s\n", __func__, params.logits_file.c_str());
+        logits_stream.write("_logits_", 8);
+        logits_stream.write(reinterpret_cast<const char *>(&n_ctx), sizeof(n_ctx));
+    }
 
     auto tim1 = std::chrono::high_resolution_clock::now();
     fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
@@ -321,17 +498,51 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
     double nll = 0.0;
     double nll2 = 0.0;
 
-    fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch);
+    const int num_batches = (n_ctx + n_batch - 1) / n_batch;
+    const int n_seq = std::max(1, n_batch / n_ctx);
+
+    GGML_ASSERT(n_batch < n_ctx || n_batch % n_ctx == 0);
+    GGML_ASSERT(params.n_ctx == n_seq * n_ctx);
+
+    llama_batch batch = llama_batch_init(std::min(n_batch, n_ctx*n_seq), 0, 1);
+
+    std::vector<float> logits;
+    if (num_batches > 1) {
+        logits.reserve((size_t)n_ctx * n_vocab);
+    }
+
+    fprintf(stderr, "%s: calculating perplexity over %d chunks, n_ctx=%d, batch_size=%d, n_seq=%d\n", __func__, n_chunk, n_ctx, n_batch, n_seq);
 
     std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
 
-    for (int i = 0; i < n_chunk; ++i) {
+    std::vector<uint16_t> log_probs;
+    if (!params.logits_file.empty()) {
+        logits_stream.write((const char *)&n_vocab, sizeof(n_vocab));
+        logits_stream.write((const char *)&n_chunk, sizeof(n_chunk));
+        logits_stream.write((const char *)tokens.data(), n_chunk*n_ctx*sizeof(tokens[0]));
+        const int nv = 2*((n_vocab + 1)/2) + 4;
+        log_probs.resize(n_ctx * nv);
+    }
+
+    // We get the logits for all the tokens in the context window (params.n_ctx)
+    // from llama_eval above.  Now, based on https://huggingface.co/docs/transformers/perplexity,
+    // calculate the perplexity over the last half of the window (so the model always has
+    // some context to predict the token).
+    //
+    // We rely on the fact that attention in the forward pass only looks at previous
+    // tokens here, so the logits returned for each token are an accurate representation
+    // of what the model would have predicted at that point.
+    //
+    // Example, we have a context window of 512, we will compute perplexity for each of the
+    // last 256 tokens.  Then, we split the input up into context window size chunks to
+    // process the entire prompt.
+    const int first = n_ctx/2;
+
+    for (int i = 0; i < n_chunk; i += n_seq) {
         const int start =     i * n_ctx;
         const int end   = start + n_ctx;
 
-        const int num_batches = (n_ctx + n_batch - 1) / n_batch;
-
-        std::vector<float> logits;
+        const int n_seq_batch = std::min(n_seq, n_chunk - i);
 
         const auto t_start = std::chrono::high_resolution_clock::now();
 
@@ -342,32 +553,54 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
             const int batch_start = start + j * n_batch;
             const int batch_size  = std::min(end - batch_start, n_batch);
 
-            // save original token and restore it after eval
-            const auto token_org = tokens[batch_start];
+            int n_outputs = 0;
 
-            // add BOS token for the first batch of each chunk
-            if (add_bos && j == 0) {
-                tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
+            batch.n_tokens = 0;
+            for (int seq = 0; seq < n_seq_batch; seq++) {
+                int seq_start = batch_start + seq*n_ctx;
+
+                // save original token and restore it after eval
+                const auto token_org = tokens[seq_start];
+
+                // add BOS token for the first batch of each chunk
+                if (add_bos && j == 0) {
+                    tokens[seq_start] = llama_token_bos(llama_get_model(ctx));
+                }
+
+                for (int k = 0; k < batch_size; ++k) {
+                    const int idx = seq*n_ctx + k;
+                    batch.token   [idx]    = tokens[seq_start + k];
+                    batch.pos     [idx]    = j*n_batch + k;
+                    batch.n_seq_id[idx]    = 1;
+                    batch.seq_id  [idx][0] = seq;
+                    batch.logits  [idx]    = batch.pos[idx] >= first ? 1 : 0;
+
+                    n_outputs += batch.logits[idx] != 0;
+                }
+                batch.n_tokens += batch_size;
+
+                // restore the original token in case it was set to BOS
+                tokens[seq_start] = token_org;
             }
 
-            if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
+            if (llama_decode(ctx, batch)) {
                 fprintf(stderr, "%s : failed to eval\n", __func__);
                 return {tokens, -1, logit_history, prob_history};
             }
 
-            // restore the original token in case it was set to BOS
-            tokens[batch_start] = token_org;
-
-            const auto * batch_logits = llama_get_logits(ctx);
-            logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
+            if (num_batches > 1 && n_outputs > 0) {
+                const auto * batch_logits = llama_get_logits(ctx);
+                logits.insert(logits.end(), batch_logits, batch_logits + n_outputs * n_vocab);
+            }
         }
 
-        const auto t_end = std::chrono::high_resolution_clock::now();
 
         if (i == 0) {
+            llama_synchronize(ctx);
+            const auto t_end = std::chrono::high_resolution_clock::now();
             const float t_total = std::chrono::duration<float>(t_end - t_start).count();
             fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total);
-            int total_seconds = (int)(t_total * n_chunk);
+            int total_seconds = (int)(t_total*n_chunk/n_seq);
             if (total_seconds >= 60*60) {
                 fprintf(stderr, "%d hours ", total_seconds / (60*60));
                 total_seconds = total_seconds % (60*60);
@@ -375,33 +608,36 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
             fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
         }
 
-        // We get the logits for all the tokens in the context window (params.n_ctx)
-        // from llama_eval above.  Now, based on https://huggingface.co/docs/transformers/perplexity,
-        // calculate the perplexity over the last half of the window (so the model always has
-        // some context to predict the token).
-        //
-        // We rely on the fact that attention in the forward pass only looks at previous
-        // tokens here, so the logits returned for each token are an accurate representation
-        // of what the model would have predicted at that point.
-        //
-        // Example, we have a context window of 512, we will compute perplexity for each of the
-        // last 256 tokens.  Then, we split the input up into context window size chunks to
-        // process the entire prompt.
-        const int first = n_ctx/2;
-        process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
-                       workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
-        count += n_ctx - first - 1;
+        for (int seq = 0; seq < n_seq_batch; seq++) {
+            const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx + first);
 
-        // perplexity is e^(average negative log-likelihood)
-        if (params.ppl_output_type == 0) {
-            printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
-        } else {
-            double av = nll/count;
-            double av2 = nll2/count - av*av;
-            if (av2 > 0) av2 = sqrt(av2/(count-1));
-            printf("%8d  %.4lf  %4lf  %4lf\n", i*n_ctx, std::exp(nll / count), av, av2);
+            llama_token * tokens_data = tokens.data() + start + seq*n_ctx + first;
+            if (!params.logits_file.empty()) {
+                process_logits(logits_stream, n_vocab, all_logits,
+                        tokens_data, n_ctx - 1 - first,
+                        workers, log_probs, nll, nll2);
+            } else {
+                process_logits(n_vocab, all_logits,
+                        tokens_data, n_ctx - 1 - first,
+                        workers, nll, nll2,
+                        logit_history.data() + start + seq*n_ctx + first,
+                        prob_history.data()  + start + seq*n_ctx + first);
+            }
+            count += n_ctx - first - 1;
+
+            // perplexity is e^(average negative log-likelihood)
+            if (params.ppl_output_type == 0) {
+                printf("[%d]%.4lf,", i + seq + 1, std::exp(nll / count));
+            } else {
+                double av = nll/count;
+                double av2 = nll2/count - av*av;
+                if (av2 > 0) av2 = sqrt(av2/(count-1));
+                printf("%8d  %.4lf  %4lf  %4lf\n", i*n_ctx, std::exp(nll / count), av, av2);
+            }
         }
         fflush(stdout);
+
+        logits.clear();
     }
     printf("\n");
 
@@ -416,29 +652,86 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
         printf("Unexpected negative standard deviation of log(prob)\n");
     }
 
+    llama_batch_free(batch);
+
     return {tokens, ppl, logit_history, prob_history};
 }
 
-static std::vector<float> hellaswag_evaluate_tokens(
-    llama_context * ctx, std::vector<int> & tokens, int n_past, int n_batch, int n_vocab
-) {
-    std::vector<float> result;
-    result.reserve(tokens.size() * n_vocab);
-    size_t n_chunk = (tokens.size() + n_batch - 1)/n_batch;
-    for (size_t i_chunk = 0; i_chunk < n_chunk; ++i_chunk) {
-        size_t n_tokens = tokens.size() - i_chunk * n_batch;
-        n_tokens = std::min(n_tokens, size_t(n_batch));
-        if (llama_decode(ctx, llama_batch_get_one(tokens.data() + i_chunk * n_batch, n_tokens, n_past, 0))) {
-            fprintf(stderr, "%s : failed to eval\n", __func__);
-            return {};
+static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<float> & batch_logits, int32_t n_batch, int32_t n_vocab) {
+    int prev_outputs = 0;
+    for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
+        const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
+
+        llama_batch batch_view = {
+            n_tokens,
+            batch.token    + i,
+            nullptr,
+            batch.pos      + i,
+            batch.n_seq_id + i,
+            batch.seq_id   + i,
+            batch.logits   + i,
+            0, 0, 0, // unused
+        };
+
+        const int ret = llama_decode(ctx, batch_view);
+        if (ret != 0) {
+            LOG_TEE("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret);
+            return false;
         }
 
-        const auto logits = llama_get_logits(ctx);
-        result.insert(result.end(), logits, logits + n_tokens * n_vocab);
+        int n_outputs = 0;
+        for (int i = 0; i < n_tokens; ++i) {
+            n_outputs += batch_view.logits[i] != 0;
+        }
 
-        n_past += n_tokens;
+        memcpy(batch_logits.data() + prev_outputs*n_vocab, llama_get_logits(ctx), n_outputs*n_vocab*sizeof(float));
+
+        prev_outputs += n_outputs;
+    }
+
+    return true;
+}
+
+#define K_TOKEN_CHUNK 4
+
+static void compute_logprobs(const float * batch_logits, int n_vocab, std::vector<std::thread>& workers,
+        const std::vector<std::pair<size_t, llama_token>>& eval_pairs, std::vector<float>& eval_results) {
+    if (eval_results.size() != eval_pairs.size()) {
+        eval_results.resize(eval_pairs.size());
+    }
+    if (eval_pairs.empty()) return;
+
+    size_t max_threads = std::min((eval_pairs.size() + K_TOKEN_CHUNK - 1)/K_TOKEN_CHUNK, workers.size());
+
+    std::atomic<int> counter(0);
+    auto compute = [&counter, &eval_pairs, &eval_results, batch_logits, n_vocab] () {
+        float local_logprobs[K_TOKEN_CHUNK];
+        while (true) {
+            size_t first = counter.fetch_add(K_TOKEN_CHUNK, std::memory_order_relaxed);
+            if (first >= eval_results.size()) break;
+            size_t last = std::min(first + K_TOKEN_CHUNK, eval_results.size());
+            for (size_t i = first; i < last; ++i) {
+                auto logits = batch_logits + eval_pairs[i].first * n_vocab;
+                float max_logit = logits[0];
+                for (int j = 1; j < n_vocab; ++j) {
+                    max_logit = std::max(max_logit, logits[j]);
+                }
+                float sum_p = 0.f;
+                for (int j = 0; j < n_vocab; ++j) {
+                    sum_p += expf(logits[j] - max_logit);
+                }
+                local_logprobs[i - first] = logits[eval_pairs[i].second] - max_logit - std::log(sum_p);
+            }
+            std::memcpy(eval_results.data() + first, local_logprobs, (last - first)*sizeof(float));
+        }
+    };
+
+    for (size_t it = 0; it < max_threads; ++it) {
+        workers[it] = std::thread(compute);
+    }
+    for (size_t it = 0; it < max_threads; ++it) {
+        workers[it].join();
     }
-    return result;
 }
 
 static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
@@ -467,7 +760,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
         prompt_lines.push_back(line);
     }
 
-    if( prompt_lines.size() % 6 != 0) {
+    if (prompt_lines.size() % 6 != 0) {
         fprintf(stderr, "%s : number of lines in prompt not a multiple of 6.\n", __func__);
         return;
     }
@@ -481,14 +774,14 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
     // This is needed as usual for LLaMA models
     const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
 
-    // Number of tasks to use when computing the score
-    if ( params.hellaswag_tasks < hs_task_count  ) {
-        hs_task_count = params.hellaswag_tasks;
-    }
-
     // The tasks should be randomized so the score stabilizes quickly.
     bool randomize_tasks = true;
 
+    // Number of tasks to use when computing the score
+    if (params.hellaswag_tasks < hs_task_count) {
+        hs_task_count = params.hellaswag_tasks;
+    }
+
     // The random seed should not impact the final result if the computation is done over enough tasks, so kept hardcoded for now
     std::mt19937 rng(1);
 
@@ -499,27 +792,53 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
         std::string ending[4];
         size_t ending_logprob_count[4];
         double ending_logprob[4];
+
+        size_t i_logits;        // starting index of logits in the llama_batch
+        size_t common_prefix;   // max number of initial tokens that are the same in all sentences
+        size_t required_tokens; // needed number of tokens to evaluate all 4 endings
+        std::vector<llama_token> seq_tokens[4];
     };
 
     fprintf(stderr, "%s : selecting %zu %s tasks.\n", __func__, hs_task_count, (randomize_tasks?"randomized":"the first")  );
 
     // Select and read data from prompt lines
-    hs_data_t *hs_data = new hs_data_t[hs_task_count];
-    for (size_t i=0; i < hs_task_count; i++) {
+    std::vector<hs_data_t> hs_data(hs_task_count);
+    for (size_t i = 0; i < hs_task_count; i++) {
         size_t idx = i;
 
+        auto & hs_cur = hs_data[i];
+
         // Select a random example of those left in the prompt
         if (randomize_tasks) {
             std::uniform_int_distribution<size_t> dist(0, prompt_lines.size()/6-1 ) ;
             idx = dist(rng);
         }
 
-        hs_data[i].context = prompt_lines[idx*6];
-        hs_data[i].gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
-        for (size_t j=0; j < 4; j++) {
-            hs_data[i].ending[j] = prompt_lines[idx*6+2+j];
+        hs_cur.context = prompt_lines[idx*6];
+        hs_cur.gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
+        for (size_t j = 0; j < 4; j++) {
+            hs_cur.ending[j] = prompt_lines[idx*6+2+j];
+            hs_cur.seq_tokens[j] = ::llama_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], add_bos);
         }
 
+        // determine the common prefix of the endings
+        hs_cur.common_prefix = 0;
+        for (size_t k = 0; k < hs_cur.seq_tokens[0].size(); k++) {
+            if (hs_cur.seq_tokens[0][k] != hs_cur.seq_tokens[1][k] ||
+                hs_cur.seq_tokens[0][k] != hs_cur.seq_tokens[2][k] ||
+                hs_cur.seq_tokens[0][k] != hs_cur.seq_tokens[3][k]) {
+                break;
+            }
+            hs_cur.common_prefix++;
+        }
+        hs_cur.required_tokens = hs_cur.common_prefix +
+            hs_cur.seq_tokens[0].size() - hs_cur.common_prefix +
+            hs_cur.seq_tokens[1].size() - hs_cur.common_prefix +
+            hs_cur.seq_tokens[2].size() - hs_cur.common_prefix +
+            hs_cur.seq_tokens[3].size() - hs_cur.common_prefix;
+
+        //GGML_ASSERT(hs_cur.common_prefix >= ::llama_tokenize(ctx, hs_cur.context, add_bos).size());
+
         // Delete the selected random example from the prompt
         if (randomize_tasks) {
             prompt_lines.erase( std::next(prompt_lines.begin(),idx*6)  , std::next(prompt_lines.begin(),idx*6+6) );
@@ -527,164 +846,1028 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
     }
 
     fprintf(stderr, "%s : calculating hellaswag score over selected tasks.\n", __func__);
+
     printf("\ntask\tacc_norm\n");
 
     double acc = 0.0f;
-    const int n_vocab = llama_n_vocab(llama_get_model(ctx));
-    const int n_ctx = llama_n_ctx(ctx);
 
-    std::vector<std::vector<int>> ending_tokens(4);
+    const int n_vocab = llama_n_vocab(llama_get_model(ctx));
+    const int n_ctx   = llama_n_ctx(ctx);
+    const int n_batch = params.n_batch;
+
+    const int max_tasks_per_batch = 32;
+    const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
+
+    llama_batch batch = llama_batch_init(n_ctx, 0, 4);
 
     std::vector<float> tok_logits(n_vocab);
+    // TODO: this could be made smaller; it's currently the worst-case size
+    std::vector<float> batch_logits(n_vocab*n_ctx);
 
-    for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) {
-        // Tokenize the context to count tokens
-        std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, add_bos);
-        size_t context_size = context_embd.size();
+    std::vector<std::pair<size_t, llama_token>> eval_pairs;
+    std::vector<float> eval_results;
+    std::vector<std::thread> workers(std::thread::hardware_concurrency());
 
-        for (int i = 0; i < 4; ++i) {
-            ending_tokens[i] = ::llama_tokenize(ctx, hs_data[task_idx].context + " " + hs_data[task_idx].ending[i], add_bos);
-            for (int k = 0; k < int(context_size); ++k) {
-                if (ending_tokens[i][k] != context_embd[k]) {
-                    fprintf(stderr, "Oops: ending %d of task %d differs from context at position %d\n",i,int(task_idx),k);
-                    break;
+    for (size_t i0 = 0; i0 < hs_task_count; i0++) {
+        int n_cur = 0;
+
+        size_t i1 = i0;
+        size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch
+
+        llama_batch_clear(batch);
+
+        // batch as much tasks as possible into the available context
+        // each task has 4 unique sequence ids - one for each ending
+        // the common prefix is shared among the 4 sequences to save tokens
+        // we extract logits only from the last common token and from all ending tokens of each sequence
+        while (n_cur + (int) hs_data[i1].required_tokens <= n_ctx) {
+            auto & hs_cur = hs_data[i1];
+            int n_logits = 0;
+
+            const int s0 = 4*(i1 - i0);
+            if (s0 + 4 > max_seq) {
+                break;
+            }
+
+            for (size_t i = 0; i < hs_cur.common_prefix; ++i) {
+                llama_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3 }, false);
+            }
+            batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
+            n_logits += 1;
+
+            for (int s = 0; s < 4; ++s) {
+                const size_t seq_tokens_size = hs_cur.seq_tokens[s].size();
+                // TODO: don't evaluate the last token of each sequence
+                for (size_t i = hs_cur.common_prefix; i < seq_tokens_size; ++i) {
+                    const bool needs_logits = i < seq_tokens_size - 1;
+                    llama_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, needs_logits);
+                    n_logits += needs_logits;
                 }
             }
+
+            hs_cur.i_logits = i_logits;
+            i_logits += n_logits;
+
+            n_cur += hs_data[i1].required_tokens;
+            if (++i1 == hs_task_count) {
+                break;
+            }
         }
 
-        // Do the 1st ending
-        // In this case we include the context when evaluating
-        //auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], add_bos);
-        auto query_embd = ending_tokens[0];
-        auto query_size = query_embd.size();
-
-        // Stop if query wont fit the ctx window
-        if (query_size > (size_t)n_ctx) {
-            fprintf(stderr, "%s : number of tokens in query %zu > n_ctxl\n", __func__, query_size);
+        if (i0 == i1) {
+            fprintf(stderr, "%s : task %zu does not fit in the context window\n", __func__, i0);
             return;
         }
 
-        // Speedup small evaluations by evaluating atleast 32 tokens
-        if (query_size < 32) {
-            query_embd.resize(32);
+        llama_kv_cache_clear(ctx);
+
+        // decode all tasks [i0, i1)
+        if (!decode_helper(ctx, batch, batch_logits, n_batch, n_vocab)) {
+            fprintf(stderr, "%s: llama_decode() failed\n", __func__);
+            return;
+        }
+
+        // Compute log-probs in parallel
+        // First we collect all tasks
+        eval_pairs.clear();
+        for (size_t i = i0; i < i1; ++i) {
+            auto & hs_cur = hs_data[i];
+            size_t li = 1; // skip the last logit of the common prefix (computed separately below)
+            for (int s = 0; s < 4; ++s) {
+                for (size_t j = hs_cur.common_prefix; j < hs_cur.seq_tokens[s].size() - 1; j++) {
+                    eval_pairs.emplace_back(hs_cur.i_logits + li++, hs_cur.seq_tokens[s][j + 1]);
+                }
+            }
+        }
+        // Then we do the actual calculation
+        compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results);
+
+        size_t ir = 0;
+
+        // compute the logprobs for each ending of the decoded tasks
+        for (size_t i = i0; i < i1; ++i) {
+            auto & hs_cur = hs_data[i];
+
+            // get the logits of the last token of the common prefix
+            std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*hs_cur.i_logits, n_vocab*sizeof(float));
+
+            const auto first_probs = softmax(tok_logits);
+
+            for (int s = 0; s < 4; ++s) {
+                hs_cur.ending_logprob_count[s] = 1;
+                hs_cur.ending_logprob[s] = std::log(first_probs[hs_cur.seq_tokens[s][hs_cur.common_prefix]]);
+                for (size_t j = hs_cur.common_prefix; j < hs_cur.seq_tokens[s].size() - 1; j++) {
+                    hs_cur.ending_logprob[s] += eval_results[ir++];
+                    hs_cur.ending_logprob_count[s]++;
+                }
+                hs_cur.ending_logprob[s] /= hs_cur.ending_logprob_count[s];
+            }
+
+            // Find the ending with maximum logprob
+            size_t ending_logprob_max_idx = 0;
+            double ending_logprob_max_val = hs_cur.ending_logprob[0];
+            for (size_t s = 1; s < 4; s++) {
+                if (hs_cur.ending_logprob[s] > ending_logprob_max_val) {
+                    ending_logprob_max_idx = s;
+                    ending_logprob_max_val =  hs_cur.ending_logprob[s];
+                }
+            }
+
+            //printf("max logprob ending idx %lu, gold ending idx %lu\n", ending_logprob_max_idx, hs_cur.gold_ending_idx);
+
+            // If the gold ending got the maximum logprobe add one accuracy point
+            if (ending_logprob_max_idx == hs_cur.gold_ending_idx) {
+                acc += 1.0;
+            }
+
+            // Print the accumulated accuracy mean x 100
+            printf("%zu\t%.8lf\n", i + 1, acc/double(i + 1)*100.0);
+            fflush(stdout);
+        }
+
+        i0 = i1 - 1;
+    }
+
+    llama_batch_free(batch);
+
+    printf("\n");
+}
+
+struct winogrande_entry {
+    std::string first;
+    std::string second;
+    std::array<std::string, 2> choices;
+    int answer;
+
+    size_t i_logits;
+    size_t common_prefix;
+    size_t required_tokens;
+    size_t n_base1; // number of tokens for context + choice 1
+    size_t n_base2; // number of tokens for context + choice 2
+    std::vector<llama_token> seq_tokens[2];
+};
+
+static std::vector<winogrande_entry> load_winogrande_from_csv(const std::string& prompt) {
+    std::vector<winogrande_entry> result;
+    std::istringstream in(prompt);
+    std::string line;
+    std::array<int, 4> comma_pos;
+    while (true) {
+        std::getline(in, line);
+        if (in.fail() || in.eof()) break;
+        int ipos = 0;
+        bool quote_open = false;
+        for (int i = 0; i < int(line.size()); ++i) {
+            if (!quote_open) {
+                if (line[i] == ',') {
+                    comma_pos[ipos++] = i;
+                    if (ipos == 4) break;
+                }
+                else if (line[i] == '"') {
+                    quote_open = true;
+                }
+            }
+            else {
+                if (line[i] == '"') {
+                    quote_open = false;
+                }
+            }
+        }
+        if (ipos != 4) {
+            printf("%s: failed to find comma separators in <%s>\n", __func__, line.c_str());
+            continue;
+        }
+        auto sentence = line[comma_pos[0]+1] == '"' ? line.substr(comma_pos[0]+2, comma_pos[1] - comma_pos[0] - 3)
+                                                    : line.substr(comma_pos[0]+1, comma_pos[1] - comma_pos[0] - 1);
+        auto choice1 = line.substr(comma_pos[1]+1, comma_pos[2] - comma_pos[1] - 1);
+        auto choice2 = line.substr(comma_pos[2]+1, comma_pos[3] - comma_pos[2] - 1);
+        auto answer  = line.substr(comma_pos[3]+1, line.size() - comma_pos[3] - 1);
+        auto index = line.substr(0, comma_pos[0]);
+        int where = 0;
+        for ( ; where < int(sentence.size()); ++where) {
+            if (sentence[where] == '_') break;
+        }
+        if (where == int(sentence.size())) {
+            printf("%s: no _ in <%s>\n", __func__, sentence.c_str());
+            continue;
+        }
+        std::istringstream stream(answer.c_str());
+        int i_answer; stream >> i_answer;
+        if (stream.fail() || i_answer < 1 || i_answer > 2) {
+            printf("%s: failed to parse answer <%s>\n", __func__, answer.c_str());
+            continue;
+        }
+        result.emplace_back();
+        auto& wg = result.back();
+        wg.first = sentence.substr(0, where);
+        wg.second = sentence.substr(where + 1, sentence.size() - where - 1);
+        wg.choices[0] = std::move(choice1);
+        wg.choices[1] = std::move(choice2);
+        wg.answer = i_answer;
+    }
+    return result;
+}
+
+/*
+ * Evaluates the Winogrande score.
+ * Uses a CSV containing task index, dentence, choice 1, choice 2, answer (1 or 2)
+ * You can get one such dataset from e.g. https://huggingface.co/datasets/ikawrakow/winogrande-eval-for-llama.cpp
+ * As an example, the 1st row in the above dataset is
+ *
+ *    0,Sarah was a much better surgeon than Maria so _ always got the easier cases.,Sarah,Maria,2
+ *
+ */
+static void winogrande_score(llama_context * ctx, const gpt_params & params) {
+
+    constexpr int k_min_trailing_ctx = 3;
+
+    auto data = load_winogrande_from_csv(params.prompt);
+    if (data.empty()) {
+        fprintf(stderr, "%s: no tasks\n", __func__);
+        return;
+    }
+
+    fprintf(stderr, "%s : loaded %zu tasks from prompt.\n", __func__, data.size());
+
+    if (params.winogrande_tasks > 0 && params.winogrande_tasks < data.size()) {
+        fprintf(stderr, "%s : selecting %zu random tasks\n", __func__, params.winogrande_tasks);
+        std::mt19937 rng(1);
+        std::vector<int> aux(data.size());
+        for (int i = 0; i < int(data.size()); ++i) {
+            aux[i] = i;
+        }
+        float scale = 1/(1.f + (float)rng.max());
+        std::vector<winogrande_entry> selected;
+        selected.resize(params.winogrande_tasks);
+        for (int i = 0; i < int(params.winogrande_tasks); ++i) {
+            int j = int(scale*rng()*aux.size());
+            selected[i] = std::move(data[aux[j]]);
+            aux[j] = aux.back();
+            aux.pop_back();
+        }
+        data = std::move(selected);
+    }
+
+    fprintf(stderr, "%s : tokenizing selected tasks\n", __func__);
+
+    // This is needed as usual for LLaMA models
+    const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
+
+    for (auto & task : data) {
+        task.seq_tokens[0] = ::llama_tokenize(ctx, task.first + task.choices[0] + task.second, add_bos);
+        task.seq_tokens[1] = ::llama_tokenize(ctx, task.first + task.choices[1] + task.second, add_bos);
+
+        task.common_prefix = 0;
+        for (size_t k = 0; k < task.seq_tokens[0].size(); k++) {
+            if (task.seq_tokens[0][k] != task.seq_tokens[1][k]) {
+                break;
+            }
+            task.common_prefix++;
+        }
+
+        // TODO: the last token of each of the sequences don't need to be evaluated
+        task.required_tokens = task.common_prefix +
+            task.seq_tokens[0].size() - task.common_prefix +
+            task.seq_tokens[1].size() - task.common_prefix;
+
+        task.n_base1 = ::llama_tokenize(ctx, task.first + task.choices[0], add_bos).size();
+        task.n_base2 = ::llama_tokenize(ctx, task.first + task.choices[1], add_bos).size();
+    }
+
+    fprintf(stderr, "%s : calculating winogrande score over selected tasks.\n", __func__);
+
+    const int n_vocab = llama_n_vocab(llama_get_model(ctx));
+    const int n_ctx   = llama_n_ctx(ctx);
+    const int n_batch = params.n_batch;
+
+    const int max_tasks_per_batch = 128;
+    const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
+
+    llama_batch batch = llama_batch_init(n_ctx, 0, 2);
+
+    std::vector<float> tok_logits(n_vocab);
+    // TODO: this could be made smaller; it's currently the worst-case size
+    std::vector<float> batch_logits(n_vocab*n_ctx);
+
+    std::vector<std::pair<size_t, llama_token>> eval_pairs;
+    std::vector<float> eval_results;
+    std::vector<std::thread> workers(std::thread::hardware_concurrency());
+
+    int n_correct = 0;
+    int n_done    = 0;
+
+    for (size_t i0 = 0; i0 < data.size(); i0++) {
+        int n_cur = 0;
+
+        size_t i1 = i0;
+        size_t i_logits = 0;
+
+        llama_batch_clear(batch);
+
+        while (n_cur + (int) data[i1].required_tokens <= n_ctx) {
+            int n_logits = 0;
+            const int s0 = 2*(i1 - i0);
+            if (s0 + 2 > max_seq) {
+                break;
+            }
+
+            for (size_t i = 0; i < data[i1].common_prefix; ++i) {
+                llama_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1 }, false);
+            }
+            batch.logits[batch.n_tokens - 1] = true;
+            n_logits += 1;
+
+            for (int s = 0; s < 2; ++s) {
+                // TODO: end before the last token, no need to predict past the end of the sequences
+                for (size_t i = data[i1].common_prefix; i < data[i1].seq_tokens[s].size(); ++i) {
+                    llama_batch_add(batch, data[i1].seq_tokens[s][i], i, { s0 + s }, true);
+                    n_logits += 1;
+                }
+            }
+
+            data[i1].i_logits = i_logits;
+            i_logits += n_logits;
+
+            n_cur += data[i1].required_tokens;
+            if (++i1 == data.size()) {
+                break;
+            }
+        }
+
+        if (i0 == i1) {
+            fprintf(stderr, "%s : task %zu does not fit in the context window\n", __func__, i0);
+            return;
+        }
+
+        llama_kv_cache_clear(ctx);
+
+        // decode all tasks [i0, i1)
+        if (!decode_helper(ctx, batch, batch_logits, n_batch, n_vocab)) {
+            fprintf(stderr, "%s: llama_decode() failed\n", __func__);
+            return;
+        }
+
+        eval_pairs.clear();
+        for (size_t i = i0; i < i1; ++i) {
+            auto & task = data[i];
+
+            const bool skip_choice =
+                task.seq_tokens[0].size() - task.common_prefix > k_min_trailing_ctx &&
+                task.seq_tokens[1].size() - task.common_prefix > k_min_trailing_ctx;
+
+            const auto& n_base1 = skip_choice ? task.n_base1 : task.common_prefix;
+            const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0;
+            size_t li = n_base1 - task.common_prefix;
+            for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) {
+                eval_pairs.emplace_back(task.i_logits + li++, task.seq_tokens[0][j+1]);
+            }
+            const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix;
+            const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0;
+            // FIXME: this uses the wrong first logits when not skipping the choice word
+            li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - task.common_prefix;
+            for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) {
+                eval_pairs.emplace_back(task.i_logits + li++, task.seq_tokens[1][j+1]);
+            }
+        }
+        compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results);
+
+        size_t ir = 0;
+        for (size_t i = i0; i < i1; ++i) {
+            auto & task = data[i];
+
+            const bool skip_choice =
+                task.seq_tokens[0].size() - task.common_prefix > k_min_trailing_ctx &&
+                task.seq_tokens[1].size() - task.common_prefix > k_min_trailing_ctx;
+
+            float score_1st = 0;
+            const auto& n_base1 = skip_choice ? task.n_base1 : task.common_prefix;
+            const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0;
+            for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) {
+                score_1st += eval_results[ir++];
+            }
+            score_1st /= (task.seq_tokens[0].size() - n_base1 - last_1st);
+
+            float score_2nd = 0;
+            const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix;
+            const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0;
+            for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) {
+                score_2nd += eval_results[ir++];
+            }
+            score_2nd /= (task.seq_tokens[1].size() - n_base2 - last_2nd);
+
+            int result = score_1st > score_2nd ? 1 : 2;
+
+            if (result == task.answer) {
+                ++n_correct;
+            }
+            ++n_done;
+
+            // print the accumulated accuracy mean x 100
+            printf("%zu\t%.4lf\t%10.6f  %10.6f  %d  %d\n", i+1, 100.0 * n_correct/n_done, score_1st, score_2nd, result, task.answer);
+            fflush(stdout);
+        }
+
+        i0 = i1 - 1;
+    }
+
+    printf("\n");
+
+    if (n_done < 100) return;
+
+    const float p = 1.f*n_correct/n_done;
+    const float sigma = 100.f*sqrt(p*(1-p)/(n_done-1));
+    printf("Final Winogrande score(%d tasks): %.4lf +/- %.4lf\n", n_done, 100*p, sigma);
+}
+
+static bool deserialize_string(std::istream & in, std::string & str) {
+    uint32_t size;
+    if (!in.read((char *)&size, sizeof(size)).fail()) {
+        str.resize(size);
+        if (!in.read((char *)&str[0], size).fail()) return true;
+    }
+    return false;
+}
+
+struct multiple_choice_answers {
+    std::vector<std::string> answers;
+    std::vector<int>         labels;
+    bool deserialize(std::istream& in) {
+        uint32_t n;
+        in.read((char *)&n, sizeof(n));
+        if (in.fail() || n > 100) return false; // 100 as max. number of answers should be good enough for any practical purpose
+        answers.resize(n);
+        labels.resize(n);
+        for (auto& a : answers) {
+            if (!deserialize_string(in, a)) return false;
+        }
+        in.read((char *)labels.data(), n*sizeof(int));
+        return !in.fail();
+    }
+};
+
+struct multiple_choice_task {
+    std::string question;         // the question (or context that needs to be continued)
+    multiple_choice_answers mc1;  // possible answers (continuations) with a single correct answer
+    multiple_choice_answers mc2;  // possible answers (continuations) with multiple correct answers - not handled yet
+    bool deserialize(std::istream& in) {
+        if (!deserialize_string(in, question)) return false;
+        return mc1.deserialize(in) && mc2.deserialize(in);
+    }
+
+    // For evaluation
+    size_t i_logits;        // starting index of logits in the llama_batch
+    size_t common_prefix;   // max number of initial tokens that are the same in all sentences
+    size_t required_tokens; // needed number of tokens to evaluate all answers
+    std::vector<std::vector<llama_token>> seq_tokens;
+    std::vector<float> log_probs;
+};
+
+static bool multiple_choice_prepare_one_task(llama_context * ctx, bool add_bos, multiple_choice_task& task, bool log_error) {
+    if (task.question.empty() || task.mc1.answers.empty()) {
+        if (log_error) {
+            printf("%s: found bad task with empty question and/or answers\n", __func__);
+        }
+        return false;
+    }
+    task.seq_tokens.reserve(task.mc1.answers.size());
+    for (auto& answer : task.mc1.answers) {
+        if (answer.empty()) {
+            if (log_error) {
+                printf("%s: found empty answer\n", __func__);
+            }
+            return false;
+        }
+        task.seq_tokens.emplace_back(::llama_tokenize(ctx, task.question + " " + answer, add_bos));
+    }
+    auto min_len = task.seq_tokens.front().size();
+    for (auto& seq : task.seq_tokens) {
+        min_len = std::min(min_len, seq.size());
+    }
+    task.common_prefix = 0;
+    for (size_t k = 0; k < min_len; ++k) {
+        auto token = task.seq_tokens[0][k];
+        bool all_same = true;
+        for (size_t i = 1; i < task.seq_tokens.size(); ++i) {
+            if (task.seq_tokens[i][k] != token) {
+                all_same = false;
+                break;
+            }
+        }
+        if (!all_same) {
+            break;
+        }
+        ++task.common_prefix;
+    }
+    task.required_tokens = task.common_prefix;
+    for (auto& seq : task.seq_tokens) {
+        task.required_tokens += seq.size() - task.common_prefix;
+    }
+    return true;
+}
+
+//
+// Calculates score for multiple choice tasks with single correct answer from prompt.
+// Commonly used LLM evaluation metrics of this type are
+//   * ARC
+//   * HellaSwag
+//   * MMLU
+//   * TruthfulQA
+//
+// Validation datasets for these 4 tests can be found at
+//     https://huggingface.co/datasets/ikawrakow/validation-datasets-for-llama.cpp
+// The data for these datasets was extracted from
+//     git@hf.co:datasets/allenai/ai2_arc
+//     https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl
+//     git@hf.co:datasets/Stevross/mmlu
+//     https://huggingface.co/datasets/truthful_qa
+//
+static void multiple_choice_score(llama_context * ctx, const gpt_params & params) {
+
+    std::istringstream strstream(params.prompt);
+    uint32_t n_task;
+    strstream.read((char *)&n_task, sizeof(n_task));
+    if (strstream.fail() || n_task == 0) {
+        printf("%s: no tasks\n", __func__);
+        return;
+    }
+    printf("%s: there are %u tasks in prompt\n", __func__, n_task);
+    std::vector<uint32_t> task_pos(n_task);
+    strstream.read((char *)task_pos.data(), task_pos.size()*sizeof(uint32_t));
+    if (strstream.fail()) {
+        printf("%s: failed to read task positions from prompt\n", __func__);
+        return;
+    }
+
+    std::vector<multiple_choice_task> tasks;
+    if (params.multiple_choice_tasks == 0 || params.multiple_choice_tasks >= (size_t)n_task) {
+        // Use all tasks
+        tasks.resize(n_task);
+        printf("%s: reading tasks", __func__);
+        int n_dot = n_task/100;
+        int i = 0;
+        for (auto& task : tasks) {
+            ++i;
+            if (!task.deserialize(strstream)) {
+                printf("%s: failed to read task %d of %u\n", __func__, i, n_task);
+                return;
+            }
+            if (i%n_dot == 0) printf(".");
+        }
+        printf("done\n");
+    }
+    else {
+        printf("%s: selecting %zu random tasks from %u tasks available\n", __func__, params.multiple_choice_tasks, n_task);
+        std::mt19937 rng(1);
+        std::vector<int> aux(n_task);
+        for (uint32_t i = 0; i < n_task; ++i) aux[i] = i;
+        float scale = 1.f/(1.f + (float)std::mt19937::max());
+        tasks.resize(params.multiple_choice_tasks);
+        for (auto& task : tasks) {
+            int j = (int)(scale * rng() * aux.size());
+            int idx = aux[j];
+            aux[j] = aux.back();
+            aux.pop_back();
+            strstream.seekg(task_pos[idx], std::ios::beg);
+            if (!task.deserialize(strstream)) {
+                printf("%s: failed to read task %d at position %u\n", __func__, idx, task_pos[idx]);
+                return;
+            }
+        }
+        n_task = params.multiple_choice_tasks;
+    }
+
+    // This is needed as usual for LLaMA models
+    const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
+
+    printf("%s: preparing task data", __func__);
+    fflush(stdout);
+    if (n_task > 500) {
+        printf("...");
+        fflush(stdout);
+        std::atomic<int> counter(0);
+        std::atomic<int> n_bad(0);
+        auto prepare = [&counter, &n_bad, &tasks, ctx, add_bos] () {
+            int num_tasks = tasks.size();
+            int n_bad_local = 0;
+            while (true) {
+                int first = counter.fetch_add(K_TOKEN_CHUNK);
+                if (first >= num_tasks) {
+                    if (n_bad_local > 0) n_bad += n_bad_local;
+                    break;
+                }
+                int last = std::min(first + K_TOKEN_CHUNK, num_tasks);
+                for (int i = first; i < last; ++i) {
+                    if (!multiple_choice_prepare_one_task(ctx, add_bos, tasks[i], false)) ++n_bad_local;
+                }
+            }
+        };
+        size_t max_thread = std::thread::hardware_concurrency();
+        max_thread = std::min(max_thread, (tasks.size() + K_TOKEN_CHUNK - 1)/K_TOKEN_CHUNK);
+        std::vector<std::thread> workers(max_thread-1);
+        for (auto& w : workers) w = std::thread(prepare);
+        prepare();
+        for (auto& w : workers) w.join();
+        printf("done\n");
+        fflush(stdout);
+        int nbad = n_bad;
+        if (nbad > 0) {
+            printf("%s: found %d malformed tasks\n", __func__, nbad);
+            return;
+        }
+    } else {
+        int n_dot = std::max((int) n_task/100, 1);
+        int i_task = 0;
+        for (auto& task : tasks) {
+            ++i_task;
+            if (!multiple_choice_prepare_one_task(ctx, add_bos, task, true)) {
+                return;
+            }
+            if (i_task%n_dot == 0) {
+                printf(".");
+                fflush(stdout);
+            }
+        }
+        printf("done\n");
+    }
+
+    printf("%s : calculating TruthfulQA score over %zu tasks.\n", __func__, tasks.size());
+
+    printf("\ntask\tacc_norm\n");
+
+    const int n_vocab = llama_n_vocab(llama_get_model(ctx));
+    const int n_ctx   = llama_n_ctx(ctx);
+    const int n_batch = params.n_batch;
+
+    const int max_tasks_per_batch = 32;
+    const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
+
+    llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
+
+    std::vector<float> tok_logits(n_vocab);
+    std::vector<float> batch_logits(n_vocab*n_ctx);
+
+    std::vector<std::pair<size_t, llama_token>> eval_pairs;
+    std::vector<float> eval_results;
+    std::vector<std::thread> workers(std::thread::hardware_concurrency());
+    std::vector<int> batch_indeces;
+
+    int n_done = 0;
+    int n_correct = 0;
+    int n_tot_answers = 0;
+
+    for (size_t i0 = 0; i0 < tasks.size(); i0++) {
+        int n_cur = 0;
+
+        size_t i1 = i0;
+        size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch
+
+        llama_batch_clear(batch);
+
+        // batch as much tasks as possible into the available context
+        // each task has 4 unique sequence ids - one for each ending
+        // the common prefix is shared among the 4 sequences to save tokens
+        // we extract logits only from the last common token and from all ending tokens of each sequence
+        int s0 = 0;
+        while (n_cur + (int) tasks[i1].required_tokens <= n_ctx) {
+            auto& cur_task = tasks[i1];
+            int n_logits = 0;
+
+            int num_answers = cur_task.seq_tokens.size();
+            if (s0 + num_answers > max_seq) {
+                break;
+            }
+
+            if (int(batch_indeces.size()) != num_answers) {
+                batch_indeces.resize(num_answers);
+            }
+            for (int s = 0; s < num_answers; ++s) batch_indeces[s] = s0 + s;
+
+            for (size_t i = 0; i < cur_task.common_prefix; ++i) {
+                //llama_batch_add(batch, cur_task.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3}, false);
+                llama_batch_add(batch, cur_task.seq_tokens[0][i], i, batch_indeces, false);
+            }
+            batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
+            n_logits += 1;
+
+            for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
+                const size_t seq_tokens_size = cur_task.seq_tokens[s].size();
+                // TODO: don't evaluate the last token of each sequence
+                for (size_t i = cur_task.common_prefix; i < seq_tokens_size; ++i) {
+                    const bool needs_logits = i < seq_tokens_size - 1;
+                    llama_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, needs_logits);
+                    n_logits += needs_logits;
+                }
+            }
+
+            s0 += num_answers;
+
+            cur_task.i_logits = i_logits;
+            i_logits += n_logits;
+
+            n_cur += cur_task.required_tokens;
+            if (++i1 == tasks.size()) {
+                break;
+            }
+        }
+
+        if (i0 == i1) {
+            fprintf(stderr, "%s : task %zu does not fit in the context window\n", __func__, i0);
+            return;
+        }
+
+        llama_kv_cache_clear(ctx);
+
+        // decode all tasks [i0, i1)
+        if (!decode_helper(ctx, batch, batch_logits, n_batch, n_vocab)) {
+            fprintf(stderr, "%s: llama_decode() failed\n", __func__);
+            return;
+        }
+
+        // Compute log-probs in parallel
+        // First we collect all tasks
+        eval_pairs.clear();
+        for (size_t i = i0; i < i1; ++i) {
+            auto& cur_task = tasks[i];
+            size_t li = 1; // skip the last logit of the common prefix (computed separately below)
+            for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
+                for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) {
+                    eval_pairs.emplace_back(cur_task.i_logits + li++, cur_task.seq_tokens[s][j + 1]);
+                }
+            }
+        }
+        // Then we do the actual calculation
+        compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results);
+
+        size_t ir = 0;
+
+        // compute the logprobs for each ending of the decoded tasks
+        for (size_t i = i0; i < i1; ++i) {
+            auto & cur_task = tasks[i];
+            //printf("==== Evaluating <%s> with correct answer ", cur_task.question.c_str());
+            //for (int j = 0; j < int(cur_task.mc1.labels.size()); ++j) {
+            //    if (cur_task.mc1.labels[j] == 1) {
+            //        printf("%d", j+1);
+            //    }
+            //}
+            //printf("\n    common_prefix: %zu\n", cur_task.common_prefix);
+
+            // get the logits of the last token of the common prefix
+            std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*cur_task.i_logits, n_vocab*sizeof(float));
+
+            const auto first_probs = softmax(tok_logits);
+
+            cur_task.log_probs.resize(cur_task.seq_tokens.size());
+            for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
+                size_t count = 1;
+                float  log_prob  = std::log(first_probs[cur_task.seq_tokens[s][cur_task.common_prefix]]);
+                for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) {
+                    //printf("        %zu  %g\n", ir, eval_results[ir]);
+                    ++count;
+                    log_prob += eval_results[ir++];
+                }
+                cur_task.log_probs[s] = log_prob / count;
+                //printf("        Final: %g\n", log_prob / count);
+                //printf("    <%s> : %g\n", cur_task.mc1.answers[s].c_str(), log_prob/count);
+            }
+
+            // Find the ending with maximum logprob
+            size_t logprob_max_idx = 0;
+            float  logprob_max_val = cur_task.log_probs[0];
+            for (size_t s = 1; s < cur_task.log_probs.size(); s++) {
+                if (cur_task.log_probs[s] > logprob_max_val) {
+                    logprob_max_val = cur_task.log_probs[s];
+                    logprob_max_idx = s;
+                }
+            }
+
+            n_tot_answers += cur_task.log_probs.size();
+            if (cur_task.mc1.labels[logprob_max_idx] == 1) {
+                ++n_correct;
+            }
+            ++n_done;
+
+            // Print the accumulated accuracy mean x 100
+            printf("%d\t%.8lf\n", n_done, 100.*n_correct/n_done);
+            fflush(stdout);
+        }
+
+        i0 = i1 - 1;
+    }
+
+    llama_batch_free(batch);
+
+    if (n_done < 100) return;
+
+    float p = 1.f*n_correct/n_done;
+    float sigma = sqrt(p*(1-p)/(n_done-1));
+    printf("\n Final result: %.4f +/- %.4f\n", 100.f*p, 100.f*sigma);
+    p = 1.f*n_done/n_tot_answers;
+    sigma = sqrt(p*(1-p)/(n_done-1));
+    printf("Random chance: %.4f +/- %.4f\n", 100.f*p, 100.f*sigma);
+
+    printf("\n");
+}
+
+static void kl_divergence(llama_context * ctx, const gpt_params & params) {
+    if (params.logits_file.empty()) {
+        fprintf(stderr, "%s: you must provide a name of a file containing the log probabilities of the base model\n", __func__);
+        return;
+    }
+    std::ifstream in(params.logits_file.c_str(), std::ios::binary);
+    if (!in) {
+        fprintf(stderr, "%s: failed to open %s\n", __func__, params.logits_file.c_str());
+        return;
+    }
+    {
+        char check[9]; check[8] = 0;
+        in.read(check, 8);
+        if (in.fail() || strncmp("_logits_", check, 8) != 0) {
+            fprintf(stderr, "%s: %s does not look like a file containing log-probabilities\n", __func__, params.logits_file.c_str());
+            return;
+        }
+    }
+
+    uint32_t n_ctx;
+    in.read((char *)&n_ctx, sizeof(n_ctx));
+    if (n_ctx > llama_n_ctx(ctx)) {
+        fprintf(stderr, "%s: %s has been computed with %u, while the current context is %d. Increase it with -c and retry\n",
+                __func__, params.logits_file.c_str(), n_ctx, params.n_ctx);
+    }
+
+    int n_vocab, n_chunk;
+    in.read((char *)&n_vocab, sizeof(n_vocab));
+    in.read((char *)&n_chunk, sizeof(n_chunk));
+    if (in.fail()) {
+        fprintf(stderr, "%s: failed reading n_vocab, n_chunk from %s\n", __func__, params.logits_file.c_str());
+        return;
+    }
+    if (n_vocab != llama_n_vocab(llama_get_model(ctx))) {
+        fprintf(stderr, "%s: inconsistent vocabulary (%d vs %d)\n", __func__, n_vocab, llama_n_vocab(llama_get_model(ctx)));
+    }
+
+    std::vector<llama_token> tokens(n_ctx * n_chunk);
+    if (in.read((char *)tokens.data(), tokens.size()*sizeof(tokens[0])).fail()) {
+        fprintf(stderr, "%s: failed reading evaluation tokens from %s\n", __func__, params.logits_file.c_str());
+        return;
+    }
+
+    const int n_batch = params.n_batch;
+    const int num_batches = (n_ctx + n_batch - 1)/n_batch;
+    const int nv = 2*((n_vocab + 1)/2) + 4;
+    const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
+
+    std::vector<uint16_t> log_probs_uint16(size_t(n_ctx - 1 - n_ctx/2) * nv);
+    std::vector<float> kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk);
+    std::vector<float> logits;
+    if (num_batches > 1) {
+        logits.reserve(n_ctx * n_vocab);
+    }
+
+    std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
+
+    auto mean_and_uncertainty = [] (double sum, double sum2, size_t count) {
+        if (count < 1) {
+            return std::make_pair(0., 0.);
+        }
+        double f = sum/count;
+        double df = sum2/count - f*f;
+        df = df > 0 && count > 10 ? sqrt(df/(count-1)) : 0.;
+        return std::make_pair(f, df);
+    };
+
+    kl_divergence_result kld;
+    auto kld_ptr = kld_values.data();
+
+    for (int i = 0; i < n_chunk; ++i) {
+        const int start =     i * n_ctx;
+        const int end   = start + n_ctx;
+
+        const auto t_start = std::chrono::high_resolution_clock::now();
+
+        if (in.read((char *)log_probs_uint16.data(), log_probs_uint16.size()*sizeof(uint16_t)).fail()) {
+            fprintf(stderr, "%s: failed reading log-probs for chunk %d\n", __func__, i);
+            return;
         }
 
         // clear the KV cache
         llama_kv_cache_clear(ctx);
 
-        auto logits = hellaswag_evaluate_tokens(ctx, query_embd, 0, params.n_batch, n_vocab);
-        if (logits.empty()) {
-            fprintf(stderr, "%s : failed to eval\n", __func__);
-            return;
-        }
+        for (int j = 0; j < num_batches; ++j) {
+            const int batch_start = start + j * n_batch;
+            const int batch_size  = std::min(end - batch_start, n_batch);
 
-        std::memcpy(tok_logits.data(), logits.data() + (context_size-1)*n_vocab, n_vocab*sizeof(float));
-        const auto first_probs = softmax(tok_logits);
+            // save original token and restore it after eval
+            const auto token_org = tokens[batch_start];
 
-        hs_data[task_idx].ending_logprob_count[0] = 1;
-        hs_data[task_idx].ending_logprob[0] = std::log(first_probs[query_embd[context_size]]);
-
-        // Calculate the logprobs over the ending
-        for (size_t j = context_size; j < query_size - 1; j++) {
-
-            std::memcpy(tok_logits.data(), logits.data() + j*n_vocab, n_vocab*sizeof(float));
-
-            const float prob = softmax(tok_logits)[query_embd[j + 1]];
-
-            hs_data[task_idx].ending_logprob[0] += std::log(prob);
-            hs_data[task_idx].ending_logprob_count[0]++;
-        }
-
-        // Calculate the mean token logprob for acc_norm
-        hs_data[task_idx].ending_logprob[0] /= hs_data[task_idx].ending_logprob_count[0];
-
-        // Do the remaining endings
-        // For these, we use the bare ending with n_past = context_size
-        //
-        for (size_t ending_idx = 1; ending_idx < 4; ending_idx++) {
-
-            // Tokenize the query
-            query_embd.resize(ending_tokens[ending_idx].size() - context_size);
-            std::memcpy(query_embd.data(), ending_tokens[ending_idx].data() + context_size, query_embd.size()*sizeof(int));
-            query_size = query_embd.size();
-
-            // Stop if query wont fit the ctx window
-            if (context_size + query_size > (size_t)n_ctx) {
-                fprintf(stderr, "%s : number of tokens in query %zu > n_ctxl\n", __func__, query_size);
-                return;
+            // add BOS token for the first batch of each chunk
+            if (add_bos && j == 0) {
+                tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
             }
 
-            // Speedup small evaluations by evaluating atleast 32 tokens
-            // No, resizing to 32 is actually slightly slower (at least on CUDA)
-            //if (query_size < 32) {
-            //    query_embd.resize(32);
-            //}
-
-            // Evaluate the query
-            logits = hellaswag_evaluate_tokens(ctx, query_embd, context_size, params.n_batch, n_vocab);
-            if (logits.empty()) {
+            // TODO: use llama_batch.logits instead of relying on logits_all == true
+            if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
                 fprintf(stderr, "%s : failed to eval\n", __func__);
                 return;
             }
 
-            hs_data[task_idx].ending_logprob_count[ending_idx] = 1;
-            hs_data[task_idx].ending_logprob[ending_idx] = std::log(first_probs[query_embd[0]]);
+            // restore the original token in case it was set to BOS
+            tokens[batch_start] = token_org;
 
-            // Calculate the logprobs over the ending
-            for (size_t j = 0; j < query_size - 1; j++) {
-                std::memcpy(tok_logits.data(), logits.data() + j*n_vocab, n_vocab*sizeof(float));
-
-                const float prob = softmax(tok_logits)[query_embd[j + 1]];
-
-                hs_data[task_idx].ending_logprob[ending_idx] += std::log(prob);
-                hs_data[task_idx].ending_logprob_count[ending_idx]++;
-            }
-
-            // Calculate the mean token logprob for acc_norm
-            hs_data[task_idx].ending_logprob[ending_idx] /= hs_data[task_idx].ending_logprob_count[ending_idx];
-
-
-//            printf("task %lu, ending %lu, whole_len %lu, context_len %lu, ending_logprob_count %lu, ending_logprob %.4f\n",
-//                task_idx,ending_idx,whole_size,context_size, hs_data[task_idx].ending_logprob_count[ending_idx], hs_data[task_idx].ending_logprob[ending_idx] );
-        }
-
-        // Find the ending with maximum logprob
-        size_t ending_logprob_max_idx = 0;
-        double ending_logprob_max_val = hs_data[task_idx].ending_logprob[0];
-        for (size_t j = 1; j < 4; j++) {
-            if (hs_data[task_idx].ending_logprob[j] > ending_logprob_max_val) {
-                ending_logprob_max_idx = j;
-                ending_logprob_max_val =  hs_data[task_idx].ending_logprob[j];
+            if (num_batches > 1) {
+                const auto * batch_logits = llama_get_logits(ctx);
+                logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
             }
         }
 
-//        printf("max logprob ending idx %lu, gold ending idx %lu\n", ending_logprob_max_idx, hs_data[task_idx].gold_ending_idx);
+        const auto t_end = std::chrono::high_resolution_clock::now();
 
-        // If the gold ending got the maximum logprobe add one accuracy point
-        if (ending_logprob_max_idx == hs_data[task_idx].gold_ending_idx) {
-            acc += 1.0;
+        if (i == 0) {
+            const float t_total = std::chrono::duration<float>(t_end - t_start).count();
+            fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total);
+            int total_seconds = (int)(t_total * n_chunk);
+            if (total_seconds >= 60*60) {
+                fprintf(stderr, "%d hours ", total_seconds / (60*60));
+                total_seconds = total_seconds % (60*60);
+            }
+            fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
+
+            printf("\nchunk        PPL          ln(PPL(Q)/PPL(base))          KL-Divergence           Same top\n");
         }
 
-        // Print the accumulated accuracy mean x 100
-        printf("%zu\t%.8lf\n",task_idx+1, acc/double(task_idx+1)*100.0);
+        const int first = n_ctx/2;
+        const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
+        process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
+                workers, log_probs_uint16, kld, kld_ptr);
+        kld_ptr += n_ctx - 1 - first;
+
+        auto ppl           = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count);
+        auto log_ppl_ratio = mean_and_uncertainty(kld.sum_nll_diff, kld.sum_nll_diff2, kld.count);
+        auto kl_div        = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count);
+        auto p_top = 1.*kld.n_same_top/kld.count;
+        auto d_p_top = sqrt(p_top*(1 - p_top)/(kld.count - 1));
+
+        printf("%4d    %10.4lf    %10.5lf ± %10.5f    %10.5f ± %10.5lf    %.5f ± %.5f\n", i+1, exp(ppl.first),
+                log_ppl_ratio.first, log_ppl_ratio.second, kl_div.first, kl_div.second,
+                p_top, d_p_top);
+
         fflush(stdout);
+
+        logits.clear();
     }
-
-    delete [] hs_data;
-
     printf("\n");
+
+    if (kld.count < 100) return; // we do not wish to do statistics on so few values
+
+    std::sort(kld_values.begin(), kld_values.end());
+
+    printf("===== KL-divergence statistics\n");
+    auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count);
+    printf("Average: %10.6f ±%10.6lf\n", kl_div.first, kl_div.second);
+    auto kld_median = kld_values.size()%2 == 0 ? 0.5f*(kld_values[kld_values.size()/2] + kld_values[kld_values.size()/2-1])
+                                               : kld_values[kld_values.size()/2];
+    printf("Median : %10.6f\n", kld_median);
+
+    auto percentile = [&kld_values] (float fraction) {
+        if (fraction <= 0) return kld_values.front();
+        if (fraction >= 1) return kld_values.back();
+        float p = fraction*(kld_values.size() - 1);
+        size_t ip = size_t(p); p -= ip;
+        return (1 - p)*kld_values[ip] + p*kld_values[std::min(ip+1, kld_values.size()-1)];
+    };
+
+    printf("Maximum: %10.6f\n", kld_values.back());
+    printf("KLD_99 : %10.6f\n", percentile(0.99f));
+    printf("KLD_95 : %10.6f\n", percentile(0.95f));
+    printf("KLD_90 : %10.6f\n", percentile(0.90f));
+
+    printf("Minimum: %10.6f\n", kld_values.front());
+    printf("KLD_01 : %10.6f\n", percentile(0.01f));
+    printf("KLD_05 : %10.6f\n", percentile(0.05f));
+    printf("KLD_10 : %10.6f\n", percentile(0.10f));
+
 }
 
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    params.n_batch = 512;
     if (!gpt_params_parse(argc, argv, params)) {
         return 1;
     }
 
     params.logits_all = true;
-    params.n_batch = std::min(params.n_batch, params.n_ctx);
+
+    const int32_t n_ctx = params.n_ctx;
+
+    const bool ppl = !params.hellaswag && !params.winogrande && !params.multiple_choice && !params.kl_divergence;
+    if (ppl) {
+        int n_seq = std::max(1, params.n_batch / n_ctx);
+        int32_t n_kv = n_seq * n_ctx;
+        params.n_parallel = n_seq;
+        params.n_ctx = n_kv;
+        params.n_batch = std::min(params.n_batch, n_kv);
+    } else {
+        params.n_batch = std::min(params.n_batch, params.n_ctx);
+    }
 
     if (params.ppl_stride > 0) {
         fprintf(stderr, "Will perform strided perplexity calculation -> adjusting context size from %d to %d\n",
@@ -705,11 +1888,15 @@ int main(int argc, char ** argv) {
         params.prompt = gpt_random_prompt(rng);
     }
 
-    llama_backend_init(params.numa);
+    llama_backend_init();
+    llama_numa_init(params.numa);
 
     llama_model * model;
     llama_context * ctx;
 
+    // ensure there's at least enough seq_ids for HellaSwag
+    params.n_parallel = std::max(4, params.n_parallel);
+
     // load the model and apply lora adapter, if any
     std::tie(model, ctx) = llama_init_from_gpt_params(params);
     if (model == NULL) {
@@ -732,8 +1919,14 @@ int main(int argc, char ** argv) {
     struct results_perplexity results;
     if (params.hellaswag) {
         hellaswag_score(ctx, params);
+    } else if (params.winogrande) {
+        winogrande_score(ctx, params);
+    } else if (params.multiple_choice) {
+        multiple_choice_score(ctx, params);
+    } else if (params.kl_divergence) {
+        kl_divergence(ctx, params);
     } else {
-        results = perplexity(ctx, params);
+        results = perplexity(ctx, params, n_ctx);
     }
 
     llama_print_timings(ctx);
diff --git a/examples/pydantic-models-to-grammar-examples.py b/examples/pydantic-models-to-grammar-examples.py
new file mode 100644
index 000000000..160966649
--- /dev/null
+++ b/examples/pydantic-models-to-grammar-examples.py
@@ -0,0 +1,224 @@
+# Function calling example using pydantic models.
+import datetime
+import importlib
+import json
+from enum import Enum
+from typing import Optional, Union
+
+import requests
+from pydantic import BaseModel, Field
+from pydantic_models_to_grammar import (add_run_method_to_dynamic_model, convert_dictionary_to_pydantic_model,
+                                        create_dynamic_model_from_function, generate_gbnf_grammar_and_documentation)
+
+
+# Function to get completion on the llama.cpp server with grammar.
+def create_completion(prompt, grammar):
+    headers = {"Content-Type": "application/json"}
+    data = {"prompt": prompt, "grammar": grammar}
+
+    response = requests.post("http://127.0.0.1:8080/completion", headers=headers, json=data)
+    data = response.json()
+
+    print(data["content"])
+    return data["content"]
+
+
+# A function for the agent to send a message to the user.
+class SendMessageToUser(BaseModel):
+    """
+    Send a message to the User.
+    """
+    chain_of_thought: str = Field(..., description="Your chain of thought while sending the message.")
+    message: str = Field(..., description="Message you want to send to the user.")
+
+    def run(self):
+        print(self.message)
+
+
+# Enum for the calculator tool.
+class MathOperation(Enum):
+    ADD = "add"
+    SUBTRACT = "subtract"
+    MULTIPLY = "multiply"
+    DIVIDE = "divide"
+
+
+# Simple pydantic calculator tool for the agent that can add, subtract, multiply, and divide. Docstring and description of fields will be used in system prompt.
+class Calculator(BaseModel):
+    """
+    Perform a math operation on two numbers.
+    """
+    number_one: Union[int, float] = Field(..., description="First number.")
+    operation: MathOperation = Field(..., description="Math operation to perform.")
+    number_two: Union[int, float] = Field(..., description="Second number.")
+
+    def run(self):
+        if self.operation == MathOperation.ADD:
+            return self.number_one + self.number_two
+        elif self.operation == MathOperation.SUBTRACT:
+            return self.number_one - self.number_two
+        elif self.operation == MathOperation.MULTIPLY:
+            return self.number_one * self.number_two
+        elif self.operation == MathOperation.DIVIDE:
+            return self.number_one / self.number_two
+        else:
+            raise ValueError("Unknown operation.")
+
+
+# Here the grammar gets generated by passing the available function models to generate_gbnf_grammar_and_documentation function. This also generates a documentation usable by the LLM.
+# pydantic_model_list is the list of pydanitc models
+# outer_object_name is an optional name for an outer object around the actual model object. Like a "function" object with "function_parameters" which contains the actual model object. If None, no outer object will be generated
+# outer_object_content is the name of outer object content.
+# model_prefix is the optional prefix for models in the documentation. (Default="Output Model")
+# fields_prefix is the prefix for the model fields in the documentation. (Default="Output Fields")
+gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation(
+    pydantic_model_list=[SendMessageToUser, Calculator], outer_object_name="function",
+    outer_object_content="function_parameters", model_prefix="Function", fields_prefix="Parameters")
+
+print(gbnf_grammar)
+print(documentation)
+
+system_message = "You are an advanced AI, tasked to assist the user by calling functions in JSON format. The following are the available functions and their parameters and types:\n\n" + documentation
+
+user_message = "What is 42 * 42?"
+prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant"
+
+text = create_completion(prompt=prompt, grammar=gbnf_grammar)
+# This should output something like this:
+# {
+#     "function": "calculator",
+#     "function_parameters": {
+#         "number_one": 42,
+#         "operation": "multiply",
+#         "number_two": 42
+#     }
+# }
+function_dictionary = json.loads(text)
+if function_dictionary["function"] == "calculator":
+    function_parameters = {**function_dictionary["function_parameters"]}
+
+    print(Calculator(**function_parameters).run())
+    # This should output: 1764
+
+
+# A example structured output based on pydantic models. The LLM will create an entry for a Book database out of an unstructured text.
+class Category(Enum):
+    """
+    The category of the book.
+    """
+    Fiction = "Fiction"
+    NonFiction = "Non-Fiction"
+
+
+class Book(BaseModel):
+    """
+    Represents an entry about a book.
+    """
+    title: str = Field(..., description="Title of the book.")
+    author: str = Field(..., description="Author of the book.")
+    published_year: Optional[int] = Field(..., description="Publishing year of the book.")
+    keywords: list[str] = Field(..., description="A list of keywords.")
+    category: Category = Field(..., description="Category of the book.")
+    summary: str = Field(..., description="Summary of the book.")
+
+
+# We need no additional parameters other than our list of pydantic models.
+gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation([Book])
+
+system_message = "You are an advanced AI, tasked to create a dataset entry in JSON for a Book. The following is the expected output model:\n\n" + documentation
+
+text = """The Feynman Lectures on Physics is a physics textbook based on some lectures by Richard Feynman, a Nobel laureate who has sometimes been called "The Great Explainer". The lectures were presented before undergraduate students at the California Institute of Technology (Caltech), during 1961–1963. The book's co-authors are Feynman, Robert B. Leighton, and Matthew Sands."""
+prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant"
+
+text = create_completion(prompt=prompt, grammar=gbnf_grammar)
+
+json_data = json.loads(text)
+
+print(Book(**json_data))
+# An example for parallel function calling with a Python function, a pydantic function model and an OpenAI like function definition.
+
+def get_current_datetime(output_format: Optional[str] = None):
+    """
+    Get the current date and time in the given format.
+    Args:
+         output_format: formatting string for the date and time, defaults to '%Y-%m-%d %H:%M:%S'
+    """
+    if output_format is None:
+        output_format = '%Y-%m-%d %H:%M:%S'
+    return datetime.datetime.now().strftime(output_format)
+
+
+# Example function to get the weather
+def get_current_weather(location, unit):
+    """Get the current weather in a given location"""
+    if "London" in location:
+        return json.dumps({"location": "London", "temperature": "42", "unit": unit.value})
+    elif "New York" in location:
+        return json.dumps({"location": "New York", "temperature": "24", "unit": unit.value})
+    elif "North Pole" in location:
+        return json.dumps({"location": "North Pole", "temperature": "-42", "unit": unit.value})
+    else:
+        return json.dumps({"location": location, "temperature": "unknown"})
+
+
+# Here is a function definition in OpenAI style
+current_weather_tool = {
+    "type": "function",
+    "function": {
+        "name": "get_current_weather",
+        "description": "Get the current weather in a given location",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "location": {
+                    "type": "string",
+                    "description": "The city and state, e.g. San Francisco, CA",
+                },
+                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+            },
+            "required": ["location"],
+        },
+    },
+}
+
+# Convert OpenAI function definition into pydantic model
+current_weather_tool_model = convert_dictionary_to_pydantic_model(current_weather_tool)
+# Add the actual function to a pydantic model
+current_weather_tool_model = add_run_method_to_dynamic_model(current_weather_tool_model, get_current_weather)
+
+# Convert normal Python function to a pydantic model
+current_datetime_model = create_dynamic_model_from_function(get_current_datetime)
+
+tool_list = [SendMessageToUser, Calculator, current_datetime_model, current_weather_tool_model]
+
+
+gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation(
+    pydantic_model_list=tool_list, outer_object_name="function",
+    outer_object_content="params", model_prefix="Function", fields_prefix="Parameters", list_of_outputs=True)
+
+system_message = "You are an advanced AI assistant. You are interacting with the user and with your environment by calling functions. You call functions by writing JSON objects, which represent specific function calls.\nBelow is a list of your available function calls:\n\n" + documentation
+
+
+text = """Get the date and time, get the current weather in celsius in London and solve the following calculation: 42 * 42"""
+prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant"
+
+text = create_completion(prompt=prompt, grammar=gbnf_grammar)
+
+json_data = json.loads(text)
+
+print(json_data)
+# Should output something like this:
+# [{'function': 'get_current_datetime', 'params': {'output_format': '%Y-%m-%d %H:%M:%S'}}, {'function': 'get_current_weather', 'params': {'location': 'London', 'unit': 'celsius'}}, {'function': 'Calculator', 'params': {'number_one': 42, 'operation': 'multiply', 'number_two': 42}}]
+
+
+for call in json_data:
+    if call["function"] == "Calculator":
+        print(Calculator(**call["params"]).run())
+    elif call["function"] == "get_current_datetime":
+        print(current_datetime_model(**call["params"]).run())
+    elif call["function"] == "get_current_weather":
+        print(current_weather_tool_model(**call["params"]).run())
+# Should output something like this:
+# 2024-01-14 13:36:06
+# {"location": "London", "temperature": "42", "unit": "celsius"}
+# 1764
diff --git a/examples/pydantic_models_to_grammar.py b/examples/pydantic_models_to_grammar.py
new file mode 100644
index 000000000..9acc7cc6d
--- /dev/null
+++ b/examples/pydantic_models_to_grammar.py
@@ -0,0 +1,1310 @@
+from __future__ import annotations
+
+import inspect
+import json
+import re
+from copy import copy
+from enum import Enum
+from inspect import getdoc, isclass
+from typing import TYPE_CHECKING, Any, Callable, List, Optional, Union, get_args, get_origin, get_type_hints
+
+from docstring_parser import parse
+from pydantic import BaseModel, Field, create_model
+
+if TYPE_CHECKING:
+    from types import GenericAlias
+else:
+    # python 3.8 compat
+    from typing import _GenericAlias as GenericAlias
+
+
+class PydanticDataType(Enum):
+    """
+    Defines the data types supported by the grammar_generator.
+
+    Attributes:
+        STRING (str): Represents a string data type.
+        BOOLEAN (str): Represents a boolean data type.
+        INTEGER (str): Represents an integer data type.
+        FLOAT (str): Represents a float data type.
+        OBJECT (str): Represents an object data type.
+        ARRAY (str): Represents an array data type.
+        ENUM (str): Represents an enum data type.
+        CUSTOM_CLASS (str): Represents a custom class data type.
+    """
+
+    STRING = "string"
+    TRIPLE_QUOTED_STRING = "triple_quoted_string"
+    MARKDOWN_CODE_BLOCK = "markdown_code_block"
+    BOOLEAN = "boolean"
+    INTEGER = "integer"
+    FLOAT = "float"
+    OBJECT = "object"
+    ARRAY = "array"
+    ENUM = "enum"
+    ANY = "any"
+    NULL = "null"
+    CUSTOM_CLASS = "custom-class"
+    CUSTOM_DICT = "custom-dict"
+    SET = "set"
+
+
+def map_pydantic_type_to_gbnf(pydantic_type: type[Any]) -> str:
+    if isclass(pydantic_type) and issubclass(pydantic_type, str):
+        return PydanticDataType.STRING.value
+    elif isclass(pydantic_type) and issubclass(pydantic_type, bool):
+        return PydanticDataType.BOOLEAN.value
+    elif isclass(pydantic_type) and issubclass(pydantic_type, int):
+        return PydanticDataType.INTEGER.value
+    elif isclass(pydantic_type) and issubclass(pydantic_type, float):
+        return PydanticDataType.FLOAT.value
+    elif isclass(pydantic_type) and issubclass(pydantic_type, Enum):
+        return PydanticDataType.ENUM.value
+
+    elif isclass(pydantic_type) and issubclass(pydantic_type, BaseModel):
+        return format_model_and_field_name(pydantic_type.__name__)
+    elif get_origin(pydantic_type) is list:
+        element_type = get_args(pydantic_type)[0]
+        return f"{map_pydantic_type_to_gbnf(element_type)}-list"
+    elif get_origin(pydantic_type) is set:
+        element_type = get_args(pydantic_type)[0]
+        return f"{map_pydantic_type_to_gbnf(element_type)}-set"
+    elif get_origin(pydantic_type) is Union:
+        union_types = get_args(pydantic_type)
+        union_rules = [map_pydantic_type_to_gbnf(ut) for ut in union_types]
+        return f"union-{'-or-'.join(union_rules)}"
+    elif get_origin(pydantic_type) is Optional:
+        element_type = get_args(pydantic_type)[0]
+        return f"optional-{map_pydantic_type_to_gbnf(element_type)}"
+    elif isclass(pydantic_type):
+        return f"{PydanticDataType.CUSTOM_CLASS.value}-{format_model_and_field_name(pydantic_type.__name__)}"
+    elif get_origin(pydantic_type) is dict:
+        key_type, value_type = get_args(pydantic_type)
+        return f"custom-dict-key-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(key_type))}-value-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(value_type))}"
+    else:
+        return "unknown"
+
+
+def format_model_and_field_name(model_name: str) -> str:
+    parts = re.findall("[A-Z][^A-Z]*", model_name)
+    if not parts:  # Check if the list is empty
+        return model_name.lower().replace("_", "-")
+    return "-".join(part.lower().replace("_", "-") for part in parts)
+
+
+def generate_list_rule(element_type):
+    """
+    Generate a GBNF rule for a list of a given element type.
+
+    :param element_type: The type of the elements in the list (e.g., 'string').
+    :return: A string representing the GBNF rule for a list of the given type.
+    """
+    rule_name = f"{map_pydantic_type_to_gbnf(element_type)}-list"
+    element_rule = map_pydantic_type_to_gbnf(element_type)
+    list_rule = rf'{rule_name} ::= "["  {element_rule} (","  {element_rule})* "]"'
+    return list_rule
+
+
+def get_members_structure(cls, rule_name):
+    if issubclass(cls, Enum):
+        # Handle Enum types
+        members = [f'"\\"{member.value}\\""' for name, member in cls.__members__.items()]
+        return f"{cls.__name__.lower()} ::= " + " | ".join(members)
+    if cls.__annotations__ and cls.__annotations__ != {}:
+        result = f'{rule_name} ::= "{{"'
+        # Modify this comprehension
+        members = [
+            f'  "\\"{name}\\"" ":"  {map_pydantic_type_to_gbnf(param_type)}'
+            for name, param_type in cls.__annotations__.items()
+            if name != "self"
+        ]
+
+        result += '"," '.join(members)
+        result += '  "}"'
+        return result
+    if rule_name == "custom-class-any":
+        result = f"{rule_name} ::= "
+        result += "value"
+        return result
+
+    init_signature = inspect.signature(cls.__init__)
+    parameters = init_signature.parameters
+    result = f'{rule_name} ::=  "{{"'
+    # Modify this comprehension too
+    members = [
+        f'  "\\"{name}\\"" ":"  {map_pydantic_type_to_gbnf(param.annotation)}'
+        for name, param in parameters.items()
+        if name != "self" and param.annotation != inspect.Parameter.empty
+    ]
+
+    result += '", "'.join(members)
+    result += '  "}"'
+    return result
+
+
+def regex_to_gbnf(regex_pattern: str) -> str:
+    """
+    Translate a basic regex pattern to a GBNF rule.
+    Note: This function handles only a subset of simple regex patterns.
+    """
+    gbnf_rule = regex_pattern
+
+    # Translate common regex components to GBNF
+    gbnf_rule = gbnf_rule.replace("\\d", "[0-9]")
+    gbnf_rule = gbnf_rule.replace("\\s", "[ \t\n]")
+
+    # Handle quantifiers and other regex syntax that is similar in GBNF
+    # (e.g., '*', '+', '?', character classes)
+
+    return gbnf_rule
+
+
+def generate_gbnf_integer_rules(max_digit=None, min_digit=None):
+    """
+
+    Generate GBNF Integer Rules
+
+    Generates GBNF (Generalized Backus-Naur Form) rules for integers based on the given maximum and minimum digits.
+
+    Parameters:
+        max_digit (int): The maximum number of digits for the integer. Default is None.
+        min_digit (int): The minimum number of digits for the integer. Default is None.
+
+    Returns:
+        integer_rule (str): The identifier for the integer rule generated.
+        additional_rules (list): A list of additional rules generated based on the given maximum and minimum digits.
+
+    """
+    additional_rules = []
+
+    # Define the rule identifier based on max_digit and min_digit
+    integer_rule = "integer-part"
+    if max_digit is not None:
+        integer_rule += f"-max{max_digit}"
+    if min_digit is not None:
+        integer_rule += f"-min{min_digit}"
+
+    # Handling Integer Rules
+    if max_digit is not None or min_digit is not None:
+        # Start with an empty rule part
+        integer_rule_part = ""
+
+        # Add mandatory digits as per min_digit
+        if min_digit is not None:
+            integer_rule_part += "[0-9] " * min_digit
+
+        # Add optional digits up to max_digit
+        if max_digit is not None:
+            optional_digits = max_digit - (min_digit if min_digit is not None else 0)
+            integer_rule_part += "".join(["[0-9]? " for _ in range(optional_digits)])
+
+        # Trim the rule part and append it to additional rules
+        integer_rule_part = integer_rule_part.strip()
+        if integer_rule_part:
+            additional_rules.append(f"{integer_rule} ::= {integer_rule_part}")
+
+    return integer_rule, additional_rules
+
+
+def generate_gbnf_float_rules(max_digit=None, min_digit=None, max_precision=None, min_precision=None):
+    """
+    Generate GBNF float rules based on the given constraints.
+
+    :param max_digit: Maximum number of digits in the integer part (default: None)
+    :param min_digit: Minimum number of digits in the integer part (default: None)
+    :param max_precision: Maximum number of digits in the fractional part (default: None)
+    :param min_precision: Minimum number of digits in the fractional part (default: None)
+    :return: A tuple containing the float rule and additional rules as a list
+
+    Example Usage:
+    max_digit = 3
+    min_digit = 1
+    max_precision = 2
+    min_precision = 1
+    generate_gbnf_float_rules(max_digit, min_digit, max_precision, min_precision)
+
+    Output:
+    ('float-3-1-2-1', ['integer-part-max3-min1 ::= [0-9] [0-9] [0-9]?', 'fractional-part-max2-min1 ::= [0-9] [0-9]?', 'float-3-1-2-1 ::= integer-part-max3-min1 "." fractional-part-max2-min
+    *1'])
+
+    Note:
+    GBNF stands for Generalized Backus-Naur Form, which is a notation technique to specify the syntax of programming languages or other formal grammars.
+    """
+    additional_rules = []
+
+    # Define the integer part rule
+    integer_part_rule = (
+        "integer-part" + (f"-max{max_digit}" if max_digit is not None else "") + (
+        f"-min{min_digit}" if min_digit is not None else "")
+    )
+
+    # Define the fractional part rule based on precision constraints
+    fractional_part_rule = "fractional-part"
+    fractional_rule_part = ""
+    if max_precision is not None or min_precision is not None:
+        fractional_part_rule += (f"-max{max_precision}" if max_precision is not None else "") + (
+            f"-min{min_precision}" if min_precision is not None else ""
+        )
+        # Minimum number of digits
+        fractional_rule_part = "[0-9]" * (min_precision if min_precision is not None else 1)
+        # Optional additional digits
+        fractional_rule_part += "".join(
+            [" [0-9]?"] * ((max_precision - (
+                min_precision if min_precision is not None else 1)) if max_precision is not None else 0)
+        )
+        additional_rules.append(f"{fractional_part_rule} ::= {fractional_rule_part}")
+
+    # Define the float rule
+    float_rule = f"float-{max_digit if max_digit is not None else 'X'}-{min_digit if min_digit is not None else 'X'}-{max_precision if max_precision is not None else 'X'}-{min_precision if min_precision is not None else 'X'}"
+    additional_rules.append(f'{float_rule} ::= {integer_part_rule} "." {fractional_part_rule}')
+
+    # Generating the integer part rule definition, if necessary
+    if max_digit is not None or min_digit is not None:
+        integer_rule_part = "[0-9]"
+        if min_digit is not None and min_digit > 1:
+            integer_rule_part += " [0-9]" * (min_digit - 1)
+        if max_digit is not None:
+            integer_rule_part += "".join([" [0-9]?"] * (max_digit - (min_digit if min_digit is not None else 1)))
+        additional_rules.append(f"{integer_part_rule} ::= {integer_rule_part.strip()}")
+
+    return float_rule, additional_rules
+
+
+def generate_gbnf_rule_for_type(
+    model_name, field_name, field_type, is_optional, processed_models, created_rules, field_info=None
+) -> tuple[str, list[str]]:
+    """
+    Generate GBNF rule for a given field type.
+
+    :param model_name: Name of the model.
+
+    :param field_name: Name of the field.
+    :param field_type: Type of the field.
+    :param is_optional: Whether the field is optional.
+    :param processed_models: List of processed models.
+    :param created_rules: List of created rules.
+    :param field_info: Additional information about the field (optional).
+
+    :return: Tuple containing the GBNF type and a list of additional rules.
+    :rtype: tuple[str, list]
+    """
+    rules = []
+
+    field_name = format_model_and_field_name(field_name)
+    gbnf_type = map_pydantic_type_to_gbnf(field_type)
+
+    if isclass(field_type) and issubclass(field_type, BaseModel):
+        nested_model_name = format_model_and_field_name(field_type.__name__)
+        nested_model_rules, _ = generate_gbnf_grammar(field_type, processed_models, created_rules)
+        rules.extend(nested_model_rules)
+        gbnf_type, rules = nested_model_name, rules
+    elif isclass(field_type) and issubclass(field_type, Enum):
+        enum_values = [f'"\\"{e.value}\\""' for e in field_type]  # Adding escaped quotes
+        enum_rule = f"{model_name}-{field_name} ::= {' | '.join(enum_values)}"
+        rules.append(enum_rule)
+        gbnf_type, rules = model_name + "-" + field_name, rules
+    elif get_origin(field_type) == list:  # Array
+        element_type = get_args(field_type)[0]
+        element_rule_name, additional_rules = generate_gbnf_rule_for_type(
+            model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules
+        )
+        rules.extend(additional_rules)
+        array_rule = f"""{model_name}-{field_name} ::= "[" ws {element_rule_name} ("," ws {element_rule_name})*  "]" """
+        rules.append(array_rule)
+        gbnf_type, rules = model_name + "-" + field_name, rules
+
+    elif get_origin(field_type) == set or field_type == set:  # Array
+        element_type = get_args(field_type)[0]
+        element_rule_name, additional_rules = generate_gbnf_rule_for_type(
+            model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules
+        )
+        rules.extend(additional_rules)
+        array_rule = f"""{model_name}-{field_name} ::= "[" ws {element_rule_name} ("," ws {element_rule_name})*  "]" """
+        rules.append(array_rule)
+        gbnf_type, rules = model_name + "-" + field_name, rules
+
+    elif gbnf_type.startswith("custom-class-"):
+        rules.append(get_members_structure(field_type, gbnf_type))
+    elif gbnf_type.startswith("custom-dict-"):
+        key_type, value_type = get_args(field_type)
+
+        additional_key_type, additional_key_rules = generate_gbnf_rule_for_type(
+            model_name, f"{field_name}-key-type", key_type, is_optional, processed_models, created_rules
+        )
+        additional_value_type, additional_value_rules = generate_gbnf_rule_for_type(
+            model_name, f"{field_name}-value-type", value_type, is_optional, processed_models, created_rules
+        )
+        gbnf_type = rf'{gbnf_type} ::= "{{"  ( {additional_key_type} ": "  {additional_value_type} ("," "\n" ws {additional_key_type} ":"  {additional_value_type})*  )? "}}" '
+
+        rules.extend(additional_key_rules)
+        rules.extend(additional_value_rules)
+    elif gbnf_type.startswith("union-"):
+        union_types = get_args(field_type)
+        union_rules = []
+
+        for union_type in union_types:
+            if isinstance(union_type, GenericAlias):
+                union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type(
+                    model_name, field_name, union_type, False, processed_models, created_rules
+                )
+                union_rules.append(union_gbnf_type)
+                rules.extend(union_rules_list)
+
+            elif not issubclass(union_type, type(None)):
+                union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type(
+                    model_name, field_name, union_type, False, processed_models, created_rules
+                )
+                union_rules.append(union_gbnf_type)
+                rules.extend(union_rules_list)
+
+        # Defining the union grammar rule separately
+        if len(union_rules) == 1:
+            union_grammar_rule = f"{model_name}-{field_name}-optional ::= {' | '.join(union_rules)} | null"
+        else:
+            union_grammar_rule = f"{model_name}-{field_name}-union ::= {' | '.join(union_rules)}"
+        rules.append(union_grammar_rule)
+        if len(union_rules) == 1:
+            gbnf_type = f"{model_name}-{field_name}-optional"
+        else:
+            gbnf_type = f"{model_name}-{field_name}-union"
+    elif isclass(field_type) and issubclass(field_type, str):
+        if field_info and hasattr(field_info, "json_schema_extra") and field_info.json_schema_extra is not None:
+            triple_quoted_string = field_info.json_schema_extra.get("triple_quoted_string", False)
+            markdown_string = field_info.json_schema_extra.get("markdown_code_block", False)
+
+            gbnf_type = PydanticDataType.TRIPLE_QUOTED_STRING.value if triple_quoted_string else PydanticDataType.STRING.value
+            gbnf_type = PydanticDataType.MARKDOWN_CODE_BLOCK.value if markdown_string else gbnf_type
+
+        elif field_info and hasattr(field_info, "pattern"):
+            # Convert regex pattern to grammar rule
+            regex_pattern = field_info.regex.pattern
+            gbnf_type = f"pattern-{field_name} ::= {regex_to_gbnf(regex_pattern)}"
+        else:
+            gbnf_type = PydanticDataType.STRING.value
+
+    elif (
+        isclass(field_type)
+        and issubclass(field_type, float)
+        and field_info
+        and hasattr(field_info, "json_schema_extra")
+        and field_info.json_schema_extra is not None
+    ):
+        # Retrieve precision attributes for floats
+        max_precision = (
+            field_info.json_schema_extra.get("max_precision") if field_info and hasattr(field_info,
+                                                                                        "json_schema_extra") else None
+        )
+        min_precision = (
+            field_info.json_schema_extra.get("min_precision") if field_info and hasattr(field_info,
+                                                                                        "json_schema_extra") else None
+        )
+        max_digits = field_info.json_schema_extra.get("max_digit") if field_info and hasattr(field_info,
+                                                                                             "json_schema_extra") else None
+        min_digits = field_info.json_schema_extra.get("min_digit") if field_info and hasattr(field_info,
+                                                                                             "json_schema_extra") else None
+
+        # Generate GBNF rule for float with given attributes
+        gbnf_type, rules = generate_gbnf_float_rules(
+            max_digit=max_digits, min_digit=min_digits, max_precision=max_precision, min_precision=min_precision
+        )
+
+    elif (
+        isclass(field_type)
+        and issubclass(field_type, int)
+        and field_info
+        and hasattr(field_info, "json_schema_extra")
+        and field_info.json_schema_extra is not None
+    ):
+        # Retrieve digit attributes for integers
+        max_digits = field_info.json_schema_extra.get("max_digit") if field_info and hasattr(field_info,
+                                                                                             "json_schema_extra") else None
+        min_digits = field_info.json_schema_extra.get("min_digit") if field_info and hasattr(field_info,
+                                                                                             "json_schema_extra") else None
+
+        # Generate GBNF rule for integer with given attributes
+        gbnf_type, rules = generate_gbnf_integer_rules(max_digit=max_digits, min_digit=min_digits)
+    else:
+        gbnf_type, rules = gbnf_type, []
+
+    return gbnf_type, rules
+
+
+def generate_gbnf_grammar(model: type[BaseModel], processed_models: set[type[BaseModel]], created_rules: dict[str, list[str]]) -> tuple[list[str], bool]:
+    """
+
+    Generate GBnF Grammar
+
+    Generates a GBnF grammar for a given model.
+
+    :param model: A Pydantic model class to generate the grammar for. Must be a subclass of BaseModel.
+    :param processed_models: A set of already processed models to prevent infinite recursion.
+    :param created_rules: A dict containing already created rules to prevent duplicates.
+    :return: A list of GBnF grammar rules in string format. And two booleans indicating if an extra markdown or triple quoted string is in the grammar.
+    Example Usage:
+    ```
+    model = MyModel
+    processed_models = set()
+    created_rules = dict()
+
+    gbnf_grammar = generate_gbnf_grammar(model, processed_models, created_rules)
+    ```
+    """
+    if model in processed_models:
+        return [], False
+
+    processed_models.add(model)
+    model_name = format_model_and_field_name(model.__name__)
+
+    if not issubclass(model, BaseModel):
+        # For non-Pydantic classes, generate model_fields from __annotations__ or __init__
+        if hasattr(model, "__annotations__") and model.__annotations__:
+            model_fields = {name: (typ, ...) for name, typ in model.__annotations__.items()}
+        else:
+            init_signature = inspect.signature(model.__init__)
+            parameters = init_signature.parameters
+            model_fields = {name: (param.annotation, param.default) for name, param in parameters.items() if
+                            name != "self"}
+    else:
+        # For Pydantic models, use model_fields and check for ellipsis (required fields)
+        model_fields = model.__annotations__
+
+    model_rule_parts = []
+    nested_rules = []
+    has_markdown_code_block = False
+    has_triple_quoted_string = False
+    look_for_markdown_code_block = False
+    look_for_triple_quoted_string = False
+    for field_name, field_info in model_fields.items():
+        if not issubclass(model, BaseModel):
+            field_type, default_value = field_info
+            # Check if the field is optional (not required)
+            is_optional = (default_value is not inspect.Parameter.empty) and (default_value is not Ellipsis)
+        else:
+            field_type = field_info
+            field_info = model.model_fields[field_name]
+            is_optional = field_info.is_required is False and get_origin(field_type) is Optional
+        rule_name, additional_rules = generate_gbnf_rule_for_type(
+            model_name, format_model_and_field_name(field_name), field_type, is_optional, processed_models,
+            created_rules, field_info
+        )
+        look_for_markdown_code_block = True if rule_name == "markdown_code_block" else False
+        look_for_triple_quoted_string = True if rule_name == "triple_quoted_string" else False
+        if not look_for_markdown_code_block and not look_for_triple_quoted_string:
+            if rule_name not in created_rules:
+                created_rules[rule_name] = additional_rules
+            model_rule_parts.append(f' ws "\\"{field_name}\\"" ":" ws {rule_name}')  # Adding escaped quotes
+            nested_rules.extend(additional_rules)
+        else:
+            has_triple_quoted_string = look_for_triple_quoted_string
+            has_markdown_code_block = look_for_markdown_code_block
+
+    fields_joined = r' "," "\n" '.join(model_rule_parts)
+    model_rule = rf'{model_name} ::= "{{" "\n" {fields_joined} "\n" ws "}}"'
+
+    has_special_string = False
+    if has_triple_quoted_string:
+        model_rule += '"\\n" ws "}"'
+        model_rule += '"\\n" triple-quoted-string'
+        has_special_string = True
+    if has_markdown_code_block:
+        model_rule += '"\\n" ws "}"'
+        model_rule += '"\\n" markdown-code-block'
+        has_special_string = True
+    all_rules = [model_rule] + nested_rules
+
+    return all_rules, has_special_string
+
+
+def generate_gbnf_grammar_from_pydantic_models(
+    models: list[type[BaseModel]], outer_object_name: str | None = None, outer_object_content: str | None = None,
+    list_of_outputs: bool = False
+) -> str:
+    """
+    Generate GBNF Grammar from Pydantic Models.
+
+    This method takes a list of Pydantic models and uses them to generate a GBNF grammar string. The generated grammar string can be used for parsing and validating data using the generated
+    * grammar.
+
+    Args:
+        models (list[type[BaseModel]]): A list of Pydantic models to generate the grammar from.
+        outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling.
+        outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling.
+        list_of_outputs (str, optional): Allows a list of output objects
+    Returns:
+        str: The generated GBNF grammar string.
+
+    Examples:
+        models = [UserModel, PostModel]
+        grammar = generate_gbnf_grammar_from_pydantic(models)
+        print(grammar)
+        # Output:
+        # root ::= UserModel | PostModel
+        # ...
+    """
+    processed_models: set[type[BaseModel]] = set()
+    all_rules = []
+    created_rules: dict[str, list[str]] = {}
+    if outer_object_name is None:
+        for model in models:
+            model_rules, _ = generate_gbnf_grammar(model, processed_models, created_rules)
+            all_rules.extend(model_rules)
+
+        if list_of_outputs:
+            root_rule = r'root ::= (" "| "\n") "[" ws grammar-models ("," ws grammar-models)* ws "]"' + "\n"
+        else:
+            root_rule = r'root ::= (" "| "\n") grammar-models' + "\n"
+        root_rule += "grammar-models ::= " + " | ".join(
+            [format_model_and_field_name(model.__name__) for model in models])
+        all_rules.insert(0, root_rule)
+        return "\n".join(all_rules)
+    elif outer_object_name is not None:
+        if list_of_outputs:
+            root_rule = (
+                rf'root ::= (" "| "\n") "[" ws {format_model_and_field_name(outer_object_name)} ("," ws {format_model_and_field_name(outer_object_name)})* ws "]"'
+                + "\n"
+            )
+        else:
+            root_rule = f"root ::= {format_model_and_field_name(outer_object_name)}\n"
+
+        model_rule = (
+            rf'{format_model_and_field_name(outer_object_name)} ::= (" "| "\n") "{{" ws "\"{outer_object_name}\""  ":" ws grammar-models'
+        )
+
+        fields_joined = " | ".join(
+            [rf"{format_model_and_field_name(model.__name__)}-grammar-model" for model in models])
+
+        grammar_model_rules = f"\ngrammar-models ::= {fields_joined}"
+        mod_rules = []
+        for model in models:
+            mod_rule = rf"{format_model_and_field_name(model.__name__)}-grammar-model ::= "
+            mod_rule += (
+                rf'"\"{model.__name__}\"" "," ws "\"{outer_object_content}\"" ":" ws {format_model_and_field_name(model.__name__)}' + "\n"
+            )
+            mod_rules.append(mod_rule)
+        grammar_model_rules += "\n" + "\n".join(mod_rules)
+
+        for model in models:
+            model_rules, has_special_string = generate_gbnf_grammar(model, processed_models,
+                                                                    created_rules)
+
+            if not has_special_string:
+                model_rules[0] += r'"\n" ws "}"'
+
+            all_rules.extend(model_rules)
+
+        all_rules.insert(0, root_rule + model_rule + grammar_model_rules)
+        return "\n".join(all_rules)
+
+
+def get_primitive_grammar(grammar):
+    """
+    Returns the needed GBNF primitive grammar for a given GBNF grammar string.
+
+    Args:
+        grammar (str): The string containing the GBNF grammar.
+
+    Returns:
+        str: GBNF primitive grammar string.
+    """
+    type_list: list[type[object]] = []
+    if "string-list" in grammar:
+        type_list.append(str)
+    if "boolean-list" in grammar:
+        type_list.append(bool)
+    if "integer-list" in grammar:
+        type_list.append(int)
+    if "float-list" in grammar:
+        type_list.append(float)
+    additional_grammar = [generate_list_rule(t) for t in type_list]
+    primitive_grammar = r"""
+boolean ::= "true" | "false"
+null ::= "null"
+string ::= "\"" (
+        [^"\\] |
+        "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+      )* "\"" ws
+ws ::= ([ \t\n] ws)?
+float ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
+
+integer ::= [0-9]+"""
+
+    any_block = ""
+    if "custom-class-any" in grammar:
+        any_block = """
+value ::= object | array | string | number | boolean | null
+
+object ::=
+  "{" ws (
+            string ":" ws value
+    ("," ws string ":" ws value)*
+  )? "}" ws
+
+array  ::=
+  "[" ws (
+            value
+    ("," ws value)*
+  )? "]" ws
+
+number ::= integer | float"""
+
+    markdown_code_block_grammar = ""
+    if "markdown-code-block" in grammar:
+        markdown_code_block_grammar = r'''
+markdown-code-block ::= opening-triple-ticks markdown-code-block-content closing-triple-ticks
+markdown-code-block-content ::= ( [^`] | "`" [^`] |  "`"  "`" [^`]  )*
+opening-triple-ticks ::= "```" "python" "\n" | "```" "c" "\n" | "```" "cpp" "\n" | "```" "txt" "\n" | "```" "text" "\n" | "```" "json" "\n" | "```" "javascript" "\n" | "```" "css" "\n" | "```" "html" "\n" | "```" "markdown" "\n"
+closing-triple-ticks ::= "```" "\n"'''
+
+    if "triple-quoted-string" in grammar:
+        markdown_code_block_grammar = r"""
+triple-quoted-string ::= triple-quotes triple-quoted-string-content triple-quotes
+triple-quoted-string-content ::= ( [^'] | "'" [^'] |  "'"  "'" [^']  )*
+triple-quotes ::= "'''" """
+    return "\n" + "\n".join(additional_grammar) + any_block + primitive_grammar + markdown_code_block_grammar
+
+
+def generate_markdown_documentation(
+    pydantic_models: list[type[BaseModel]], model_prefix="Model", fields_prefix="Fields",
+    documentation_with_field_description=True
+) -> str:
+    """
+    Generate markdown documentation for a list of Pydantic models.
+
+    Args:
+        pydantic_models (list[type[BaseModel]]): list of Pydantic model classes.
+        model_prefix (str): Prefix for the model section.
+        fields_prefix (str): Prefix for the fields section.
+        documentation_with_field_description (bool): Include field descriptions in the documentation.
+
+    Returns:
+        str: Generated text documentation.
+    """
+    documentation = ""
+    pyd_models = [(model, True) for model in pydantic_models]
+    for model, add_prefix in pyd_models:
+        if add_prefix:
+            documentation += f"{model_prefix}: {model.__name__}\n"
+        else:
+            documentation += f"Model: {model.__name__}\n"
+
+        # Handling multi-line model description with proper indentation
+
+        class_doc = getdoc(model)
+        base_class_doc = getdoc(BaseModel)
+        class_description = class_doc if class_doc and class_doc != base_class_doc else ""
+        if class_description != "":
+            documentation += "  Description: "
+            documentation += format_multiline_description(class_description, 0) + "\n"
+
+        if add_prefix:
+            # Indenting the fields section
+            documentation += f"  {fields_prefix}:\n"
+        else:
+            documentation += f"  Fields:\n"
+        if isclass(model) and issubclass(model, BaseModel):
+            for name, field_type in model.__annotations__.items():
+                # if name == "markdown_code_block":
+                #    continue
+                if get_origin(field_type) == list:
+                    element_type = get_args(field_type)[0]
+                    if isclass(element_type) and issubclass(element_type, BaseModel):
+                        pyd_models.append((element_type, False))
+                if get_origin(field_type) == Union:
+                    element_types = get_args(field_type)
+                    for element_type in element_types:
+                        if isclass(element_type) and issubclass(element_type, BaseModel):
+                            pyd_models.append((element_type, False))
+                documentation += generate_field_markdown(
+                    name, field_type, model, documentation_with_field_description=documentation_with_field_description
+                )
+            documentation += "\n"
+
+        if hasattr(model, "Config") and hasattr(model.Config,
+                                                "json_schema_extra") and "example" in model.Config.json_schema_extra:
+            documentation += f"  Expected Example Output for {format_model_and_field_name(model.__name__)}:\n"
+            json_example = json.dumps(model.Config.json_schema_extra["example"])
+            documentation += format_multiline_description(json_example, 2) + "\n"
+
+    return documentation
+
+
+def generate_field_markdown(
+    field_name: str, field_type: type[Any], model: type[BaseModel], depth=1,
+    documentation_with_field_description=True
+) -> str:
+    """
+    Generate markdown documentation for a Pydantic model field.
+
+    Args:
+        field_name (str): Name of the field.
+        field_type (type[Any]): Type of the field.
+        model (type[BaseModel]): Pydantic model class.
+        depth (int): Indentation depth in the documentation.
+        documentation_with_field_description (bool): Include field descriptions in the documentation.
+
+    Returns:
+        str: Generated text documentation for the field.
+    """
+    indent = "    " * depth
+
+    field_info = model.model_fields.get(field_name)
+    field_description = field_info.description if field_info and field_info.description else ""
+
+    if get_origin(field_type) == list:
+        element_type = get_args(field_type)[0]
+        field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)} of {format_model_and_field_name(element_type.__name__)})"
+        if field_description != "":
+            field_text += ":\n"
+        else:
+            field_text += "\n"
+    elif get_origin(field_type) == Union:
+        element_types = get_args(field_type)
+        types = []
+        for element_type in element_types:
+            types.append(format_model_and_field_name(element_type.__name__))
+        field_text = f"{indent}{field_name} ({' or '.join(types)})"
+        if field_description != "":
+            field_text += ":\n"
+        else:
+            field_text += "\n"
+    else:
+        field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)})"
+        if field_description != "":
+            field_text += ":\n"
+        else:
+            field_text += "\n"
+
+    if not documentation_with_field_description:
+        return field_text
+
+    if field_description != "":
+        field_text += f"        Description: " + field_description + "\n"
+
+    # Check for and include field-specific examples if available
+    if hasattr(model, "Config") and hasattr(model.Config,
+                                            "json_schema_extra") and "example" in model.Config.json_schema_extra:
+        field_example = model.Config.json_schema_extra["example"].get(field_name)
+        if field_example is not None:
+            example_text = f"'{field_example}'" if isinstance(field_example, str) else field_example
+            field_text += f"{indent}  Example: {example_text}\n"
+
+    if isclass(field_type) and issubclass(field_type, BaseModel):
+        field_text += f"{indent}  Details:\n"
+        for name, type_ in field_type.__annotations__.items():
+            field_text += generate_field_markdown(name, type_, field_type, depth + 2)
+
+    return field_text
+
+
+def format_json_example(example: dict[str, Any], depth: int) -> str:
+    """
+    Format a JSON example into a readable string with indentation.
+
+    Args:
+        example (dict): JSON example to be formatted.
+        depth (int): Indentation depth.
+
+    Returns:
+        str: Formatted JSON example string.
+    """
+    indent = "    " * depth
+    formatted_example = "{\n"
+    for key, value in example.items():
+        value_text = f"'{value}'" if isinstance(value, str) else value
+        formatted_example += f"{indent}{key}: {value_text},\n"
+    formatted_example = formatted_example.rstrip(",\n") + "\n" + indent + "}"
+    return formatted_example
+
+
+def generate_text_documentation(
+    pydantic_models: list[type[BaseModel]], model_prefix="Model", fields_prefix="Fields",
+    documentation_with_field_description=True
+) -> str:
+    """
+    Generate text documentation for a list of Pydantic models.
+
+    Args:
+        pydantic_models (list[type[BaseModel]]): List of Pydantic model classes.
+        model_prefix (str): Prefix for the model section.
+        fields_prefix (str): Prefix for the fields section.
+        documentation_with_field_description (bool): Include field descriptions in the documentation.
+
+    Returns:
+        str: Generated text documentation.
+    """
+    documentation = ""
+    pyd_models = [(model, True) for model in pydantic_models]
+    for model, add_prefix in pyd_models:
+        if add_prefix:
+            documentation += f"{model_prefix}: {model.__name__}\n"
+        else:
+            documentation += f"Model: {model.__name__}\n"
+
+        # Handling multi-line model description with proper indentation
+
+        class_doc = getdoc(model)
+        base_class_doc = getdoc(BaseModel)
+        class_description = class_doc if class_doc and class_doc != base_class_doc else ""
+        if class_description != "":
+            documentation += "  Description: "
+            documentation += "\n" + format_multiline_description(class_description, 2) + "\n"
+
+        if isclass(model) and issubclass(model, BaseModel):
+            documentation_fields = ""
+            for name, field_type in model.__annotations__.items():
+                # if name == "markdown_code_block":
+                #    continue
+                if get_origin(field_type) == list:
+                    element_type = get_args(field_type)[0]
+                    if isclass(element_type) and issubclass(element_type, BaseModel):
+                        pyd_models.append((element_type, False))
+                if get_origin(field_type) == Union:
+                    element_types = get_args(field_type)
+                    for element_type in element_types:
+                        if isclass(element_type) and issubclass(element_type, BaseModel):
+                            pyd_models.append((element_type, False))
+                documentation_fields += generate_field_text(
+                    name, field_type, model, documentation_with_field_description=documentation_with_field_description
+                )
+            if documentation_fields != "":
+                if add_prefix:
+                    documentation += f"  {fields_prefix}:\n{documentation_fields}"
+                else:
+                    documentation += f"  Fields:\n{documentation_fields}"
+            documentation += "\n"
+
+        if hasattr(model, "Config") and hasattr(model.Config,
+                                                "json_schema_extra") and "example" in model.Config.json_schema_extra:
+            documentation += f"  Expected Example Output for {format_model_and_field_name(model.__name__)}:\n"
+            json_example = json.dumps(model.Config.json_schema_extra["example"])
+            documentation += format_multiline_description(json_example, 2) + "\n"
+
+    return documentation
+
+
+def generate_field_text(
+    field_name: str, field_type: type[Any], model: type[BaseModel], depth=1,
+    documentation_with_field_description=True
+) -> str:
+    """
+    Generate text documentation for a Pydantic model field.
+
+    Args:
+        field_name (str): Name of the field.
+        field_type (type[Any]): Type of the field.
+        model (type[BaseModel]): Pydantic model class.
+        depth (int): Indentation depth in the documentation.
+        documentation_with_field_description (bool): Include field descriptions in the documentation.
+
+    Returns:
+        str: Generated text documentation for the field.
+    """
+    indent = "    " * depth
+
+    field_info = model.model_fields.get(field_name)
+    field_description = field_info.description if field_info and field_info.description else ""
+
+    if get_origin(field_type) == list:
+        element_type = get_args(field_type)[0]
+        field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)} of {format_model_and_field_name(element_type.__name__)})"
+        if field_description != "":
+            field_text += ":\n"
+        else:
+            field_text += "\n"
+    elif get_origin(field_type) == Union:
+        element_types = get_args(field_type)
+        types = []
+        for element_type in element_types:
+            types.append(format_model_and_field_name(element_type.__name__))
+        field_text = f"{indent}{field_name} ({' or '.join(types)})"
+        if field_description != "":
+            field_text += ":\n"
+        else:
+            field_text += "\n"
+    else:
+        field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)})"
+        if field_description != "":
+            field_text += ":\n"
+        else:
+            field_text += "\n"
+
+    if not documentation_with_field_description:
+        return field_text
+
+    if field_description != "":
+        field_text += f"{indent}  Description: " + field_description + "\n"
+
+    # Check for and include field-specific examples if available
+    if hasattr(model, "Config") and hasattr(model.Config,
+                                            "json_schema_extra") and "example" in model.Config.json_schema_extra:
+        field_example = model.Config.json_schema_extra["example"].get(field_name)
+        if field_example is not None:
+            example_text = f"'{field_example}'" if isinstance(field_example, str) else field_example
+            field_text += f"{indent}  Example: {example_text}\n"
+
+    if isclass(field_type) and issubclass(field_type, BaseModel):
+        field_text += f"{indent}  Details:\n"
+        for name, type_ in field_type.__annotations__.items():
+            field_text += generate_field_text(name, type_, field_type, depth + 2)
+
+    return field_text
+
+
+def format_multiline_description(description: str, indent_level: int) -> str:
+    """
+    Format a multiline description with proper indentation.
+
+    Args:
+        description (str): Multiline description.
+        indent_level (int): Indentation level.
+
+    Returns:
+        str: Formatted multiline description.
+    """
+    indent = "    " * indent_level
+    return indent + description.replace("\n", "\n" + indent)
+
+
+def save_gbnf_grammar_and_documentation(
+    grammar, documentation, grammar_file_path="./grammar.gbnf", documentation_file_path="./grammar_documentation.md"
+):
+    """
+    Save GBNF grammar and documentation to specified files.
+
+    Args:
+        grammar (str): GBNF grammar string.
+        documentation (str): Documentation string.
+        grammar_file_path (str): File path to save the GBNF grammar.
+        documentation_file_path (str): File path to save the documentation.
+
+    Returns:
+        None
+    """
+    try:
+        with open(grammar_file_path, "w") as file:
+            file.write(grammar + get_primitive_grammar(grammar))
+        print(f"Grammar successfully saved to {grammar_file_path}")
+    except IOError as e:
+        print(f"An error occurred while saving the grammar file: {e}")
+
+    try:
+        with open(documentation_file_path, "w") as file:
+            file.write(documentation)
+        print(f"Documentation successfully saved to {documentation_file_path}")
+    except IOError as e:
+        print(f"An error occurred while saving the documentation file: {e}")
+
+
+def remove_empty_lines(string):
+    """
+    Remove empty lines from a string.
+
+    Args:
+        string (str): Input string.
+
+    Returns:
+        str: String with empty lines removed.
+    """
+    lines = string.splitlines()
+    non_empty_lines = [line for line in lines if line.strip() != ""]
+    string_no_empty_lines = "\n".join(non_empty_lines)
+    return string_no_empty_lines
+
+
+def generate_and_save_gbnf_grammar_and_documentation(
+    pydantic_model_list,
+    grammar_file_path="./generated_grammar.gbnf",
+    documentation_file_path="./generated_grammar_documentation.md",
+    outer_object_name: str | None = None,
+    outer_object_content: str | None = None,
+    model_prefix: str = "Output Model",
+    fields_prefix: str = "Output Fields",
+    list_of_outputs: bool = False,
+    documentation_with_field_description=True,
+):
+    """
+    Generate GBNF grammar and documentation, and save them to specified files.
+
+    Args:
+        pydantic_model_list: List of Pydantic model classes.
+        grammar_file_path (str): File path to save the generated GBNF grammar.
+        documentation_file_path (str): File path to save the generated documentation.
+        outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling.
+        outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling.
+        model_prefix (str): Prefix for the model section in the documentation.
+        fields_prefix (str): Prefix for the fields section in the documentation.
+        list_of_outputs (bool): Whether the output is a list of items.
+        documentation_with_field_description (bool): Include field descriptions in the documentation.
+
+    Returns:
+        None
+    """
+    documentation = generate_markdown_documentation(
+        pydantic_model_list, model_prefix, fields_prefix,
+        documentation_with_field_description=documentation_with_field_description
+    )
+    grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, outer_object_content,
+                                                         list_of_outputs)
+    grammar = remove_empty_lines(grammar)
+    save_gbnf_grammar_and_documentation(grammar, documentation, grammar_file_path, documentation_file_path)
+
+
+def generate_gbnf_grammar_and_documentation(
+    pydantic_model_list,
+    outer_object_name: str | None = None,
+    outer_object_content: str | None = None,
+    model_prefix: str = "Output Model",
+    fields_prefix: str = "Output Fields",
+    list_of_outputs: bool = False,
+    documentation_with_field_description=True,
+):
+    """
+    Generate GBNF grammar and documentation for a list of Pydantic models.
+
+    Args:
+        pydantic_model_list: List of Pydantic model classes.
+        outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling.
+        outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling.
+        model_prefix (str): Prefix for the model section in the documentation.
+        fields_prefix (str): Prefix for the fields section in the documentation.
+        list_of_outputs (bool): Whether the output is a list of items.
+        documentation_with_field_description (bool): Include field descriptions in the documentation.
+
+    Returns:
+        tuple: GBNF grammar string, documentation string.
+    """
+    documentation = generate_markdown_documentation(
+        copy(pydantic_model_list), model_prefix, fields_prefix,
+        documentation_with_field_description=documentation_with_field_description
+    )
+    grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, outer_object_content,
+                                                         list_of_outputs)
+    grammar = remove_empty_lines(grammar + get_primitive_grammar(grammar))
+    return grammar, documentation
+
+
+def generate_gbnf_grammar_and_documentation_from_dictionaries(
+    dictionaries: list[dict[str, Any]],
+    outer_object_name: str | None = None,
+    outer_object_content: str | None = None,
+    model_prefix: str = "Output Model",
+    fields_prefix: str = "Output Fields",
+    list_of_outputs: bool = False,
+    documentation_with_field_description=True,
+):
+    """
+    Generate GBNF grammar and documentation from a list of dictionaries.
+
+    Args:
+        dictionaries (list[dict]): List of dictionaries representing Pydantic models.
+        outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling.
+        outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling.
+        model_prefix (str): Prefix for the model section in the documentation.
+        fields_prefix (str): Prefix for the fields section in the documentation.
+        list_of_outputs (bool): Whether the output is a list of items.
+        documentation_with_field_description (bool): Include field descriptions in the documentation.
+
+    Returns:
+        tuple: GBNF grammar string, documentation string.
+    """
+    pydantic_model_list = create_dynamic_models_from_dictionaries(dictionaries)
+    documentation = generate_markdown_documentation(
+        copy(pydantic_model_list), model_prefix, fields_prefix,
+        documentation_with_field_description=documentation_with_field_description
+    )
+    grammar = generate_gbnf_grammar_from_pydantic_models(pydantic_model_list, outer_object_name, outer_object_content,
+                                                         list_of_outputs)
+    grammar = remove_empty_lines(grammar + get_primitive_grammar(grammar))
+    return grammar, documentation
+
+
+def create_dynamic_model_from_function(func: Callable[..., Any]):
+    """
+    Creates a dynamic Pydantic model from a given function's type hints and adds the function as a 'run' method.
+
+    Args:
+        func (Callable): A function with type hints from which to create the model.
+
+    Returns:
+        A dynamic Pydantic model class with the provided function as a 'run' method.
+    """
+
+    # Get the signature of the function
+    sig = inspect.signature(func)
+
+    # Parse the docstring
+    assert func.__doc__ is not None
+    docstring = parse(func.__doc__)
+
+    dynamic_fields = {}
+    param_docs = []
+    for param in sig.parameters.values():
+        # Exclude 'self' parameter
+        if param.name == "self":
+            continue
+
+        # Assert that the parameter has a type annotation
+        if param.annotation == inspect.Parameter.empty:
+            raise TypeError(f"Parameter '{param.name}' in function '{func.__name__}' lacks a type annotation")
+
+        # Find the parameter's description in the docstring
+        param_doc = next((d for d in docstring.params if d.arg_name == param.name), None)
+
+        # Assert that the parameter has a description
+        if not param_doc or not param_doc.description:
+            raise ValueError(
+                f"Parameter '{param.name}' in function '{func.__name__}' lacks a description in the docstring")
+
+        # Add parameter details to the schema
+        param_docs.append((param.name, param_doc))
+        if param.default == inspect.Parameter.empty:
+            default_value = ...
+        else:
+            default_value = param.default
+        dynamic_fields[param.name] = (
+            param.annotation if param.annotation != inspect.Parameter.empty else str, default_value)
+    # Creating the dynamic model
+    dynamic_model = create_model(f"{func.__name__}", **dynamic_fields)  # type: ignore[call-overload]
+
+    for name, param_doc in param_docs:
+        dynamic_model.model_fields[name].description = param_doc.description
+
+    dynamic_model.__doc__ = docstring.short_description
+
+    def run_method_wrapper(self):
+        func_args = {name: getattr(self, name) for name, _ in dynamic_fields.items()}
+        return func(**func_args)
+
+    # Adding the wrapped function as a 'run' method
+    setattr(dynamic_model, "run", run_method_wrapper)
+    return dynamic_model
+
+
+def add_run_method_to_dynamic_model(model: type[BaseModel], func: Callable[..., Any]):
+    """
+    Add a 'run' method to a dynamic Pydantic model, using the provided function.
+
+    Args:
+        model (type[BaseModel]): Dynamic Pydantic model class.
+        func (Callable): Function to be added as a 'run' method to the model.
+
+    Returns:
+        type[BaseModel]: Pydantic model class with the added 'run' method.
+    """
+
+    def run_method_wrapper(self):
+        func_args = {name: getattr(self, name) for name in model.model_fields}
+        return func(**func_args)
+
+    # Adding the wrapped function as a 'run' method
+    setattr(model, "run", run_method_wrapper)
+
+    return model
+
+
+def create_dynamic_models_from_dictionaries(dictionaries: list[dict[str, Any]]):
+    """
+    Create a list of dynamic Pydantic model classes from a list of dictionaries.
+
+    Args:
+        dictionaries (list[dict]): List of dictionaries representing model structures.
+
+    Returns:
+        list[type[BaseModel]]: List of generated dynamic Pydantic model classes.
+    """
+    dynamic_models = []
+    for func in dictionaries:
+        model_name = format_model_and_field_name(func.get("name", ""))
+        dyn_model = convert_dictionary_to_pydantic_model(func, model_name)
+        dynamic_models.append(dyn_model)
+    return dynamic_models
+
+
+def map_grammar_names_to_pydantic_model_class(pydantic_model_list):
+    output = {}
+    for model in pydantic_model_list:
+        output[format_model_and_field_name(model.__name__)] = model
+
+    return output
+
+
+from enum import Enum
+
+
+def json_schema_to_python_types(schema):
+    type_map = {
+        "any": Any,
+        "string": str,
+        "number": float,
+        "integer": int,
+        "boolean": bool,
+        "array": list,
+    }
+    return type_map[schema]
+
+
+def list_to_enum(enum_name, values):
+    return Enum(enum_name, {value: value for value in values})
+
+
+def convert_dictionary_to_pydantic_model(dictionary: dict[str, Any], model_name: str = "CustomModel") -> type[Any]:
+    """
+    Convert a dictionary to a Pydantic model class.
+
+    Args:
+        dictionary (dict): Dictionary representing the model structure.
+        model_name (str): Name of the generated Pydantic model.
+
+    Returns:
+        type[BaseModel]: Generated Pydantic model class.
+    """
+    fields: dict[str, Any] = {}
+
+    if "properties" in dictionary:
+        for field_name, field_data in dictionary.get("properties", {}).items():
+            if field_data == "object":
+                submodel = convert_dictionary_to_pydantic_model(dictionary, f"{model_name}_{field_name}")
+                fields[field_name] = (submodel, ...)
+            else:
+                field_type = field_data.get("type", "str")
+
+                if field_data.get("enum", []):
+                    fields[field_name] = (list_to_enum(field_name, field_data.get("enum", [])), ...)
+                elif field_type == "array":
+                    items = field_data.get("items", {})
+                    if items != {}:
+                        array = {"properties": items}
+                        array_type = convert_dictionary_to_pydantic_model(array, f"{model_name}_{field_name}_items")
+                        fields[field_name] = (List[array_type], ...)  # type: ignore[valid-type]
+                    else:
+                        fields[field_name] = (list, ...)
+                elif field_type == "object":
+                    submodel = convert_dictionary_to_pydantic_model(field_data, f"{model_name}_{field_name}")
+                    fields[field_name] = (submodel, ...)
+                elif field_type == "required":
+                    required = field_data.get("enum", [])
+                    for key, field in fields.items():
+                        if key not in required:
+                            fields[key] = (Optional[fields[key][0]], ...)
+                else:
+                    field_type = json_schema_to_python_types(field_type)
+                    fields[field_name] = (field_type, ...)
+    if "function" in dictionary:
+        for field_name, field_data in dictionary.get("function", {}).items():
+            if field_name == "name":
+                model_name = field_data
+            elif field_name == "description":
+                fields["__doc__"] = field_data
+            elif field_name == "parameters":
+                return convert_dictionary_to_pydantic_model(field_data, f"{model_name}")
+
+    if "parameters" in dictionary:
+        field_data = {"function": dictionary}
+        return convert_dictionary_to_pydantic_model(field_data, f"{model_name}")
+    if "required" in dictionary:
+        required = dictionary.get("required", [])
+        for key, field in fields.items():
+            if key not in required:
+                fields[key] = (Optional[fields[key][0]], ...)
+    custom_model = create_model(model_name, **fields)
+    return custom_model
diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp
index 773024160..1d05f1391 100644
--- a/examples/quantize-stats/quantize-stats.cpp
+++ b/examples/quantize-stats/quantize-stats.cpp
@@ -257,13 +257,13 @@ int main(int argc, char ** argv) {
                 invalid_param = true;
                 break;
             }
-            params.include_layers.push_back(argv[i]);
+            params.include_layers.emplace_back(argv[i]);
         } else if (arg == "-L" || arg == "--exclude-layer") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params.exclude_layers.push_back(argv[i]);
+            params.exclude_layers.emplace_back(argv[i]);
         } else if (arg == "-t" || arg == "--type") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -378,6 +378,8 @@ int main(int argc, char ** argv) {
                 printf("testing %s ...\n",  ggml_type_name(type));
             }
 
+            ggml_quantize_init(type);
+
             error_stats global_stats {};
 
             for (const auto& kv_tensor : tensors) {
diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp
index d27ea5e91..64cb6db19 100644
--- a/examples/quantize/quantize.cpp
+++ b/examples/quantize/quantize.cpp
@@ -5,6 +5,10 @@
 #include <cstring>
 #include <vector>
 #include <string>
+#include <unordered_map>
+#include <fstream>
+#include <cmath>
+#include <algorithm>
 
 struct quant_option {
     std::string name;
@@ -17,18 +21,31 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
     { "Q4_1",   LLAMA_FTYPE_MOSTLY_Q4_1,   " 3.90G, +0.1585 ppl @ LLaMA-v1-7B", },
     { "Q5_0",   LLAMA_FTYPE_MOSTLY_Q5_0,   " 4.33G, +0.0683 ppl @ LLaMA-v1-7B", },
     { "Q5_1",   LLAMA_FTYPE_MOSTLY_Q5_1,   " 4.70G, +0.0349 ppl @ LLaMA-v1-7B", },
+    { "IQ2_XXS",LLAMA_FTYPE_MOSTLY_IQ2_XXS," 2.06 bpw quantization",            },
+    { "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization",            },
+    { "IQ2_S",  LLAMA_FTYPE_MOSTLY_IQ2_S,  " 2.5  bpw quantization",            },
+    { "IQ2_M",  LLAMA_FTYPE_MOSTLY_IQ2_M,  " 2.7  bpw quantization",            },
+    { "IQ1_S",  LLAMA_FTYPE_MOSTLY_IQ1_S,  " 1.56 bpw quantization",            },
+    { "IQ1_M",  LLAMA_FTYPE_MOSTLY_IQ1_M,  " 1.75 bpw quantization",            },
     { "Q2_K",   LLAMA_FTYPE_MOSTLY_Q2_K,   " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", },
+    { "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", },
+    { "IQ3_XXS",LLAMA_FTYPE_MOSTLY_IQ3_XXS," 3.06 bpw quantization",            },
+    { "IQ3_S",  LLAMA_FTYPE_MOSTLY_IQ3_S,  " 3.44 bpw quantization",            },
+    { "IQ3_M",  LLAMA_FTYPE_MOSTLY_IQ3_M,  " 3.66 bpw quantization mix",        },
     { "Q3_K",   LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" },
+    { "IQ3_XS", LLAMA_FTYPE_MOSTLY_IQ3_XS, " 3.3 bpw quantization"   ,          },
     { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", },
     { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.07G, +0.2496 ppl @ LLaMA-v1-7B", },
     { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1764 ppl @ LLaMA-v1-7B", },
+    { "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.50 bpw non-linear quantization", },
+    { "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", },
     { "Q4_K",   LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", },
     { "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 3.59G, +0.0992 ppl @ LLaMA-v1-7B", },
     { "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 3.80G, +0.0532 ppl @ LLaMA-v1-7B", },
     { "Q5_K",   LLAMA_FTYPE_MOSTLY_Q5_K_M, "alias for Q5_K_M", },
     { "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S, " 4.33G, +0.0400 ppl @ LLaMA-v1-7B", },
     { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 4.45G, +0.0122 ppl @ LLaMA-v1-7B", },
-    { "Q6_K",   LLAMA_FTYPE_MOSTLY_Q6_K,   " 5.15G, -0.0008 ppl @ LLaMA-v1-7B", },
+    { "Q6_K",   LLAMA_FTYPE_MOSTLY_Q6_K,   " 5.15G, +0.0008 ppl @ LLaMA-v1-7B", },
     { "Q8_0",   LLAMA_FTYPE_MOSTLY_Q8_0,   " 6.70G, +0.0004 ppl @ LLaMA-v1-7B", },
     { "F16",    LLAMA_FTYPE_MOSTLY_F16,    "13.00G              @ 7B", },
     { "F32",    LLAMA_FTYPE_ALL_F32,       "26.00G              @ 7B", },
@@ -71,10 +88,18 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp
 //
 [[noreturn]]
 static void usage(const char * executable) {
-    printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
+    printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
     printf("  --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
     printf("  --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
     printf("  --pure: Disable k-quant mixtures and quantize all tensors to the same type\n");
+    printf("  --imatrix file_name: use data in file_name as importance matrix for quant optimizations\n");
+    printf("  --include-weights tensor_name: use importance matrix for this/these tensor(s)\n");
+    printf("  --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n");
+    printf("  --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor\n");
+    printf("  --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n");
+    printf("  --override-kv KEY=TYPE:VALUE\n");
+    printf("      Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n");
+    printf("Note: --include-weights and --exclude-weights cannot be used together\n");
     printf("\nAllowed quantization types:\n");
     for (auto & it : QUANT_OPTIONS) {
         if (it.name != "COPY") {
@@ -82,11 +107,146 @@ static void usage(const char * executable) {
         } else {
             printf("          ");
         }
-        printf("%-6s : %s\n", it.name.c_str(), it.desc.c_str());
+        printf("%-7s : %s\n", it.name.c_str(), it.desc.c_str());
     }
     exit(1);
 }
 
+static void load_imatrix(const std::string & imatrix_file, std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
+    std::ifstream in(imatrix_file.c_str(), std::ios::binary);
+    if (!in) {
+        printf("%s: failed to open %s\n",__func__, imatrix_file.c_str());
+        exit(1);
+    }
+    int n_entries;
+    in.read((char *)&n_entries, sizeof(n_entries));
+    if (in.fail() || n_entries < 1) {
+        printf("%s: no data in file %s\n", __func__, imatrix_file.c_str());
+        exit(1);
+    }
+    for (int i = 0; i < n_entries; ++i) {
+        int len; in.read((char *)&len, sizeof(len));
+        std::vector<char> name_as_vec(len+1);
+        in.read((char *)name_as_vec.data(), len);
+        if (in.fail()) {
+            printf("%s: failed reading name for entry %d from %s\n", __func__, i+1, imatrix_file.c_str());
+            exit(1);
+        }
+        name_as_vec[len] = 0;
+        std::string name{name_as_vec.data()};
+        auto & e = imatrix_data[name];
+        int ncall;
+        in.read((char *)&ncall, sizeof(ncall));
+        int nval;
+        in.read((char *)&nval, sizeof(nval));
+        if (in.fail() || nval < 1) {
+            printf("%s: failed reading number of values for entry %d\n", __func__, i);
+            imatrix_data = {};
+            exit(1);
+        }
+        e.resize(nval);
+        in.read((char *)e.data(), nval*sizeof(float));
+        if (in.fail()) {
+            printf("%s: failed reading data for entry %d\n", __func__, i);
+            imatrix_data = {};
+            exit(1);
+        }
+        if (ncall > 0) {
+            for (auto& v : e) v /= ncall;
+        }
+
+        if (getenv("LLAMA_TRACE")) {
+            printf("%s: loaded data (size = %6d, ncall = %6d) for '%s'\n", __func__, int(e.size()), ncall, name.c_str());
+        }
+    }
+    printf("%s: loaded %d importance matrix entries from %s\n", __func__, int(imatrix_data.size()), imatrix_file.c_str());
+}
+
+static void prepare_imatrix(const std::string & imatrix_file,
+        const std::vector<std::string> & included_weights,
+        const std::vector<std::string> & excluded_weights,
+        std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
+    if (!imatrix_file.empty()) {
+        load_imatrix(imatrix_file, imatrix_data);
+    }
+    if (imatrix_data.empty()) {
+        return;
+    }
+    if (!excluded_weights.empty()) {
+        for (auto& name : excluded_weights) {
+            for (auto it = imatrix_data.begin(); it != imatrix_data.end(); ) {
+                auto pos = it->first.find(name);
+                if (pos != std::string::npos) it = imatrix_data.erase(it);
+                else ++it;
+            }
+        }
+    }
+    if (!included_weights.empty()) {
+        std::unordered_map<std::string, std::vector<float>> tmp;
+        for (auto& name : included_weights) {
+            for (auto& e : imatrix_data) {
+                auto pos = e.first.find(name);
+                if (pos != std::string::npos) {
+                    tmp.emplace(std::move(e));
+                }
+            }
+        }
+        imatrix_data = std::move(tmp);
+    }
+    if (!imatrix_data.empty()) {
+        printf("%s: have %d importance matrix entries\n", __func__, int(imatrix_data.size()));
+    }
+}
+
+static ggml_type parse_ggml_type(const char * arg) {
+    ggml_type result = GGML_TYPE_COUNT;
+    for (int j = 0; j < GGML_TYPE_COUNT; ++j) {
+        auto type = ggml_type(j);
+        const auto * name = ggml_type_name(type);
+        if (name && strcmp(arg, name) == 0) {
+            result = type; break;
+        }
+    }
+    return result;
+}
+
+static bool parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
+    const char* sep = strchr(data, '=');
+    if (sep == nullptr || sep - data >= 128) {
+        fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
+        return false;
+    }
+    llama_model_kv_override kvo;
+    std::strncpy(kvo.key, data, sep - data);
+    kvo.key[sep - data] = 0;
+    sep++;
+    if (strncmp(sep, "int:", 4) == 0) {
+        sep += 4;
+        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
+        kvo.int_value = std::atol(sep);
+    } else if (strncmp(sep, "float:", 6) == 0) {
+        sep += 6;
+        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
+        kvo.float_value = std::atof(sep);
+    } else if (strncmp(sep, "bool:", 5) == 0) {
+        sep += 5;
+        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
+        if (std::strcmp(sep, "true") == 0) {
+            kvo.bool_value = true;
+        } else if (std::strcmp(sep, "false") == 0) {
+            kvo.bool_value = false;
+        } else {
+            fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
+            return false;
+        }
+    } else {
+        fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
+        return false;
+    }
+    overrides.emplace_back(std::move(kvo));
+    return true;
+}
+
 int main(int argc, char ** argv) {
     if (argc < 3) {
         usage(argv[0]);
@@ -95,24 +255,76 @@ int main(int argc, char ** argv) {
     llama_model_quantize_params params = llama_model_quantize_default_params();
 
     int arg_idx = 1;
+    std::string imatrix_file;
+    std::vector<std::string> included_weights, excluded_weights;
+    std::vector<llama_model_kv_override> kv_overrides;
 
     for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) {
         if (strcmp(argv[arg_idx], "--leave-output-tensor") == 0) {
             params.quantize_output_tensor = false;
+        } else if (strcmp(argv[arg_idx], "--output-tensor-type") == 0) {
+            if (arg_idx < argc-1) {
+                params.output_tensor_type = parse_ggml_type(argv[++arg_idx]);
+            } else {
+                usage(argv[0]);
+            }
+        } else if (strcmp(argv[arg_idx], "--token-embedding-type") == 0) {
+            if (arg_idx < argc-1) {
+                params.token_embedding_type = parse_ggml_type(argv[++arg_idx]);
+            } else {
+                usage(argv[0]);
+            }
+        } else if (strcmp(argv[arg_idx], "--override-kv") == 0) {
+            if (arg_idx == argc-1 || !parse_kv_override(argv[++arg_idx], kv_overrides)) {
+                usage(argv[0]);
+            }
         } else if (strcmp(argv[arg_idx], "--allow-requantize") == 0) {
             params.allow_requantize = true;
         } else if (strcmp(argv[arg_idx], "--pure") == 0) {
             params.pure = true;
+        } else if (strcmp(argv[arg_idx], "--imatrix") == 0) {
+            if (arg_idx < argc-1) {
+                imatrix_file = argv[++arg_idx];
+            } else {
+                usage(argv[0]);
+            }
+        } else if (strcmp(argv[arg_idx], "--include-weights") == 0) {
+            if (arg_idx < argc-1) {
+                included_weights.emplace_back(argv[++arg_idx]);
+            } else {
+                usage(argv[0]);
+            }
+        } else if (strcmp(argv[arg_idx], "--exclude-weights") == 0) {
+            if (arg_idx < argc-1) {
+                excluded_weights.emplace_back(argv[++arg_idx]);
+            } else {
+                usage(argv[0]);
+            }
         } else {
             usage(argv[0]);
         }
     }
 
     if (argc - arg_idx < 2) {
+        printf("%s: bad arguments\n", argv[0]);
+        usage(argv[0]);
+    }
+    if (!included_weights.empty() && !excluded_weights.empty()) {
         usage(argv[0]);
     }
 
-    llama_backend_init(false);
+    std::unordered_map<std::string, std::vector<float>> imatrix_data;
+    prepare_imatrix(imatrix_file, included_weights, excluded_weights, imatrix_data);
+    if (!imatrix_data.empty()) {
+        params.imatrix = &imatrix_data;
+    }
+    if (!kv_overrides.empty()) {
+        kv_overrides.emplace_back();
+        kv_overrides.back().key[0] = 0;
+        params.kv_overrides = &kv_overrides;
+    }
+
+    llama_backend_init();
 
     // parse command line arguments
     const std::string fname_inp = argv[arg_idx];
@@ -132,8 +344,7 @@ int main(int argc, char ** argv) {
         if (ftype_str == "COPY") {
             params.only_copy = true;
         }
-    }
-    else {
+    } else {
         fname_out = argv[arg_idx];
         arg_idx++;
 
@@ -162,6 +373,17 @@ int main(int argc, char ** argv) {
         }
     }
 
+    if ((params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS ||
+         params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_S  ||
+         params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S ||
+         params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S  ||
+         params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) && imatrix_data.empty()) {
+        fprintf(stderr, "\n==========================================================================================================\n");
+        fprintf(stderr, "Please do not use IQ1_S, IQ1_M, IQ2_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n");
+        fprintf(stderr, "==========================================================================================================\n\n\n");
+        return 1;
+    }
+
     print_build_info();
 
     fprintf(stderr, "%s: quantizing '%s' to '%s' as %s", __func__, fname_inp.c_str(), fname_out.c_str(), ftype_str.c_str());
diff --git a/examples/regex-to-grammar.py b/examples/regex-to-grammar.py
new file mode 100644
index 000000000..c98118e95
--- /dev/null
+++ b/examples/regex-to-grammar.py
@@ -0,0 +1,20 @@
+import json, subprocess, sys, os
+
+assert len(sys.argv) >= 2
+[_, pattern, *rest] = sys.argv
+
+print(subprocess.check_output(
+    [
+        "python",
+        os.path.join(
+        os.path.dirname(os.path.realpath(__file__)),
+        "json-schema-to-grammar.py"),
+        *rest,
+        "-",
+        "--raw-pattern",
+    ],
+    text=True,
+    input=json.dumps({
+        "type": "string",
+        "pattern": pattern,
+    }, indent=2)))
diff --git a/examples/retrieval/CMakeLists.txt b/examples/retrieval/CMakeLists.txt
new file mode 100644
index 000000000..eaabae08d
--- /dev/null
+++ b/examples/retrieval/CMakeLists.txt
@@ -0,0 +1,5 @@
+set(TARGET retrieval)
+add_executable(${TARGET} retrieval.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/retrieval/README.md b/examples/retrieval/README.md
new file mode 100644
index 000000000..2b2595c46
--- /dev/null
+++ b/examples/retrieval/README.md
@@ -0,0 +1,69 @@
+# llama.cpp/examples/retrieval
+
+Demonstration of simple retrieval technique based on cosine similarity
+
+More info:
+https://github.com/ggerganov/llama.cpp/pull/6193
+
+### How to use
+
+`retieval.cpp` has parameters of its own:
+- `--context-file`: file to be embedded - state this option multiple times to embed multiple files
+- `--chunk-size`: minimum size of each text chunk to be embedded
+- `--chunk-separator`: STRING to divide chunks by. newline by default
+
+`retrieval` example can be tested as follows:
+
+```bash
+make -j && ./retrieval --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .
+```
+
+This chunks and embeds all given files and starts a loop requesting query inputs:
+
+```
+Enter query:
+```
+
+On each query input, top k chunks are shown along with file name, chunk position within file and original text:
+
+```
+Enter query: describe the mit license
+batch_decode: n_tokens = 6, n_seq = 1
+Top 3 similar chunks:
+filename: README.md
+filepos: 119
+similarity: 0.762334
+textdata:
+png)
+
+[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
+
+[Roadmap](https://github.
+--------------------
+filename: License
+filepos: 0
+similarity: 0.725146
+textdata:
+MIT License
+
+Copyright (c) 2023 Georgi Gerganov
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+--------------------
+filename: README.md
+filepos: 9178
+similarity: 0.621722
+textdata:
+com/cztomsik/ava) (MIT)
+- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal)
+- [pythops/tenere](https://github.
+--------------------
+```
diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp
new file mode 100644
index 000000000..5ba71e76a
--- /dev/null
+++ b/examples/retrieval/retrieval.cpp
@@ -0,0 +1,350 @@
+#include "common.h"
+#include "llama.h"
+
+#include <algorithm>
+#include <fstream>
+
+struct retrieval_params {
+    std::vector<std::string> context_files; // context files to embed
+    int32_t chunk_size            = 64;     // chunk size for context embedding
+    std::string chunk_separator   = "\n";   // chunk separator for context embedding
+};
+
+static void retrieval_params_print_usage(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & params) {
+    gpt_print_usage(argc, argv, gpt_params);
+    printf("retrieval options:\n");
+    printf("  --context-file FNAME  file containing context to embed.\n");
+    printf("                        specify multiple files by providing --context-file option multiple times.\n");
+    printf("  --chunk-size N        minimum length of embedded text chunk (default:%d)\n", params.chunk_size);
+    printf("  --chunk-separator STRING\n");
+    printf("                        string to separate chunks (default: \"\\n\")\n");
+    printf("\n");
+}
+
+static void retrieval_params_parse(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & retrieval_params) {
+    int i = 1;
+    std::string arg;
+    while (i < argc) {
+        arg = argv[i];
+        bool invalid_gpt_param = false;
+        if(gpt_params_find_arg(argc, argv, argv[i], gpt_params, i, invalid_gpt_param)) {
+            if (invalid_gpt_param) {
+                fprintf(stderr, "error: invalid argument: %s\n", arg.c_str());
+                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
+                exit(1);
+            }
+            // option was parsed by gpt_params_find_arg
+        } else if (arg == "--context-file") {
+            if (++i >= argc) {
+                fprintf(stderr, "error: missing argument for --context-file\n");
+                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
+                exit(1);
+            }
+            std::ifstream file(argv[i]);
+            if (!file) {
+                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
+                exit(1);
+            }
+            // store the external file name in params
+            retrieval_params.context_files.push_back(argv[i]);
+        } else if (arg == "--chunk-size") {
+            if (++i >= argc) {
+                fprintf(stderr, "error: missing argument for --chunk-size\n");
+                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
+                exit(1);
+            }
+            retrieval_params.chunk_size = std::stoi(argv[i]);
+        } else if (arg == "--chunk-separator") {
+            if (++i >= argc) {
+                fprintf(stderr, "error: missing argument for --chunk-separator\n");
+                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
+                exit(1);
+            }
+            retrieval_params.chunk_separator = argv[i];
+        } else {
+            // unknown argument
+            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
+            retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
+            exit(1);
+        }
+        i++;
+    }
+}
+
+struct chunk {
+    // filename
+    std::string filename;
+    // original file position
+    size_t filepos;
+    // original text data
+    std::string textdata = "";
+    // tokenized text data
+    std::vector<llama_token> tokens;
+    // embedding
+    std::vector<float> embedding;
+};
+
+// chunk file data to chunks of size >= chunk_size
+// chunk_separator is the separator between chunks
+static std::vector<chunk> chunk_file(const std::string & filename, int chunk_size, const std::string & chunk_separator) {
+    std::vector<chunk> chunks;
+    std::ifstream f(filename.c_str());
+
+    if (!f.is_open()) {
+        fprintf(stderr, "Error: could not open file %s\n", filename.c_str());
+        return chunks;
+    }
+
+    chunk current_chunk;
+    char buffer[1024];
+    int64_t filepos = 0;
+    std::string current = "";
+    while (f.read(buffer, 1024)) {
+        current += std::string(buffer, f.gcount());
+        size_t pos;
+        while ((pos = current.find(chunk_separator)) != std::string::npos) {
+            current_chunk.textdata += current.substr(0, pos + chunk_separator.size());
+            if ((int) current_chunk.textdata.size() > chunk_size) {
+                // save chunk
+                current_chunk.filepos = filepos;
+                current_chunk.filename = filename;
+                chunks.push_back(current_chunk);
+                // update filepos
+                filepos += (int) current_chunk.textdata.size();
+                // reset current_chunk
+                current_chunk = chunk();
+            }
+            current = current.substr(pos + chunk_separator.size());
+        }
+
+    }
+    // add leftover data to last chunk
+    if (current_chunk.textdata.size() > 0) {
+        if (chunks.empty()) {
+            current_chunk.filepos = filepos;
+            current_chunk.filename = filename;
+            chunks.push_back(current_chunk);
+        } else {
+            chunks.back().textdata += current_chunk.textdata;
+        }
+    }
+    f.close();
+    return chunks;
+}
+
+static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, int seq_id) {
+    for (size_t i = 0; i < tokens.size(); i++) {
+        llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
+    }
+}
+
+static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) {
+    // clear previous kv_cache values (irrelevant for embeddings)
+    llama_kv_cache_clear(ctx);
+
+    // run model
+    fprintf(stderr, "%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
+    if (llama_decode(ctx, batch) < 0) {
+        fprintf(stderr, "%s : failed to decode\n", __func__);
+    }
+
+    for (int i = 0; i < batch.n_tokens; i++) {
+        if (!batch.logits[i]) {
+            continue;
+        }
+
+        // try to get sequence embeddings - supported only when pooling_type is not NONE
+        const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
+        if (embd == NULL) {
+            embd = llama_get_embeddings_ith(ctx, i);
+            if (embd == NULL) {
+                fprintf(stderr, "%s: failed to get embeddings for token %d\n", __func__, i);
+                continue;
+            }
+        }
+
+        float * out = output + batch.seq_id[i][0] * n_embd;
+        llama_embd_normalize(embd, out, n_embd);
+    }
+}
+
+int main(int argc, char ** argv) {
+    gpt_params params;
+    retrieval_params retrieval_params;
+
+    retrieval_params_parse(argc, argv, params, retrieval_params);
+
+    // For BERT models, batch size must be equal to ubatch size
+    params.n_ubatch = params.n_batch;
+
+    if (retrieval_params.chunk_size <= 0) {
+        fprintf(stderr, "chunk_size must be positive\n");
+        return 1;
+    }
+    if (retrieval_params.context_files.empty()) {
+        fprintf(stderr, "context_files must be specified\n");
+        return 1;
+    }
+    params.embedding = true;
+
+    print_build_info();
+
+    printf("processing files:\n");
+    for (auto & context_file : retrieval_params.context_files) {
+        printf("%s\n", context_file.c_str());
+    }
+
+    std::vector<chunk> chunks;
+    for (auto & context_file : retrieval_params.context_files) {
+        std::vector<chunk> file_chunk = chunk_file(context_file, retrieval_params.chunk_size, retrieval_params.chunk_separator);
+        chunks.insert(chunks.end(), file_chunk.begin(), file_chunk.end());
+    }
+    printf("Number of chunks: %ld\n", chunks.size());
+
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    llama_model * model;
+    llama_context * ctx;
+
+    // load the model
+    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    if (model == NULL) {
+        fprintf(stderr, "%s: error: unable to load model\n", __func__);
+        return 1;
+    }
+
+    const int n_ctx_train = llama_n_ctx_train(model);
+    const int n_ctx = llama_n_ctx(ctx);
+
+    if (n_ctx > n_ctx_train) {
+        fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
+                __func__, n_ctx_train, n_ctx);
+    }
+
+    // print system information
+    {
+        fprintf(stderr, "\n");
+        fprintf(stderr, "%s\n", get_system_info(params).c_str());
+    }
+
+    // max batch size
+    const uint64_t n_batch = params.n_batch;
+    GGML_ASSERT(params.n_batch >= params.n_ctx);
+
+    // tokenize the prompts and trim
+    for (auto & chunk : chunks) {
+        auto inp = ::llama_tokenize(ctx, chunk.textdata, true, false);
+        if (inp.size() > n_batch) {
+            fprintf(stderr, "%s: error: chunk size (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
+                    __func__, (long long int) inp.size(), (long long int) n_batch);
+            return 1;
+        }
+        // add eos if not present
+        if (inp.empty() || inp.back() != llama_token_eos(model)) {
+            inp.push_back(llama_token_eos(model));
+        }
+        chunk.tokens = inp;
+    }
+
+    // tokenization stats
+    if (params.verbose_prompt) {
+        for (int i = 0; i < (int) chunks.size(); i++) {
+            fprintf(stderr, "%s: prompt %d: '%s'\n", __func__, i, chunks[i].textdata.c_str());
+            fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, chunks[i].tokens.size());
+            for (int j = 0; j < (int) chunks[i].tokens.size(); j++) {
+                fprintf(stderr, "%6d -> '%s'\n", chunks[i].tokens[j], llama_token_to_piece(ctx, chunks[i].tokens[j]).c_str());
+            }
+            fprintf(stderr, "\n\n");
+        }
+    }
+
+    // initialize batch
+    const int n_chunks = chunks.size();
+    struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
+
+    // allocate output
+    const int n_embd = llama_n_embd(model);
+    std::vector<float> embeddings(n_chunks * n_embd, 0);
+    float * emb = embeddings.data();
+
+    // break into batches
+    int p = 0; // number of prompts processed already
+    int s = 0; // number of prompts in current batch
+    for (int k = 0; k < n_chunks; k++) {
+        // clamp to n_batch tokens
+        auto & inp = chunks[k].tokens;
+
+        const uint64_t n_toks = inp.size();
+
+        // encode if at capacity
+        if (batch.n_tokens + n_toks > n_batch) {
+            float * out = emb + p * n_embd;
+            batch_decode(ctx, batch, out, s, n_embd);
+            llama_batch_clear(batch);
+            p += s;
+            s = 0;
+        }
+
+        // add to batch
+        batch_add_seq(batch, inp, s);
+        s += 1;
+    }
+
+    // final batch
+    float * out = emb + p * n_embd;
+    batch_decode(ctx, batch, out, s, n_embd);
+
+    // save embeddings to chunks
+    for (int i = 0; i < n_chunks; i++) {
+        chunks[i].embedding = std::vector<float>(emb + i * n_embd, emb + (i + 1) * n_embd);
+        // clear tokens as they are no longer needed
+        chunks[i].tokens.clear();
+    }
+
+    // start loop, receive query and return top k similar chunks based on cosine similarity
+    std::string query;
+    while (true) {
+        printf("Enter query: ");
+        std::getline(std::cin, query);
+        std::vector<int32_t> query_tokens = llama_tokenize(ctx, query, true);
+
+        struct llama_batch query_batch = llama_batch_init(n_batch, 0, 1);
+        batch_add_seq(query_batch, query_tokens, 0);
+
+        std::vector<float> query_emb(n_embd, 0);
+        batch_decode(ctx, query_batch, query_emb.data(), 1, n_embd);
+
+        llama_batch_clear(query_batch);
+
+        // compute cosine similarities
+        {
+            std::vector<std::pair<int, float>> similarities;
+            for (int i = 0; i < n_chunks; i++) {
+                float sim = llama_embd_similarity_cos(chunks[i].embedding.data(), query_emb.data(), n_embd);
+                similarities.push_back(std::make_pair(i, sim));
+            }
+
+            // sort similarities
+            std::sort(similarities.begin(), similarities.end(), [](const std::pair<int, float> & a, const std::pair<int, float> & b) {
+                return a.second > b.second;
+            });
+
+            printf("Top %d similar chunks:\n", params.sparams.top_k);
+            for (int i = 0; i < std::min(params.sparams.top_k, (int) chunks.size()); i++) {
+                printf("filename: %s\n", chunks[similarities[i].first].filename.c_str());
+                printf("filepos: %lld\n", (long long int) chunks[similarities[i].first].filepos);
+                printf("similarity: %f\n", similarities[i].second);
+                printf("textdata:\n%s\n", chunks[similarities[i].first].textdata.c_str());
+                printf("--------------------\n");
+            }
+        }
+    }
+
+    // clean up
+    llama_print_timings(ctx);
+    llama_free(ctx);
+    llama_free_model(model);
+    llama_backend_free();
+}
diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp
index 48d801110..ef952e2bd 100644
--- a/examples/save-load-state/save-load-state.cpp
+++ b/examples/save-load-state/save-load-state.cpp
@@ -45,13 +45,13 @@ int main(int argc, char ** argv) {
     // save state (rng, logits, embedding and kv_cache) to file
     {
         std::vector<uint8_t> state_mem(llama_get_state_size(ctx));
+        const size_t written = llama_copy_state_data(ctx, state_mem.data());
 
-        {
-            FILE *fp_write = fopen("dump_state.bin", "wb");
-            llama_copy_state_data(ctx, state_mem.data()); // could also copy directly to memory mapped file
-            fwrite(state_mem.data(), 1, state_mem.size(), fp_write);
-            fclose(fp_write);
-        }
+        FILE *fp_write = fopen("dump_state.bin", "wb");
+        fwrite(state_mem.data(), 1, written, fp_write);
+        fclose(fp_write);
+
+        fprintf(stderr, "%s : serialized state into %zd out of a maximum of %zd bytes\n", __func__, written, state_mem.size());
     }
 
     // save state (last tokens)
@@ -100,18 +100,17 @@ int main(int argc, char ** argv) {
         std::vector<uint8_t> state_mem(llama_get_state_size(ctx2));
 
         FILE * fp_read = fopen("dump_state.bin", "rb");
+        const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read);
+        fclose(fp_read);
 
-        const size_t ret = fread(state_mem.data(), 1, state_mem.size(), fp_read);
-        if (ret != state_mem.size()) {
+        if (read != llama_set_state_data(ctx2, state_mem.data())) {
             fprintf(stderr, "\n%s : failed to read state\n", __func__);
             llama_free(ctx2);
             llama_free_model(model);
             return 1;
         }
 
-        llama_set_state_data(ctx2, state_mem.data());
-
-        fclose(fp_read);
+        fprintf(stderr, "%s : deserialized state from %zd out of a maximum of %zd bytes\n", __func__, read, state_mem.size());
     }
 
     // restore state (last tokens)
diff --git a/examples/server-embd.py b/examples/server-embd.py
new file mode 100644
index 000000000..118e04271
--- /dev/null
+++ b/examples/server-embd.py
@@ -0,0 +1,34 @@
+import asyncio
+import requests
+import numpy as np
+
+n = 8
+
+result = []
+
+async def requests_post_async(*args, **kwargs):
+    return await asyncio.to_thread(requests.post, *args, **kwargs)
+
+async def main():
+    model_url = "http://127.0.0.1:6900"
+    responses: list[requests.Response] = await asyncio.gather(*[requests_post_async(
+        url= f"{model_url}/embedding",
+        json= {"content": str(0)*1024}
+    ) for i in range(n)])
+
+    for response in responses:
+        embedding = response.json()["embedding"]
+        print(embedding[-8:])
+        result.append(embedding)
+
+asyncio.run(main())
+
+# compute cosine similarity
+
+for i in range(n-1):
+    for j in range(i+1, n):
+        embedding1 = np.array(result[i])
+        embedding2 = np.array(result[j])
+        similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
+        print(f"Similarity between {i} and {j}: {similarity:.2f}")
+
diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt
index 81709e448..d2ee47d01 100644
--- a/examples/server/CMakeLists.txt
+++ b/examples/server/CMakeLists.txt
@@ -1,12 +1,22 @@
 set(TARGET server)
 option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
+option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF)
 include_directories(${CMAKE_CURRENT_SOURCE_DIR})
-add_executable(${TARGET} server.cpp json.hpp httplib.h)
+add_executable(${TARGET}
+    server.cpp
+    utils.hpp
+    httplib.h
+)
 install(TARGETS ${TARGET} RUNTIME)
 target_compile_definitions(${TARGET} PRIVATE
     SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
 )
-target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE common json-schema-to-grammar ${CMAKE_THREAD_LIBS_INIT})
+if (LLAMA_SERVER_SSL)
+    find_package(OpenSSL REQUIRED)
+    target_link_libraries(${TARGET} PRIVATE OpenSSL::SSL OpenSSL::Crypto)
+    target_compile_definitions(${TARGET} PRIVATE CPPHTTPLIB_OPENSSL_SUPPORT)
+endif()
 if (WIN32)
     TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
 endif()
diff --git a/examples/server/README.md b/examples/server/README.md
index d85a14f89..0d8564a1f 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -1,38 +1,73 @@
-# llama.cpp/example/server
+# LLaMA.cpp HTTP Server
 
-This example demonstrates a simple HTTP API server and a simple web front end to interact with llama.cpp.
+Fast, lightweight, pure C/C++ HTTP server based on [httplib](https://github.com/yhirose/cpp-httplib), [nlohmann::json](https://github.com/nlohmann/json) and **llama.cpp**.
 
-Command line options:
+Set of LLM REST APIs and a simple web front end to interact with llama.cpp.
 
--   `--threads N`, `-t N`: Set the number of threads to use during generation.
--   `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation.
--   `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`).
--   `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
--   `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096.
--   `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
--   `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.
--   `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS.
--   `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `512`.
--   `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. Not recommended.
--   `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped.
--   `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed.
--   `--numa`: Attempt optimizations that help on some NUMA systems.
--   `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
--   `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.
--   `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`.
--   `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`.
--   `--port`: Set the port to listen. Default: `8080`.
--   `--path`: path from which to serve static files (default examples/server/public)
--   `--api-key`: Set an api key for request authorization. By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token.
--   `--embedding`: Enable embedding extraction, Default: disabled.
--   `-np N`, `--parallel N`: Set the number of slots for process requests (default: 1)
--   `-cb`, `--cont-batching`: enable continuous batching (a.k.a dynamic batching) (default: disabled)
--   `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load "a system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
--   `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA.
+**Features:**
+ * LLM inference of F16 and quantum models on GPU and CPU
+ * [OpenAI API](https://github.com/openai/openai-openapi) compatible chat completions and embeddings routes
+ * Parallel decoding with multi-user support
+ * Continuous batching
+ * Multimodal (wip)
+ * Monitoring endpoints
+
+The project is under active development, and we are [looking for feedback and contributors](https://github.com/ggerganov/llama.cpp/issues/4216).
+
+**Command line options:**
+
+- `--threads N`, `-t N`: Set the number of threads to use during generation. Not used if model layers are offloaded to GPU. The server is using batching. This parameter is used only if one token is to be processed on CPU backend.
+- `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. Not used if model layers are offloaded to GPU.
+- `--threads-http N`: Number of threads in the http server pool to process requests. Default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)`
+- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`).
+- `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file. Default: unused
+- `-hfr REPO, --hf-repo REPO`: Hugging Face model repository. Default: unused
+- `-hff FILE, --hf-file FILE`: Hugging Face model file. Default: unused
+- `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
+- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is `512`, but LLaMA models were built with a context of `2048`, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of `4096`.
+- `-ngl N`, `--n-gpu-layers N`: When compiled with GPU support, this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
+- `-mg i, --main-gpu i`: When using multiple GPUs, this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default, GPU `0` is used.
+- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs, this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default, the data is split in proportion to VRAM, but this may not be optimal for performance.
+- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `2048`
+- `-ub N`, `--ubatch-size N`: Physical maximum batch size. Default: `512`
+- `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped.
+- `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed.
+- `--numa STRATEGY`: Attempt one of the below optimization strategies that may help on some NUMA systems
+- `--numa distribute`: Spread execution evenly over all nodes
+- `--numa isolate`: Only spawn threads on CPUs on the node that execution started on
+- `--numa numactl`: Use the CPU map provided by numactl. If run without this previously, it is recommended to drop the system
+page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/1437
+
+- `--numa`: Attempt optimizations that may help on some NUMA systems.
+- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
+- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.
+- `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`
+- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`
+- `--port`: Set the port to listen. Default: `8080`
+- `--path`: Path from which to serve static files. Default: disabled
+- `--api-key`: Set an api key for request authorization. By default, the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. May be used multiple times to enable multiple valid keys.
+- `--api-key-file`: Path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access. May be used in conjunction with `--api-key`s.
+- `--embedding`: Enable embedding extraction. Default: disabled
+- `-np N`, `--parallel N`: Set the number of slots for process requests. Default: `1`
+- `-cb`, `--cont-batching`: Enable continuous batching (a.k.a dynamic batching).  Default: disabled
+- `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load a system prompt (initial prompt of all slots). This is useful for chat applications. [See more](#change-system-prompt-on-runtime)
+- `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA.
+- `--grp-attn-n`: Set the group attention factor to extend context size through self-extend. Used together with group attention width `--grp-attn-w`. Default: `1`, which is disabled.
+- `--grp-attn-w`: Set the group attention width to extend context size through self-extend.  Used together with group attention factor `--grp-attn-n`. Default: `512`
+- `-n N, --n-predict N`: Set the maximum tokens to predict. Default: `-1`
+- `--slots-endpoint-disable`: To disable slots state monitoring endpoint. Slots state may contain user data, prompts included.
+- `--metrics`: enable prometheus `/metrics` compatible endpoint. Default: disabled
+- `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name.  Default: template taken from model's metadata. We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
+- `--log-disable`: Output logs to stdout only, not to `llama.log`. Default: enabled
+- `--log-format FORMAT`: Define the log output to FORMAT: json or text Default: `json`
+
+**If compiled with `LLAMA_SERVER_SSL=ON`**
+- `--ssl-key-file FNAME`: path to file a PEM-encoded SSL private key
+- `--ssl-cert-file FNAME`: path to file a PEM-encoded SSL certificate
 
 ## Build
 
-server is build alongside everything else from the root of the project
+`server` is built alongside everything else from the root of the project
 
 - Using `make`:
 
@@ -46,27 +81,59 @@ server is build alongside everything else from the root of the project
   cmake --build . --config Release
   ```
 
+## Build with SSL
+
+`server` can also be built with SSL support using OpenSSL 3
+
+- Using `make`:
+
+  ```bash
+  # NOTE: For non-system openssl, use the following:
+  #   CXXFLAGS="-I /path/to/openssl/include"
+  #   LDFLAGS="-L /path/to/openssl/lib"
+  make LLAMA_SERVER_SSL=true server
+  ```
+
+- Using `CMake`:
+
+  ```bash
+  mkdir build
+  cd build
+  cmake .. -DLLAMA_SERVER_SSL=ON
+  make server
+  ```
+
 ## Quick Start
 
 To get started right away, run the following command, making sure to use the correct path for the model you have:
 
-### Unix-based systems (Linux, macOS, etc.):
+### Unix-based systems (Linux, macOS, etc.)
 
 ```bash
 ./server -m models/7B/ggml-model.gguf -c 2048
 ```
 
-### Windows:
+### Windows
 
 ```powershell
 server.exe -m models\7B\ggml-model.gguf -c 2048
 ```
+
 The above command will start a server that by default listens on `127.0.0.1:8080`.
 You can consume the endpoints with Postman or NodeJS with axios library. You can visit the web front end at the same url.
 
+### Docker
+
+```bash
+docker run -p 8080:8080 -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080
+
+# or, with CUDA:
+docker run -p 8080:8080 -v /path/to/models:/models --gpus all ghcr.io/ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99
+```
+
 ## Testing with CURL
 
-Using [curl](https://curl.se/). On Windows `curl.exe` should be available in the base OS.
+Using [curl](https://curl.se/). On Windows, `curl.exe` should be available in the base OS.
 
 ```sh
 curl --request POST \
@@ -75,6 +142,12 @@ curl --request POST \
     --data '{"prompt": "Building a website can be done in 10 simple steps:","n_predict": 128}'
 ```
 
+## Advanced testing
+
+We implemented a [server test framework](./tests/README.md) using human-readable scenario.
+
+*Before submitting an issue, please try to reproduce it with this format.*
+
 ## Node JS Test
 
 You need to have [Node.js](https://nodejs.org/en) installed.
@@ -84,7 +157,7 @@ mkdir llama-client
 cd llama-client
 ```
 
-Create a index.js file and put inside this:
+Create a index.js file and put this inside:
 
 ```javascript
 const prompt = `Building a website can be done in 10 simple steps:`;
@@ -111,78 +184,98 @@ node index.js
 
 ## API Endpoints
 
--   **POST** `/completion`: Given a `prompt`, it returns the predicted completion.
+- **GET** `/health`: Returns the current state of the server:
+  - 503 -> `{"status": "loading model"}` if the model is still being loaded.
+  - 500 -> `{"status": "error"}` if the model failed to load.
+  - 200 -> `{"status": "ok", "slots_idle": 1, "slots_processing": 2 }` if the model is successfully loaded and the server is ready for further requests mentioned below.
+  - 200 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if no slots are currently available.
+  - 503 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if the query parameter `fail_on_no_slot` is provided and no slots are currently available.
+
+  If the query parameter `include_slots` is passed, `slots` field will contain internal slots data except if `--slots-endpoint-disable` is set.
+
+- **POST** `/completion`: Given a `prompt`, it returns the predicted completion.
 
     *Options:*
 
-    `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. If the prompt is a string or an array with the first element given as a string, a `bos` token is inserted in the front like `main` does.
+    `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, if `cache_prompt` is `true`, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. A `BOS` token is inserted at the start, if all of the following conditions are true:
 
-    `temperature`: Adjust the randomness of the generated text (default: 0.8).
+      - The prompt is a string or an array with the first element given as a string
+      - The model's `tokenizer.ggml.add_bos_token` metadata is `true`
+      - The system prompt is empty
 
-    `top_k`: Limit the next token selection to the K most probable tokens (default: 40).
+    `temperature`: Adjust the randomness of the generated text. Default: `0.8`
 
-    `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.95).
+    `dynatemp_range`: Dynamic temperature range. The final temperature will be in the range of `[temperature - dynatemp_range; temperature + dynatemp_range]` Default: `0.0`, which is disabled.
 
-    `min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token (default: 0.05).
+    `dynatemp_exponent`: Dynamic temperature exponent. Default: `1.0`
 
-    `n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. (default: -1, -1 = infinity).
+    `top_k`: Limit the next token selection to the K most probable tokens.  Default: `40`
+
+    `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P. Default: `0.95`
+
+    `min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token. Default: `0.05`
+
+    `n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. Default: `-1`, where `-1` is infinity.
 
     `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded.
-    By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the prompt.
+    By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to retain all tokens from the prompt.
 
     `stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.
 
     `stop`: Specify a JSON array of stopping strings.
-    These words will not be included in the completion, so make sure to add them to the prompt for the next iteration (default: []).
+    These words will not be included in the completion, so make sure to add them to the prompt for the next iteration. Default: `[]`
 
-    `tfs_z`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled).
+    `tfs_z`: Enable tail free sampling with parameter z. Default: `1.0`, which is disabled.
 
-    `typical_p`: Enable locally typical sampling with parameter p (default: 1.0, 1.0 = disabled).
+    `typical_p`: Enable locally typical sampling with parameter p. Default: `1.0`, which is disabled.
 
-    `repeat_penalty`: Control the repetition of token sequences in the generated text (default: 1.1).
+    `repeat_penalty`: Control the repetition of token sequences in the generated text. Default: `1.1`
 
-    `repeat_last_n`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size).
+    `repeat_last_n`: Last n tokens to consider for penalizing repetition. Default: `64`, where `0` is disabled and `-1` is ctx-size.
 
-    `penalize_nl`: Penalize newline tokens when applying the repeat penalty (default: true).
+    `penalize_nl`: Penalize newline tokens when applying the repeat penalty. Default: `true`
 
-    `presence_penalty`: Repeat alpha presence penalty (default: 0.0, 0.0 = disabled).
+    `presence_penalty`: Repeat alpha presence penalty. Default: `0.0`, which is disabled.
 
-    `frequency_penalty`: Repeat alpha frequency penalty (default: 0.0, 0.0 = disabled);
+    `frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled.
 
-    `penalty_prompt`: This will replace the `prompt` for the purpose of the penalty evaluation. Can be either `null`, a string or an array of numbers representing tokens (default: `null` = use the original `prompt`).
+    `penalty_prompt`: This will replace the `prompt` for the purpose of the penalty evaluation. Can be either `null`, a string or an array of numbers representing tokens. Default: `null`, which is to use the original `prompt`.
 
-    `mirostat`: Enable Mirostat sampling, controlling perplexity during text generation (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0).
+    `mirostat`: Enable Mirostat sampling, controlling perplexity during text generation. Default: `0`, where `0` is disabled, `1` is Mirostat, and `2` is Mirostat 2.0.
 
-    `mirostat_tau`: Set the Mirostat target entropy, parameter tau (default: 5.0).
+    `mirostat_tau`: Set the Mirostat target entropy, parameter tau. Default: `5.0`
 
-    `mirostat_eta`: Set the Mirostat learning rate, parameter eta (default: 0.1).
+    `mirostat_eta`: Set the Mirostat learning rate, parameter eta.  Default: `0.1`
 
-    `grammar`: Set grammar for grammar-based sampling (default: no grammar)
+    `grammar`: Set grammar for grammar-based sampling.  Default: no grammar
 
-    `seed`: Set the random number generator (RNG) seed (default: -1, -1 = random seed).
+    `seed`: Set the random number generator (RNG) seed.  Default: `-1`, which is a random seed.
 
-    `ignore_eos`: Ignore end of stream token and continue generating (default: false).
+    `ignore_eos`: Ignore end of stream token and continue generating.  Default: `false`
 
-    `logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced (default: []).
+    `logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced. The tokens can also be represented as strings, e.g. `[["Hello, World!",-0.5]]` will reduce the likelihood of all the individual tokens that represent the string `Hello, World!`, just like the `presence_penalty` does. Default: `[]`
 
-    `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0)
+    `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token. Default: `0`
+
+    `min_keep`: If greater than 0, force samplers to return N possible tokens at minimum. Default: `0`
 
     `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
 
-    `slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1)
+    `id_slot`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot.  Default: `-1`
 
-    `cache_prompt`: Save the prompt and generation for avoid reprocess entire prompt if a part of this isn't change (default: false)
+    `cache_prompt`: Re-use previously cached prompt from the last request if possible. This may prevent re-caching the prompt from scratch.  Default: `false`
 
     `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
 
-### Result JSON:
+    `samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["top_k", "tfs_z", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values.
 
-* Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion.
+### Result JSON
 
+- Note: When using streaming mode (`stream`), only `content` and `stop` will be returned until end of completion.
 
 - `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has the following structure:
 
-```
+```json
 {
   "content": "<the token selected by the model>",
   "probs": [
@@ -192,17 +285,18 @@ node index.js
     },
     {
       "prob": float,
-      "tok_str": "<second most likely tonen>"
+      "tok_str": "<second most likely token>"
     },
     ...
   ]
 },
 ```
+
 Notice that each `probs` is an array of length `n_probs`.
 
 - `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string.
 - `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options)
-- `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`
+- `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`. These options may differ from the original ones in some way (e.g. bad values filtered out, strings converted to tokens, etc.).
 - `model`: The path to the model loaded with `-m`
 - `prompt`: The provided `prompt`
 - `stopped_eos`: Indicating whether the completion has stopped because it encountered the EOS token
@@ -214,21 +308,21 @@ Notice that each `probs` is an array of length `n_probs`.
 - `tokens_evaluated`: Number of tokens evaluated in total from the prompt
 - `truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`)
 
--   **POST** `/tokenize`: Tokenize a given text.
+- **POST** `/tokenize`: Tokenize a given text.
 
     *Options:*
 
     `content`: Set the text to tokenize.
 
-    Note that the special `BOS` token is not added in front of the text and also a space character is not inserted automatically as it is for `/completion`.
+    Note that a special `BOS` token is never inserted.
 
--   **POST** `/detokenize`: Convert tokens to text.
+- **POST** `/detokenize`: Convert tokens to text.
 
     *Options:*
 
     `tokens`: Set the tokens to detokenize.
 
--   **POST** `/embedding`: Generate embedding of a given text just as [the embedding example](../embedding) does.
+- **POST** `/embedding`: Generate embedding of a given text just as [the embedding example](../embedding) does.
 
     *Options:*
 
@@ -236,7 +330,7 @@ Notice that each `probs` is an array of length `n_probs`.
 
     `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `content`. You can determine the place of the image in the content as in the following: `Image: [img-21].\nCaption: This is a picture of a house`. In this case, `[img-21]` will be replaced by the embeddings of the image with id `21` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 21}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
 
--   **POST** `/infill`: For code infilling. Takes a prefix and a suffix and returns the predicted completion as stream.
+- **POST** `/infill`: For code infilling. Takes a prefix and a suffix and returns the predicted completion as stream.
 
     *Options:*
 
@@ -246,13 +340,29 @@ Notice that each `probs` is an array of length `n_probs`.
 
     It also accepts all the options of `/completion` except `stream` and `prompt`.
 
--   **GET** `/props`: Return the required assistant name and anti-prompt to generate the prompt in case you have specified a system prompt for all slots.
+- **GET** `/props`: Return current server settings.
 
--   **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served.
+### Result JSON
+
+```json
+{
+  "assistant_name": "",
+  "user_name": "",
+  "default_generation_settings": { ... },
+  "total_slots": 1
+}
+```
+
+- `assistant_name` - the required assistant name to generate the prompt in case you have specified a system prompt for all slots.
+- `user_name` - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots.
+- `default_generation_settings` - the default generation settings for the `/completion` endpoint, which has the same fields as the `generation_settings` response object from the `/completion` endpoint.
+- `total_slots` - the total number of slots for process requests (defined by `--parallel` option)
+
+- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only model with [supported chat template](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, ChatML template will be used.
 
     *Options:*
 
-    See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such are `mirostat` are supported.
+    See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such as `mirostat` are supported.
 
     *Examples:*
 
@@ -276,6 +386,7 @@ Notice that each `probs` is an array of length `n_probs`.
 
     print(completion.choices[0].message)
     ```
+
     ... or raw HTTP requests:
 
     ```shell
@@ -297,11 +408,120 @@ Notice that each `probs` is an array of length `n_probs`.
     }'
     ```
 
+- **POST** `/v1/embeddings`: OpenAI-compatible embeddings API.
+
+    *Options:*
+
+    See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-reference/embeddings).
+
+    *Examples:*
+
+  - input as string
+
+    ```shell
+    curl http://localhost:8080/v1/embeddings \
+    -H "Content-Type: application/json" \
+    -H "Authorization: Bearer no-key" \
+    -d '{
+            "input": "hello",
+            "model":"GPT-4",
+            "encoding_format": "float"
+    }'
+    ```
+
+  - `input` as string array
+
+    ```shell
+    curl http://localhost:8080/v1/embeddings \
+    -H "Content-Type: application/json" \
+    -H "Authorization: Bearer no-key" \
+    -d '{
+            "input": ["hello", "world"],
+            "model":"GPT-4",
+            "encoding_format": "float"
+    }'
+    ```
+
+- **GET** `/slots`: Returns the current slots processing state. Can be disabled with `--slots-endpoint-disable`.
+
+### Result JSON
+
+```json
+[
+    {
+        "dynatemp_exponent": 1.0,
+        "dynatemp_range": 0.0,
+        "frequency_penalty": 0.0,
+        "grammar": "",
+        "id": 0,
+        "ignore_eos": false,
+        "logit_bias": [],
+        "min_p": 0.05000000074505806,
+        "mirostat": 0,
+        "mirostat_eta": 0.10000000149011612,
+        "mirostat_tau": 5.0,
+        "model": "llama-2-7b-32k-instruct.Q2_K.gguf",
+        "n_ctx": 2048,
+        "n_keep": 0,
+        "n_predict": 100000,
+        "n_probs": 0,
+        "next_token": {
+            "has_next_token": true,
+            "n_remain": -1,
+            "n_decoded": 0,
+            "stopped_eos": false,
+            "stopped_limit": false,
+            "stopped_word": false,
+            "stopping_word": ""
+        },
+        "penalize_nl": true,
+        "penalty_prompt_tokens": [],
+        "presence_penalty": 0.0,
+        "prompt": "Say hello to llama.cpp",
+        "repeat_last_n": 64,
+        "repeat_penalty": 1.100000023841858,
+        "samplers": [
+            "top_k",
+            "tfs_z",
+            "typical_p",
+            "top_p",
+            "min_p",
+            "temperature"
+        ],
+        "seed": 42,
+        "state": 1,
+        "stop": [
+            "\n"
+        ],
+        "stream": false,
+        "task_id": 0,
+        "temperature": 0.0,
+        "tfs_z": 1.0,
+        "top_k": 40,
+        "top_p": 0.949999988079071,
+        "typical_p": 1.0,
+        "use_penalty_prompt_tokens": false
+    }
+]
+```
+
+- **GET** `/metrics`: [Prometheus](https://prometheus.io/) compatible metrics exporter endpoint if `--metrics` is enabled:
+
+Available metrics:
+- `llamacpp:prompt_tokens_total`: Number of prompt tokens processed.
+- `llamacpp:tokens_predicted_total`: Number of generation tokens processed.
+- `llamacpp:prompt_tokens_seconds`: Average prompt throughput in tokens/s.
+- `llamacpp:predicted_tokens_seconds`: Average generation throughput in tokens/s.
+- `llamacpp:kv_cache_usage_ratio`: KV-cache usage. `1` means 100 percent usage.
+- `llamacpp:kv_cache_tokens`: KV-cache tokens.
+- `llamacpp:requests_processing`: Number of requests processing.
+- `llamacpp:requests_deferred`: Number of requests deferred.
+
 ## More examples
 
 ### Change system prompt on runtime
 
-To use the server example to serve multiple chat-type clients while keeping the same system prompt, you can utilize the option `system_prompt` to achieve that. This only needs to be done once to establish it.
+To use the server example to serve multiple chat-type clients while keeping the same system prompt, you can utilize the option `system_prompt`. This only needs to be used once.
 
 `prompt`: Specify a context that you want all connecting clients to respect.
 
@@ -338,25 +558,55 @@ Run with bash:
 bash chat.sh
 ```
 
-### API like OAI
+### OAI-like API
 
-API example using Python Flask: [api_like_OAI.py](api_like_OAI.py)
-This example must be used with server.cpp
+The HTTP `server` supports an OAI-like API: https://github.com/openai/openai-openapi
 
-```sh
-python api_like_OAI.py
+### API errors
+
+`server` returns errors in the same format as OAI: https://github.com/openai/openai-openapi
+
+Example of an error:
+
+```json
+{
+    "error": {
+        "code": 401,
+        "message": "Invalid API Key",
+        "type": "authentication_error"
+    }
+}
 ```
 
-After running the API server, you can use it in Python by setting the API base URL.
-```python
-openai.api_base = "http://<Your api-server IP>:port"
+Apart from error types supported by OAI, we also have custom types that are specific to functionalities of llama.cpp:
+
+**When /metrics or /slots endpoint is disabled**
+
+```json
+{
+    "error": {
+        "code": 501,
+        "message": "This server does not support metrics endpoint.",
+        "type": "not_supported_error"
+    }
+}
 ```
 
-Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API
+**When the server receives invalid grammar via */completions endpoint**
+
+```json
+{
+    "error": {
+        "code": 400,
+        "message": "Failed to parse grammar",
+        "type": "invalid_request_error"
+    }
+}
+```
 
 ### Extending or building alternative Web Front End
 
-The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.
+You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.
 
 Read the documentation in `/completion.js` to see convenient ways to access llama.
 
diff --git a/examples/server/api_like_OAI.py b/examples/server/api_like_OAI.py
deleted file mode 100755
index 607fe49d3..000000000
--- a/examples/server/api_like_OAI.py
+++ /dev/null
@@ -1,228 +0,0 @@
-#!/usr/bin/env python3
-import argparse
-from flask import Flask, jsonify, request, Response
-import urllib.parse
-import requests
-import time
-import json
-
-
-app = Flask(__name__)
-slot_id = -1
-
-parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.")
-parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')
-parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: 'USER: ')", default="USER: ")
-parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: 'ASSISTANT: ')", default="ASSISTANT: ")
-parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: 'ASSISTANT's RULE: ')", default="ASSISTANT's RULE: ")
-parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '</s>')", default="</s>")
-parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080')
-parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="")
-parser.add_argument("--host", type=str, help="Set the ip address to listen.(default: 127.0.0.1)", default='127.0.0.1')
-parser.add_argument("--port", type=int, help="Set the port to listen.(default: 8081)", default=8081)
-
-args = parser.parse_args()
-
-def is_present(json, key):
-    try:
-        buf = json[key]
-    except KeyError:
-        return False
-    if json[key] == None:
-        return False
-    return True
-
-#convert chat to prompt
-def convert_chat(messages):
-
-    system_n = args.system_name
-    user_n = args.user_name
-    ai_n = args.ai_name
-    stop = args.stop
-
-    prompt = "" + args.chat_prompt + stop
-
-    for line in messages:
-        if (line["role"] == "system"):
-            prompt += f"{system_n}{line['content']}{stop}"
-        if (line["role"] == "user"):
-            prompt += f"{user_n}{line['content']}{stop}"
-        if (line["role"] == "assistant"):
-            prompt += f"{ai_n}{line['content']}{stop}"
-    prompt += ai_n.rstrip()
-
-    return prompt
-
-def make_postData(body, chat=False, stream=False):
-    postData = {}
-    if (chat):
-        postData["prompt"] = convert_chat(body["messages"])
-    else:
-        postData["prompt"] = body["prompt"]
-    if(is_present(body, "temperature")): postData["temperature"] = body["temperature"]
-    if(is_present(body, "top_k")): postData["top_k"] = body["top_k"]
-    if(is_present(body, "top_p")): postData["top_p"] = body["top_p"]
-    if(is_present(body, "max_tokens")): postData["n_predict"] = body["max_tokens"]
-    if(is_present(body, "presence_penalty")): postData["presence_penalty"] = body["presence_penalty"]
-    if(is_present(body, "frequency_penalty")): postData["frequency_penalty"] = body["frequency_penalty"]
-    if(is_present(body, "repeat_penalty")): postData["repeat_penalty"] = body["repeat_penalty"]
-    if(is_present(body, "mirostat")): postData["mirostat"] = body["mirostat"]
-    if(is_present(body, "mirostat_tau")): postData["mirostat_tau"] = body["mirostat_tau"]
-    if(is_present(body, "mirostat_eta")): postData["mirostat_eta"] = body["mirostat_eta"]
-    if(is_present(body, "seed")): postData["seed"] = body["seed"]
-    if(is_present(body, "grammar")): postData["grammar"] = body["grammar"]
-    if(is_present(body, "logit_bias")): postData["logit_bias"] = [[int(token), body["logit_bias"][token]] for token in body["logit_bias"].keys()]
-    if (args.stop != ""):
-        postData["stop"] = [args.stop]
-    else:
-        postData["stop"] = []
-    if(is_present(body, "stop")): postData["stop"] += body["stop"]
-    postData["n_keep"] = -1
-    postData["stream"] = stream
-    postData["cache_prompt"] = True
-    postData["slot_id"] = slot_id
-    return postData
-
-def make_resData(data, chat=False, promptToken=[]):
-    resData = {
-        "id": "chatcmpl" if (chat) else "cmpl",
-        "object": "chat.completion" if (chat) else "text_completion",
-        "created": int(time.time()),
-        "truncated": data["truncated"],
-        "model": "LLaMA_CPP",
-        "usage": {
-            "prompt_tokens": data["tokens_evaluated"],
-            "completion_tokens": data["tokens_predicted"],
-            "total_tokens": data["tokens_evaluated"] + data["tokens_predicted"]
-        }
-    }
-    if (len(promptToken) != 0):
-        resData["promptToken"] = promptToken
-    if (chat):
-        #only one choice is supported
-        resData["choices"] = [{
-            "index": 0,
-            "message": {
-                "role": "assistant",
-                "content": data["content"],
-            },
-            "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
-        }]
-    else:
-        #only one choice is supported
-        resData["choices"] = [{
-            "text": data["content"],
-            "index": 0,
-            "logprobs": None,
-            "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
-        }]
-    return resData
-
-def make_resData_stream(data, chat=False, time_now = 0, start=False):
-    resData = {
-        "id": "chatcmpl" if (chat) else "cmpl",
-        "object": "chat.completion.chunk" if (chat) else "text_completion.chunk",
-        "created": time_now,
-        "model": "LLaMA_CPP",
-        "choices": [
-            {
-                "finish_reason": None,
-                "index": 0
-            }
-        ]
-    }
-    slot_id = data.get("slot_id")
-    if (chat):
-        if (start):
-            resData["choices"][0]["delta"] =  {
-                "role": "assistant"
-            }
-        else:
-            resData["choices"][0]["delta"] =  {
-                "content": data["content"]
-            }
-            if (data["stop"]):
-                resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
-    else:
-        resData["choices"][0]["text"] = data["content"]
-        if (data["stop"]):
-            resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
-
-    return resData
-
-
-@app.route('/chat/completions', methods=['POST', 'OPTIONS'])
-@app.route('/v1/chat/completions', methods=['POST', 'OPTIONS'])
-def chat_completions():
-    if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
-        return Response(status=403)
-    if request.method == 'OPTIONS':
-        return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
-    body = request.get_json()
-    stream = False
-    tokenize = False
-    if(is_present(body, "stream")): stream = body["stream"]
-    if(is_present(body, "tokenize")): tokenize = body["tokenize"]
-    postData = make_postData(body, chat=True, stream=stream)
-
-    promptToken = []
-    if (tokenize):
-        tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
-        promptToken = tokenData["tokens"]
-
-    if (not stream):
-        data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
-        print(data.json())
-        resData = make_resData(data.json(), chat=True, promptToken=promptToken)
-        return jsonify(resData)
-    else:
-        def generate():
-            data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
-            time_now = int(time.time())
-            resData = make_resData_stream({}, chat=True, time_now=time_now, start=True)
-            yield 'data: {}\n\n'.format(json.dumps(resData))
-            for line in data.iter_lines():
-                if line:
-                    decoded_line = line.decode('utf-8')
-                    resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now)
-                    yield 'data: {}\n\n'.format(json.dumps(resData))
-        return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
-
-
-@app.route('/completions', methods=['POST', 'OPTIONS'])
-@app.route('/v1/completions', methods=['POST', 'OPTIONS'])
-def completion():
-    if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
-        return Response(status=403)
-    if request.method == 'OPTIONS':
-        return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
-    body = request.get_json()
-    stream = False
-    tokenize = False
-    if(is_present(body, "stream")): stream = body["stream"]
-    if(is_present(body, "tokenize")): tokenize = body["tokenize"]
-    postData = make_postData(body, chat=False, stream=stream)
-
-    promptToken = []
-    if (tokenize):
-        tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
-        promptToken = tokenData["tokens"]
-
-    if (not stream):
-        data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
-        print(data.json())
-        resData = make_resData(data.json(), chat=False, promptToken=promptToken)
-        return jsonify(resData)
-    else:
-        def generate():
-            data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
-            time_now = int(time.time())
-            for line in data.iter_lines():
-                if line:
-                    decoded_line = line.decode('utf-8')
-                    resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now)
-                    yield 'data: {}\n\n'.format(json.dumps(resData))
-        return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
-
-if __name__ == '__main__':
-    app.run(args.host, port=args.port)
diff --git a/examples/server/bench/README.md b/examples/server/bench/README.md
new file mode 100644
index 000000000..23a3ec975
--- /dev/null
+++ b/examples/server/bench/README.md
@@ -0,0 +1,120 @@
+### Server benchmark tools
+
+Benchmark is using [k6](https://k6.io/).
+
+##### Install k6 and sse extension
+
+SSE is not supported by default in k6, you have to build k6 with the [xk6-sse](https://github.com/phymbert/xk6-sse) extension.
+
+Example:
+```shell
+go install go.k6.io/xk6/cmd/xk6@latest
+xk6 build master \
+--with github.com/phymbert/xk6-sse
+```
+
+#### Download a dataset
+
+This dataset was originally proposed in [vLLM benchmarks](https://github.com/vllm-project/vllm/blob/main/benchmarks/README.md).
+
+```shell
+wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+```
+
+#### Download a model
+Example for PHI-2
+
+```shell
+../../../scripts/hf.sh --repo ggml-org/models --file phi-2/ggml-model-q4_0.gguf
+```
+
+#### Start the server
+The server must answer OAI Chat completion requests on `http://localhost:8080/v1` or according to the environment variable `SERVER_BENCH_URL`.
+
+Example:
+```shell
+server --host localhost --port 8080 \
+  --model ggml-model-q4_0.gguf \
+  --cont-batching \
+  --metrics \
+  --parallel 8 \
+  --batch-size 512 \
+  --ctx-size 4096 \
+  --log-format text \
+  -ngl 33
+```
+
+#### Run the benchmark
+
+For 500 chat completions request with 8 concurrent users during maximum 10 minutes, run:
+```shell
+./k6 run script.js --duration 10m --iterations 500 --vus 8
+```
+
+The benchmark values can be overridden with:
+- `SERVER_BENCH_URL` server url prefix for chat completions, default `http://localhost:8080/v1`
+- `SERVER_BENCH_N_PROMPTS` total prompts to randomly select in the benchmark, default `480`
+- `SERVER_BENCH_MODEL_ALIAS` model alias to pass in the completion request, default `my-model`
+- `SERVER_BENCH_MAX_TOKENS` max tokens to predict, default: `512`
+- `SERVER_BENCH_DATASET` path to the benchmark dataset file
+- `SERVER_BENCH_MAX_PROMPT_TOKENS` maximum prompt tokens to filter out in the dataset: default `1024`
+- `SERVER_BENCH_MAX_CONTEXT` maximum context size of the completions request to filter out in the dataset: prompt + predicted tokens, default `2048`
+
+Note: the local tokenizer is just a string space split, real number of tokens will differ.
+
+Or with [k6 options](https://k6.io/docs/using-k6/k6-options/reference/):
+
+```shell
+SERVER_BENCH_N_PROMPTS=500 k6 run script.js --duration 10m --iterations 500 --vus 8
+```
+
+To [debug http request](https://k6.io/docs/using-k6/http-debugging/) use `--http-debug="full"`.
+
+#### Metrics
+
+Following metrics are available computed from the OAI chat completions response `usage`:
+- `llamacpp_tokens_second` Trend of `usage.total_tokens / request duration`
+- `llamacpp_prompt_tokens` Trend of `usage.prompt_tokens`
+- `llamacpp_prompt_tokens_total_counter` Counter of `usage.prompt_tokens`
+- `llamacpp_completion_tokens` Trend of `usage.completion_tokens`
+- `llamacpp_completion_tokens_total_counter` Counter of `usage.completion_tokens`
+- `llamacpp_completions_truncated_rate` Rate of completions truncated, i.e. if `finish_reason === 'length'`
+- `llamacpp_completions_stop_rate` Rate of completions stopped by the model, i.e. if `finish_reason === 'stop'`
+
+The script will fail if too many completions are truncated, see `llamacpp_completions_truncated_rate`.
+
+K6 metrics might be compared against [server metrics](../README.md), with:
+
+```shell
+curl http://localhost:8080/metrics
+```
+
+### Using the CI python script
+The `bench.py` script does several steps:
+- start the server
+- define good variable for k6
+- run k6 script
+- extract metrics from prometheus
+
+It aims to be used in the CI, but you can run it manually:
+
+```shell
+LLAMA_SERVER_BIN_PATH=../../../cmake-build-release/bin/server python bench.py \
+              --runner-label local \
+              --name local \
+              --branch `git rev-parse --abbrev-ref HEAD` \
+              --commit `git rev-parse HEAD` \
+              --scenario script.js \
+              --duration 5m \
+              --hf-repo ggml-org/models	 \
+              --hf-file phi-2/ggml-model-q4_0.gguf \
+              --model-path-prefix models \
+              --parallel 4 \
+              -ngl 33 \
+              --batch-size 2048 \
+              --ubatch-size	256 \
+              --ctx-size 4096 \
+              --n-prompts 200 \
+              --max-prompt-tokens 256 \
+              --max-tokens 256
+```
diff --git a/examples/server/bench/bench.py b/examples/server/bench/bench.py
new file mode 100644
index 000000000..6ca637bdd
--- /dev/null
+++ b/examples/server/bench/bench.py
@@ -0,0 +1,308 @@
+import argparse
+import json
+import os
+import re
+import signal
+import socket
+import subprocess
+import sys
+import threading
+import time
+import traceback
+from contextlib import closing
+from datetime import datetime
+
+import matplotlib
+import matplotlib.dates
+import matplotlib.pyplot as plt
+import requests
+from statistics import mean
+
+
+def main(args_in: list[str] | None = None) -> None:
+    parser = argparse.ArgumentParser(description="Start server benchmark scenario")
+    parser.add_argument("--name", type=str, help="Bench name", required=True)
+    parser.add_argument("--runner-label", type=str, help="Runner label", required=True)
+    parser.add_argument("--branch", type=str, help="Branch name", default="detached")
+    parser.add_argument("--commit", type=str, help="Commit name", default="dirty")
+    parser.add_argument("--host", type=str, help="Server listen host", default="0.0.0.0")
+    parser.add_argument("--port", type=int, help="Server listen host", default="8080")
+    parser.add_argument("--model-path-prefix", type=str, help="Prefix where to store the model files", default="models")
+    parser.add_argument("--n-prompts", type=int,
+                        help="SERVER_BENCH_N_PROMPTS: total prompts to randomly select in the benchmark", required=True)
+    parser.add_argument("--max-prompt-tokens", type=int,
+                        help="SERVER_BENCH_MAX_PROMPT_TOKENS: maximum prompt tokens to filter out in the dataset",
+                        required=True)
+    parser.add_argument("--max-tokens", type=int,
+                        help="SERVER_BENCH_MAX_CONTEXT: maximum context size of the completions request to filter out in the dataset: prompt + predicted tokens",
+                        required=True)
+    parser.add_argument("--hf-repo", type=str, help="Hugging Face model repository", required=True)
+    parser.add_argument("--hf-file", type=str, help="Hugging Face model file", required=True)
+    parser.add_argument("-ngl", "--n-gpu-layers", type=int, help="layers to the GPU for computation", required=True)
+    parser.add_argument("--ctx-size", type=int, help="Set the size of the prompt context", required=True)
+    parser.add_argument("--parallel", type=int, help="Set the number of slots for process requests", required=True)
+    parser.add_argument("--batch-size", type=int, help="Set the batch size for prompt processing", required=True)
+    parser.add_argument("--ubatch-size", type=int, help="physical maximum batch size", required=True)
+    parser.add_argument("--scenario", type=str, help="Scenario to run", required=True)
+    parser.add_argument("--duration", type=str, help="Bench scenario", required=True)
+
+    args = parser.parse_args(args_in)
+
+    start_time = time.time()
+
+    # Start the server and performance scenario
+    try:
+        server_process = start_server(args)
+    except Exception:
+        print("bench: server start error :")
+        traceback.print_exc(file=sys.stdout)
+        sys.exit(1)
+
+    # start the benchmark
+    try:
+        start_benchmark(args)
+
+        iterations = 0
+        with open("results.github.env", 'w') as github_env:
+            # parse output
+            with open('k6-results.json', 'r') as bench_results:
+                # Load JSON data from file
+                data = json.load(bench_results)
+                for metric_name in data['metrics']:
+                    for metric_metric in data['metrics'][metric_name]:
+                        value = data['metrics'][metric_name][metric_metric]
+                        if isinstance(value, float) or isinstance(value, int):
+                            value = round(value, 2)
+                            data['metrics'][metric_name][metric_metric]=value
+                            github_env.write(
+                                f"{escape_metric_name(metric_name)}_{escape_metric_name(metric_metric)}={value}\n")
+                iterations = data['root_group']['checks']['success completion']['passes']
+
+    except Exception:
+        print("bench: error :")
+        traceback.print_exc(file=sys.stdout)
+
+    # Stop the server
+    if server_process:
+        try:
+            print(f"bench: shutting down server pid={server_process.pid} ...")
+            if os.name == 'nt':
+                interrupt = signal.CTRL_C_EVENT
+            else:
+                interrupt = signal.SIGINT
+            server_process.send_signal(interrupt)
+            server_process.wait(0.5)
+
+        except subprocess.TimeoutExpired:
+            print(f"server still alive after 500ms, force-killing pid={server_process.pid} ...")
+            server_process.kill()  # SIGKILL
+            server_process.wait()
+
+        while is_server_listening(args.host, args.port):
+            time.sleep(0.1)
+
+    title = (f"llama.cpp {args.name} on {args.runner_label}\n "
+             f"duration={args.duration} {iterations} iterations")
+    xlabel = (f"{args.hf_repo}/{args.hf_file}\n"
+              f"parallel={args.parallel} ctx-size={args.ctx_size} ngl={args.n_gpu_layers} batch-size={args.batch_size} ubatch-size={args.ubatch_size} pp={args.max_prompt_tokens} pp+tg={args.max_tokens}\n"
+              f"branch={args.branch} commit={args.commit}")
+
+    # Prometheus
+    end_time = time.time()
+    prometheus_metrics = {}
+    if is_server_listening("0.0.0.0", 9090):
+        metrics = ['prompt_tokens_seconds', 'predicted_tokens_seconds',
+                   'kv_cache_usage_ratio', 'requests_processing', 'requests_deferred']
+
+        for metric in metrics:
+            resp = requests.get(f"http://localhost:9090/api/v1/query_range",
+                                params={'query': 'llamacpp:' + metric, 'start': start_time, 'end': end_time, 'step': 2})
+
+            with open(f"{metric}.json", 'w') as metric_json:
+                metric_json.write(resp.text)
+
+            if resp.status_code != 200:
+                print(f"bench: unable to extract prometheus metric {metric}: {resp.text}")
+            else:
+                metric_data = resp.json()
+                values = metric_data['data']['result'][0]['values']
+                timestamps, metric_values = zip(*values)
+                metric_values = [float(value) for value in metric_values]
+                prometheus_metrics[metric] = metric_values
+                timestamps_dt = [datetime.fromtimestamp(int(ts)) for ts in timestamps]
+                plt.figure(figsize=(16, 10), dpi=80)
+                plt.plot(timestamps_dt, metric_values, label=metric)
+                plt.xticks(rotation=0, fontsize=14, horizontalalignment='center', alpha=.7)
+                plt.yticks(fontsize=12, alpha=.7)
+
+                ylabel = f"llamacpp:{metric}"
+                plt.title(title,
+                          fontsize=14, wrap=True)
+                plt.grid(axis='both', alpha=.3)
+                plt.ylabel(ylabel, fontsize=22)
+                plt.xlabel(xlabel, fontsize=14, wrap=True)
+                plt.gca().xaxis.set_major_locator(matplotlib.dates.MinuteLocator())
+                plt.gca().xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%Y-%m-%d %H:%M:%S"))
+                plt.gcf().autofmt_xdate()
+
+                # Remove borders
+                plt.gca().spines["top"].set_alpha(0.0)
+                plt.gca().spines["bottom"].set_alpha(0.3)
+                plt.gca().spines["right"].set_alpha(0.0)
+                plt.gca().spines["left"].set_alpha(0.3)
+
+                # Save the plot as a jpg image
+                plt.savefig(f'{metric}.jpg', dpi=60)
+                plt.close()
+
+                # Mermaid format in case images upload failed
+                with (open(f"{metric}.mermaid", 'w') as mermaid_f):
+                    mermaid = (
+                    f"""---
+config:
+    xyChart:
+        titleFontSize: 12
+        width: 900
+        height: 600
+    themeVariables:
+        xyChart:
+            titleColor: "#000000"
+---
+xychart-beta
+    title "{title}"
+    y-axis "llamacpp:{metric}"
+    x-axis "llamacpp:{metric}" {int(min(timestamps))} --> {int(max(timestamps))}
+    line [{', '.join([str(round(float(value), 2)) for value in metric_values])}]
+                    """)
+                    mermaid_f.write(mermaid)
+
+    # 140 chars max for commit status description
+    bench_results = {
+        "i": iterations,
+        "req": {
+            "p95": round(data['metrics']["http_req_duration"]["p(95)"], 2),
+            "avg": round(data['metrics']["http_req_duration"]["avg"], 2),
+        },
+        "pp": {
+            "p95": round(data['metrics']["llamacpp_prompt_processing_second"]["p(95)"], 2),
+            "avg": round(data['metrics']["llamacpp_prompt_processing_second"]["avg"], 2),
+            "0": round(mean(prometheus_metrics['prompt_tokens_seconds']), 2),
+        },
+        "tg": {
+            "p95": round(data['metrics']["llamacpp_tokens_second"]["p(95)"], 2),
+            "avg": round(data['metrics']["llamacpp_tokens_second"]["avg"], 2),
+            "0": round(mean(prometheus_metrics['predicted_tokens_seconds']), 2),
+        },
+    }
+    with open("results.github.env", 'a') as github_env:
+        github_env.write(f"BENCH_RESULTS={json.dumps(bench_results, indent=None, separators=(',', ':') )}\n")
+        github_env.write(f"BENCH_ITERATIONS={iterations}\n")
+
+        title = title.replace('\n', ' ')
+        xlabel = xlabel.replace('\n', ' ')
+        github_env.write(f"BENCH_GRAPH_TITLE={title}\n")
+        github_env.write(f"BENCH_GRAPH_XLABEL={xlabel}\n")
+
+
+def start_benchmark(args):
+    k6_path = './k6'
+    if 'BENCH_K6_BIN_PATH' in os.environ:
+        k6_path = os.environ['BENCH_K6_BIN_PATH']
+    k6_args = [
+        'run', args.scenario,
+        '--no-color',
+    ]
+    k6_args.extend(['--duration', args.duration])
+    k6_args.extend(['--iterations', args.n_prompts])
+    k6_args.extend(['--vus', args.parallel])
+    k6_args.extend(['--summary-export', 'k6-results.json'])
+    args = f"SERVER_BENCH_N_PROMPTS={args.n_prompts} SERVER_BENCH_MAX_PROMPT_TOKENS={args.max_prompt_tokens} SERVER_BENCH_MAX_CONTEXT={args.max_tokens} "
+    args = args + ' '.join([str(arg) for arg in [k6_path, *k6_args]])
+    print(f"bench: starting k6 with: {args}")
+    k6_completed = subprocess.run(args, shell=True, stdout=sys.stdout, stderr=sys.stderr)
+    if k6_completed.returncode != 0:
+        raise Exception("bench: unable to run k6")
+
+
+def start_server(args):
+    server_process = start_server_background(args)
+
+    attempts = 0
+    max_attempts = 20
+    if 'GITHUB_ACTIONS' in os.environ:
+        max_attempts *= 2
+
+    while not is_server_listening(args.host, args.port):
+        attempts += 1
+        if attempts > max_attempts:
+            assert False, "server not started"
+        print(f"bench:     waiting for server to start ...")
+        time.sleep(0.5)
+
+    print("bench: server started.")
+    return server_process
+
+
+def start_server_background(args):
+    # Start the server
+    server_path = '../../../build/bin/server'
+    if 'LLAMA_SERVER_BIN_PATH' in os.environ:
+        server_path = os.environ['LLAMA_SERVER_BIN_PATH']
+    server_args = [
+        '--host', args.host,
+        '--port', args.port,
+    ]
+    model_file = args.model_path_prefix + os.path.sep + args.hf_file
+    model_dir  = os.path.dirname(model_file)
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    server_args.extend(['--model', model_file])
+    server_args.extend(['--hf-repo', args.hf_repo])
+    server_args.extend(['--hf-file', args.hf_file])
+    server_args.extend(['--n-gpu-layers', args.n_gpu_layers])
+    server_args.extend(['--ctx-size', args.ctx_size])
+    server_args.extend(['--parallel', args.parallel])
+    server_args.extend(['--batch-size', args.batch_size])
+    server_args.extend(['--ubatch-size', args.ubatch_size])
+    server_args.extend(['--n-predict', args.max_tokens * 2])
+    server_args.extend(['--defrag-thold', "0.1"])
+    server_args.append('--cont-batching')
+    server_args.append('--metrics')
+    server_args.extend(['--log-format', "text"])
+    args = [str(arg) for arg in [server_path, *server_args]]
+    print(f"bench: starting server with: {' '.join(args)}")
+    pkwargs = {
+        'stdout': subprocess.PIPE,
+        'stderr': subprocess.PIPE
+    }
+    server_process = subprocess.Popen(
+        args,
+        **pkwargs)
+
+    def server_log(in_stream, out_stream):
+        for line in iter(in_stream.readline, b''):
+            print(line.decode('utf-8'), end='', file=out_stream)
+
+    thread_stdout = threading.Thread(target=server_log, args=(server_process.stdout, sys.stdout))
+    thread_stdout.start()
+    thread_stderr = threading.Thread(target=server_log, args=(server_process.stderr, sys.stderr))
+    thread_stderr.start()
+
+    return server_process
+
+
+def is_server_listening(server_fqdn, server_port):
+    with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
+        result = sock.connect_ex((server_fqdn, server_port))
+        _is_server_listening = result == 0
+        if _is_server_listening:
+            print(f"server is listening on {server_fqdn}:{server_port}...")
+        return _is_server_listening
+
+
+def escape_metric_name(metric_name):
+    return re.sub('[^A-Z0-9]', '_', metric_name.upper())
+
+
+if __name__ == '__main__':
+    main()
diff --git a/examples/server/bench/prometheus.yml b/examples/server/bench/prometheus.yml
new file mode 100644
index 000000000..b15ee5244
--- /dev/null
+++ b/examples/server/bench/prometheus.yml
@@ -0,0 +1,9 @@
+global:
+  scrape_interval:     10s
+  external_labels:
+    llamacpp: 'server'
+
+scrape_configs:
+  - job_name: 'llama.cpp server'
+    static_configs:
+      - targets: ['localhost:8080']
diff --git a/examples/server/bench/requirements.txt b/examples/server/bench/requirements.txt
new file mode 100644
index 000000000..66ed226ed
--- /dev/null
+++ b/examples/server/bench/requirements.txt
@@ -0,0 +1,2 @@
+matplotlib
+requests
diff --git a/examples/server/bench/script.js b/examples/server/bench/script.js
new file mode 100644
index 000000000..c4c486cdf
--- /dev/null
+++ b/examples/server/bench/script.js
@@ -0,0 +1,149 @@
+import sse from 'k6/x/sse'
+import {check, sleep} from 'k6'
+import {SharedArray} from 'k6/data'
+import {Counter, Rate, Trend} from 'k6/metrics'
+import exec from 'k6/execution';
+
+// Server chat completions prefix
+const server_url = __ENV.SERVER_BENCH_URL ? __ENV.SERVER_BENCH_URL : 'http://localhost:8080/v1'
+
+// Number of total prompts in the dataset - default 10m / 10 seconds/request * number of users
+const n_prompt = __ENV.SERVER_BENCH_N_PROMPTS ? parseInt(__ENV.SERVER_BENCH_N_PROMPTS) : 600 / 10 * 8
+
+// Model name to request
+const model = __ENV.SERVER_BENCH_MODEL_ALIAS ? __ENV.SERVER_BENCH_MODEL_ALIAS : 'my-model'
+
+// Dataset path
+const dataset_path = __ENV.SERVER_BENCH_DATASET ? __ENV.SERVER_BENCH_DATASET : './ShareGPT_V3_unfiltered_cleaned_split.json'
+
+// Max tokens to predict
+const max_tokens = __ENV.SERVER_BENCH_MAX_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_TOKENS) : 512
+
+// Max prompt tokens
+const n_prompt_tokens = __ENV.SERVER_BENCH_MAX_PROMPT_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_PROMPT_TOKENS) : 1024
+
+// Max slot context
+const n_ctx_slot = __ENV.SERVER_BENCH_MAX_CONTEXT ? parseInt(__ENV.SERVER_BENCH_MAX_CONTEXT) : 2048
+
+export function setup() {
+    console.info(`Benchmark config: server_url=${server_url} n_prompt=${n_prompt} model=${model} dataset_path=${dataset_path} max_tokens=${max_tokens}`)
+}
+
+const data = new SharedArray('conversations', function () {
+    const tokenizer = (message) => message.split(/[\s,'".?]/)
+
+    return JSON.parse(open(dataset_path))
+        // Filter out the conversations with less than 2 turns.
+        .filter(data => data["conversations"].length >= 2)
+        .filter(data => data["conversations"][0]["from"] === "human")
+        .map(data => {
+            return {
+                prompt: data["conversations"][0]["value"],
+                n_prompt_tokens: tokenizer(data["conversations"][0]["value"]).length,
+                n_completion_tokens: tokenizer(data["conversations"][1]["value"]).length,
+            }
+        })
+        // Filter out too short sequences
+        .filter(conv => conv.n_prompt_tokens >= 4 && conv.n_completion_tokens >= 4)
+        // Filter out too long sequences.
+        .filter(conv => conv.n_prompt_tokens <= n_prompt_tokens && conv.n_prompt_tokens + conv.n_completion_tokens <= n_ctx_slot)
+        // Keep only first n prompts
+        .slice(0, n_prompt)
+})
+
+const llamacpp_prompt_tokens = new Trend('llamacpp_prompt_tokens')
+const llamacpp_completion_tokens = new Trend('llamacpp_completion_tokens')
+
+const llamacpp_tokens_second = new Trend('llamacpp_tokens_second')
+const llamacpp_prompt_processing_second = new Trend('llamacpp_prompt_processing_second')
+
+const llamacpp_prompt_tokens_total_counter = new Counter('llamacpp_prompt_tokens_total_counter')
+const llamacpp_completion_tokens_total_counter = new Counter('llamacpp_completion_tokens_total_counter')
+
+const llamacpp_completions_truncated_rate = new Rate('llamacpp_completions_truncated_rate')
+const llamacpp_completions_stop_rate = new Rate('llamacpp_completions_stop_rate')
+
+export const options = {
+    thresholds: {
+        llamacpp_completions_truncated_rate: [
+            // more than 80% of truncated input will abort the test
+            {threshold: 'rate < 0.8', abortOnFail: true, delayAbortEval: '1m'},
+        ],
+    },
+    duration: '10m',
+    vus: 8,
+}
+
+export default function () {
+    const conversation = data[exec.scenario.iterationInInstance % data.length]
+    const payload = {
+        "messages": [
+            {
+                "role": "system",
+                "content": "You are ChatGPT, an AI assistant.",
+            },
+            {
+                "role": "user",
+                "content": conversation.prompt,
+            }
+        ],
+        "model": model,
+        "stream": true,
+        "seed": 42,
+        "max_tokens": max_tokens
+    }
+
+    const params = {method: 'POST', body: JSON.stringify(payload)};
+
+    const startTime = new Date()
+    let promptEvalEndTime = null
+    let prompt_tokens = 0
+    let completions_tokens = 0
+    let finish_reason = null
+    const res = sse.open(`${server_url}/chat/completions`, params, function (client) {
+        client.on('event', function (event) {
+            if (promptEvalEndTime == null) {
+                promptEvalEndTime = new Date()
+            }
+
+            let chunk = JSON.parse(event.data)
+            let choice = chunk.choices[0]
+            if (choice.finish_reason) {
+                finish_reason = choice.finish_reason
+            }
+
+            if (chunk.usage) {
+                prompt_tokens = chunk.usage.prompt_tokens
+                llamacpp_prompt_tokens.add(prompt_tokens)
+                llamacpp_prompt_tokens_total_counter.add(prompt_tokens)
+
+                completions_tokens = chunk.usage.completion_tokens
+                llamacpp_completion_tokens.add(completions_tokens)
+                llamacpp_completion_tokens_total_counter.add(completions_tokens)
+            }
+        })
+
+        client.on('error', function (e) {
+            console.log('An unexpected error occurred: ', e.error());
+            throw e;
+        })
+    })
+
+    check(res, {'success completion': (r) => r.status === 200})
+
+    const endTime = new Date()
+
+    const promptEvalTime = promptEvalEndTime - startTime
+    if (promptEvalTime > 0) {
+        llamacpp_prompt_processing_second.add(prompt_tokens / (promptEvalEndTime - startTime) * 1.e3)
+    }
+
+    const completion_time = endTime - promptEvalEndTime
+    if (completions_tokens > 0 && completion_time > 0) {
+        llamacpp_tokens_second.add(completions_tokens / completion_time * 1.e3)
+    }
+    llamacpp_completions_truncated_rate.add(finish_reason === 'length')
+    llamacpp_completions_stop_rate.add(finish_reason === 'stop')
+
+    sleep(0.3)
+}
diff --git a/examples/server/chat.mjs b/examples/server/chat.mjs
index 219ebb51a..a79c8a3cd 100644
--- a/examples/server/chat.mjs
+++ b/examples/server/chat.mjs
@@ -26,8 +26,9 @@ const propOrder = grammarJsonSchemaPropOrder
 
 let grammar = null
 if (grammarJsonSchemaFile) {
-    const schema = JSON.parse(readFileSync(grammarJsonSchemaFile, 'utf-8'))
-    const converter = new SchemaConverter(propOrder)
+    let schema = JSON.parse(readFileSync(grammarJsonSchemaFile, 'utf-8'))
+    const converter = new SchemaConverter({prop_order: propOrder, allow_fetch: true})
+    schema = await converter.resolveRefs(schema, grammarJsonSchemaFile)
     converter.visit(schema, '')
     grammar = converter.formatGrammar()
 }
diff --git a/examples/server/chat.sh b/examples/server/chat.sh
index 014360121..da0a6ca68 100755
--- a/examples/server/chat.sh
+++ b/examples/server/chat.sh
@@ -48,6 +48,7 @@ chat_completion() {
         top_p: 0.9,
         n_keep: $n_keep,
         n_predict: 256,
+        cache_prompt: true,
         stop: ["\n### Human:"],
         stream: true
     }')"
diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp
index fe5f81228..9a7f3986d 100644
--- a/examples/server/completion.js.hpp
+++ b/examples/server/completion.js.hpp
@@ -43,407 +43,454 @@ unsigned char completion_js[] = {
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 };
-unsigned int completion_js_len = 5346;
+unsigned int completion_js_len = 5909;
diff --git a/examples/server/httplib.h b/examples/server/httplib.h
index 28746000c..f360bd93e 100644
--- a/examples/server/httplib.h
+++ b/examples/server/httplib.h
@@ -1,14 +1,14 @@
 //
 //  httplib.h
 //
-//  Copyright (c) 2023 Yuji Hirose. All rights reserved.
+//  Copyright (c) 2024 Yuji Hirose. All rights reserved.
 //  MIT License
 //
 
 #ifndef CPPHTTPLIB_HTTPLIB_H
 #define CPPHTTPLIB_HTTPLIB_H
 
-#define CPPHTTPLIB_VERSION "0.12.2"
+#define CPPHTTPLIB_VERSION "0.15.3"
 
 /*
  * Configuration
@@ -82,6 +82,10 @@
 #define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 8192
 #endif
 
+#ifndef CPPHTTPLIB_RANGE_MAX_COUNT
+#define CPPHTTPLIB_RANGE_MAX_COUNT 1024
+#endif
+
 #ifndef CPPHTTPLIB_TCP_NODELAY
 #define CPPHTTPLIB_TCP_NODELAY false
 #endif
@@ -160,10 +164,6 @@ using ssize_t = long;
 #define WSA_FLAG_NO_HANDLE_INHERIT 0x80
 #endif
 
-#ifndef strcasecmp
-#define strcasecmp _stricmp
-#endif // strcasecmp
-
 using socket_t = SOCKET;
 #ifdef CPPHTTPLIB_USE_POLL
 #define poll(fds, nfds, timeout) WSAPoll(fds, nfds, timeout)
@@ -172,9 +172,15 @@ using socket_t = SOCKET;
 #else // not _WIN32
 
 #include <arpa/inet.h>
-#ifndef _AIX
+#if !defined(_AIX) && !defined(__MVS__)
 #include <ifaddrs.h>
 #endif
+#ifdef __MVS__
+#include <strings.h>
+#ifndef NI_MAXHOST
+#define NI_MAXHOST 1025
+#endif
+#endif
 #include <net/if.h>
 #include <netdb.h>
 #include <netinet/in.h>
@@ -187,6 +193,7 @@ using socket_t = SOCKET;
 #endif
 #include <csignal>
 #include <pthread.h>
+#include <sys/mman.h>
 #include <sys/select.h>
 #include <sys/socket.h>
 #include <sys/un.h>
@@ -207,6 +214,7 @@ using socket_t = int;
 #include <condition_variable>
 #include <cstring>
 #include <errno.h>
+#include <exception>
 #include <fcntl.h>
 #include <fstream>
 #include <functional>
@@ -223,6 +231,9 @@ using socket_t = int;
 #include <string>
 #include <sys/stat.h>
 #include <thread>
+#include <unordered_map>
+#include <unordered_set>
+#include <utility>
 
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
 #ifdef _WIN32
@@ -237,7 +248,6 @@ using socket_t = int;
 
 #ifdef _MSC_VER
 #pragma comment(lib, "crypt32.lib")
-#pragma comment(lib, "cryptui.lib")
 #endif
 #elif defined(CPPHTTPLIB_USE_CERTS_FROM_MACOSX_KEYCHAIN) && defined(__APPLE__)
 #include <TargetConditionals.h>
@@ -259,10 +269,8 @@ using socket_t = int;
 #include <iostream>
 #include <sstream>
 
-#if OPENSSL_VERSION_NUMBER < 0x1010100fL
-#error Sorry, OpenSSL versions prior to 1.1.1 are not supported
-#elif OPENSSL_VERSION_NUMBER < 0x30000000L
-#define SSL_get1_peer_certificate SSL_get_peer_certificate
+#if OPENSSL_VERSION_NUMBER < 0x30000000L
+#error Sorry, OpenSSL versions prior to 3.0.0 are not supported
 #endif
 
 #endif
@@ -321,7 +329,7 @@ struct scope_exit {
   explicit scope_exit(std::function<void(void)> &&f)
       : exit_function(std::move(f)), execute_on_destruction{true} {}
 
-  scope_exit(scope_exit &&rhs)
+  scope_exit(scope_exit &&rhs) noexcept
       : exit_function(std::move(rhs.exit_function)),
         execute_on_destruction{rhs.execute_on_destruction} {
     rhs.release();
@@ -344,6 +352,81 @@ private:
 
 } // namespace detail
 
+enum StatusCode {
+  // Information responses
+  Continue_100 = 100,
+  SwitchingProtocol_101 = 101,
+  Processing_102 = 102,
+  EarlyHints_103 = 103,
+
+  // Successful responses
+  OK_200 = 200,
+  Created_201 = 201,
+  Accepted_202 = 202,
+  NonAuthoritativeInformation_203 = 203,
+  NoContent_204 = 204,
+  ResetContent_205 = 205,
+  PartialContent_206 = 206,
+  MultiStatus_207 = 207,
+  AlreadyReported_208 = 208,
+  IMUsed_226 = 226,
+
+  // Redirection messages
+  MultipleChoices_300 = 300,
+  MovedPermanently_301 = 301,
+  Found_302 = 302,
+  SeeOther_303 = 303,
+  NotModified_304 = 304,
+  UseProxy_305 = 305,
+  unused_306 = 306,
+  TemporaryRedirect_307 = 307,
+  PermanentRedirect_308 = 308,
+
+  // Client error responses
+  BadRequest_400 = 400,
+  Unauthorized_401 = 401,
+  PaymentRequired_402 = 402,
+  Forbidden_403 = 403,
+  NotFound_404 = 404,
+  MethodNotAllowed_405 = 405,
+  NotAcceptable_406 = 406,
+  ProxyAuthenticationRequired_407 = 407,
+  RequestTimeout_408 = 408,
+  Conflict_409 = 409,
+  Gone_410 = 410,
+  LengthRequired_411 = 411,
+  PreconditionFailed_412 = 412,
+  PayloadTooLarge_413 = 413,
+  UriTooLong_414 = 414,
+  UnsupportedMediaType_415 = 415,
+  RangeNotSatisfiable_416 = 416,
+  ExpectationFailed_417 = 417,
+  ImATeapot_418 = 418,
+  MisdirectedRequest_421 = 421,
+  UnprocessableContent_422 = 422,
+  Locked_423 = 423,
+  FailedDependency_424 = 424,
+  TooEarly_425 = 425,
+  UpgradeRequired_426 = 426,
+  PreconditionRequired_428 = 428,
+  TooManyRequests_429 = 429,
+  RequestHeaderFieldsTooLarge_431 = 431,
+  UnavailableForLegalReasons_451 = 451,
+
+  // Server error responses
+  InternalServerError_500 = 500,
+  NotImplemented_501 = 501,
+  BadGateway_502 = 502,
+  ServiceUnavailable_503 = 503,
+  GatewayTimeout_504 = 504,
+  HttpVersionNotSupported_505 = 505,
+  VariantAlsoNegotiates_506 = 506,
+  InsufficientStorage_507 = 507,
+  LoopDetected_508 = 508,
+  NotExtended_510 = 510,
+  NetworkAuthenticationRequired_511 = 511,
+};
+
 using Headers = std::multimap<std::string, std::string, detail::ci>;
 
 using Params = std::multimap<std::string, std::string>;
@@ -373,17 +456,18 @@ public:
   DataSink &operator=(DataSink &&) = delete;
 
   std::function<bool(const char *data, size_t data_len)> write;
+  std::function<bool()> is_writable;
   std::function<void()> done;
   std::function<void(const Headers &trailer)> done_with_trailer;
   std::ostream os;
 
 private:
-  class data_sink_streambuf : public std::streambuf {
+  class data_sink_streambuf final : public std::streambuf {
   public:
     explicit data_sink_streambuf(DataSink &sink) : sink_(sink) {}
 
   protected:
-    std::streamsize xsputn(const char *s, std::streamsize n) {
+    std::streamsize xsputn(const char *s, std::streamsize n) override {
       sink_.write(s, static_cast<size_t>(n));
       return n;
     }
@@ -465,6 +549,7 @@ struct Request {
   MultipartFormDataMap files;
   Ranges ranges;
   Match matches;
+  std::unordered_map<std::string, std::string> path_params;
 
   // for client
   ResponseHandler response_handler;
@@ -476,8 +561,7 @@ struct Request {
 
   bool has_header(const std::string &key) const;
   std::string get_header_value(const std::string &key, size_t id = 0) const;
-  template <typename T>
-  T get_header_value(const std::string &key, size_t id = 0) const;
+  uint64_t get_header_value_u64(const std::string &key, size_t id = 0) const;
   size_t get_header_value_count(const std::string &key) const;
   void set_header(const std::string &key, const std::string &val);
 
@@ -509,14 +593,14 @@ struct Response {
 
   bool has_header(const std::string &key) const;
   std::string get_header_value(const std::string &key, size_t id = 0) const;
-  template <typename T>
-  T get_header_value(const std::string &key, size_t id = 0) const;
+  uint64_t get_header_value_u64(const std::string &key, size_t id = 0) const;
   size_t get_header_value_count(const std::string &key) const;
   void set_header(const std::string &key, const std::string &val);
 
-  void set_redirect(const std::string &url, int status = 302);
+  void set_redirect(const std::string &url, int status = StatusCode::Found_302);
   void set_content(const char *s, size_t n, const std::string &content_type);
   void set_content(const std::string &s, const std::string &content_type);
+  void set_content(std::string &&s, const std::string &content_type);
 
   void set_content_provider(
       size_t length, const std::string &content_type, ContentProvider provider,
@@ -573,15 +657,16 @@ public:
   TaskQueue() = default;
   virtual ~TaskQueue() = default;
 
-  virtual void enqueue(std::function<void()> fn) = 0;
+  virtual bool enqueue(std::function<void()> fn) = 0;
   virtual void shutdown() = 0;
 
   virtual void on_idle() {}
 };
 
-class ThreadPool : public TaskQueue {
+class ThreadPool final : public TaskQueue {
 public:
-  explicit ThreadPool(size_t n) : shutdown_(false) {
+  explicit ThreadPool(size_t n, size_t mqr = 0)
+      : shutdown_(false), max_queued_requests_(mqr) {
     while (n) {
       threads_.emplace_back(worker(*this));
       n--;
@@ -591,13 +676,17 @@ public:
   ThreadPool(const ThreadPool &) = delete;
   ~ThreadPool() override = default;
 
-  void enqueue(std::function<void()> fn) override {
+  bool enqueue(std::function<void()> fn) override {
     {
       std::unique_lock<std::mutex> lock(mutex_);
+      if (max_queued_requests_ > 0 && jobs_.size() >= max_queued_requests_) {
+        return false;
+      }
       jobs_.push_back(std::move(fn));
     }
 
     cond_.notify_one();
+    return true;
   }
 
   void shutdown() override {
@@ -647,6 +736,7 @@ private:
   std::list<std::function<void()>> jobs_;
 
   bool shutdown_;
+  size_t max_queued_requests_ = 0;
 
   std::condition_variable cond_;
   std::mutex mutex_;
@@ -658,6 +748,82 @@ using SocketOptions = std::function<void(socket_t sock)>;
 
 void default_socket_options(socket_t sock);
 
+const char *status_message(int status);
+
+std::string get_bearer_token_auth(const Request &req);
+
+namespace detail {
+
+class MatcherBase {
+public:
+  virtual ~MatcherBase() = default;
+
+  // Match request path and populate its matches and
+  virtual bool match(Request &request) const = 0;
+};
+
+/**
+ * Captures parameters in request path and stores them in Request::path_params
+ *
+ * Capture name is a substring of a pattern from : to /.
+ * The rest of the pattern is matched agains the request path directly
+ * Parameters are captured starting from the next character after
+ * the end of the last matched static pattern fragment until the next /.
+ *
+ * Example pattern:
+ * "/path/fragments/:capture/more/fragments/:second_capture"
+ * Static fragments:
+ * "/path/fragments/", "more/fragments/"
+ *
+ * Given the following request path:
+ * "/path/fragments/:1/more/fragments/:2"
+ * the resulting capture will be
+ * {{"capture", "1"}, {"second_capture", "2"}}
+ */
+class PathParamsMatcher final : public MatcherBase {
+public:
+  PathParamsMatcher(const std::string &pattern);
+
+  bool match(Request &request) const override;
+
+private:
+  static constexpr char marker = ':';
+  // Treat segment separators as the end of path parameter capture
+  // Does not need to handle query parameters as they are parsed before path
+  // matching
+  static constexpr char separator = '/';
+
+  // Contains static path fragments to match against, excluding the '/' after
+  // path params
+  // Fragments are separated by path params
+  std::vector<std::string> static_fragments_;
+  // Stores the names of the path parameters to be used as keys in the
+  // Request::path_params map
+  std::vector<std::string> param_names_;
+};
+
+/**
+ * Performs std::regex_match on request path
+ * and stores the result in Request::matches
+ *
+ * Note that regex match is performed directly on the whole request.
+ * This means that wildcard patterns may match multiple path segments with /:
+ * "/begin/(.*)/end" will match both "/begin/middle/end" and "/begin/1/2/end".
+ */
+class RegexMatcher final : public MatcherBase {
+public:
+  RegexMatcher(const std::string &pattern) : regex_(pattern) {}
+
+  bool match(Request &request) const override;
+
+private:
+  std::regex regex_;
+};
+
+ssize_t write_headers(Stream &strm, const Headers &headers);
+
+} // namespace detail
+
 class Server {
 public:
   using Handler = std::function<void(const Request &, Response &)>;
@@ -702,6 +868,7 @@ public:
   bool remove_mount_point(const std::string &mount_point);
   Server &set_file_extension_and_mimetype_mapping(const std::string &ext,
                                                   const std::string &mime);
+  Server &set_default_file_mimetype(const std::string &mime);
   Server &set_file_request_handler(Handler handler);
 
   Server &set_error_handler(HandlerWithResponse handler);
@@ -718,6 +885,8 @@ public:
   Server &set_socket_options(SocketOptions socket_options);
 
   Server &set_default_headers(Headers headers);
+  Server &
+  set_header_writer(std::function<ssize_t(Stream &, Headers &)> const &writer);
 
   Server &set_keep_alive_max_count(size_t count);
   Server &set_keep_alive_timeout(time_t sec);
@@ -765,9 +934,14 @@ protected:
   size_t payload_max_length_ = CPPHTTPLIB_PAYLOAD_MAX_LENGTH;
 
 private:
-  using Handlers = std::vector<std::pair<std::regex, Handler>>;
+  using Handlers =
+      std::vector<std::pair<std::unique_ptr<detail::MatcherBase>, Handler>>;
   using HandlersForContentReader =
-      std::vector<std::pair<std::regex, HandlerWithContentReader>>;
+      std::vector<std::pair<std::unique_ptr<detail::MatcherBase>,
+                            HandlerWithContentReader>>;
+
+  static std::unique_ptr<detail::MatcherBase>
+  make_matcher(const std::string &pattern);
 
   socket_t create_server_socket(const std::string &host, int port,
                                 int socket_flags,
@@ -778,16 +952,16 @@ private:
   bool routing(Request &req, Response &res, Stream &strm);
   bool handle_file_request(const Request &req, Response &res,
                            bool head = false);
-  bool dispatch_request(Request &req, Response &res, const Handlers &handlers);
-  bool
-  dispatch_request_for_content_reader(Request &req, Response &res,
-                                      ContentReader content_reader,
-                                      const HandlersForContentReader &handlers);
+  bool dispatch_request(Request &req, Response &res,
+                        const Handlers &handlers) const;
+  bool dispatch_request_for_content_reader(
+      Request &req, Response &res, ContentReader content_reader,
+      const HandlersForContentReader &handlers) const;
 
-  bool parse_request_line(const char *s, Request &req);
+  bool parse_request_line(const char *s, Request &req) const;
   void apply_ranges(const Request &req, Response &res,
-                    std::string &content_type, std::string &boundary);
-  bool write_response(Stream &strm, bool close_connection, const Request &req,
+                    std::string &content_type, std::string &boundary) const;
+  bool write_response(Stream &strm, bool close_connection, Request &req,
                       Response &res);
   bool write_response_with_content(Stream &strm, bool close_connection,
                                    const Request &req, Response &res);
@@ -806,21 +980,23 @@ private:
   bool read_content_core(Stream &strm, Request &req, Response &res,
                          ContentReceiver receiver,
                          MultipartContentHeader multipart_header,
-                         ContentReceiver multipart_receiver);
+                         ContentReceiver multipart_receiver) const;
 
   virtual bool process_and_close_socket(socket_t sock);
 
+  std::atomic<bool> is_running_{false};
+  std::atomic<bool> done_{false};
+
   struct MountPointEntry {
     std::string mount_point;
     std::string base_dir;
     Headers headers;
   };
   std::vector<MountPointEntry> base_dirs_;
-
-  std::atomic<bool> is_running_{false};
-  std::atomic<bool> done_{false};
   std::map<std::string, std::string> file_extension_and_mimetype_map_;
+  std::string default_file_mimetype_ = "application/octet-stream";
   Handler file_request_handler_;
+
   Handlers get_handlers_;
   Handlers post_handlers_;
   HandlersForContentReader post_handlers_for_content_reader_;
@@ -831,18 +1007,22 @@ private:
   Handlers delete_handlers_;
   HandlersForContentReader delete_handlers_for_content_reader_;
   Handlers options_handlers_;
+
   HandlerWithResponse error_handler_;
   ExceptionHandler exception_handler_;
   HandlerWithResponse pre_routing_handler_;
   Handler post_routing_handler_;
-  Logger logger_;
   Expect100ContinueHandler expect_100_continue_handler_;
 
+  Logger logger_;
+
   int address_family_ = AF_UNSPEC;
   bool tcp_nodelay_ = CPPHTTPLIB_TCP_NODELAY;
   SocketOptions socket_options_ = default_socket_options;
 
   Headers default_headers_;
+  std::function<ssize_t(Stream &, Headers &)> header_writer_ =
+      detail::write_headers;
 };
 
 enum class Error {
@@ -860,17 +1040,19 @@ enum class Error {
   UnsupportedMultipartBoundaryChars,
   Compression,
   ConnectionTimeout,
+  ProxyConnection,
 
   // For internal use only
   SSLPeerCouldBeClosed_,
 };
 
-std::string to_string(const Error error);
+std::string to_string(Error error);
 
 std::ostream &operator<<(std::ostream &os, const Error &obj);
 
 class Result {
 public:
+  Result() = default;
   Result(std::unique_ptr<Response> &&res, Error err,
          Headers &&request_headers = Headers{})
       : res_(std::move(res)), err_(err),
@@ -893,13 +1075,13 @@ public:
   bool has_request_header(const std::string &key) const;
   std::string get_request_header_value(const std::string &key,
                                        size_t id = 0) const;
-  template <typename T>
-  T get_request_header_value(const std::string &key, size_t id = 0) const;
+  uint64_t get_request_header_value_u64(const std::string &key,
+                                        size_t id = 0) const;
   size_t get_request_header_value_count(const std::string &key) const;
 
 private:
   std::unique_ptr<Response> res_;
-  Error err_;
+  Error err_ = Error::Unknown;
   Headers request_headers_;
 };
 
@@ -1059,16 +1241,21 @@ public:
   bool send(Request &req, Response &res, Error &error);
   Result send(const Request &req);
 
-  size_t is_socket_open() const;
-
-  socket_t socket() const;
-
   void stop();
 
+  std::string host() const;
+  int port() const;
+
+  size_t is_socket_open() const;
+  socket_t socket() const;
+
   void set_hostname_addr_map(std::map<std::string, std::string> addr_map);
 
   void set_default_headers(Headers headers);
 
+  void
+  set_header_writer(std::function<ssize_t(Stream &, Headers &)> const &writer);
+
   void set_address_family(int family);
   void set_tcp_nodelay(bool on);
   void set_socket_options(SocketOptions socket_options);
@@ -1117,6 +1304,7 @@ public:
   void set_ca_cert_path(const std::string &ca_cert_file_path,
                         const std::string &ca_cert_dir_path = std::string());
   void set_ca_cert_store(X509_STORE *ca_cert_store);
+  X509_STORE *create_ca_cert_store(const char *ca_cert, std::size_t size) const;
 #endif
 
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
@@ -1145,14 +1333,14 @@ protected:
   // Also, shutdown_ssl and close_socket should also NOT be called concurrently
   // with a DIFFERENT thread sending requests using that socket.
   virtual void shutdown_ssl(Socket &socket, bool shutdown_gracefully);
-  void shutdown_socket(Socket &socket);
+  void shutdown_socket(Socket &socket) const;
   void close_socket(Socket &socket);
 
   bool process_request(Stream &strm, Request &req, Response &res,
                        bool close_connection, Error &error);
 
   bool write_content_with_provider(Stream &strm, const Request &req,
-                                   Error &error);
+                                   Error &error) const;
 
   void copy_settings(const ClientImpl &rhs);
 
@@ -1177,6 +1365,10 @@ protected:
   // Default headers
   Headers default_headers_;
 
+  // Header writer
+  std::function<ssize_t(Stream &, Headers &)> header_writer_ =
+      detail::write_headers;
+
   // Settings
   std::string client_cert_path_;
   std::string client_key_path_;
@@ -1239,7 +1431,8 @@ private:
   Result send_(Request &&req);
 
   socket_t create_client_socket(Error &error) const;
-  bool read_response_line(Stream &strm, const Request &req, Response &res);
+  bool read_response_line(Stream &strm, const Request &req,
+                          Response &res) const;
   bool write_request(Stream &strm, Request &req, bool close_connection,
                      Error &error);
   bool redirect(Request &req, Response &res, Error &error);
@@ -1258,7 +1451,7 @@ private:
       const std::string &content_type);
   ContentProviderWithoutLength get_multipart_content_provider(
       const std::string &boundary, const MultipartFormDataItems &items,
-      const MultipartFormDataProviderItems &provider_items);
+      const MultipartFormDataProviderItems &provider_items) const;
 
   std::string adjust_host_string(const std::string &host) const;
 
@@ -1431,16 +1624,21 @@ public:
   bool send(Request &req, Response &res, Error &error);
   Result send(const Request &req);
 
-  size_t is_socket_open() const;
-
-  socket_t socket() const;
-
   void stop();
 
+  std::string host() const;
+  int port() const;
+
+  size_t is_socket_open() const;
+  socket_t socket() const;
+
   void set_hostname_addr_map(std::map<std::string, std::string> addr_map);
 
   void set_default_headers(Headers headers);
 
+  void
+  set_header_writer(std::function<ssize_t(Stream &, Headers &)> const &writer);
+
   void set_address_family(int family);
   void set_tcp_nodelay(bool on);
   void set_socket_options(SocketOptions socket_options);
@@ -1497,6 +1695,7 @@ public:
                         const std::string &ca_cert_dir_path = std::string());
 
   void set_ca_cert_store(X509_STORE *ca_cert_store);
+  void load_ca_cert_store(const char *ca_cert, std::size_t size);
 
   long get_openssl_verify_result() const;
 
@@ -1538,7 +1737,7 @@ private:
   std::mutex ctx_mutex_;
 };
 
-class SSLClient : public ClientImpl {
+class SSLClient final : public ClientImpl {
 public:
   explicit SSLClient(const std::string &host);
 
@@ -1546,16 +1745,19 @@ public:
 
   explicit SSLClient(const std::string &host, int port,
                      const std::string &client_cert_path,
-                     const std::string &client_key_path);
+                     const std::string &client_key_path,
+                     const std::string &private_key_password = std::string());
 
   explicit SSLClient(const std::string &host, int port, X509 *client_cert,
-                     EVP_PKEY *client_key);
+                     EVP_PKEY *client_key,
+                     const std::string &private_key_password = std::string());
 
   ~SSLClient() override;
 
   bool is_valid() const override;
 
   void set_ca_cert_store(X509_STORE *ca_cert_store);
+  void load_ca_cert_store(const char *ca_cert, std::size_t size);
 
   long get_openssl_verify_result() const;
 
@@ -1564,7 +1766,7 @@ public:
 private:
   bool create_and_connect_socket(Socket &socket, Error &error) override;
   void shutdown_ssl(Socket &socket, bool shutdown_gracefully) override;
-  void shutdown_ssl_impl(Socket &socket, bool shutdown_socket);
+  void shutdown_ssl_impl(Socket &socket, bool shutdown_gracefully);
 
   bool process_socket(const Socket &socket,
                       std::function<bool(Stream &strm)> callback) override;
@@ -1608,15 +1810,9 @@ inline void duration_to_sec_and_usec(const T &duration, U callback) {
   callback(static_cast<time_t>(sec), static_cast<time_t>(usec));
 }
 
-template <typename T>
-inline T get_header_value(const Headers & /*headers*/,
-                          const std::string & /*key*/, size_t /*id*/ = 0,
-                          uint64_t /*def*/ = 0) {}
-
-template <>
-inline uint64_t get_header_value<uint64_t>(const Headers &headers,
-                                           const std::string &key, size_t id,
-                                           uint64_t def) {
+inline uint64_t get_header_value_u64(const Headers &headers,
+                                     const std::string &key, size_t id,
+                                     uint64_t def) {
   auto rng = headers.equal_range(key);
   auto it = rng.first;
   std::advance(it, static_cast<ssize_t>(id));
@@ -1628,14 +1824,14 @@ inline uint64_t get_header_value<uint64_t>(const Headers &headers,
 
 } // namespace detail
 
-template <typename T>
-inline T Request::get_header_value(const std::string &key, size_t id) const {
-  return detail::get_header_value<T>(headers, key, id, 0);
+inline uint64_t Request::get_header_value_u64(const std::string &key,
+                                              size_t id) const {
+  return detail::get_header_value_u64(headers, key, id, 0);
 }
 
-template <typename T>
-inline T Response::get_header_value(const std::string &key, size_t id) const {
-  return detail::get_header_value<T>(headers, key, id, 0);
+inline uint64_t Response::get_header_value_u64(const std::string &key,
+                                               size_t id) const {
+  return detail::get_header_value_u64(headers, key, id, 0);
 }
 
 template <typename... Args>
@@ -1665,21 +1861,106 @@ inline ssize_t Stream::write_format(const char *fmt, const Args &...args) {
 inline void default_socket_options(socket_t sock) {
   int yes = 1;
 #ifdef _WIN32
-  setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, reinterpret_cast<char *>(&yes),
-             sizeof(yes));
+  setsockopt(sock, SOL_SOCKET, SO_REUSEADDR,
+             reinterpret_cast<const char *>(&yes), sizeof(yes));
   setsockopt(sock, SOL_SOCKET, SO_EXCLUSIVEADDRUSE,
-             reinterpret_cast<char *>(&yes), sizeof(yes));
+             reinterpret_cast<const char *>(&yes), sizeof(yes));
 #else
 #ifdef SO_REUSEPORT
-  setsockopt(sock, SOL_SOCKET, SO_REUSEPORT, reinterpret_cast<void *>(&yes),
-             sizeof(yes));
+  setsockopt(sock, SOL_SOCKET, SO_REUSEPORT,
+             reinterpret_cast<const void *>(&yes), sizeof(yes));
 #else
-  setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, reinterpret_cast<void *>(&yes),
-             sizeof(yes));
+  setsockopt(sock, SOL_SOCKET, SO_REUSEADDR,
+             reinterpret_cast<const void *>(&yes), sizeof(yes));
 #endif
 #endif
 }
 
+inline const char *status_message(int status) {
+  switch (status) {
+  case StatusCode::Continue_100: return "Continue";
+  case StatusCode::SwitchingProtocol_101: return "Switching Protocol";
+  case StatusCode::Processing_102: return "Processing";
+  case StatusCode::EarlyHints_103: return "Early Hints";
+  case StatusCode::OK_200: return "OK";
+  case StatusCode::Created_201: return "Created";
+  case StatusCode::Accepted_202: return "Accepted";
+  case StatusCode::NonAuthoritativeInformation_203:
+    return "Non-Authoritative Information";
+  case StatusCode::NoContent_204: return "No Content";
+  case StatusCode::ResetContent_205: return "Reset Content";
+  case StatusCode::PartialContent_206: return "Partial Content";
+  case StatusCode::MultiStatus_207: return "Multi-Status";
+  case StatusCode::AlreadyReported_208: return "Already Reported";
+  case StatusCode::IMUsed_226: return "IM Used";
+  case StatusCode::MultipleChoices_300: return "Multiple Choices";
+  case StatusCode::MovedPermanently_301: return "Moved Permanently";
+  case StatusCode::Found_302: return "Found";
+  case StatusCode::SeeOther_303: return "See Other";
+  case StatusCode::NotModified_304: return "Not Modified";
+  case StatusCode::UseProxy_305: return "Use Proxy";
+  case StatusCode::unused_306: return "unused";
+  case StatusCode::TemporaryRedirect_307: return "Temporary Redirect";
+  case StatusCode::PermanentRedirect_308: return "Permanent Redirect";
+  case StatusCode::BadRequest_400: return "Bad Request";
+  case StatusCode::Unauthorized_401: return "Unauthorized";
+  case StatusCode::PaymentRequired_402: return "Payment Required";
+  case StatusCode::Forbidden_403: return "Forbidden";
+  case StatusCode::NotFound_404: return "Not Found";
+  case StatusCode::MethodNotAllowed_405: return "Method Not Allowed";
+  case StatusCode::NotAcceptable_406: return "Not Acceptable";
+  case StatusCode::ProxyAuthenticationRequired_407:
+    return "Proxy Authentication Required";
+  case StatusCode::RequestTimeout_408: return "Request Timeout";
+  case StatusCode::Conflict_409: return "Conflict";
+  case StatusCode::Gone_410: return "Gone";
+  case StatusCode::LengthRequired_411: return "Length Required";
+  case StatusCode::PreconditionFailed_412: return "Precondition Failed";
+  case StatusCode::PayloadTooLarge_413: return "Payload Too Large";
+  case StatusCode::UriTooLong_414: return "URI Too Long";
+  case StatusCode::UnsupportedMediaType_415: return "Unsupported Media Type";
+  case StatusCode::RangeNotSatisfiable_416: return "Range Not Satisfiable";
+  case StatusCode::ExpectationFailed_417: return "Expectation Failed";
+  case StatusCode::ImATeapot_418: return "I'm a teapot";
+  case StatusCode::MisdirectedRequest_421: return "Misdirected Request";
+  case StatusCode::UnprocessableContent_422: return "Unprocessable Content";
+  case StatusCode::Locked_423: return "Locked";
+  case StatusCode::FailedDependency_424: return "Failed Dependency";
+  case StatusCode::TooEarly_425: return "Too Early";
+  case StatusCode::UpgradeRequired_426: return "Upgrade Required";
+  case StatusCode::PreconditionRequired_428: return "Precondition Required";
+  case StatusCode::TooManyRequests_429: return "Too Many Requests";
+  case StatusCode::RequestHeaderFieldsTooLarge_431:
+    return "Request Header Fields Too Large";
+  case StatusCode::UnavailableForLegalReasons_451:
+    return "Unavailable For Legal Reasons";
+  case StatusCode::NotImplemented_501: return "Not Implemented";
+  case StatusCode::BadGateway_502: return "Bad Gateway";
+  case StatusCode::ServiceUnavailable_503: return "Service Unavailable";
+  case StatusCode::GatewayTimeout_504: return "Gateway Timeout";
+  case StatusCode::HttpVersionNotSupported_505:
+    return "HTTP Version Not Supported";
+  case StatusCode::VariantAlsoNegotiates_506: return "Variant Also Negotiates";
+  case StatusCode::InsufficientStorage_507: return "Insufficient Storage";
+  case StatusCode::LoopDetected_508: return "Loop Detected";
+  case StatusCode::NotExtended_510: return "Not Extended";
+  case StatusCode::NetworkAuthenticationRequired_511:
+    return "Network Authentication Required";
+
+  default:
+  case StatusCode::InternalServerError_500: return "Internal Server Error";
+  }
+}
+
+inline std::string get_bearer_token_auth(const Request &req) {
+  if (req.has_header("Authorization")) {
+    static std::string BearerHeaderPrefix = "Bearer ";
+    return req.get_header_value("Authorization")
+        .substr(BearerHeaderPrefix.length());
+  }
+  return "";
+}
+
 template <class Rep, class Period>
 inline Server &
 Server::set_read_timeout(const std::chrono::duration<Rep, Period> &duration) {
@@ -1720,6 +2001,7 @@ inline std::string to_string(const Error error) {
     return "Unsupported HTTP multipart boundary characters";
   case Error::Compression: return "Compression failed";
   case Error::ConnectionTimeout: return "Connection timed out";
+  case Error::ProxyConnection: return "Proxy connection failed";
   case Error::Unknown: return "Unknown";
   default: break;
   }
@@ -1733,10 +2015,9 @@ inline std::ostream &operator<<(std::ostream &os, const Error &obj) {
   return os;
 }
 
-template <typename T>
-inline T Result::get_request_header_value(const std::string &key,
-                                          size_t id) const {
-  return detail::get_header_value<T>(request_headers_, key, id, 0);
+inline uint64_t Result::get_request_header_value_u64(const std::string &key,
+                                                     size_t id) const {
+  return detail::get_header_value_u64(request_headers_, key, id, 0);
 }
 
 template <class Rep, class Period>
@@ -1790,7 +2071,7 @@ void hosted_at(const std::string &hostname, std::vector<std::string> &addrs);
 
 std::string append_query_params(const std::string &path, const Params &params);
 
-std::pair<std::string, std::string> make_range_header(Ranges ranges);
+std::pair<std::string, std::string> make_range_header(const Ranges &ranges);
 
 std::pair<std::string, std::string>
 make_basic_authentication_header(const std::string &username,
@@ -1810,6 +2091,9 @@ std::string trim_copy(const std::string &s);
 void split(const char *b, const char *e, char d,
            std::function<void(const char *, const char *)> fn);
 
+void split(const char *b, const char *e, char d, size_t m,
+           std::function<void(const char *, const char *)> fn);
+
 bool process_client_socket(socket_t sock, time_t read_timeout_sec,
                            time_t read_timeout_usec, time_t write_timeout_sec,
                            time_t write_timeout_usec,
@@ -1844,7 +2128,7 @@ enum class EncodingType { None = 0, Gzip, Brotli };
 
 EncodingType encoding_type(const Request &req, const Response &res);
 
-class BufferStream : public Stream {
+class BufferStream final : public Stream {
 public:
   BufferStream() = default;
   ~BufferStream() override = default;
@@ -1884,19 +2168,19 @@ public:
                           Callback callback) = 0;
 };
 
-class nocompressor : public compressor {
+class nocompressor final : public compressor {
 public:
-  virtual ~nocompressor() = default;
+  ~nocompressor() override = default;
 
   bool compress(const char *data, size_t data_length, bool /*last*/,
                 Callback callback) override;
 };
 
 #ifdef CPPHTTPLIB_ZLIB_SUPPORT
-class gzip_compressor : public compressor {
+class gzip_compressor final : public compressor {
 public:
   gzip_compressor();
-  ~gzip_compressor();
+  ~gzip_compressor() override;
 
   bool compress(const char *data, size_t data_length, bool last,
                 Callback callback) override;
@@ -1906,10 +2190,10 @@ private:
   z_stream strm_;
 };
 
-class gzip_decompressor : public decompressor {
+class gzip_decompressor final : public decompressor {
 public:
   gzip_decompressor();
-  ~gzip_decompressor();
+  ~gzip_decompressor() override;
 
   bool is_valid() const override;
 
@@ -1923,7 +2207,7 @@ private:
 #endif
 
 #ifdef CPPHTTPLIB_BROTLI_SUPPORT
-class brotli_compressor : public compressor {
+class brotli_compressor final : public compressor {
 public:
   brotli_compressor();
   ~brotli_compressor();
@@ -1935,7 +2219,7 @@ private:
   BrotliEncoderState *state_ = nullptr;
 };
 
-class brotli_decompressor : public decompressor {
+class brotli_decompressor final : public decompressor {
 public:
   brotli_decompressor();
   ~brotli_decompressor();
@@ -1972,6 +2256,29 @@ private:
   std::string glowable_buffer_;
 };
 
+class mmap {
+public:
+  mmap(const char *path);
+  ~mmap();
+
+  bool open(const char *path);
+  void close();
+
+  bool is_open() const;
+  size_t size() const;
+  const char *data() const;
+
+private:
+#if defined(_WIN32)
+  HANDLE hFile_;
+  HANDLE hMapping_;
+#else
+  int fd_;
+#endif
+  size_t size_;
+  void *addr_;
+};
+
 } // namespace detail
 
 // ----------------------------------------------------------------------------
@@ -2003,7 +2310,7 @@ inline bool from_hex_to_i(const std::string &s, size_t i, size_t cnt,
   val = 0;
   for (; cnt; i++, cnt--) {
     if (!s[i]) { return false; }
-    int v = 0;
+    auto v = 0;
     if (is_hex(s[i], v)) {
       val = val * 16 + v;
     } else {
@@ -2014,7 +2321,7 @@ inline bool from_hex_to_i(const std::string &s, size_t i, size_t cnt,
 }
 
 inline std::string from_i_to_hex(size_t n) {
-  const char *charset = "0123456789abcdef";
+  static const auto charset = "0123456789abcdef";
   std::string ret;
   do {
     ret = charset[n & 15] + ret;
@@ -2025,7 +2332,7 @@ inline std::string from_i_to_hex(size_t n) {
 
 inline size_t to_utf8(int code, char *buff) {
   if (code < 0x0080) {
-    buff[0] = (code & 0x7F);
+    buff[0] = static_cast<char>(code & 0x7F);
     return 1;
   } else if (code < 0x0800) {
     buff[0] = static_cast<char>(0xC0 | ((code >> 6) & 0x1F));
@@ -2064,8 +2371,8 @@ inline std::string base64_encode(const std::string &in) {
   std::string out;
   out.reserve(in.size());
 
-  int val = 0;
-  int valb = -6;
+  auto val = 0;
+  auto valb = -6;
 
   for (auto c : in) {
     val = (val << 8) + static_cast<uint8_t>(c);
@@ -2112,6 +2419,11 @@ inline bool is_valid_path(const std::string &path) {
     // Read component
     auto beg = i;
     while (i < path.size() && path[i] != '/') {
+      if (path[i] == '\0') {
+        return false;
+      } else if (path[i] == '\\') {
+        return false;
+      }
       i++;
     }
 
@@ -2196,7 +2508,7 @@ inline std::string decode_url(const std::string &s,
   for (size_t i = 0; i < s.size(); i++) {
     if (s[i] == '%' && i + 1 < s.size()) {
       if (s[i + 1] == 'u') {
-        int val = 0;
+        auto val = 0;
         if (from_hex_to_i(s, i + 2, 4, val)) {
           // 4 digits Unicode codes
           char buff[4];
@@ -2207,7 +2519,7 @@ inline std::string decode_url(const std::string &s,
           result += s[i];
         }
       } else {
-        int val = 0;
+        auto val = 0;
         if (from_hex_to_i(s, i + 1, 2, val)) {
           // 2 digits hex codes
           result += static_cast<char>(val);
@@ -2260,16 +2572,30 @@ inline std::string trim_copy(const std::string &s) {
   return s.substr(r.first, r.second - r.first);
 }
 
+inline std::string trim_double_quotes_copy(const std::string &s) {
+  if (s.length() >= 2 && s.front() == '"' && s.back() == '"') {
+    return s.substr(1, s.size() - 2);
+  }
+  return s;
+}
+
 inline void split(const char *b, const char *e, char d,
                   std::function<void(const char *, const char *)> fn) {
+  return split(b, e, d, (std::numeric_limits<size_t>::max)(), std::move(fn));
+}
+
+inline void split(const char *b, const char *e, char d, size_t m,
+                  std::function<void(const char *, const char *)> fn) {
   size_t i = 0;
   size_t beg = 0;
+  size_t count = 1;
 
   while (e ? (b + i < e) : (b[i] != '\0')) {
-    if (b[i] == d) {
+    if (b[i] == d && count < m) {
       auto r = trim(b, e, beg, i);
       if (r.first < r.second) { fn(&b[r.first], &b[r.second]); }
       beg = i + 1;
+      count++;
     }
     i++;
   }
@@ -2345,6 +2671,105 @@ inline void stream_line_reader::append(char c) {
   }
 }
 
+inline mmap::mmap(const char *path)
+#if defined(_WIN32)
+    : hFile_(NULL), hMapping_(NULL)
+#else
+    : fd_(-1)
+#endif
+      ,
+      size_(0), addr_(nullptr) {
+  open(path);
+}
+
+inline mmap::~mmap() { close(); }
+
+inline bool mmap::open(const char *path) {
+  close();
+
+#if defined(_WIN32)
+  std::wstring wpath;
+  for (size_t i = 0; i < strlen(path); i++) {
+    wpath += path[i];
+  }
+
+  hFile_ = ::CreateFile2(wpath.c_str(), GENERIC_READ, FILE_SHARE_READ,
+                         OPEN_EXISTING, NULL);
+
+  if (hFile_ == INVALID_HANDLE_VALUE) { return false; }
+
+  LARGE_INTEGER size{};
+  if (!::GetFileSizeEx(hFile_, &size)) { return false; }
+  size_ = static_cast<size_t>(size.QuadPart);
+
+  hMapping_ =
+      ::CreateFileMappingFromApp(hFile_, NULL, PAGE_READONLY, size_, NULL);
+
+  if (hMapping_ == NULL) {
+    close();
+    return false;
+  }
+
+  addr_ = ::MapViewOfFileFromApp(hMapping_, FILE_MAP_READ, 0, 0);
+#else
+  fd_ = ::open(path, O_RDONLY);
+  if (fd_ == -1) { return false; }
+
+  struct stat sb;
+  if (fstat(fd_, &sb) == -1) {
+    close();
+    return false;
+  }
+  size_ = static_cast<size_t>(sb.st_size);
+
+  addr_ = ::mmap(NULL, size_, PROT_READ, MAP_PRIVATE, fd_, 0);
+#endif
+
+  if (addr_ == nullptr) {
+    close();
+    return false;
+  }
+
+  return true;
+}
+
+inline bool mmap::is_open() const { return addr_ != nullptr; }
+
+inline size_t mmap::size() const { return size_; }
+
+inline const char *mmap::data() const {
+  return static_cast<const char *>(addr_);
+}
+
+inline void mmap::close() {
+#if defined(_WIN32)
+  if (addr_) {
+    ::UnmapViewOfFile(addr_);
+    addr_ = nullptr;
+  }
+
+  if (hMapping_) {
+    ::CloseHandle(hMapping_);
+    hMapping_ = NULL;
+  }
+
+  if (hFile_ != INVALID_HANDLE_VALUE) {
+    ::CloseHandle(hFile_);
+    hFile_ = INVALID_HANDLE_VALUE;
+  }
+#else
+  if (addr_ != nullptr) {
+    munmap(addr_, size_);
+    addr_ = nullptr;
+  }
+
+  if (fd_ != -1) {
+    ::close(fd_);
+    fd_ = -1;
+  }
+#endif
+  size_ = 0;
+}
 inline int close_socket(socket_t sock) {
 #ifdef _WIN32
   return closesocket(sock);
@@ -2354,7 +2779,7 @@ inline int close_socket(socket_t sock) {
 }
 
 template <typename T> inline ssize_t handle_EINTR(T fn) {
-  ssize_t res = false;
+  ssize_t res = 0;
   while (true) {
     res = fn();
     if (res < 0 && errno == EINTR) { continue; }
@@ -2399,7 +2824,7 @@ inline ssize_t select_read(socket_t sock, time_t sec, time_t usec) {
   return handle_EINTR([&]() { return poll(&pfd_read, 1, timeout); });
 #else
 #ifndef _WIN32
-  if (sock >= FD_SETSIZE) { return 1; }
+  if (sock >= FD_SETSIZE) { return -1; }
 #endif
 
   fd_set fds;
@@ -2427,7 +2852,7 @@ inline ssize_t select_write(socket_t sock, time_t sec, time_t usec) {
   return handle_EINTR([&]() { return poll(&pfd_read, 1, timeout); });
 #else
 #ifndef _WIN32
-  if (sock >= FD_SETSIZE) { return 1; }
+  if (sock >= FD_SETSIZE) { return -1; }
 #endif
 
   fd_set fds;
@@ -2458,7 +2883,7 @@ inline Error wait_until_socket_is_ready(socket_t sock, time_t sec,
   if (poll_res == 0) { return Error::ConnectionTimeout; }
 
   if (poll_res > 0 && pfd_read.revents & (POLLIN | POLLOUT)) {
-    int error = 0;
+    auto error = 0;
     socklen_t len = sizeof(error);
     auto res = getsockopt(sock, SOL_SOCKET, SO_ERROR,
                           reinterpret_cast<char *>(&error), &len);
@@ -2490,7 +2915,7 @@ inline Error wait_until_socket_is_ready(socket_t sock, time_t sec,
   if (ret == 0) { return Error::ConnectionTimeout; }
 
   if (ret > 0 && (FD_ISSET(sock, &fdsr) || FD_ISSET(sock, &fdsw))) {
-    int error = 0;
+    auto error = 0;
     socklen_t len = sizeof(error);
     auto res = getsockopt(sock, SOL_SOCKET, SO_ERROR,
                           reinterpret_cast<char *>(&error), &len);
@@ -2512,7 +2937,7 @@ inline bool is_socket_alive(socket_t sock) {
   return detail::read_socket(sock, &buf[0], sizeof(buf), MSG_PEEK) > 0;
 }
 
-class SocketStream : public Stream {
+class SocketStream final : public Stream {
 public:
   SocketStream(socket_t sock, time_t read_timeout_sec, time_t read_timeout_usec,
                time_t write_timeout_sec, time_t write_timeout_usec);
@@ -2537,11 +2962,11 @@ private:
   size_t read_buff_off_ = 0;
   size_t read_buff_content_size_ = 0;
 
-  static const size_t read_buff_size_ = 1024 * 4;
+  static const size_t read_buff_size_ = 1024l * 4;
 };
 
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
-class SSLSocketStream : public Stream {
+class SSLSocketStream final : public Stream {
 public:
   SSLSocketStream(socket_t sock, SSL *ssl, time_t read_timeout_sec,
                   time_t read_timeout_usec, time_t write_timeout_sec,
@@ -2666,7 +3091,7 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port,
 #ifndef _WIN32
   if (hints.ai_family == AF_UNIX) {
     const auto addrlen = host.length();
-    if (addrlen > sizeof(sockaddr_un::sun_path)) return INVALID_SOCKET;
+    if (addrlen > sizeof(sockaddr_un::sun_path)) { return INVALID_SOCKET; }
 
     auto sock = socket(hints.ai_family, hints.ai_socktype, hints.ai_protocol);
     if (sock != INVALID_SOCKET) {
@@ -2735,17 +3160,27 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port,
 #endif
 
     if (tcp_nodelay) {
-      int yes = 1;
-      setsockopt(sock, IPPROTO_TCP, TCP_NODELAY, reinterpret_cast<char *>(&yes),
-                 sizeof(yes));
+      auto yes = 1;
+#ifdef _WIN32
+      setsockopt(sock, IPPROTO_TCP, TCP_NODELAY,
+                 reinterpret_cast<const char *>(&yes), sizeof(yes));
+#else
+      setsockopt(sock, IPPROTO_TCP, TCP_NODELAY,
+                 reinterpret_cast<const void *>(&yes), sizeof(yes));
+#endif
     }
 
     if (socket_options) { socket_options(sock); }
 
     if (rp->ai_family == AF_INET6) {
-      int no = 0;
-      setsockopt(sock, IPPROTO_IPV6, IPV6_V6ONLY, reinterpret_cast<char *>(&no),
-                 sizeof(no));
+      auto no = 0;
+#ifdef _WIN32
+      setsockopt(sock, IPPROTO_IPV6, IPV6_V6ONLY,
+                 reinterpret_cast<const char *>(&no), sizeof(no));
+#else
+      setsockopt(sock, IPPROTO_IPV6, IPV6_V6ONLY,
+                 reinterpret_cast<const void *>(&no), sizeof(no));
+#endif
     }
 
     // bind or connect
@@ -2804,7 +3239,7 @@ inline bool bind_ip_address(socket_t sock, const std::string &host) {
   return ret;
 }
 
-#if !defined _WIN32 && !defined ANDROID && !defined _AIX
+#if !defined _WIN32 && !defined ANDROID && !defined _AIX && !defined __MVS__
 #define USE_IF2IP
 #endif
 
@@ -2860,7 +3295,7 @@ inline socket_t create_client_socket(
 #ifdef USE_IF2IP
           auto ip_from_if = if2ip(address_family, intf);
           if (ip_from_if.empty()) { ip_from_if = intf; }
-          if (!bind_ip_address(sock2, ip_from_if.c_str())) {
+          if (!bind_ip_address(sock2, ip_from_if)) {
             error = Error::BindIPAddress;
             return false;
           }
@@ -2888,13 +3323,14 @@ inline socket_t create_client_socket(
 #ifdef _WIN32
           auto timeout = static_cast<uint32_t>(read_timeout_sec * 1000 +
                                                read_timeout_usec / 1000);
-          setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO, (char *)&timeout,
-                     sizeof(timeout));
+          setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO,
+                     reinterpret_cast<const char *>(&timeout), sizeof(timeout));
 #else
           timeval tv;
           tv.tv_sec = static_cast<long>(read_timeout_sec);
           tv.tv_usec = static_cast<decltype(tv.tv_usec)>(read_timeout_usec);
-          setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO, (char *)&tv, sizeof(tv));
+          setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO,
+                     reinterpret_cast<const void *>(&tv), sizeof(tv));
 #endif
         }
         {
@@ -2902,13 +3338,14 @@ inline socket_t create_client_socket(
 #ifdef _WIN32
           auto timeout = static_cast<uint32_t>(write_timeout_sec * 1000 +
                                                write_timeout_usec / 1000);
-          setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO, (char *)&timeout,
-                     sizeof(timeout));
+          setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO,
+                     reinterpret_cast<const char *>(&timeout), sizeof(timeout));
 #else
           timeval tv;
           tv.tv_sec = static_cast<long>(write_timeout_sec);
           tv.tv_usec = static_cast<decltype(tv.tv_usec)>(write_timeout_usec);
-          setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO, (char *)&tv, sizeof(tv));
+          setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO,
+                     reinterpret_cast<const void *>(&tv), sizeof(tv));
 #endif
         }
 
@@ -3008,18 +3445,20 @@ inline constexpr unsigned int operator"" _t(const char *s, size_t l) {
 
 } // namespace udl
 
-inline const char *
+inline std::string
 find_content_type(const std::string &path,
-                  const std::map<std::string, std::string> &user_data) {
+                  const std::map<std::string, std::string> &user_data,
+                  const std::string &default_content_type) {
   auto ext = file_extension(path);
 
   auto it = user_data.find(ext);
-  if (it != user_data.end()) { return it->second.c_str(); }
+  if (it != user_data.end()) { return it->second; }
 
   using udl::operator""_t;
 
   switch (str2tag(ext)) {
-  default: return nullptr;
+  default: return default_content_type;
+
   case "css"_t: return "text/css";
   case "csv"_t: return "text/csv";
   case "htm"_t:
@@ -3072,76 +3511,6 @@ find_content_type(const std::string &path,
   }
 }
 
-inline const char *status_message(int status) {
-  switch (status) {
-  case 100: return "Continue";
-  case 101: return "Switching Protocol";
-  case 102: return "Processing";
-  case 103: return "Early Hints";
-  case 200: return "OK";
-  case 201: return "Created";
-  case 202: return "Accepted";
-  case 203: return "Non-Authoritative Information";
-  case 204: return "No Content";
-  case 205: return "Reset Content";
-  case 206: return "Partial Content";
-  case 207: return "Multi-Status";
-  case 208: return "Already Reported";
-  case 226: return "IM Used";
-  case 300: return "Multiple Choice";
-  case 301: return "Moved Permanently";
-  case 302: return "Found";
-  case 303: return "See Other";
-  case 304: return "Not Modified";
-  case 305: return "Use Proxy";
-  case 306: return "unused";
-  case 307: return "Temporary Redirect";
-  case 308: return "Permanent Redirect";
-  case 400: return "Bad Request";
-  case 401: return "Unauthorized";
-  case 402: return "Payment Required";
-  case 403: return "Forbidden";
-  case 404: return "Not Found";
-  case 405: return "Method Not Allowed";
-  case 406: return "Not Acceptable";
-  case 407: return "Proxy Authentication Required";
-  case 408: return "Request Timeout";
-  case 409: return "Conflict";
-  case 410: return "Gone";
-  case 411: return "Length Required";
-  case 412: return "Precondition Failed";
-  case 413: return "Payload Too Large";
-  case 414: return "URI Too Long";
-  case 415: return "Unsupported Media Type";
-  case 416: return "Range Not Satisfiable";
-  case 417: return "Expectation Failed";
-  case 418: return "I'm a teapot";
-  case 421: return "Misdirected Request";
-  case 422: return "Unprocessable Entity";
-  case 423: return "Locked";
-  case 424: return "Failed Dependency";
-  case 425: return "Too Early";
-  case 426: return "Upgrade Required";
-  case 428: return "Precondition Required";
-  case 429: return "Too Many Requests";
-  case 431: return "Request Header Fields Too Large";
-  case 451: return "Unavailable For Legal Reasons";
-  case 501: return "Not Implemented";
-  case 502: return "Bad Gateway";
-  case 503: return "Service Unavailable";
-  case 504: return "Gateway Timeout";
-  case 505: return "HTTP Version Not Supported";
-  case 506: return "Variant Also Negotiates";
-  case 507: return "Insufficient Storage";
-  case 508: return "Loop Detected";
-  case 510: return "Not Extended";
-  case 511: return "Network Authentication Required";
-
-  default:
-  case 500: return "Internal Server Error";
-  }
-}
-
 inline bool can_compress_content_type(const std::string &content_type) {
   using udl::operator""_t;
 
@@ -3218,7 +3587,7 @@ inline bool gzip_compressor::compress(const char *data, size_t data_length,
     data += strm_.avail_in;
 
     auto flush = (last && data_length == 0) ? Z_FINISH : Z_NO_FLUSH;
-    int ret = Z_OK;
+    auto ret = Z_OK;
 
     std::array<char, CPPHTTPLIB_COMPRESSION_BUFSIZ> buff{};
     do {
@@ -3262,7 +3631,7 @@ inline bool gzip_decompressor::decompress(const char *data, size_t data_length,
                                           Callback callback) {
   assert(is_valid_);
 
-  int ret = Z_OK;
+  auto ret = Z_OK;
 
   do {
     constexpr size_t max_avail_in =
@@ -3276,16 +3645,12 @@ inline bool gzip_decompressor::decompress(const char *data, size_t data_length,
     data += strm_.avail_in;
 
     std::array<char, CPPHTTPLIB_COMPRESSION_BUFSIZ> buff{};
-    while (strm_.avail_in > 0) {
+    while (strm_.avail_in > 0 && ret == Z_OK) {
       strm_.avail_out = static_cast<uInt>(buff.size());
       strm_.next_out = reinterpret_cast<Bytef *>(buff.data());
 
-      auto prev_avail_in = strm_.avail_in;
-
       ret = inflate(&strm_, Z_NO_FLUSH);
 
-      if (prev_avail_in - strm_.avail_in == 0) { return false; }
-
       assert(ret != Z_STREAM_ERROR);
       switch (ret) {
       case Z_NEED_DICT:
@@ -3298,7 +3663,7 @@ inline bool gzip_decompressor::decompress(const char *data, size_t data_length,
       }
     }
 
-    if (ret != Z_OK && ret != Z_STREAM_END) return false;
+    if (ret != Z_OK && ret != Z_STREAM_END) { return false; }
 
   } while (data_length > 0);
 
@@ -3367,7 +3732,7 @@ inline bool brotli_decompressor::decompress(const char *data,
     return 0;
   }
 
-  const uint8_t *next_in = (const uint8_t *)data;
+  auto next_in = reinterpret_cast<const uint8_t *>(data);
   size_t avail_in = data_length;
   size_t total_out;
 
@@ -3437,6 +3802,9 @@ inline bool parse_header(const char *beg, const char *end, T fn) {
   }
 
   if (p < end) {
+    auto key_len = key_end - beg;
+    if (!key_len) { return false; }
+
     auto key = std::string(beg, key_end);
     auto val = compare_case_ignore(key, "Location")
                    ? std::string(p, end)
@@ -3526,11 +3894,7 @@ inline bool read_content_without_length(Stream &strm,
   uint64_t r = 0;
   for (;;) {
     auto n = strm.read(buf, CPPHTTPLIB_RECV_BUFSIZ);
-    if (n < 0) {
-      return false;
-    } else if (n == 0) {
-      return true;
-    }
+    if (n <= 0) { return true; }
 
     if (!out(buf, static_cast<size_t>(n), r, 0)) { return false; }
     r += static_cast<uint64_t>(n);
@@ -3566,7 +3930,7 @@ inline bool read_content_chunked(Stream &strm, T &x,
 
     if (!line_reader.getline()) { return false; }
 
-    if (strcmp(line_reader.ptr(), "\r\n")) { return false; }
+    if (strcmp(line_reader.ptr(), "\r\n") != 0) { return false; }
 
     if (!line_reader.getline()) { return false; }
   }
@@ -3576,7 +3940,7 @@ inline bool read_content_chunked(Stream &strm, T &x,
   // Trailer
   if (!line_reader.getline()) { return false; }
 
-  while (strcmp(line_reader.ptr(), "\r\n")) {
+  while (strcmp(line_reader.ptr(), "\r\n") != 0) {
     if (line_reader.size() > CPPHTTPLIB_HEADER_MAX_LENGTH) { return false; }
 
     // Exclude line terminator
@@ -3595,8 +3959,8 @@ inline bool read_content_chunked(Stream &strm, T &x,
 }
 
 inline bool is_chunked_transfer_encoding(const Headers &headers) {
-  return !strcasecmp(get_header_value(headers, "Transfer-Encoding", 0, ""),
-                     "chunked");
+  return compare_case_ignore(
+      get_header_value(headers, "Transfer-Encoding", 0, ""), "chunked");
 }
 
 template <typename T, typename U>
@@ -3611,14 +3975,14 @@ bool prepare_content_receiver(T &x, int &status,
 #ifdef CPPHTTPLIB_ZLIB_SUPPORT
       decompressor = detail::make_unique<gzip_decompressor>();
 #else
-      status = 415;
+      status = StatusCode::UnsupportedMediaType_415;
       return false;
 #endif
     } else if (encoding.find("br") != std::string::npos) {
 #ifdef CPPHTTPLIB_BROTLI_SUPPORT
       decompressor = detail::make_unique<brotli_decompressor>();
 #else
-      status = 415;
+      status = StatusCode::UnsupportedMediaType_415;
       return false;
 #endif
     }
@@ -3634,7 +3998,7 @@ bool prepare_content_receiver(T &x, int &status,
         };
         return callback(std::move(out));
       } else {
-        status = 500;
+        status = StatusCode::InternalServerError_500;
         return false;
       }
     }
@@ -3662,7 +4026,7 @@ bool read_content(Stream &strm, T &x, size_t payload_max_length, int &status,
         } else if (!has_header(x.headers, "Content-Length")) {
           ret = read_content_without_length(strm, out);
         } else {
-          auto len = get_header_value<uint64_t>(x.headers, "Content-Length");
+          auto len = get_header_value_u64(x.headers, "Content-Length", 0, 0);
           if (len > payload_max_length) {
             exceed_payload_max_length = true;
             skip_content_with_length(strm, len);
@@ -3672,7 +4036,10 @@ bool read_content(Stream &strm, T &x, size_t payload_max_length, int &status,
           }
         }
 
-        if (!ret) { status = exceed_payload_max_length ? 413 : 400; }
+        if (!ret) {
+          status = exceed_payload_max_length ? StatusCode::PayloadTooLarge_413
+                                             : StatusCode::BadRequest_400;
+        }
         return ret;
       });
 } // namespace detail
@@ -3720,6 +4087,8 @@ inline bool write_content(Stream &strm, const ContentProvider &content_provider,
     return ok;
   };
 
+  data_sink.is_writable = [&]() -> bool { return strm.is_writable(); };
+
   while (offset < end_offset && !is_shutting_down()) {
     if (!strm.is_writable()) {
       error = Error::Write;
@@ -3764,6 +4133,8 @@ write_content_without_length(Stream &strm,
     return ok;
   };
 
+  data_sink.is_writable = [&]() -> bool { return strm.is_writable(); };
+
   data_sink.done = [&](void) { data_available = false; };
 
   while (data_available && !is_shutting_down()) {
@@ -3814,6 +4185,8 @@ write_content_chunked(Stream &strm, const ContentProvider &content_provider,
     return ok;
   };
 
+  data_sink.is_writable = [&]() -> bool { return strm.is_writable(); };
+
   auto done_with_trailer = [&](const Headers *trailer) {
     if (!ok) { return; }
 
@@ -3898,7 +4271,8 @@ inline bool redirect(T &cli, Request &req, Response &res,
   new_req.path = path;
   new_req.redirect_count_ -= 1;
 
-  if (res.status == 303 && (req.method != "GET" && req.method != "HEAD")) {
+  if (res.status == StatusCode::SeeOther_303 &&
+      (req.method != "GET" && req.method != "HEAD")) {
     new_req.method = "GET";
     new_req.body.clear();
     new_req.headers.clear();
@@ -3910,7 +4284,8 @@ inline bool redirect(T &cli, Request &req, Response &res,
   if (ret) {
     req = new_req;
     res = new_res;
-    res.location = location;
+
+    if (res.location.empty()) { res.location = location; }
   }
   return ret;
 }
@@ -3957,14 +4332,34 @@ inline bool parse_multipart_boundary(const std::string &content_type,
   if (pos == std::string::npos) { return false; }
   auto end = content_type.find(';', pos);
   auto beg = pos + strlen(boundary_keyword);
-  boundary = content_type.substr(beg, end - beg);
-  if (boundary.length() >= 2 && boundary.front() == '"' &&
-      boundary.back() == '"') {
-    boundary = boundary.substr(1, boundary.size() - 2);
-  }
+  boundary = trim_double_quotes_copy(content_type.substr(beg, end - beg));
   return !boundary.empty();
 }
 
+inline void parse_disposition_params(const std::string &s, Params &params) {
+  std::set<std::string> cache;
+  split(s.data(), s.data() + s.size(), ';', [&](const char *b, const char *e) {
+    std::string kv(b, e);
+    if (cache.find(kv) != cache.end()) { return; }
+    cache.insert(kv);
+
+    std::string key;
+    std::string val;
+    split(b, e, '=', [&](const char *b2, const char *e2) {
+      if (key.empty()) {
+        key.assign(b2, e2);
+      } else {
+        val.assign(b2, e2);
+      }
+    });
+
+    if (!key.empty()) {
+      params.emplace(trim_double_quotes_copy((key)),
+                     trim_double_quotes_copy((val)));
+    }
+  });
+}
+
 #ifdef CPPHTTPLIB_NO_EXCEPTIONS
 inline bool parse_range_header(const std::string &s, Ranges &ranges) {
 #else
@@ -3975,9 +4370,9 @@ inline bool parse_range_header(const std::string &s, Ranges &ranges) try {
   if (std::regex_match(s, m, re_first_range)) {
     auto pos = static_cast<size_t>(m.position(1));
     auto len = static_cast<size_t>(m.length(1));
-    bool all_valid_ranges = true;
+    auto all_valid_ranges = true;
     split(&s[pos], &s[pos + len], ',', [&](const char *b, const char *e) {
-      if (!all_valid_ranges) return;
+      if (!all_valid_ranges) { return; }
       static auto re_another_range = std::regex(R"(\s*(\d*)-(\d*))");
       std::cmatch cm;
       if (std::regex_match(b, e, cm, re_another_range)) {
@@ -4022,11 +4417,6 @@ public:
   bool parse(const char *buf, size_t n, const ContentReceiver &content_callback,
              const MultipartContentHeader &header_callback) {
 
-    // TODO: support 'filename*'
-    static const std::regex re_content_disposition(
-        R"~(^Content-Disposition:\s*form-data;\s*name="(.*?)"(?:;\s*filename="(.*?)")?(?:;\s*filename\*=\S+)?\s*$)~",
-        std::regex_constants::icase);
-
     buf_append(buf, n);
 
     while (buf_size() > 0) {
@@ -4059,18 +4449,54 @@ public:
             break;
           }
 
-          static const std::string header_name = "content-type:";
           const auto header = buf_head(pos);
-          if (start_with_case_ignore(header, header_name)) {
-            file_.content_type = trim_copy(header.substr(header_name.size()));
+
+          if (!parse_header(header.data(), header.data() + header.size(),
+                            [&](std::string &&, std::string &&) {})) {
+            is_valid_ = false;
+            return false;
+          }
+
+          static const std::string header_content_type = "Content-Type:";
+
+          if (start_with_case_ignore(header, header_content_type)) {
+            file_.content_type =
+                trim_copy(header.substr(header_content_type.size()));
           } else {
+            static const std::regex re_content_disposition(
+                R"~(^Content-Disposition:\s*form-data;\s*(.*)$)~",
+                std::regex_constants::icase);
+
             std::smatch m;
             if (std::regex_match(header, m, re_content_disposition)) {
-              file_.name = m[1];
-              file_.filename = m[2];
-            } else {
-              is_valid_ = false;
-              return false;
+              Params params;
+              parse_disposition_params(m[1], params);
+
+              auto it = params.find("name");
+              if (it != params.end()) {
+                file_.name = it->second;
+              } else {
+                is_valid_ = false;
+                return false;
+              }
+
+              it = params.find("filename");
+              if (it != params.end()) { file_.filename = it->second; }
+
+              it = params.find("filename*");
+              if (it != params.end()) {
+                // Only allow UTF-8 enconnding...
+                static const std::regex re_rfc5987_encoding(
+                    R"~(^UTF-8''(.+?)$)~", std::regex_constants::icase);
+
+                std::smatch m2;
+                if (std::regex_match(it->second, m2, re_rfc5987_encoding)) {
+                  file_.filename = decode_url(m2[1], false); // override...
+                } else {
+                  is_valid_ = false;
+                  return false;
+                }
+              }
             }
           }
           buf_erase(pos + crlf_.size());
@@ -4108,9 +4534,9 @@ public:
           buf_erase(crlf_.size());
           state_ = 1;
         } else {
-          if (dash_crlf_.size() > buf_size()) { return true; }
-          if (buf_start_with(dash_crlf_)) {
-            buf_erase(dash_crlf_.size());
+          if (dash_.size() > buf_size()) { return true; }
+          if (buf_start_with(dash_)) {
+            buf_erase(dash_.size());
             is_valid_ = true;
             buf_erase(buf_size()); // Remove epilogue
           } else {
@@ -4143,7 +4569,6 @@ private:
 
   const std::string dash_ = "--";
   const std::string crlf_ = "\r\n";
-  const std::string dash_crlf_ = "--\r\n";
   std::string boundary_;
   std::string dash_boundary_crlf_;
   std::string crlf_dash_boundary_;
@@ -4230,28 +4655,32 @@ inline std::string to_lower(const char *beg, const char *end) {
   return out;
 }
 
-inline std::string make_multipart_data_boundary() {
+inline std::string random_string(size_t length) {
   static const char data[] =
       "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
 
   // std::random_device might actually be deterministic on some
   // platforms, but due to lack of support in the c++ standard library,
   // doing better requires either some ugly hacks or breaking portability.
-  std::random_device seed_gen;
+  static std::random_device seed_gen;
 
   // Request 128 bits of entropy for initialization
-  std::seed_seq seed_sequence{seed_gen(), seed_gen(), seed_gen(), seed_gen()};
-  std::mt19937 engine(seed_sequence);
+  static std::seed_seq seed_sequence{seed_gen(), seed_gen(), seed_gen(),
+                                     seed_gen()};
 
-  std::string result = "--cpp-httplib-multipart-data-";
+  static std::mt19937 engine(seed_sequence);
 
-  for (auto i = 0; i < 16; i++) {
+  std::string result;
+  for (size_t i = 0; i < length; i++) {
     result += data[engine() % (sizeof(data) - 1)];
   }
-
   return result;
 }
 
+inline std::string make_multipart_data_boundary() {
+  return "--cpp-httplib-multipart-data-" + detail::random_string(16);
+}
+
 inline bool is_multipart_boundary_chars_valid(const std::string &boundary) {
   auto valid = true;
   for (size_t i = 0; i < boundary.size(); i++) {
@@ -4304,48 +4733,97 @@ serialize_multipart_formdata(const MultipartFormDataItems &items,
     body += item.content + serialize_multipart_formdata_item_end();
   }
 
-  if (finish) body += serialize_multipart_formdata_finish(boundary);
+  if (finish) { body += serialize_multipart_formdata_finish(boundary); }
 
   return body;
 }
 
+inline bool range_error(Request &req, Response &res) {
+  if (!req.ranges.empty() && 200 <= res.status && res.status < 300) {
+    ssize_t contant_len = static_cast<ssize_t>(
+        res.content_length_ ? res.content_length_ : res.body.size());
+
+    ssize_t prev_first_pos = -1;
+    ssize_t prev_last_pos = -1;
+    size_t overwrapping_count = 0;
+
+    // NOTE: The following Range check is based on '14.2. Range' in RFC 9110
+    // 'HTTP Semantics' to avoid potential denial-of-service attacks.
+    // https://www.rfc-editor.org/rfc/rfc9110#section-14.2
+
+    // Too many ranges
+    if (req.ranges.size() > CPPHTTPLIB_RANGE_MAX_COUNT) { return true; }
+
+    for (auto &r : req.ranges) {
+      auto &first_pos = r.first;
+      auto &last_pos = r.second;
+
+      if (first_pos == -1 && last_pos == -1) {
+        first_pos = 0;
+        last_pos = contant_len;
+      }
+
+      if (first_pos == -1) {
+        first_pos = contant_len - last_pos;
+        last_pos = contant_len - 1;
+      }
+
+      if (last_pos == -1) { last_pos = contant_len - 1; }
+
+      // Range must be within content length
+      if (!(0 <= first_pos && first_pos <= last_pos &&
+            last_pos <= contant_len - 1)) {
+        return true;
+      }
+
+      // Ranges must be in ascending order
+      if (first_pos <= prev_first_pos) { return true; }
+
+      // Request must not have more than two overlapping ranges
+      if (first_pos <= prev_last_pos) {
+        overwrapping_count++;
+        if (overwrapping_count > 2) { return true; }
+      }
+
+      prev_first_pos = (std::max)(prev_first_pos, first_pos);
+      prev_last_pos = (std::max)(prev_last_pos, last_pos);
+    }
+  }
+
+  return false;
+}
+
 inline std::pair<size_t, size_t>
-get_range_offset_and_length(const Request &req, size_t content_length,
-                            size_t index) {
-  auto r = req.ranges[index];
+get_range_offset_and_length(Range r, size_t content_length) {
+  (void)(content_length); // patch to get rid of "unused parameter" on release build
+  assert(r.first != -1 && r.second != -1);
+  assert(0 <= r.first && r.first < static_cast<ssize_t>(content_length));
+  assert(r.first <= r.second &&
+         r.second < static_cast<ssize_t>(content_length));
 
-  if (r.first == -1 && r.second == -1) {
-    return std::make_pair(0, content_length);
-  }
-
-  auto slen = static_cast<ssize_t>(content_length);
-
-  if (r.first == -1) {
-    r.first = (std::max)(static_cast<ssize_t>(0), slen - r.second);
-    r.second = slen - 1;
-  }
-
-  if (r.second == -1) { r.second = slen - 1; }
   return std::make_pair(r.first, static_cast<size_t>(r.second - r.first) + 1);
 }
 
-inline std::string make_content_range_header_field(size_t offset, size_t length,
-                                                   size_t content_length) {
+inline std::string make_content_range_header_field(
+    const std::pair<size_t, size_t> &offset_and_length, size_t content_length) {
+  auto st = offset_and_length.first;
+  auto ed = st + offset_and_length.second - 1;
+
   std::string field = "bytes ";
-  field += std::to_string(offset);
+  field += std::to_string(st);
   field += "-";
-  field += std::to_string(offset + length - 1);
+  field += std::to_string(ed);
   field += "/";
   field += std::to_string(content_length);
   return field;
 }
 
 template <typename SToken, typename CToken, typename Content>
-bool process_multipart_ranges_data(const Request &req, Response &res,
+bool process_multipart_ranges_data(const Request &req,
                                    const std::string &boundary,
                                    const std::string &content_type,
-                                   SToken stoken, CToken ctoken,
-                                   Content content) {
+                                   size_t content_length, SToken stoken,
+                                   CToken ctoken, Content content) {
   for (size_t i = 0; i < req.ranges.size(); i++) {
     ctoken("--");
     stoken(boundary);
@@ -4356,50 +4834,51 @@ bool process_multipart_ranges_data(const Request &req, Response &res,
       ctoken("\r\n");
     }
 
-    auto offsets = get_range_offset_and_length(req, res.body.size(), i);
-    auto offset = offsets.first;
-    auto length = offsets.second;
+    auto offset_and_length =
+        get_range_offset_and_length(req.ranges[i], content_length);
 
     ctoken("Content-Range: ");
-    stoken(make_content_range_header_field(offset, length, res.body.size()));
+    stoken(make_content_range_header_field(offset_and_length, content_length));
     ctoken("\r\n");
     ctoken("\r\n");
-    if (!content(offset, length)) { return false; }
+
+    if (!content(offset_and_length.first, offset_and_length.second)) {
+      return false;
+    }
     ctoken("\r\n");
   }
 
   ctoken("--");
   stoken(boundary);
-  ctoken("--\r\n");
+  ctoken("--");
 
   return true;
 }
 
-inline bool make_multipart_ranges_data(const Request &req, Response &res,
+inline void make_multipart_ranges_data(const Request &req, Response &res,
                                        const std::string &boundary,
                                        const std::string &content_type,
+                                       size_t content_length,
                                        std::string &data) {
-  return process_multipart_ranges_data(
-      req, res, boundary, content_type,
+  process_multipart_ranges_data(
+      req, boundary, content_type, content_length,
       [&](const std::string &token) { data += token; },
       [&](const std::string &token) { data += token; },
       [&](size_t offset, size_t length) {
-        if (offset < res.body.size()) {
-          data += res.body.substr(offset, length);
-          return true;
-        }
-        return false;
+        assert(offset + length <= content_length);
+        data += res.body.substr(offset, length);
+        return true;
       });
 }
 
-inline size_t
-get_multipart_ranges_data_length(const Request &req, Response &res,
-                                 const std::string &boundary,
-                                 const std::string &content_type) {
+inline size_t get_multipart_ranges_data_length(const Request &req,
+                                               const std::string &boundary,
+                                               const std::string &content_type,
+                                               size_t content_length) {
   size_t data_length = 0;
 
   process_multipart_ranges_data(
-      req, res, boundary, content_type,
+      req, boundary, content_type, content_length,
       [&](const std::string &token) { data_length += token.size(); },
       [&](const std::string &token) { data_length += token.size(); },
       [&](size_t /*offset*/, size_t length) {
@@ -4411,13 +4890,13 @@ get_multipart_ranges_data_length(const Request &req, Response &res,
 }
 
 template <typename T>
-inline bool write_multipart_ranges_data(Stream &strm, const Request &req,
-                                        Response &res,
-                                        const std::string &boundary,
-                                        const std::string &content_type,
-                                        const T &is_shutting_down) {
+inline bool
+write_multipart_ranges_data(Stream &strm, const Request &req, Response &res,
+                            const std::string &boundary,
+                            const std::string &content_type,
+                            size_t content_length, const T &is_shutting_down) {
   return process_multipart_ranges_data(
-      req, res, boundary, content_type,
+      req, boundary, content_type, content_length,
       [&](const std::string &token) { strm.write(token); },
       [&](const std::string &token) { strm.write(token); },
       [&](size_t offset, size_t length) {
@@ -4426,18 +4905,6 @@ inline bool write_multipart_ranges_data(Stream &strm, const Request &req,
       });
 }
 
-inline std::pair<size_t, size_t>
-get_range_offset_and_length(const Request &req, const Response &res,
-                            size_t index) {
-  auto r = req.ranges[index];
-
-  if (r.second == -1) {
-    r.second = static_cast<ssize_t>(res.content_length_) - 1;
-  }
-
-  return std::make_pair(r.first, r.second - r.first + 1);
-}
-
 inline bool expect_content(const Request &req) {
   if (req.method == "POST" || req.method == "PUT" || req.method == "PATCH" ||
       req.method == "PRI" || req.method == "DELETE") {
@@ -4471,7 +4938,7 @@ inline std::string message_digest(const std::string &s, const EVP_MD *algo) {
   std::stringstream ss;
   for (auto i = 0u; i < hash_length; ++i) {
     ss << std::hex << std::setw(2) << std::setfill('0')
-       << (unsigned int)hash[i];
+       << static_cast<unsigned int>(hash[i]);
   }
 
   return ss.str();
@@ -4564,7 +5031,7 @@ inline bool retrieve_root_certs_from_keychain(CFObjectPtr<CFArrayRef> &certs) {
 
 inline bool add_certs_to_x509_store(CFArrayRef certs, X509_STORE *store) {
   auto result = false;
-  for (int i = 0; i < CFArrayGetCount(certs); ++i) {
+  for (auto i = 0; i < CFArrayGetCount(certs); ++i) {
     const auto cert = reinterpret_cast<const __SecCertificate *>(
         CFArrayGetValueAtIndex(certs, i));
 
@@ -4707,7 +5174,7 @@ inline bool parse_www_authenticate(const Response &res,
         s = s.substr(pos + 1);
         auto beg = std::sregex_iterator(s.begin(), s.end(), re);
         for (auto i = beg; i != std::sregex_iterator(); ++i) {
-          auto m = *i;
+          const auto &m = *i;
           auto key = s.substr(static_cast<size_t>(m.position(1)),
                               static_cast<size_t>(m.length(1)));
           auto val = m.length(2) > 0
@@ -4724,20 +5191,6 @@ inline bool parse_www_authenticate(const Response &res,
   return false;
 }
 
-// https://stackoverflow.com/questions/440133/how-do-i-create-a-random-alpha-numeric-string-in-c/440240#answer-440240
-inline std::string random_string(size_t length) {
-  auto randchar = []() -> char {
-    const char charset[] = "0123456789"
-                           "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
-                           "abcdefghijklmnopqrstuvwxyz";
-    const size_t max_index = (sizeof(charset) - 1);
-    return charset[static_cast<size_t>(std::rand()) % max_index];
-  };
-  std::string str(length, 0);
-  std::generate_n(str.begin(), length, randchar);
-  return str;
-}
-
 class ContentProviderAdapter {
 public:
   explicit ContentProviderAdapter(
@@ -4782,7 +5235,7 @@ inline void hosted_at(const std::string &hostname,
     const auto &addr =
         *reinterpret_cast<struct sockaddr_storage *>(rp->ai_addr);
     std::string ip;
-    int dummy = -1;
+    auto dummy = -1;
     if (detail::get_ip_and_port(addr, sizeof(struct sockaddr_storage), ip,
                                 dummy)) {
       addrs.push_back(ip);
@@ -4802,10 +5255,11 @@ inline std::string append_query_params(const std::string &path,
 }
 
 // Header utilities
-inline std::pair<std::string, std::string> make_range_header(Ranges ranges) {
+inline std::pair<std::string, std::string>
+make_range_header(const Ranges &ranges) {
   std::string field = "bytes=";
   auto i = 0;
-  for (auto r : ranges) {
+  for (const auto &r : ranges) {
     if (i != 0) { field += ", "; }
     if (r.first != -1) { field += std::to_string(r.first); }
     field += '-';
@@ -4924,7 +5378,7 @@ inline void Response::set_redirect(const std::string &url, int stat) {
     if (300 <= stat && stat < 400) {
       this->status = stat;
     } else {
-      this->status = 302;
+      this->status = StatusCode::Found_302;
     }
   }
 }
@@ -4943,13 +5397,22 @@ inline void Response::set_content(const std::string &s,
   set_content(s.data(), s.size(), content_type);
 }
 
+inline void Response::set_content(std::string &&s,
+                                  const std::string &content_type) {
+  body = std::move(s);
+
+  auto rng = headers.equal_range("Content-Type");
+  headers.erase(rng.first, rng.second);
+  set_header("Content-Type", content_type);
+}
+
 inline void Response::set_content_provider(
     size_t in_length, const std::string &content_type, ContentProvider provider,
     ContentProviderResourceReleaser resource_releaser) {
   set_header("Content-Type", content_type);
   content_length_ = in_length;
   if (in_length > 0) { content_provider_ = std::move(provider); }
-  content_provider_resource_releaser_ = resource_releaser;
+  content_provider_resource_releaser_ = std::move(resource_releaser);
   is_chunked_content_provider_ = false;
 }
 
@@ -4959,7 +5422,7 @@ inline void Response::set_content_provider(
   set_header("Content-Type", content_type);
   content_length_ = 0;
   content_provider_ = detail::ContentProviderAdapter(std::move(provider));
-  content_provider_resource_releaser_ = resource_releaser;
+  content_provider_resource_releaser_ = std::move(resource_releaser);
   is_chunked_content_provider_ = false;
 }
 
@@ -4969,7 +5432,7 @@ inline void Response::set_chunked_content_provider(
   set_header("Content-Type", content_type);
   content_length_ = 0;
   content_provider_ = detail::ContentProviderAdapter(std::move(provider));
-  content_provider_resource_releaser_ = resource_releaser;
+  content_provider_resource_releaser_ = std::move(resource_releaser);
   is_chunked_content_provider_ = true;
 }
 
@@ -5010,7 +5473,7 @@ inline SocketStream::SocketStream(socket_t sock, time_t read_timeout_sec,
       write_timeout_sec_(write_timeout_sec),
       write_timeout_usec_(write_timeout_usec), read_buff_(read_buff_size_, 0) {}
 
-inline SocketStream::~SocketStream() {}
+inline SocketStream::~SocketStream() = default;
 
 inline bool SocketStream::is_readable() const {
   return select_read(sock_, read_timeout_sec_, read_timeout_usec_) > 0;
@@ -5120,6 +5583,99 @@ inline socket_t BufferStream::socket() const { return 0; }
 
 inline const std::string &BufferStream::get_buffer() const { return buffer; }
 
+inline PathParamsMatcher::PathParamsMatcher(const std::string &pattern) {
+  // One past the last ending position of a path param substring
+  std::size_t last_param_end = 0;
+
+#ifndef CPPHTTPLIB_NO_EXCEPTIONS
+  // Needed to ensure that parameter names are unique during matcher
+  // construction
+  // If exceptions are disabled, only last duplicate path
+  // parameter will be set
+  std::unordered_set<std::string> param_name_set;
+#endif
+
+  while (true) {
+    const auto marker_pos = pattern.find(marker, last_param_end);
+    if (marker_pos == std::string::npos) { break; }
+
+    static_fragments_.push_back(
+        pattern.substr(last_param_end, marker_pos - last_param_end));
+
+    const auto param_name_start = marker_pos + 1;
+
+    auto sep_pos = pattern.find(separator, param_name_start);
+    if (sep_pos == std::string::npos) { sep_pos = pattern.length(); }
+
+    auto param_name =
+        pattern.substr(param_name_start, sep_pos - param_name_start);
+
+#ifndef CPPHTTPLIB_NO_EXCEPTIONS
+    if (param_name_set.find(param_name) != param_name_set.cend()) {
+      std::string msg = "Encountered path parameter '" + param_name +
+                        "' multiple times in route pattern '" + pattern + "'.";
+      throw std::invalid_argument(msg);
+    }
+#endif
+
+    param_names_.push_back(std::move(param_name));
+
+    last_param_end = sep_pos + 1;
+  }
+
+  if (last_param_end < pattern.length()) {
+    static_fragments_.push_back(pattern.substr(last_param_end));
+  }
+}
+
+inline bool PathParamsMatcher::match(Request &request) const {
+  request.matches = std::smatch();
+  request.path_params.clear();
+  request.path_params.reserve(param_names_.size());
+
+  // One past the position at which the path matched the pattern last time
+  std::size_t starting_pos = 0;
+  for (size_t i = 0; i < static_fragments_.size(); ++i) {
+    const auto &fragment = static_fragments_[i];
+
+    if (starting_pos + fragment.length() > request.path.length()) {
+      return false;
+    }
+
+    // Avoid unnecessary allocation by using strncmp instead of substr +
+    // comparison
+    if (std::strncmp(request.path.c_str() + starting_pos, fragment.c_str(),
+                     fragment.length()) != 0) {
+      return false;
+    }
+
+    starting_pos += fragment.length();
+
+    // Should only happen when we have a static fragment after a param
+    // Example: '/users/:id/subscriptions'
+    // The 'subscriptions' fragment here does not have a corresponding param
+    if (i >= param_names_.size()) { continue; }
+
+    auto sep_pos = request.path.find(separator, starting_pos);
+    if (sep_pos == std::string::npos) { sep_pos = request.path.length(); }
+
+    const auto &param_name = param_names_[i];
+
+    request.path_params.emplace(
+        param_name, request.path.substr(starting_pos, sep_pos - starting_pos));
+
+    // Mark everythin up to '/' as matched
+    starting_pos = sep_pos + 1;
+  }
+  // Returns false if the path is longer than the pattern
+  return starting_pos >= request.path.length();
+}
+
+inline bool RegexMatcher::match(Request &request) const {
+  request.path_params.clear();
+  return std::regex_match(request.path, request.matches, regex_);
+}
+
 } // namespace detail
 
 // HTTP server implementation
@@ -5131,69 +5687,72 @@ inline Server::Server()
 #endif
 }
 
-inline Server::~Server() {}
+inline Server::~Server() = default;
+
+inline std::unique_ptr<detail::MatcherBase>
+Server::make_matcher(const std::string &pattern) {
+  if (pattern.find("/:") != std::string::npos) {
+    return detail::make_unique<detail::PathParamsMatcher>(pattern);
+  } else {
+    return detail::make_unique<detail::RegexMatcher>(pattern);
+  }
+}
 
 inline Server &Server::Get(const std::string &pattern, Handler handler) {
-  get_handlers_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  get_handlers_.emplace_back(make_matcher(pattern), std::move(handler));
   return *this;
 }
 
 inline Server &Server::Post(const std::string &pattern, Handler handler) {
-  post_handlers_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  post_handlers_.emplace_back(make_matcher(pattern), std::move(handler));
   return *this;
 }
 
 inline Server &Server::Post(const std::string &pattern,
                             HandlerWithContentReader handler) {
-  post_handlers_for_content_reader_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  post_handlers_for_content_reader_.emplace_back(make_matcher(pattern),
+                                                 std::move(handler));
   return *this;
 }
 
 inline Server &Server::Put(const std::string &pattern, Handler handler) {
-  put_handlers_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  put_handlers_.emplace_back(make_matcher(pattern), std::move(handler));
   return *this;
 }
 
 inline Server &Server::Put(const std::string &pattern,
                            HandlerWithContentReader handler) {
-  put_handlers_for_content_reader_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  put_handlers_for_content_reader_.emplace_back(make_matcher(pattern),
+                                                std::move(handler));
   return *this;
 }
 
 inline Server &Server::Patch(const std::string &pattern, Handler handler) {
-  patch_handlers_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  patch_handlers_.emplace_back(make_matcher(pattern), std::move(handler));
   return *this;
 }
 
 inline Server &Server::Patch(const std::string &pattern,
                              HandlerWithContentReader handler) {
-  patch_handlers_for_content_reader_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  patch_handlers_for_content_reader_.emplace_back(make_matcher(pattern),
+                                                  std::move(handler));
   return *this;
 }
 
 inline Server &Server::Delete(const std::string &pattern, Handler handler) {
-  delete_handlers_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  delete_handlers_.emplace_back(make_matcher(pattern), std::move(handler));
   return *this;
 }
 
 inline Server &Server::Delete(const std::string &pattern,
                               HandlerWithContentReader handler) {
-  delete_handlers_for_content_reader_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  delete_handlers_for_content_reader_.emplace_back(make_matcher(pattern),
+                                                   std::move(handler));
   return *this;
 }
 
 inline Server &Server::Options(const std::string &pattern, Handler handler) {
-  options_handlers_.push_back(
-      std::make_pair(std::regex(pattern), std::move(handler)));
+  options_handlers_.emplace_back(make_matcher(pattern), std::move(handler));
   return *this;
 }
 
@@ -5231,6 +5790,11 @@ Server::set_file_extension_and_mimetype_mapping(const std::string &ext,
   return *this;
 }
 
+inline Server &Server::set_default_file_mimetype(const std::string &mime) {
+  default_file_mimetype_ = mime;
+  return *this;
+}
+
 inline Server &Server::set_file_request_handler(Handler handler) {
   file_request_handler_ = std::move(handler);
   return *this;
@@ -5272,7 +5836,6 @@ inline Server &Server::set_logger(Logger logger) {
 inline Server &
 Server::set_expect_100_continue_handler(Expect100ContinueHandler handler) {
   expect_100_continue_handler_ = std::move(handler);
-
   return *this;
 }
 
@@ -5296,6 +5859,12 @@ inline Server &Server::set_default_headers(Headers headers) {
   return *this;
 }
 
+inline Server &Server::set_header_writer(
+    std::function<ssize_t(Stream &, Headers &)> const &writer) {
+  header_writer_ = writer;
+  return *this;
+}
+
 inline Server &Server::set_keep_alive_max_count(size_t count) {
   keep_alive_max_count_ = count;
   return *this;
@@ -5331,8 +5900,7 @@ inline Server &Server::set_payload_max_length(size_t length) {
 
 inline bool Server::bind_to_port(const std::string &host, int port,
                                  int socket_flags) {
-  if (bind_internal(host, port, socket_flags) < 0) return false;
-  return true;
+  return bind_internal(host, port, socket_flags) >= 0;
 }
 inline int Server::bind_to_any_port(const std::string &host, int socket_flags) {
   return bind_internal(host, 0, socket_flags);
@@ -5366,7 +5934,7 @@ inline void Server::stop() {
   }
 }
 
-inline bool Server::parse_request_line(const char *s, Request &req) {
+inline bool Server::parse_request_line(const char *s, Request &req) const {
   auto len = strlen(s);
   if (len < 2 || s[len - 2] != '\r' || s[len - 1] != '\n') { return false; }
   len -= 2;
@@ -5407,7 +5975,7 @@ inline bool Server::parse_request_line(const char *s, Request &req) {
     size_t count = 0;
 
     detail::split(req.target.data(), req.target.data() + req.target.size(), '?',
-                  [&](const char *b, const char *e) {
+                  2, [&](const char *b, const char *e) {
                     switch (count) {
                     case 0:
                       req.path = detail::decode_url(std::string(b, e), false);
@@ -5430,7 +5998,10 @@ inline bool Server::parse_request_line(const char *s, Request &req) {
 }
 
 inline bool Server::write_response(Stream &strm, bool close_connection,
-                                   const Request &req, Response &res) {
+                                   Request &req, Response &res) {
+  // NOTE: `req.ranges` should be empty, otherwise it will be applied
+  // incorrectly to the error content.
+  req.ranges.clear();
   return write_response_core(strm, close_connection, req, res, false);
 }
 
@@ -5486,11 +6057,11 @@ inline bool Server::write_response_core(Stream &strm, bool close_connection,
     detail::BufferStream bstrm;
 
     if (!bstrm.write_format("HTTP/1.1 %d %s\r\n", res.status,
-                            detail::status_message(res.status))) {
+                            status_message(res.status))) {
       return false;
     }
 
-    if (!detail::write_headers(bstrm, res.headers)) { return false; }
+    if (!header_writer_(bstrm, res.headers)) { return false; }
 
     // Flush buffer
     auto &data = bstrm.get_buffer();
@@ -5508,7 +6079,6 @@ inline bool Server::write_response_core(Stream &strm, bool close_connection,
       if (write_content_with_provider(strm, req, res, boundary, content_type)) {
         res.content_provider_success_ = true;
       } else {
-        res.content_provider_success_ = false;
         ret = false;
       }
     }
@@ -5533,15 +6103,16 @@ Server::write_content_with_provider(Stream &strm, const Request &req,
       return detail::write_content(strm, res.content_provider_, 0,
                                    res.content_length_, is_shutting_down);
     } else if (req.ranges.size() == 1) {
-      auto offsets =
-          detail::get_range_offset_and_length(req, res.content_length_, 0);
-      auto offset = offsets.first;
-      auto length = offsets.second;
-      return detail::write_content(strm, res.content_provider_, offset, length,
-                                   is_shutting_down);
+      auto offset_and_length = detail::get_range_offset_and_length(
+          req.ranges[0], res.content_length_);
+
+      return detail::write_content(strm, res.content_provider_,
+                                   offset_and_length.first,
+                                   offset_and_length.second, is_shutting_down);
     } else {
       return detail::write_multipart_ranges_data(
-          strm, req, res, boundary, content_type, is_shutting_down);
+          strm, req, res, boundary, content_type, res.content_length_,
+          is_shutting_down);
     }
   } else {
     if (res.is_chunked_content_provider_) {
@@ -5598,7 +6169,7 @@ inline bool Server::read_content(Stream &strm, Request &req, Response &res) {
     const auto &content_type = req.get_header_value("Content-Type");
     if (!content_type.find("application/x-www-form-urlencoded")) {
       if (req.body.size() > CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH) {
-        res.status = 413; // NOTE: should be 414?
+        res.status = StatusCode::PayloadTooLarge_413; // NOTE: should be 414?
         return false;
       }
       detail::parse_query_text(req.body, req.params);
@@ -5617,10 +6188,11 @@ inline bool Server::read_content_with_content_receiver(
                            std::move(multipart_receiver));
 }
 
-inline bool Server::read_content_core(Stream &strm, Request &req, Response &res,
-                                      ContentReceiver receiver,
-                                      MultipartContentHeader multipart_header,
-                                      ContentReceiver multipart_receiver) {
+inline bool
+Server::read_content_core(Stream &strm, Request &req, Response &res,
+                          ContentReceiver receiver,
+                          MultipartContentHeader multipart_header,
+                          ContentReceiver multipart_receiver) const {
   detail::MultipartFormDataParser multipart_form_data_parser;
   ContentReceiverWithProgress out;
 
@@ -5628,7 +6200,7 @@ inline bool Server::read_content_core(Stream &strm, Request &req, Response &res,
     const auto &content_type = req.get_header_value("Content-Type");
     std::string boundary;
     if (!detail::parse_multipart_boundary(content_type, boundary)) {
-      res.status = 400;
+      res.status = StatusCode::BadRequest_400;
       return false;
     }
 
@@ -5664,7 +6236,7 @@ inline bool Server::read_content_core(Stream &strm, Request &req, Response &res,
 
   if (req.is_multipart_form_data()) {
     if (!multipart_form_data_parser.is_valid()) {
-      res.status = 400;
+      res.status = StatusCode::BadRequest_400;
       return false;
     }
   }
@@ -5683,17 +6255,26 @@ inline bool Server::handle_file_request(const Request &req, Response &res,
         if (path.back() == '/') { path += "index.html"; }
 
         if (detail::is_file(path)) {
-          detail::read_file(path, res.body);
-          auto type =
-              detail::find_content_type(path, file_extension_and_mimetype_map_);
-          if (type) { res.set_header("Content-Type", type); }
           for (const auto &kv : entry.headers) {
-            res.set_header(kv.first.c_str(), kv.second);
+            res.set_header(kv.first, kv.second);
           }
-          res.status = req.has_header("Range") ? 206 : 200;
+
+          auto mm = std::make_shared<detail::mmap>(path.c_str());
+          if (!mm->is_open()) { return false; }
+
+          res.set_content_provider(
+              mm->size(),
+              detail::find_content_type(path, file_extension_and_mimetype_map_,
+                                        default_file_mimetype_),
+              [mm](size_t offset, size_t length, DataSink &sink) -> bool {
+                sink.write(mm->data() + offset, length);
+                return true;
+              });
+
           if (!head && file_request_handler_) {
             file_request_handler_(req, res);
           }
+
           return true;
         }
       }
@@ -5789,13 +6370,14 @@ inline bool Server::listen_internal() {
 #ifdef _WIN32
         auto timeout = static_cast<uint32_t>(read_timeout_sec_ * 1000 +
                                              read_timeout_usec_ / 1000);
-        setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO, (char *)&timeout,
-                   sizeof(timeout));
+        setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO,
+                   reinterpret_cast<const char *>(&timeout), sizeof(timeout));
 #else
         timeval tv;
         tv.tv_sec = static_cast<long>(read_timeout_sec_);
         tv.tv_usec = static_cast<decltype(tv.tv_usec)>(read_timeout_usec_);
-        setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO, (char *)&tv, sizeof(tv));
+        setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO,
+                   reinterpret_cast<const void *>(&tv), sizeof(tv));
 #endif
       }
       {
@@ -5803,17 +6385,22 @@ inline bool Server::listen_internal() {
 #ifdef _WIN32
         auto timeout = static_cast<uint32_t>(write_timeout_sec_ * 1000 +
                                              write_timeout_usec_ / 1000);
-        setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO, (char *)&timeout,
-                   sizeof(timeout));
+        setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO,
+                   reinterpret_cast<const char *>(&timeout), sizeof(timeout));
 #else
         timeval tv;
         tv.tv_sec = static_cast<long>(write_timeout_sec_);
         tv.tv_usec = static_cast<decltype(tv.tv_usec)>(write_timeout_usec_);
-        setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO, (char *)&tv, sizeof(tv));
+        setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO,
+                   reinterpret_cast<const void *>(&tv), sizeof(tv));
 #endif
       }
 
-      task_queue->enqueue([this, sock]() { process_and_close_socket(sock); });
+      if (!task_queue->enqueue(
+              [this, sock]() { process_and_close_socket(sock); })) {
+        detail::shutdown_socket(sock);
+        detail::close_socket(sock);
+      }
     }
 
     task_queue->shutdown();
@@ -5829,7 +6416,7 @@ inline bool Server::routing(Request &req, Response &res, Stream &strm) {
   }
 
   // File handler
-  bool is_head_request = req.method == "HEAD";
+  auto is_head_request = req.method == "HEAD";
   if ((req.method == "GET" || is_head_request) &&
       handle_file_request(req, res, is_head_request)) {
     return true;
@@ -5895,17 +6482,17 @@ inline bool Server::routing(Request &req, Response &res, Stream &strm) {
     return dispatch_request(req, res, patch_handlers_);
   }
 
-  res.status = 400;
+  res.status = StatusCode::BadRequest_400;
   return false;
 }
 
 inline bool Server::dispatch_request(Request &req, Response &res,
-                                     const Handlers &handlers) {
+                                     const Handlers &handlers) const {
   for (const auto &x : handlers) {
-    const auto &pattern = x.first;
+    const auto &matcher = x.first;
     const auto &handler = x.second;
 
-    if (std::regex_match(req.path, req.matches, pattern)) {
+    if (matcher->match(req)) {
       handler(req, res);
       return true;
     }
@@ -5915,18 +6502,18 @@ inline bool Server::dispatch_request(Request &req, Response &res,
 
 inline void Server::apply_ranges(const Request &req, Response &res,
                                  std::string &content_type,
-                                 std::string &boundary) {
+                                 std::string &boundary) const {
   if (req.ranges.size() > 1) {
-    boundary = detail::make_multipart_data_boundary();
-
     auto it = res.headers.find("Content-Type");
     if (it != res.headers.end()) {
       content_type = it->second;
       res.headers.erase(it);
     }
 
-    res.headers.emplace("Content-Type",
-                        "multipart/byteranges; boundary=" + boundary);
+    boundary = detail::make_multipart_data_boundary();
+
+    res.set_header("Content-Type",
+                   "multipart/byteranges; boundary=" + boundary);
   }
 
   auto type = detail::encoding_type(req, res);
@@ -5937,16 +6524,17 @@ inline void Server::apply_ranges(const Request &req, Response &res,
       if (req.ranges.empty()) {
         length = res.content_length_;
       } else if (req.ranges.size() == 1) {
-        auto offsets =
-            detail::get_range_offset_and_length(req, res.content_length_, 0);
-        auto offset = offsets.first;
-        length = offsets.second;
+        auto offset_and_length = detail::get_range_offset_and_length(
+            req.ranges[0], res.content_length_);
+
+        length = offset_and_length.second;
+
         auto content_range = detail::make_content_range_header_field(
-            offset, length, res.content_length_);
+            offset_and_length, res.content_length_);
         res.set_header("Content-Range", content_range);
       } else {
-        length = detail::get_multipart_ranges_data_length(req, res, boundary,
-                                                          content_type);
+        length = detail::get_multipart_ranges_data_length(
+            req, boundary, content_type, res.content_length_);
       }
       res.set_header("Content-Length", std::to_string(length));
     } else {
@@ -5965,28 +6553,22 @@ inline void Server::apply_ranges(const Request &req, Response &res,
     if (req.ranges.empty()) {
       ;
     } else if (req.ranges.size() == 1) {
-      auto offsets =
-          detail::get_range_offset_and_length(req, res.body.size(), 0);
-      auto offset = offsets.first;
-      auto length = offsets.second;
+      auto offset_and_length =
+          detail::get_range_offset_and_length(req.ranges[0], res.body.size());
+      auto offset = offset_and_length.first;
+      auto length = offset_and_length.second;
+
       auto content_range = detail::make_content_range_header_field(
-          offset, length, res.body.size());
+          offset_and_length, res.body.size());
       res.set_header("Content-Range", content_range);
-      if (offset < res.body.size()) {
-        res.body = res.body.substr(offset, length);
-      } else {
-        res.body.clear();
-        res.status = 416;
-      }
+
+      assert(offset + length <= res.body.size());
+      res.body = res.body.substr(offset, length);
     } else {
       std::string data;
-      if (detail::make_multipart_ranges_data(req, res, boundary, content_type,
-                                             data)) {
-        res.body.swap(data);
-      } else {
-        res.body.clear();
-        res.status = 416;
-      }
+      detail::make_multipart_ranges_data(req, res, boundary, content_type,
+                                         res.body.size(), data);
+      res.body.swap(data);
     }
 
     if (type != detail::EncodingType::None) {
@@ -6025,12 +6607,12 @@ inline void Server::apply_ranges(const Request &req, Response &res,
 
 inline bool Server::dispatch_request_for_content_reader(
     Request &req, Response &res, ContentReader content_reader,
-    const HandlersForContentReader &handlers) {
+    const HandlersForContentReader &handlers) const {
   for (const auto &x : handlers) {
-    const auto &pattern = x.first;
+    const auto &matcher = x.first;
     const auto &handler = x.second;
 
-    if (std::regex_match(req.path, req.matches, pattern)) {
+    if (matcher->match(req)) {
       handler(req, res, content_reader);
       return true;
     }
@@ -6050,15 +6632,10 @@ Server::process_request(Stream &strm, bool close_connection,
   if (!line_reader.getline()) { return false; }
 
   Request req;
+
   Response res;
-
   res.version = "HTTP/1.1";
-
-  for (const auto &header : default_headers_) {
-    if (res.headers.find(header.first) == res.headers.end()) {
-      res.headers.insert(header);
-    }
-  }
+  res.headers = default_headers_;
 
 #ifdef _WIN32
   // TODO: Increase FD_SETSIZE statically (libzmq), dynamically (MySQL).
@@ -6068,7 +6645,7 @@ Server::process_request(Stream &strm, bool close_connection,
   if (strm.socket() >= FD_SETSIZE) {
     Headers dummy;
     detail::read_headers(strm, dummy);
-    res.status = 500;
+    res.status = StatusCode::InternalServerError_500;
     return write_response(strm, close_connection, req, res);
   }
 #endif
@@ -6078,14 +6655,14 @@ Server::process_request(Stream &strm, bool close_connection,
   if (line_reader.size() > CPPHTTPLIB_REQUEST_URI_MAX_LENGTH) {
     Headers dummy;
     detail::read_headers(strm, dummy);
-    res.status = 414;
+    res.status = StatusCode::UriTooLong_414;
     return write_response(strm, close_connection, req, res);
   }
 
   // Request line and headers
   if (!parse_request_line(line_reader.ptr(), req) ||
       !detail::read_headers(strm, req.headers)) {
-    res.status = 400;
+    res.status = StatusCode::BadRequest_400;
     return write_response(strm, close_connection, req, res);
   }
 
@@ -6109,7 +6686,7 @@ Server::process_request(Stream &strm, bool close_connection,
   if (req.has_header("Range")) {
     const auto &range_header_value = req.get_header_value("Range");
     if (!detail::parse_range_header(range_header_value, req.ranges)) {
-      res.status = 416;
+      res.status = StatusCode::RangeNotSatisfiable_416;
       return write_response(strm, close_connection, req, res);
     }
   }
@@ -6117,22 +6694,22 @@ Server::process_request(Stream &strm, bool close_connection,
   if (setup_request) { setup_request(req); }
 
   if (req.get_header_value("Expect") == "100-continue") {
-    auto status = 100;
+    int status = StatusCode::Continue_100;
     if (expect_100_continue_handler_) {
       status = expect_100_continue_handler_(req, res);
     }
     switch (status) {
-    case 100:
-    case 417:
+    case StatusCode::Continue_100:
+    case StatusCode::ExpectationFailed_417:
       strm.write_format("HTTP/1.1 %d %s\r\n\r\n", status,
-                        detail::status_message(status));
+                        status_message(status));
       break;
     default: return write_response(strm, close_connection, req, res);
     }
   }
 
-  // Rounting
-  bool routed = false;
+  // Routing
+  auto routed = false;
 #ifdef CPPHTTPLIB_NO_EXCEPTIONS
   routed = routing(req, res, strm);
 #else
@@ -6144,7 +6721,7 @@ Server::process_request(Stream &strm, bool close_connection,
       exception_handler_(req, res, ep);
       routed = true;
     } else {
-      res.status = 500;
+      res.status = StatusCode::InternalServerError_500;
       std::string val;
       auto s = e.what();
       for (size_t i = 0; s[i]; i++) {
@@ -6162,17 +6739,29 @@ Server::process_request(Stream &strm, bool close_connection,
       exception_handler_(req, res, ep);
       routed = true;
     } else {
-      res.status = 500;
+      res.status = StatusCode::InternalServerError_500;
       res.set_header("EXCEPTION_WHAT", "UNKNOWN");
     }
   }
 #endif
-
   if (routed) {
-    if (res.status == -1) { res.status = req.ranges.empty() ? 200 : 206; }
+    if (res.status == -1) {
+      res.status = req.ranges.empty() ? StatusCode::OK_200
+                                      : StatusCode::PartialContent_206;
+    }
+
+    if (detail::range_error(req, res)) {
+      res.body.clear();
+      res.content_length_ = 0;
+      res.content_provider_ = nullptr;
+      res.status = StatusCode::RangeNotSatisfiable_416;
+      return write_response(strm, close_connection, req, res);
+    }
+
     return write_response_with_content(strm, close_connection, req, res);
   } else {
-    if (res.status == -1) { res.status = 404; }
+    if (res.status == -1) { res.status = StatusCode::NotFound_404; }
+
     return write_response(strm, close_connection, req, res);
   }
 }
@@ -6272,7 +6861,7 @@ inline socket_t ClientImpl::create_client_socket(Error &error) const {
   // Check is custom IP specified for host_
   std::string ip;
   auto it = addr_map_.find(host_);
-  if (it != addr_map_.end()) ip = it->second;
+  if (it != addr_map_.end()) { ip = it->second; }
 
   return detail::create_client_socket(
       host_, ip, port_, address_family_, tcp_nodelay_, socket_options_,
@@ -6297,7 +6886,7 @@ inline void ClientImpl::shutdown_ssl(Socket & /*socket*/,
          socket_requests_are_from_thread_ == std::this_thread::get_id());
 }
 
-inline void ClientImpl::shutdown_socket(Socket &socket) {
+inline void ClientImpl::shutdown_socket(Socket &socket) const {
   if (socket.sock == INVALID_SOCKET) { return; }
   detail::shutdown_socket(socket.sock);
 }
@@ -6322,7 +6911,7 @@ inline void ClientImpl::close_socket(Socket &socket) {
 }
 
 inline bool ClientImpl::read_response_line(Stream &strm, const Request &req,
-                                           Response &res) {
+                                           Response &res) const {
   std::array<char, 2048> buf{};
 
   detail::stream_line_reader line_reader(strm, buf.data(), buf.size());
@@ -6330,9 +6919,9 @@ inline bool ClientImpl::read_response_line(Stream &strm, const Request &req,
   if (!line_reader.getline()) { return false; }
 
 #ifdef CPPHTTPLIB_ALLOW_LF_AS_LINE_TERMINATOR
-  const static std::regex re("(HTTP/1\\.[01]) (\\d{3})(?: (.*?))?\r\n");
-#else
   const static std::regex re("(HTTP/1\\.[01]) (\\d{3})(?: (.*?))?\r?\n");
+#else
+  const static std::regex re("(HTTP/1\\.[01]) (\\d{3})(?: (.*?))?\r\n");
 #endif
 
   std::cmatch m;
@@ -6344,7 +6933,7 @@ inline bool ClientImpl::read_response_line(Stream &strm, const Request &req,
   res.reason = std::string(m[3]);
 
   // Ignore '100 Continue'
-  while (res.status == 100) {
+  while (res.status == StatusCode::Continue_100) {
     if (!line_reader.getline()) { return false; } // CRLF
     if (!line_reader.getline()) { return false; } // next response line
 
@@ -6492,15 +7081,31 @@ inline bool ClientImpl::handle_request(Stream &strm, Request &req,
 
   if (!ret) { return false; }
 
+  if (res.get_header_value("Connection") == "close" ||
+      (res.version == "HTTP/1.0" && res.reason != "Connection established")) {
+    // TODO this requires a not-entirely-obvious chain of calls to be correct
+    // for this to be safe.
+
+    // This is safe to call because handle_request is only called by send_
+    // which locks the request mutex during the process. It would be a bug
+    // to call it from a different thread since it's a thread-safety issue
+    // to do these things to the socket if another thread is using the socket.
+    std::lock_guard<std::mutex> guard(socket_mutex_);
+    shutdown_ssl(socket_, true);
+    shutdown_socket(socket_);
+    close_socket(socket_);
+  }
+
   if (300 < res.status && res.status < 400 && follow_location_) {
     req = req_save;
     ret = redirect(req, res, error);
   }
 
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
-  if ((res.status == 401 || res.status == 407) &&
+  if ((res.status == StatusCode::Unauthorized_401 ||
+       res.status == StatusCode::ProxyAuthenticationRequired_407) &&
       req.authorization_count_ < 5) {
-    auto is_proxy = res.status == 407;
+    auto is_proxy = res.status == StatusCode::ProxyAuthenticationRequired_407;
     const auto &username =
         is_proxy ? proxy_digest_auth_username_ : digest_auth_username_;
     const auto &password =
@@ -6571,7 +7176,7 @@ inline bool ClientImpl::redirect(Request &req, Response &res, Error &error) {
   } else {
     if (next_scheme == "https") {
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
-      SSLClient cli(next_host.c_str(), next_port);
+      SSLClient cli(next_host, next_port);
       cli.copy_settings(*this);
       if (ca_cert_store_) { cli.set_ca_cert_store(ca_cert_store_); }
       return detail::redirect(cli, req, res, path, location, error);
@@ -6579,7 +7184,7 @@ inline bool ClientImpl::redirect(Request &req, Response &res, Error &error) {
       return false;
 #endif
     } else {
-      ClientImpl cli(next_host.c_str(), next_port);
+      ClientImpl cli(next_host, next_port);
       cli.copy_settings(*this);
       return detail::redirect(cli, req, res, path, location, error);
     }
@@ -6588,7 +7193,7 @@ inline bool ClientImpl::redirect(Request &req, Response &res, Error &error) {
 
 inline bool ClientImpl::write_content_with_provider(Stream &strm,
                                                     const Request &req,
-                                                    Error &error) {
+                                                    Error &error) const {
   auto is_shutting_down = []() { return false; };
 
   if (req.is_chunked_content_provider_) {
@@ -6616,32 +7221,32 @@ inline bool ClientImpl::write_request(Stream &strm, Request &req,
   // Prepare additional headers
   if (close_connection) {
     if (!req.has_header("Connection")) {
-      req.headers.emplace("Connection", "close");
+      req.set_header("Connection", "close");
     }
   }
 
   if (!req.has_header("Host")) {
     if (is_ssl()) {
       if (port_ == 443) {
-        req.headers.emplace("Host", host_);
+        req.set_header("Host", host_);
       } else {
-        req.headers.emplace("Host", host_and_port_);
+        req.set_header("Host", host_and_port_);
       }
     } else {
       if (port_ == 80) {
-        req.headers.emplace("Host", host_);
+        req.set_header("Host", host_);
       } else {
-        req.headers.emplace("Host", host_and_port_);
+        req.set_header("Host", host_and_port_);
       }
     }
   }
 
-  if (!req.has_header("Accept")) { req.headers.emplace("Accept", "*/*"); }
+  if (!req.has_header("Accept")) { req.set_header("Accept", "*/*"); }
 
 #ifndef CPPHTTPLIB_NO_DEFAULT_USER_AGENT
   if (!req.has_header("User-Agent")) {
     auto agent = std::string("cpp-httplib/") + CPPHTTPLIB_VERSION;
-    req.headers.emplace("User-Agent", agent);
+    req.set_header("User-Agent", agent);
   }
 #endif
 
@@ -6650,23 +7255,23 @@ inline bool ClientImpl::write_request(Stream &strm, Request &req,
       if (!req.is_chunked_content_provider_) {
         if (!req.has_header("Content-Length")) {
           auto length = std::to_string(req.content_length_);
-          req.headers.emplace("Content-Length", length);
+          req.set_header("Content-Length", length);
         }
       }
     } else {
       if (req.method == "POST" || req.method == "PUT" ||
           req.method == "PATCH") {
-        req.headers.emplace("Content-Length", "0");
+        req.set_header("Content-Length", "0");
       }
     }
   } else {
     if (!req.has_header("Content-Type")) {
-      req.headers.emplace("Content-Type", "text/plain");
+      req.set_header("Content-Type", "text/plain");
     }
 
     if (!req.has_header("Content-Length")) {
       auto length = std::to_string(req.body.size());
-      req.headers.emplace("Content-Length", length);
+      req.set_header("Content-Length", length);
     }
   }
 
@@ -6706,7 +7311,7 @@ inline bool ClientImpl::write_request(Stream &strm, Request &req,
     const auto &path = url_encode_ ? detail::encode_url(req.path) : req.path;
     bstrm.write_format("%s %s HTTP/1.1\r\n", req.method.c_str(), path.c_str());
 
-    detail::write_headers(bstrm, req.headers);
+    header_writer_(bstrm, req.headers);
 
     // Flush buffer
     auto &data = bstrm.get_buffer();
@@ -6734,12 +7339,10 @@ inline std::unique_ptr<Response> ClientImpl::send_with_content_provider(
     ContentProvider content_provider,
     ContentProviderWithoutLength content_provider_without_length,
     const std::string &content_type, Error &error) {
-  if (!content_type.empty()) {
-    req.headers.emplace("Content-Type", content_type);
-  }
+  if (!content_type.empty()) { req.set_header("Content-Type", content_type); }
 
 #ifdef CPPHTTPLIB_ZLIB_SUPPORT
-  if (compress_) { req.headers.emplace("Content-Encoding", "gzip"); }
+  if (compress_) { req.set_header("Content-Encoding", "gzip"); }
 #endif
 
 #ifdef CPPHTTPLIB_ZLIB_SUPPORT
@@ -6800,10 +7403,9 @@ inline std::unique_ptr<Response> ClientImpl::send_with_content_provider(
       req.content_provider_ = detail::ContentProviderAdapter(
           std::move(content_provider_without_length));
       req.is_chunked_content_provider_ = true;
-      req.headers.emplace("Transfer-Encoding", "chunked");
+      req.set_header("Transfer-Encoding", "chunked");
     } else {
       req.body.assign(body, content_length);
-      ;
     }
   }
 
@@ -6864,7 +7466,8 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req,
   }
 
   // Body
-  if ((res.status != 204) && req.method != "HEAD" && req.method != "CONNECT") {
+  if ((res.status != StatusCode::NoContent_204) && req.method != "HEAD" &&
+      req.method != "CONNECT") {
     auto redirect = 300 < res.status && res.status < 400 && follow_location_;
 
     if (req.response_handler && !redirect) {
@@ -6909,24 +7512,6 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req,
     }
   }
 
-  if (res.get_header_value("Connection") == "close" ||
-      (res.version == "HTTP/1.0" && res.reason != "Connection established")) {
-    // TODO this requires a not-entirely-obvious chain of calls to be correct
-    // for this to be safe. Maybe a code refactor (such as moving this out to
-    // the send function and getting rid of the recursiveness of the mutex)
-    // could make this more obvious.
-
-    // This is safe to call because process_request is only called by
-    // handle_request which is only called by send, which locks the request
-    // mutex during the process. It would be a bug to call it from a different
-    // thread since it's a thread-safety issue to do these things to the socket
-    // if another thread is using the socket.
-    std::lock_guard<std::mutex> guard(socket_mutex_);
-    shutdown_ssl(socket_, true);
-    shutdown_socket(socket_);
-    close_socket(socket_);
-  }
-
   // Log
   if (logger_) { logger_(req, res); }
 
@@ -6935,13 +7520,14 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req,
 
 inline ContentProviderWithoutLength ClientImpl::get_multipart_content_provider(
     const std::string &boundary, const MultipartFormDataItems &items,
-    const MultipartFormDataProviderItems &provider_items) {
-  size_t cur_item = 0, cur_start = 0;
+    const MultipartFormDataProviderItems &provider_items) const {
+  size_t cur_item = 0;
+  size_t cur_start = 0;
   // cur_item and cur_start are copied to within the std::function and maintain
   // state between successive calls
   return [&, cur_item, cur_start](size_t offset,
                                   DataSink &sink) mutable -> bool {
-    if (!offset && items.size()) {
+    if (!offset && !items.empty()) {
       sink.os << detail::serialize_multipart_formdata(items, boundary, false);
       return true;
     } else if (cur_item < provider_items.size()) {
@@ -6954,12 +7540,13 @@ inline ContentProviderWithoutLength ClientImpl::get_multipart_content_provider(
       }
 
       DataSink cur_sink;
-      bool has_data = true;
+      auto has_data = true;
       cur_sink.write = sink.write;
       cur_sink.done = [&]() { has_data = false; };
 
-      if (!provider_items[cur_item].provider(offset - cur_start, cur_sink))
+      if (!provider_items[cur_item].provider(offset - cur_start, cur_sink)) {
         return false;
+      }
 
       if (!has_data) {
         sink.os << detail::serialize_multipart_formdata_item_end();
@@ -7078,14 +7665,15 @@ inline Result ClientImpl::Get(const std::string &path, const Params &params,
   if (params.empty()) { return Get(path, headers); }
 
   std::string path_with_query = append_query_params(path, params);
-  return Get(path_with_query.c_str(), headers, progress);
+  return Get(path_with_query, headers, std::move(progress));
 }
 
 inline Result ClientImpl::Get(const std::string &path, const Params &params,
                               const Headers &headers,
                               ContentReceiver content_receiver,
                               Progress progress) {
-  return Get(path, params, headers, nullptr, content_receiver, progress);
+  return Get(path, params, headers, nullptr, std::move(content_receiver),
+             std::move(progress));
 }
 
 inline Result ClientImpl::Get(const std::string &path, const Params &params,
@@ -7094,12 +7682,13 @@ inline Result ClientImpl::Get(const std::string &path, const Params &params,
                               ContentReceiver content_receiver,
                               Progress progress) {
   if (params.empty()) {
-    return Get(path, headers, response_handler, content_receiver, progress);
+    return Get(path, headers, std::move(response_handler),
+               std::move(content_receiver), std::move(progress));
   }
 
   std::string path_with_query = append_query_params(path, params);
-  return Get(path_with_query.c_str(), headers, response_handler,
-             content_receiver, progress);
+  return Get(path_with_query, headers, std::move(response_handler),
+             std::move(content_receiver), std::move(progress));
 }
 
 inline Result ClientImpl::Head(const std::string &path) {
@@ -7201,7 +7790,7 @@ inline Result ClientImpl::Post(const std::string &path, const Headers &headers,
   const auto &content_type =
       detail::serialize_multipart_formdata_get_content_type(boundary);
   const auto &body = detail::serialize_multipart_formdata(items, boundary);
-  return Post(path, headers, body, content_type.c_str());
+  return Post(path, headers, body, content_type);
 }
 
 inline Result ClientImpl::Post(const std::string &path, const Headers &headers,
@@ -7214,7 +7803,7 @@ inline Result ClientImpl::Post(const std::string &path, const Headers &headers,
   const auto &content_type =
       detail::serialize_multipart_formdata_get_content_type(boundary);
   const auto &body = detail::serialize_multipart_formdata(items, boundary);
-  return Post(path, headers, body, content_type.c_str());
+  return Post(path, headers, body, content_type);
 }
 
 inline Result
@@ -7423,9 +8012,7 @@ inline Result ClientImpl::Delete(const std::string &path,
   req.headers = headers;
   req.path = path;
 
-  if (!content_type.empty()) {
-    req.headers.emplace("Content-Type", content_type);
-  }
+  if (!content_type.empty()) { req.set_header("Content-Type", content_type); }
   req.body.assign(body, content_length);
 
   return send_(std::move(req));
@@ -7458,13 +8045,6 @@ inline Result ClientImpl::Options(const std::string &path,
   return send_(std::move(req));
 }
 
-inline size_t ClientImpl::is_socket_open() const {
-  std::lock_guard<std::mutex> guard(socket_mutex_);
-  return socket_.is_open();
-}
-
-inline socket_t ClientImpl::socket() const { return socket_.sock; }
-
 inline void ClientImpl::stop() {
   std::lock_guard<std::mutex> guard(socket_mutex_);
 
@@ -7488,6 +8068,17 @@ inline void ClientImpl::stop() {
   close_socket(socket_);
 }
 
+inline std::string ClientImpl::host() const { return host_; }
+
+inline int ClientImpl::port() const { return port_; }
+
+inline size_t ClientImpl::is_socket_open() const {
+  std::lock_guard<std::mutex> guard(socket_mutex_);
+  return socket_.is_open();
+}
+
+inline socket_t ClientImpl::socket() const { return socket_.sock; }
+
 inline void ClientImpl::set_connection_timeout(time_t sec, time_t usec) {
   connection_timeout_sec_ = sec;
   connection_timeout_usec_ = usec;
@@ -7536,6 +8127,11 @@ inline void ClientImpl::set_default_headers(Headers headers) {
   default_headers_ = std::move(headers);
 }
 
+inline void ClientImpl::set_header_writer(
+    std::function<ssize_t(Stream &, Headers &)> const &writer) {
+  header_writer_ = writer;
+}
+
 inline void ClientImpl::set_address_family(int family) {
   address_family_ = family;
 }
@@ -7575,9 +8171,7 @@ inline void ClientImpl::set_proxy_digest_auth(const std::string &username,
   proxy_digest_auth_username_ = username;
   proxy_digest_auth_password_ = password;
 }
-#endif
 
-#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
 inline void ClientImpl::set_ca_cert_path(const std::string &ca_cert_file_path,
                                          const std::string &ca_cert_dir_path) {
   ca_cert_file_path_ = ca_cert_file_path;
@@ -7589,9 +8183,34 @@ inline void ClientImpl::set_ca_cert_store(X509_STORE *ca_cert_store) {
     ca_cert_store_ = ca_cert_store;
   }
 }
-#endif
 
-#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
+inline X509_STORE *ClientImpl::create_ca_cert_store(const char *ca_cert,
+                                                    std::size_t size) const {
+  auto mem = BIO_new_mem_buf(ca_cert, static_cast<int>(size));
+  if (!mem) { return nullptr; }
+
+  auto inf = PEM_X509_INFO_read_bio(mem, nullptr, nullptr, nullptr);
+  if (!inf) {
+    BIO_free_all(mem);
+    return nullptr;
+  }
+
+  auto cts = X509_STORE_new();
+  if (cts) {
+    for (auto i = 0; i < static_cast<int>(sk_X509_INFO_num(inf)); i++) {
+      auto itmp = sk_X509_INFO_value(inf, i);
+      if (!itmp) { continue; }
+
+      if (itmp->x509) { X509_STORE_add_cert(cts, itmp->x509); }
+      if (itmp->crl) { X509_STORE_add_crl(cts, itmp->crl); }
+    }
+  }
+
+  sk_X509_INFO_pop_free(inf, X509_INFO_free);
+  BIO_free_all(mem);
+  return cts;
+}
+
 inline void ClientImpl::enable_server_certificate_verification(bool enabled) {
   server_certificate_verification_ = enabled;
 }
@@ -7655,7 +8274,7 @@ bool ssl_connect_or_accept_nonblocking(socket_t sock, SSL *ssl,
                                        U ssl_connect_or_accept,
                                        time_t timeout_sec,
                                        time_t timeout_usec) {
-  int res = 0;
+  auto res = 0;
   while ((res = ssl_connect_or_accept(ssl)) != 1) {
     auto err = SSL_get_error(ssl, res);
     switch (err) {
@@ -7718,7 +8337,7 @@ inline SSLSocketStream::SSLSocketStream(socket_t sock, SSL *ssl,
   SSL_clear_mode(ssl, SSL_MODE_AUTO_RETRY);
 }
 
-inline SSLSocketStream::~SSLSocketStream() {}
+inline SSLSocketStream::~SSLSocketStream() = default;
 
 inline bool SSLSocketStream::is_readable() const {
   return detail::select_read(sock_, read_timeout_sec_, read_timeout_usec_) > 0;
@@ -7736,7 +8355,7 @@ inline ssize_t SSLSocketStream::read(char *ptr, size_t size) {
     auto ret = SSL_read(ssl_, ptr, static_cast<int>(size));
     if (ret < 0) {
       auto err = SSL_get_error(ssl_, ret);
-      int n = 1000;
+      auto n = 1000;
 #ifdef _WIN32
       while (--n >= 0 && (err == SSL_ERROR_WANT_READ ||
                           (err == SSL_ERROR_SYSCALL &&
@@ -7769,7 +8388,7 @@ inline ssize_t SSLSocketStream::write(const char *ptr, size_t size) {
     auto ret = SSL_write(ssl_, ptr, static_cast<int>(handle_size));
     if (ret < 0) {
       auto err = SSL_get_error(ssl_, ret);
-      int n = 1000;
+      auto n = 1000;
 #ifdef _WIN32
       while (--n >= 0 && (err == SSL_ERROR_WANT_WRITE ||
                           (err == SSL_ERROR_SYSCALL &&
@@ -7822,10 +8441,10 @@ inline SSLServer::SSLServer(const char *cert_path, const char *private_key_path,
 
     SSL_CTX_set_min_proto_version(ctx_, TLS1_1_VERSION);
 
-    // add default password callback before opening encrypted private key
     if (private_key_password != nullptr && (private_key_password[0] != '\0')) {
-      SSL_CTX_set_default_passwd_cb_userdata(ctx_,
-                                             (char *)private_key_password);
+      SSL_CTX_set_default_passwd_cb_userdata(
+          ctx_,
+          reinterpret_cast<void *>(const_cast<char *>(private_key_password)));
     }
 
     if (SSL_CTX_use_certificate_chain_file(ctx_, cert_path) != 1 ||
@@ -7927,16 +8546,23 @@ inline SSLClient::SSLClient(const std::string &host, int port)
 
 inline SSLClient::SSLClient(const std::string &host, int port,
                             const std::string &client_cert_path,
-                            const std::string &client_key_path)
+                            const std::string &client_key_path,
+                            const std::string &private_key_password)
     : ClientImpl(host, port, client_cert_path, client_key_path) {
   ctx_ = SSL_CTX_new(TLS_client_method());
 
   detail::split(&host_[0], &host_[host_.size()], '.',
                 [&](const char *b, const char *e) {
-                  host_components_.emplace_back(std::string(b, e));
+                  host_components_.emplace_back(b, e);
                 });
 
   if (!client_cert_path.empty() && !client_key_path.empty()) {
+    if (!private_key_password.empty()) {
+      SSL_CTX_set_default_passwd_cb_userdata(
+          ctx_, reinterpret_cast<void *>(
+                    const_cast<char *>(private_key_password.c_str())));
+    }
+
     if (SSL_CTX_use_certificate_file(ctx_, client_cert_path.c_str(),
                                      SSL_FILETYPE_PEM) != 1 ||
         SSL_CTX_use_PrivateKey_file(ctx_, client_key_path.c_str(),
@@ -7948,16 +8574,23 @@ inline SSLClient::SSLClient(const std::string &host, int port,
 }
 
 inline SSLClient::SSLClient(const std::string &host, int port,
-                            X509 *client_cert, EVP_PKEY *client_key)
+                            X509 *client_cert, EVP_PKEY *client_key,
+                            const std::string &private_key_password)
     : ClientImpl(host, port) {
   ctx_ = SSL_CTX_new(TLS_client_method());
 
   detail::split(&host_[0], &host_[host_.size()], '.',
                 [&](const char *b, const char *e) {
-                  host_components_.emplace_back(std::string(b, e));
+                  host_components_.emplace_back(b, e);
                 });
 
   if (client_cert != nullptr && client_key != nullptr) {
+    if (!private_key_password.empty()) {
+      SSL_CTX_set_default_passwd_cb_userdata(
+          ctx_, reinterpret_cast<void *>(
+                    const_cast<char *>(private_key_password.c_str())));
+    }
+
     if (SSL_CTX_use_certificate(ctx_, client_cert) != 1 ||
         SSL_CTX_use_PrivateKey(ctx_, client_key) != 1) {
       SSL_CTX_free(ctx_);
@@ -7989,6 +8622,11 @@ inline void SSLClient::set_ca_cert_store(X509_STORE *ca_cert_store) {
   }
 }
 
+inline void SSLClient::load_ca_cert_store(const char *ca_cert,
+                                          std::size_t size) {
+  set_ca_cert_store(ClientImpl::create_ca_cert_store(ca_cert, size));
+}
+
 inline long SSLClient::get_openssl_verify_result() const {
   return verify_result_;
 }
@@ -8003,14 +8641,14 @@ inline bool SSLClient::create_and_connect_socket(Socket &socket, Error &error) {
 inline bool SSLClient::connect_with_proxy(Socket &socket, Response &res,
                                           bool &success, Error &error) {
   success = true;
-  Response res2;
+  Response proxy_res;
   if (!detail::process_client_socket(
           socket.sock, read_timeout_sec_, read_timeout_usec_,
           write_timeout_sec_, write_timeout_usec_, [&](Stream &strm) {
             Request req2;
             req2.method = "CONNECT";
             req2.path = host_and_port_;
-            return process_request(strm, req2, res2, false, error);
+            return process_request(strm, req2, proxy_res, false, error);
           })) {
     // Thread-safe to close everything because we are assuming there are no
     // requests in flight
@@ -8021,12 +8659,12 @@ inline bool SSLClient::connect_with_proxy(Socket &socket, Response &res,
     return false;
   }
 
-  if (res2.status == 407) {
+  if (proxy_res.status == StatusCode::ProxyAuthenticationRequired_407) {
     if (!proxy_digest_auth_username_.empty() &&
         !proxy_digest_auth_password_.empty()) {
       std::map<std::string, std::string> auth;
-      if (detail::parse_www_authenticate(res2, auth, true)) {
-        Response res3;
+      if (detail::parse_www_authenticate(proxy_res, auth, true)) {
+        proxy_res = Response();
         if (!detail::process_client_socket(
                 socket.sock, read_timeout_sec_, read_timeout_usec_,
                 write_timeout_sec_, write_timeout_usec_, [&](Stream &strm) {
@@ -8037,7 +8675,7 @@ inline bool SSLClient::connect_with_proxy(Socket &socket, Response &res,
                       req3, auth, 1, detail::random_string(10),
                       proxy_digest_auth_username_, proxy_digest_auth_password_,
                       true));
-                  return process_request(strm, req3, res3, false, error);
+                  return process_request(strm, req3, proxy_res, false, error);
                 })) {
           // Thread-safe to close everything because we are assuming there are
           // no requests in flight
@@ -8048,17 +8686,28 @@ inline bool SSLClient::connect_with_proxy(Socket &socket, Response &res,
           return false;
         }
       }
-    } else {
-      res = res2;
-      return false;
     }
   }
 
+  // If status code is not 200, proxy request is failed.
+  // Set error to ProxyConnection and return proxy response
+  // as the response of the request
+  if (proxy_res.status != StatusCode::OK_200) {
+    error = Error::ProxyConnection;
+    res = std::move(proxy_res);
+    // Thread-safe to close everything because we are assuming there are
+    // no requests in flight
+    shutdown_ssl(socket, true);
+    shutdown_socket(socket);
+    close_socket(socket);
+    return false;
+  }
+
   return true;
 }
 
 inline bool SSLClient::load_certs() {
-  bool ret = true;
+  auto ret = true;
 
   std::call_once(initialize_cert_, [&]() {
     std::lock_guard<std::mutex> guard(ctx_mutex_);
@@ -8134,7 +8783,11 @@ inline bool SSLClient::initialize_ssl(Socket &socket, Error &error) {
         return true;
       },
       [&](SSL *ssl2) {
-        SSL_set_tlsext_host_name(ssl2, host_.c_str());
+        // NOTE: Direct call instead of using the OpenSSL macro to suppress
+        // -Wold-style-cast warning
+        // SSL_set_tlsext_host_name(ssl2, host_.c_str());
+        SSL_ctrl(ssl2, SSL_CTRL_SET_TLSEXT_HOSTNAME, TLSEXT_NAMETYPE_host_name,
+                 static_cast<void *>(const_cast<char *>(host_.c_str())));
         return true;
       });
 
@@ -8208,8 +8861,8 @@ SSLClient::verify_host_with_subject_alt_name(X509 *server_cert) const {
 
   auto type = GEN_DNS;
 
-  struct in6_addr addr6;
-  struct in_addr addr;
+  struct in6_addr addr6 {};
+  struct in_addr addr {};
   size_t addr_len = 0;
 
 #ifndef __MINGW32__
@@ -8234,8 +8887,9 @@ SSLClient::verify_host_with_subject_alt_name(X509 *server_cert) const {
     for (decltype(count) i = 0; i < count && !dsn_matched; i++) {
       auto val = sk_GENERAL_NAME_value(alt_names, i);
       if (val->type == type) {
-        auto name = (const char *)ASN1_STRING_get0_data(val->d.ia5);
-        auto name_len = (size_t)ASN1_STRING_length(val->d.ia5);
+        auto name =
+            reinterpret_cast<const char *>(ASN1_STRING_get0_data(val->d.ia5));
+        auto name_len = static_cast<size_t>(ASN1_STRING_length(val->d.ia5));
 
         switch (type) {
         case GEN_DNS: dsn_matched = check_host_name(name, name_len); break;
@@ -8253,7 +8907,8 @@ SSLClient::verify_host_with_subject_alt_name(X509 *server_cert) const {
     if (dsn_matched || ip_matched) { ret = true; }
   }
 
-  GENERAL_NAMES_free((STACK_OF(GENERAL_NAME) *)alt_names);
+  GENERAL_NAMES_free(const_cast<STACK_OF(GENERAL_NAME) *>(
+      reinterpret_cast<const STACK_OF(GENERAL_NAME) *>(alt_names)));
   return ret;
 }
 
@@ -8282,7 +8937,7 @@ inline bool SSLClient::check_host_name(const char *pattern,
   std::vector<std::string> pattern_components;
   detail::split(&pattern[0], &pattern[pattern_len], '.',
                 [&](const char *b, const char *e) {
-                  pattern_components.emplace_back(std::string(b, e));
+                  pattern_components.emplace_back(b, e);
                 });
 
   if (host_components_.size() != pattern_components.size()) { return false; }
@@ -8361,7 +9016,7 @@ inline Client::Client(const std::string &host, int port,
     : cli_(detail::make_unique<ClientImpl>(host, port, client_cert_path,
                                            client_key_path)) {}
 
-inline Client::~Client() {}
+inline Client::~Client() = default;
 
 inline bool Client::is_valid() const {
   return cli_ != nullptr && cli_->is_valid();
@@ -8421,19 +9076,20 @@ inline Result Client::Get(const std::string &path, const Headers &headers,
 }
 inline Result Client::Get(const std::string &path, const Params &params,
                           const Headers &headers, Progress progress) {
-  return cli_->Get(path, params, headers, progress);
+  return cli_->Get(path, params, headers, std::move(progress));
 }
 inline Result Client::Get(const std::string &path, const Params &params,
                           const Headers &headers,
                           ContentReceiver content_receiver, Progress progress) {
-  return cli_->Get(path, params, headers, content_receiver, progress);
+  return cli_->Get(path, params, headers, std::move(content_receiver),
+                   std::move(progress));
 }
 inline Result Client::Get(const std::string &path, const Params &params,
                           const Headers &headers,
                           ResponseHandler response_handler,
                           ContentReceiver content_receiver, Progress progress) {
-  return cli_->Get(path, params, headers, response_handler, content_receiver,
-                   progress);
+  return cli_->Get(path, params, headers, std::move(response_handler),
+                   std::move(content_receiver), std::move(progress));
 }
 
 inline Result Client::Head(const std::string &path) { return cli_->Head(path); }
@@ -8665,12 +9321,16 @@ inline bool Client::send(Request &req, Response &res, Error &error) {
 
 inline Result Client::send(const Request &req) { return cli_->send(req); }
 
+inline void Client::stop() { cli_->stop(); }
+
+inline std::string Client::host() const { return cli_->host(); }
+
+inline int Client::port() const { return cli_->port(); }
+
 inline size_t Client::is_socket_open() const { return cli_->is_socket_open(); }
 
 inline socket_t Client::socket() const { return cli_->socket(); }
 
-inline void Client::stop() { cli_->stop(); }
-
 inline void
 Client::set_hostname_addr_map(std::map<std::string, std::string> addr_map) {
   cli_->set_hostname_addr_map(std::move(addr_map));
@@ -8680,6 +9340,11 @@ inline void Client::set_default_headers(Headers headers) {
   cli_->set_default_headers(std::move(headers));
 }
 
+inline void Client::set_header_writer(
+    std::function<ssize_t(Stream &, Headers &)> const &writer) {
+  cli_->set_header_writer(writer);
+}
+
 inline void Client::set_address_family(int family) {
   cli_->set_address_family(family);
 }
@@ -8754,7 +9419,9 @@ inline void Client::enable_server_certificate_verification(bool enabled) {
 }
 #endif
 
-inline void Client::set_logger(Logger logger) { cli_->set_logger(logger); }
+inline void Client::set_logger(Logger logger) {
+  cli_->set_logger(std::move(logger));
+}
 
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
 inline void Client::set_ca_cert_path(const std::string &ca_cert_file_path,
@@ -8770,6 +9437,10 @@ inline void Client::set_ca_cert_store(X509_STORE *ca_cert_store) {
   }
 }
 
+inline void Client::load_ca_cert_store(const char *ca_cert, std::size_t size) {
+  set_ca_cert_store(cli_->create_ca_cert_store(ca_cert, size));
+}
+
 inline long Client::get_openssl_verify_result() const {
   if (is_ssl_) {
     return static_cast<SSLClient &>(*cli_).get_openssl_verify_result();
diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp
index 20551520e..2de8a5fd2 100644
--- a/examples/server/index.html.hpp
+++ b/examples/server/index.html.hpp
@@ -307,1299 +307,1388 @@ unsigned char index_html[] = {
   0x6c, 0x2c, 0x20, 0x75, 0x73, 0x65, 0x45, 0x66, 0x66, 0x65, 0x63, 0x74,
   0x2c, 0x20, 0x75, 0x73, 0x65, 0x52, 0x65, 0x66, 0x2c, 0x20, 0x43, 0x6f,
   0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x0a, 0x20, 0x20, 0x20, 0x20,
-  0x7d, 0x20, 0x66, 0x72, 0x6f, 0x6d, 0x20, 0x27, 0x2f, 0x69, 0x6e, 0x64,
-  0x65, 0x78, 0x2e, 0x6a, 0x73, 0x27, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20,
-  0x20, 0x69, 0x6d, 0x70, 0x6f, 0x72, 0x74, 0x20, 0x7b, 0x20, 0x6c, 0x6c,
-  0x61, 0x6d, 0x61, 0x20, 0x7d, 0x20, 0x66, 0x72, 0x6f, 0x6d, 0x20, 0x27,
-  0x2f, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x2e,
-  0x6a, 0x73, 0x27, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x69, 0x6d, 0x70,
-  0x6f, 0x72, 0x74, 0x20, 0x7b, 0x20, 0x53, 0x63, 0x68, 0x65, 0x6d, 0x61,
-  0x43, 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x74, 0x65, 0x72, 0x20, 0x7d, 0x20,
-  0x66, 0x72, 0x6f, 0x6d, 0x20, 0x27, 0x2f, 0x6a, 0x73, 0x6f, 0x6e, 0x2d,
-  0x73, 0x63, 0x68, 0x65, 0x6d, 0x61, 0x2d, 0x74, 0x6f, 0x2d, 0x67, 0x72,
-  0x61, 0x6d, 0x6d, 0x61, 0x72, 0x2e, 0x6d, 0x6a, 0x73, 0x27, 0x3b, 0x0a,
-  0x20, 0x20, 0x20, 0x20, 0x6c, 0x65, 0x74, 0x20, 0x73, 0x65, 0x6c, 0x65,
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+  0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x70,
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+  0x3e, 0x0a, 0x3c, 0x2f, 0x62, 0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x0a, 0x3c,
+  0x2f, 0x68, 0x74, 0x6d, 0x6c, 0x3e, 0x0a, 0x0a
 };
-unsigned int index_html_len = 33456;
+unsigned int index_html_len = 34412;
diff --git a/examples/server/index.js.hpp b/examples/server/index.js.hpp
index e09b3c8c5..256aab5e5 100644
--- a/examples/server/index.js.hpp
+++ b/examples/server/index.js.hpp
@@ -12,7 +12,7 @@ unsigned char index_js[] = {
   0x74, 0x2c, 0x6e, 0x3d, 0x21, 0x31, 0x3b, 0x77, 0x68, 0x69, 0x6c, 0x65,
   0x28, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x6f, 0x29,
   0x7b, 0x6c, 0x65, 0x74, 0x20, 0x5f, 0x3d, 0x6f, 0x3b, 0x6f, 0x3d, 0x76,
-  0x6f, 0x69, 0x64, 0x20, 0x30, 0x3b, 0x73, 0x2b, 0x2b, 0x3b, 0x77, 0x68,
+  0x6f, 0x69, 0x64, 0x20, 0x30, 0x3b, 0x6c, 0x2b, 0x2b, 0x3b, 0x77, 0x68,
   0x69, 0x6c, 0x65, 0x28, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d,
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   0x5f, 0x2e, 0x6f, 0x3b, 0x5f, 0x2e, 0x6f, 0x3d, 0x76, 0x6f, 0x69, 0x64,
@@ -21,7 +21,7 @@ unsigned char index_js[] = {
   0x70, 0x28, 0x5f, 0x29, 0x29, 0x74, 0x72, 0x79, 0x7b, 0x5f, 0x2e, 0x63,
   0x28, 0x29, 0x7d, 0x63, 0x61, 0x74, 0x63, 0x68, 0x28, 0x65, 0x29, 0x7b,
   0x69, 0x66, 0x28, 0x21, 0x6e, 0x29, 0x7b, 0x74, 0x3d, 0x65, 0x3b, 0x6e,
-  0x3d, 0x21, 0x30, 0x7d, 0x7d, 0x5f, 0x3d, 0x69, 0x7d, 0x7d, 0x73, 0x3d,
+  0x3d, 0x21, 0x30, 0x7d, 0x7d, 0x5f, 0x3d, 0x69, 0x7d, 0x7d, 0x6c, 0x3d,
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@@ -37,8 +37,8 @@ unsigned char index_js[] = {
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-  0x30, 0x2c, 0x6c, 0x3d, 0x30, 0x3b, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69,
+  0x7d, 0x7d, 0x6c, 0x65, 0x74, 0x20, 0x66, 0x3d, 0x30, 0x2c, 0x6c, 0x3d,
+  0x30, 0x2c, 0x73, 0x3d, 0x30, 0x3b, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69,
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@@ -141,9 +141,9 @@ unsigned char index_js[] = {
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@@ -191,7 +191,7 @@ unsigned char index_js[] = {
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@@ -201,9 +201,9 @@ unsigned char index_js[] = {
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@@ -437,321 +437,340 @@ unsigned char index_js[] = {
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@@ -1851,53 +1879,53 @@ unsigned char index_js[] = {
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 };
-unsigned int index_js_len = 22800;
+unsigned int index_js_len = 23136;
diff --git a/examples/server/json-schema-to-grammar.mjs.hpp b/examples/server/json-schema-to-grammar.mjs.hpp
index 0a05c369d..ef1d5dcf3 100644
--- a/examples/server/json-schema-to-grammar.mjs.hpp
+++ b/examples/server/json-schema-to-grammar.mjs.hpp
@@ -1,152 +1,1027 @@
 unsigned char json_schema_to_grammar_mjs[] = {
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+  0x65, 0x6c, 0x64, 0x20, 0x5b, 0x6c, 0x61, 0x73, 0x74, 0x4b, 0x65, 0x79,
+  0x2c, 0x20, 0x67, 0x72, 0x6f, 0x75, 0x70, 0x5d, 0x3b, 0x0a, 0x20, 0x20,
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+  0x20, 0x20, 0x67, 0x72, 0x6f, 0x75, 0x70, 0x2e, 0x70, 0x75, 0x73, 0x68,
+  0x28, 0x65, 0x6c, 0x65, 0x6d, 0x65, 0x6e, 0x74, 0x29, 0x3b, 0x0a, 0x20,
+  0x20, 0x20, 0x20, 0x6c, 0x61, 0x73, 0x74, 0x4b, 0x65, 0x79, 0x20, 0x3d,
+  0x20, 0x6b, 0x65, 0x79, 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20,
+  0x69, 0x66, 0x20, 0x28, 0x67, 0x72, 0x6f, 0x75, 0x70, 0x2e, 0x6c, 0x65,
+  0x6e, 0x67, 0x74, 0x68, 0x20, 0x3e, 0x20, 0x30, 0x29, 0x20, 0x7b, 0x0a,
+  0x20, 0x20, 0x20, 0x20, 0x79, 0x69, 0x65, 0x6c, 0x64, 0x20, 0x5b, 0x6c,
+  0x61, 0x73, 0x74, 0x4b, 0x65, 0x79, 0x2c, 0x20, 0x67, 0x72, 0x6f, 0x75,
+  0x70, 0x5d, 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x7d, 0x0a
 };
-unsigned int json_schema_to_grammar_mjs_len = 3695;
+unsigned int json_schema_to_grammar_mjs_len = 19690;
diff --git a/examples/server/public/completion.js b/examples/server/public/completion.js
index baaec1d60..987b9a3b8 100644
--- a/examples/server/public/completion.js
+++ b/examples/server/public/completion.js
@@ -21,6 +21,7 @@ let generation_settings = null;
 //
 export async function* llama(prompt, params = {}, config = {}) {
   let controller = config.controller;
+  const api_url = config.api_url || "";
 
   if (!controller) {
     controller = new AbortController();
@@ -28,7 +29,7 @@ export async function* llama(prompt, params = {}, config = {}) {
 
   const completionParams = { ...paramDefaults, ...params, prompt };
 
-  const response = await fetch("/completion", {
+  const response = await fetch(`${api_url}/completion`, {
     method: 'POST',
     body: JSON.stringify(completionParams),
     headers: {
@@ -96,18 +97,18 @@ export async function* llama(prompt, params = {}, config = {}) {
             }
           }
           if (result.error) {
-            result.error = JSON.parse(result.error);
-            if (result.error.content.includes('slot unavailable')) {
-              // Throw an error to be caught by upstream callers
-              throw new Error('slot unavailable');
-            } else {
-              console.error(`llama.cpp error: ${result.error.content}`);
+            try {
+              result.error = JSON.parse(result.error);
+              if (result.error.message.includes('slot unavailable')) {
+                // Throw an error to be caught by upstream callers
+                throw new Error('slot unavailable');
+              } else {
+                console.error(`llama.cpp error [${result.error.code} - ${result.error.type}]: ${result.error.message}`);
+              }
+            } catch(e) {
+              console.error(`llama.cpp error ${result.error}`)
             }
           }
-          if (result.error) {
-            result.error = JSON.parse(result.error);
-            console.error(`llama.cpp error: ${result.error.content}`);
-          }
         }
       }
     }
@@ -193,9 +194,11 @@ export const llamaComplete = async (params, controller, callback) => {
 }
 
 // Get the model info from the server. This is useful for getting the context window and so on.
-export const llamaModelInfo = async () => {
+export const llamaModelInfo = async (config = {}) => {
   if (!generation_settings) {
-    generation_settings = await fetch("/model.json").then(r => r.json());
+    const api_url = config.api_url || "";
+    const props = await fetch(`${api_url}/props`).then(r => r.json());
+    generation_settings = props.default_generation_settings;
   }
   return generation_settings;
 }
diff --git a/examples/server/public/index.html b/examples/server/public/index.html
index b059c75f2..c91a357c1 100644
--- a/examples/server/public/index.html
+++ b/examples/server/public/index.html
@@ -199,10 +199,10 @@
   <script type="module">
     import {
       html, h, signal, effect, computed, render, useSignal, useEffect, useRef, Component
-    } from '/index.js';
+    } from './index.js';
 
-    import { llama } from '/completion.js';
-    import { SchemaConverter } from '/json-schema-to-grammar.mjs';
+    import { llama } from './completion.js';
+    import { SchemaConverter } from './json-schema-to-grammar.mjs';
     let selected_image = false;
     var slot_id = -1;
 
@@ -222,6 +222,7 @@
       temperature: 0.7,
       repeat_last_n: 256, // 0 = disable penalty, -1 = context size
       repeat_penalty: 1.18, // 1.0 = disabled
+      penalize_nl: false,
       top_k: 40, // <= 0 to use vocab size
       top_p: 0.95, // 1.0 = disabled
       min_p: 0.05, // 0 = disabled
@@ -234,6 +235,7 @@
       mirostat_eta: 0.1, // learning rate
       grammar: '',
       n_probs: 0, // no completion_probabilities,
+      min_keep: 0, // min probs from each sampler,
       image_data: [],
       cache_prompt: true,
       api_key: ''
@@ -404,7 +406,7 @@
         throw new Error("already running");
       }
       controller.value = new AbortController();
-      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) {
+      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: document.baseURI.replace(/\/+$/, '') })) {
         const data = chunk.data;
 
         if (data.stop) {
@@ -626,17 +628,20 @@
       const updateParams = (el) => params.value = { ...params.value, [el.target.name]: el.target.value }
       const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) }
       const updateParamsInt = (el) => params.value = { ...params.value, [el.target.name]: Math.floor(parseFloat(el.target.value)) }
+      const updateParamsBool = (el) => params.value = { ...params.value, [el.target.name]: el.target.checked }
 
       const grammarJsonSchemaPropOrder = signal('')
       const updateGrammarJsonSchemaPropOrder = (el) => grammarJsonSchemaPropOrder.value = el.target.value
-      const convertJSONSchemaGrammar = () => {
+      const convertJSONSchemaGrammar = async () => {
         try {
-          const schema = JSON.parse(params.value.grammar)
-          const converter = new SchemaConverter(
-            grammarJsonSchemaPropOrder.value
+          let schema = JSON.parse(params.value.grammar)
+          const converter = new SchemaConverter({
+            prop_order: grammarJsonSchemaPropOrder.value
               .split(',')
-              .reduce((acc, cur, i) => ({ ...acc, [cur.trim()]: i }), {})
-          )
+              .reduce((acc, cur, i) => ({ ...acc, [cur.trim()]: i }), {}),
+            allow_fetch: true,
+          })
+          schema = await converter.resolveRefs(schema, 'input')
           converter.visit(schema, '')
           params.value = {
             ...params.value,
@@ -667,6 +672,15 @@
         `
       };
 
+      const BoolField = ({ label, name, value }) => {
+        return html`
+          <div>
+            <label for="${name}">${label}</label>
+            <input type="checkbox" id="${name}" name="${name}" checked="${value}" onclick=${updateParamsBool} />
+          </div>
+        `
+      };
+
       const userTemplateReset = (e) => {
         e.preventDefault();
         userTemplateResetToDefaultAndApply()
@@ -766,6 +780,7 @@
             ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
             ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
             ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
+            ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
             ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
             ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
             ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}
@@ -791,6 +806,9 @@
             <fieldset>
               ${IntField({ label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs })}
             </fieldset>
+            <fieldset>
+              ${IntField({ label: "Min Probabilities from each Sampler", max: 10, min: 0, name: "min_keep", value: params.value.min_keep })}
+            </fieldset>
             <fieldset>
               <label for="api_key">API Key</label>
               <input type="text" name="api_key" value="${params.value.api_key}" placeholder="Enter API key" oninput=${updateParams} />
diff --git a/examples/server/public/index.js b/examples/server/public/index.js
index 9db5a9d9f..aa3ffb5e0 100644
--- a/examples/server/public/index.js
+++ b/examples/server/public/index.js
@@ -1 +1 @@
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e=this.__$u;if(!(e&&void 0!==e.s||4&this.__$f))return!0;if(3&this.__$f)return!0;for(let _ in n)return!0;for(let _ in t)if("__source"!==_&&t[_]!==this.props[_])return!0;for(let _ in this.props)if(!(_ in t))return!0;return!1};function tn(t){return At(()=>a(t),[])}function nn(t){const n=Tt(t);n.current=t;Kt.__$f|=4;return At(()=>m(()=>n.current()),[])}function en(t){const n=Tt(t);n.current=t;$t(()=>w(()=>n.current()),[])}var _n=function(t,n,e,_){var i;n[0]=0;for(var o=1;o<n.length;o++){var r=n[o++],u=n[o]?(n[0]|=r?1:2,e[n[o++]]):n[++o];3===r?_[0]=u:4===r?_[1]=Object.assign(_[1]||{},u):5===r?(_[1]=_[1]||{})[n[++o]]=u:6===r?_[1][n[++o]]+=u+"":r?(i=t.apply(u,_n(t,u,e,["",null])),_.push(i),u[0]?n[0]|=2:(n[o-2]=0,n[o]=i)):_.push(u)}return _},on=new Map;function rn(t){var n=on.get(this);return n||(n=new Map,on.set(this,n)),(n=_n(this,n.get(t)||(n.set(t,n=function(t){for(var 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diff --git a/examples/server/public/json-schema-to-grammar.mjs b/examples/server/public/json-schema-to-grammar.mjs
index 3f1b255c2..17bd60fde 100644
--- a/examples/server/public/json-schema-to-grammar.mjs
+++ b/examples/server/public/json-schema-to-grammar.mjs
@@ -1,112 +1,538 @@
+// WARNING: This file was ported from json-schema-to-grammar.py, please fix bugs / add features there first.
 const SPACE_RULE = '" "?';
 
 const PRIMITIVE_RULES = {
   boolean: '("true" | "false") space',
   number: '("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space',
   integer: '("-"? ([0-9] | [1-9] [0-9]*)) space',
+  value: 'object | array | string | number | boolean',
+  object: '"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space',
+  array: '"[" space ( value ("," space value)* )? "]" space',
+  uuid: '"\\"" ' + [8, 4, 4, 4, 12].map(n => [...new Array(n)].map(_ => '[0-9a-fA-F]').join('')).join(' "-" ') + ' "\\"" space',
   string: ` "\\"" (
         [^"\\\\] |
         "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
       )* "\\"" space`,
   null: '"null" space',
 };
+const OBJECT_RULE_NAMES = ['object', 'array', 'string', 'number', 'boolean', 'null', 'value'];
+
+// TODO: support "uri", "email" string formats
+const DATE_RULES = {
+    'date'   : '[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )',
+    'time'   : '([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )',
+    'date-time': 'date "T" time',
+    'date-string': '"\\"" date "\\"" space',
+    'time-string': '"\\"" time "\\"" space',
+    'date-time-string': '"\\"" date-time "\\"" space',
+};
+
+const RESERVED_NAMES = {'root': true, ...PRIMITIVE_RULES, ...DATE_RULES};
 
 const INVALID_RULE_CHARS_RE = /[^\dA-Za-z-]+/g;
 const GRAMMAR_LITERAL_ESCAPE_RE = /[\n\r"]/g;
-const GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"'};
+const GRAMMAR_RANGE_LITERAL_ESCAPE_RE = /[\n\r"\]\-\\]/g;
+const GRAMMAR_LITERAL_ESCAPES = { '\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']': '\\]' };
+
+const NON_LITERAL_SET = new Set('|.()[]{}*+?');
+const ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = new Set('[]()|{}*+?');
 
 export class SchemaConverter {
-  constructor(propOrder) {
-    this._propOrder = propOrder || {};
-    this._rules = new Map();
-    this._rules.set('space', SPACE_RULE);
+  constructor(options) {
+    this._propOrder = options.prop_order || {};
+    this._allowFetch = options.allow_fetch || false;
+    this._dotall = options.dotall || false;
+    this._rules = {'space': SPACE_RULE};
+    this._refs = {};
+    this._refsBeingResolved = new Set();
   }
 
   _formatLiteral(literal) {
-    const escaped = JSON.stringify(literal).replace(
+    const escaped = literal.replace(
       GRAMMAR_LITERAL_ESCAPE_RE,
       m => GRAMMAR_LITERAL_ESCAPES[m]
     );
     return `"${escaped}"`;
   }
 
+  _formatRangeChar(literal) {
+    return JSON.stringify(literal).slice(1, -1).replace(
+      GRAMMAR_RANGE_LITERAL_ESCAPE_RE,
+      m => GRAMMAR_LITERAL_ESCAPES[m]
+    );
+  }
+
   _addRule(name, rule) {
     let escName = name.replace(INVALID_RULE_CHARS_RE, '-');
     let key = escName;
 
-    if (this._rules.has(escName)) {
-      if (this._rules.get(escName) === rule) {
+    if (escName in this._rules) {
+      if (this._rules[escName] === rule) {
         return key;
       }
 
       let i = 0;
-      while (this._rules.has(`${escName}${i}`)) {
+      while ((`${escName}${i}` in this._rules) && (this._rules[`${escName}${i}`] !== rule)) {
         i += 1;
       }
       key = `${escName}${i}`;
     }
 
-    this._rules.set(key, rule);
+    this._rules[key] = rule;
     return key;
   }
 
+  async resolveRefs(schema, url) {
+    const visit = async (n) => {
+      if (Array.isArray(n)) {
+        return Promise.all(n.map(visit));
+      } else if (typeof n === 'object' && n !== null) {
+        let ref = n.$ref;
+        let target;
+        if (ref !== undefined && !this._refs[ref]) {
+          if (ref.startsWith('https://')) {
+            if (!this._allowFetch) {
+              throw new Error('Fetching remote schemas is not allowed (use --allow-fetch for force)');
+            }
+            const fetch = (await import('node-fetch')).default;
+
+            const fragSplit = ref.split('#');
+            const baseUrl = fragSplit[0];
+
+            target = this._refs[baseUrl];
+            if (!target) {
+              target = await this.resolveRefs(await fetch(ref).then(res => res.json()), baseUrl);
+              this._refs[baseUrl] = target;
+            }
+
+            if (fragSplit.length === 1 || fragSplit[fragSplit.length - 1] === '') {
+              return target;
+            }
+          } else if (ref.startsWith('#/')) {
+            target = schema;
+            ref = `${url}${ref}`;
+            n.$ref = ref;
+          } else {
+            throw new Error(`Unsupported ref ${ref}`);
+          }
+
+          const selectors = ref.split('#')[1].split('/').slice(1);
+          for (const sel of selectors) {
+            if (!target || !(sel in target)) {
+              throw new Error(`Error resolving ref ${ref}: ${sel} not in ${JSON.stringify(target)}`);
+            }
+            target = target[sel];
+          }
+
+          this._refs[ref] = target;
+        } else {
+          await Promise.all(Object.values(n).map(visit));
+        }
+      }
+
+      return n;
+    };
+
+    return visit(schema);
+  }
+
+  _generateUnionRule(name, altSchemas) {
+    return altSchemas
+      .map((altSchema, i) => this.visit(altSchema, `${name ?? ''}${name ? '-' : 'alternative-'}${i}`))
+      .join(' | ');
+  }
+
+  _visitPattern(pattern, name) {
+    if (!pattern.startsWith('^') || !pattern.endsWith('$')) {
+      throw new Error('Pattern must start with "^" and end with "$"');
+    }
+    pattern = pattern.slice(1, -1);
+    const subRuleIds = {};
+
+    let i = 0;
+    const length = pattern.length;
+
+    const getDot = () => {
+      let rule;
+      if (this._dotall) {
+        rule = '[\\U00000000-\\U0010FFFF]';
+      } else {
+        // Accept any character... except \n and \r line break chars (\x0A and \xOD)
+        rule = '[\\U00000000-\\x09\\x0B\\x0C\\x0E-\\U0010FFFF]';
+      }
+      return this._addRule('dot', rule);
+    };
+
+
+    const toRule = ([s, isLiteral]) => isLiteral ? "\"" + s + "\"" : s;
+
+    const transform = () => {
+      const start = i;
+      // For each component of this sequence, store its string representation and whether it's a literal.
+      // We only need a flat structure here to apply repetition operators to the last item, and
+      // to merge literals at the and (we're parsing grouped ( sequences ) recursively and don't treat '|' specially
+      // (GBNF's syntax is luckily very close to regular expressions!)
+      const seq = [];
+
+      const joinSeq = () => {
+        const ret = [];
+        for (const [isLiteral, g] of groupBy(seq, x => x[1])) {
+          if (isLiteral) {
+            ret.push([[...g].map(x => x[0]).join(''), true]);
+          } else {
+            ret.push(...g);
+          }
+        }
+        if (ret.length === 1) {
+          return ret[0];
+        }
+        return [ret.map(x => toRule(x)).join(' '), false];
+      };
+
+      while (i < length) {
+        const c = pattern[i];
+        if (c === '.') {
+          seq.push([getDot(), false]);
+          i += 1;
+        } else if (c === '(') {
+          i += 1;
+          if (i < length) {
+            if (pattern[i] === '?') {
+              throw new Error(`Unsupported pattern syntax "${pattern[i]}" at index ${i} of /${pattern}/`);
+            }
+          }
+          seq.push([`(${toRule(transform())})`, false]);
+        } else if (c === ')') {
+          i += 1;
+          if (start <= 0 || pattern[start - 1] !== '(') {
+            throw new Error(`Unbalanced parentheses; start = ${start}, i = ${i}, pattern = ${pattern}`);
+          }
+          return joinSeq();
+        } else if (c === '[') {
+          let squareBrackets = c;
+          i += 1;
+          while (i < length && pattern[i] !== ']') {
+            if (pattern[i] === '\\') {
+              squareBrackets += pattern.slice(i, i + 2);
+              i += 2;
+            } else {
+              squareBrackets += pattern[i];
+              i += 1;
+            }
+          }
+          if (i >= length) {
+            throw new Error(`Unbalanced square brackets; start = ${start}, i = ${i}, pattern = ${pattern}`);
+          }
+          squareBrackets += ']';
+          i += 1;
+          seq.push([squareBrackets, false]);
+        } else if (c === '|') {
+          seq.push(['|', false]);
+          i += 1;
+        } else if (c === '*' || c === '+' || c === '?') {
+          seq[seq.length - 1] = [toRule(seq[seq.length - 1]) + c, false];
+          i += 1;
+        } else if (c === '{') {
+          let curlyBrackets = c;
+          i += 1;
+          while (i < length && pattern[i] !== '}') {
+            curlyBrackets += pattern[i];
+            i += 1;
+          }
+          if (i >= length) {
+            throw new Error(`Unbalanced curly brackets; start = ${start}, i = ${i}, pattern = ${pattern}`);
+          }
+          curlyBrackets += '}';
+          i += 1;
+          const nums = curlyBrackets.slice(1, -1).split(',').map(s => s.trim());
+          let minTimes, maxTimes;
+          if (nums.length === 1) {
+            minTimes = parseInt(nums[0], 10);
+            maxTimes = minTimes;
+          } else {
+            if (nums.length !== 2) {
+              throw new Error(`Invalid quantifier ${curlyBrackets}`);
+            }
+            minTimes = nums[0] ? parseInt(nums[0], 10) : 0;
+            maxTimes = nums[1] ? parseInt(nums[1], 10) : Infinity;
+          }
+
+          let [sub, subIsLiteral] = seq[seq.length - 1];
+
+          if (minTimes === 0 && maxTimes === Infinity) {
+            seq[seq.length - 1] = [`${sub}*`, false];
+          } else if (minTimes === 0 && maxTimes === 1) {
+            seq[seq.length - 1] = [`${sub}?`, false];
+          } else if (minTimes === 1 && maxTimes === Infinity) {
+            seq[seq.length - 1] = [`${sub}+`, false];
+          } else {
+            if (!subIsLiteral) {
+              let id = subRuleIds[sub];
+              if (id === undefined) {
+                id = this._addRule(`${name}-${Object.keys(subRuleIds).length + 1}`, sub);
+                subRuleIds[sub] = id;
+              }
+              sub = id;
+            }
+
+            const repeatedSub = Array.from({ length: minTimes }, () => subIsLiteral ? `"${sub.slice(1, -1).repeat(minTimes)}"` : sub);
+            const optionalSub = maxTimes !== undefined ? Array.from({ length: maxTimes - minTimes }, () => `${sub}?`) : [`${sub}*`];
+            seq[seq.length - 1] = [repeatedSub.concat(optionalSub).join(' '), false];
+          }
+        } else {
+          let literal = '';
+          while (i < length) {
+            if (pattern[i] === '\\' && i < length - 1) {
+              const next = pattern[i + 1];
+              if (ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS.has(next)) {
+                i += 1;
+                literal += pattern[i];
+                i += 1;
+              } else {
+                literal += pattern.slice(i, i + 2);
+                i += 2;
+              }
+            } else if (pattern[i] === '"') {
+              literal += '\\"';
+              i += 1;
+            } else if (!NON_LITERAL_SET.has(pattern[i]) &&
+                (i === length - 1 || literal === '' || pattern[i + 1] === '.' || !NON_LITERAL_SET.has(pattern[i+1]))) {
+              literal += pattern[i];
+              i += 1;
+            } else {
+              break;
+            }
+          }
+          if (literal !== '') {
+            seq.push([literal, true]);
+          }
+        }
+      }
+
+      return joinSeq();
+    };
+
+    return this._addRule(name, "\"\\\"\" " + toRule(transform()) + " \"\\\"\" space")
+  }
+
+  _resolveRef(ref) {
+    let refName = ref.split('/').pop();
+    if (!(refName in this._rules) && !this._refsBeingResolved.has(ref)) {
+      this._refsBeingResolved.add(ref);
+      const resolved = this._refs[ref];
+      refName = this.visit(resolved, refName);
+      this._refsBeingResolved.delete(ref);
+    }
+    return refName;
+  }
+
+  _generateConstantRule(value) {
+    return this._formatLiteral(JSON.stringify(value));
+  }
+
   visit(schema, name) {
     const schemaType = schema.type;
-    const ruleName = name || 'root';
+    const schemaFormat = schema.format;
+    const ruleName = name in RESERVED_NAMES ? name + '-' : name == '' ? 'root' : name;
 
-    if (schema.oneOf || schema.anyOf) {
-      const rule = (schema.oneOf || schema.anyOf).map((altSchema, i) =>
-        this.visit(altSchema, `${name}${name ? "-" : ""}${i}`)
-      ).join(' | ');
-
-      return this._addRule(ruleName, rule);
+    const ref = schema.$ref;
+    if (ref !== undefined) {
+      return this._addRule(ruleName, this._resolveRef(ref));
+    } else if (schema.oneOf || schema.anyOf) {
+      return this._addRule(ruleName, this._generateUnionRule(name, schema.oneOf || schema.anyOf));
+    } else if (Array.isArray(schemaType)) {
+      return this._addRule(ruleName, this._generateUnionRule(name, schemaType.map(t => ({ type: t }))));
     } else if ('const' in schema) {
-      return this._addRule(ruleName, this._formatLiteral(schema.const));
+      return this._addRule(ruleName, this._generateConstantRule(schema.const));
     } else if ('enum' in schema) {
-      const rule = schema.enum.map(v => this._formatLiteral(v)).join(' | ');
+      const rule = schema.enum.map(v => this._generateConstantRule(v)).join(' | ');
       return this._addRule(ruleName, rule);
-    } else if (schemaType === 'object' && 'properties' in schema) {
-      // TODO: `required` keyword (from python implementation)
-      const propOrder = this._propOrder;
-      const propPairs = Object.entries(schema.properties).sort((a, b) => {
-        // sort by position in prop_order (if specified) then by key
-        const orderA = typeof propOrder[a[0]] === 'number' ? propOrder[a[0]] : Infinity;
-        const orderB = typeof propOrder[b[0]] === 'number' ? propOrder[b[0]] : Infinity;
-        return orderA - orderB || a[0].localeCompare(b[0]);
-      });
-
-      let rule = '"{" space';
-      propPairs.forEach(([propName, propSchema], i) => {
-        const propRuleName = this.visit(propSchema, `${name}${name ? "-" : ""}${propName}`);
-        if (i > 0) {
-          rule += ' "," space';
+    } else if ((schemaType === undefined || schemaType === 'object') &&
+               ('properties' in schema ||
+                ('additionalProperties' in schema && schema.additionalProperties !== true))) {
+      const required = new Set(schema.required || []);
+      const properties = Object.entries(schema.properties ?? {});
+      return this._addRule(ruleName, this._buildObjectRule(properties, required, name, schema.additionalProperties));
+    } else if ((schemaType === undefined || schemaType === 'object') && 'allOf' in schema) {
+      const required = new Set();
+      const properties = [];
+      const addComponent = (compSchema, isRequired) => {
+        const ref = compSchema.$ref;
+        if (ref !== undefined) {
+          compSchema = this._refs[ref];
         }
-        rule += ` ${this._formatLiteral(propName)} space ":" space ${propRuleName}`;
-      });
-      rule += ' "}" space';
 
-      return this._addRule(ruleName, rule);
-    } else if (schemaType === 'array' && 'items' in schema) {
-      // TODO `prefixItems` keyword (from python implementation)
-      const itemRuleName = this.visit(schema.items, `${name}${name ? "-" : ""}item`);
-      const rule = `"[" space (${itemRuleName} ("," space ${itemRuleName})*)? "]" space`;
-      return this._addRule(ruleName, rule);
+        if ('properties' in compSchema) {
+          for (const [propName, propSchema] of Object.entries(compSchema.properties)) {
+            properties.push([propName, propSchema]);
+            if (isRequired) {
+              required.add(propName);
+            }
+          }
+        }
+      };
+
+      for (const t of schema.allOf) {
+        if ('anyOf' in t) {
+          for (const tt of t.anyOf) {
+            addComponent(tt, false);
+          }
+        } else {
+          addComponent(t, true);
+        }
+      }
+
+      return this._addRule(ruleName, this._buildObjectRule(properties, required, name, /* additionalProperties= */ false));
+    } else if ((schemaType === undefined || schemaType === 'array') && ('items' in schema || 'prefixItems' in schema)) {
+      const items = schema.items ?? schema.prefixItems;
+      if (Array.isArray(items)) {
+        return this._addRule(
+          ruleName,
+          '"[" space ' +
+            items.map((item, i) => this.visit(item, `${name ?? ''}${name ? '-' : ''}tuple-${i}`)).join(' "," space ') +
+            ' "]" space'
+        );
+      } else {
+        const itemRuleName = this.visit(items, `${name ?? ''}${name ? '-' : ''}item`);
+        const listItemOperator = `( "," space ${itemRuleName} )`;
+        let successiveItems = '';
+        let minItems = schema.minItems || 0;
+        const maxItems = schema.maxItems;
+        if (minItems > 0) {
+          successiveItems = listItemOperator.repeat(minItems - 1);
+          minItems--;
+        }
+        if (maxItems !== undefined && maxItems > minItems) {
+          successiveItems += `${listItemOperator}?`.repeat(maxItems - minItems - 1);
+        } else {
+          successiveItems += `${listItemOperator}*`;
+        }
+        const rule = minItems === 0
+          ? `"[" space ( ${itemRuleName} ${successiveItems} )? "]" space`
+          : `"[" space ${itemRuleName} ${successiveItems} "]" space`;
+        return this._addRule(ruleName, rule);
+      }
+    } else if ((schemaType === undefined || schemaType === 'string') && 'pattern' in schema) {
+      return this._visitPattern(schema.pattern, ruleName);
+    } else if ((schemaType === undefined || schemaType === 'string') && /^uuid[1-5]?$/.test(schema.format || '')) {
+      return this._addRule(
+          ruleName === 'root' ? 'root' : schemaFormat,
+          PRIMITIVE_RULES['uuid'])
+    } else if ((schemaType === undefined || schemaType === 'string') && schema.format in DATE_RULES) {
+      for (const [t, r] of Object.entries(DATE_RULES)) {
+        this._addRule(t, r);
+      }
+      return schemaFormat + '-string';
+    } else if ((schemaType === 'object') || (Object.keys(schema).length === 0)) {
+      for (const n of OBJECT_RULE_NAMES) {
+        this._addRule(n, PRIMITIVE_RULES[n]);
+      }
+      return this._addRule(ruleName, 'object');
     } else {
-      if (!PRIMITIVE_RULES[schemaType]) {
+      if (!(schemaType in PRIMITIVE_RULES)) {
         throw new Error(`Unrecognized schema: ${JSON.stringify(schema)}`);
       }
-      return this._addRule(
-        ruleName === 'root' ? 'root' : schemaType,
-        PRIMITIVE_RULES[schemaType]
+      // TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero
+      return this._addRule(ruleName === 'root' ? 'root' : schemaType, PRIMITIVE_RULES[schemaType]);
+    }
+  }
+
+  _buildObjectRule(properties, required, name, additionalProperties) {
+    const propOrder = this._propOrder;
+    // sort by position in prop_order (if specified) then by original order
+    const sortedProps = properties.map(([k]) => k).sort((a, b) => {
+      const orderA = propOrder[a] || Infinity;
+      const orderB = propOrder[b] || Infinity;
+      return orderA - orderB || properties.findIndex(([k]) => k === a) - properties.findIndex(([k]) => k === b);
+    });
+
+    const propKvRuleNames = {};
+    for (const [propName, propSchema] of properties) {
+      const propRuleName = this.visit(propSchema, `${name ?? ''}${name ? '-' : ''}${propName}`);
+      propKvRuleNames[propName] = this._addRule(
+        `${name ?? ''}${name ? '-' : ''}${propName}-kv`,
+        `${this._formatLiteral(JSON.stringify(propName))} space ":" space ${propRuleName}`
       );
     }
+    const requiredProps = sortedProps.filter(k => required.has(k));
+    const optionalProps = sortedProps.filter(k => !required.has(k));
+
+    if (typeof additionalProperties === 'object' || additionalProperties === true) {
+      const subName = `${name ?? ''}${name ? '-' : ''}additional`;
+      const valueRule = this.visit(additionalProperties === true ? {} : additionalProperties, `${subName}-value`);
+      propKvRuleNames['*'] = this._addRule(
+        `${subName}-kv`,
+        `${this._addRule('string', PRIMITIVE_RULES['string'])} ":" space ${valueRule}`);
+      optionalProps.push('*');
+    }
+
+    let rule = '"{" space ';
+    rule += requiredProps.map(k => propKvRuleNames[k]).join(' "," space ');
+
+    if (optionalProps.length > 0) {
+      rule += ' (';
+      if (requiredProps.length > 0) {
+        rule += ' "," space ( ';
+      }
+
+      const getRecursiveRefs = (ks, firstIsOptional) => {
+        const [k, ...rest] = ks;
+        const kvRuleName = propKvRuleNames[k];
+        let res;
+        if (k === '*') {
+            res = this._addRule(
+                `${name ?? ''}${name ? '-' : ''}additional-kvs`,
+                `${kvRuleName} ( "," space ` + kvRuleName + ` )*`
+            )
+        } else if (firstIsOptional) {
+          res = `( "," space ${kvRuleName} )?`;
+        } else {
+          res = kvRuleName;
+        }
+        if (rest.length > 0) {
+          res += ' ' + this._addRule(
+            `${name ?? ''}${name ? '-' : ''}${k}-rest`,
+            getRecursiveRefs(rest, true)
+          );
+        }
+        return res;
+      };
+
+      rule += optionalProps.map((_, i) => getRecursiveRefs(optionalProps.slice(i), false)).join(' | ');
+      if (requiredProps.length > 0) {
+        rule += ' )';
+      }
+      rule += ' )?';
+    }
+
+    rule += ' "}" space';
+
+    return rule;
   }
 
   formatGrammar() {
     let grammar = '';
-    this._rules.forEach((rule, name) => {
+    for (const [name, rule] of Object.entries(this._rules).sort(([a], [b]) => a.localeCompare(b))) {
       grammar += `${name} ::= ${rule}\n`;
-    });
+    }
     return grammar;
   }
 }
+
+// Helper function to group elements by a key function
+function* groupBy(iterable, keyFn) {
+  let lastKey = null;
+  let group = [];
+  for (const element of iterable) {
+    const key = keyFn(element);
+    if (lastKey !== null && key !== lastKey) {
+      yield [lastKey, group];
+      group = [];
+    }
+    group.push(element);
+    lastKey = key;
+  }
+  if (group.length > 0) {
+    yield [lastKey, group];
+  }
+}
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 6c7fcd176..183f0d3c8 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1,11 +1,10 @@
+#include "utils.hpp"
+
 #include "common.h"
+#include "json-schema-to-grammar.h"
 #include "llama.h"
 #include "grammar-parser.h"
 
-#include "../llava/clip.h"
-
-#include "stb_image.h"
-
 #ifndef NDEBUG
 // crash the server in debug mode, otherwise send an http 500 error
 #define CPPHTTPLIB_NO_EXCEPTIONS 1
@@ -21,181 +20,86 @@
 #include "completion.js.hpp"
 #include "json-schema-to-grammar.mjs.hpp"
 
-#include <cstddef>
-#include <thread>
-#include <mutex>
+#include <atomic>
 #include <chrono>
 #include <condition_variable>
+#include <cstddef>
+#include <set>
+#include <mutex>
+#include <thread>
+#include <signal.h>
+#include <memory>
 
-#ifndef SERVER_VERBOSE
-#define SERVER_VERBOSE 1
-#endif
+using json = nlohmann::ordered_json;
 
-#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
+bool server_verbose = false;
+bool server_log_json = true;
 
-using json = nlohmann::json;
-
-struct server_params
-{
-    std::string hostname = "127.0.0.1";
-    std::string api_key;
-    std::string public_path = "examples/server/public";
-    int32_t port = 8080;
-    int32_t read_timeout = 600;
-    int32_t write_timeout = 600;
+enum stop_type {
+    STOP_TYPE_FULL,
+    STOP_TYPE_PARTIAL,
 };
 
-static bool server_verbose = false;
-
-#if SERVER_VERBOSE != 1
-#define LOG_VERBOSE(MSG, ...)
-#else
-#define LOG_VERBOSE(MSG, ...)                                            \
-    do                                                                   \
-    {                                                                    \
-        if (server_verbose)                                              \
-        {                                                                \
-            server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \
-        }                                                                \
-    } while (0)
-#endif
-
-#define LOG_ERROR(  MSG, ...) server_log("ERROR",   __func__, __LINE__, MSG, __VA_ARGS__)
-#define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__)
-#define LOG_INFO(   MSG, ...) server_log("INFO",    __func__, __LINE__, MSG, __VA_ARGS__)
-
-json oaicompat_completion_params_parse(const json &body);
-std::string format_chatml(std::vector<json> messages);
-
-
-//
-// base64 utils (TODO: move to common in the future)
-//
-
-static const std::string base64_chars =
-             "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
-             "abcdefghijklmnopqrstuvwxyz"
-             "0123456789+/";
-
-static inline bool is_base64(uint8_t c)
-{
-    return (isalnum(c) || (c == '+') || (c == '/'));
-}
-
-static std::vector<uint8_t> base64_decode(const std::string & encoded_string)
-{
-    int i = 0;
-    int j = 0;
-    int in_ = 0;
-
-    int in_len = encoded_string.size();
-
-    uint8_t char_array_4[4];
-    uint8_t char_array_3[3];
-
-    std::vector<uint8_t> ret;
-
-    while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_]))
-    {
-        char_array_4[i++] = encoded_string[in_]; in_++;
-        if (i == 4)
-        {
-            for (i = 0; i <4; i++)
-            {
-                char_array_4[i] = base64_chars.find(char_array_4[i]);
-            }
-
-            char_array_3[0] = ((char_array_4[0]      ) << 2) + ((char_array_4[1] & 0x30) >> 4);
-            char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
-            char_array_3[2] = ((char_array_4[2] & 0x3) << 6) +   char_array_4[3];
-
-            for (i = 0; (i < 3); i++)
-            {
-                ret.push_back(char_array_3[i]);
-            }
-            i = 0;
-        }
-    }
-
-    if (i)
-    {
-        for (j = i; j <4; j++)
-        {
-            char_array_4[j] = 0;
-        }
-
-        for (j = 0; j <4; j++)
-        {
-            char_array_4[j] = base64_chars.find(char_array_4[j]);
-        }
-
-        char_array_3[0] = ((char_array_4[0]      ) << 2) + ((char_array_4[1] & 0x30) >> 4);
-        char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
-        char_array_3[2] = ((char_array_4[2] & 0x3) << 6) +   char_array_4[3];
-
-        for (j = 0; (j < i - 1); j++)
-        {
-            ret.push_back(char_array_3[j]);
-        }
-    }
-
-    return ret;
-}
-
-//
-// parallel
-//
-
-enum task_type {
-    COMPLETION_TASK,
-    CANCEL_TASK
+enum slot_state {
+    SLOT_STATE_IDLE,
+    SLOT_STATE_PROCESSING,
 };
 
-struct task_server {
-    int id;
-    int target_id;
-    task_type type;
+enum slot_command {
+    SLOT_COMMAND_NONE,
+    SLOT_COMMAND_LOAD_PROMPT,
+    SLOT_COMMAND_RELEASE,
+};
+
+enum server_state {
+    SERVER_STATE_LOADING_MODEL,  // Server is starting up, model not fully loaded yet
+    SERVER_STATE_READY,          // Server is ready and model is loaded
+    SERVER_STATE_ERROR           // An error occurred, load_model failed
+};
+
+enum server_task_type {
+    SERVER_TASK_TYPE_COMPLETION,
+    SERVER_TASK_TYPE_CANCEL,
+    SERVER_TASK_TYPE_NEXT_RESPONSE,
+    SERVER_TASK_TYPE_METRICS
+};
+
+struct server_task {
+    int id        = -1; // to be filled by server_queue
+    int id_multi  = -1;
+    int id_target = -1;
+
+    server_task_type type;
     json data;
-    bool infill_mode = false;
-    bool embedding_mode = false;
-    int multitask_id = -1;
+
+    bool infill    = false;
+    bool embedding = false;
 };
 
-struct task_result {
-    int id;
-    int multitask_id = -1;
+struct server_task_result {
+    int id       = -1;
+    int id_multi = -1;
+
+    json data;
+
     bool stop;
     bool error;
-    json result_json;
 };
 
-struct task_multi {
-    int id;
-    std::set<int> subtasks_remaining{};
-    std::vector<task_result> results{};
+struct server_task_multi {
+    int id = -1;
+
+    std::set<int> subtasks_remaining;
+    std::vector<server_task_result> results;
 };
 
-// TODO: can become bool if we can't find use of more states
-enum slot_state
-{
-    IDLE,
-    PROCESSING,
-};
-
-enum slot_command
-{
-    NONE,
-    LOAD_PROMPT,
-    RELEASE,
-};
-
-struct slot_params
-{
+struct slot_params {
     bool stream       = true;
     bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt
 
     uint32_t seed      = -1; // RNG seed
     int32_t  n_keep    =  0; // number of tokens to keep from initial prompt
+    int32_t  n_discard =  0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half
     int32_t  n_predict = -1; // new tokens to predict
 
     std::vector<std::string> antiprompt;
@@ -204,168 +108,37 @@ struct slot_params
     json input_suffix;
 };
 
-struct slot_image
-{
-    int32_t id;
+struct server_params {
+    int32_t port           = 8080;
+    int32_t read_timeout   = 600;
+    int32_t write_timeout  = 600;
+    int32_t n_threads_http = -1;
 
-    bool request_encode_image = false;
-    float * image_embedding = nullptr;
-    int32_t image_tokens = 0;
+    std::string hostname      = "127.0.0.1";
+    std::string public_path   = "";
+    std::string chat_template = "";
+    std::string system_prompt = "";
 
-    clip_image_u8 * img_data;
+    std::vector<std::string> api_keys;
 
-    std::string prefix_prompt; // before of this image
+#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
+    std::string ssl_key_file = "";
+    std::string ssl_cert_file = "";
+#endif
+
+    bool slots_endpoint   = true;
+    bool metrics_endpoint = false;
 };
 
-// completion token output with probabilities
-struct completion_token_output
-{
-    struct token_prob
-    {
-        llama_token tok;
-        float prob;
-    };
-
-    std::vector<token_prob> probs;
-    llama_token tok;
-    std::string text_to_send;
-};
-
-static size_t common_part(const std::vector<llama_token> &a, const std::vector<llama_token> &b)
-{
-    size_t i;
-    for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++)
-    {
-    }
-    return i;
-}
-
-enum stop_type
-{
-    STOP_FULL,
-    STOP_PARTIAL,
-};
-
-static bool ends_with(const std::string &str, const std::string &suffix)
-{
-    return str.size() >= suffix.size() &&
-           0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
-}
-
-static size_t find_partial_stop_string(const std::string &stop,
-                                       const std::string &text)
-{
-    if (!text.empty() && !stop.empty())
-    {
-        const char text_last_char = text.back();
-        for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--)
-        {
-            if (stop[char_index] == text_last_char)
-            {
-                const std::string current_partial = stop.substr(0, char_index + 1);
-                if (ends_with(text, current_partial))
-                {
-                    return text.size() - char_index - 1;
-                }
-            }
-        }
-    }
-    return std::string::npos;
-}
-
-// TODO: reuse llama_detokenize
-template <class Iter>
-static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end)
-{
-    std::string ret;
-    for (; begin != end; ++begin)
-    {
-        ret += llama_token_to_piece(ctx, *begin);
-    }
-    return ret;
-}
-
-static void server_log(const char *level, const char *function, int line,
-                       const char *message, const nlohmann::ordered_json &extra)
-{
-    nlohmann::ordered_json log
-    {
-        {"timestamp", time(nullptr)},
-        {"level",     level},
-        {"function",  function},
-        {"line",      line},
-        {"message",   message},
-    };
-
-    if (!extra.empty())
-    {
-        log.merge_patch(extra);
-    }
-
-    const std::string str = log.dump(-1, ' ', false, json::error_handler_t::replace);
-    printf("%.*s\n", (int)str.size(), str.data());
-    fflush(stdout);
-}
-
-// format incomplete utf-8 multibyte character for output
-static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token)
-{
-    std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token);
-    // if the size is 1 and first bit is 1, meaning it's a partial character
-    //   (size > 1 meaning it's already a known token)
-    if (out.size() == 1 && (out[0] & 0x80) == 0x80)
-    {
-        std::stringstream ss;
-        ss << std::hex << (out[0] & 0xff);
-        std::string res(ss.str());
-        out = "byte: \\x" + res;
-    }
-    return out;
-}
-
-// convert a vector of completion_token_output to json
-static json probs_vector_to_json(const llama_context *ctx, const std::vector<completion_token_output> &probs)
-{
-    json out = json::array();
-    for (const auto &prob : probs)
-    {
-        json probs_for_token = json::array();
-        for (const auto &p : prob.probs)
-        {
-            std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok);
-            probs_for_token.push_back(json
-            {
-                {"tok_str", tok_str},
-                {"prob",    p.prob},
-            });
-        }
-        std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok);
-        out.push_back(json{
-            {"content", tok_str},
-            {"probs",   probs_for_token},
-        });
-    }
-    return out;
-}
-
-template <typename T>
-static T json_value(const json &body, const std::string &key, const T &default_value)
-{
-    // Fallback null to default value
-    return body.contains(key) && !body.at(key).is_null()
-        ? body.value(key, default_value)
-        : default_value;
-}
-
-struct llama_client_slot
-{
+struct server_slot {
     int id;
-    int task_id = -1;
+    int id_task = -1;
+    int id_multi = -1;
 
     struct slot_params params;
 
-    slot_state state = IDLE;
-    slot_command command = NONE;
+    slot_state state = SLOT_STATE_IDLE;
+    slot_command command = SLOT_COMMAND_NONE;
 
     // used to determine the slot that has been used the longest
     int64_t t_last_used = -1;
@@ -376,90 +149,83 @@ struct llama_client_slot
     int32_t n_decoded   = 0;
     int32_t n_remaining = -1;
     int32_t i_batch     = -1;
+    int32_t n_predict   = -1; // TODO: disambiguate from params.n_predict
 
-    int32_t num_prompt_tokens           = 0;
-    int32_t num_prompt_tokens_processed = 0;
+    int32_t n_prompt_tokens           = 0;
+    int32_t n_prompt_tokens_processed = 0;
 
     json prompt;
+
+    // when a task is submitted, we first tokenize the prompt and store it here
+    std::vector<llama_token> prompt_tokens;
+
     std::string generated_text;
-    llama_token sampled;
     std::vector<llama_token> cache_tokens;
     std::vector<completion_token_output> generated_token_probs;
 
-    bool infill = false;
-    bool embedding = false;
+    bool infill         = false;
+    bool embedding      = false;
     bool has_next_token = true;
-    bool truncated = false;
-    bool stopped_eos = false;
-    bool stopped_word = false;
-    bool stopped_limit = false;
+    bool truncated      = false;
+    bool stopped_eos    = false;
+    bool stopped_word   = false;
+    bool stopped_limit  = false;
 
     bool oaicompat = false;
-    std::string oaicompat_model;
 
+    std::string oaicompat_model;
     std::string stopping_word;
 
     // sampling
+    llama_token sampled;
     struct llama_sampling_params sparams;
-    llama_sampling_context *ctx_sampling = nullptr;
+    llama_sampling_context * ctx_sampling = nullptr;
+    json json_schema;
 
-    // multimodal
-    std::vector<slot_image> images;
+    int32_t ga_i = 0;   // group-attention state
+    int32_t ga_n = 1;   // group-attention factor
+    int32_t ga_w = 512; // group-attention width
+
+    int32_t n_past_se = 0; // self-extend
 
     // stats
-    size_t sent_count = 0;
-    size_t sent_token_probs_index = 0;
+    size_t n_sent_text = 0; // number of sent text character
+    size_t n_sent_token_probs = 0;
 
     int64_t t_start_process_prompt;
-    int64_t t_start_genereration;
+    int64_t t_start_generation;
 
     double t_prompt_processing; // ms
     double t_token_generation; // ms
 
-    // multitasks
-    int multitask_id = -1;
-
     void reset() {
-        num_prompt_tokens      = 0;
-        generated_text         = "";
-        truncated              = false;
-        stopped_eos            = false;
-        stopped_word           = false;
-        stopped_limit          = false;
-        stopping_word          = "";
-        n_past                 = 0;
-        sent_count             = 0;
-        sent_token_probs_index = 0;
-        infill                 = false;
+        n_prompt_tokens    = 0;
+        generated_text     = "";
+        truncated          = false;
+        stopped_eos        = false;
+        stopped_word       = false;
+        stopped_limit      = false;
+        stopping_word      = "";
+        n_past             = 0;
+        n_sent_text        = 0;
+        n_sent_token_probs = 0;
+        infill             = false;
+        ga_i               = 0;
+        n_past_se          = 0;
 
         generated_token_probs.clear();
-
-        for (slot_image & img : images)
-        {
-            free(img.image_embedding);
-            if (img.img_data) {
-                clip_image_u8_free(img.img_data);
-            }
-            img.prefix_prompt = "";
-        }
-
-        images.clear();
     }
 
     bool has_budget(gpt_params &global_params) {
-        if (params.n_predict == -1 && global_params.n_predict == -1)
-        {
+        if (params.n_predict == -1 && global_params.n_predict == -1) {
             return true; // limitless
         }
 
         n_remaining = -1;
 
-        if (params.n_predict != -1)
-        {
+        if (params.n_predict != -1) {
             n_remaining = params.n_predict - n_decoded;
-        }
-        else if (global_params.n_predict != -1)
-        {
+        } else if (global_params.n_predict != -1) {
             n_remaining = global_params.n_predict - n_decoded;
         }
 
@@ -467,37 +233,33 @@ struct llama_client_slot
     }
 
     bool available() const {
-        return state == IDLE && command == NONE;
+        return state == SLOT_STATE_IDLE && command == SLOT_COMMAND_NONE;
     }
 
     bool is_processing() const {
-        return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING;
+        return (state == SLOT_STATE_IDLE && command == SLOT_COMMAND_LOAD_PROMPT) || state == SLOT_STATE_PROCESSING;
     }
 
-    void add_token_string(const completion_token_output &token) {
-        if (command == RELEASE)
-        {
+    void add_token_string(const completion_token_output & token) {
+        if (command == SLOT_COMMAND_RELEASE) {
             return;
         }
-        cache_tokens.push_back(token.tok);
         generated_token_probs.push_back(token);
     }
 
     void release() {
-        if (state == IDLE || state == PROCESSING)
-        {
-            t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3;
-            command = RELEASE;
+        if (state == SLOT_STATE_PROCESSING) {
+            t_token_generation = (ggml_time_us() - t_start_generation) / 1e3;
+            command = SLOT_COMMAND_RELEASE;
         }
     }
 
-    json get_formated_timings() {
-        return json
-        {
-            {"prompt_n",               num_prompt_tokens_processed},
+    json get_formated_timings() const {
+        return json {
+            {"prompt_n",               n_prompt_tokens_processed},
             {"prompt_ms",              t_prompt_processing},
-            {"prompt_per_token_ms",    t_prompt_processing / num_prompt_tokens_processed},
-            {"prompt_per_second",      1e3 / t_prompt_processing * num_prompt_tokens_processed},
+            {"prompt_per_token_ms",    t_prompt_processing / n_prompt_tokens_processed},
+            {"prompt_per_second",      1e3 / t_prompt_processing * n_prompt_tokens_processed},
 
             {"predicted_n",            n_decoded},
             {"predicted_ms",           t_token_generation},
@@ -506,34 +268,376 @@ struct llama_client_slot
         };
     }
 
+    size_t find_stopping_strings(const std::string & text, const size_t last_token_size, const stop_type type) {
+        size_t stop_pos = std::string::npos;
+
+        for (const std::string & word : params.antiprompt) {
+            size_t pos;
+
+            if (type == STOP_TYPE_FULL) {
+                const size_t tmp      = word.size() + last_token_size;
+                const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0;
+
+                pos = text.find(word, from_pos);
+            } else {
+                pos = find_partial_stop_string(word, text);
+            }
+
+            if (pos != std::string::npos && (stop_pos == std::string::npos || pos < stop_pos)) {
+                if (type == STOP_TYPE_FULL) {
+                    stopped_word   = true;
+                    stopping_word  = word;
+                    has_next_token = false;
+                }
+                stop_pos = pos;
+            }
+        }
+
+        return stop_pos;
+    }
+
     void print_timings() const {
-        LOG_TEE("\n");
-        LOG_TEE("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
-            __func__, t_prompt_processing, num_prompt_tokens_processed, t_prompt_processing / num_prompt_tokens_processed, 1e3 / t_prompt_processing * num_prompt_tokens_processed);
-        LOG_TEE("%s:        eval time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
-            __func__, t_token_generation, n_decoded,t_token_generation / n_decoded, 1e3 / t_token_generation * n_decoded);
-        LOG_TEE("%s:       total time = %10.2f ms\n", __func__, t_prompt_processing + t_token_generation);
+        char buffer[512];
+
+        double t_token = t_prompt_processing / n_prompt_tokens_processed;
+        double n_tokens_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed;
+
+        snprintf(buffer, 512, "prompt eval time     = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)",
+                t_prompt_processing, n_prompt_tokens_processed,
+                t_token, n_tokens_second);
+
+        LOG_INFO(buffer, {
+            {"id_slot",                   id},
+            {"id_task",                   id_task},
+            {"t_prompt_processing",       t_prompt_processing},
+            {"n_prompt_tokens_processed", n_prompt_tokens_processed},
+            {"t_token",                   t_token},
+            {"n_tokens_second",           n_tokens_second},
+        });
+
+        t_token = t_token_generation / n_decoded;
+        n_tokens_second = 1e3 / t_token_generation * n_decoded;
+
+        snprintf(buffer, 512, "generation eval time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)",
+                t_token_generation, n_decoded,
+                t_token, n_tokens_second);
+
+        LOG_INFO(buffer, {
+            {"id_slot",            id},
+            {"id_task",            id_task},
+            {"t_token_generation", t_token_generation},
+            {"n_decoded",          n_decoded},
+            {"t_token",            t_token},
+            {"n_tokens_second",    n_tokens_second},
+        });
+
+        snprintf(buffer, 512, "          total time = %10.2f ms", t_prompt_processing + t_token_generation);
+
+        LOG_INFO(buffer, {
+            {"id_slot",             id},
+            {"id_task",             id_task},
+            {"t_prompt_processing", t_prompt_processing},
+            {"t_token_generation",  t_token_generation},
+            {"t_total",             t_prompt_processing + t_token_generation},
+        });
     }
 };
 
-struct llama_server_context
-{
-    llama_model *model = nullptr;
-    llama_context *ctx = nullptr;
+struct server_metrics {
+    int64_t t_start = 0;
 
-    clip_ctx *clp_ctx = nullptr;
+    uint64_t n_prompt_tokens_processed_total = 0;
+    uint64_t t_prompt_processing_total       = 0;
+    uint64_t n_tokens_predicted_total        = 0;
+    uint64_t t_tokens_generation_total       = 0;
+
+    uint64_t n_prompt_tokens_processed = 0;
+    uint64_t t_prompt_processing       = 0;
+
+    uint64_t n_tokens_predicted  = 0;
+    uint64_t t_tokens_generation = 0;
+
+    void init() {
+        t_start = ggml_time_us();
+    }
+
+    void on_prompt_eval(const server_slot & slot) {
+        n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed;
+        n_prompt_tokens_processed       += slot.n_prompt_tokens_processed;
+        t_prompt_processing             += slot.t_prompt_processing;
+        t_prompt_processing_total       += slot.t_prompt_processing;
+    }
+
+    void on_prediction(const server_slot & slot) {
+        n_tokens_predicted_total   += slot.n_decoded;
+        n_tokens_predicted         += slot.n_decoded;
+        t_tokens_generation        += slot.t_token_generation;
+        t_tokens_generation_total  += slot.t_token_generation;
+    }
+
+    void reset_bucket() {
+        n_prompt_tokens_processed = 0;
+        t_prompt_processing       = 0;
+        n_tokens_predicted        = 0;
+        t_tokens_generation       = 0;
+    }
+};
+
+struct server_queue {
+    int id = 0;
+    bool running;
+
+    // queues
+    std::vector<server_task> queue_tasks;
+    std::vector<server_task> queue_tasks_deferred;
+
+    std::vector<server_task_multi> queue_multitasks;
+
+    std::mutex mutex_tasks;
+    std::condition_variable condition_tasks;
+
+    // callback functions
+    std::function<void(server_task       &)> callback_new_task;
+    std::function<void(server_task_multi &)> callback_finish_multitask;
+    std::function<void(void)>                callback_update_slots;
+
+    // Add a new task to the end of the queue
+    int post(server_task task) {
+        std::unique_lock<std::mutex> lock(mutex_tasks);
+        if (task.id == -1) {
+            task.id = id++;
+            LOG_VERBOSE("new task id", {{"new_id", task.id}});
+        }
+        queue_tasks.push_back(std::move(task));
+        condition_tasks.notify_one();
+        return task.id;
+    }
+
+    // Add a new task, but defer until one slot is available
+    void defer(server_task task) {
+        std::unique_lock<std::mutex> lock(mutex_tasks);
+        queue_tasks_deferred.push_back(std::move(task));
+    }
+
+    // Get the next id for creating anew task
+    int get_new_id() {
+        std::unique_lock<std::mutex> lock(mutex_tasks);
+        int new_id = id++;
+        LOG_VERBOSE("new task id", {{"new_id", new_id}});
+        return new_id;
+    }
+
+    // Register function to process a new task
+    void on_new_task(std::function<void(server_task &)> callback) {
+        callback_new_task = std::move(callback);
+    }
+
+    // Register function to process a multitask when it is finished
+    void on_finish_multitask(std::function<void(server_task_multi&)> callback) {
+        callback_finish_multitask = std::move(callback);
+    }
+
+    // Register the function to be called when all slots data is ready to be processed
+    void on_update_slots(std::function<void(void)> callback) {
+        callback_update_slots = std::move(callback);
+    }
+
+    // Call when the state of one slot is changed
+    void notify_slot_changed() {
+        // move deferred tasks back to main loop
+        std::unique_lock<std::mutex> lock(mutex_tasks);
+        for (auto & task : queue_tasks_deferred) {
+            queue_tasks.push_back(std::move(task));
+        }
+        queue_tasks_deferred.clear();
+    }
+
+    // end the start_loop routine
+    void terminate() {
+        std::unique_lock<std::mutex> lock(mutex_tasks);
+        running = false;
+        condition_tasks.notify_all();
+    }
+
+    /**
+     * Main loop consists of these steps:
+     * - Wait until a new task arrives
+     * - Process the task (i.e. maybe copy data into slot)
+     * - Check if multitask is finished
+     * - Update all slots
+     */
+    void start_loop() {
+        running = true;
+
+        while (true) {
+            LOG_VERBOSE("new task may arrive", {});
+
+            while (true) {
+                std::unique_lock<std::mutex> lock(mutex_tasks);
+                if (queue_tasks.empty()) {
+                    lock.unlock();
+                    break;
+                }
+                server_task task = queue_tasks.front();
+                queue_tasks.erase(queue_tasks.begin());
+                lock.unlock();
+                LOG_VERBOSE("callback_new_task", {{"id_task", task.id}});
+                callback_new_task(task);
+            }
+
+            LOG_VERBOSE("update_multitasks", {});
+
+            // check if we have any finished multitasks
+            auto queue_iterator = queue_multitasks.begin();
+            while (queue_iterator != queue_multitasks.end()) {
+                if (queue_iterator->subtasks_remaining.empty()) {
+                    // all subtasks done == multitask is done
+                    server_task_multi current_multitask = *queue_iterator;
+                    callback_finish_multitask(current_multitask);
+                    // remove this multitask
+                    queue_iterator = queue_multitasks.erase(queue_iterator);
+                } else {
+                    ++queue_iterator;
+                }
+            }
+
+            // all tasks in the current loop is processed, slots data is now ready
+            LOG_VERBOSE("callback_update_slots", {});
+
+            callback_update_slots();
+
+            LOG_VERBOSE("wait for new task", {});
+            {
+                std::unique_lock<std::mutex> lock(mutex_tasks);
+                if (queue_tasks.empty()) {
+                    if (!running) {
+                        LOG_VERBOSE("ending start_loop", {});
+                        return;
+                    }
+                    condition_tasks.wait(lock, [&]{
+                        return (!queue_tasks.empty() || !running);
+                    });
+                }
+            }
+        }
+    }
+
+    //
+    // functions to manage multitasks
+    //
+
+    // add a multitask by specifying the id of all subtask (subtask is a server_task)
+    void add_multitask(int id_multi, std::vector<int> & sub_ids) {
+        std::lock_guard<std::mutex> lock(mutex_tasks);
+        server_task_multi multi;
+        multi.id = id_multi;
+        std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end()));
+        queue_multitasks.push_back(multi);
+    }
+
+    // updatethe remaining subtasks, while appending results to multitask
+    void update_multitask(int id_multi, int id_sub, server_task_result & result) {
+        std::lock_guard<std::mutex> lock(mutex_tasks);
+        for (auto & multitask : queue_multitasks) {
+            if (multitask.id == id_multi) {
+                multitask.subtasks_remaining.erase(id_sub);
+                multitask.results.push_back(result);
+            }
+        }
+    }
+};
+
+struct server_response {
+    typedef std::function<void(int, int, server_task_result &)> callback_multitask_t;
+    callback_multitask_t callback_update_multitask;
+
+    // for keeping track of all tasks waiting for the result
+    std::set<int> waiting_task_ids;
+
+    // the main result queue
+    std::vector<server_task_result> queue_results;
+
+    std::mutex mutex_results;
+    std::condition_variable condition_results;
+
+    // add the id_task to the list of tasks waiting for response
+    void add_waiting_task_id(int id_task) {
+        LOG_VERBOSE("waiting for task id", {{"id_task", id_task}});
+
+        std::unique_lock<std::mutex> lock(mutex_results);
+        waiting_task_ids.insert(id_task);
+    }
+
+    // when the request is finished, we can remove task associated with it
+    void remove_waiting_task_id(int id_task) {
+        LOG_VERBOSE("remove waiting for task id", {{"id_task", id_task}});
+
+        std::unique_lock<std::mutex> lock(mutex_results);
+        waiting_task_ids.erase(id_task);
+    }
+
+    // This function blocks the thread until there is a response for this id_task
+    server_task_result recv(int id_task) {
+        while (true) {
+            std::unique_lock<std::mutex> lock(mutex_results);
+            condition_results.wait(lock, [&]{
+                return !queue_results.empty();
+            });
+
+            for (int i = 0; i < (int) queue_results.size(); i++) {
+                if (queue_results[i].id == id_task) {
+                    assert(queue_results[i].id_multi == -1);
+                    server_task_result res = queue_results[i];
+                    queue_results.erase(queue_results.begin() + i);
+                    return res;
+                }
+            }
+        }
+
+        // should never reach here
+    }
+
+    // Register the function to update multitask
+    void on_multitask_update(callback_multitask_t callback) {
+        callback_update_multitask = std::move(callback);
+    }
+
+    // Send a new result to a waiting id_task
+    void send(server_task_result result) {
+        LOG_VERBOSE("send new result", {{"id_task", result.id}});
+
+        std::unique_lock<std::mutex> lock(mutex_results);
+        for (const auto & id_task : waiting_task_ids) {
+            // LOG_TEE("waiting task id %i \n", id_task);
+            // for now, tasks that have associated parent multitasks just get erased once multitask picks up the result
+            if (result.id_multi == id_task) {
+                LOG_VERBOSE("callback_update_multitask", {{"id_task", id_task}});
+                callback_update_multitask(id_task, result.id, result);
+                continue;
+            }
+
+            if (result.id == id_task) {
+                LOG_VERBOSE("queue_results.push_back", {{"id_task", id_task}});
+                queue_results.push_back(result);
+                condition_results.notify_all();
+                return;
+            }
+        }
+    }
+};
+
+struct server_context {
+    llama_model * model = nullptr;
+    llama_context * ctx = nullptr;
 
     gpt_params params;
 
     llama_batch batch;
 
-    bool multimodal         = false;
-    bool clean_kv_cache     = true;
-    bool all_slots_are_idle = false;
-    bool add_bos_token      = true;
+    bool clean_kv_cache = true;
+    bool add_bos_token  = true;
 
-    int32_t id_gen;
-    int32_t n_ctx;  // total context for all clients / slots
+    int32_t n_ctx; // total context for all clients / slots
 
     // system prompt
     bool system_need_update = false;
@@ -545,65 +649,39 @@ struct llama_server_context
     std::string name_assistant;
 
     // slots / clients
-    std::vector<llama_client_slot> slots;
+    std::vector<server_slot> slots;
+    json default_generation_settings_for_props;
 
-    std::vector<task_server> queue_tasks;
-    std::vector<task_result> queue_results;
-    std::vector<task_multi>  queue_multitasks;
-    std::mutex mutex_tasks; // also guards id_gen, and queue_multitasks
-    std::condition_variable condition_tasks;
-    std::mutex mutex_results;
-    std::condition_variable condition_results;
+    server_queue    queue_tasks;
+    server_response queue_results;
 
-    ~llama_server_context()
-    {
-        if (ctx)
-        {
+    server_metrics metrics;
+
+    ~server_context() {
+        if (ctx) {
             llama_free(ctx);
             ctx = nullptr;
         }
-        if (model)
-        {
+
+        if (model) {
             llama_free_model(model);
             model = nullptr;
         }
     }
 
-    bool load_model(const gpt_params &params_)
-    {
+    bool load_model(const gpt_params & params_) {
         params = params_;
-        if (!params.mmproj.empty()) {
-            multimodal = true;
-            LOG_TEE("Multi Modal Mode Enabled");
-            clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1);
-            if(clp_ctx == nullptr) {
-                LOG_ERROR("unable to load clip model", {{"model", params.mmproj}});
-                return false;
-            }
 
-            if (params.n_ctx < 2048) { // request larger context for the image embedding
-                params.n_ctx = 2048;
-            }
-        }
+        // dedicate one sequence to the system prompt
+        params.n_parallel += 1;
 
         std::tie(model, ctx) = llama_init_from_gpt_params(params);
-        if (model == nullptr)
-        {
+        params.n_parallel -= 1; // but be sneaky about it
+        if (model == nullptr) {
             LOG_ERROR("unable to load model", {{"model", params.model}});
             return false;
         }
 
-        if (multimodal) {
-            const int n_embd_clip = clip_n_mmproj_embd(clp_ctx);
-            const int n_embd_llm  = llama_n_embd(model);
-            if (n_embd_clip != n_embd_llm) {
-                LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_embd_clip, n_embd_llm);
-                llama_free(ctx);
-                llama_free_model(model);
-                return false;
-            }
-        }
-
         n_ctx = llama_n_ctx(ctx);
 
         add_bos_token = llama_should_add_bos_token(model);
@@ -611,36 +689,72 @@ struct llama_server_context
         return true;
     }
 
-    void initialize() {
-        id_gen = 0;
+    bool validate_model_chat_template() const {
+        llama_chat_message chat[] = {{"user", "test"}};
 
-        // create slots
-        all_slots_are_idle = true;
+        const int res = llama_chat_apply_template(model, nullptr, chat, 1, true, nullptr, 0);
 
+        return res > 0;
+    }
+
+    void init() {
         const int32_t n_ctx_slot = n_ctx / params.n_parallel;
 
-        LOG_TEE("Available slots:\n");
-        for (int i = 0; i < params.n_parallel; i++)
-        {
-            llama_client_slot slot;
+        LOG_INFO("initializing slots", {{"n_slots", params.n_parallel}});
+
+        for (int i = 0; i < params.n_parallel; i++) {
+            server_slot slot;
 
             slot.id = i;
             slot.n_ctx = n_ctx_slot;
+            slot.n_predict = params.n_predict;
+
+            LOG_INFO("new slot", {
+                {"id_slot",    slot.id},
+                {"n_ctx_slot", slot.n_ctx}
+            });
+
+            const int ga_n = params.grp_attn_n;
+            const int ga_w = params.grp_attn_w;
+
+            if (ga_n != 1) {
+                GGML_ASSERT(ga_n > 0                    && "ga_n must be positive");                       // NOLINT
+                GGML_ASSERT(ga_w % ga_n == 0            && "ga_w must be a multiple of ga_n");             // NOLINT
+                //GGML_ASSERT(n_ctx_train % ga_w == 0     && "n_ctx_train must be a multiple of ga_w");    // NOLINT
+                //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT
+
+                LOG_INFO("slot self-extend", {
+                    {"id_slot", slot.id},
+                    {"ga_n",    ga_n},
+                    {"ga_w",    ga_w}
+                });
+            }
+
+            slot.ga_i = 0;
+            slot.ga_n = ga_n;
+            slot.ga_w = ga_w;
+
             slot.reset();
 
-            LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, n_ctx_slot);
             slots.push_back(slot);
         }
 
-        batch = llama_batch_init(n_ctx, 0, params.n_parallel);
+        default_generation_settings_for_props = get_formated_generation(slots.front());
+        default_generation_settings_for_props["seed"] = -1;
 
-        // empty system prompt
-        system_prompt = "";
-        system_tokens.clear();
+        // the update_slots() logic will always submit a maximum of n_batch tokens
+        // note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used)
+        {
+            const int32_t n_batch = llama_n_batch(ctx);
+
+            // only a single seq_id per token is needed
+            batch = llama_batch_init(n_batch, 0, 1);
+        }
+
+        metrics.init();
     }
 
-    std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const
-    {
+    std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const {
         // TODO: currently, we tokenize using special tokens by default
         //       this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216)
         //       but it's better compared to completely ignoring ChatML and other chat templates
@@ -650,38 +764,30 @@ struct llama_server_context
         // or the first element of the json_prompt array is a string.
         std::vector<llama_token> prompt_tokens;
 
-        if (json_prompt.is_array())
-        {
+        if (json_prompt.is_array()) {
             bool first = true;
-            for (const auto& p : json_prompt)
-            {
-                if (p.is_string())
-                {
+            for (const auto & p : json_prompt) {
+                if (p.is_string()) {
                     auto s = p.template get<std::string>();
+
                     std::vector<llama_token> p;
-                    if (first)
-                    {
+                    if (first) {
                         p = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
                         first = false;
-                    }
-                    else
-                    {
+                    } else {
                         p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL);
                     }
+
                     prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
-                }
-                else
-                {
-                    if (first)
-                    {
+                } else {
+                    if (first) {
                         first = false;
                     }
+
                     prompt_tokens.push_back(p.template get<llama_token>());
                 }
             }
-        }
-        else
-        {
+        } else {
             auto s = json_prompt.template get<std::string>();
             prompt_tokens = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
         }
@@ -689,19 +795,18 @@ struct llama_server_context
         return prompt_tokens;
     }
 
-    llama_client_slot* get_slot(int id) {
+    server_slot * get_slot(int id) {
         int64_t t_last = ggml_time_us();
-        llama_client_slot *last_used = nullptr;
 
-        for (llama_client_slot & slot : slots)
-        {
-            if (slot.id == id && slot.available())
-            {
+        server_slot * last_used = nullptr;
+
+        for (server_slot & slot : slots) {
+            if (slot.id == id && slot.available()) {
                 return &slot;
             }
 
-            if (slot.available() && slot.t_last_used < t_last)
-            {
+            // among all available slots, find the one that has been least recently used
+            if (slot.available() && slot.t_last_used < t_last) {
                 last_used = &slot;
                 t_last = slot.t_last_used;
             }
@@ -710,321 +815,303 @@ struct llama_server_context
         return last_used;
     }
 
-    bool launch_slot_with_data(llama_client_slot* &slot, json data) {
+    bool launch_slot_with_task(server_slot & slot, const server_task & task) {
         slot_params default_params;
         llama_sampling_params default_sparams;
+        auto & data = task.data;
 
         if (data.count("__oaicompat") != 0) {
-            slot->oaicompat = true;
-            slot->oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
+            slot.oaicompat = true;
+            slot.oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
         } else {
-            slot->oaicompat = false;
-            slot->oaicompat_model = "";
+            slot.oaicompat = false;
+            slot.oaicompat_model = "";
         }
 
-        slot->params.stream           = json_value(data, "stream",            false);
-        slot->params.cache_prompt     = json_value(data, "cache_prompt",      false);
-        slot->params.n_predict        = json_value(data, "n_predict",         default_params.n_predict);
-        slot->sparams.top_k           = json_value(data, "top_k",             default_sparams.top_k);
-        slot->sparams.top_p           = json_value(data, "top_p",             default_sparams.top_p);
-        slot->sparams.min_p           = json_value(data, "min_p",             default_sparams.min_p);
-        slot->sparams.tfs_z           = json_value(data, "tfs_z",             default_sparams.tfs_z);
-        slot->sparams.typical_p       = json_value(data, "typical_p",         default_sparams.typical_p);
-        slot->sparams.temp            = json_value(data, "temperature",       default_sparams.temp);
-        slot->sparams.penalty_last_n  = json_value(data, "repeat_last_n",     default_sparams.penalty_last_n);
-        slot->sparams.penalty_repeat  = json_value(data, "repeat_penalty",    default_sparams.penalty_repeat);
-        slot->sparams.penalty_freq    = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
-        slot->sparams.penalty_present = json_value(data, "presence_penalty",  default_sparams.penalty_present);
-        slot->sparams.mirostat        = json_value(data, "mirostat",          default_sparams.mirostat);
-        slot->sparams.mirostat_tau    = json_value(data, "mirostat_tau",      default_sparams.mirostat_tau);
-        slot->sparams.mirostat_eta    = json_value(data, "mirostat_eta",      default_sparams.mirostat_eta);
-        slot->sparams.penalize_nl     = json_value(data, "penalize_nl",       default_sparams.penalize_nl);
-        slot->params.n_keep           = json_value(data, "n_keep",            slot->params.n_keep);
-        slot->params.seed             = json_value(data, "seed",              default_params.seed);
-        slot->sparams.grammar         = json_value(data, "grammar",           default_sparams.grammar);
-        slot->sparams.n_probs         = json_value(data, "n_probs",           default_sparams.n_probs);
+        slot.params.stream             = json_value(data, "stream",            false);
+        slot.params.cache_prompt       = json_value(data, "cache_prompt",      false);
+        slot.params.n_predict          = json_value(data, "n_predict",         default_params.n_predict);
+        slot.sparams.top_k             = json_value(data, "top_k",             default_sparams.top_k);
+        slot.sparams.top_p             = json_value(data, "top_p",             default_sparams.top_p);
+        slot.sparams.min_p             = json_value(data, "min_p",             default_sparams.min_p);
+        slot.sparams.tfs_z             = json_value(data, "tfs_z",             default_sparams.tfs_z);
+        slot.sparams.typical_p         = json_value(data, "typical_p",         default_sparams.typical_p);
+        slot.sparams.temp              = json_value(data, "temperature",       default_sparams.temp);
+        slot.sparams.dynatemp_range    = json_value(data, "dynatemp_range",    default_sparams.dynatemp_range);
+        slot.sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent);
+        slot.sparams.penalty_last_n    = json_value(data, "repeat_last_n",     default_sparams.penalty_last_n);
+        slot.sparams.penalty_repeat    = json_value(data, "repeat_penalty",    default_sparams.penalty_repeat);
+        slot.sparams.penalty_freq      = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
+        slot.sparams.penalty_present   = json_value(data, "presence_penalty",  default_sparams.penalty_present);
+        slot.sparams.mirostat          = json_value(data, "mirostat",          default_sparams.mirostat);
+        slot.sparams.mirostat_tau      = json_value(data, "mirostat_tau",      default_sparams.mirostat_tau);
+        slot.sparams.mirostat_eta      = json_value(data, "mirostat_eta",      default_sparams.mirostat_eta);
+        slot.sparams.penalize_nl       = json_value(data, "penalize_nl",       default_sparams.penalize_nl);
+        slot.params.n_keep             = json_value(data, "n_keep",            slot.params.n_keep);
+        slot.params.n_discard          = json_value(data, "n_discard",         default_params.n_discard);
+        slot.params.seed               = json_value(data, "seed",              default_params.seed);
+        slot.sparams.n_probs           = json_value(data, "n_probs",           default_sparams.n_probs);
+        slot.sparams.min_keep          = json_value(data, "min_keep",          default_sparams.min_keep);
+
+        // process "json_schema" and "grammar"
+        if (data.contains("json_schema") && data.contains("grammar")) {
+            send_error(task, "Either \"json_schema\" or \"grammar\" can be specified, but not both", ERROR_TYPE_INVALID_REQUEST);
+            return false;
+        } else if (data.contains("json_schema") && !data.contains("grammar")) {
+            try {
+                auto schema                = json_value(data, "json_schema", json::object());
+                slot.sparams.grammar       = json_schema_to_grammar(schema);
+            } catch (const std::exception & e) {
+                send_error(task, std::string("\"json_schema\": ") + e.what(), ERROR_TYPE_INVALID_REQUEST);
+                return false;
+            }
+        } else {
+            slot.sparams.grammar       = json_value(data, "grammar",           default_sparams.grammar);
+        }
+
+        if (slot.params.cache_prompt && slot.ga_n != 1) {
+            LOG_WARNING("cache_prompt is not supported with group-attention", {});
+            slot.params.cache_prompt = false;
+        }
+
+        if (slot.n_predict > 0 && slot.params.n_predict > slot.n_predict) {
+            // Might be better to reject the request with a 400 ?
+            LOG_WARNING("Max tokens to predict exceeds server configuration", {
+                {"params.n_predict", slot.params.n_predict},
+                {"slot.n_predict",   slot.n_predict},
+            });
+            slot.params.n_predict = slot.n_predict;
+        }
 
         // infill
-        if (data.count("input_prefix") != 0)
-        {
-            slot->params.input_prefix = data["input_prefix"];
-        }
-        else
-        {
-            slot->params.input_prefix = "";
-        }
+        slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix);
+        slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix);
 
-        if (data.count("input_suffix") != 0)
+        // get prompt
         {
-            slot->params.input_suffix = data["input_suffix"];
-        }
-        else
-        {
-            slot->params.input_suffix = "";
-        }
-
-        if (data.count("prompt") != 0)
-        {
-            slot->prompt = data["prompt"];
-        }
-        else
-        {
-            slot->prompt = "";
-        }
-
-        slot->sparams.penalty_prompt_tokens.clear();
-        slot->sparams.use_penalty_prompt_tokens = false;
-        const auto &penalty_prompt = data.find("penalty_prompt");
-        if (penalty_prompt != data.end())
-        {
-            if (penalty_prompt->is_string())
-            {
-                const auto penalty_prompt_string = penalty_prompt->get<std::string>();
-                auto penalty_tokens = llama_tokenize(model, penalty_prompt_string, false);
-                slot->sparams.penalty_prompt_tokens.swap(penalty_tokens);
-                if (slot->params.n_predict > 0)
-                {
-                    slot->sparams.penalty_prompt_tokens.reserve(slot->sparams.penalty_prompt_tokens.size() + slot->params.n_predict);
-                }
-                slot->sparams.use_penalty_prompt_tokens = true;
+            const auto & prompt = data.find("prompt");
+            if (prompt == data.end()) {
+                send_error(task, "Either \"prompt\" or \"messages\" must be provided", ERROR_TYPE_INVALID_REQUEST);
+                return false;
+            } else {
+                slot.prompt = *prompt;
             }
-            else if (penalty_prompt->is_array())
-            {
-                const auto n_tokens = penalty_prompt->size();
-                slot->sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot->params.n_predict));
-                const int n_vocab = llama_n_vocab(model);
-                for (const auto &penalty_token : *penalty_prompt)
-                {
-                    if (penalty_token.is_number_integer())
-                    {
-                        const auto tok = penalty_token.get<llama_token>();
-                        if (tok >= 0 && tok < n_vocab)
-                        {
-                            slot->sparams.penalty_prompt_tokens.push_back(tok);
-                        }
+            if (slot.prompt.is_array() && slot.prompt.size() == 0) {
+                send_error(task, "\"prompt\" cannot be an empty array", ERROR_TYPE_INVALID_REQUEST);
+                return false;
+            }
+        }
+
+        // penalize user-provided tokens
+        {
+            slot.sparams.penalty_prompt_tokens.clear();
+            slot.sparams.use_penalty_prompt_tokens = false;
+
+            const auto & penalty_prompt = data.find("penalty_prompt");
+
+            if (penalty_prompt != data.end()) {
+                if (penalty_prompt->is_string()) {
+                    const auto penalty_prompt_string = penalty_prompt->get<std::string>();
+                    slot.sparams.penalty_prompt_tokens = llama_tokenize(model, penalty_prompt_string, false);
+
+                    if (slot.params.n_predict > 0) {
+                        slot.sparams.penalty_prompt_tokens.reserve(slot.sparams.penalty_prompt_tokens.size() + slot.params.n_predict);
                     }
+                    slot.sparams.use_penalty_prompt_tokens = true;
+
+                    LOG_VERBOSE("penalty_prompt_tokens", {
+                        {"id_slot", slot.id},
+                        {"tokens",  slot.sparams.penalty_prompt_tokens},
+                    });
                 }
-                slot->sparams.use_penalty_prompt_tokens = true;
-            }
-        }
+                else if (penalty_prompt->is_array()) {
+                    const auto n_tokens = penalty_prompt->size();
+                    slot.sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot.params.n_predict));
 
-        slot->sparams.logit_bias.clear();
-
-        if (json_value(data, "ignore_eos", false))
-        {
-            slot->sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
-        }
-
-        const auto &logit_bias = data.find("logit_bias");
-        if (logit_bias != data.end() && logit_bias->is_array())
-        {
-            const int n_vocab = llama_n_vocab(model);
-            for (const auto &el : *logit_bias)
-            {
-                if (el.is_array() && el.size() == 2 && el[0].is_number_integer())
-                {
-                    llama_token tok = el[0].get<llama_token>();
-                    if (tok >= 0 && tok < n_vocab)
-                    {
-                        if (el[1].is_number())
-                        {
-                            slot->sparams.logit_bias[tok] = el[1].get<float>();
-                        }
-                        else if (el[1].is_boolean() && !el[1].get<bool>())
-                        {
-                            slot->sparams.logit_bias[tok] = -INFINITY;
-                        }
-                    }
-                }
-            }
-        }
-
-        slot->params.antiprompt.clear();
-
-        const auto &stop = data.find("stop");
-        if (stop != data.end() && stop->is_array())
-        {
-            for (const auto &word : *stop)
-            {
-                if (!word.empty())
-                {
-                    slot->params.antiprompt.push_back(word);
-                }
-            }
-        }
-
-        if (multimodal)
-        {
-            const auto &images_data = data.find("image_data");
-            if (images_data != data.end() && images_data->is_array())
-            {
-                for (const auto &img : *images_data)
-                {
-                    const std::vector<uint8_t> image_buffer = base64_decode(img["data"].get<std::string>());
-
-                    slot_image img_sl;
-                    img_sl.id = img.count("id") != 0 ? img["id"].get<int>() : slot->images.size();
-                    img_sl.img_data = clip_image_u8_init();
-                    if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), img_sl.img_data))
-                    {
-                        LOG_TEE("slot %i - failed to load image [id: %i]\n", slot->id, img_sl.id);
-                        return false;
-                    }
-                    LOG_TEE("slot %i - loaded image\n", slot->id);
-                    img_sl.request_encode_image = true;
-                    slot->images.push_back(img_sl);
-                }
-                // process prompt
-                // example: system prompt [img-102] user [img-103] describe [img-134] -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]}
-                if (slot->images.size() > 0 && !slot->prompt.is_array())
-                {
-                    std::string prompt = slot->prompt.get<std::string>();
-                    size_t pos = 0, begin_prefix = 0;
-                    std::string pattern = "[img-";
-                    while ((pos = prompt.find(pattern, pos)) != std::string::npos) {
-                        size_t end_prefix = pos;
-                        pos += pattern.length();
-                        size_t end_pos = prompt.find("]", pos);
-                        if (end_pos != std::string::npos)
-                        {
-                            std::string image_id = prompt.substr(pos, end_pos - pos);
-                            try
-                            {
-                                int img_id = std::stoi(image_id);
-                                bool found = false;
-                                for (slot_image &img : slot->images)
-                                {
-                                    if (img.id == img_id) {
-                                        found = true;
-                                        img.prefix_prompt = prompt.substr(begin_prefix, end_prefix - begin_prefix);
-                                        begin_prefix = end_pos + 1;
-                                        break;
-                                    }
-                                }
-                                if (!found) {
-                                    LOG_TEE("ERROR: Image with id: %i, not found.\n", img_id);
-                                    slot->images.clear();
-                                    return false;
-                                }
-                            } catch (const std::invalid_argument& e) {
-                                LOG_TEE("Invalid image number id in prompt\n");
-                                slot->images.clear();
-                                return false;
+                    const int n_vocab = llama_n_vocab(model);
+                    for (const auto & penalty_token : *penalty_prompt) {
+                        if (penalty_token.is_number_integer()) {
+                            const auto tok = penalty_token.get<llama_token>();
+                            if (tok >= 0 && tok < n_vocab) {
+                                slot.sparams.penalty_prompt_tokens.push_back(tok);
                             }
                         }
                     }
-                    slot->prompt = "";
-                    slot->params.input_suffix = prompt.substr(begin_prefix);
-                    slot->params.cache_prompt = false; // multimodal doesn't support cache prompt
+                    slot.sparams.use_penalty_prompt_tokens = true;
+
+                    LOG_VERBOSE("penalty_prompt_tokens", {
+                        {"id_slot", slot.id},
+                        {"tokens",  slot.sparams.penalty_prompt_tokens},
+                    });
                 }
             }
         }
 
-        if (slot->ctx_sampling != nullptr)
         {
-            llama_sampling_free(slot->ctx_sampling);
+            slot.sparams.logit_bias.clear();
+
+            if (json_value(data, "ignore_eos", false)) {
+                slot.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
+            }
+
+            const auto & logit_bias = data.find("logit_bias");
+            if (logit_bias != data.end() && logit_bias->is_array()) {
+                const int n_vocab = llama_n_vocab(model);
+                for (const auto & el : *logit_bias) {
+                    // TODO: we may want to throw errors here, in case "el" is incorrect
+                    if (el.is_array() && el.size() == 2) {
+                        float bias;
+                        if (el[1].is_number()) {
+                            bias = el[1].get<float>();
+                        } else if (el[1].is_boolean() && !el[1].get<bool>()) {
+                            bias = -INFINITY;
+                        } else {
+                            continue;
+                        }
+
+                        if (el[0].is_number_integer()) {
+                            llama_token tok = el[0].get<llama_token>();
+                            if (tok >= 0 && tok < n_vocab) {
+                                slot.sparams.logit_bias[tok] = bias;
+                            }
+                        } else if (el[0].is_string()) {
+                            auto toks = llama_tokenize(model, el[0].get<std::string>(), false);
+                            for (auto tok : toks) {
+                                slot.sparams.logit_bias[tok] = bias;
+                            }
+                        }
+                    }
+                }
+            }
         }
-        slot->ctx_sampling = llama_sampling_init(slot->sparams);
-        llama_set_rng_seed(ctx, slot->params.seed);
-        slot->command = LOAD_PROMPT;
 
-        all_slots_are_idle = false;
+        {
+            slot.params.antiprompt.clear();
 
-        LOG_TEE("slot %i is processing [task id: %i]\n", slot->id, slot->task_id);
+            const auto & stop = data.find("stop");
+            if (stop != data.end() && stop->is_array()) {
+                for (const auto & word : *stop) {
+                    if (!word.empty()) {
+                        slot.params.antiprompt.push_back(word);
+                    }
+                }
+            }
+        }
+
+        {
+            const auto & samplers_sequence = data.find("samplers");
+            if (samplers_sequence != data.end() && samplers_sequence->is_array()) {
+                std::vector<std::string> sampler_names;
+                for (const auto & sampler_name : *samplers_sequence) {
+                    if (sampler_name.is_string()) {
+                        sampler_names.emplace_back(sampler_name);
+                    }
+                }
+                slot.sparams.samplers_sequence = sampler_types_from_names(sampler_names, false);
+            } else {
+                slot.sparams.samplers_sequence = default_sparams.samplers_sequence;
+            }
+        }
+
+        {
+            if (slot.ctx_sampling != nullptr) {
+                llama_sampling_free(slot.ctx_sampling);
+            }
+            slot.ctx_sampling = llama_sampling_init(slot.sparams);
+            if (slot.ctx_sampling == nullptr) {
+                // for now, the only error that may happen here is invalid grammar
+                send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST);
+                return false;
+            }
+            llama_set_rng_seed(ctx, slot.params.seed);
+        }
+
+        slot.command = SLOT_COMMAND_LOAD_PROMPT;
+        slot.prompt_tokens.clear();
+
+        LOG_INFO("slot is processing task", {
+            {"id_slot", slot.id},
+            {"id_task", slot.id_task},
+        });
 
         return true;
     }
 
     void kv_cache_clear() {
+        LOG_VERBOSE("clearing KV cache", {});
+
         // clear the entire KV cache
         llama_kv_cache_clear(ctx);
         clean_kv_cache = false;
     }
 
-    void update_system_prompt() {
-        system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token);
-
-        llama_batch_clear(batch);
+    void system_prompt_update() {
+        LOG_VERBOSE("system prompt update", {
+            {"system_prompt", system_prompt},
+        });
 
         kv_cache_clear();
+        system_tokens.clear();
 
-        for (int i = 0; i < (int) system_tokens.size(); ++i)
-        {
-            llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
+        if (!system_prompt.empty()) {
+            system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token);
+
+            llama_batch_clear(batch);
+
+            for (int i = 0; i < (int)system_tokens.size(); ++i) {
+                llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
+            }
+
+            const int32_t n_batch = llama_n_batch(ctx);
+
+            for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
+                const int32_t n_tokens = std::min(params.n_batch, batch.n_tokens - i);
+                llama_batch batch_view = {
+                    n_tokens,
+                    batch.token    + i,
+                    nullptr,
+                    batch.pos      + i,
+                    batch.n_seq_id + i,
+                    batch.seq_id   + i,
+                    batch.logits   + i,
+                    0, 0, 0, // unused
+                };
+
+                if (llama_decode(ctx, batch_view) != 0) {
+                    LOG_TEE("%s: llama_decode() failed\n", __func__);
+                    return;
+                }
+            }
+
+            // assign the system KV cache to all parallel sequences
+            for (int32_t i = 1; i <= params.n_parallel; ++i) {
+                llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
+            }
         }
 
-        if (llama_decode(ctx, batch) != 0)
-        {
-            LOG_TEE("%s: llama_decode() failed\n", __func__);
-            return;
-        }
-
-        // assign the system KV cache to all parallel sequences
-        for (int32_t i = 1; i < params.n_parallel; ++i)
-        {
-            llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size());
-        }
-
-        LOG_TEE("system prompt updated\n");
         system_need_update = false;
     }
 
-    void notify_system_prompt_changed() {
+    void system_prompt_set(const json & sys_props) {
+        system_prompt  = sys_props.value("prompt", "");
+        name_user      = sys_props.value("anti_prompt", "");
+        name_assistant = sys_props.value("assistant_name", "");
+
+        LOG_VERBOSE("system prompt process", {
+            {"system_prompt",  system_prompt},
+            {"name_user",      name_user},
+            {"name_assistant", name_assistant},
+        });
+
         // release all slots
-        for (llama_client_slot &slot : slots)
-        {
+        for (server_slot & slot : slots) {
             slot.release();
         }
 
         system_need_update = true;
     }
 
-    void process_system_prompt_data(const json &sys_props) {
-        system_prompt  = sys_props.value("prompt", "");
-        name_user      = sys_props.value("anti_prompt", "");
-        name_assistant = sys_props.value("assistant_name", "");
-
-        if (slots.size() > 0)
-        {
-            notify_system_prompt_changed();
-        }
-    }
-
-    static size_t find_stopping_strings(const std::string &text, const size_t last_token_size,
-                                        const stop_type type, llama_client_slot &slot)
-    {
-        size_t stop_pos = std::string::npos;
-
-        for (const std::string &word : slot.params.antiprompt)
-        {
-            size_t pos;
-            if (type == STOP_FULL)
-            {
-                const size_t tmp = word.size() + last_token_size;
-                const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0;
-                pos = text.find(word, from_pos);
-            }
-            else
-            {
-                pos = find_partial_stop_string(word, text);
-            }
-            if (pos != std::string::npos &&
-                (stop_pos == std::string::npos || pos < stop_pos))
-            {
-                if (type == STOP_FULL)
-                {
-                    slot.stopped_word = true;
-                    slot.stopping_word = word;
-                    slot.has_next_token = false;
-                }
-                stop_pos = pos;
-            }
-        }
-
-        return stop_pos;
-    }
-
-    bool process_token(completion_token_output &result, llama_client_slot &slot) {
+    bool process_token(completion_token_output & result, server_slot & slot) {
         // remember which tokens were sampled - used for repetition penalties during sampling
         const std::string token_str = llama_token_to_piece(ctx, result.tok);
         slot.sampled = result.tok;
@@ -1033,34 +1120,26 @@ struct llama_server_context
         slot.generated_text += token_str;
         slot.has_next_token = true;
 
-        if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1)
-        {
+        if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1) {
             // we can change penalty_prompt_tokens because it is always created from scratch each request
             slot.ctx_sampling->params.penalty_prompt_tokens.push_back(result.tok);
         }
 
         // check if there is incomplete UTF-8 character at the end
         bool incomplete = false;
-        for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i)
-        {
+        for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i) {
             unsigned char c = slot.generated_text[slot.generated_text.size() - i];
-            if ((c & 0xC0) == 0x80)
-            {
+            if ((c & 0xC0) == 0x80) {
                 // continuation byte: 10xxxxxx
                 continue;
             }
-            if ((c & 0xE0) == 0xC0)
-            {
+            if ((c & 0xE0) == 0xC0) {
                 // 2-byte character: 110xxxxx ...
                 incomplete = i < 2;
-            }
-            else if ((c & 0xF0) == 0xE0)
-            {
+            } else if ((c & 0xF0) == 0xE0) {
                 // 3-byte character: 1110xxxx ...
                 incomplete = i < 3;
-            }
-            else if ((c & 0xF8) == 0xF0)
-            {
+            } else if ((c & 0xF8) == 0xF0) {
                 // 4-byte character: 11110xxx ...
                 incomplete = i < 4;
             }
@@ -1068,247 +1147,197 @@ struct llama_server_context
             break;
         }
 
-        if (!incomplete)
-        {
-            size_t pos = std::min(slot.sent_count, slot.generated_text.size());
+        if (!incomplete) {
+            size_t pos = std::min(slot.n_sent_text, slot.generated_text.size());
+
             const std::string str_test = slot.generated_text.substr(pos);
             bool is_stop_full = false;
-            size_t stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_FULL, slot);
-            if (stop_pos != std::string::npos)
-            {
+
+            size_t stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_FULL);
+            if (stop_pos != std::string::npos) {
                 is_stop_full = true;
                 slot.generated_text.erase(
                     slot.generated_text.begin() + pos + stop_pos,
                     slot.generated_text.end());
-                pos = std::min(slot.sent_count, slot.generated_text.size());
-            }
-            else
-            {
+                pos = std::min(slot.n_sent_text, slot.generated_text.size());
+            } else {
                 is_stop_full = false;
-                stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_PARTIAL, slot);
+                stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_PARTIAL);
             }
 
             // check if there is any token to predict
-            if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0))
-            {
+            if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0)) {
                 // no send the stop word in the response
                 result.text_to_send = slot.generated_text.substr(pos, std::string::npos);
-                slot.sent_count += result.text_to_send.size();
+                slot.n_sent_text += result.text_to_send.size();
                 // add the token to slot queue and cache
             }
+
             slot.add_token_string(result);
-            if (slot.params.stream)
-            {
+            if (slot.params.stream) {
                 send_partial_response(slot, result);
             }
         }
 
-        if (incomplete)
-        {
+        if (incomplete) {
             slot.has_next_token = true;
         }
 
         // check the limits
-        if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params))
-        {
-            slot.stopped_limit = true;
+        if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params)) {
+            slot.stopped_limit  = true;
             slot.has_next_token = false;
+
+            LOG_VERBOSE("stopped by limit", {
+                {"id_slot",   slot.id},
+                {"id_task",   slot.id_task},
+                {"n_decoded", slot.n_decoded},
+                {"n_predict", slot.params.n_predict},
+            });
         }
 
-        if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(model))
-        {
-            slot.stopped_eos = true;
+        if (result.tok == llama_token_eos(model)) {
+            slot.stopped_eos    = true;
             slot.has_next_token = false;
+
             LOG_VERBOSE("eos token found", {});
         }
 
         LOG_VERBOSE("next token", {
-                                      {"token", result.tok},
-                                      {"token_text", tokens_to_output_formatted_string(ctx, result.tok)},
-                                      {"has_next_token", slot.has_next_token},
-                                      {"n_remain", slot.n_remaining},
-                                      {"num_tokens_predicted", slot.n_decoded},
-                                      {"stopped_eos", slot.stopped_eos},
-                                      {"stopped_word", slot.stopped_word},
-                                      {"stopped_limit", slot.stopped_limit},
-                                      {"stopping_word", slot.stopping_word},
-                                  });
+            {"id_slot",        slot.id},
+            {"id_task",        slot.id_task},
+            {"token",          result.tok},
+            {"token_text",     tokens_to_output_formatted_string(ctx, result.tok)},
+            {"has_next_token", slot.has_next_token},
+            {"n_remain",       slot.n_remaining},
+            {"n_decoded",      slot.n_decoded},
+            {"stopped_eos",    slot.stopped_eos},
+            {"stopped_word",   slot.stopped_word},
+            {"stopped_limit",  slot.stopped_limit},
+            {"stopping_word",  slot.stopping_word},
+        });
 
         return slot.has_next_token; // continue
     }
 
-    bool process_images(llama_client_slot &slot) const
-    {
-        for (slot_image &img : slot.images)
-        {
-            if (!img.request_encode_image)
-            {
-                continue;
-            }
-            clip_image_f32 * img_res = clip_image_f32_init();
-            if (!clip_image_preprocess(clp_ctx, img.img_data, img_res, /*pad2square =*/ true))
-            {
-                LOG_TEE("Error processing the given image");
-                clip_free(clp_ctx);
-                return false;
-            }
-            img.image_tokens = clip_n_patches(clp_ctx);
-            img.image_embedding = (float *)malloc(clip_embd_nbytes(clp_ctx));
-            if (!img.image_embedding)
-            {
-                LOG_TEE("Unable to allocate memory for image embeddings\n");
-                clip_free(clp_ctx);
-                return false;
-            }
-            LOG_TEE("slot %i - encoding image [id: %i]\n", slot.id, img.id);
-            if (!clip_image_encode(clp_ctx, params.n_threads, img_res, img.image_embedding))
-            {
-                LOG_TEE("Unable to encode image\n");
-                return false;
-            }
-            clip_image_f32_free(img_res);
-            img.request_encode_image = false;
-        }
-
-        return slot.images.size() > 0;
-    }
-
-    void send_error(task_server& task, std::string error)
-    {
-        std::unique_lock<std::mutex> lock(mutex_results);
-        task_result res;
-        res.id = task.id;
-        res.multitask_id = task.multitask_id;
-        res.stop = false;
-        res.error = true;
-        res.result_json = { { "content", error } };
-        queue_results.push_back(res);
-        condition_results.notify_all();
-    }
-
-    void add_multi_task(int id, std::vector<int>& sub_ids)
-    {
-        std::lock_guard<std::mutex> lock(mutex_tasks);
-        task_multi multi;
-        multi.id = id;
-        std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end()));
-        queue_multitasks.push_back(multi);
-        condition_tasks.notify_one();
-    }
-
-    void update_multi_task(int multitask_id, int subtask_id, task_result& result)
-    {
-        std::lock_guard<std::mutex> lock(mutex_tasks);
-        for (auto& multitask : queue_multitasks)
-        {
-            if (multitask.id == multitask_id)
-            {
-                multitask.subtasks_remaining.erase(subtask_id);
-                multitask.results.push_back(result);
-                condition_tasks.notify_one();
-            }
-        }
-    }
-
-    json get_model_props()
-    {
-        return get_formated_generation(slots[0]);
-    }
-
-    json get_formated_generation(llama_client_slot &slot)
-    {
+    json get_formated_generation(const server_slot & slot) const {
         const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
-        const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() &&
-                                eos_bias->second < 0.0f && std::isinf(eos_bias->second);
+        const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second);
+
+        std::vector<std::string> samplers_sequence;
+        samplers_sequence.reserve(slot.sparams.samplers_sequence.size());
+        for (const auto & sampler_type : slot.sparams.samplers_sequence) {
+            samplers_sequence.emplace_back(sampler_type_to_name_string(sampler_type));
+        }
+
         return json {
-            {"n_ctx",             slot.n_ctx},
-            {"model",             params.model_alias},
-            {"seed",              slot.params.seed},
-            {"temperature",       slot.sparams.temp},
-            {"top_k",             slot.sparams.top_k},
-            {"top_p",             slot.sparams.top_p},
-            {"min_p",             slot.sparams.min_p},
-            {"tfs_z",             slot.sparams.tfs_z},
-            {"typical_p",         slot.sparams.typical_p},
-            {"repeat_last_n",     slot.sparams.penalty_last_n},
-            {"repeat_penalty",    slot.sparams.penalty_repeat},
-            {"presence_penalty",  slot.sparams.penalty_present},
-            {"frequency_penalty", slot.sparams.penalty_freq},
-            {"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens},
+            {"n_ctx",                     slot.n_ctx},
+            {"n_predict",                 slot.n_predict},
+            {"model",                     params.model_alias},
+            {"seed",                      slot.params.seed},
+            {"temperature",               slot.sparams.temp},
+            {"dynatemp_range",            slot.sparams.dynatemp_range},
+            {"dynatemp_exponent",         slot.sparams.dynatemp_exponent},
+            {"top_k",                     slot.sparams.top_k},
+            {"top_p",                     slot.sparams.top_p},
+            {"min_p",                     slot.sparams.min_p},
+            {"tfs_z",                     slot.sparams.tfs_z},
+            {"typical_p",                 slot.sparams.typical_p},
+            {"repeat_last_n",             slot.sparams.penalty_last_n},
+            {"repeat_penalty",            slot.sparams.penalty_repeat},
+            {"presence_penalty",          slot.sparams.penalty_present},
+            {"frequency_penalty",         slot.sparams.penalty_freq},
+            {"penalty_prompt_tokens",     slot.sparams.penalty_prompt_tokens},
             {"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens},
-            {"mirostat",          slot.sparams.mirostat},
-            {"mirostat_tau",      slot.sparams.mirostat_tau},
-            {"mirostat_eta",      slot.sparams.mirostat_eta},
-            {"penalize_nl",       slot.sparams.penalize_nl},
-            {"stop",              slot.params.antiprompt},
-            {"n_predict",         slot.params.n_predict},
-            {"n_keep",            params.n_keep},
-            {"ignore_eos",        ignore_eos},
-            {"stream",            slot.params.stream},
-            {"logit_bias",        slot.sparams.logit_bias},
-            {"n_probs",           slot.sparams.n_probs},
-            {"grammar",           slot.sparams.grammar},
+            {"mirostat",                  slot.sparams.mirostat},
+            {"mirostat_tau",              slot.sparams.mirostat_tau},
+            {"mirostat_eta",              slot.sparams.mirostat_eta},
+            {"penalize_nl",               slot.sparams.penalize_nl},
+            {"stop",                      slot.params.antiprompt},
+            {"n_predict",                 slot.params.n_predict}, // TODO: fix duplicate key n_predict
+            {"n_keep",                    slot.params.n_keep},
+            {"n_discard",                 slot.params.n_discard},
+            {"ignore_eos",                ignore_eos},
+            {"stream",                    slot.params.stream},
+            {"logit_bias",                slot.sparams.logit_bias},
+            {"n_probs",                   slot.sparams.n_probs},
+            {"min_keep",                  slot.sparams.min_keep},
+            {"grammar",                   slot.sparams.grammar},
+            {"samplers",                  samplers_sequence}
         };
     }
 
-    void send_partial_response(llama_client_slot &slot, completion_token_output tkn)
-    {
-        std::unique_lock<std::mutex> lock(mutex_results);
-        task_result res;
-        res.id = slot.task_id;
-        res.multitask_id = slot.multitask_id;
-        res.error = false;
-        res.stop = false;
+    void send_error(const server_task & task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
+        send_error(task.id, task.id_multi, error, type);
+    }
 
-        res.result_json = json
-        {
+    void send_error(const server_slot & slot, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
+        send_error(slot.id_task, slot.id_multi, error, type);
+    }
+
+    void send_error(const int id_task, const int id_multi, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
+        LOG_TEE("task %i - error: %s\n", id_task, error.c_str());
+
+        server_task_result res;
+        res.id       = id_task;
+        res.id_multi = id_multi;
+        res.stop     = false;
+        res.error    = true;
+        res.data     = format_error_response(error, type);
+
+        queue_results.send(res);
+    }
+
+    void send_partial_response(server_slot & slot, completion_token_output tkn) {
+        server_task_result res;
+        res.id       = slot.id_task;
+        res.id_multi = slot.id_multi;
+        res.error    = false;
+        res.stop     = false;
+        res.data     = json {
             {"content",    tkn.text_to_send},
             {"stop",       false},
-            {"slot_id",    slot.id},
-            {"multimodal", multimodal}
+            {"id_slot",    slot.id},
+            {"multimodal", false}
         };
 
-        if (slot.sparams.n_probs > 0)
-        {
-            std::vector<completion_token_output> probs_output = {};
+        if (slot.sparams.n_probs > 0) {
             const std::vector<llama_token> to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false);
-            size_t probs_pos      = std::min(slot.sent_token_probs_index,                       slot.generated_token_probs.size());
-            size_t probs_stop_pos = std::min(slot.sent_token_probs_index + to_send_toks.size(), slot.generated_token_probs.size());
-            if (probs_pos < probs_stop_pos)
-            {
-                probs_output = std::vector<completion_token_output>(slot.generated_token_probs.begin() + probs_pos, slot.generated_token_probs.begin() + probs_stop_pos);
+            const size_t probs_pos      = std::min(slot.n_sent_token_probs,                       slot.generated_token_probs.size());
+            const size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size());
+
+            std::vector<completion_token_output> probs_output;
+            if (probs_pos < probs_stop_pos) {
+                probs_output = std::vector<completion_token_output>(
+                        slot.generated_token_probs.begin() + probs_pos,
+                        slot.generated_token_probs.begin() + probs_stop_pos);
             }
-            slot.sent_token_probs_index = probs_stop_pos;
-            res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output);
+            slot.n_sent_token_probs = probs_stop_pos;
+
+            res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs_output);
         }
 
-        if (slot.oaicompat)
-        {
-            res.result_json["oaicompat_token_ctr"] = slot.n_decoded;
-            res.result_json["model"] = slot.oaicompat_model;
+        if (slot.oaicompat) {
+            res.data["oaicompat_token_ctr"] = slot.n_decoded;
+            res.data["model"] = slot.oaicompat_model;
         }
 
-        queue_results.push_back(res);
-        condition_results.notify_all();
+        queue_results.send(res);
     }
 
-    void send_final_response(llama_client_slot &slot)
-    {
-        std::unique_lock<std::mutex> lock(mutex_results);
-        task_result res;
-        res.id = slot.task_id;
-        res.multitask_id = slot.multitask_id;
-        res.error = false;
-        res.stop = true;
-
-        res.result_json = json
-        {
+    void send_final_response(const server_slot & slot) {
+        server_task_result res;
+        res.id       = slot.id_task;
+        res.id_multi = slot.id_multi;
+        res.error    = false;
+        res.stop     = true;
+        res.data     = json {
             {"content",             !slot.params.stream ? slot.generated_text : ""},
-            {"slot_id",             slot.id},
+            {"id_slot",             slot.id},
             {"stop",                true},
             {"model",               params.model_alias},
             {"tokens_predicted",    slot.n_decoded},
-            {"tokens_evaluated",    slot.num_prompt_tokens},
+            {"tokens_evaluated",    slot.n_prompt_tokens},
             {"generation_settings", get_formated_generation(slot)},
             {"prompt",              slot.prompt},
             {"truncated",           slot.truncated},
@@ -1320,564 +1349,722 @@ struct llama_server_context
             {"timings",             slot.get_formated_timings()}
         };
 
-        if (slot.sparams.n_probs > 0)
-        {
-            std::vector<completion_token_output> probs = {};
-            if (!slot.params.stream && slot.stopped_word)
-            {
+        if (slot.sparams.n_probs > 0) {
+            std::vector<completion_token_output> probs;
+            if (!slot.params.stream && slot.stopped_word) {
                 const std::vector<llama_token> stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false);
-                probs = std::vector<completion_token_output>(slot.generated_token_probs.begin(), slot.generated_token_probs.end() - stop_word_toks.size());
-            }
-            else
-            {
+
                 probs = std::vector<completion_token_output>(
-                                    slot.generated_token_probs.begin(),
-                                    slot.generated_token_probs.end());
+                        slot.generated_token_probs.begin(),
+                        slot.generated_token_probs.end() - stop_word_toks.size());
+            } else {
+                probs = std::vector<completion_token_output>(
+                        slot.generated_token_probs.begin(),
+                        slot.generated_token_probs.end());
             }
-            res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs);
+
+            res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs);
         }
 
-        if (slot.oaicompat)
-        {
-            res.result_json["oaicompat_token_ctr"] = slot.n_decoded;
-            res.result_json["model"] = slot.oaicompat_model;
+        if (slot.oaicompat) {
+            res.data["oaicompat_token_ctr"] = slot.n_decoded;
+            res.data["model"] = slot.oaicompat_model;
         }
 
-        // parent multitask, if any, needs to be updated
-        if (slot.multitask_id != -1)
-        {
-            update_multi_task(slot.multitask_id, slot.task_id, res);
-        }
-
-        queue_results.push_back(res);
-        condition_results.notify_all();
+        queue_results.send(res);
     }
 
-    void send_embedding(llama_client_slot &slot)
-    {
-        std::unique_lock<std::mutex> lock(mutex_results);
-        task_result res;
-        res.id = slot.task_id;
-        res.multitask_id = slot.multitask_id;
-        res.error = false;
-        res.stop = true;
+    void send_embedding(const server_slot & slot, const llama_batch & batch) {
+        server_task_result res;
+        res.id       = slot.id_task;
+        res.id_multi = slot.id_multi;
+        res.error    = false;
+        res.stop     = true;
 
         const int n_embd = llama_n_embd(model);
-        if (!params.embedding)
-        {
-            LOG_WARNING("embedding disabled", {
-                                                  {"params.embedding", params.embedding},
-                                              });
-            res.result_json = json
-            {
-                {"embedding", std::vector<float>(n_embd, 0.0f)},
+
+        std::vector<float> embd_res(n_embd, 0.0f);
+
+        for (int i = 0; i < batch.n_tokens; ++i) {
+            if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) {
+                continue;
+            }
+
+            const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
+            if (embd == NULL) {
+                embd = llama_get_embeddings_ith(ctx, i);
+            }
+
+            if (embd == NULL) {
+                LOG_ERROR("failed to get embeddings", {
+                    {"token",  batch.token [i]},
+                        {"seq_id", batch.seq_id[i][0]}
+                });
+
+                res.data = json {
+                    {"embedding", std::vector<float>(n_embd, 0.0f)},
+                };
+
+                continue;
+            }
+
+            llama_embd_normalize(embd, embd_res.data(), n_embd);
+
+            res.data = json {
+                {"embedding", embd_res},
             };
         }
-        else
-        {
-            const float *data = llama_get_embeddings(ctx);
-            std::vector<float> embedding(data, data + n_embd);
-            res.result_json = json
-            {
-                {"embedding", embedding },
-            };
-        }
-        queue_results.push_back(res);
-        condition_results.notify_all();
+
+        queue_results.send(res);
     }
 
-    int request_completion(json data, bool infill, bool embedding, int multitask_id)
-    {
-        std::unique_lock<std::mutex> lock(mutex_tasks);
-        task_server task;
-        task.id = id_gen++;
-        task.target_id = 0;
-        task.data = std::move(data);
-        task.infill_mode = infill;
-        task.embedding_mode = embedding;
-        task.type = COMPLETION_TASK;
-        task.multitask_id = multitask_id;
+    void request_completion(int id_task, int id_multi, json data, bool infill, bool embedding) {
+        server_task task;
+        task.id        = id_task;
+        task.id_multi  = id_multi;
+        task.id_target = 0;
+        task.data      = std::move(data);
+        task.infill    = infill;
+        task.embedding = embedding;
+        task.type      = SERVER_TASK_TYPE_COMPLETION;
 
         // when a completion task's prompt array is not a singleton, we split it into multiple requests
-        if (task.data.at("prompt").size() > 1)
-        {
-            lock.unlock(); // entering new func scope
-            return split_multiprompt_task(task);
-        }
-
         // otherwise, it's a single-prompt task, we actually queue it
-        queue_tasks.push_back(task);
-        condition_tasks.notify_one();
-        return task.id;
-    }
-
-    task_result next_result(int task_id)
-    {
-        while (true)
-        {
-            std::unique_lock<std::mutex> lock(mutex_results);
-            condition_results.wait(lock, [&]{
-                return !queue_results.empty();
-            });
-
-            for (int i = 0; i < (int) queue_results.size(); i++)
-            {
-                // for now, tasks that have associated parent multitasks just get erased once multitask picks up the result
-                if (queue_results[i].multitask_id == task_id)
-                {
-                    update_multi_task(task_id, queue_results[i].id, queue_results[i]);
-                    queue_results.erase(queue_results.begin() + i);
-                    continue;
-                }
-
-                if (queue_results[i].id == task_id)
-                {
-                    assert(queue_results[i].multitask_id == -1);
-                    task_result res = queue_results[i];
-                    queue_results.erase(queue_results.begin() + i);
-                    return res;
+        // if there's numbers in the prompt array it will be treated as an array of tokens
+        if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1) {
+            bool numbers = false;
+            for (const auto & e : task.data.at("prompt")) {
+                if (e.is_number()) {
+                    numbers = true;
+                    break;
                 }
             }
+
+            // NOTE: split_multiprompt_task() does not handle a mix of strings and numbers,
+            // it will completely stall the server. I don't know where the bug for this is.
+            //
+            // if there are numbers, it needs to be treated like a single prompt,
+            // queue_tasks handles a mix of strings and numbers just fine.
+            if (numbers) {
+                queue_tasks.post(task);
+            } else {
+                split_multiprompt_task(id_task, task);
+            }
+        } else {
+            queue_tasks.post(task);
+        }
+    }
+
+    void request_cancel(int id_task) {
+        server_task task;
+        task.type      = SERVER_TASK_TYPE_CANCEL;
+        task.id_target = id_task;
+
+        queue_tasks.post(task);
+    }
+
+    void split_multiprompt_task(int id_multi, const server_task & multiprompt_task) {
+        const int prompt_count = multiprompt_task.data.at("prompt").size();
+        if (prompt_count <= 1) {
+            send_error(multiprompt_task, "error while handling multiple prompts");
+            return;
         }
 
-        // never reached
-        //return task_result{-1, false, false, {}};
-    }
-
-    // for multiple images processing
-    bool ingest_images(llama_client_slot &slot, int n_batch)
-    {
-        int image_idx = 0;
-
-        while (image_idx < (int) slot.images.size())
-        {
-            slot_image &img = slot.images[image_idx];
-
-            // process prefix prompt
-            for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch)
-            {
-                const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
-                llama_batch batch_view = {
-                    n_tokens,
-                    batch.token    + i,
-                    nullptr,
-                    batch.pos      + i,
-                    batch.n_seq_id + i,
-                    batch.seq_id   + i,
-                    batch.logits   + i,
-                    0, 0, 0, // unused
-                };
-                if (llama_decode(ctx, batch_view))
-                {
-                    LOG_TEE("%s : failed to eval\n", __func__);
-                    return false;
-                }
-            }
-
-            // process image with llm
-            for (int i = 0; i < img.image_tokens; i += n_batch)
-            {
-                int n_eval = img.image_tokens - i;
-                if (n_eval > n_batch)
-                {
-                    n_eval = n_batch;
-                }
-
-                const int n_embd = llama_n_embd(model);
-                llama_batch batch_img = { n_eval, nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, nullptr, slot.n_past, 1, 0, };
-                if (llama_decode(ctx, batch_img))
-                {
-                    LOG_TEE("%s : failed to eval image\n", __func__);
-                    return false;
-                }
-                slot.n_past += n_eval;
-            }
-            image_idx++;
-
-            llama_batch_clear(batch);
-
-            // append prefix of next image
-            const auto json_prompt = (image_idx >= (int) slot.images.size()) ?
-                slot.params.input_suffix : // no more images, then process suffix prompt
-                (json)(slot.images[image_idx].prefix_prompt);
-
-            std::vector<llama_token> append_tokens = tokenize(json_prompt, false); // has next image
-            for (int i = 0; i < (int) append_tokens.size(); ++i)
-            {
-                llama_batch_add(batch, append_tokens[i], slot.n_past, { slot.id }, true);
-                slot.n_past += 1;
-            }
-        }
-
-        return true;
-    }
-
-    void request_cancel(int task_id)
-    {
-        std::unique_lock<std::mutex> lock(mutex_tasks);
-        task_server task;
-        task.id = id_gen++;
-        task.type = CANCEL_TASK;
-        task.target_id = task_id;
-        queue_tasks.push_back(task);
-        condition_tasks.notify_one();
-    }
-
-    int split_multiprompt_task(task_server& multiprompt_task)
-    {
-        int prompt_count = multiprompt_task.data.at("prompt").size();
-        assert(prompt_count > 1);
-
-        int multitask_id = id_gen++;
+        // generate all the ID for subtask
         std::vector<int> subtask_ids(prompt_count);
-        for (int i = 0; i < prompt_count; i++)
-        {
+        for (int i = 0; i < prompt_count; i++) {
+            subtask_ids[i] = queue_tasks.get_new_id();
+        }
+
+        // queue up the multitask so we can track its subtask progression
+        queue_tasks.add_multitask(id_multi, subtask_ids);
+
+        // add subtasks
+        for (int i = 0; i < prompt_count; i++) {
             json subtask_data = multiprompt_task.data;
             subtask_data["prompt"] = subtask_data["prompt"][i];
 
             // subtasks inherit everything else (infill mode, embedding mode, etc.)
-            subtask_ids[i] = request_completion(subtask_data, multiprompt_task.infill_mode, multiprompt_task.embedding_mode, multitask_id);
+            request_completion(subtask_ids[i], id_multi, subtask_data, multiprompt_task.infill, multiprompt_task.embedding);
         }
-
-        // queue up the multitask so we can track its subtask progression
-        add_multi_task(multitask_id, subtask_ids);
-        return multitask_id;
     }
 
-    void process_tasks()
-    {
-        std::unique_lock<std::mutex> lock(mutex_tasks);
-        while (!queue_tasks.empty())
-        {
-            task_server task = queue_tasks.front();
-            queue_tasks.erase(queue_tasks.begin());
-            switch (task.type)
-            {
-                case COMPLETION_TASK: {
-                    llama_client_slot *slot = get_slot(json_value(task.data, "slot_id", -1));
-                    if (slot == nullptr)
-                    {
-                        LOG_TEE("slot unavailable\n");
-                        // send error result
-                        send_error(task, "slot unavailable");
-                        return;
+    void process_single_task(const server_task & task) {
+        switch (task.type) {
+            case SERVER_TASK_TYPE_COMPLETION:
+                {
+                    server_slot * slot = get_slot(json_value(task.data, "id_slot", -1));
+                    if (slot == nullptr) {
+                        // if no slot is available, we defer this task for processing later
+                        LOG_VERBOSE("no slot is available", {{"id_task", task.id}});
+                        queue_tasks.defer(task);
+                        break;
                     }
 
-                    if (task.data.contains("system_prompt"))
-                    {
-                        process_system_prompt_data(task.data["system_prompt"]);
+                    if (task.data.contains("system_prompt")) {
+                        system_prompt_set(task.data["system_prompt"]);
+
+                        for (server_slot & slot : slots) {
+                            slot.n_past    = 0;
+                            slot.n_past_se = 0;
+                        }
                     }
 
                     slot->reset();
 
-                    slot->infill = task.infill_mode;
-                    slot->embedding = task.embedding_mode;
-                    slot->task_id = task.id;
-                    slot->multitask_id = task.multitask_id;
+                    slot->id_task   = task.id;
+                    slot->id_multi  = task.id_multi;
+                    slot->infill    = task.infill;
+                    slot->embedding = task.embedding;
 
-                    if (!launch_slot_with_data(slot, task.data))
-                    {
-                        // send error result
-                        send_error(task, "internal_error");
+                    if (!launch_slot_with_task(*slot, task)) {
+                        LOG_ERROR("error while launching slot", task.data);
                         break;
                     }
                 } break;
-                case CANCEL_TASK: { // release slot linked with the task id
-                    for (auto & slot : slots)
-                    {
-                        if (slot.task_id == task.target_id)
-                        {
+            case SERVER_TASK_TYPE_CANCEL:
+                {
+                    // release slot linked with the task id
+                    for (auto & slot : slots) {
+                        if (slot.id_task == task.id_target) {
                             slot.release();
                             break;
                         }
                     }
                 } break;
-            }
-        }
-
-        // remove finished multitasks from the queue of multitasks, and add the corresponding result to the result queue
-        auto queue_iterator = queue_multitasks.begin();
-        while (queue_iterator != queue_multitasks.end())
-        {
-            if (queue_iterator->subtasks_remaining.empty())
-            {
-                // all subtasks done == multitask is done
-                task_result aggregate_result;
-                aggregate_result.id = queue_iterator->id;
-                aggregate_result.stop = true;
-                aggregate_result.error = false;
-
-                // collect json results into one json result
-                std::vector<json> result_jsons;
-                for (auto& subres : queue_iterator->results)
+            case SERVER_TASK_TYPE_NEXT_RESPONSE:
                 {
-                    result_jsons.push_back(subres.result_json);
-                    aggregate_result.error = aggregate_result.error && subres.error;
-                }
-                aggregate_result.result_json = json{ "results", result_jsons };
+                    // do nothing
+                } break;
+            case SERVER_TASK_TYPE_METRICS:
+                {
+                    json slots_data = json::array();
 
-                std::lock_guard<std::mutex> lock(mutex_results);
-                queue_results.push_back(aggregate_result);
-                condition_results.notify_all();
+                    int n_idle_slots       = 0;
+                    int n_processing_slots = 0;
 
-                queue_iterator = queue_multitasks.erase(queue_iterator);
-            }
-            else
-            {
-                ++queue_iterator;
-            }
+                    for (server_slot & slot : slots) {
+                        json slot_data = get_formated_generation(slot);
+                        slot_data["id"]         = slot.id;
+                        slot_data["id_task"]    = slot.id_task;
+                        slot_data["state"]      = slot.state;
+                        slot_data["prompt"]     = slot.prompt;
+                        slot_data["next_token"] = {
+                            {"has_next_token", slot.has_next_token},
+                            {"n_remain",       slot.n_remaining},
+                            {"n_decoded",      slot.n_decoded},
+                            {"stopped_eos",    slot.stopped_eos},
+                            {"stopped_word",   slot.stopped_word},
+                            {"stopped_limit",  slot.stopped_limit},
+                            {"stopping_word",  slot.stopping_word},
+                        };
+
+                        if (slot_data["state"] == SLOT_STATE_IDLE) {
+                            n_idle_slots++;
+                        } else {
+                            n_processing_slots++;
+                        }
+
+                        slots_data.push_back(slot_data);
+                    }
+                    LOG_INFO("slot data", {
+                        {"id_task",            task.id},
+                        {"n_idle_slots",       n_idle_slots},
+                        {"n_processing_slots", n_processing_slots}
+                    });
+
+                    LOG_VERBOSE("slot data", {
+                        {"id_task",            task.id},
+                        {"n_idle_slots",       n_idle_slots},
+                        {"n_processing_slots", n_processing_slots},
+                        {"slots",              slots_data}
+                    });
+
+                    server_task_result res;
+                    res.id       = task.id;
+                    res.id_multi = task.id_multi;
+                    res.stop     = true;
+                    res.error    = false;
+                    res.data     = {
+                        { "idle",                            n_idle_slots       },
+                        { "processing",                      n_processing_slots },
+                        { "deferred",                        queue_tasks.queue_tasks_deferred.size() },
+                        { "t_start",                         metrics.t_start},
+
+                        { "n_prompt_tokens_processed_total", metrics.n_prompt_tokens_processed_total},
+                        { "t_tokens_generation_total",       metrics.t_tokens_generation_total},
+                        { "n_tokens_predicted_total",        metrics.n_tokens_predicted_total},
+                        { "t_prompt_processing_total",       metrics.t_prompt_processing_total},
+
+                        { "n_prompt_tokens_processed",       metrics.n_prompt_tokens_processed},
+                        { "t_prompt_processing",             metrics.t_prompt_processing},
+                        { "n_tokens_predicted",              metrics.n_tokens_predicted},
+                        { "t_tokens_generation",             metrics.t_tokens_generation},
+
+                        { "kv_cache_tokens_count",           llama_get_kv_cache_token_count(ctx)},
+                        { "kv_cache_used_cells",             llama_get_kv_cache_used_cells(ctx)},
+
+                        { "slots",                           slots_data },
+                    };
+
+                    if (json_value(task.data, "reset_bucket", false)) {
+                        metrics.reset_bucket();
+                    }
+                    queue_results.send(res);
+                } break;
         }
     }
 
-    bool update_slots() {
-        // attend tasks
-        process_tasks();
+    void on_finish_multitask(const server_task_multi & multitask) {
+        // all subtasks done == multitask is done
+        server_task_result result;
+        result.id    = multitask.id;
+        result.stop  = true;
+        result.error = false;
 
-        // update the system prompt wait until all slots are idle state
-        if (system_need_update && all_slots_are_idle)
-        {
-            LOG_TEE("updating system prompt\n");
-            update_system_prompt();
+        // collect json results into one json result
+        std::vector<json> result_jsons;
+        for (const auto & subres : multitask.results) {
+            result_jsons.push_back(subres.data);
+            result.error = result.error && subres.error;
+        }
+        result.data = json {
+            { "results", result_jsons }
+        };
+
+        queue_results.send(result);
+    }
+
+    void update_slots() {
+        if (system_need_update) {
+            system_prompt_update();
         }
 
-        llama_batch_clear(batch);
-
-        if (all_slots_are_idle)
-        {
-            if (system_prompt.empty() && clean_kv_cache)
-            {
-                LOG_TEE("all slots are idle and system prompt is empty, clear the KV cache\n");
-                kv_cache_clear();
-            }
-            std::unique_lock<std::mutex> lock(mutex_tasks);
-            condition_tasks.wait(lock, [&]{
-                return !queue_tasks.empty();
-            });
-        }
-
-        for (llama_client_slot &slot : slots)
-        {
-            if (slot.is_processing() && slot.cache_tokens.size() >= (size_t) slot.n_ctx)
-            {
-                // Shift context
-                const int n_left    = slot.n_past - slot.params.n_keep - 1;
-                const int n_discard = n_left / 2;
-
-                LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard);
-                llama_kv_cache_seq_rm   (ctx, slot.id, slot.params.n_keep + 1            , slot.params.n_keep + n_discard + 1);
-                llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, slot.n_past, -n_discard);
-
-                for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++)
-                {
-                    slot.cache_tokens[i - n_discard] = slot.cache_tokens[i];
-                }
-
-                slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard);
-
-                slot.n_past -= n_discard;
-
-                slot.truncated = true;
-
-                LOG_VERBOSE("context shift", {
-                                                {"n_ctx",  n_ctx},
-                                                {"n_keep", params.n_keep},
-                                                {"n_left", n_left},
-                                            });
-            }
-        }
-
-        // decode any currently ongoing sequences
-        for (auto & slot : slots)
-        {
-            // release the slot
-            if (slot.command == RELEASE)
-            {
-                slot.state = IDLE;
-                slot.command = NONE;
+        // release slots
+        for (auto & slot : slots) {
+            if (slot.command == SLOT_COMMAND_RELEASE) {
+                slot.state       = SLOT_STATE_IDLE;
+                slot.command     = SLOT_COMMAND_NONE;
                 slot.t_last_used = ggml_time_us();
 
-                LOG_TEE("slot %d released (%d tokens in cache)\n", slot.id, (int) slot.cache_tokens.size());
+                LOG_INFO("slot released", {
+                    {"id_slot",         slot.id},
+                    {"id_task",         slot.id_task},
+                    {"n_ctx",           n_ctx},
+                    {"n_past",          slot.n_past},
+                    {"n_system_tokens", system_tokens.size()},
+                    {"n_cache_tokens",  slot.cache_tokens.size()},
+                    {"truncated",       slot.truncated}
+                });
 
-                continue;
+                queue_tasks.notify_slot_changed();
+            }
+        }
+
+        // check if all slots are idle
+        {
+            bool all_idle = true;
+
+            for (auto & slot : slots) {
+                if (slot.state != SLOT_STATE_IDLE || slot.command != SLOT_COMMAND_NONE) {
+                    all_idle = false;
+                    break;
+                }
             }
 
-            if (slot.state == IDLE)
-            {
+            if (all_idle) {
+                LOG_INFO("all slots are idle", {});
+                if (system_prompt.empty() && clean_kv_cache) {
+                    kv_cache_clear();
+                }
+
+                return;
+            }
+        }
+
+        {
+            LOG_VERBOSE("posting NEXT_RESPONSE", {});
+
+            server_task task;
+            task.type      = SERVER_TASK_TYPE_NEXT_RESPONSE;
+            task.id_target = -1;
+
+            queue_tasks.post(task);
+        }
+
+        // apply context-shift if needed
+        // TODO: simplify and improve
+        for (server_slot & slot : slots) {
+            if (slot.ga_n == 1) {
+                if (slot.is_processing() && (int) system_tokens.size() + slot.n_past >= slot.n_ctx - 1) {
+                    // Shift context
+                    const int n_keep    = slot.params.n_keep + add_bos_token;
+                    const int n_left    = (int) system_tokens.size() + slot.n_past - n_keep;
+                    const int n_discard = slot.params.n_discard ? slot.params.n_discard : (n_left / 2);
+
+                    LOG_INFO("slot context shift", {
+                        {"id_slot",         slot.id},
+                        {"id_task",         slot.id_task},
+                        {"n_keep",          n_keep},
+                        {"n_left",          n_left},
+                        {"n_discard",       n_discard},
+                        {"n_ctx",           n_ctx},
+                        {"n_past",          slot.n_past},
+                        {"n_system_tokens", system_tokens.size()},
+                        {"n_cache_tokens",  slot.cache_tokens.size()}
+                    });
+
+                    llama_kv_cache_seq_rm (ctx, slot.id + 1, n_keep            , n_keep + n_discard);
+                    llama_kv_cache_seq_add(ctx, slot.id + 1, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard);
+
+                    if (slot.params.cache_prompt) {
+                        for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) {
+                            slot.cache_tokens[i - n_discard] = slot.cache_tokens[i];
+                        }
+
+                        slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard);
+                    }
+
+                    slot.n_past -= n_discard;
+
+                    slot.truncated = true;
+                }
+            }
+        }
+
+        // start populating the batch for this iteration
+        llama_batch_clear(batch);
+
+        // frist, add sampled tokens from any ongoing sequences
+        for (auto & slot : slots) {
+            if (slot.state == SLOT_STATE_IDLE) {
                 continue;
             }
 
             slot.i_batch = batch.n_tokens;
 
-            llama_batch_add(batch, slot.sampled, system_tokens.size() + slot.n_past, { slot.id }, true);
+            const int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
+
+            // TODO: we always have to take into account the "system_tokens"
+            //       this is not great and needs to be improved somehow
+            llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id + 1 }, true);
 
             slot.n_past += 1;
+
+            if (slot.params.cache_prompt) {
+                slot.cache_tokens.push_back(slot.sampled);
+            }
+
+            LOG_VERBOSE("slot decode token", {
+                {"id_slot",         slot.id},
+                {"id_task",         slot.id_task},
+                {"n_ctx",           n_ctx},
+                {"n_past",          slot.n_past},
+                {"n_system_tokens", system_tokens.size()},
+                {"n_cache_tokens",  slot.cache_tokens.size()},
+                {"truncated",       slot.truncated}
+            });
         }
 
         // process in chunks of params.n_batch
-        int32_t n_batch = params.n_batch;
+        int32_t n_batch  = llama_n_batch(ctx);
+        int32_t n_ubatch = llama_n_ubatch(ctx);
 
-        // assign workload to the slots
-        if (params.cont_batching || batch.n_tokens == 0)
-        {
-            for (auto & slot : slots)
-            {
-                const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get<std::string>().empty()) || !slot.images.empty();
+        // next, batch any pending prompts without exceeding n_batch
+        if (params.cont_batching || batch.n_tokens == 0) {
+            for (auto & slot : slots) {
+                // this slot still has a prompt to be processed
+                if (slot.state == SLOT_STATE_IDLE && slot.command == SLOT_COMMAND_LOAD_PROMPT) {
+                    auto & prompt_tokens = slot.prompt_tokens;
 
-                // empty prompt passed -> release the slot and send empty response
-                if (slot.state == IDLE && slot.command == LOAD_PROMPT && !has_prompt)
-                {
-                    slot.release();
-                    slot.print_timings();
-                    send_final_response(slot);
-                    continue;
-                }
-
-                // need process the prompt
-                if (slot.state == IDLE && slot.command == LOAD_PROMPT)
-                {
-                    slot.state = PROCESSING;
-                    slot.command = NONE;
-                    std::vector<llama_token> prompt_tokens;
-                    slot.t_start_process_prompt = ggml_time_us();
-                    slot.t_start_genereration = 0;
-
-                    if (slot.infill)
-                    {
-                        bool suff_rm_leading_spc = true;
-                        if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1)
-                        {
-                            params.input_suffix.erase(0, 1);
-                            suff_rm_leading_spc = false;
-                        }
-                        auto prefix_tokens = tokenize(slot.params.input_prefix, false);
-                        auto suffix_tokens = tokenize(slot.params.input_suffix, false);
-
-                        const int space_token = 29871; // TODO: this should not be hardcoded
-                        if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) {
-                            suffix_tokens.erase(suffix_tokens.begin());
-                        }
-
-                        prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model));
-                        prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS
-                        prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model));
-                        prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end());
-                        prefix_tokens.push_back(llama_token_middle(model));
-                        prompt_tokens = prefix_tokens;
-                    }
-                    else
-                    {
-                        prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token);  // add BOS if there isn't system prompt
-                    }
-
-                    slot.num_prompt_tokens = prompt_tokens.size();
-
-                    if (slot.params.n_keep < 0)
-                    {
-                        slot.params.n_keep = slot.num_prompt_tokens;
-                    }
-                    slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep);
-
-                    // if input prompt is too big, truncate it
-                    if (slot.num_prompt_tokens >= slot.n_ctx)
-                    {
-                        const int n_left = slot.n_ctx - slot.params.n_keep;
-                        const int n_block_size = n_left / 2;
-                        const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size;
-
-                        std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep);
-                        new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end());
-
-                        LOG_VERBOSE("input truncated", {
-                            {"n_ctx",  slot.n_ctx},
-                            {"n_keep", slot.params.n_keep},
-                            {"n_left", n_left},
-                            {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
+                    // we haven't tokenized the prompt yet - do it now:
+                    if (prompt_tokens.empty()) {
+                        LOG_VERBOSE("tokenizing prompt", {
+                            {"id_slot", slot.id},
+                            {"id_task", slot.id_task}
                         });
-                        slot.truncated = true;
-                        prompt_tokens = new_tokens;
 
-                        slot.num_prompt_tokens = prompt_tokens.size();
-                        GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx);
-                    }
+                        slot.t_start_process_prompt = ggml_time_us();
+                        slot.t_start_generation = 0;
 
-                    if (!slot.params.cache_prompt)
-                    {
-                        llama_sampling_reset(slot.ctx_sampling);
+                        if (slot.infill) {
+                            bool suff_rm_leading_spc = true;
+                            if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) {
+                                params.input_suffix.erase(0, 1);
+                                suff_rm_leading_spc = false;
+                            }
+
+                            auto prefix_tokens = tokenize(slot.params.input_prefix, false);
+                            auto suffix_tokens = tokenize(slot.params.input_suffix, false);
+
+                            const int space_token = 29871; // TODO: this should not be hardcoded
+                            if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) {
+                                suffix_tokens.erase(suffix_tokens.begin());
+                            }
+
+                            prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model));
+                            prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS
+                            prefix_tokens.insert(prefix_tokens.end(),   llama_token_suffix(model));
+                            prefix_tokens.insert(prefix_tokens.end(),   suffix_tokens.begin(), suffix_tokens.end());
+                            prefix_tokens.push_back(llama_token_middle(model));
+                            prompt_tokens = prefix_tokens;
+                        } else {
+                            prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token);  // add BOS if there isn't system prompt
+                        }
 
                         slot.n_past = 0;
-                        slot.num_prompt_tokens_processed = slot.num_prompt_tokens;
-                    }
-                    else
-                    {
-                        // push the prompt into the sampling context (do not apply grammar)
-                        for (auto &token : prompt_tokens)
-                        {
-                            llama_sampling_accept(slot.ctx_sampling, ctx, token, false);
+                        slot.n_prompt_tokens = prompt_tokens.size();
+
+                        LOG_VERBOSE("prompt tokenized", {
+                            {"id_slot",         slot.id},
+                            {"id_task",         slot.id_task},
+                            {"n_ctx",           slot.n_ctx},
+                            {"n_keep",          slot.params.n_keep},
+                            {"n_prompt_tokens", slot.n_prompt_tokens},
+                            {"prompt_tokens",   tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())},
+                        });
+
+                        // empty prompt passed -> release the slot and send empty response
+                        if (prompt_tokens.empty()) {
+                            LOG_INFO("empty prompt - releasing slot", {
+                                {"id_slot", slot.id},
+                                {"id_task", slot.id_task}
+                            });
+
+                            slot.state = SLOT_STATE_PROCESSING;
+                            slot.command = SLOT_COMMAND_NONE;
+                            slot.release();
+                            slot.print_timings();
+                            send_final_response(slot);
+                            continue;
                         }
 
-                        slot.n_past = common_part(slot.cache_tokens, prompt_tokens);
-                        slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past;
+                        if (slot.embedding) {
+                            // this prompt is too large to process - discard it
+                            if (slot.n_prompt_tokens > n_ubatch) {
+                                slot.state = SLOT_STATE_PROCESSING;
+                                slot.command = SLOT_COMMAND_NONE;
+                                slot.release();
+                                slot.print_timings();
+                                send_final_response(slot);
+                                continue;
+                            }
+                        } else {
+                            if (slot.params.n_keep < 0) {
+                                slot.params.n_keep = slot.n_prompt_tokens;
+                            }
+                            slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep);
 
-                        LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed);
+                            // if input prompt is too big, truncate it (if group attention self-extend is disabled)
+                            if (slot.ga_n == 1 && slot.n_prompt_tokens >= slot.n_ctx) {
+                                const int n_left = slot.n_ctx - slot.params.n_keep;
+
+                                const int n_block_size = n_left / 2;
+                                const int erased_blocks = (slot.n_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size;
+
+                                std::vector<llama_token> new_tokens(
+                                        prompt_tokens.begin(),
+                                        prompt_tokens.begin() + slot.params.n_keep);
+
+                                new_tokens.insert(
+                                        new_tokens.end(),
+                                        prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size,
+                                        prompt_tokens.end());
+
+                                prompt_tokens = std::move(new_tokens);
+
+                                slot.truncated = true;
+                                slot.n_prompt_tokens = prompt_tokens.size();
+
+                                LOG_VERBOSE("input truncated", {
+                                    {"id_slot",         slot.id},
+                                    {"id_task",         slot.id_task},
+                                    {"n_ctx",           slot.n_ctx},
+                                    {"n_keep",          slot.params.n_keep},
+                                    {"n_left",          n_left},
+                                    {"n_prompt_tokens", slot.n_prompt_tokens},
+                                    {"prompt_tokens",   tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())},
+                                });
+
+                                GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx);
+                            }
+
+                            llama_sampling_reset(slot.ctx_sampling);
+
+                            if (!slot.params.cache_prompt) {
+                                slot.n_past_se = 0;
+                                slot.ga_i      = 0;
+                            } else {
+                                GGML_ASSERT(slot.ga_n == 1);
+
+                                // reuse any previously computed tokens that are common with the new prompt
+                                slot.n_past = common_part(slot.cache_tokens, prompt_tokens);
+
+                                // push the prompt into the sampling context (do not apply grammar)
+                                for (int i = 0; i < slot.n_past; ++i) {
+                                    llama_sampling_accept(slot.ctx_sampling, ctx, slot.cache_tokens[i], false);
+                                }
+                            }
+                        }
+
+                        if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0) {
+                            // we have to evaluate at least 1 token to generate logits.
+                            LOG_INFO("we have to evaluate at least 1 token to generate logits", {
+                                { "id_slot", slot.id },
+                                { "id_task", slot.id_task }
+                            });
+
+                            slot.n_past--;
+                            if (slot.ga_i > 0) {
+                                slot.n_past_se--;
+                            }
+                        }
+
+                        slot.n_prompt_tokens_processed = 0;
                     }
 
-                    LOG_TEE("slot %d : kv cache rm - [%d, end)\n", slot.id, (int) system_tokens.size() + slot.n_past);
-
-                    llama_kv_cache_seq_rm(ctx, slot.id, system_tokens.size() + slot.n_past, -1);
-
-                    slot.cache_tokens = prompt_tokens;
-
-                    if (slot.n_past == slot.num_prompt_tokens)
-                    {
-                        // we have to evaluate at least 1 token to generate logits.
-                        LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id);
-                        slot.n_past--;
+                    if (slot.embedding) {
+                        // cannot fit the prompt in the current batch - will try next iter
+                        if (batch.n_tokens + slot.n_prompt_tokens > n_batch) {
+                            continue;
+                        }
                     }
 
-                    LOG_VERBOSE("prompt ingested", {
-                                                    {"n_past", slot.n_past},
-                                                    {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)},
-                                                    {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())},
-                                                });
+                    // keep only the common part
+                    int p0 = (int) system_tokens.size() + slot.n_past;
+                    if (!llama_kv_cache_seq_rm(ctx, slot.id + 1, p0, -1)) {
+                        // could not partially delete (likely using a non-Transformer model)
+                        llama_kv_cache_seq_rm(ctx, slot.id + 1, -1, -1);
 
-                    const bool has_images = process_images(slot);
+                        p0 = (int) system_tokens.size();
+                        if (p0 != 0) {
+                            // copy over the system prompt when there is one
+                            llama_kv_cache_seq_cp(ctx, 0, slot.id + 1, -1, -1);
+                        }
 
-                    // process the prefix of first image
-                    std::vector<llama_token> prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens;
-                    for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past)
-                    {
-                       llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot.n_past, { slot.id }, false);
+                        // there is no common part left (except for the system prompt)
+                        slot.n_past = 0;
+                        slot.n_past_se = 0;
+                        slot.ga_i = 0;
+                        // TODO: is the system prompt ever in the sampling context?
+                        llama_sampling_reset(slot.ctx_sampling);
                     }
 
-                    if (has_images && !ingest_images(slot, n_batch))
-                    {
-                        LOG_TEE("failed processing images\n");
-                        return false;
+                    // remove the non-common part from the cache
+                    slot.cache_tokens.resize(slot.n_past);
+
+                    LOG_INFO("kv cache rm [p0, end)", {
+                        { "id_slot", slot.id },
+                        { "id_task", slot.id_task },
+                        { "p0",      p0 }
+                    });
+
+                    int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
+
+                    int32_t ga_i = slot.ga_i;
+                    int32_t ga_n = slot.ga_n;
+                    int32_t ga_w = slot.ga_w;
+
+                    // add prompt tokens for processing in the current batch
+                    // TODO: the self-extend stuff here is a mess - simplify and/or abstract it somehow
+                    for (; slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch; ++slot.n_past) {
+                        if (slot.ga_n != 1) {
+                            while (slot_npast >= ga_i + ga_w) {
+                                const int bd = (ga_w/ga_n)*(ga_n - 1);
+                                slot_npast -= bd;
+                                ga_i += ga_w/ga_n;
+                            }
+                        }
+
+                        llama_batch_add(batch, prompt_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id + 1 }, false);
+
+                        if (slot.params.cache_prompt) {
+                            slot.cache_tokens.push_back(prompt_tokens[slot.n_past]);
+                        }
+
+                        slot.n_prompt_tokens_processed++;
+                        slot_npast++;
                     }
 
-                    // extract the logits only for the last token
-                    if (batch.n_tokens > 0)
-                    {
+                    LOG_VERBOSE("prompt processing progress", {
+                        {"id_slot",  slot.id},
+                        {"n_past",   slot.n_past},
+                        {"n_ctx",    n_ctx},
+                        {"n_tokens", batch.n_tokens},
+                        {"progress", (float) slot.n_prompt_tokens_processed / slot.n_prompt_tokens},
+                    });
+
+                    // entire prompt has been processed - start decoding new tokens
+                    if (slot.n_past == slot.n_prompt_tokens) {
+                        slot.state   = SLOT_STATE_PROCESSING;
+                        slot.command = SLOT_COMMAND_NONE;
+
+                        GGML_ASSERT(batch.n_tokens > 0);
+
+                        // extract the logits only for the last token
                         batch.logits[batch.n_tokens - 1] = true;
-                    }
 
-                    slot.n_decoded = 0;
-                    slot.i_batch   = batch.n_tokens - 1;
+                        slot.n_decoded = 0;
+                        slot.i_batch   = batch.n_tokens - 1;
+
+                        LOG_VERBOSE("prompt done", {
+                            {"id_slot",  slot.id},
+                            {"n_past",   slot.n_past},
+                            {"n_ctx",    n_ctx},
+                            {"n_tokens", batch.n_tokens},
+                        });
+                    }
+                }
+
+                if (batch.n_tokens >= n_batch) {
+                    break;
                 }
             }
         }
 
-        if (batch.n_tokens == 0)
-        {
-            all_slots_are_idle = true;
-            return true;
+        if (batch.n_tokens == 0) {
+            LOG_VERBOSE("no tokens to decode", {});
+            return;
         }
 
-        for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch)
-        {
-            const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
-            llama_batch batch_view =
-            {
+        LOG_VERBOSE("decoding batch", {
+            {"n_tokens", batch.n_tokens},
+        });
+
+        // process the created batch of tokens
+        for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
+            const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);
+
+            for (auto & slot : slots) {
+                if (slot.ga_n != 1) {
+                    // context extension via Self-Extend
+                    // TODO: simplify and/or abstract this
+                    while (slot.n_past_se >= slot.ga_i + slot.ga_w) {
+                        const int ib = (slot.ga_n * slot.ga_i) / slot.ga_w;
+                        const int bd = (slot.ga_w / slot.ga_n) * (slot.ga_n - 1);
+                        const int dd = (slot.ga_w / slot.ga_n) - ib * bd - slot.ga_w;
+
+                        LOG_TEE("\n");
+                        LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i, slot.n_past_se, ib * bd, slot.ga_i + ib * bd, slot.n_past_se + ib * bd);
+                        LOG_TEE("div:   [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n);
+                        LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd);
+
+                        llama_kv_cache_seq_add(ctx, slot.id + 1, slot.ga_i, slot.n_past_se, ib * bd);
+                        llama_kv_cache_seq_div(ctx, slot.id + 1, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n);
+                        llama_kv_cache_seq_add(ctx, slot.id + 1, slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd);
+
+                        slot.n_past_se -= bd;
+
+                        slot.ga_i += slot.ga_w / slot.ga_n;
+
+                        LOG_TEE("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past_se + bd, slot.n_past_se, slot.ga_i);
+                    }
+
+                    slot.n_past_se += n_tokens;
+                }
+            }
+
+            llama_batch batch_view = {
                 n_tokens,
                 batch.token    + i,
                 nullptr,
@@ -1889,13 +2076,18 @@ struct llama_server_context
             };
 
             const int ret = llama_decode(ctx, batch_view);
-            if (ret != 0)
-            {
-                if (n_batch == 1 || ret < 0)
-                {
+
+            if (ret != 0) {
+                if (n_batch == 1 || ret < 0) {
                     // if you get here, it means the KV cache is full - try increasing it via the context size
                     LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret);
-                    return false;
+                    for (auto & slot : slots) {
+                        slot.state = SLOT_STATE_PROCESSING;
+                        slot.command = SLOT_COMMAND_NONE;
+                        slot.release();
+                        send_error(slot, "Input prompt is too big compared to KV size. Please try increasing KV size.");
+                    }
+                    break; // break loop of n_batch
                 }
 
                 LOG_TEE("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2);
@@ -1903,23 +2095,21 @@ struct llama_server_context
                 // retry with half the batch size to try to find a free slot in the KV cache
                 n_batch /= 2;
                 i -= n_batch;
-                continue;
+
+                continue; // continue loop of n_batch
             }
 
-            for (auto & slot : slots)
-            {
-                if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens))
-                {
-                    continue;
+            for (auto & slot : slots) {
+                if (slot.state != SLOT_STATE_PROCESSING || slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) {
+                    continue; // continue loop of slots
                 }
 
                 // prompt evaluated for embedding
-                if (slot.embedding)
-                {
-                    send_embedding(slot);
+                if (slot.embedding) {
+                    send_embedding(slot, batch_view);
                     slot.release();
                     slot.i_batch = -1;
-                    return true;
+                    continue; // continue loop of slots
                 }
 
                 completion_token_output result;
@@ -1928,44 +2118,55 @@ struct llama_server_context
                 llama_sampling_accept(slot.ctx_sampling, ctx, id, true);
 
                 slot.n_decoded += 1;
-                if (slot.n_decoded == 1)
-                {
-                    slot.t_start_genereration = ggml_time_us();
-                    slot.t_prompt_processing = (slot.t_start_genereration - slot.t_start_process_prompt) / 1e3;
+                if (slot.n_decoded == 1) {
+                    slot.t_start_generation = ggml_time_us();
+                    slot.t_prompt_processing = (slot.t_start_generation - slot.t_start_process_prompt) / 1e3;
+                    metrics.on_prompt_eval(slot);
                 }
 
                 llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false };
                 result.tok = id;
 
                 const int32_t n_probs = slot.sparams.n_probs;
-                if (slot.sparams.temp <= 0 && n_probs > 0)
-                {
+                if (slot.sparams.temp <= 0 && n_probs > 0) {
                     // for llama_sample_token_greedy we need to sort candidates
                     llama_sample_softmax(ctx, &cur_p);
                 }
 
-                for (size_t i = 0; i < std::min(cur_p.size, (size_t)n_probs); ++i)
-                {
-                    result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p});
+                for (size_t i = 0; i < std::min(cur_p.size, (size_t) n_probs); ++i) {
+                    result.probs.push_back({
+                        cur_p.data[i].id,
+                        cur_p.data[i].p
+                    });
                 }
 
-                if (!process_token(result, slot))
-                {
+                if (!process_token(result, slot)) {
                     slot.release();
                     slot.print_timings();
                     send_final_response(slot);
+                    metrics.on_prediction(slot);
                 }
 
                 slot.i_batch = -1;
             }
         }
-        return true;
+
+        LOG_VERBOSE("run slots completed", {});
+    }
+
+    json model_meta() const {
+        return json {
+            {"vocab_type",  llama_vocab_type    (model)},
+            {"n_vocab",     llama_n_vocab       (model)},
+            {"n_ctx_train", llama_n_ctx_train   (model)},
+            {"n_embd",      llama_n_embd        (model)},
+            {"n_params",    llama_model_n_params(model)},
+            {"size",        llama_model_size    (model)},
+        };
     }
 };
 
-static void server_print_usage(const char *argv0, const gpt_params &params,
-                               const server_params &sparams)
-{
+static void server_print_usage(const char * argv0, const gpt_params & params, const server_params & sparams) {
     printf("usage: %s [options]\n", argv0);
     printf("\n");
     printf("options:\n");
@@ -1973,6 +2174,7 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     printf("  -v, --verbose             verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
     printf("  -t N, --threads N         number of threads to use during computation (default: %d)\n", params.n_threads);
     printf("  -tb N, --threads-batch N  number of threads to use during batch and prompt processing (default: same as --threads)\n");
+    printf("  --threads-http N          number of threads in the http server pool to process requests (default: max(hardware concurrency - 1, --parallel N + 2))\n");
     printf("  -c N, --ctx-size N        size of the prompt context (default: %d)\n", params.n_ctx);
     printf("  --rope-scaling {none,linear,yarn}\n");
     printf("                            RoPE frequency scaling method, defaults to linear unless specified by the model\n");
@@ -1982,383 +2184,439 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     printf("  --yarn-attn-factor N      YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n");
     printf("  --yarn-beta-slow N        YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow);
     printf("  --yarn-beta-fast N        YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast);
-    printf("  -b N, --batch-size N      batch size for prompt processing (default: %d)\n", params.n_batch);
-    printf("  --memory-f32              use f32 instead of f16 for memory key+value (default: disabled)\n");
-    printf("                            not recommended: doubles context memory required and no measurable increase in quality\n");
-    if (llama_mlock_supported())
-    {
-        printf("  --mlock               force system to keep model in RAM rather than swapping or compressing\n");
+    printf("  --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\n");
+    printf("  -dt N, --defrag-thold N\n");
+    printf("                            KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
+    printf("  -b N, --batch-size N      logical maximum batch size (default: %d)\n", params.n_batch);
+    printf("  -ub N, --ubatch-size N    physical maximum batch size (default: %d)\n", params.n_ubatch);
+    if (llama_supports_mlock()) {
+        printf("  --mlock                   force system to keep model in RAM rather than swapping or compressing\n");
     }
-    if (llama_mmap_supported())
-    {
-        printf("  --no-mmap             do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
+    if (llama_supports_mmap()) {
+        printf("  --no-mmap                 do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
+    }
+    printf("  --numa TYPE               attempt optimizations that help on some NUMA systems\n");
+    printf("                              - distribute: spread execution evenly over all nodes\n");
+    printf("                              - isolate: only spawn threads on CPUs on the node that execution started on\n");
+    printf("                              - numactl: use the CPU map provided my numactl\n");
+    if (llama_supports_gpu_offload()) {
+        printf("  -ngl N, --n-gpu-layers N\n");
+        printf("                            number of layers to store in VRAM\n");
+        printf("  -sm SPLIT_MODE, --split-mode SPLIT_MODE\n");
+        printf("                            how to split the model across multiple GPUs, one of:\n");
+        printf("                              - none: use one GPU only\n");
+        printf("                              - layer (default): split layers and KV across GPUs\n");
+        printf("                              - row: split rows across GPUs\n");
+        printf("  -ts SPLIT --tensor-split SPLIT\n");
+        printf("                            fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n");
+        printf("  -mg i, --main-gpu i       the GPU to use for the model (with split-mode = none),\n");
+        printf("                            or for intermediate results and KV (with split-mode = row)\n");
+        printf("  -nkvo, --no-kv-offload\n");
+        printf("                            disable KV offload\n");
     }
-    printf("  --numa                attempt optimizations that help on some NUMA systems\n");
-#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
-    printf("  -ngl N, --n-gpu-layers N\n");
-    printf("                        number of layers to store in VRAM\n");
-    printf("  -ts SPLIT --tensor-split SPLIT\n");
-    printf("                        how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
-    printf("  -mg i, --main-gpu i   the GPU to use for scratch and small tensors\n");
-    printf("  -nommq, --no-mul-mat-q\n");
-    printf("                        use cuBLAS instead of custom mul_mat_q CUDA kernels.\n");
-    printf("                        Not recommended since this is both slower and uses more VRAM.\n");
-#endif
     printf("  -m FNAME, --model FNAME\n");
-    printf("                        model path (default: %s)\n", params.model.c_str());
+    printf("                            model path (default: %s)\n", params.model.c_str());
+    printf("  -mu MODEL_URL, --model-url MODEL_URL\n");
+    printf("                            model download url (default: unused)\n");
+    printf("  -hfr REPO, --hf-repo REPO\n");
+    printf("                            Hugging Face model repository (default: unused)\n");
+    printf("  -hff FILE, --hf-file FILE\n");
+    printf("                            Hugging Face model file (default: unused)\n");
     printf("  -a ALIAS, --alias ALIAS\n");
-    printf("                        set an alias for the model, will be added as `model` field in completion response\n");
-    printf("  --lora FNAME          apply LoRA adapter (implies --no-mmap)\n");
-    printf("  --lora-base FNAME     optional model to use as a base for the layers modified by the LoRA adapter\n");
-    printf("  --host                ip address to listen (default  (default: %s)\n", sparams.hostname.c_str());
-    printf("  --port PORT           port to listen (default  (default: %d)\n", sparams.port);
-    printf("  --path PUBLIC_PATH    path from which to serve static files (default %s)\n", sparams.public_path.c_str());
-    printf("  --api-key API_KEY     optional api key to enhance server security. If set, requests must include this key for access.\n");
-    printf("  -to N, --timeout N    server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
-    printf("  --embedding           enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
-    printf("  -np N, --parallel N   number of slots for process requests (default: %d)\n", params.n_parallel);
-    printf("  -cb, --cont-batching  enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
-    printf("    -spf FNAME, --system-prompt-file FNAME\n");
-    printf("                        Set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n");
-    printf("  --mmproj MMPROJ_FILE  path to a multimodal projector file for LLaVA.\n");
-    printf("  --log-disable         disables logging to a file.\n");
+    printf("                            set an alias for the model, will be added as `model` field in completion response\n");
+    printf("  --lora FNAME              apply LoRA adapter (implies --no-mmap)\n");
+    printf("  --lora-base FNAME         optional model to use as a base for the layers modified by the LoRA adapter\n");
+    printf("  --host                    ip address to listen (default  (default: %s)\n", sparams.hostname.c_str());
+    printf("  --port PORT               port to listen (default  (default: %d)\n", sparams.port);
+    printf("  --path PUBLIC_PATH        path from which to serve static files (default: disabled)\n");
+    printf("  --api-key API_KEY         optional api key to enhance server security. If set, requests must include this key for access.\n");
+    printf("  --api-key-file FNAME      path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n");
+#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
+    printf("  --ssl-key-file FNAME      path to file a PEM-encoded SSL private key\n");
+    printf("  --ssl-cert-file FNAME     path to file a PEM-encoded SSL certificate\n");
+#endif
+    printf("  -to N, --timeout N        server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
+    printf("  --embeddings              enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
+    printf("  -np N, --parallel N       number of slots for process requests (default: %d)\n", params.n_parallel);
+    printf("  -cb, --cont-batching      enable continuous batching (a.k.a dynamic batching) (default: enabled)\n");
+    printf("  -spf FNAME, --system-prompt-file FNAME\n");
+    printf("                            set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n");
+    printf("  -ctk TYPE, --cache-type-k TYPE\n");
+    printf("                            KV cache data type for K (default: f16)\n");
+    printf("  -ctv TYPE, --cache-type-v TYPE\n");
+    printf("                            KV cache data type for V (default: f16)\n");
+    printf("  --log-format              log output format: json or text (default: json)\n");
+    printf("  --log-disable             disables logging to a file.\n");
+    printf("  --slots-endpoint-disable  disables slots monitoring endpoint.\n");
+    printf("  --metrics                 enable prometheus compatible metrics endpoint (default: %s).\n", sparams.metrics_endpoint ? "enabled" : "disabled");
     printf("\n");
+    printf("  -n, --n-predict           maximum tokens to predict (default: %d)\n", params.n_predict);
     printf("  --override-kv KEY=TYPE:VALUE\n");
-    printf("                        advanced option to override model metadata by key. may be specified multiple times.\n");
-    printf("                        types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
+    printf("                            advanced option to override model metadata by key. may be specified multiple times.\n");
+    printf("                            types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
+    printf("  -gan N, --grp-attn-n N    set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`\n");
+    printf("  -gaw N, --grp-attn-w N    set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n");
+    printf("  --chat-template JINJA_TEMPLATE\n");
+    printf("                            set custom jinja chat template (default: template taken from model's metadata)\n");
+    printf("                            only commonly used templates are accepted:\n");
+    printf("                            https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template\n");
     printf("\n");
 }
 
-static void server_params_parse(int argc, char **argv, server_params &sparams,
-                                gpt_params &params, llama_server_context& llama)
-{
-    gpt_params default_params;
+static void server_params_parse(int argc, char ** argv, server_params & sparams, gpt_params & params) {
+    gpt_params    default_params;
     server_params default_sparams;
+
     std::string arg;
     bool invalid_param = false;
 
-    for (int i = 1; i < argc; i++)
-    {
+    for (int i = 1; i < argc; i++) {
         arg = argv[i];
-        if (arg == "--port")
-        {
-            if (++i >= argc)
-            {
+        if (arg == "--port") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             sparams.port = std::stoi(argv[i]);
-        }
-        else if (arg == "--host")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--host") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             sparams.hostname = argv[i];
-        }
-        else if (arg == "--path")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--path") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             sparams.public_path = argv[i];
-        }
-        else if (arg == "--api-key")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--api-key") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            sparams.api_key = argv[i];
+            sparams.api_keys.push_back(argv[i]);
+        } else if (arg == "--api-key-file") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            std::ifstream key_file(argv[i]);
+            if (!key_file) {
+                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+                invalid_param = true;
+                break;
+            }
+            std::string key;
+            while (std::getline(key_file, key)) {
+               if (key.size() > 0) {
+                   sparams.api_keys.push_back(key);
+               }
+            }
+            key_file.close();
+
         }
-        else if (arg == "--timeout" || arg == "-to")
-        {
-            if (++i >= argc)
-            {
+#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
+        else if (arg == "--ssl-key-file") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            sparams.ssl_key_file = argv[i];
+        } else if (arg == "--ssl-cert-file") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            sparams.ssl_cert_file = argv[i];
+        }
+#endif
+        else if (arg == "--timeout" || arg == "-to") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             sparams.read_timeout = std::stoi(argv[i]);
             sparams.write_timeout = std::stoi(argv[i]);
-        }
-        else if (arg == "-m" || arg == "--model")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "-m" || arg == "--model") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.model = argv[i];
-        }
-        else if (arg == "-a" || arg == "--alias")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "-mu" || arg == "--model-url") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.model_url = argv[i];
+        } else if (arg == "-hfr" || arg == "--hf-repo") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.hf_repo = argv[i];
+        } else if (arg == "-hff" || arg == "--hf-file") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.hf_file = argv[i];
+        } else if (arg == "-a" || arg == "--alias") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.model_alias = argv[i];
-        }
-        else if (arg == "-h" || arg == "--help")
-        {
+        } else if (arg == "-h" || arg == "--help") {
             server_print_usage(argv[0], default_params, default_sparams);
             exit(0);
-        }
-        else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.n_ctx = std::stoi(argv[i]);
-        }
-        else if (arg == "--rope-scaling")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--rope-scaling") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             std::string value(argv[i]);
-            /**/ if (value == "none")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; }
-            else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; }
-            else if (value == "yarn")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; }
+            /**/ if (value == "none")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
+            else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
+            else if (value == "yarn")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
             else { invalid_param = true; break; }
-        }
-        else if (arg == "--rope-freq-base")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--rope-freq-base") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.rope_freq_base = std::stof(argv[i]);
-        }
-        else if (arg == "--rope-freq-scale")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--rope-freq-scale") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.rope_freq_scale = std::stof(argv[i]);
-        }
-        else if (arg == "--yarn-ext-factor")
-        {
+        } else if (arg == "--yarn-ext-factor") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.yarn_ext_factor = std::stof(argv[i]);
         }
-        else if (arg == "--yarn-attn-factor")
-        {
+        else if (arg == "--yarn-attn-factor") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.yarn_attn_factor = std::stof(argv[i]);
-        }
-        else if (arg == "--yarn-beta-fast")
-        {
+        } else if (arg == "--yarn-beta-fast") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.yarn_beta_fast = std::stof(argv[i]);
-        }
-        else if (arg == "--yarn-beta-slow")
-        {
+        } else if (arg == "--yarn-beta-slow") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.yarn_beta_slow = std::stof(argv[i]);
-        }
-        else if (arg == "--threads" || arg == "-t")
-        {
+        } else if (arg == "--pooling") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            std::string value(argv[i]);
+            /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
+            else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
+            else if (value == "cls")  { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
+            else { invalid_param = true; break; }
+        } else if (arg == "--defrag-thold" || arg == "-dt") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.defrag_thold = std::stof(argv[i]);
+        } else if (arg == "--threads" || arg == "-t") {
             if (++i >= argc)
             {
                 invalid_param = true;
                 break;
             }
             params.n_threads = std::stoi(argv[i]);
-        }
-        else if (arg == "--threads-batch" || arg == "-tb")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--grp-attn-n" || arg == "-gan") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+
+            params.grp_attn_n = std::stoi(argv[i]);
+        } else if (arg == "--grp-attn-w" || arg == "-gaw") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+
+            params.grp_attn_w = std::stoi(argv[i]);
+        } else if (arg == "--threads-batch" || arg == "-tb") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.n_threads_batch = std::stoi(argv[i]);
-        }
-        else if (arg == "-b" || arg == "--batch-size")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--threads-http") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            sparams.n_threads_http = std::stoi(argv[i]);
+        } else if (arg == "-b" || arg == "--batch-size") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.n_batch = std::stoi(argv[i]);
-            params.n_batch = std::min(512, params.n_batch);
-        }
-        else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "-ub" || arg == "--ubatch-size") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
-            params.n_gpu_layers = std::stoi(argv[i]);
-#else
-            LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. "
-                        "See main README.md for information on enabling GPU BLAS support",
-                        {{"n_gpu_layers", params.n_gpu_layers}});
-#endif
-        }
-        else if (arg == "--tensor-split" || arg == "-ts")
-        {
-            if (++i >= argc)
-            {
+            params.n_ubatch = std::stoi(argv[i]);
+        } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-#ifdef GGML_USE_CUBLAS
+            if (llama_supports_gpu_offload()) {
+                params.n_gpu_layers = std::stoi(argv[i]);
+            } else {
+                LOG_WARNING(
+                    "Not compiled with GPU offload support, --n-gpu-layers option will be ignored. "
+                    "See main README.md for information on enabling GPU BLAS support",
+                    {{"n_gpu_layers", params.n_gpu_layers}});
+            }
+        } else if (arg == "-nkvo" || arg == "--no-kv-offload") {
+            params.no_kv_offload = true;
+        } else if (arg == "--split-mode" || arg == "-sm") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            std::string arg_next = argv[i];
+            if (arg_next == "none") {
+                params.split_mode = LLAMA_SPLIT_MODE_NONE;
+            } else if (arg_next == "layer") {
+                params.split_mode = LLAMA_SPLIT_MODE_LAYER;
+            } else if (arg_next == "row") {
+                params.split_mode = LLAMA_SPLIT_MODE_ROW;
+            } else {
+                invalid_param = true;
+                break;
+            }
+#ifndef GGML_USE_CUDA
+            fprintf(stderr, "warning: llama.cpp was compiled without CUDA. Setting the split mode has no effect.\n");
+#endif // GGML_USE_CUDA
+        } else if (arg == "--tensor-split" || arg == "-ts") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)
             std::string arg_next = argv[i];
 
             // split string by , and /
             const std::regex regex{R"([,/]+)"};
             std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
             std::vector<std::string> split_arg{it, {}};
-            GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
+            GGML_ASSERT(split_arg.size() <= llama_max_devices());
 
-            for (size_t i_device = 0; i_device < LLAMA_MAX_DEVICES; ++i_device)
-            {
-                if (i_device < split_arg.size())
-                {
+            for (size_t i_device = 0; i_device < llama_max_devices(); ++i_device) {
+                if (i_device < split_arg.size()) {
                     params.tensor_split[i_device] = std::stof(split_arg[i_device]);
-                }
-                else
-                {
+                } else {
                     params.tensor_split[i_device] = 0.0f;
                 }
             }
 #else
-            LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {});
-#endif // GGML_USE_CUBLAS
-        }
-        else if (arg == "--no-mul-mat-q" || arg == "-nommq")
-        {
-#ifdef GGML_USE_CUBLAS
-            params.mul_mat_q = false;
-#else
-            LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n", {});
-#endif // GGML_USE_CUBLAS
-        }
-        else if (arg == "--main-gpu" || arg == "-mg")
-        {
-            if (++i >= argc)
-            {
+            LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a tensor split.\n", {});
+#endif // GGML_USE_CUDA
+        } else if (arg == "--main-gpu" || arg == "-mg") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-#ifdef GGML_USE_CUBLAS
+#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)
             params.main_gpu = std::stoi(argv[i]);
 #else
-            LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {});
+            LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a main GPU.", {});
 #endif
-        }
-        else if (arg == "--lora")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--lora") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f));
+            params.lora_adapter.emplace_back(argv[i], 1.0f);
             params.use_mmap = false;
-        }
-        else if (arg == "--lora-scaled")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--lora-scaled") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             const char * lora_adapter = argv[i];
-            if (++i >= argc)
-            {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params.lora_adapter.push_back(std::make_tuple(lora_adapter, std::stof(argv[i])));
+            params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i]));
             params.use_mmap = false;
-        }
-        else if (arg == "--lora-base")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "--lora-base") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.lora_base = argv[i];
-        }
-        else if (arg == "-v" || arg == "--verbose")
-        {
+        } else if (arg == "-v" || arg == "--verbose") {
 #if SERVER_VERBOSE != 1
             LOG_WARNING("server.cpp is not built with verbose logging.", {});
 #else
             server_verbose = true;
 #endif
-        }
-        else if (arg == "--mlock")
-        {
+        } else if (arg == "--mlock") {
             params.use_mlock = true;
-        }
-        else if (arg == "--no-mmap")
-        {
+        } else if (arg == "--no-mmap") {
             params.use_mmap = false;
-        }
-        else if (arg == "--numa")
-        {
-            params.numa = true;
-        }
-        else if (arg == "--embedding")
-        {
+        } else if (arg == "--numa") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            } else {
+                std::string value(argv[i]);
+                /**/ if (value == "distribute" || value == "" ) { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
+                else if (value == "isolate")                    { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
+                else if (value == "numactl")                    { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
+                else { invalid_param = true; break; }
+            }
+        } else if (arg == "--embedding" || arg == "--embeddings") {
             params.embedding = true;
-        }
-        else if (arg == "-cb" || arg == "--cont-batching")
-        {
+        } else if (arg == "-cb" || arg == "--cont-batching") {
             params.cont_batching = true;
-        }
-        else if (arg == "-np" || arg == "--parallel")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "-np" || arg == "--parallel") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.n_parallel = std::stoi(argv[i]);
-        } else if (arg == "-n" || arg == "--n-predict")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "-n" || arg == "--n-predict") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
             params.n_predict = std::stoi(argv[i]);
-        } else if (arg == "-spf" || arg == "--system-prompt-file")
-        {
-            if (++i >= argc)
-            {
+        } else if (arg == "-spf" || arg == "--system-prompt-file") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
@@ -2368,30 +2626,50 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
                 invalid_param = true;
                 break;
             }
-            std::string systm_content;
+            std::string system_prompt;
             std::copy(
                 std::istreambuf_iterator<char>(file),
                 std::istreambuf_iterator<char>(),
-                std::back_inserter(systm_content)
+                std::back_inserter(system_prompt)
             );
-            llama.process_system_prompt_data(json::parse(systm_content));
-        }
-        else if(arg == "--mmproj")
-        {
-            if (++i >= argc)
-            {
+            sparams.system_prompt = system_prompt;
+        } else if (arg == "-ctk" || arg == "--cache-type-k") {
+            params.cache_type_k = argv[++i];
+        } else if (arg == "-ctv" || arg == "--cache-type-v") {
+            params.cache_type_v = argv[++i];
+        } else if (arg == "--log-format") {
+            if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params.mmproj = argv[i];
-        }
-        else if (arg == "--log-disable")
-        {
+            if (std::strcmp(argv[i], "json") == 0) {
+                server_log_json = true;
+            } else if (std::strcmp(argv[i], "text") == 0) {
+                server_log_json = false;
+            } else {
+                invalid_param = true;
+                break;
+            }
+        } else if (arg == "--log-disable") {
             log_set_target(stdout);
             LOG_INFO("logging to file is disabled.", {});
-        }
-        else if (arg == "--override-kv")
-        {
+        } else if (arg == "--slots-endpoint-disable") {
+            sparams.slots_endpoint = false;
+        } else if (arg == "--metrics") {
+            sparams.metrics_endpoint = true;
+        } else if (arg == "--chat-template") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            if (!verify_custom_template(argv[i])) {
+                fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]);
+                fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n");
+                invalid_param = true;
+                break;
+            }
+            sparams.chat_template = argv[i];
+        } else if (arg == "--override-kv") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
@@ -2402,21 +2680,22 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
                 invalid_param = true;
                 break;
             }
+
             struct llama_model_kv_override kvo;
             std::strncpy(kvo.key, argv[i], sep - argv[i]);
             kvo.key[sep - argv[i]] = 0;
             sep++;
             if (strncmp(sep, "int:", 4) == 0) {
                 sep += 4;
-                kvo.tag = LLAMA_KV_OVERRIDE_INT;
+                kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
                 kvo.int_value = std::atol(sep);
             } else if (strncmp(sep, "float:", 6) == 0) {
                 sep += 6;
-                kvo.tag = LLAMA_KV_OVERRIDE_FLOAT;
+                kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
                 kvo.float_value = std::atof(sep);
             } else if (strncmp(sep, "bool:", 5) == 0) {
                 sep += 5;
-                kvo.tag = LLAMA_KV_OVERRIDE_BOOL;
+                kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
                 if (std::strcmp(sep, "true") == 0) {
                     kvo.bool_value = true;
                 } else if (std::strcmp(sep, "false") == 0) {
@@ -2432,820 +2711,877 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
                 break;
             }
             params.kv_overrides.push_back(kvo);
-        }
-        else
-        {
+        } else {
             fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
             server_print_usage(argv[0], default_params, default_sparams);
             exit(1);
         }
     }
+
     if (!params.kv_overrides.empty()) {
-        params.kv_overrides.emplace_back(llama_model_kv_override());
+        params.kv_overrides.emplace_back();
         params.kv_overrides.back().key[0] = 0;
     }
 
-    if (invalid_param)
-    {
+    if (invalid_param) {
         fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
         server_print_usage(argv[0], default_params, default_sparams);
         exit(1);
     }
 }
 
-
-static std::string random_string()
-{
-    static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz");
-
-    std::random_device rd;
-    std::mt19937 generator(rd());
-
-    std::string result(32, ' ');
-
-    for (int i = 0; i < 32; ++i) {
-        result[i] = str[generator() % str.size()];
+static void log_server_request(const httplib::Request & req, const httplib::Response & res) {
+    // skip GH copilot requests when using default port
+    if (req.path == "/v1/health" || req.path == "/v1/completions") {
+        return;
     }
 
-    return result;
-}
-
-static std::string gen_chatcmplid()
-{
-    std::stringstream chatcmplid;
-    chatcmplid << "chatcmpl-" << random_string();
-    return chatcmplid.str();
-}
-
-std::string format_chatml(std::vector<json> messages)
-{
-    std::ostringstream chatml_msgs;
-
-    for (auto it = messages.begin(); it != messages.end(); ++it) {
-        chatml_msgs << "<|im_start|>"
-                    << json_value(*it, "role",    std::string("user")) << '\n';
-        chatml_msgs << json_value(*it, "content", std::string(""))
-                    << "<|im_end|>\n";
-    }
-
-    chatml_msgs << "<|im_start|>assistant" << '\n';
-
-    return chatml_msgs.str();
-}
-
-/* llama.cpp completion api semantics */
-json oaicompat_completion_params_parse(
-    const json &body /* openai api json semantics */)
-{
-    json llama_params;
-
-    llama_params["__oaicompat"] = true;
-
-    // Map OpenAI parameters to llama.cpp parameters
-    //
-    // For parameters that are defined by the OpenAI documentation (e.g.
-    // temperature), we explicitly specify OpenAI's intended default; we
-    // need to do that because sometimes OpenAI disagrees with llama.cpp
-    //
-    // https://platform.openai.com/docs/api-reference/chat/create
-    llama_sampling_params default_sparams;
-    llama_params["model"]             = json_value(body, "model", std::string("uknown"));
-    llama_params["prompt"]            = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt'
-    llama_params["cache_prompt"]      = json_value(body, "cache_prompt", false);
-    llama_params["temperature"]       = json_value(body, "temperature", 0.0);
-    llama_params["top_k"]             = json_value(body, "top_k", default_sparams.top_k);
-    llama_params["top_p"]             = json_value(body, "top_p", 1.0);
-    llama_params["n_predict"]         = json_value(body, "max_tokens", -1);
-    llama_params["logit_bias"]        = json_value(body, "logit_bias",json::object());
-    llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0);
-    llama_params["presence_penalty"]  = json_value(body, "presence_penalty", 0.0);
-    llama_params["seed"]              = json_value(body, "seed", LLAMA_DEFAULT_SEED);
-    llama_params["stream"]            = json_value(body, "stream", false);
-    llama_params["mirostat"]          = json_value(body, "mirostat", default_sparams.mirostat);
-    llama_params["mirostat_tau"]      = json_value(body, "mirostat_tau", default_sparams.mirostat_tau);
-    llama_params["mirostat_eta"]      = json_value(body, "mirostat_eta", default_sparams.mirostat_eta);
-    llama_params["penalize_nl"]       = json_value(body, "penalize_nl", default_sparams.penalize_nl);
-    llama_params["typical_p"]         = json_value(body, "typical_p", default_sparams.typical_p);
-    llama_params["repeat_last_n"]     = json_value(body, "repeat_last_n", default_sparams.penalty_last_n);
-    llama_params["ignore_eos"]        = json_value(body, "ignore_eos", false);
-    llama_params["tfs_z"]             = json_value(body, "tfs_z", default_sparams.tfs_z);
-
-    if (body.count("grammar") != 0) {
-        llama_params["grammar"] = json_value(body, "grammar", json::object());
-    }
-
-    // Handle 'stop' field
-    if (body.contains("stop") && body["stop"].is_string()) {
-        llama_params["stop"] = json::array({body["stop"].get<std::string>()});
-    } else {
-        llama_params["stop"] = json_value(body, "stop", json::array());
-    }
-
-    // Ensure there is ChatML-specific end sequence among stop words
-    llama_params["stop"].push_back("<|im_end|>");
-
-    return llama_params;
-}
-
-static json format_final_response_oaicompat(const json &request, const task_result &response, bool streaming = false)
-{
-    json result = response.result_json;
-
-    bool stopped_word        = result.count("stopped_word") != 0;
-    bool stopped_eos         = json_value(result, "stopped_eos", false);
-    int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
-    int num_prompt_tokens    = json_value(result, "tokens_evaluated", 0);
-    std::string content      = json_value(result, "content", std::string(""));
-
-    std::string finish_reason = "length";
-    if (stopped_word || stopped_eos) {
-        finish_reason = "stop";
-    }
-
-    json choices =
-        streaming ? json::array({json{{"finish_reason", finish_reason},
-                                        {"index", 0},
-                                        {"delta", json::object()}}})
-                  : json::array({json{{"finish_reason", finish_reason},
-                                        {"index", 0},
-                                        {"message", json{{"content", content},
-                                                         {"role", "assistant"}}}}});
-
-    std::time_t t = std::time(0);
-
-    json res =
-        json{{"choices", choices},
-            {"created", t},
-            {"model",
-                json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
-            {"object", streaming ? "chat.completion.chunk" : "chat.completion"},
-            {"usage",
-                json{{"completion_tokens", num_tokens_predicted},
-                    {"prompt_tokens", num_prompt_tokens},
-                    {"total_tokens", num_tokens_predicted + num_prompt_tokens}}},
-            {"id", gen_chatcmplid()}};
-
-    if (server_verbose) {
-        res["__verbose"] = result;
-    }
-
-    if (result.contains("completion_probabilities")) {
-        res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array());
-    }
-
-    return res;
-}
-
-// return value is vector as there is one case where we might need to generate two responses
-static std::vector<json> format_partial_response_oaicompat(const task_result &response) {
-    json result = response.result_json;
-
-    if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
-        return std::vector<json>({response.result_json});
-    }
-
-    bool first = json_value(result, "oaicompat_token_ctr", 0) == 0;
-    std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
-
-    bool stopped_word   = json_value(result, "stopped_word", false);
-    bool stopped_eos    = json_value(result, "stopped_eos", false);
-    bool stopped_limit  = json_value(result, "stopped_limit", false);
-    std::string content = json_value(result, "content", std::string(""));
-
-    std::string finish_reason;
-    if (stopped_word || stopped_eos) {
-        finish_reason = "stop";
-    }
-    if (stopped_limit) {
-        finish_reason = "length";
-    }
-
-    std::time_t t = std::time(0);
-
-    json choices;
-
-    if (!finish_reason.empty()) {
-        choices = json::array({json{{"finish_reason", finish_reason},
-                                    {"index", 0},
-                                    {"delta", json::object()}}});
-    } else {
-        if (first) {
-            if (content.empty()) {
-                choices = json::array({json{{"finish_reason", nullptr},
-                                            {"index", 0},
-                                            {"delta", json{{"role", "assistant"}}}}});
-            } else {
-                // We have to send this as two updates to conform to openai behavior
-                json initial_ret = json{{"choices", json::array({json{
-                                        {"finish_reason", nullptr},
-                                        {"index", 0},
-                                        {"delta", json{
-                                            {"role", "assistant"}
-                                        }}}})},
-                            {"created", t},
-                            {"id", gen_chatcmplid()},
-                            {"model", modelname},
-                            {"object", "chat.completion.chunk"}};
-
-                json second_ret = json{
-                            {"choices", json::array({json{{"finish_reason", nullptr},
-                                                            {"index", 0},
-                                                            {"delta", json{
-                                                            {"content", content}}}
-                                                            }})},
-                            {"created", t},
-                            {"id", gen_chatcmplid()},
-                            {"model", modelname},
-                            {"object", "chat.completion.chunk"}};
-
-                return std::vector<json>({initial_ret, second_ret});
-            }
-        } else {
-            // Some idiosyncrasy in task processing logic makes several trailing calls
-            // with empty content, we ignore these at the calee site.
-            if (content.empty()) {
-                return std::vector<json>({json::object()});
-            }
-
-            choices = json::array({json{
-                {"finish_reason", nullptr},
-                {"index", 0},
-                {"delta",
-                json{
-                    {"content", content},
-                }},
-            }});
-        }
-    }
-
-    json ret = json{{"choices", choices},
-                    {"created", t},
-                    {"id", gen_chatcmplid()},
-                    {"model", modelname},
-                    {"object", "chat.completion.chunk"}};
-
-    return std::vector<json>({ret});
-}
-
-static json format_partial_response(
-    llama_server_context &llama, llama_client_slot *slot, const std::string &content, const std::vector<completion_token_output> &probs
-) {
-    json res = json
-    {
-        {"content",    content },
-        {"stop",       false},
-        {"slot_id",    slot->id },
-        {"multimodal", llama.multimodal }
-    };
-
-    if (slot->sparams.n_probs > 0)
-    {
-        res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs);
-    }
-
-    return res;
-}
-
-static json format_tokenizer_response(const std::vector<llama_token> &tokens)
-{
-    return json{
-        {"tokens", tokens}};
-}
-
-static json format_detokenized_response(std::string content)
-{
-    return json{
-        {"content", content}};
-}
-
-
-static void log_server_request(const httplib::Request &req, const httplib::Response &res)
-{
     LOG_INFO("request", {
-                            {"remote_addr", req.remote_addr},
-                            {"remote_port", req.remote_port},
-                            {"status", res.status},
-                            {"method", req.method},
-                            {"path", req.path},
-                            {"params", req.params},
-                        });
+        {"remote_addr", req.remote_addr},
+        {"remote_port", req.remote_port},
+        {"status",      res.status},
+        {"method",      req.method},
+        {"path",        req.path},
+        {"params",      req.params},
+    });
 
     LOG_VERBOSE("request", {
-                               {"request", req.body},
-                               {"response", res.body},
-                           });
+        {"request",  req.body},
+        {"response", res.body},
+    });
 }
 
-struct token_translator
-{
-    llama_context * ctx;
-    std::string operator()(llama_token tok)                    const { return llama_token_to_piece(ctx, tok); }
-    std::string operator()(const completion_token_output &cto) const { return (*this)(cto.tok); }
-};
+std::function<void(int)> shutdown_handler;
+std::atomic_flag is_terminating = ATOMIC_FLAG_INIT;
 
-static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, llama_client_slot *slot)
-{
-    auto & gtps = slot->generated_token_probs;
-    auto translator = token_translator{llama.ctx};
-    auto add_strlen = [=](size_t sum, const completion_token_output & cto) { return sum + translator(cto).size(); };
-    const size_t len = std::accumulate(gtps.begin(), gtps.end(), size_t(0), add_strlen);
-    if (slot->generated_text.capacity() < slot->generated_text.size() + len)
-    {
-        slot->generated_text.reserve(slot->generated_text.size() + len);
-    }
-    for (const completion_token_output & cto : gtps)
-    {
-        slot->generated_text += translator(cto);
+inline void signal_handler(int signal) {
+    if (is_terminating.test_and_set()) {
+        // in case it hangs, we can force terminate the server by hitting Ctrl+C twice
+        // this is for better developer experience, we can remove when the server is stable enough
+        fprintf(stderr, "Received second interrupt, terminating immediately.\n");
+        exit(1);
     }
+
+    shutdown_handler(signal);
 }
 
-int main(int argc, char **argv)
-{
+int main(int argc, char ** argv) {
 #if SERVER_VERBOSE != 1
     log_disable();
 #endif
     // own arguments required by this example
-    gpt_params params;
+    gpt_params    params;
     server_params sparams;
 
     // struct that contains llama context and inference
-    llama_server_context llama;
+    server_context ctx_server;
 
-    server_params_parse(argc, argv, sparams, params, llama);
+    server_params_parse(argc, argv, sparams, params);
 
-    if (params.model_alias == "unknown")
-    {
+    if (!sparams.system_prompt.empty()) {
+        ctx_server.system_prompt_set(json::parse(sparams.system_prompt));
+    }
+
+    if (params.model_alias == "unknown") {
         params.model_alias = params.model;
     }
 
-    llama_backend_init(params.numa);
+    llama_backend_init();
+    llama_numa_init(params.numa);
 
-    LOG_INFO("build info", {{"build", LLAMA_BUILD_NUMBER},
-                            {"commit", LLAMA_COMMIT}});
+    LOG_INFO("build info", {
+        {"build",  LLAMA_BUILD_NUMBER},
+        {"commit", LLAMA_COMMIT}
+    });
 
     LOG_INFO("system info", {
-                                {"n_threads", params.n_threads},
-                                {"n_threads_batch", params.n_threads_batch},
-                                {"total_threads", std::thread::hardware_concurrency()},
-                                {"system_info", llama_print_system_info()},
-                            });
+        {"n_threads",       params.n_threads},
+        {"n_threads_batch", params.n_threads_batch},
+        {"total_threads",   std::thread::hardware_concurrency()},
+        {"system_info",     llama_print_system_info()},
+    });
 
-    // load the model
-    if (!llama.load_model(params))
-    {
+    std::unique_ptr<httplib::Server> svr;
+#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
+    if (sparams.ssl_key_file != "" && sparams.ssl_cert_file != "") {
+        LOG_INFO("Running with SSL", {{"key", sparams.ssl_key_file}, {"cert", sparams.ssl_cert_file}});
+        svr.reset(
+            new httplib::SSLServer(sparams.ssl_cert_file.c_str(), sparams.ssl_key_file.c_str())
+        );
+    } else {
+        LOG_INFO("Running without SSL", {});
+        svr.reset(new httplib::Server());
+    }
+#else
+    svr.reset(new httplib::Server());
+#endif
+
+    std::atomic<server_state> state{SERVER_STATE_LOADING_MODEL};
+
+    svr->set_default_headers({{"Server", "llama.cpp"}});
+
+    // CORS preflight
+    svr->Options(R"(.*)", [](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin",      req.get_header_value("Origin"));
+        res.set_header("Access-Control-Allow-Credentials", "true");
+        res.set_header("Access-Control-Allow-Methods",     "POST");
+        res.set_header("Access-Control-Allow-Headers",     "*");
+        return res.set_content("", "application/json; charset=utf-8");
+    });
+
+    svr->set_logger(log_server_request);
+
+    auto res_error = [](httplib::Response & res, json error_data) {
+        json final_response {{"error", error_data}};
+        res.set_content(final_response.dump(), "application/json; charset=utf-8");
+        res.status = json_value(error_data, "code", 500);
+    };
+
+    svr->set_exception_handler([&res_error](const httplib::Request &, httplib::Response & res, std::exception_ptr ep) {
+        std::string message;
+        try {
+            std::rethrow_exception(std::move(ep));
+        } catch (std::exception & e) {
+            message = e.what();
+        } catch (...) {
+            message = "Unknown Exception";
+        }
+
+        json formatted_error = format_error_response(message, ERROR_TYPE_SERVER);
+        LOG_VERBOSE("Got exception", formatted_error);
+        res_error(res, formatted_error);
+    });
+
+    svr->set_error_handler([&res_error](const httplib::Request &, httplib::Response & res) {
+        if (res.status == 404) {
+            res_error(res, format_error_response("File Not Found", ERROR_TYPE_NOT_FOUND));
+        }
+        // for other error codes, we skip processing here because it's already done by res_error()
+    });
+
+    // set timeouts and change hostname and port
+    svr->set_read_timeout (sparams.read_timeout);
+    svr->set_write_timeout(sparams.write_timeout);
+
+    if (!svr->bind_to_port(sparams.hostname, sparams.port)) {
+        fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port);
         return 1;
     }
 
-    llama.initialize();
+    std::unordered_map<std::string, std::string> log_data;
 
-    httplib::Server svr;
+    log_data["hostname"] = sparams.hostname;
+    log_data["port"]     = std::to_string(sparams.port);
+
+    if (sparams.api_keys.size() == 1) {
+        auto key = sparams.api_keys[0];
+        log_data["api_key"] = "api_key: ****" + key.substr(std::max((int)(key.length() - 4), 0));
+    } else if (sparams.api_keys.size() > 1) {
+        log_data["api_key"] = "api_key: " + std::to_string(sparams.api_keys.size()) + " keys loaded";
+    }
+
+    // load the model
+    if (!ctx_server.load_model(params)) {
+        state.store(SERVER_STATE_ERROR);
+        return 1;
+    } else {
+        ctx_server.init();
+        state.store(SERVER_STATE_READY);
+    }
+
+    LOG_INFO("model loaded", {});
+
+    const auto model_meta = ctx_server.model_meta();
+
+    // if a custom chat template is not supplied, we will use the one that comes with the model (if any)
+    if (sparams.chat_template.empty()) {
+        if (!ctx_server.validate_model_chat_template()) {
+            LOG_ERROR("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {});
+            sparams.chat_template = "chatml";
+        }
+    }
+
+    // print sample chat example to make it clear which template is used
+    {
+        json chat;
+        chat.push_back({{"role", "system"},    {"content", "You are a helpful assistant"}});
+        chat.push_back({{"role", "user"},      {"content", "Hello"}});
+        chat.push_back({{"role", "assistant"}, {"content", "Hi there"}});
+        chat.push_back({{"role", "user"},      {"content", "How are you?"}});
+
+        const std::string chat_example = format_chat(ctx_server.model, sparams.chat_template, chat);
+
+        LOG_INFO("chat template", {
+            {"chat_example", chat_example},
+            {"built_in", sparams.chat_template.empty()},
+        });
+    }
+
+    //
+    // Middlewares
+    //
+
+    auto middleware_validate_api_key = [&sparams, &res_error](const httplib::Request & req, httplib::Response & res) {
+        // TODO: should we apply API key to all endpoints, including "/health" and "/models"?
+        static const std::set<std::string> protected_endpoints = {
+            "/props",
+            "/completion",
+            "/completions",
+            "/v1/completions",
+            "/chat/completions",
+            "/v1/chat/completions",
+            "/infill",
+            "/tokenize",
+            "/detokenize",
+            "/embedding",
+            "/embeddings",
+            "/v1/embeddings",
+        };
 
-    // Middleware for API key validation
-    auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool {
         // If API key is not set, skip validation
-        if (sparams.api_key.empty()) {
+        if (sparams.api_keys.empty()) {
+            return true;
+        }
+
+        // If path is not in protected_endpoints list, skip validation
+        if (protected_endpoints.find(req.path) == protected_endpoints.end()) {
             return true;
         }
 
         // Check for API key in the header
         auto auth_header = req.get_header_value("Authorization");
+
         std::string prefix = "Bearer ";
         if (auth_header.substr(0, prefix.size()) == prefix) {
             std::string received_api_key = auth_header.substr(prefix.size());
-            if (received_api_key == sparams.api_key) {
+            if (std::find(sparams.api_keys.begin(), sparams.api_keys.end(), received_api_key) != sparams.api_keys.end()) {
                 return true; // API key is valid
             }
         }
 
         // API key is invalid or not provided
-        res.set_content("Unauthorized: Invalid API Key", "text/plain; charset=utf-8");
-        res.status = 401; // Unauthorized
+        // TODO: make another middleware for CORS related logic
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+        res_error(res, format_error_response("Invalid API Key", ERROR_TYPE_AUTHENTICATION));
 
         LOG_WARNING("Unauthorized: Invalid API Key", {});
 
         return false;
     };
 
-    svr.set_default_headers({{"Server", "llama.cpp"},
-                             {"Access-Control-Allow-Origin", "*"},
-                             {"Access-Control-Allow-Headers", "content-type"}});
+    // register server middlewares
+    svr->set_pre_routing_handler([&middleware_validate_api_key](const httplib::Request & req, httplib::Response & res) {
+        if (!middleware_validate_api_key(req, res)) {
+            return httplib::Server::HandlerResponse::Handled;
+        }
+        return httplib::Server::HandlerResponse::Unhandled;
+    });
 
-    // this is only called if no index.html is found in the public --path
-    svr.Get("/", [](const httplib::Request &, httplib::Response &res)
-            {
-                res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html; charset=utf-8");
-                return false;
-            });
+    //
+    // Route handlers (or controllers)
+    //
 
-    // this is only called if no index.js is found in the public --path
-    svr.Get("/index.js", [](const httplib::Request &, httplib::Response &res)
-            {
-                res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript; charset=utf-8");
-                return false;
-            });
+    const auto handle_health = [&](const httplib::Request & req, httplib::Response & res) {
+        server_state current_state = state.load();
+        switch (current_state) {
+            case SERVER_STATE_READY:
+                {
+                    // request slots data using task queue
+                    server_task task;
+                    task.id   = ctx_server.queue_tasks.get_new_id();
+                    task.type = SERVER_TASK_TYPE_METRICS;
+                    task.id_target = -1;
 
-    // this is only called if no index.html is found in the public --path
-    svr.Get("/completion.js", [](const httplib::Request &, httplib::Response &res)
-            {
-                res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript; charset=utf-8");
-                return false;
-            });
+                    ctx_server.queue_results.add_waiting_task_id(task.id);
+                    ctx_server.queue_tasks.post(task);
 
-    // this is only called if no index.html is found in the public --path
-    svr.Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response &res)
-            {
-                res.set_content(reinterpret_cast<const char*>(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript; charset=utf-8");
-                return false;
-            });
+                    // get the result
+                    server_task_result result = ctx_server.queue_results.recv(task.id);
+                    ctx_server.queue_results.remove_waiting_task_id(task.id);
 
-    svr.Get("/props", [&llama](const httplib::Request & /*req*/, httplib::Response &res)
-            {
-                res.set_header("Access-Control-Allow-Origin", "*");
-                json data = {
-                    { "user_name",      llama.name_user.c_str() },
-                    { "assistant_name", llama.name_assistant.c_str() }
-                };
-                res.set_content(data.dump(), "application/json; charset=utf-8");
-            });
+                    const int n_idle_slots       = result.data["idle"];
+                    const int n_processing_slots = result.data["processing"];
 
-    svr.Post("/completion", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
-            {
-                if (!validate_api_key(req, res)) {
-                    return;
+                    json health = {
+                        {"status",           "ok"},
+                        {"slots_idle",       n_idle_slots},
+                        {"slots_processing", n_processing_slots}
+                    };
+
+                    res.status = 200; // HTTP OK
+                    if (sparams.slots_endpoint && req.has_param("include_slots")) {
+                        health["slots"] = result.data["slots"];
+                    }
+
+                    if (n_idle_slots == 0) {
+                        health["status"] = "no slot available";
+                        if (req.has_param("fail_on_no_slot")) {
+                            res.status = 503; // HTTP Service Unavailable
+                        }
+                    }
+
+                    res.set_content(health.dump(), "application/json");
+                    break;
                 }
-                json data = json::parse(req.body);
-                const int task_id = llama.request_completion(data, false, false, -1);
-                if (!json_value(data, "stream", false)) {
-                    std::string completion_text;
-                    task_result result = llama.next_result(task_id);
-                    if (!result.error && result.stop) {
-                        res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
+            case SERVER_STATE_LOADING_MODEL:
+                {
+                    res_error(res, format_error_response("Loading model", ERROR_TYPE_UNAVAILABLE));
+                } break;
+            case SERVER_STATE_ERROR:
+                {
+                    res_error(res, format_error_response("Model failed to load", ERROR_TYPE_SERVER));
+                } break;
+        }
+    };
+
+    const auto handle_slots = [&](const httplib::Request &, httplib::Response & res) {
+        if (!sparams.slots_endpoint) {
+            res_error(res, format_error_response("This server does not support slots endpoint.", ERROR_TYPE_NOT_SUPPORTED));
+            return;
+        }
+
+        // request slots data using task queue
+        server_task task;
+        task.id = ctx_server.queue_tasks.get_new_id();
+        task.id_multi  = -1;
+        task.id_target = -1;
+        task.type = SERVER_TASK_TYPE_METRICS;
+
+        ctx_server.queue_results.add_waiting_task_id(task.id);
+        ctx_server.queue_tasks.post(task);
+
+        // get the result
+        server_task_result result = ctx_server.queue_results.recv(task.id);
+        ctx_server.queue_results.remove_waiting_task_id(task.id);
+
+        res.set_content(result.data["slots"].dump(), "application/json");
+        res.status = 200; // HTTP OK
+    };
+
+    const auto handle_metrics = [&](const httplib::Request &, httplib::Response & res) {
+        if (!sparams.metrics_endpoint) {
+            res_error(res, format_error_response("This server does not support metrics endpoint.", ERROR_TYPE_NOT_SUPPORTED));
+            return;
+        }
+
+        // request slots data using task queue
+        server_task task;
+        task.id = ctx_server.queue_tasks.get_new_id();
+        task.id_multi  = -1;
+        task.id_target = -1;
+        task.type = SERVER_TASK_TYPE_METRICS;
+        task.data.push_back({{"reset_bucket", true}});
+
+        ctx_server.queue_results.add_waiting_task_id(task.id);
+        ctx_server.queue_tasks.post(task);
+
+        // get the result
+        server_task_result result = ctx_server.queue_results.recv(task.id);
+        ctx_server.queue_results.remove_waiting_task_id(task.id);
+
+        json data = result.data;
+
+        const uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"];
+        const uint64_t t_prompt_processing       = data["t_prompt_processing"];
+
+        const uint64_t n_tokens_predicted  = data["n_tokens_predicted"];
+        const uint64_t t_tokens_generation = data["t_tokens_generation"];
+
+        const int32_t kv_cache_used_cells = data["kv_cache_used_cells"];
+
+        // metrics definition: https://prometheus.io/docs/practices/naming/#metric-names
+        json all_metrics_def = json {
+            {"counter", {{
+                    {"name",  "prompt_tokens_total"},
+                    {"help",  "Number of prompt tokens processed."},
+                    {"value",  (uint64_t) data["n_prompt_tokens_processed_total"]}
+            }, {
+                    {"name",  "prompt_seconds_total"},
+                    {"help",  "Prompt process time"},
+                    {"value",  (uint64_t) data["t_prompt_processing_total"] / 1.e3}
+            }, {
+                    {"name",  "tokens_predicted_total"},
+                    {"help",  "Number of generation tokens processed."},
+                    {"value",  (uint64_t) data["n_tokens_predicted_total"]}
+            }, {
+                    {"name",  "tokens_predicted_seconds_total"},
+                    {"help",  "Predict process time"},
+                    {"value",  (uint64_t) data["t_tokens_generation_total"] / 1.e3}
+            }}},
+            {"gauge", {{
+                    {"name",  "prompt_tokens_seconds"},
+                    {"help",  "Average prompt throughput in tokens/s."},
+                    {"value",  n_prompt_tokens_processed ? 1.e3 / t_prompt_processing * n_prompt_tokens_processed : 0.}
+            },{
+                    {"name",  "predicted_tokens_seconds"},
+                    {"help",  "Average generation throughput in tokens/s."},
+                    {"value",  n_tokens_predicted ? 1.e3 / t_tokens_generation * n_tokens_predicted : 0.}
+            },{
+                    {"name",  "kv_cache_usage_ratio"},
+                    {"help",  "KV-cache usage. 1 means 100 percent usage."},
+                    {"value",  1. * kv_cache_used_cells / params.n_ctx}
+            },{
+                    {"name",  "kv_cache_tokens"},
+                    {"help",  "KV-cache tokens."},
+                    {"value",  (uint64_t) data["kv_cache_tokens_count"]}
+            },{
+                    {"name",  "requests_processing"},
+                    {"help",  "Number of request processing."},
+                    {"value",  (uint64_t) data["processing"]}
+            },{
+                    {"name",  "requests_deferred"},
+                    {"help",  "Number of request deferred."},
+                    {"value",  (uint64_t) data["deferred"]}
+            }}}
+        };
+
+        std::stringstream prometheus;
+
+        for (const auto & el : all_metrics_def.items()) {
+            const auto & type        = el.key();
+            const auto & metrics_def = el.value();
+
+            for (const auto & metric_def : metrics_def) {
+                const std::string name = metric_def["name"];
+                const std::string help = metric_def["help"];
+
+                auto value = json_value(metric_def, "value", 0.);
+                prometheus << "# HELP llamacpp:" << name << " " << help  << "\n"
+                            << "# TYPE llamacpp:" << name << " " << type  << "\n"
+                            << "llamacpp:"        << name << " " << value << "\n";
+            }
+        }
+
+        const int64_t t_start = data["t_start"];
+        res.set_header("Process-Start-Time-Unix", std::to_string(t_start));
+
+        res.set_content(prometheus.str(), "text/plain; version=0.0.4");
+        res.status = 200; // HTTP OK
+    };
+
+    const auto handle_props = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+        json data = {
+            { "user_name",                   ctx_server.name_user.c_str() },
+            { "assistant_name",              ctx_server.name_assistant.c_str() },
+            { "default_generation_settings", ctx_server.default_generation_settings_for_props },
+            { "total_slots",                 ctx_server.params.n_parallel }
+        };
+
+        res.set_content(data.dump(), "application/json; charset=utf-8");
+    };
+
+    const auto handle_completions = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+
+        json data = json::parse(req.body);
+
+        const int id_task = ctx_server.queue_tasks.get_new_id();
+
+        ctx_server.queue_results.add_waiting_task_id(id_task);
+        ctx_server.request_completion(id_task, -1, data, false, false);
+
+        if (!json_value(data, "stream", false)) {
+            server_task_result result = ctx_server.queue_results.recv(id_task);
+            if (!result.error && result.stop) {
+                res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
+            } else {
+                res_error(res, result.data);
+            }
+
+            ctx_server.queue_results.remove_waiting_task_id(id_task);
+        } else {
+            const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) {
+                while (true) {
+                    server_task_result result = ctx_server.queue_results.recv(id_task);
+                    if (!result.error) {
+                        const std::string str =
+                            "data: " +
+                            result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
+                            "\n\n";
+
+                        LOG_VERBOSE("data stream", {
+                            { "to_send", str }
+                        });
+
+                        if (!sink.write(str.c_str(), str.size())) {
+                            ctx_server.queue_results.remove_waiting_task_id(id_task);
+                            return false;
+                        }
+
+                        if (result.stop) {
+                            break;
+                        }
+                    } else {
+                        const std::string str =
+                            "error: " +
+                            result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
+                            "\n\n";
+
+                        LOG_VERBOSE("data stream", {
+                            { "to_send", str }
+                        });
+
+                        if (!sink.write(str.c_str(), str.size())) {
+                            ctx_server.queue_results.remove_waiting_task_id(id_task);
+                            return false;
+                        }
+
+                        break;
                     }
-                    else
-                    {
-                        res.status = 404;
-                        res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
-                        return;
-                    }
-                } else {
-                    const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink)
-                    {
-                        while (true)
-                        {
-                            task_result result = llama.next_result(task_id);
-                            if (!result.error) {
+                }
+
+                ctx_server.queue_results.remove_waiting_task_id(id_task);
+                sink.done();
+
+                return true;
+            };
+
+            auto on_complete = [id_task, &ctx_server] (bool) {
+                // cancel
+                ctx_server.request_cancel(id_task);
+                ctx_server.queue_results.remove_waiting_task_id(id_task);
+            };
+
+            res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
+        }
+    };
+
+    const auto handle_models = [&params, &model_meta](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+
+        json models = {
+            {"object", "list"},
+            {"data", {
+                 {
+                     {"id",       params.model_alias},
+                     {"object",   "model"},
+                     {"created",  std::time(0)},
+                     {"owned_by", "llamacpp"},
+                     {"meta",     model_meta}
+                 },
+             }}
+        };
+
+        res.set_content(models.dump(), "application/json; charset=utf-8");
+    };
+
+    const auto handle_chat_completions = [&ctx_server, &sparams, &res_error](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+        json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), sparams.chat_template);
+
+        const int id_task = ctx_server.queue_tasks.get_new_id();
+
+        ctx_server.queue_results.add_waiting_task_id(id_task);
+        ctx_server.request_completion(id_task, -1, data, false, false);
+
+        const auto completion_id = gen_chatcmplid();
+        if (!json_value(data, "stream", false)) {
+            server_task_result result = ctx_server.queue_results.recv(id_task);
+
+            if (!result.error && result.stop) {
+                json result_oai = format_final_response_oaicompat(data, result.data, completion_id);
+
+                res.set_content(result_oai.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
+            } else {
+                res_error(res, result.data);
+            }
+            ctx_server.queue_results.remove_waiting_task_id(id_task);
+        } else {
+            const auto chunked_content_provider = [id_task, &ctx_server, completion_id](size_t, httplib::DataSink & sink) {
+                while (true) {
+                    server_task_result result = ctx_server.queue_results.recv(id_task);
+                    if (!result.error) {
+                        std::vector<json> result_array = format_partial_response_oaicompat(result.data, completion_id);
+
+                        for (auto it = result_array.begin(); it != result_array.end(); ++it) {
+                            if (!it->empty()) {
                                 const std::string str =
                                     "data: " +
-                                    result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
-                                    "\n\n";
-                                LOG_VERBOSE("data stream", {
-                                    { "to_send", str }
-                                });
-                                if (!sink.write(str.c_str(), str.size()))
-                                {
-                                    return false;
-                                }
-                                if (result.stop) {
-                                    break;
-                                }
-                            } else {
-                                const std::string str =
-                                    "error: " +
-                                    result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
-                                    "\n\n";
-                                LOG_VERBOSE("data stream", {
-                                    { "to_send", str }
-                                });
-                                if (!sink.write(str.c_str(), str.size()))
-                                {
-                                    return false;
-                                }
-                                break;
-                            }
-                        }
-                        sink.done();
-                        return true;
-                    };
-
-                    auto on_complete = [task_id, &llama] (bool)
-                    {
-                        // cancel
-                        llama.request_cancel(task_id);
-                    };
-
-                    res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
-                }
-            });
-
-
-
-    svr.Get("/v1/models", [&params](const httplib::Request&, httplib::Response& res)
-            {
-                std::time_t t = std::time(0);
-
-                json models = {
-                    {"object", "list"},
-                    {"data", {
-                        {
-                            {"id", params.model_alias},
-                            {"object", "model"},
-                            {"created", t},
-                            {"owned_by", "llamacpp"}
-                        },
-                    }}
-                };
-
-                res.set_content(models.dump(), "application/json; charset=utf-8");
-            });
-
-    // TODO: add mount point without "/v1" prefix -- how?
-    svr.Post("/v1/chat/completions", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
-            {
-                if (!validate_api_key(req, res)) {
-                    return;
-                }
-                json data = oaicompat_completion_params_parse(json::parse(req.body));
-
-                const int task_id = llama.request_completion(data, false, false, -1);
-
-                if (!json_value(data, "stream", false)) {
-                    std::string completion_text;
-                    task_result result = llama.next_result(task_id);
-
-                    if (!result.error && result.stop) {
-                        json oaicompat_result = format_final_response_oaicompat(data, result);
-
-                        res.set_content(oaicompat_result.dump(-1, ' ', false,
-                                            json::error_handler_t::replace),
-                                            "application/json; charset=utf-8");
-                    } else {
-                        res.status = 500;
-                        res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
-                        return;
-                    }
-                } else {
-                    const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) {
-                        while (true) {
-                            task_result llama_result = llama.next_result(task_id);
-                            if (!llama_result.error) {
-                                std::vector<json> result_array = format_partial_response_oaicompat( llama_result);
-
-                                for (auto it = result_array.begin(); it != result_array.end(); ++it)
-                                {
-                                    if (!it->empty()) {
-                                        const std::string str =
-                                            "data: " +
-                                            it->dump(-1, ' ', false, json::error_handler_t::replace) +
-                                            "\n\n";
-                                        LOG_VERBOSE("data stream", {{"to_send", str}});
-                                        if (!sink.write(str.c_str(), str.size())) {
-                                            return false;
-                                        }
-                                    }
-                                }
-                                if (llama_result.stop) {
-                                    break;
-                                }
-                            } else {
-                                const std::string str =
-                                    "error: " +
-                                    llama_result.result_json.dump(-1, ' ', false,
-                                            json::error_handler_t::replace) +
+                                    it->dump(-1, ' ', false, json::error_handler_t::replace) +
                                     "\n\n";
                                 LOG_VERBOSE("data stream", {{"to_send", str}});
                                 if (!sink.write(str.c_str(), str.size())) {
+                                    ctx_server.queue_results.remove_waiting_task_id(id_task);
                                     return false;
                                 }
-                                break;
                             }
                         }
-                        sink.done();
-                        return true;
-                    };
-
-                    auto on_complete = [task_id, &llama](bool) {
-                        // cancel request
-                        llama.request_cancel(task_id);
-                    };
-
-                    res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
-                }
-            });
-
-    svr.Post("/infill", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
-            {
-                if (!validate_api_key(req, res)) {
-                    return;
-                }
-                json data = json::parse(req.body);
-                const int task_id = llama.request_completion(data, true, false, -1);
-                if (!json_value(data, "stream", false)) {
-                    std::string completion_text;
-                    task_result result = llama.next_result(task_id);
-                    if (!result.error && result.stop)
-                    {
-                        res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
+                        if (result.stop) {
+                            break;
+                        }
+                    } else {
+                        const std::string str =
+                            "error: " +
+                            result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
+                            "\n\n";
+                        LOG_VERBOSE("data stream", {{"to_send", str}});
+                        if (!sink.write(str.c_str(), str.size())) {
+                            ctx_server.queue_results.remove_waiting_task_id(id_task);
+                            return false;
+                        }
+                        break;
                     }
-                    else
-                    {
-                        res.status = 404;
-                        res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
-                        return;
+                }
+                sink.done();
+                ctx_server.queue_results.remove_waiting_task_id(id_task);
+                return true;
+            };
+
+            auto on_complete = [id_task, &ctx_server](bool) {
+                // cancel request
+                ctx_server.request_cancel(id_task);
+                ctx_server.queue_results.remove_waiting_task_id(id_task);
+            };
+
+            res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
+        }
+    };
+
+    const auto handle_infill = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+
+        json data = json::parse(req.body);
+
+        const int id_task = ctx_server.queue_tasks.get_new_id();
+
+        ctx_server.queue_results.add_waiting_task_id(id_task);
+        ctx_server.request_completion(id_task, -1, data, true, false);
+
+        if (!json_value(data, "stream", false)) {
+            server_task_result result = ctx_server.queue_results.recv(id_task);
+            if (!result.error && result.stop) {
+                res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
+            } else {
+                res_error(res, result.data);
+            }
+
+            ctx_server.queue_results.remove_waiting_task_id(id_task);
+        } else {
+            const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) {
+                while (true) {
+                    server_task_result result = ctx_server.queue_results.recv(id_task);
+                    if (!result.error) {
+                        const std::string str =
+                            "data: " +
+                            result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
+                            "\n\n";
+
+                        LOG_VERBOSE("data stream", {
+                            { "to_send", str }
+                        });
+
+                        if (!sink.write(str.c_str(), str.size())) {
+                            ctx_server.queue_results.remove_waiting_task_id(id_task);
+                            return false;
+                        }
+
+                        if (result.stop) {
+                            break;
+                        }
+                    } else {
+                        break;
                     }
+                }
+
+                ctx_server.queue_results.remove_waiting_task_id(id_task);
+                sink.done();
+
+                return true;
+            };
+
+            auto on_complete = [id_task, &ctx_server] (bool) {
+                ctx_server.request_cancel(id_task);
+            };
+
+            res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
+        }
+    };
+
+    const auto handle_tokenize = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+        const json body = json::parse(req.body);
+
+        std::vector<llama_token> tokens;
+        if (body.count("content") != 0) {
+            tokens = ctx_server.tokenize(body["content"], false);
+        }
+        const json data = format_tokenizer_response(tokens);
+        return res.set_content(data.dump(), "application/json; charset=utf-8");
+    };
+
+    const auto handle_detokenize = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+        const json body = json::parse(req.body);
+
+        std::string content;
+        if (body.count("tokens") != 0) {
+            const std::vector<llama_token> tokens = body["tokens"];
+            content = tokens_to_str(ctx_server.ctx, tokens.cbegin(), tokens.cend());
+        }
+
+        const json data = format_detokenized_response(content);
+        return res.set_content(data.dump(), "application/json; charset=utf-8");
+    };
+
+    const auto handle_embeddings = [&params, &ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+        if (!params.embedding) {
+            res.status = 501;
+            res.set_content("This server does not support embeddings. Start it with `--embeddings`", "text/plain; charset=utf-8");
+            return;
+        }
+
+        const json body = json::parse(req.body);
+        bool is_openai = false;
+
+        // an input prompt can be a string or a list of tokens (integer)
+        json prompt;
+        if (body.count("input") != 0) {
+            is_openai = true;
+            prompt = body["input"];
+        } else if (body.count("content") != 0) {
+            // with "content", we only support single prompt
+            prompt = std::vector<std::string>{body["content"]};
+        } else {
+            res_error(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST));
+            return;
+        }
+
+        // create and queue the task
+        json responses;
+        {
+            const int id_task = ctx_server.queue_tasks.get_new_id();
+            ctx_server.queue_results.add_waiting_task_id(id_task);
+            ctx_server.request_completion(id_task, -1, {{"prompt", prompt}}, false, true);
+
+            // get the result
+            server_task_result result = ctx_server.queue_results.recv(id_task);
+            ctx_server.queue_results.remove_waiting_task_id(id_task);
+            if (!result.error) {
+                if (result.data.count("results")) {
+                    // result for multi-task
+                    responses = result.data["results"];
                 } else {
-                    const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) {
-                        while (true)
-                        {
-                            task_result result = llama.next_result(task_id);
-                            if (!result.error) {
-                                const std::string str =
-                                "data: " +
-                                result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
-                                "\n\n";
-                                LOG_VERBOSE("data stream", {
-                                    { "to_send", str }
-                                });
-                                if (!sink.write(str.c_str(), str.size()))
-                                {
-                                    return false;
-                                }
-                                if (result.stop)
-                                {
-                                    break;
-                                }
-                            }
-                            else
-                            {
-                                break;
-                            }
-                        }
-
-                        sink.done();
-
-                        return true;
-                    };
-
-                    auto on_complete = [task_id, &llama] (bool)
-                    {
-                        // cancel
-                        llama.request_cancel(task_id);
-                    };
-
-                    res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
+                    // result for single task
+                    responses = std::vector<json>{result.data};
                 }
-            });
+            } else {
+                // error received, ignore everything else
+                res_error(res, result.data);
+                return;
+            }
+        }
 
-    svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res)
-            {
-                const json data = llama.get_model_props();
-                return res.set_content(data.dump(), "application/json; charset=utf-8");
-            });
+        // write JSON response
+        json root = is_openai
+            ? format_embeddings_response_oaicompat(body, responses)
+            : responses[0];
+        return res.set_content(root.dump(), "application/json; charset=utf-8");
+    };
 
-    svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res)
-                { return res.set_content("", "application/json; charset=utf-8"); });
+    auto handle_static_file = [](unsigned char * content, size_t len, const char * mime_type) {
+        return [content, len, mime_type](const httplib::Request &, httplib::Response & res) {
+            res.set_content(reinterpret_cast<const char*>(content), len, mime_type);
+            return false;
+        };
+    };
 
-    svr.Post("/tokenize", [&llama](const httplib::Request &req, httplib::Response &res)
-            {
-                const json body = json::parse(req.body);
-                std::vector<llama_token> tokens;
-                if (body.count("content") != 0)
-                {
-                    tokens = llama.tokenize(body["content"], false);
-                }
-                const json data = format_tokenizer_response(tokens);
-                return res.set_content(data.dump(), "application/json; charset=utf-8");
-            });
+    //
+    // Router
+    //
 
-    svr.Post("/detokenize", [&llama](const httplib::Request &req, httplib::Response &res)
-            {
-                const json body = json::parse(req.body);
-                std::string content;
-                if (body.count("tokens") != 0)
-                {
-                    const std::vector<llama_token> tokens = body["tokens"];
-                    content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend());
-                }
-
-                const json data = format_detokenized_response(content);
-                return res.set_content(data.dump(), "application/json; charset=utf-8");
-            });
-
-    svr.Post("/embedding", [&llama](const httplib::Request &req, httplib::Response &res)
-            {
-                const json body = json::parse(req.body);
-                json prompt;
-                if (body.count("content") != 0)
-                {
-                    prompt = body["content"];
-                }
-                else
-                {
-                    prompt = "";
-                }
-
-                json image_data;
-                if (body.count("image_data") != 0) {
-                    image_data = body["image_data"];
-                }
-                else
-                {
-                    image_data = "";
-                }
-
-                const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0}, {"image_data", image_data} }, false, true, -1);
-                task_result result = llama.next_result(task_id);
-                return res.set_content(result.result_json.dump(), "application/json; charset=utf-8");
-            });
-
-    svr.set_logger(log_server_request);
-
-    svr.set_exception_handler([](const httplib::Request &, httplib::Response &res, std::exception_ptr ep)
-            {
-                const char fmt[] = "500 Internal Server Error\n%s";
-                char buf[BUFSIZ];
-                try
-                {
-                    std::rethrow_exception(std::move(ep));
-                }
-                catch (std::exception &e)
-                {
-                    snprintf(buf, sizeof(buf), fmt, e.what());
-                }
-                catch (...)
-                {
-                    snprintf(buf, sizeof(buf), fmt, "Unknown Exception");
-                }
-                res.set_content(buf, "text/plain; charset=utf-8");
-                res.status = 500;
-            });
-
-    svr.set_error_handler([](const httplib::Request &, httplib::Response &res)
-            {
-                if (res.status == 401)
-                {
-                    res.set_content("Unauthorized", "text/plain; charset=utf-8");
-                }
-                if (res.status == 400)
-                {
-                    res.set_content("Invalid request", "text/plain; charset=utf-8");
-                }
-                else if (res.status == 404)
-                {
-                    res.set_content("File Not Found", "text/plain; charset=utf-8");
-                    res.status = 404;
-                }
-            });
-
-    // set timeouts and change hostname and port
-    svr.set_read_timeout (sparams.read_timeout);
-    svr.set_write_timeout(sparams.write_timeout);
-
-    if (!svr.bind_to_port(sparams.hostname, sparams.port))
-    {
-        fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port);
-        return 1;
+    // register static assets routes
+    if (!sparams.public_path.empty()) {
+        // Set the base directory for serving static files
+        svr->set_base_dir(sparams.public_path);
     }
 
-    // Set the base directory for serving static files
-    svr.set_base_dir(sparams.public_path);
+    // using embedded static files
+    svr->Get("/", handle_static_file(index_html, index_html_len, "text/html; charset=utf-8"));
+    svr->Get("/index.js", handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8"));
+    svr->Get("/completion.js", handle_static_file(completion_js, completion_js_len, "text/javascript; charset=utf-8"));
+    svr->Get("/json-schema-to-grammar.mjs", handle_static_file(
+        json_schema_to_grammar_mjs, json_schema_to_grammar_mjs_len, "text/javascript; charset=utf-8"));
 
-    // to make it ctrl+clickable:
-    LOG_TEE("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port);
+    // register API routes
+    svr->Get ("/health",              handle_health);
+    svr->Get ("/slots",               handle_slots);
+    svr->Get ("/metrics",             handle_metrics);
+    svr->Get ("/props",               handle_props);
+    svr->Get ("/v1/models",           handle_models);
+    svr->Post("/completion",          handle_completions); // legacy
+    svr->Post("/completions",         handle_completions);
+    svr->Post("/v1/completions",      handle_completions);
+    svr->Post("/chat/completions",    handle_chat_completions);
+    svr->Post("/v1/chat/completions", handle_chat_completions);
+    svr->Post("/infill",              handle_infill);
+    svr->Post("/embedding",           handle_embeddings); // legacy
+    svr->Post("/embeddings",          handle_embeddings);
+    svr->Post("/v1/embeddings",       handle_embeddings);
+    svr->Post("/tokenize",            handle_tokenize);
+    svr->Post("/detokenize",          handle_detokenize);
 
-    std::unordered_map<std::string, std::string> log_data;
-    log_data["hostname"] = sparams.hostname;
-    log_data["port"] = std::to_string(sparams.port);
-
-    if (!sparams.api_key.empty()) {
-        log_data["api_key"] = "api_key: ****" + sparams.api_key.substr(sparams.api_key.length() - 4);
+    //
+    // Start the server
+    //
+    if (sparams.n_threads_http < 1) {
+        // +2 threads for monitoring endpoints
+        sparams.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1);
     }
+    log_data["n_threads_http"] =  std::to_string(sparams.n_threads_http);
+    svr->new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); };
 
     LOG_INFO("HTTP server listening", log_data);
+
     // run the HTTP server in a thread - see comment below
-    std::thread t([&]()
-            {
-                if (!svr.listen_after_bind())
-                {
-                    return 1;
-                }
-
-                return 0;
-            });
-
-    // GG: if I put the main loop inside a thread, it crashes on the first request when build in Debug!?
-    //     "Bus error: 10" - this is on macOS, it does not crash on Linux
-    //std::thread t2([&]()
-    {
-        bool running = true;
-        while (running)
-        {
-            running = llama.update_slots();
+    std::thread t([&]() {
+        if (!svr->listen_after_bind()) {
+            state.store(SERVER_STATE_ERROR);
+            return 1;
         }
-    }
-    //);
 
+        return 0;
+    });
+
+    ctx_server.queue_tasks.on_new_task(std::bind(
+        &server_context::process_single_task, &ctx_server, std::placeholders::_1));
+    ctx_server.queue_tasks.on_finish_multitask(std::bind(
+        &server_context::on_finish_multitask, &ctx_server, std::placeholders::_1));
+    ctx_server.queue_tasks.on_update_slots(std::bind(
+        &server_context::update_slots, &ctx_server));
+    ctx_server.queue_results.on_multitask_update(std::bind(
+        &server_queue::update_multitask,
+        &ctx_server.queue_tasks,
+        std::placeholders::_1,
+        std::placeholders::_2,
+        std::placeholders::_3
+    ));
+
+    shutdown_handler = [&](int) {
+        ctx_server.queue_tasks.terminate();
+    };
+
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
+    struct sigaction sigint_action;
+    sigint_action.sa_handler = signal_handler;
+    sigemptyset (&sigint_action.sa_mask);
+    sigint_action.sa_flags = 0;
+    sigaction(SIGINT, &sigint_action, NULL);
+    sigaction(SIGTERM, &sigint_action, NULL);
+#elif defined (_WIN32)
+    auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
+        return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false;
+    };
+    SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
+#endif
+
+    ctx_server.queue_tasks.start_loop();
+
+    svr->stop();
     t.join();
 
     llama_backend_free();
+
     return 0;
 }
diff --git a/examples/server/tests/README.md b/examples/server/tests/README.md
new file mode 100644
index 000000000..feb2b1d6c
--- /dev/null
+++ b/examples/server/tests/README.md
@@ -0,0 +1,67 @@
+# Server tests
+
+Python based server tests scenario using [BDD](https://en.wikipedia.org/wiki/Behavior-driven_development)
+and [behave](https://behave.readthedocs.io/en/latest/):
+
+* [issues.feature](./features/issues.feature) Pending issues scenario
+* [parallel.feature](./features/parallel.feature) Scenario involving multi slots and concurrent requests
+* [security.feature](./features/security.feature) Security, CORS and API Key
+* [server.feature](./features/server.feature) Server base scenario: completion, embedding, tokenization, etc...
+
+Tests target GitHub workflows job runners with 4 vCPU.
+
+Requests are
+using [aiohttp](https://docs.aiohttp.org/en/stable/client_reference.html), [asyncio](https://docs.python.org/fr/3/library/asyncio.html)
+based http client.
+
+Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail.
+To mitigate it, you can increase values in `n_predict`, `kv_size`.
+
+### Install dependencies
+
+`pip install -r requirements.txt`
+
+### Run tests
+
+1. Build the server
+
+```shell
+cd ../../..
+mkdir build
+cd build
+cmake ../
+cmake --build . --target server
+```
+
+2. Start the test: `./tests.sh`
+
+It's possible to override some scenario steps values with environment variables:
+
+| variable                 | description                                                                                    |
+|--------------------------|------------------------------------------------------------------------------------------------|
+| `PORT`                   | `context.server_port` to set the listening port of the server during scenario, default: `8080` |
+| `LLAMA_SERVER_BIN_PATH`  | to change the server binary path, default: `../../../build/bin/server`                         |
+| `DEBUG`                  | "ON" to enable steps and server verbose mode `--verbose`                                       |
+| `SERVER_LOG_FORMAT_JSON` | if set switch server logs to json format                                                       |
+| `N_GPU_LAYERS`           | number of model layers to offload to VRAM `-ngl --n-gpu-layers`                                |
+
+### Run @bug, @wip or @wrong_usage annotated scenario
+
+Feature or Scenario must be annotated with `@llama.cpp` to be included in the default scope.
+
+- `@bug` annotation aims to link a scenario with a GitHub issue.
+- `@wrong_usage` are meant to show user issue that are actually an expected behavior
+- `@wip` to focus on a scenario working in progress
+- `@slow` heavy test, disabled by default
+
+To run a scenario annotated with `@bug`, start:
+
+```shell
+DEBUG=ON ./tests.sh --no-skipped --tags bug --stop
+```
+
+After changing logic in `steps.py`, ensure that `@bug` and `@wrong_usage` scenario are updated.
+
+```shell
+./tests.sh --no-skipped --tags bug,wrong_usage || echo "should failed but compile"
+```
diff --git a/examples/server/tests/features/embeddings.feature b/examples/server/tests/features/embeddings.feature
new file mode 100644
index 000000000..dcf1434f9
--- /dev/null
+++ b/examples/server/tests/features/embeddings.feature
@@ -0,0 +1,96 @@
+@llama.cpp
+@embeddings
+Feature: llama.cpp server
+
+  Background: Server startup
+    Given a server listening on localhost:8080
+    And   a model url https://huggingface.co/ggml-org/models/resolve/main/bert-bge-small/ggml-model-f16.gguf
+    And   a model file ggml-model-f16.gguf
+    And   a model alias bert-bge-small
+    And   42 as server seed
+    And   2 slots
+    And   1024 as batch size
+    And   1024 as ubatch size
+    And   2048 KV cache size
+    And   embeddings extraction
+    Then  the server is starting
+    Then  the server is healthy
+
+  Scenario: Embedding
+    When embeddings are computed for:
+    """
+    What is the capital of Bulgaria ?
+    """
+    Then embeddings are generated
+
+  Scenario: OAI Embeddings compatibility
+    Given a model bert-bge-small
+    When an OAI compatible embeddings computation request for:
+    """
+    What is the capital of Spain ?
+    """
+    Then embeddings are generated
+
+  Scenario: OAI Embeddings compatibility with multiple inputs
+    Given a model bert-bge-small
+    Given a prompt:
+      """
+      In which country Paris is located ?
+      """
+    And a prompt:
+      """
+      Is Madrid the capital of Spain ?
+      """
+    When an OAI compatible embeddings computation request for multiple inputs
+    Then embeddings are generated
+
+  Scenario: Multi users embeddings
+    Given a prompt:
+      """
+      Write a very long story about AI.
+      """
+    And a prompt:
+      """
+      Write another very long music lyrics.
+      """
+    And a prompt:
+      """
+      Write a very long poem.
+      """
+    And a prompt:
+      """
+      Write a very long joke.
+      """
+    Given concurrent embedding requests
+    Then the server is busy
+    Then the server is idle
+    Then all embeddings are generated
+
+  Scenario: Multi users OAI compatibility embeddings
+    Given a prompt:
+      """
+      In which country Paris is located ?
+      """
+    And a prompt:
+      """
+      Is Madrid the capital of Spain ?
+      """
+    And a prompt:
+      """
+      What is the biggest US city ?
+      """
+    And a prompt:
+      """
+      What is the capital of Bulgaria ?
+      """
+    And   a model bert-bge-small
+    Given concurrent OAI embedding requests
+    Then the server is busy
+    Then the server is idle
+    Then all embeddings are generated
+
+  Scenario: All embeddings should be the same
+    Given 10 fixed prompts
+    And   a model bert-bge-small
+    Given concurrent OAI embedding requests
+    Then all embeddings are the same
diff --git a/examples/server/tests/features/environment.py b/examples/server/tests/features/environment.py
new file mode 100644
index 000000000..e7845dc2f
--- /dev/null
+++ b/examples/server/tests/features/environment.py
@@ -0,0 +1,71 @@
+import os
+import signal
+import socket
+import sys
+import time
+import traceback
+from contextlib import closing
+from subprocess import TimeoutExpired
+
+
+def before_scenario(context, scenario):
+    context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON'
+    if context.debug:
+        print("DEBUG=ON")
+    print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m")
+    port = 8080
+    if 'PORT' in os.environ:
+        port = int(os.environ['PORT'])
+    if is_server_listening("localhost", port):
+        assert False, "Server already started"
+
+
+def after_scenario(context, scenario):
+    try:
+        if 'server_process' not in context or context.server_process is None:
+            return
+        if scenario.status == "failed":
+            if 'GITHUB_ACTIONS' in os.environ:
+                print(f"\x1b[33;101mSCENARIO FAILED: {scenario.name} server logs:\x1b[0m\n")
+                if os.path.isfile('llama.log'):
+                    with closing(open('llama.log', 'r')) as f:
+                        for line in f:
+                            print(line)
+            if not is_server_listening(context.server_fqdn, context.server_port):
+                print("\x1b[33;101mERROR: Server stopped listening\x1b[0m")
+
+        if context.server_process.poll() is not None:
+            assert False, f"Server not running pid={context.server_process.pid} ..."
+
+        server_graceful_shutdown(context)  # SIGINT
+
+        try:
+            context.server_process.wait(0.5)
+        except TimeoutExpired:
+            print(f"server still alive after 500ms, force-killing pid={context.server_process.pid} ...")
+            context.server_process.kill()  # SIGKILL
+            context.server_process.wait()
+
+        while is_server_listening(context.server_fqdn, context.server_port):
+            time.sleep(0.1)
+    except Exception:
+        print("ignoring error in after_scenario:")
+        traceback.print_exc(file=sys.stdout)
+
+
+def server_graceful_shutdown(context):
+    print(f"shutting down server pid={context.server_process.pid} ...")
+    if os.name == 'nt':
+        interrupt = signal.CTRL_C_EVENT
+    else:
+        interrupt = signal.SIGINT
+    context.server_process.send_signal(interrupt)
+
+
+def is_server_listening(server_fqdn, server_port):
+    with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
+        result = sock.connect_ex((server_fqdn, server_port))
+        _is_server_listening = result == 0
+        if _is_server_listening:
+            print(f"server is listening on {server_fqdn}:{server_port}...")
+        return _is_server_listening
diff --git a/examples/server/tests/features/issues.feature b/examples/server/tests/features/issues.feature
new file mode 100644
index 000000000..7b13e44ca
--- /dev/null
+++ b/examples/server/tests/features/issues.feature
@@ -0,0 +1,5 @@
+# List of ongoing issues
+# run with: DEBUG=ON ./tests.sh --no-skipped --tags bug
+@bug
+Feature: Issues
+  # No confirmed issue at the moment
diff --git a/examples/server/tests/features/parallel.feature b/examples/server/tests/features/parallel.feature
new file mode 100644
index 000000000..6cd306a2b
--- /dev/null
+++ b/examples/server/tests/features/parallel.feature
@@ -0,0 +1,102 @@
+@llama.cpp
+@parallel
+Feature: Parallel
+
+  Background: Server startup
+    Given a server listening on localhost:8080
+    And   a model file tinyllamas/split/stories15M-00001-of-00003.gguf from HF repo ggml-org/models
+    And   a model file test-model-00001-of-00003.gguf
+    And   42 as server seed
+    And   128 as batch size
+    And   256 KV cache size
+    And   2 slots
+    And   continuous batching
+    Then  the server is starting
+    Then  the server is healthy
+
+  Scenario Outline: Multi users completion
+    Given a prompt:
+      """
+      Write a very long story about AI.
+      """
+    And a prompt:
+      """
+      Write another very long music lyrics.
+      """
+    And <n_predict> max tokens to predict
+    Given concurrent completion requests
+    Then the server is busy
+    Then the server is idle
+    And  all slots are idle
+    Then all prompts are predicted with <n_predict> tokens
+    Examples:
+      | n_predict |
+      | 128       |
+
+  Scenario Outline: Multi users OAI completions compatibility
+    Given a system prompt You are a writer.
+    And   a model tinyllama-2
+    Given a prompt:
+      """
+      Write a very long book.
+      """
+    And a prompt:
+      """
+      Write another a poem.
+      """
+    And <n_predict> max tokens to predict
+    And streaming is <streaming>
+    Given concurrent OAI completions requests
+    Then the server is busy
+    Then the server is idle
+    Then all prompts are predicted with <n_predict> tokens
+    Examples:
+      | streaming | n_predict |
+      | disabled  | 128       |
+      | enabled   | 64        |
+
+  Scenario Outline: Multi users OAI completions compatibility no v1
+    Given a system prompt You are a writer.
+    And   a model tinyllama-2
+    Given a prompt:
+      """
+      Write a very long book.
+      """
+    And a prompt:
+      """
+      Write another a poem.
+      """
+    And <n_predict> max tokens to predict
+    And streaming is <streaming>
+    Given concurrent OAI completions requests no v1
+    Then the server is busy
+    Then the server is idle
+    Then all prompts are predicted with <n_predict> tokens
+    Examples:
+      | streaming | n_predict |
+      | disabled  | 128       |
+      | enabled   | 64        |
+
+
+  Scenario:  Multi users with total number of tokens to predict exceeds the KV Cache size #3969
+    Given a prompt:
+      """
+      Write a very long story about AI.
+      """
+    And a prompt:
+      """
+      Write another very long music lyrics.
+      """
+    And a prompt:
+      """
+      Write a very long poem.
+      """
+    And a prompt:
+      """
+      Write a very long joke.
+      """
+    And 128 max tokens to predict
+    Given concurrent completion requests
+    Then the server is busy
+    Then the server is idle
+    Then all prompts are predicted
diff --git a/examples/server/tests/features/passkey.feature b/examples/server/tests/features/passkey.feature
new file mode 100644
index 000000000..1bde7aab8
--- /dev/null
+++ b/examples/server/tests/features/passkey.feature
@@ -0,0 +1,55 @@
+# run with: ./tests.sh --no-skipped --tags passkey
+@passkey
+@slow
+Feature: Passkey / Self-extend with context shift
+
+  Background: Server startup
+    Given a server listening on localhost:8080
+
+  # Generates a long text of junk and inserts a secret passkey number inside it.
+  # Then we query the LLM for the secret passkey.
+  # see #3856 and #4810
+  Scenario Outline: Passkey
+    Given a model file <hf_file> from HF repo <hf_repo>
+    And   <n_batch> as batch size
+    And   <n_junk> as number of junk
+    And   <n_predicted> server max tokens to predict
+    And   42 as seed
+    And   <n_ctx> KV cache size
+    And   1 slots
+    And   <n_ga> group attention factor to extend context size through self-extend
+    And   <n_ga_w> group attention width to extend context size through self-extend
+    # Can be override with N_GPU_LAYERS
+    And   <ngl> GPU offloaded layers
+    Then  the server is starting
+    Then  the server is healthy
+    Given available models
+    Then  model 0 is trained on <n_ctx_train> tokens context
+    Given a prefix prompt:
+    """
+    here is an important info hidden inside a lot of irrelevant text. Find it and memorize them. I will quiz you about the important information there.
+    """
+    And a passkey prompt template:
+    """
+    The pass key is <passkey> Remember it. <passkey> is the pass key.
+    """
+    And a junk suffix prompt:
+    """
+    The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.
+    """
+    And a suffix prompt:
+    """
+    What is the pass key? The pass key is
+    """
+    Given a "<passkey>" passkey challenge prompt with the passkey inserted every <i_pos> junk
+    And  a completion request with no api error
+    Then <n_predicted> tokens are predicted matching <re_content>
+
+    Examples:
+      | hf_repo                         | hf_file                     | n_ctx_train | ngl | n_ctx | n_batch | n_ga | n_ga_w | n_junk | i_pos | passkey | n_predicted | re_content     |
+      | TheBloke/phi-2-GGUF             | phi-2.Q4_K_M.gguf           | 2048        | 5   | 8192  | 512     | 4    | 512    | 250    | 50    | 42      | 1           | 42             |
+      | TheBloke/phi-2-GGUF             | phi-2.Q4_K_M.gguf           | 2048        | 5   | 8192  | 512     | 2    | 512    | 250    | 50    | 42      | 1           | \b((?!42)\w)+\b  |
+      #| TheBloke/Llama-2-7B-GGUF        | llama-2-7b.Q2_K.gguf        | 4096        | 3   | 16384 | 512     | 4    | 512    | 500    | 300   | 1234    | 5           | 1234           |
+      #| TheBloke/Mixtral-8x7B-v0.1-GGUF | mixtral-8x7b-v0.1.Q2_K.gguf | 32768       | 2   | 16384 | 512     | 4    | 512    | 500    | 100   | 0987    | 5           | 0
+      # 987           |
+
diff --git a/examples/server/tests/features/security.feature b/examples/server/tests/features/security.feature
new file mode 100644
index 000000000..eb82e7aca
--- /dev/null
+++ b/examples/server/tests/features/security.feature
@@ -0,0 +1,68 @@
+@llama.cpp
+@security
+Feature: Security
+
+  Background: Server startup with an api key defined
+    Given a server listening on localhost:8080
+    And   a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
+    And   a server api key llama.cpp
+    Then  the server is starting
+    Then  the server is healthy
+
+  Scenario Outline: Completion with some user api key
+    Given a prompt test
+    And   a user api key <api_key>
+    And   4 max tokens to predict
+    And   a completion request with <api_error> api error
+
+    Examples: Prompts
+      | api_key   | api_error |
+      | llama.cpp | no        |
+      | llama.cpp | no        |
+      | hackeme   | raised    |
+      |           | raised    |
+
+  Scenario Outline: OAI Compatibility
+    Given a system prompt test
+    And   a user prompt test
+    And   a model test
+    And   2 max tokens to predict
+    And   streaming is disabled
+    And   a user api key <api_key>
+    Given an OAI compatible chat completions request with <api_error> api error
+
+    Examples: Prompts
+      | api_key   | api_error |
+      | llama.cpp | no        |
+      | llama.cpp | no        |
+      | hackme    | raised    |
+
+  Scenario Outline: OAI Compatibility (invalid response formats)
+    Given a system prompt test
+    And   a user prompt test
+    And   a response format <response_format>
+    And   a model test
+    And   2 max tokens to predict
+    And   streaming is disabled
+    Given an OAI compatible chat completions request with raised api error
+
+    Examples: Prompts
+      | response_format                                       |
+      | {"type": "sound"}                                     |
+      | {"type": "json_object", "schema": 123}                |
+      | {"type": "json_object", "schema": {"type": 123}}      |
+      | {"type": "json_object", "schema": {"type": "hiccup"}} |
+
+
+  Scenario Outline: CORS Options
+    Given a user api key llama.cpp
+    When  an OPTIONS request is sent from <origin>
+    Then  CORS header <cors_header> is set to <cors_header_value>
+
+    Examples: Headers
+      | origin          | cors_header                      | cors_header_value |
+      | localhost       | Access-Control-Allow-Origin      | localhost         |
+      | web.mydomain.fr | Access-Control-Allow-Origin      | web.mydomain.fr   |
+      | origin          | Access-Control-Allow-Credentials | true              |
+      | web.mydomain.fr | Access-Control-Allow-Methods     | POST              |
+      | web.mydomain.fr | Access-Control-Allow-Headers     | *                 |
diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature
new file mode 100644
index 000000000..646a4e49d
--- /dev/null
+++ b/examples/server/tests/features/server.feature
@@ -0,0 +1,100 @@
+@llama.cpp
+@server
+Feature: llama.cpp server
+
+  Background: Server startup
+    Given a server listening on localhost:8080
+    And   a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
+    And   a model file test-model.gguf
+    And   a model alias tinyllama-2
+    And   42 as server seed
+      # KV Cache corresponds to the total amount of tokens
+      # that can be stored across all independent sequences: #4130
+      # see --ctx-size and #5568
+    And   256 KV cache size
+    And   32 as batch size
+    And   2 slots
+    And   64 server max tokens to predict
+    And   prometheus compatible metrics exposed
+    Then  the server is starting
+    Then  the server is healthy
+
+  Scenario: Health
+    Then the server is ready
+    And  all slots are idle
+
+
+  Scenario Outline: Completion
+    Given a prompt <prompt>
+    And   <n_predict> max tokens to predict
+    And   a completion request with no api error
+    Then  <n_predicted> tokens are predicted matching <re_content>
+    And   the completion is <truncated> truncated
+    And   <n_prompt> prompt tokens are processed
+    And   prometheus metrics are exposed
+    And   metric llamacpp:tokens_predicted is <n_predicted>
+
+    Examples: Prompts
+      | prompt                                                                    | n_predict | re_content                                  | n_prompt | n_predicted | truncated |
+      | I believe the meaning of life is                                          | 8         | (read\|going)+                              | 18       | 8           | not       |
+      | Write a joke about AI from a very long prompt which will not be truncated | 256       | (princesses\|everyone\|kids\|Anna\|forest)+ | 46       | 64          | not       |
+
+  Scenario: Completion prompt truncated
+    Given a prompt:
+    """
+    Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
+    Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
+    Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
+    Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
+    """
+    And   a completion request with no api error
+    Then  64 tokens are predicted matching fun|Annaks|popcorns|pictry|bowl
+    And   the completion is  truncated
+    And   109 prompt tokens are processed
+
+
+  Scenario Outline: OAI Compatibility
+    Given a model <model>
+    And   a system prompt <system_prompt>
+    And   a user prompt <user_prompt>
+    And   <max_tokens> max tokens to predict
+    And   streaming is <enable_streaming>
+    Given an OAI compatible chat completions request with no api error
+    Then  <n_predicted> tokens are predicted matching <re_content>
+    And   <n_prompt> prompt tokens are processed
+    And   the completion is <truncated> truncated
+
+    Examples: Prompts
+      | model        | system_prompt               | user_prompt                          | max_tokens | re_content                        | n_prompt | n_predicted | enable_streaming | truncated |
+      | llama-2      | Book                        | What is the best book                | 8          | (Here\|what)+                     | 77       | 8           | disabled         | not       |
+      | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128        | (thanks\|happy\|bird\|Annabyear)+ | -1       | 64          | enabled          |           |
+
+
+  Scenario Outline: OAI Compatibility w/ response format
+    Given a model test
+    And   a system prompt test
+    And   a user prompt test
+    And   a response format <response_format>
+    And   10 max tokens to predict
+    Given an OAI compatible chat completions request with no api error
+    Then  <n_predicted> tokens are predicted matching <re_content>
+
+    Examples: Prompts
+      | response_format                                                     | n_predicted | re_content             |
+      | {"type": "json_object", "schema": {"const": "42"}}                  | 5           | "42"                   |
+      | {"type": "json_object", "schema": {"items": [{"type": "integer"}]}} | 10          | \[ -300 \]             |
+      | {"type": "json_object"}                                             | 10          | \{ " Jacky.            |
+
+
+  Scenario: Tokenize / Detokenize
+    When tokenizing:
+    """
+    What is the capital of France ?
+    """
+    Then tokens can be detokenize
+
+  Scenario: Models available
+    Given available models
+    Then  1 models are supported
+    Then  model 0 is identified by tinyllama-2
+    Then  model 0 is trained on 128 tokens context
diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py
new file mode 100644
index 000000000..9a6cf7d6a
--- /dev/null
+++ b/examples/server/tests/features/steps/steps.py
@@ -0,0 +1,1146 @@
+import asyncio
+import collections
+import json
+import os
+import re
+import socket
+import subprocess
+import sys
+import threading
+import time
+from contextlib import closing
+from re import RegexFlag
+
+import aiohttp
+import numpy as np
+import openai
+from behave import step
+from behave.api.async_step import async_run_until_complete
+from prometheus_client import parser
+
+
+@step("a server listening on {server_fqdn}:{server_port}")
+def step_server_config(context, server_fqdn, server_port):
+    context.server_fqdn = server_fqdn
+    context.server_port = int(server_port)
+    context.n_gpu_layer = None
+    if 'PORT' in os.environ:
+        context.server_port = int(os.environ['PORT'])
+        print(f"$PORT set, overriding server port with to {context.server_port}")
+    if 'FQDN' in os.environ:
+        context.server_fqdn = os.environ['FQDN']
+        print(f"$FQDN set, overriding server fqdn with to {context.server_fqdn}")
+    if 'N_GPU_LAYERS' in os.environ:
+        context.n_gpu_layer = int(os.environ['N_GPU_LAYERS'])
+        print(f"$N_GPU_LAYERS set, overriding n_gpu_layer with to {context.n_gpu_layer}")
+
+    context.base_url = f'http://{context.server_fqdn}:{context.server_port}'
+
+    context.model_alias = None
+    context.model_file = None
+    context.model_hf_repo = None
+    context.model_hf_file = None
+    context.model_url = None
+    context.n_batch = None
+    context.n_ubatch = None
+    context.n_ctx = None
+    context.n_ga = None
+    context.n_ga_w = None
+    context.n_predict = None
+    context.n_prompts = 0
+    context.n_server_predict = None
+    context.n_slots = None
+    context.prompt_prefix = None
+    context.prompt_suffix = None
+    context.server_api_key = None
+    context.server_continuous_batching = False
+    context.server_embeddings = False
+    context.server_metrics = False
+    context.server_process = None
+    context.seed = None
+    context.server_seed = None
+    context.user_api_key = None
+    context.response_format = None
+
+    context.tasks_result = []
+    context.concurrent_tasks = []
+    context.prompts = []
+
+
+@step('a model file {hf_file} from HF repo {hf_repo}')
+def step_download_hf_model(context, hf_file, hf_repo):
+    context.model_hf_repo = hf_repo
+    context.model_hf_file = hf_file
+    context.model_file = os.path.basename(hf_file)
+
+
+@step('a model file {model_file}')
+def step_model_file(context, model_file):
+    context.model_file = model_file
+
+
+@step('a model url {model_url}')
+def step_model_url(context, model_url):
+    context.model_url = model_url
+
+
+@step('a model alias {model_alias}')
+def step_model_alias(context, model_alias):
+    context.model_alias = model_alias
+
+
+@step('{seed:d} as server seed')
+def step_seed(context, seed):
+    context.server_seed = seed
+
+
+@step('{ngl:d} GPU offloaded layers')
+def step_n_gpu_layer(context, ngl):
+    if 'N_GPU_LAYERS' in os.environ:
+        new_ngl = int(os.environ['N_GPU_LAYERS'])
+        if context.debug:
+            print(f"-ngl upgraded from {ngl} to {new_ngl}")
+        ngl = new_ngl
+    context.n_gpu_layer = ngl
+
+
+@step('{n_ctx:d} KV cache size')
+def step_n_ctx(context, n_ctx):
+    context.n_ctx = n_ctx
+
+
+@step('{n_slots:d} slots')
+def step_n_slots(context, n_slots):
+    context.n_slots = n_slots
+
+
+@step('{n_predict:d} server max tokens to predict')
+def step_server_n_predict(context, n_predict):
+    context.n_server_predict = n_predict
+
+
+@step('continuous batching')
+def step_server_continuous_batching(context):
+    context.server_continuous_batching = True
+
+
+@step('embeddings extraction')
+def step_server_embeddings(context):
+    context.server_embeddings = True
+
+
+@step('prometheus compatible metrics exposed')
+def step_server_metrics(context):
+    context.server_metrics = True
+
+
+@step("the server is starting")
+def step_start_server(context):
+    start_server_background(context)
+    attempts = 0
+    max_attempts = 20
+    if 'GITHUB_ACTIONS' in os.environ:
+        max_attempts *= 2
+
+    addrs = socket.getaddrinfo(context.server_fqdn, context.server_port, type=socket.SOCK_STREAM)
+    family, typ, proto, _, sockaddr = addrs[0]
+
+    while True:
+        with closing(socket.socket(family, typ, proto)) as sock:
+            result = sock.connect_ex(sockaddr)
+            if result == 0:
+                print("\x1b[33;46mserver started!\x1b[0m")
+                return
+            attempts += 1
+            if attempts > max_attempts:
+                assert False, "server not started"
+            print(f"waiting for server to start, connect error code = {result}...")
+            time.sleep(0.1)
+
+
+@step("the server is {expecting_status}")
+@async_run_until_complete
+async def step_wait_for_the_server_to_be_started(context, expecting_status):
+    match expecting_status:
+        case 'healthy':
+            await wait_for_health_status(context, context.base_url, 200, 'ok',
+                                         timeout=30)
+
+        case 'ready' | 'idle':
+            await wait_for_health_status(context, context.base_url, 200, 'ok',
+                                         timeout=10,
+                                         params={'fail_on_no_slot': 0, 'include_slots': 0},
+                                         slots_idle=context.n_slots,
+                                         slots_processing=0,
+                                         expected_slots=[{'id': slot_id, 'state': 0}
+                                                         for slot_id in
+                                                         range(context.n_slots if context.n_slots else 1)])
+        case 'busy':
+            await wait_for_health_status(context, context.base_url, 503,
+                                         'no slot available',
+                                         params={'fail_on_no_slot': 0, 'include_slots': 0},
+                                         slots_idle=0,
+                                         slots_processing=context.n_slots,
+                                         expected_slots=[{'id': slot_id, 'state': 1}
+                                                         for slot_id in
+                                                         range(context.n_slots if context.n_slots else 1)])
+        case _:
+            assert False, "unknown status"
+
+
+@step('all slots are {expected_slot_status_string}')
+@async_run_until_complete
+async def step_all_slots_status(context, expected_slot_status_string):
+    match expected_slot_status_string:
+        case 'idle':
+            expected_slot_status = 0
+        case 'busy':
+            expected_slot_status = 1
+        case _:
+            assert False, "unknown status"
+
+    expected_slots = [{'id': slot_id, 'state': expected_slot_status}
+                      for slot_id in range(context.n_slots)]
+    await request_slots_status(context, expected_slots)
+
+
+@step('a completion request with {api_error} api error')
+@async_run_until_complete
+async def step_request_completion(context, api_error):
+    expect_api_error = api_error == 'raised'
+    completion = await request_completion(context.prompts.pop(),
+                                          context.base_url,
+                                          debug=context.debug,
+                                          n_predict=context.n_predict,
+                                          seed=await completions_seed(context),
+                                          expect_api_error=expect_api_error,
+                                          user_api_key=context.user_api_key)
+    context.tasks_result.append(completion)
+    if context.debug:
+        print(f"Completion response: {completion}")
+    if expect_api_error:
+        assert completion == 401, f"completion must be an 401 status code: {completion}"
+
+
+@step('{predicted_n:d} tokens are predicted matching {re_content}')
+def step_n_tokens_predicted_with_content(context, predicted_n, re_content):
+    context.completion = context.tasks_result.pop()
+    assert_n_tokens_predicted(context.completion, predicted_n, re_content)
+
+
+@step('{predicted_n:d} tokens are predicted')
+def step_n_tokens_predicted(context, predicted_n):
+    context.completion = context.tasks_result.pop()
+    assert_n_tokens_predicted(context.completion, predicted_n)
+
+
+@step('the completion is  truncated')
+def step_assert_completion_truncated(context):
+    step_assert_completion_truncated(context, '')
+
+
+@step('the completion is {truncated} truncated')
+def step_assert_completion_truncated(context, truncated):
+    truncated = truncated != "not"
+    assert context.completion['truncated'] == truncated, f'{context.completion}'
+
+
+@step('{n_prompt:d} prompt tokens are processed')
+def step_impl(context, n_prompt):
+    assert n_prompt < 0 or n_prompt == context.completion['timings']['prompt_n'], f"n_prompt={context.completion['timings']['prompt_n']}"
+
+
+@step('a user prompt {user_prompt}')
+def step_user_prompt(context, user_prompt):
+    context.prompts.append(user_prompt)
+    context.n_prompts = len(context.prompts)
+
+
+@step('a system prompt {system_prompt}')
+def step_system_prompt(context, system_prompt):
+    context.system_prompt = system_prompt
+
+
+@step('a model {model}')
+def step_model(context, model):
+    context.model = model
+
+
+@step('{max_tokens:d} max tokens to predict')
+def step_max_tokens(context, max_tokens):
+    context.n_predict = max_tokens
+
+
+@step('a response format {response_format}')
+def step_response_format(context, response_format):
+    context.response_format = json.loads(response_format)
+
+
+@step('streaming is {enable_streaming}')
+def step_streaming(context, enable_streaming):
+    context.enable_streaming = enable_streaming == 'enabled'
+
+
+@step('a user api key {user_api_key}')
+def step_user_api_key(context, user_api_key):
+    context.user_api_key = user_api_key
+
+
+@step('no user api key')
+def step_no_user_api_key(context):
+    context.user_api_key = None
+
+
+@step('a user api key ')
+def step_no_user_api_key_space(context):
+    context.user_api_key = None
+
+
+@step('a server api key {server_api_key}')
+def step_server_api_key(context, server_api_key):
+    context.server_api_key = server_api_key
+
+
+@step('{n_junk:d} as number of junk')
+def step_n_junk(context, n_junk):
+    context.n_junk = n_junk
+
+
+@step('{n_batch:d} as batch size')
+def step_n_batch(context, n_batch):
+    context.n_batch = n_batch
+
+
+@step('{n_ubatch:d} as ubatch size')
+def step_n_ubatch(context, n_ubatch):
+    context.n_ubatch = n_ubatch
+
+
+@step('{seed:d} as seed')
+def step_seed(context, seed):
+    context.seed = seed
+
+
+@step('a prefix prompt')
+def step_prompt_prefix(context):
+    context.prompt_prefix = context_text(context)
+
+
+@step('a junk suffix prompt')
+def step_prompt_junk_suffix(context):
+    context.prompt_junk_suffix = context_text(context)
+
+
+@step('a suffix prompt')
+def step_prompt_suffix(context):
+    context.prompt_suffix = context_text(context)
+
+
+@step('{n_ga:d} group attention factor'
+      ' to extend context size through self-extend')
+def step_impl(context, n_ga):
+    context.n_ga = n_ga
+
+
+@step('{n_ga_w:d} group attention width to extend context size through self-extend')
+def step_impl(context, n_ga_w):
+    context.n_ga_w = n_ga_w
+
+
+@step('a passkey prompt template')
+def step_prompt_passkey(context):
+    context.prompt_passkey = context_text(context)
+
+
+@step('{n_prompts:d} fixed prompts')
+def step_fixed_prompts(context, n_prompts):
+    context.prompts.extend([str(0)*(context.n_batch if context.n_batch is not None else 512) for i in range(n_prompts)])
+    context.n_prompts = n_prompts
+
+
+@step('a "{passkey}" passkey challenge prompt with the passkey inserted every {i_pos:d} junk')
+def step_prompt_passkey(context, passkey, i_pos):
+    prompt = ""
+    for i in range(context.n_junk):
+        if i % context.n_junk == i_pos:
+            prompt += context.prompt_passkey # the passkey is already substituted
+        prompt += context.prompt_junk_suffix
+    if context.debug:
+        passkey_highlight = "\x1b[33m" + passkey + "\x1b[0m"
+        print(f"Passkey challenge:\n```{prompt.replace(passkey, passkey_highlight)}```")
+    context.prompts.append(context.prompt_prefix + prompt + context.prompt_suffix)
+    context.n_prompts = len(context.prompts)
+
+
+@step('an OAI compatible chat completions request with {api_error} api error')
+@async_run_until_complete
+async def step_oai_chat_completions(context, api_error):
+    if context.debug:
+        print(f"Submitting OAI compatible completions request...")
+    expect_api_error = api_error == 'raised'
+    completion = await oai_chat_completions(context.prompts.pop(),
+                                            context.system_prompt,
+                                            context.base_url,
+                                            '/v1/chat',
+                                            False,
+                                            model=context.model if hasattr(context, 'model') else None,
+
+                                            n_predict=context.n_predict
+                                            if hasattr(context, 'n_predict') else None,
+
+                                            enable_streaming=context.enable_streaming
+                                            if hasattr(context, 'enable_streaming') else None,
+
+                                            response_format=context.response_format
+                                            if hasattr(context, 'response_format') else None,
+
+                                            seed=await completions_seed(context),
+
+                                            user_api_key=context.user_api_key
+                                            if hasattr(context, 'user_api_key') else None,
+
+                                            expect_api_error=expect_api_error)
+    context.tasks_result.append(completion)
+    if context.debug:
+        print(f"Completion response: {completion}")
+    if expect_api_error:
+        assert completion == 401, f"completion must be an 401 status code: {completion}"
+
+    if context.debug:
+        print(f"Completion response: {completion}")
+
+
+@step('a prompt')
+def step_a_prompt(context):
+    context.prompts.append(context_text(context))
+    context.n_prompts = len(context.prompts)
+
+
+@step('a prompt {prompt}')
+def step_a_prompt_prompt(context, prompt):
+    context.prompts.append(prompt)
+    context.n_prompts = len(context.prompts)
+
+
+@step('concurrent completion requests')
+@async_run_until_complete()
+async def step_concurrent_completion_requests(context):
+    await concurrent_requests(context,
+                              request_completion,
+                              # prompt is inserted automatically
+                              context.base_url,
+                              debug=context.debug,
+                              prompt_prefix=context.prompt_prefix,
+                              prompt_suffix=context.prompt_suffix,
+                              n_predict=context.n_predict if hasattr(context, 'n_predict') else None,
+                              seed=await completions_seed(context),
+                              user_api_key=context.user_api_key if hasattr(context,
+                                                                           'user_api_key') else None)
+
+
+@step('concurrent OAI completions requests')
+@async_run_until_complete
+async def step_oai_chat_completions(context):
+    await concurrent_requests(context, oai_chat_completions,
+                              # user_prompt is inserted automatically
+                              context.system_prompt,
+                              context.base_url,
+                              '/v1/chat/completions',
+                              True,  # async_client
+                              model=context.model
+                              if hasattr(context, 'model') else None,
+                              n_predict=context.n_predict
+                              if hasattr(context, 'n_predict') else None,
+                              enable_streaming=context.enable_streaming
+                              if hasattr(context, 'enable_streaming') else None,
+                              response_format=context.response_format
+                              if hasattr(context, 'response_format') else None,
+                              seed=await completions_seed(context),
+                              user_api_key=context.user_api_key
+                              if hasattr(context, 'user_api_key') else None)
+
+
+@step('concurrent OAI completions requests no v1')
+@async_run_until_complete
+async def step_oai_chat_completions(context):
+    await concurrent_requests(context, oai_chat_completions,
+                              # user_prompt is inserted automatically
+                              context.system_prompt,
+                              context.base_url,
+                              '/chat/completions',
+                              True,  # async_client
+                              model=context.model
+                              if hasattr(context, 'model') else None,
+                              n_predict=context.n_predict
+                              if hasattr(context, 'n_predict') else None,
+                              enable_streaming=context.enable_streaming
+                              if hasattr(context, 'enable_streaming') else None,
+                              response_format=context.response_format
+                              if hasattr(context, 'response_format') else None,
+                              seed=context.seed
+                              if hasattr(context, 'seed') else
+                              context.server_seed
+                              if hasattr(context, 'server_seed') else None,
+                              user_api_key=context.user_api_key
+                              if hasattr(context, 'user_api_key') else None)
+
+
+@step('all prompts are predicted')
+@async_run_until_complete
+async def step_all_prompts_are_predicted(context):
+    await all_prompts_are_predicted(context)
+
+
+@step('all prompts are predicted with {n_expected_predicted:d} tokens')
+@async_run_until_complete
+async def step_all_prompts_are_predicted_with_n_tokens(context, n_expected_predicted):
+    await all_prompts_are_predicted(context, n_expected_predicted)
+
+
+async def all_prompts_are_predicted(context, expected_predicted_n=None):
+    n_completions = await gather_tasks_results(context)
+    assert n_completions > 0
+    for i in range(n_completions):
+        assert_n_tokens_predicted(context.tasks_result.pop(), expected_predicted_n=expected_predicted_n)
+    assert len(context.concurrent_tasks) == 0, f"{len(context.concurrent_tasks)} pending requests"
+
+
+@step('embeddings are computed for')
+@async_run_until_complete
+async def step_compute_embedding(context):
+    context.n_prompts = 1
+    context.embeddings = await request_embedding(context_text(context), base_url=context.base_url)
+
+
+@step('all embeddings are the same')
+@async_run_until_complete
+async def step_all_embeddings_are_the_same(context):
+    n_embedding_requests = await gather_tasks_results(context)
+    assert n_embedding_requests > 0
+    embeddings = []
+    for i in range(n_embedding_requests):
+        embedding = context.tasks_result.pop().pop()
+        embeddings.append(embedding)
+        assert_embeddings(embedding)
+    n = len(embeddings)
+    for i in range(n-1):
+        for j in range(i+1, n):
+            embedding1 = np.array(embeddings[i])
+            embedding2 = np.array(embeddings[j])
+            if context.debug:
+                print(f"embedding1: {embedding1[-8:]}")
+                print(f"embedding2: {embedding2[-8:]}")
+            similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
+            msg = f"Similarity between {i} and {j}: {similarity:.10f}"
+            if context.debug:
+                print(f"{msg}")
+            assert np.isclose(similarity, 1.0, rtol=1e-05, atol=1e-08, equal_nan=False), msg
+
+
+@step('embeddings are generated')
+def step_assert_embeddings(context):
+    assert context.n_prompts == len(context.embeddings), (f"unexpected response:\n"
+                                                             f"context.n_prompts={context.n_prompts}\n"
+                                                             f"context.embeddings={context.embeddings}")
+    for embedding in context.embeddings:
+        assert_embeddings(embedding)
+
+
+@step('an OAI compatible embeddings computation request for')
+@async_run_until_complete
+async def step_oai_compute_embeddings(context):
+    context.n_prompts = 1
+    context.embeddings = await request_oai_embeddings(context_text(context),
+                                                      base_url=context.base_url,
+                                                      user_api_key=context.user_api_key,
+                                                      model=context.model)
+
+
+@step('an OAI compatible embeddings computation request for multiple inputs')
+@async_run_until_complete
+async def step_oai_compute_embeddings_multiple_inputs(context):
+    context.embeddings = await request_oai_embeddings(context.prompts,
+                                                      base_url=context.base_url,
+                                                      user_api_key=context.user_api_key,
+                                                      model=context.model)
+    context.prompts.clear()
+
+
+@step('concurrent embedding requests')
+@async_run_until_complete()
+async def step_concurrent_embedding_requests(context):
+    await concurrent_requests(context,
+                              request_embedding,
+                              # prompt is inserted automatically
+                              base_url=context.base_url)
+
+
+@step('concurrent OAI embedding requests')
+@async_run_until_complete()
+async def step_concurrent_oai_embedding_requests(context):
+    await concurrent_requests(context,
+                              request_oai_embeddings,
+                              # prompt is inserted automatically
+                              base_url=context.base_url,
+                              async_client=True,
+                              model=context.model)
+
+
+@step('all embeddings are generated')
+@async_run_until_complete()
+async def all_embeddings_are_generated(context):
+    n_embedding_requests = await gather_tasks_results(context)
+    assert n_embedding_requests == context.n_prompts
+    for i in range(n_embedding_requests):
+        assert_embeddings(context.tasks_result.pop().pop())
+
+
+@step('tokenizing')
+@async_run_until_complete
+async def step_tokenize(context):
+    context.tokenized_text = context_text(context)
+    async with aiohttp.ClientSession() as session:
+        async with session.post(f'{context.base_url}/tokenize',
+                                json={
+                                    "content": context.tokenized_text,
+                                }) as response:
+            assert response.status == 200
+            tokenize_json = await response.json()
+            context.tokens = tokenize_json['tokens']
+
+
+@step('tokens can be detokenize')
+@async_run_until_complete
+async def step_detokenize(context):
+    assert len(context.tokens) > 0
+    async with aiohttp.ClientSession() as session:
+        async with session.post(f'{context.base_url}/detokenize',
+                                json={
+                                    "tokens": context.tokens,
+                                }) as response:
+            assert response.status == 200
+            detokenize_json = await response.json()
+            # SPM tokenizer adds a whitespace prefix: https://github.com/google/sentencepiece/issues/15
+            assert context.tokenized_text == detokenize_json['content'].strip()
+
+
+@step('an OPTIONS request is sent from {origin}')
+@async_run_until_complete
+async def step_options_request(context, origin):
+    async with aiohttp.ClientSession() as session:
+        headers = {'Authorization': f'Bearer {context.user_api_key}', 'Origin': origin}
+        async with session.options(f'{context.base_url}/v1/chat/completions',
+                                    headers=headers) as response:
+            assert response.status == 200
+            context.options_response = response
+
+
+@step('CORS header {cors_header} is set to {cors_header_value}')
+def step_check_options_header_value(context, cors_header, cors_header_value):
+    assert context.options_response.headers[cors_header] == cors_header_value
+
+
+@step('prometheus metrics are exposed')
+@async_run_until_complete
+async def step_prometheus_metrics_exported(context):
+    async with aiohttp.ClientSession() as session:
+        async with await session.get(f'{context.base_url}/metrics') as metrics_response:
+            assert metrics_response.status == 200
+            assert metrics_response.headers['Content-Type'] == "text/plain; version=0.0.4"
+            metrics_raw = await metrics_response.text()
+            metric_exported = False
+            if context.debug:
+                print(f"/metrics answer:\n{metrics_raw}")
+            context.metrics = {}
+            for metric in parser.text_string_to_metric_families(metrics_raw):
+                match metric.name:
+                    case "llamacpp:kv_cache_usage_ratio":
+                        assert len(metric.samples) > 0
+                        metric_exported = True
+                context.metrics[metric.name] = metric
+            assert int(metrics_response.headers["Process-Start-Time-Unix"]) > 0, "no header process start time"
+            assert metric_exported, "No metrics exported"
+
+
+@step('metric {metric_name} is {metric_value:d}')
+def step_assert_metric_value(context, metric_name, metric_value):
+    if metric_name not in context.metrics:
+        assert False, f"no metric {metric_name} in {context.metrics.keys()}"
+    assert context.metrics[metric_name].samples[0].value == metric_value, f"metric: {context.metrics[metric_name]}"
+
+
+@step('available models')
+def step_available_models(context):
+    # openai client always expects an api_key
+    openai.api_key = context.user_api_key if context.user_api_key is not None else 'nope'
+    openai.api_base = f'{context.base_url}/v1'
+    context.models = openai.Model.list().data
+
+
+@step('{n_model:d} models are supported')
+def step_supported_models(context, n_model):
+    if context.debug:
+        print("server models available:", context.models)
+    assert len(context.models) == n_model
+
+
+@step('model {i_model:d} is {param} {preposition} {param_value}')
+def step_supported_models(context, i_model, param, preposition, param_value):
+    assert i_model < len(context.models)
+    model = context.models[i_model]
+
+    param_value = param_value.split(' ', 1)[0]
+    match param:
+        case 'identified':
+            value = model.id
+        case 'trained':
+            value = str(model.meta.n_ctx_train)
+        case _:
+            assert False, "param {param} not supported"
+    assert param_value == value, f"model param {param} {value} != {param_value}"
+
+
+async def concurrent_requests(context, f_completion, *args, **kwargs):
+    context.n_prompts = len(context.prompts)
+    if context.debug:
+        print(f"starting {context.n_prompts} concurrent completion requests...")
+    assert context.n_prompts > 0
+    for prompt_no in range(context.n_prompts):
+        shifted_args = [context.prompts.pop(), *args]
+        context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs)))
+    await asyncio.sleep(0.1)
+
+
+async def request_completion(prompt,
+                             base_url,
+                             debug=False,
+                             prompt_prefix=None,
+                             prompt_suffix=None,
+                             n_predict=None,
+                             seed=None,
+                             expect_api_error=None,
+                             user_api_key=None):
+    if debug:
+        print(f"Sending completion request: {prompt}")
+    origin = "my.super.domain"
+    headers = {
+        'Origin': origin
+    }
+    if user_api_key is not None:
+        if debug:
+            print(f"Set user_api_key: {user_api_key}")
+        headers['Authorization'] = f'Bearer {user_api_key}'
+
+    async with aiohttp.ClientSession() as session:
+        async with session.post(f'{base_url}/completion',
+                                json={
+                                    "input_prefix": prompt_prefix,
+                                    "prompt": prompt,
+                                    "input_suffix": prompt_suffix,
+                                    "n_predict": n_predict if n_predict is not None else -1,
+                                    "seed": seed if seed is not None else 42
+                                },
+                                headers=headers,
+                                timeout=3600) as response:
+            if expect_api_error is None or not expect_api_error:
+                assert response.status == 200
+                assert response.headers['Access-Control-Allow-Origin'] == origin
+                return await response.json()
+            else:
+                return response.status
+
+
+async def oai_chat_completions(user_prompt,
+                               system_prompt,
+                               base_url,
+                               base_path,
+                               async_client,
+                               debug=False,
+                               model=None,
+                               n_predict=None,
+                               enable_streaming=None,
+                               response_format=None,
+                               seed=None,
+                               user_api_key=None,
+                               expect_api_error=None):
+    if debug:
+        print(f"Sending OAI Chat completions request: {user_prompt}")
+    # openai client always expects an api key
+    user_api_key = user_api_key if user_api_key is not None else 'nope'
+    seed = seed if seed is not None else 42
+    enable_streaming = enable_streaming if enable_streaming is not None else False
+    payload = {
+        "messages": [
+            {
+                "role": "system",
+                "content": system_prompt,
+            },
+            {
+                "role": "user",
+                "content": user_prompt,
+            }
+        ],
+        "model": model,
+        "max_tokens": n_predict,
+        "stream": enable_streaming,
+        "seed": seed
+    }
+    if response_format is not None:
+        payload['response_format'] = response_format
+    completion_response = {
+        'content': '',
+        'timings': {
+            'predicted_n': 0,
+            'prompt_n': 0
+        }
+    }
+    if async_client:
+        origin = 'llama.cpp'
+        headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin}
+        async with aiohttp.ClientSession() as session:
+            async with session.post(f'{base_url}{base_path}',
+                                    json=payload,
+                                    headers=headers) as response:
+                if enable_streaming:
+                    assert response.status == 200
+                    assert response.headers['Access-Control-Allow-Origin'] == origin
+                    assert response.headers['Content-Type'] == "text/event-stream"
+                    event_received = True
+                    while event_received:
+                        event_received = False
+                        async for line_in_bytes in response.content:
+                            line = line_in_bytes.decode('utf8')
+                            line = line.rstrip('\n').rstrip('\r')
+                            if line == '':
+                                continue
+                            event_data = line.split(': ', 1)
+                            assert event_data[0] == 'data', f'Bad event code received: ```{event_data}```'
+                            chunk_raw = event_data[1]
+
+                            chunk = json.loads(chunk_raw)
+                            assert len(chunk['choices']) == 1, f"no choices provided, line ```{line}```"
+                            delta = chunk['choices'][0]['delta']
+                            if 'content' in delta:
+                                completion_response['content'] += delta['content']
+                                completion_response['timings']['predicted_n'] += 1
+                else:
+                    if expect_api_error is None or not expect_api_error:
+                        assert response.status == 200
+                        assert response.headers['Access-Control-Allow-Origin'] == origin
+                        assert response.headers['Content-Type'] == "application/json; charset=utf-8"
+                        chat_completion_raw = await response.json()
+                        completion_response = {
+                            'content': chat_completion_raw['choices'][0]['message'],
+                            'timings': {
+                                'predicted_n': chat_completion_raw['usage']['completion_tokens'],
+                                'prompt_n': chat_completion_raw['usage']['prompt_tokens']
+                            }
+                        }
+                    else:
+                        return response.status
+    else:
+        try:
+            openai.api_key = user_api_key
+            openai.api_base = f'{base_url}{base_path}'
+            chat_completion = openai.Completion.create(
+                messages=payload['messages'],
+                model=model,
+                max_tokens=n_predict,
+                stream=enable_streaming,
+                response_format=payload.get('response_format'),
+                seed=seed
+            )
+        except openai.error.AuthenticationError as e:
+            if expect_api_error is not None and expect_api_error:
+                return 401
+            else:
+                assert False, f'error raised: {e}'
+
+        if enable_streaming:
+            for chunk in chat_completion:
+                assert len(chunk.choices) == 1
+                delta = chunk.choices[0].delta
+                if 'content' in delta:
+                    completion_response['content'] += delta['content']
+                    completion_response['timings']['predicted_n'] += 1
+                completion_response['truncated'] = chunk.choices[0].finish_reason != 'stop'
+        else:
+            assert len(chat_completion.choices) == 1
+            completion_response = {
+                'content': chat_completion.choices[0].message.content,
+                'timings': {
+                    'predicted_n': chat_completion.usage.completion_tokens,
+                    'prompt_n': chat_completion.usage.prompt_tokens
+                    },
+                'truncated': chat_completion.choices[0].finish_reason != 'stop'
+            }
+    if debug:
+        print("OAI response formatted to llama.cpp:", completion_response)
+    return completion_response
+
+
+async def request_embedding(content, base_url=None):
+    async with aiohttp.ClientSession() as session:
+        async with session.post(f'{base_url}/embedding',
+                                json={
+                                    "content": content,
+                                }) as response:
+            assert response.status == 200
+            response_json = await response.json()
+            return [response_json['embedding']]
+
+
+async def request_oai_embeddings(input,
+                                 base_url=None, user_api_key=None,
+                                 model=None, async_client=False):
+    # openai client always expects an api_key
+    user_api_key = user_api_key if user_api_key is not None else 'nope'
+    if async_client:
+        origin = 'llama.cpp'
+        headers=[]
+        if user_api_key is not None:
+            headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin}
+        async with aiohttp.ClientSession() as session:
+            async with session.post(f'{base_url}/v1/embeddings',
+                                    json={
+                                        "input": input,
+                                        "model": model,
+                                    },
+                                    headers=headers,
+                                    timeout=3600) as response:
+                assert response.status == 200, f"received status code not expected: {response.status}"
+                assert response.headers['Access-Control-Allow-Origin'] == origin
+                assert response.headers['Content-Type'] == "application/json; charset=utf-8"
+                response_json = await response.json()
+                assert response_json['model'] == model, f"invalid model received: {response_json['model']}"
+                assert response_json['object'] == 'list'
+                if isinstance(input, collections.abc.Sequence):
+                    embeddings = []
+                    for an_oai_embeddings in response_json['data']:
+                        embeddings.append(an_oai_embeddings['embedding'])
+                else:
+                    embeddings = [response_json['data']['embedding']]
+                return embeddings
+    else:
+        openai.api_key = user_api_key
+        openai.api_base = f'{base_url}/v1'
+        oai_embeddings = openai.Embedding.create(
+            model=model,
+            input=input,
+        )
+
+        if isinstance(input, collections.abc.Sequence):
+            embeddings = []
+            for an_oai_embeddings in oai_embeddings.data:
+                embeddings.append(an_oai_embeddings.embedding)
+        else:
+            embeddings = [oai_embeddings.data.embedding]
+        return embeddings
+
+
+def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re_content=None):
+    content = completion_response['content']
+    n_predicted = completion_response['timings']['predicted_n']
+    assert len(content) > 0, "no token predicted"
+    if re_content is not None:
+        p = re.compile(re_content, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL)
+        matches = p.finditer(content)
+        last_match = 0
+        highlighted = ''
+        for match in matches:
+            start, end = match.span()
+            highlighted += content[last_match: start]
+            highlighted += '\x1b[33m'
+            highlighted += content[start: end]
+            highlighted += '\x1b[0m'
+            last_match = end
+        highlighted += content[last_match:]
+        if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
+          print(f"Checking completion response: {highlighted}")
+        assert last_match > 0, f'/{re_content}/ must match ```{highlighted}```'
+    if expected_predicted_n and expected_predicted_n > 0:
+        assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:'
+                                                     f' {n_predicted} <> {expected_predicted_n}')
+
+
+async def gather_tasks_results(context):
+    n_tasks = len(context.concurrent_tasks)
+    if context.debug:
+        print(f"Waiting for all {n_tasks} tasks results...")
+    for task_no in range(n_tasks):
+        context.tasks_result.append(await context.concurrent_tasks.pop())
+    n_completions = len(context.tasks_result)
+    return n_completions
+
+
+async def wait_for_health_status(context,
+                                 base_url,
+                                 expected_http_status_code,
+                                 expected_health_status,
+                                 timeout=3,
+                                 params=None,
+                                 slots_idle=None,
+                                 slots_processing=None,
+                                 expected_slots=None):
+    if context.debug:
+        print(f"Starting checking for health for expected_health_status={expected_health_status}")
+    interval = 0.5
+    counter = 0
+    if 'GITHUB_ACTIONS' in os.environ:
+        timeout *= 2
+
+    async with aiohttp.ClientSession() as session:
+        while True:
+            async with await session.get(f'{base_url}/health', params=params) as health_response:
+                status_code = health_response.status
+                health = await health_response.json()
+                if context.debug:
+                    print(f"HEALTH - response for expected health status='{expected_health_status}' on "
+                          f"'{base_url}/health'?{params} is {health}\n")
+                if (status_code == expected_http_status_code
+                        and health['status'] == expected_health_status
+                        and (slots_idle is None or health['slots_idle'] == slots_idle)
+                        and (slots_processing is None or health['slots_processing'] == slots_processing)):
+                    if expected_slots is not None:
+                        assert_slots_status(health['slots'], expected_slots)
+                    return
+                if (status_code == expected_http_status_code
+                        and health['status'] == expected_health_status
+                        and (slots_idle is None or health['slots_idle'] == slots_idle)
+                        and (slots_processing is None or health['slots_processing'] == slots_processing)):
+                    if expected_slots is not None:
+                        assert_slots_status(health['slots'], expected_slots)
+                    return
+            await asyncio.sleep(interval)
+
+            counter += interval
+            if counter >= timeout:
+                # Sometimes health requests are triggered after completions are predicted
+                if expected_http_status_code == 503:
+                    if len(context.tasks_result) == 0:
+                        print("\x1b[5;37;43mWARNING: forcing concurrent tasks,"
+                              " busy health check missed, probably too fast inference\x1b[0m\n")
+                        n_completions = await gather_tasks_results(context)
+                        if n_completions > 0:
+                            return
+
+                assert False, f'{expected_health_status} timeout exceeded {counter}s>={timeout}'
+
+
+def assert_embeddings(embeddings):
+    assert len(embeddings) > 0
+    embeddings_computed = False
+    for emb in embeddings:
+        if not isinstance(emb, float):
+            assert False, f"Bad embeddings: {embeddings}"
+        if emb != 0:
+            embeddings_computed = True
+    assert embeddings_computed, f"Embeddings: {embeddings}"
+
+
+async def request_slots_status(context, expected_slots):
+    async with aiohttp.ClientSession() as session:
+        async with await session.get(f'{context.base_url}/slots') as slots_response:
+            assert slots_response.status == 200
+            slots = await slots_response.json()
+            assert_slots_status(slots, expected_slots)
+
+
+def assert_slots_status(slots, expected_slots):
+    assert len(slots) == len(expected_slots)
+    for slot_id, (expected, slot) in enumerate(zip(expected_slots, slots)):
+        for key in expected:
+            assert expected[key] == slot[key], (f"invalid slot {slot_id}"
+                                                f" expected[{key}] != slot[{key}]"
+                                                f" = {expected[key]} != {slot[key]}")
+
+
+async def completions_seed(context):
+    return context.seed if hasattr(context, 'seed') and context.seed is not None \
+        else context.server_seed if hasattr(context, 'server_seed') else None
+
+
+def context_text(context):
+    return context.text.replace('\r', '')
+
+
+def start_server_background(context):
+    if os.name == 'nt':
+        context.server_path = '../../../build/bin/Release/server.exe'
+    else:
+        context.server_path = '../../../build/bin/server'
+    if 'LLAMA_SERVER_BIN_PATH' in os.environ:
+        context.server_path = os.environ['LLAMA_SERVER_BIN_PATH']
+    server_listen_addr = context.server_fqdn
+    server_args = [
+        '--host', server_listen_addr,
+        '--port', context.server_port,
+    ]
+    if context.model_file:
+        server_args.extend(['--model', context.model_file])
+    if context.model_url:
+        server_args.extend(['--model-url', context.model_url])
+    if context.model_hf_repo:
+        server_args.extend(['--hf-repo', context.model_hf_repo])
+    if context.model_hf_file:
+        server_args.extend(['--hf-file', context.model_hf_file])
+    if context.n_batch:
+        server_args.extend(['--batch-size', context.n_batch])
+    if context.n_ubatch:
+        server_args.extend(['--ubatch-size', context.n_ubatch])
+    if context.n_gpu_layer:
+        server_args.extend(['--n-gpu-layers', context.n_gpu_layer])
+    if context.server_continuous_batching:
+        server_args.append('--cont-batching')
+    if context.server_embeddings:
+        server_args.append('--embedding')
+    if context.server_metrics:
+        server_args.append('--metrics')
+    if context.model_alias:
+        server_args.extend(['--alias', context.model_alias])
+    if context.n_ctx:
+        server_args.extend(['--ctx-size', context.n_ctx])
+    if context.n_slots:
+        server_args.extend(['--parallel', context.n_slots])
+    if context.n_server_predict:
+        server_args.extend(['--n-predict', context.n_server_predict])
+    if context.server_api_key:
+        server_args.extend(['--api-key', context.server_api_key])
+    if context.n_ga:
+        server_args.extend(['--grp-attn-n', context.n_ga])
+    if context.n_ga_w:
+        server_args.extend(['--grp-attn-w', context.n_ga_w])
+    if context.debug:
+        server_args.append('--verbose')
+    if 'SERVER_LOG_FORMAT_JSON' not in os.environ:
+        server_args.extend(['--log-format', "text"])
+
+    args = [str(arg) for arg in [context.server_path, *server_args]]
+    print(f"bench: starting server with: {' '.join(args)}")
+
+    flags = 0
+    if 'nt' == os.name:
+        flags |= subprocess.DETACHED_PROCESS
+        flags |= subprocess.CREATE_NEW_PROCESS_GROUP
+        flags |= subprocess.CREATE_NO_WINDOW
+
+    pkwargs = {
+        'creationflags': flags,
+        'stdout': subprocess.PIPE,
+        'stderr': subprocess.PIPE
+    }
+    context.server_process = subprocess.Popen(
+        [str(arg) for arg in [context.server_path, *server_args]],
+        **pkwargs)
+
+    def server_log(in_stream, out_stream):
+        for line in iter(in_stream.readline, b''):
+            print(line.decode('utf-8'), end='', file=out_stream)
+
+    thread_stdout = threading.Thread(target=server_log, args=(context.server_process.stdout, sys.stdout))
+    thread_stdout.start()
+
+    thread_stderr = threading.Thread(target=server_log, args=(context.server_process.stderr, sys.stderr))
+    thread_stderr.start()
+
+    print(f"server pid={context.server_process.pid}, behave pid={os.getpid()}")
diff --git a/examples/server/tests/features/wrong_usages.feature b/examples/server/tests/features/wrong_usages.feature
new file mode 100644
index 000000000..cf14b3b44
--- /dev/null
+++ b/examples/server/tests/features/wrong_usages.feature
@@ -0,0 +1,22 @@
+# run with: ./tests.sh --no-skipped --tags wrong_usage
+@wrong_usage
+Feature: Wrong usage of llama.cpp server
+
+  #3969 The user must always set --n-predict option
+  # to cap the number of tokens any completion request can generate
+  # or pass n_predict/max_tokens in the request.
+  Scenario: Infinite loop
+    Given a server listening on localhost:8080
+    And   a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
+    # Uncomment below to fix the issue
+    #And   64 server max tokens to predict
+    Then  the server is starting
+    Given a prompt:
+      """
+      Go to: infinite loop
+      """
+    # Uncomment below to fix the issue
+    #And   128 max tokens to predict
+    Given concurrent completion requests
+    Then the server is idle
+    Then all prompts are predicted
diff --git a/examples/server/tests/requirements.txt b/examples/server/tests/requirements.txt
new file mode 100644
index 000000000..2e4f42ad2
--- /dev/null
+++ b/examples/server/tests/requirements.txt
@@ -0,0 +1,6 @@
+aiohttp~=3.9.3
+behave~=1.2.6
+huggingface_hub~=0.20.3
+numpy~=1.24.4
+openai~=0.25.0
+prometheus-client~=0.20.0
diff --git a/examples/server/tests/tests.sh b/examples/server/tests/tests.sh
new file mode 100755
index 000000000..1c6c5695f
--- /dev/null
+++ b/examples/server/tests/tests.sh
@@ -0,0 +1,12 @@
+#!/bin/bash
+
+set -eu
+
+if [ $# -lt 1 ]
+then
+  # Start @llama.cpp scenario
+  behave --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp
+else
+  behave "$@"
+fi
+
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
new file mode 100644
index 000000000..a8d43ac63
--- /dev/null
+++ b/examples/server/utils.hpp
@@ -0,0 +1,657 @@
+#pragma once
+
+#include "llama.h"
+#include "common.h"
+
+#include "json.hpp"
+
+#include <string>
+#include <vector>
+#include <sstream>
+#include <random>
+
+#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
+
+using json = nlohmann::ordered_json;
+
+// https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11
+enum error_type {
+    ERROR_TYPE_INVALID_REQUEST,
+    ERROR_TYPE_AUTHENTICATION,
+    ERROR_TYPE_SERVER,
+    ERROR_TYPE_NOT_FOUND,
+    ERROR_TYPE_PERMISSION,
+    ERROR_TYPE_UNAVAILABLE, // custom error
+    ERROR_TYPE_NOT_SUPPORTED, // custom error
+};
+
+extern bool server_verbose;
+extern bool server_log_json;
+
+#ifndef SERVER_VERBOSE
+#define SERVER_VERBOSE 1
+#endif
+
+#if SERVER_VERBOSE != 1
+#define LOG_VERBOSE(MSG, ...)
+#else
+#define LOG_VERBOSE(MSG, ...)                                            \
+    do                                                                   \
+    {                                                                    \
+        if (server_verbose)                                              \
+        {                                                                \
+            server_log("VERB", __func__, __LINE__, MSG, __VA_ARGS__); \
+        }                                                                \
+    } while (0)
+#endif
+
+#define LOG_ERROR(  MSG, ...) server_log("ERR",  __func__, __LINE__, MSG, __VA_ARGS__)
+#define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__)
+#define LOG_INFO(   MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
+
+static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra);
+
+template <typename T>
+static T json_value(const json &body, const std::string &key, const T &default_value) {
+    // Fallback null to default value
+    if (body.contains(key) && !body.at(key).is_null()){
+        try {
+            return body.value(key, default_value);
+        }
+        catch (nlohmann::json_abi_v3_11_3::detail::type_error const&){
+            std::string message = "Wrong type supplied for parameter '" + key + "'. Expected '" + typeid(default_value).name() + "', using default value.";
+            server_log("WARN", __func__, __LINE__, message.c_str(), body);
+            return default_value;
+        }
+    } else {
+        return default_value;
+    }
+}
+
+static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) {
+    std::stringstream ss_tid;
+    ss_tid << std::this_thread::get_id();
+    json log = nlohmann::ordered_json{
+        {"tid",       ss_tid.str()},
+        {"timestamp", time(nullptr)},
+    };
+
+    if (server_log_json) {
+        log.merge_patch( {
+            {"level",    level},
+            {"function", function},
+            {"line",     line},
+            {"msg",      message},
+        });
+
+        if (!extra.empty()) {
+            log.merge_patch(extra);
+        }
+
+        printf("%s\n", log.dump(-1, ' ', false, json::error_handler_t::replace).c_str());
+    } else {
+        char buf[1024];
+        snprintf(buf, 1024, "%4s [%24s] %s", level, function, message);
+
+        if (!extra.empty()) {
+            log.merge_patch(extra);
+        }
+        std::stringstream ss;
+        ss << buf << " |";
+        for (const auto& el : log.items())
+        {
+            const std::string value = el.value().dump(-1, ' ', false, json::error_handler_t::replace);
+            ss << " " << el.key() << "=" << value;
+        }
+
+        const std::string str = ss.str();
+        printf("%.*s\n", (int)str.size(), str.data());
+    }
+    fflush(stdout);
+}
+
+//
+// chat template utils
+//
+
+// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
+inline bool verify_custom_template(const std::string & tmpl) {
+    llama_chat_message chat[] = {{"user", "test"}};
+    int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
+    return res >= 0;
+}
+
+// Format given chat. If tmpl is empty, we take the template from model metadata
+inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector<json> & messages) {
+    size_t alloc_size = 0;
+    // vector holding all allocated string to be passed to llama_chat_apply_template
+    std::vector<std::string> str(messages.size() * 2);
+    std::vector<llama_chat_message> chat(messages.size());
+
+    for (size_t i = 0; i < messages.size(); ++i) {
+        const auto & curr_msg = messages[i];
+        str[i*2 + 0]    = json_value(curr_msg, "role",    std::string(""));
+        str[i*2 + 1]    = json_value(curr_msg, "content", std::string(""));
+        alloc_size     += str[i*2 + 1].length();
+        chat[i].role    = str[i*2 + 0].c_str();
+        chat[i].content = str[i*2 + 1].c_str();
+    }
+
+    const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
+    std::vector<char> buf(alloc_size * 2);
+
+    // run the first time to get the total output length
+    int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size());
+
+    // if it turns out that our buffer is too small, we resize it
+    if ((size_t) res > buf.size()) {
+        buf.resize(res);
+        res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size());
+    }
+
+    const std::string formatted_chat(buf.data(), res);
+
+    LOG_VERBOSE("formatted_chat", {{"text", formatted_chat.c_str()}});
+
+    return formatted_chat;
+}
+
+//
+// base64 utils (TODO: move to common in the future)
+//
+
+static const std::string base64_chars =
+             "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
+             "abcdefghijklmnopqrstuvwxyz"
+             "0123456789+/";
+
+static inline bool is_base64(uint8_t c) {
+    return (isalnum(c) || (c == '+') || (c == '/'));
+}
+
+static inline std::vector<uint8_t> base64_decode(const std::string & encoded_string) {
+    int i = 0;
+    int j = 0;
+    int in_ = 0;
+
+    int in_len = encoded_string.size();
+
+    uint8_t char_array_4[4];
+    uint8_t char_array_3[3];
+
+    std::vector<uint8_t> ret;
+
+    while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) {
+        char_array_4[i++] = encoded_string[in_]; in_++;
+        if (i == 4) {
+            for (i = 0; i < 4; i++) {
+                char_array_4[i] = base64_chars.find(char_array_4[i]);
+            }
+
+            char_array_3[0] = ((char_array_4[0]      ) << 2) + ((char_array_4[1] & 0x30) >> 4);
+            char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
+            char_array_3[2] = ((char_array_4[2] & 0x3) << 6) +   char_array_4[3];
+
+            for (i = 0; (i < 3); i++) {
+                ret.push_back(char_array_3[i]);
+            }
+
+            i = 0;
+        }
+    }
+
+    if (i) {
+        for (j = i; j < 4; j++) {
+            char_array_4[j] = 0;
+        }
+
+        for (j = 0; j < 4; j++) {
+            char_array_4[j] = base64_chars.find(char_array_4[j]);
+        }
+
+        char_array_3[0] = ((char_array_4[0]      ) << 2) + ((char_array_4[1] & 0x30) >> 4);
+        char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
+        char_array_3[2] = ((char_array_4[2] & 0x3) << 6) +   char_array_4[3];
+
+        for (j = 0; j < i - 1; j++) {
+            ret.push_back(char_array_3[j]);
+        }
+    }
+
+    return ret;
+}
+
+//
+// random string / id
+//
+
+static std::string random_string() {
+    static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz");
+
+    std::random_device rd;
+    std::mt19937 generator(rd());
+
+    std::string result(32, ' ');
+
+    for (int i = 0; i < 32; ++i) {
+        result[i] = str[generator() % str.size()];
+    }
+
+    return result;
+}
+
+static std::string gen_chatcmplid() {
+    std::stringstream chatcmplid;
+    chatcmplid << "chatcmpl-" << random_string();
+
+    return chatcmplid.str();
+}
+
+//
+// other common utils
+//
+
+static size_t common_part(const std::vector<llama_token> & a, const std::vector<llama_token> & b) {
+    size_t i;
+    for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
+
+    return i;
+}
+
+static bool ends_with(const std::string & str, const std::string & suffix) {
+    return str.size() >= suffix.size() && 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
+}
+
+static size_t find_partial_stop_string(const std::string &stop, const std::string &text) {
+    if (!text.empty() && !stop.empty()) {
+        const char text_last_char = text.back();
+        for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) {
+            if (stop[char_index] == text_last_char) {
+                const std::string current_partial = stop.substr(0, char_index + 1);
+                if (ends_with(text, current_partial)) {
+                    return text.size() - char_index - 1;
+                }
+            }
+        }
+    }
+
+    return std::string::npos;
+}
+
+// TODO: reuse llama_detokenize
+template <class Iter>
+static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
+    std::string ret;
+    for (; begin != end; ++begin) {
+        ret += llama_token_to_piece(ctx, *begin);
+    }
+
+    return ret;
+}
+
+// format incomplete utf-8 multibyte character for output
+static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) {
+    std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token);
+
+    // if the size is 1 and first bit is 1, meaning it's a partial character
+    //   (size > 1 meaning it's already a known token)
+    if (out.size() == 1 && (out[0] & 0x80) == 0x80) {
+        std::stringstream ss;
+        ss << std::hex << (out[0] & 0xff);
+        std::string res(ss.str());
+        out = "byte: \\x" + res;
+    }
+
+    return out;
+}
+
+struct completion_token_output {
+    llama_token tok;
+    std::string text_to_send;
+
+    struct token_prob {
+        llama_token tok;
+        float prob;
+    };
+
+    std::vector<token_prob> probs;
+};
+
+// convert a vector of completion_token_output to json
+static json probs_vector_to_json(const llama_context * ctx, const std::vector<completion_token_output> & probs) {
+    json out = json::array();
+
+    for (const auto & prob : probs) {
+        json probs_for_token = json::array();
+
+        for (const auto & p : prob.probs) {
+            const std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok);
+            probs_for_token.push_back(json {
+                {"tok_str", tok_str},
+                {"prob",    p.prob},
+            });
+        }
+
+        const std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok);
+        out.push_back(json {
+            {"content", tok_str},
+            {"probs",   probs_for_token},
+        });
+    }
+
+    return out;
+}
+
+//
+// OAI utils
+//
+
+static json oaicompat_completion_params_parse(
+    const struct llama_model * model,
+    const json & body, /* openai api json semantics */
+    const std::string & chat_template) {
+    json llama_params;
+
+    llama_params["__oaicompat"] = true;
+
+    // Map OpenAI parameters to llama.cpp parameters
+    //
+    // For parameters that are defined by the OpenAI documentation (e.g.
+    // temperature), we explicitly specify OpenAI's intended default; we
+    // need to do that because sometimes OpenAI disagrees with llama.cpp
+    //
+    // https://platform.openai.com/docs/api-reference/chat/create
+    llama_sampling_params default_sparams;
+    llama_params["model"]             = json_value(body,   "model",             std::string("unknown"));
+    llama_params["frequency_penalty"] = json_value(body,   "frequency_penalty", 0.0);
+    llama_params["logit_bias"]        = json_value(body,   "logit_bias",        json::object());
+    llama_params["n_predict"]         = json_value(body,   "max_tokens",        -1);
+    llama_params["presence_penalty"]  = json_value(body,   "presence_penalty",  0.0);
+    llama_params["seed"]              = json_value(body,   "seed",              LLAMA_DEFAULT_SEED);
+    llama_params["stream"]            = json_value(body,   "stream",            false);
+    llama_params["temperature"]       = json_value(body,   "temperature",       0.0);
+    llama_params["top_p"]             = json_value(body,   "top_p",             1.0);
+
+    // Apply chat template to the list of messages
+    llama_params["prompt"] = format_chat(model, chat_template, body["messages"]);
+
+    // Handle "stop" field
+    if (body.contains("stop") && body["stop"].is_string()) {
+        llama_params["stop"] = json::array({body["stop"].get<std::string>()});
+    } else {
+        llama_params["stop"] = json_value(body, "stop", json::array());
+    }
+    // Some chat templates don't use EOS token to stop generation
+    // We must add their end sequences to list of stop words
+    llama_params["stop"].push_back("<|im_end|>"); // chatml
+    llama_params["stop"].push_back("<end_of_turn>"); // gemma
+
+    // Handle "response_format" field
+    if (body.contains("response_format")) {
+        json response_format      = json_value(body, "response_format", json::object());
+        std::string response_type = json_value(response_format, "type", std::string());
+        if (response_type == "json_object") {
+            llama_params["json_schema"] = json_value(response_format, "schema", json::object());
+        } else if (!response_type.empty() && response_type != "text") {
+            throw std::runtime_error("response_format type must be one of \"text\" or \"json_object\", but got: " + response_type);
+        }
+    }
+
+    // Handle "n" field
+    int n_choices = json_value(body, "n", 1);
+    if (n_choices != 1) {
+        throw std::runtime_error("Only one completion choice is allowed");
+    }
+
+    // Handle "logprobs" field
+    // TODO: The response format of this option is not yet OAI-compatible, but seems like no one really using it; We may need to fix it in the future
+    if (body.contains("logprobs")) {
+        llama_params["n_probs"] = json_value(body, "top_logprobs", 20);
+    } else if (body.contains("top_logprobs")) {
+        throw std::runtime_error("top_logprobs requires logprobs to be set to true");
+    }
+
+    // Params supported by OAI but unsupported by llama.cpp
+    static const std::vector<std::string> unsupported_params { "tools", "tool_choice" };
+    for (auto & param : unsupported_params) {
+        if (body.contains(param)) {
+            throw std::runtime_error("Unsupported param: " + param);
+        }
+    }
+
+    // Copy remaining properties to llama_params
+    // This allows user to use llama.cpp-specific params like "mirostat", "tfs_z",... via OAI endpoint.
+    // See "launch_slot_with_task()" for a complete list of params supported by llama.cpp
+    for (const auto & item : body.items()) {
+        // Exception: if "n_predict" is present, we overwrite the value specified earlier by "max_tokens"
+        if (!llama_params.contains(item.key()) || item.key() == "n_predict") {
+            llama_params[item.key()] = item.value();
+        }
+    }
+
+    return llama_params;
+}
+
+static json format_final_response_oaicompat(const json & request, json result, const std::string & completion_id, bool streaming = false) {
+    bool stopped_word        = result.count("stopped_word") != 0;
+    bool stopped_eos         = json_value(result, "stopped_eos", false);
+    int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
+    int num_prompt_tokens    = json_value(result, "tokens_evaluated", 0);
+    std::string content      = json_value(result, "content", std::string(""));
+
+    std::string finish_reason = "length";
+    if (stopped_word || stopped_eos) {
+        finish_reason = "stop";
+    }
+
+    json choices =
+        streaming ? json::array({json{{"finish_reason", finish_reason},
+                                        {"index", 0},
+                                        {"delta", json::object()}}})
+                  : json::array({json{{"finish_reason", finish_reason},
+                                        {"index", 0},
+                                        {"message", json{{"content", content},
+                                                         {"role", "assistant"}}}}});
+
+    std::time_t t = std::time(0);
+
+    json res = json {
+        {"choices", choices},
+        {"created", t},
+        {"model",
+            json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
+        {"object", streaming ? "chat.completion.chunk" : "chat.completion"},
+        {"usage", json {
+            {"completion_tokens", num_tokens_predicted},
+            {"prompt_tokens",     num_prompt_tokens},
+            {"total_tokens",      num_tokens_predicted + num_prompt_tokens}
+        }},
+        {"id", completion_id}
+    };
+
+    if (server_verbose) {
+        res["__verbose"] = result;
+    }
+
+    if (result.contains("completion_probabilities")) {
+        res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array());
+    }
+
+    return res;
+}
+
+// return value is vector as there is one case where we might need to generate two responses
+static std::vector<json> format_partial_response_oaicompat(json result, const std::string & completion_id) {
+    if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
+        return std::vector<json>({result});
+    }
+
+    bool first = json_value(result, "oaicompat_token_ctr", 0) == 0;
+    std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
+
+    bool stopped_word   = json_value(result, "stopped_word",  false);
+    bool stopped_eos    = json_value(result, "stopped_eos",   false);
+    bool stopped_limit  = json_value(result, "stopped_limit", false);
+    std::string content = json_value(result, "content",       std::string(""));
+
+    std::string finish_reason;
+    if (stopped_word || stopped_eos) {
+        finish_reason = "stop";
+    }
+    if (stopped_limit) {
+        finish_reason = "length";
+    }
+
+    std::time_t t = std::time(0);
+
+    json choices;
+
+    if (!finish_reason.empty()) {
+        choices = json::array({json{{"finish_reason", finish_reason},
+                                    {"index", 0},
+                                    {"delta", json::object()}}});
+    } else {
+        if (first) {
+            if (content.empty()) {
+                choices = json::array({json{{"finish_reason", nullptr},
+                                            {"index", 0},
+                                            {"delta", json{{"role", "assistant"}}}}});
+            } else {
+                // We have to send this as two updates to conform to openai behavior
+                json initial_ret = json{{"choices", json::array({json{
+                                        {"finish_reason", nullptr},
+                                        {"index", 0},
+                                        {"delta", json{
+                                            {"role", "assistant"}
+                                        }}}})},
+                            {"created", t},
+                            {"id", completion_id},
+                            {"model", modelname},
+                            {"object", "chat.completion.chunk"}};
+
+                json second_ret = json{
+                            {"choices", json::array({json{{"finish_reason", nullptr},
+                                                            {"index", 0},
+                                                            {"delta", json{
+                                                            {"content", content}}}
+                                                            }})},
+                            {"created", t},
+                            {"id", completion_id},
+                            {"model", modelname},
+                            {"object", "chat.completion.chunk"}};
+
+                return std::vector<json>({initial_ret, second_ret});
+            }
+        } else {
+            // Some idiosyncrasy in task processing logic makes several trailing calls
+            // with empty content, we ignore these at the calee site.
+            if (content.empty()) {
+                return std::vector<json>({json::object()});
+            }
+
+            choices = json::array({json{
+                {"finish_reason", nullptr},
+                {"index", 0},
+                {"delta",
+                json{
+                    {"content", content},
+                }},
+            }});
+        }
+    }
+
+    json ret = json {
+        {"choices", choices},
+        {"created", t},
+        {"id",      completion_id},
+        {"model",   modelname},
+        {"object",  "chat.completion.chunk"}
+    };
+    if (!finish_reason.empty()) {
+        int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
+        int num_prompt_tokens    = json_value(result, "tokens_evaluated", 0);
+        ret.push_back({"usage", json {
+            {"completion_tokens", num_tokens_predicted},
+            {"prompt_tokens",     num_prompt_tokens},
+            {"total_tokens",      num_tokens_predicted + num_prompt_tokens}
+        }});
+    }
+
+    return std::vector<json>({ret});
+}
+
+static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) {
+    json data = json::array();
+    int i = 0;
+    for (auto & elem : embeddings) {
+        data.push_back(json{
+            {"embedding", json_value(elem, "embedding", json::array())},
+            {"index",     i++},
+            {"object",    "embedding"}
+        });
+    }
+
+    json res = json {
+        {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
+        {"object", "list"},
+        {"usage", json {
+            {"prompt_tokens", 0},
+            {"total_tokens", 0}
+        }},
+        {"data", data}
+    };
+
+    return res;
+}
+
+static json format_tokenizer_response(const std::vector<llama_token> & tokens) {
+    return json {
+        {"tokens", tokens}
+    };
+}
+
+static json format_detokenized_response(const std::string & content) {
+    return json {
+        {"content", content}
+    };
+}
+
+static json format_error_response(const std::string & message, const enum error_type type) {
+    std::string type_str;
+    int code = 500;
+    switch (type) {
+        case ERROR_TYPE_INVALID_REQUEST:
+            type_str = "invalid_request_error";
+            code = 400;
+            break;
+        case ERROR_TYPE_AUTHENTICATION:
+            type_str = "authentication_error";
+            code = 401;
+            break;
+        case ERROR_TYPE_NOT_FOUND:
+            type_str = "not_found_error";
+            code = 404;
+            break;
+        case ERROR_TYPE_SERVER:
+            type_str = "server_error";
+            code = 500;
+            break;
+        case ERROR_TYPE_PERMISSION:
+            type_str = "permission_error";
+            code = 403;
+            break;
+        case ERROR_TYPE_NOT_SUPPORTED:
+            type_str = "not_supported_error";
+            code = 501;
+            break;
+        case ERROR_TYPE_UNAVAILABLE:
+            type_str = "unavailable_error";
+            code = 503;
+            break;
+    }
+    return json {
+        {"code", code},
+        {"message", message},
+        {"type", type_str},
+    };
+}
diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp
index 9cfde8308..39e2d8ea4 100644
--- a/examples/simple/simple.cpp
+++ b/examples/simple/simple.cpp
@@ -31,7 +31,8 @@ int main(int argc, char ** argv) {
 
     // init LLM
 
-    llama_backend_init(params.numa);
+    llama_backend_init();
+    llama_numa_init(params.numa);
 
     // initialize the model
 
diff --git a/examples/speculative/README.md b/examples/speculative/README.md
index 814efa592..a6608c5fe 100644
--- a/examples/speculative/README.md
+++ b/examples/speculative/README.md
@@ -6,3 +6,4 @@ More info:
 
 - https://github.com/ggerganov/llama.cpp/pull/2926
 - https://github.com/ggerganov/llama.cpp/pull/3624
+- https://github.com/ggerganov/llama.cpp/pull/5625
diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp
index 20f1fb5bf..6e0815b36 100644
--- a/examples/speculative/speculative.cpp
+++ b/examples/speculative/speculative.cpp
@@ -5,6 +5,7 @@
 #include <cstdio>
 #include <string>
 #include <vector>
+#include <set>
 
 #define SPEC_VOCAB_MAX_SIZE_DIFFERENCE  100
 #define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
@@ -18,6 +19,7 @@ struct seq_draft {
     std::vector<int> i_batch_tgt;
 
     std::vector<llama_token> tokens;
+    std::vector<std::vector<llama_token_data>> dists;
 
     struct llama_sampling_context * ctx_sampling;
 };
@@ -37,12 +39,15 @@ int main(int argc, char ** argv) {
     // max number of parallel drafting sequences (i.e. tree branches)
     const int n_seq_dft = params.n_parallel;
 
-    // probability threshold for accepting a token from the draft model
-    const float p_accept = params.p_accept;
-
     // probability threshold for splitting a draft branch (only for n_seq_dft > 1)
     const float p_split  = params.p_split;
 
+    if (params.seed == LLAMA_DEFAULT_SEED) {
+        params.seed = time(NULL);
+    }
+    std::default_random_engine rng(params.seed);
+    std::uniform_real_distribution<> u_dist;
+
 #ifndef LOG_DISABLE_LOGS
     log_set_target(log_filename_generator("speculative", "log"));
     LOG_TEE("Log start\n");
@@ -50,7 +55,8 @@ int main(int argc, char ** argv) {
 #endif // LOG_DISABLE_LOGS
 
     // init llama.cpp
-    llama_backend_init(params.numa);
+    llama_backend_init();
+    llama_numa_init(params.numa);
 
     llama_model * model_tgt = NULL;
     llama_model * model_dft = NULL;
@@ -59,12 +65,15 @@ int main(int argc, char ** argv) {
     llama_context * ctx_dft = NULL;
 
     // load the target model
-    params.logits_all = true;
     std::tie(model_tgt, ctx_tgt) = llama_init_from_gpt_params(params);
 
     // load the draft model
     params.model = params.model_draft;
     params.n_gpu_layers = params.n_gpu_layers_draft;
+    if (params.n_threads_draft > 0) {
+        params.n_threads = params.n_threads_draft;
+    }
+    params.n_threads_batch = params.n_threads_batch_draft;
     std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params);
 
     {
@@ -161,7 +170,9 @@ int main(int argc, char ** argv) {
     std::vector<seq_draft> drafts(n_seq_dft);
 
     params.sparams.grammar.clear(); // the draft samplers will copy the target sampler's grammar
-    params.sparams.temp = -1.0f;    // force greedy sampling with probs for the draft model
+    if (params.sparams.temp == 0) {
+        params.sparams.temp = -1.0f; // force greedy sampling with probs for the draft model
+    }
 
     for (int s = 0; s < n_seq_dft; ++s) {
         drafts[s].ctx_sampling = llama_sampling_init(params.sparams);
@@ -177,12 +188,15 @@ int main(int argc, char ** argv) {
     drafts[0].i_batch_tgt[0] = 0;
 
     while (true) {
+        std::set<int> active_seqs = {};
+
         // print current draft sequences
         for (int s = 0; s < n_seq_dft; ++s) {
             if (!drafts[s].active) {
                 continue;
             }
 
+            active_seqs.insert(s);
             const auto & tokens = drafts[s].tokens;
 
             LOG("draft %d: %s\n", s, LOG_TOKENS_TOSTR_PRETTY(ctx_dft, tokens).c_str());
@@ -191,48 +205,157 @@ int main(int argc, char ** argv) {
         int i_dft  = 0;
         int s_keep = 0;
 
+        llama_token token_id;
+        std::string token_str;
+
+        // loop until we fail to accept a drafted token or we run out of drafted tokens
         while (true) {
-            LOG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]);
-
-            // sample from the target model
-            llama_token id = llama_sampling_sample(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]);
-
-            llama_sampling_accept(ctx_sampling, ctx_tgt, id, true);
-
-            //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, ctx_sampling->prev).c_str());
-
-            const std::string token_str = llama_token_to_piece(ctx_tgt, id);
-
-            if (!params.use_color) {
-                printf("%s", token_str.c_str());
-            }
-
-            if (id == llama_token_eos(model_tgt)) {
-                has_eos = true;
-            }
-
-            ++n_predict;
 
             // check if the target token matches any of the drafts
+            // for stochastic sampling, attempt to match the token with the drafted tokens
             {
-                bool matches = false;
+                bool accept = false;
+                if (params.sparams.temp > 0) {
+                    // stochastic verification
 
-                for (int s = 0; s < n_seq_dft; ++s) {
-                    if (!drafts[s].active) {
-                        continue;
+                    llama_token_data_array dist_tgt = llama_sampling_prepare(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft], true, NULL);
+                    llama_sample_softmax(ctx_tgt, &dist_tgt);
+                    float p_tgt = 0, p_dft = 0;
+
+                    // GGML_ASSERT(dist_tgt.size() == dist_dft.size());
+
+                    while (active_seqs.size() > 0) {
+                        // randomly select a sequence to verify from active sequences
+                        std::uniform_int_distribution<unsigned int> u_int_dist(0, active_seqs.size() - 1);
+                        int s = *std::next(active_seqs.begin(), u_int_dist(rng));
+                        if (i_dft >= (int) drafts[s].tokens.size()) {
+                            drafts[s].active = false;
+                            active_seqs.erase(s);
+                            continue;
+                        }
+                        if (accept) {
+                            // if we already accepted a token, we can skip the rest
+                            if (drafts[s].tokens[i_dft] != drafts[s_keep].tokens[i_dft]) {
+                                drafts[s].active = false;
+                                active_seqs.erase(s);
+                            }
+                            continue;
+                        }
+                        LOG("verifying sequence #%d at pos #%d from %d active sequence(s)\n", s, i_dft, (int) active_seqs.size());
+                        float r = u_dist(rng);
+                        llama_token_data_array dist_dft = { drafts[s].dists[i_dft].data() , drafts[s].dists[i_dft].size(), true };
+                        // acquire the token probabilities assigned by the draft and target models
+                        for (size_t i = 0; i < dist_tgt.size; i++) {
+                            if (dist_tgt.data[i].id == drafts[s].tokens[i_dft]) {
+                                p_tgt = dist_tgt.data[i].p;
+                            }
+                            if (dist_dft.data[i].id == drafts[s].tokens[i_dft]) {
+                                p_dft = dist_dft.data[i].p;
+                            }
+                            if (p_tgt && p_dft) {
+                                break;
+                            }
+                        }
+                        LOG("r = %f, p_dft = %f, p_tgt = %f\n", r, p_dft, p_tgt);
+                        if (r <= p_tgt / p_dft) {
+                            s_keep = s;
+                            accept = true;
+                            token_id = drafts[s].tokens[i_dft];
+                            token_str = llama_token_to_piece(ctx_tgt, token_id);
+                            llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true);
+
+                            LOG("draft token %d of sequence %d (%d, '%s') accepted\n", i_dft, s, token_id, token_str.c_str());
+                            break;
+                        } else {
+                            LOG("draft token %d of sequence %d (%d, '%s') rejected\n", i_dft, s, drafts[s].tokens[i_dft], llama_token_to_piece(ctx_tgt, drafts[s].tokens[i_dft]).c_str());
+                            drafts[s].active = false;
+
+                            // calculate residual probability
+                            GGML_ASSERT(dist_tgt.sorted);
+                            GGML_ASSERT(dist_dft.sorted);
+                            float sum_probs = 0.0f;
+
+                            // sort dist by id
+                            std::sort(dist_tgt.data, dist_tgt.data + dist_tgt.size, [](const llama_token_data &a, const llama_token_data &b) {
+                                return a.id < b.id;
+                            });
+                            std::sort(dist_dft.data, dist_dft.data + dist_dft.size, [](const llama_token_data &a, const llama_token_data &b) {
+                                return a.id < b.id;
+                            });
+
+                            for (size_t i = 0; i < dist_tgt.size; i++) {
+                                dist_tgt.data[i].p = std::max(0.0f, dist_tgt.data[i].p - dist_dft.data[i].p);
+                                sum_probs += dist_tgt.data[i].p;
+                            }
+                            for (size_t i = 0; i < dist_tgt.size; i++) {
+                                dist_tgt.data[i].p /= sum_probs;
+                            }
+
+                            // sort dist_tgt by p desc
+                            std::sort(dist_tgt.data, dist_tgt.data + dist_tgt.size, [](const llama_token_data &a, const llama_token_data &b) {
+                                return a.p > b.p;
+                            });
+                        }
+
+                        active_seqs.erase(s);
+                        for(int i = 0; i < n_seq_dft; i++) {
+                            if (i == s) {
+                                continue;
+                            }
+                            if (drafts[i].tokens[i_dft] == drafts[s].tokens[i_dft]) {
+                                // synchronize active status for sequences with the same drafted token
+                                drafts[i].active = drafts[i].active && accept;
+                                if (!drafts[i].active) {
+                                    active_seqs.erase(s);
+                                }
+                            }
+                        }
                     }
 
-                    if (i_dft < (int) drafts[s].tokens.size() && id == drafts[s].tokens[i_dft]) {
-                        LOG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, id, token_str.c_str());
+                    if (!accept) {
+                        // all drafted tokens were rejected
+                        // sample from the target model
+                        LOG("all drafted tokens were rejected, sampling from residual distribution\n");
+                        token_id = llama_sample_token(ctx_tgt, &dist_tgt);
+                        llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true);
+                        token_str = llama_token_to_piece(ctx_tgt, token_id);
+                    }
 
-                        s_keep = s;
-                        matches = true;
-                    } else {
-                        drafts[s].active = false;
+                } else {
+                    // greedy verification
+
+                    // sample from the target model
+                    LOG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]);
+                    token_id = llama_sampling_sample(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]);
+
+                    llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true);
+
+                    //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, ctx_sampling->prev).c_str());
+
+                    token_str = llama_token_to_piece(ctx_tgt, token_id);
+
+                    for (int s = 0; s < n_seq_dft; ++s) {
+                        if (!drafts[s].active) {
+                            continue;
+                        }
+
+                        if (i_dft < (int) drafts[s].tokens.size() && token_id == drafts[s].tokens[i_dft]) {
+                            LOG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, token_id, token_str.c_str());
+
+                            s_keep = s;
+                            accept = true;
+                        } else {
+                            drafts[s].active = false;
+                        }
                     }
                 }
 
-                if (matches) {
+                if (token_id == llama_token_eos(model_tgt)) {
+                    has_eos = true;
+                }
+                ++n_predict;
+
+                if (accept) {
                     ++n_accept;
                     ++n_past_tgt;
                     ++n_past_dft;
@@ -240,17 +363,21 @@ int main(int argc, char ** argv) {
                     if (params.use_color) {
                         // Color token according to its origin sequence
                         printf("\u001b[%dm%s\u001b[37m", (36 - s_keep % 6), token_str.c_str());
-                        fflush(stdout);
+                    } else {
+                        printf("%s", token_str.c_str());
                     }
+                    fflush(stdout);
                     continue;
+                } else {
+                    printf("%s", token_str.c_str());
+                    fflush(stdout);
+                    break;
                 }
             }
-            if (params.use_color) {
-                printf("%s", token_str.c_str());
-            }
-            fflush(stdout);
+        }
 
-            LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str());
+        {
+            LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", token_id, token_str.c_str());
 
             // TODO: simplify
             {
@@ -270,21 +397,21 @@ int main(int argc, char ** argv) {
                 drafts[s].active = false;
                 drafts[s].tokens.clear();
                 drafts[s].i_batch_tgt.clear();
+                drafts[s].dists.clear();
             }
             // note: will be erased after the speculation phase
-            drafts[0].tokens.push_back(id);
+            drafts[0].tokens.push_back(token_id);
+            drafts[0].dists.push_back(std::vector<llama_token_data>());
             drafts[0].i_batch_tgt.push_back(0);
 
             llama_batch_clear(batch_dft);
-            llama_batch_add  (batch_dft, id, n_past_dft, { 0 }, true);
+            llama_batch_add  (batch_dft, token_id, n_past_dft, { 0 }, true);
 
             llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1);
             // LOG("dft batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_dft, batch_dft).c_str());
-            llama_decode         (ctx_dft, batch_dft);
+            llama_decode(ctx_dft, batch_dft);
 
             ++n_past_dft;
-
-            break;
         }
 
         if (n_predict > params.n_predict || has_eos) {
@@ -329,12 +456,6 @@ int main(int argc, char ** argv) {
                             k, s, i, cur_p[k].id, cur_p[k].p, llama_token_to_piece(ctx_dft, cur_p[k].id).c_str());
                 }
 
-                if (cur_p[0].p < p_accept) {
-                    LOG("stopping drafting for seq %3d, probability too low: %.3f < %.3f\n", s, cur_p[0].p, p_accept);
-                    drafts[s].drafting = false;
-                    continue;
-                }
-
                 std::vector<int> sa(1, s);
 
                 // attempt to split the branch if the probability is high enough
@@ -362,6 +483,7 @@ int main(int argc, char ** argv) {
                         drafts[n_seq_cur].skip     = true;
 
                         drafts[n_seq_cur].tokens      = drafts[s].tokens;
+                        drafts[n_seq_cur].dists       = drafts[s].dists;
                         drafts[n_seq_cur].i_batch_dft = drafts[s].i_batch_dft;
                         drafts[n_seq_cur].i_batch_tgt = drafts[s].i_batch_tgt;
 
@@ -384,6 +506,8 @@ int main(int argc, char ** argv) {
                     llama_sampling_accept(drafts[s].ctx_sampling, ctx_dft, id, true);
 
                     drafts[s].tokens.push_back(id);
+                    // save cur_p.data into drafts[s].dists
+                    drafts[s].dists.push_back(cur_p);
 
                     // add unique drafted tokens to the target batch
                     drafts[s].i_batch_tgt.push_back(batch_tgt.n_tokens);
@@ -435,6 +559,7 @@ int main(int argc, char ** argv) {
             }
 
             drafts[s].tokens.erase(drafts[s].tokens.begin());
+            drafts[s].dists.erase(drafts[s].dists.begin());
         }
     }
 
diff --git a/examples/sycl/CMakeLists.txt b/examples/sycl/CMakeLists.txt
new file mode 100644
index 000000000..69cf8932e
--- /dev/null
+++ b/examples/sycl/CMakeLists.txt
@@ -0,0 +1,9 @@
+#  MIT license
+#  Copyright (C) 2024 Intel Corporation
+#  SPDX-License-Identifier: MIT
+
+set(TARGET ls-sycl-device)
+add_executable(${TARGET} ls-sycl-device.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_17)
diff --git a/examples/sycl/README.md b/examples/sycl/README.md
new file mode 100644
index 000000000..b46f17f39
--- /dev/null
+++ b/examples/sycl/README.md
@@ -0,0 +1,47 @@
+# llama.cpp/example/sycl
+
+This example program provide the tools for llama.cpp for SYCL on Intel GPU.
+
+## Tool
+
+|Tool Name| Function|Status|
+|-|-|-|
+|ls-sycl-device| List all SYCL devices with ID, compute capability, max work group size, ect.|Support|
+
+### ls-sycl-device
+
+List all SYCL devices with ID, compute capability, max work group size, ect.
+
+1. Build the llama.cpp for SYCL for all targets.
+
+2. Enable oneAPI running environment
+
+```
+source /opt/intel/oneapi/setvars.sh
+```
+
+3. Execute
+
+```
+./build/bin/ls-sycl-device
+```
+
+Check the ID in startup log, like:
+
+```
+found 4 SYCL devices:
+  Device 0: Intel(R) Arc(TM) A770 Graphics,	compute capability 1.3,
+    max compute_units 512,	max work group size 1024,	max sub group size 32,	global mem size 16225243136
+  Device 1: Intel(R) FPGA Emulation Device,	compute capability 1.2,
+    max compute_units 24,	max work group size 67108864,	max sub group size 64,	global mem size 67065057280
+  Device 2: 13th Gen Intel(R) Core(TM) i7-13700K,	compute capability 3.0,
+    max compute_units 24,	max work group size 8192,	max sub group size 64,	global mem size 67065057280
+  Device 3: Intel(R) Arc(TM) A770 Graphics,	compute capability 3.0,
+    max compute_units 512,	max work group size 1024,	max sub group size 32,	global mem size 16225243136
+
+```
+
+|Attribute|Note|
+|-|-|
+|compute capability 1.3|Level-zero running time, recommended |
+|compute capability 3.0|OpenCL running time, slower than level-zero in most cases|
diff --git a/examples/sycl/build.sh b/examples/sycl/build.sh
new file mode 100755
index 000000000..f20391d7a
--- /dev/null
+++ b/examples/sycl/build.sh
@@ -0,0 +1,23 @@
+
+#  MIT license
+#  Copyright (C) 2024 Intel Corporation
+#  SPDX-License-Identifier: MIT
+
+mkdir -p build
+cd build
+source /opt/intel/oneapi/setvars.sh
+
+#for FP16
+#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON # faster for long-prompt inference
+
+#for FP32
+cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
+
+#build example/main
+#cmake --build . --config Release --target main
+
+#build example/llama-bench
+#cmake --build . --config Release --target llama-bench
+
+#build all binary
+cmake --build . --config Release -v
diff --git a/examples/sycl/ls-sycl-device.cpp b/examples/sycl/ls-sycl-device.cpp
new file mode 100644
index 000000000..74a8b7fd8
--- /dev/null
+++ b/examples/sycl/ls-sycl-device.cpp
@@ -0,0 +1,13 @@
+//
+//  MIT license
+//  Copyright (C) 2024 Intel Corporation
+//  SPDX-License-Identifier: MIT
+//
+
+
+#include "ggml-sycl.h"
+
+int main() {
+    ggml_backend_sycl_print_sycl_devices();
+    return 0;
+}
diff --git a/examples/sycl/run-llama2.sh b/examples/sycl/run-llama2.sh
new file mode 100755
index 000000000..c979a52f6
--- /dev/null
+++ b/examples/sycl/run-llama2.sh
@@ -0,0 +1,36 @@
+#!/bin/bash
+
+#  MIT license
+#  Copyright (C) 2024 Intel Corporation
+#  SPDX-License-Identifier: MIT
+
+INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
+source /opt/intel/oneapi/setvars.sh
+
+if [ $# -gt 0 ]; then
+    GGML_SYCL_DEVICE=$1
+    GGML_SYCL_SINGLE_GPU=1
+else
+    GGML_SYCL_DEVICE=0
+fi
+
+#export GGML_SYCL_DEBUG=1
+
+
+#ZES_ENABLE_SYSMAN=1, Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory. Recommended to use when --split-mode = layer.
+
+if [ $GGML_SYCL_SINGLE_GPU -eq 1 ]; then
+    echo "use $GGML_SYCL_DEVICE as main GPU"
+    #use signle GPU only
+    ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none
+else
+    #use multiple GPUs with same max compute units
+    ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
+fi
+
+#use main GPU only
+#ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none
+
+#use multiple GPUs with same max compute units
+#ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
+
diff --git a/examples/sycl/win-build-sycl.bat b/examples/sycl/win-build-sycl.bat
new file mode 100644
index 000000000..1b0dc41ba
--- /dev/null
+++ b/examples/sycl/win-build-sycl.bat
@@ -0,0 +1,34 @@
+
+::  MIT license
+::  Copyright (C) 2024 Intel Corporation
+::  SPDX-License-Identifier: MIT
+
+
+IF not exist build (mkdir build)
+cd build
+if %errorlevel% neq 0 goto ERROR
+
+@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
+if %errorlevel% neq 0 goto ERROR
+
+::  for FP16
+::  faster for long-prompt inference
+::  cmake -G "MinGW Makefiles" ..  -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
+
+::  for FP32
+cmake -G "MinGW Makefiles" ..  -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release
+if %errorlevel% neq 0 goto ERROR
+::  build example/main only
+::  make main
+
+::  build all binary
+make -j
+if %errorlevel% neq 0 goto ERROR
+
+cd ..
+exit /B 0
+
+:ERROR
+echo comomand error: %errorlevel%
+exit /B %errorlevel%
+
diff --git a/examples/sycl/win-run-llama2.bat b/examples/sycl/win-run-llama2.bat
new file mode 100644
index 000000000..1d4d7d2cd
--- /dev/null
+++ b/examples/sycl/win-run-llama2.bat
@@ -0,0 +1,11 @@
+::  MIT license
+::  Copyright (C) 2024 Intel Corporation
+::  SPDX-License-Identifier: MIT
+
+set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
+@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
+
+
+.\build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p %INPUT2% -n 400 -e -ngl 33 -s 0
+
+
diff --git a/examples/tokenize/tokenize.cpp b/examples/tokenize/tokenize.cpp
index 4ff8e3fa7..d95a92475 100644
--- a/examples/tokenize/tokenize.cpp
+++ b/examples/tokenize/tokenize.cpp
@@ -17,7 +17,7 @@ int main(int argc, char ** argv) {
 
     const bool printing_ids = argc > 3 && std::string(argv[3]) == "--ids";
 
-    llama_backend_init(false);
+    llama_backend_init();
 
     llama_model_params model_params = llama_model_default_params();
     model_params.vocab_only = true;
diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp
index 4a9a2340b..7d06e401b 100644
--- a/examples/train-text-from-scratch/train-text-from-scratch.cpp
+++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp
@@ -1,5 +1,6 @@
 #include "ggml.h"
 #include "ggml-alloc.h"
+#include "ggml-backend.h"
 #include "common.h"
 #include "train.h"
 #include "llama.h"
@@ -19,8 +20,6 @@
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
-static const size_t tensor_alignment = 32;
-
 struct my_llama_hparams {
     uint32_t n_vocab = 32000;
     uint32_t n_ctx   = 512;
@@ -51,14 +50,14 @@ struct my_llama_layer {
     struct ggml_tensor * ffn_norm;
 
     // ff
-    struct ggml_tensor * w1;
-    struct ggml_tensor * w2;
-    struct ggml_tensor * w3;
+    struct ggml_tensor * ffn_gate; // w1
+    struct ggml_tensor * ffn_down; // w2
+    struct ggml_tensor * ffn_up;   // w3
 };
 
 struct my_llama_model {
     struct ggml_context * ctx = NULL;
-    std::vector<uint8_t> data;
+    ggml_backend_buffer_t data = NULL;
 
     my_llama_hparams hparams;
 
@@ -112,13 +111,13 @@ static const char * LLM_TENSOR_FFN_DOWN      = "blk.%d.ffn_down";
 static const char * LLM_TENSOR_FFN_UP        = "blk.%d.ffn_up";
 
 static void print_params(struct my_llama_hparams * params) {
-    printf("%s: n_vocab: %d\n", __func__, params->n_vocab);
-    printf("%s: n_ctx:   %d\n", __func__, params->n_ctx);
-    printf("%s: n_embd:  %d\n", __func__, params->n_embd);
-    printf("%s: n_head:  %d\n", __func__, params->n_head);
-    printf("%s: n_ff:    %d\n", __func__, params->n_ff);
-    printf("%s: n_layer: %d\n", __func__, params->n_layer);
-    printf("%s: n_rot:   %d\n", __func__, params->n_rot);
+    printf("%s: n_vocab: %u\n", __func__, params->n_vocab);
+    printf("%s: n_ctx:   %u\n", __func__, params->n_ctx);
+    printf("%s: n_embd:  %u\n", __func__, params->n_embd);
+    printf("%s: n_head:  %u\n", __func__, params->n_head);
+    printf("%s: n_ff:    %u\n", __func__, params->n_ff);
+    printf("%s: n_layer: %u\n", __func__, params->n_layer);
+    printf("%s: n_rot:   %u\n", __func__, params->n_rot);
 }
 
 static void set_param_model(struct my_llama_model * model) {
@@ -141,42 +140,9 @@ static void set_param_model(struct my_llama_model * model) {
         ggml_set_param(ctx, layer.wv);
         ggml_set_param(ctx, layer.wo);
         ggml_set_param(ctx, layer.ffn_norm);
-        ggml_set_param(ctx, layer.w1);
-        ggml_set_param(ctx, layer.w2);
-        ggml_set_param(ctx, layer.w3);
-    }
-}
-
-static void alloc_model(struct ggml_allocr * alloc, struct my_llama_model * model) {
-    ggml_allocr_alloc(alloc, model->tok_embeddings);
-    ggml_allocr_alloc(alloc, model->norm);
-    ggml_allocr_alloc(alloc, model->output);
-    for (uint32_t i = 0; i < model->layers.size(); ++i) {
-        auto & layer = model->layers[i];
-        ggml_allocr_alloc(alloc, layer.attention_norm);
-        ggml_allocr_alloc(alloc, layer.wq);
-        ggml_allocr_alloc(alloc, layer.wk);
-        ggml_allocr_alloc(alloc, layer.wv);
-        ggml_allocr_alloc(alloc, layer.wo);
-        ggml_allocr_alloc(alloc, layer.ffn_norm);
-        ggml_allocr_alloc(alloc, layer.w1);
-        ggml_allocr_alloc(alloc, layer.w2);
-        ggml_allocr_alloc(alloc, layer.w3);
-    }
-    ggml_allocr_alloc(alloc, model->tok_embeddings->grad);
-    ggml_allocr_alloc(alloc, model->norm->grad);
-    ggml_allocr_alloc(alloc, model->output->grad);
-    for (uint32_t i = 0; i < model->layers.size(); ++i) {
-        auto & layer = model->layers[i];
-        ggml_allocr_alloc(alloc, layer.attention_norm->grad);
-        ggml_allocr_alloc(alloc, layer.wq->grad);
-        ggml_allocr_alloc(alloc, layer.wk->grad);
-        ggml_allocr_alloc(alloc, layer.wv->grad);
-        ggml_allocr_alloc(alloc, layer.wo->grad);
-        ggml_allocr_alloc(alloc, layer.ffn_norm->grad);
-        ggml_allocr_alloc(alloc, layer.w1->grad);
-        ggml_allocr_alloc(alloc, layer.w2->grad);
-        ggml_allocr_alloc(alloc, layer.w3->grad);
+        ggml_set_param(ctx, layer.ffn_gate);
+        ggml_set_param(ctx, layer.ffn_down);
+        ggml_set_param(ctx, layer.ffn_up);
     }
 }
 
@@ -232,9 +198,9 @@ static void init_model(struct my_llama_model * model) {
 
         layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
 
-        layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd,   n_ff);
-        layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32,   n_ff, n_embd);
-        layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd,   n_ff);
+        layer.ffn_gate = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd,   n_ff);
+        layer.ffn_down = ggml_new_tensor_2d(ctx, GGML_TYPE_F32,   n_ff, n_embd);
+        layer.ffn_up   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd,   n_ff);
 
         ggml_set_name(layer.attention_norm, tni(LLM_TENSOR_ATTN_NORM, i));
 
@@ -245,25 +211,15 @@ static void init_model(struct my_llama_model * model) {
 
         ggml_set_name(layer.ffn_norm,       tni(LLM_TENSOR_FFN_NORM, i));
 
-        ggml_set_name(layer.w1,             tni(LLM_TENSOR_FFN_GATE, i));
-        ggml_set_name(layer.w2,             tni(LLM_TENSOR_FFN_DOWN, i));
-        ggml_set_name(layer.w3,             tni(LLM_TENSOR_FFN_UP, i));
+        ggml_set_name(layer.ffn_gate,       tni(LLM_TENSOR_FFN_GATE, i));
+        ggml_set_name(layer.ffn_down,       tni(LLM_TENSOR_FFN_DOWN, i));
+        ggml_set_name(layer.ffn_up,         tni(LLM_TENSOR_FFN_UP, i));
     }
 
     set_param_model(model);
 
-    // measure data size
-    size_t size = 0;
-    for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
-        size += GGML_PAD(ggml_nbytes(t), tensor_alignment);
-    }
-
     // allocate data
-    struct ggml_allocr * alloc = NULL;
-    model->data.resize(size + tensor_alignment);
-    alloc = ggml_allocr_new(model->data.data(), model->data.size(), tensor_alignment);
-    alloc_model(alloc, model);
-    ggml_allocr_free(alloc);
+    model->data = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_cpu_buffer_type());
 }
 
 static void randomize_model(struct my_llama_model * model, int seed, float mean, float std, float min, float max) {
@@ -288,9 +244,9 @@ static void randomize_model(struct my_llama_model * model, int seed, float mean,
 
         randomize_tensor_normal(layer.ffn_norm, rnd);
 
-        randomize_tensor_normal(layer.w1, rnd);
-        randomize_tensor_normal(layer.w2, rnd);
-        randomize_tensor_normal(layer.w3, rnd);
+        randomize_tensor_normal(layer.ffn_gate, rnd);
+        randomize_tensor_normal(layer.ffn_down, rnd);
+        randomize_tensor_normal(layer.ffn_up,   rnd);
     }
 
     free_random_normal_distribution(rnd);
@@ -298,7 +254,7 @@ static void randomize_model(struct my_llama_model * model, int seed, float mean,
 
 static struct ggml_tensor * llama_build_train_graphs(
         struct my_llama_model * model,
-        struct ggml_allocr    * alloc,
+        ggml_gallocr_t          alloc,
         struct ggml_context   * ctx,
         struct ggml_cgraph    * gf,
         struct ggml_cgraph    * gb,
@@ -309,7 +265,8 @@ static struct ggml_tensor * llama_build_train_graphs(
         const  int              n_tokens,
         const  int              n_batch,
         const  bool             enable_flash_attn,
-        const  bool             enable_checkpointing) {
+        const  bool             enable_checkpointing,
+        const  bool             measure_only) {
 
     ggml_set_scratch(ctx, { 0, 0, nullptr, });
     const int n_past = 0;
@@ -335,13 +292,7 @@ static struct ggml_tensor * llama_build_train_graphs(
 
     // KQ_pos - contains the positions
     struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, N);
-    ggml_allocr_alloc(alloc, KQ_pos);
-    if (!ggml_allocr_is_measure(alloc)) {
-        int * data = (int *) KQ_pos->data;
-        for (int i = 0; i < N; ++i) {
-            data[i] = n_past + i;
-        }
-    }
+    ggml_set_input(KQ_pos);
 
     // rope has so much parameters that we make a custom function for it
     auto rope = [ctx, KQ_pos, n_rot, n_ctx, rope_freq_base, rope_freq_scale]
@@ -405,11 +356,11 @@ static struct ggml_tensor * llama_build_train_graphs(
         struct ggml_tensor * t22 = ggml_rms_norm     (ctx, t21, f_norm_rms_eps);                    set_name(t22, "t22");     assert_shape_2d(t22, n_embd, N*n_batch);
         struct ggml_tensor * t23 = ggml_repeat       (ctx, layer.ffn_norm, t22);                    set_name(t23, "t23");     assert_shape_2d(t23, n_embd, N*n_batch);
         struct ggml_tensor * t24 = ggml_mul          (ctx, t23, t22);                               set_name(t24, "t24");     assert_shape_2d(t24, n_embd, N*n_batch);
-        struct ggml_tensor * t25 = ggml_mul_mat      (ctx, layer.w3, t24);                          set_name(t25, "t25");     assert_shape_2d(t25, n_ff, N*n_batch);
-        struct ggml_tensor * t26 = ggml_mul_mat      (ctx, layer.w1, t24);                          set_name(t26, "t26");     assert_shape_2d(t26, n_ff, N*n_batch);
+        struct ggml_tensor * t25 = ggml_mul_mat      (ctx, layer.ffn_up, t24);                      set_name(t25, "t25");     assert_shape_2d(t25, n_ff, N*n_batch);
+        struct ggml_tensor * t26 = ggml_mul_mat      (ctx, layer.ffn_gate, t24);                    set_name(t26, "t26");     assert_shape_2d(t26, n_ff, N*n_batch);
         struct ggml_tensor * t27 = ggml_silu         (ctx, t26);                                    set_name(t27, "t27");     assert_shape_2d(t27, n_ff, N*n_batch);
         struct ggml_tensor * t28 = ggml_mul          (ctx, t27, t25);                               set_name(t28, "t28");     assert_shape_2d(t28, n_ff, N*n_batch);
-        struct ggml_tensor * t29 = ggml_mul_mat      (ctx, layer.w2, t28);                          set_name(t29, "t29");     assert_shape_2d(t29, n_embd, N*n_batch);
+        struct ggml_tensor * t29 = ggml_mul_mat      (ctx, layer.ffn_down, t28);                    set_name(t29, "t29");     assert_shape_2d(t29, n_embd, N*n_batch);
         struct ggml_tensor * t30 = ggml_add          (ctx, t29, t21);                               set_name(t30, "t30");     assert_shape_2d(t30, n_embd, N*n_batch);
         cur = t30;
         checkpoints.push_back(cur);
@@ -449,21 +400,31 @@ static struct ggml_tensor * llama_build_train_graphs(
         // KQ_pos
         ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, 1.0f));
         GGML_ASSERT(t36->grad->data == NULL && t36->grad->view_src == NULL);
-
-        ggml_allocr_alloc(alloc, t36->grad);
+        ggml_set_input(t36->grad);
 
         // allocating checkpoints in one block to reduce memory fragmentation
         // note: they will be freed in reverse order
         for (int i = 0; i < (int) checkpoints.size(); ++i) {
             if (checkpoints[i]->data == NULL && checkpoints[i]->view_src == NULL) {
-                ggml_allocr_alloc(alloc, checkpoints[i]);
+                ggml_set_input(checkpoints[i]);
             }
         }
 
         //int n_leafs_after = gb->n_leafs;
         //int n_nodes_after = gb->n_nodes;
+        if (measure_only) {
+            // FIXME: will still allocate
+            ggml_gallocr_reserve(alloc, gb);
+        } else {
+            ggml_gallocr_alloc_graph(alloc, gb);
 
-        ggml_allocr_alloc_graph(alloc, gb);
+            if (!measure_only) {
+                int * data = (int *) KQ_pos->data;
+                for (int i = 0; i < N; ++i) {
+                    data[i] = n_past + i;
+                }
+            }
+        }
 
         // remove the additional nodes and leafs
         for (int i = n_leafs_before; i < gb->n_leafs; ++i) {
@@ -560,9 +521,9 @@ static void load_llama_model_gguf(struct gguf_context * fctx, struct ggml_contex
         copy_tensor_by_name(layer.wv,             f_ggml_ctx, tni(LLM_TENSOR_ATTN_V, i));
         copy_tensor_by_name(layer.wo,             f_ggml_ctx, tni(LLM_TENSOR_ATTN_OUT, i));
         copy_tensor_by_name(layer.ffn_norm,       f_ggml_ctx, tni(LLM_TENSOR_FFN_NORM, i));
-        copy_tensor_by_name(layer.w1,             f_ggml_ctx, tni(LLM_TENSOR_FFN_GATE, i));
-        copy_tensor_by_name(layer.w2,             f_ggml_ctx, tni(LLM_TENSOR_FFN_DOWN, i));
-        copy_tensor_by_name(layer.w3,             f_ggml_ctx, tni(LLM_TENSOR_FFN_UP, i));
+        copy_tensor_by_name(layer.ffn_gate,       f_ggml_ctx, tni(LLM_TENSOR_FFN_GATE, i));
+        copy_tensor_by_name(layer.ffn_down,       f_ggml_ctx, tni(LLM_TENSOR_FFN_DOWN, i));
+        copy_tensor_by_name(layer.ffn_up,         f_ggml_ctx, tni(LLM_TENSOR_FFN_UP, i));
     }
 }
 
@@ -703,9 +664,9 @@ static void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vo
         gguf_add_tensor(fctx, layer.wv);
         gguf_add_tensor(fctx, layer.wo);
         gguf_add_tensor(fctx, layer.ffn_norm);
-        gguf_add_tensor(fctx, layer.w1);
-        gguf_add_tensor(fctx, layer.w2);
-        gguf_add_tensor(fctx, layer.w3);
+        gguf_add_tensor(fctx, layer.ffn_gate);
+        gguf_add_tensor(fctx, layer.ffn_down);
+        gguf_add_tensor(fctx, layer.ffn_up);
     }
 }
 
@@ -750,6 +711,7 @@ static bool load_checkpoint_file(const char * filename, struct my_llama_model *
 
     load_checkpoint_gguf(fctx, f_ggml_ctx, model, train);
 
+    gguf_free(fctx);
     return true;
 }
 
@@ -954,9 +916,9 @@ static int64_t get_parameter_count(struct my_llama_model* model) {
         nx += ggml_nelements(layer.wv);
         nx += ggml_nelements(layer.wo);
         nx += ggml_nelements(layer.ffn_norm);
-        nx += ggml_nelements(layer.w1);
-        nx += ggml_nelements(layer.w2);
-        nx += ggml_nelements(layer.w3);
+        nx += ggml_nelements(layer.ffn_gate);
+        nx += ggml_nelements(layer.ffn_down);
+        nx += ggml_nelements(layer.ffn_up);
     }
     return nx;
 }
@@ -999,7 +961,7 @@ int main(int argc, char ** argv) {
     struct ggml_opt_context * opt   = train->opt;
 
     // set opt params from command line
-    opt->params = ggml_opt_default_params(GGML_OPT_ADAM);
+    opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
     opt->params.print_forward_graph     = false;
     opt->params.print_backward_graph    = false;
     opt->params.graph_size              = LLAMA_TRAIN_MAX_NODES;
@@ -1047,7 +1009,7 @@ int main(int argc, char ** argv) {
     printf("%s: seen train_samples     %llu\n", __func__, (long long unsigned) train->train_samples);
     printf("%s: seen train_tokens      %llu\n", __func__, (long long unsigned) train->train_tokens);
     printf("%s: completed train_epochs %llu\n", __func__, (long long unsigned) train->train_epochs);
-    printf("%s: model_size = %zu bytes (%.1f MB)\n", __func__, (ggml_used_mem(model.ctx) + model.data.size()), (float) (ggml_used_mem(model.ctx) + model.data.size()) / (1024.0f*1024.0f));
+    printf("%s: model_size = %zu bytes (%.1f MB)\n", __func__, (ggml_used_mem(model.ctx) + ggml_backend_buffer_get_size(model.data)), (float) (ggml_used_mem(model.ctx) + ggml_backend_buffer_get_size(model.data)) / (1024.0f*1024.0f));
 
     if (params.only_write_model) {
         save_train_files_data save_data;
@@ -1074,11 +1036,6 @@ int main(int argc, char ** argv) {
     int n_vocab  = model.hparams.n_vocab;
     int n_batch  = params.common.n_batch;
 
-    std::vector<uint8_t> mem_input_data;
-    std::vector<uint8_t> mem_compute_data;
-
-    ggml_allocr * alloc = NULL;
-
     // context for input tensors without their data
     struct ggml_init_params ctx_input_params = {
         ggml_tensor_overhead() * 2, // mem_size
@@ -1092,17 +1049,10 @@ int main(int argc, char ** argv) {
     struct ggml_tensor * target_probs  = ggml_new_tensor_3d(ctx_input, GGML_TYPE_F32, n_vocab,  n_tokens, n_batch);
 
     // measure required memory for input tensors
-    size_t max_input_size = GGML_PAD(ggml_nbytes(tokens_input), tensor_alignment) +
-                            GGML_PAD(ggml_nbytes(target_probs), tensor_alignment) +
-                            tensor_alignment;
-    printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f));
-
     // allocate input tensors
-    mem_input_data.resize(max_input_size);
-    alloc = ggml_allocr_new(mem_input_data.data(), mem_input_data.size(), tensor_alignment);
-    ggml_allocr_alloc(alloc, tokens_input);
-    ggml_allocr_alloc(alloc, target_probs);
-    ggml_allocr_free(alloc);
+    ggml_backend_buffer_t input_data = ggml_backend_alloc_ctx_tensors_from_buft(ctx_input, ggml_backend_cpu_buffer_type());
+    size_t max_input_size = ggml_backend_buffer_get_size(input_data);
+    printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f));
 
     // context for compute tensors without their data
     const size_t estimated_compute_size_wo_data = (
@@ -1129,7 +1079,7 @@ int main(int argc, char ** argv) {
     // find best evaluation order
     for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) {
         ctx_compute = ggml_init(ctx_compute_params);
-        alloc = ggml_allocr_new_measure(tensor_alignment);
+        ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
         gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
         gf->order = (enum ggml_cgraph_eval_order) order;
         gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
@@ -1142,14 +1092,14 @@ int main(int argc, char ** argv) {
             &logits, tokens_input, target_probs,
             n_tokens, n_batch,
             params.common.use_flash,
-            params.common.use_checkpointing
+            params.common.use_checkpointing,
+            true
         );
-        size_t max_compute_size = ggml_allocr_max_size(alloc) + tensor_alignment;
+        size_t max_compute_size = ggml_gallocr_get_buffer_size(alloc, 0); // FIXME: this will still allocate the buffer
         if (max_compute_size < best_compute_size) {
             best_compute_size = max_compute_size;
             best_order = gf->order;
         }
-        ggml_allocr_free(alloc);
         ggml_free(ctx_compute);
     }
     size_t max_compute_size = best_compute_size;
@@ -1160,9 +1110,8 @@ int main(int argc, char ** argv) {
         "invalid");
 
     // allocate compute tensors
-    mem_compute_data.resize(max_compute_size);
     ctx_compute = ggml_init(ctx_compute_params);
-    alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment);
+    ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
     gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
     gf->order = best_order;
     gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
@@ -1175,9 +1124,9 @@ int main(int argc, char ** argv) {
         &logits, tokens_input, target_probs,
         n_tokens, n_batch,
         params.common.use_flash,
-        params.common.use_checkpointing
+        params.common.use_checkpointing,
+        false
     );
-    ggml_allocr_free(alloc);
 
     std::vector<llama_token> train_tokens;
     std::vector<size_t> train_samples_begin;
diff --git a/examples/ts-type-to-grammar.sh b/examples/ts-type-to-grammar.sh
new file mode 100755
index 000000000..a7ad00173
--- /dev/null
+++ b/examples/ts-type-to-grammar.sh
@@ -0,0 +1,28 @@
+#!/bin/bash
+#
+# ./examples/ts-type-to-grammar.sh "{a:string,b:string,c?:string}"
+# python examples/json-schema-to-grammar.py https://json.schemastore.org/tsconfig.json
+#
+set -euo pipefail
+
+readonly type="$1"
+
+# Create a temporary directory
+TMPDIR=""
+trap 'rm -fR "$TMPDIR"' EXIT
+TMPDIR=$(mktemp -d)
+
+DTS_FILE="$TMPDIR/type.d.ts"
+SCHEMA_FILE="$TMPDIR/schema.json"
+
+echo "export type MyType = $type" > "$DTS_FILE"
+
+# This is a fork of typescript-json-schema, actively maintained as of March 2024:
+# https://github.com/vega/ts-json-schema-generator
+npx ts-json-schema-generator --unstable --no-top-ref --path "$DTS_FILE" --type MyType -e none > "$SCHEMA_FILE"
+
+# Alternative, not actively maintained as of March 2024:
+# https://github.com/YousefED/typescript-json-schema
+# npx typescript-json-schema --defaultProps --required "$DTS_FILE" MyType | tee "$SCHEMA_FILE" >&2
+
+./examples/json-schema-to-grammar.py "$SCHEMA_FILE"
diff --git a/flake.lock b/flake.lock
index 15a0a1a8e..c32725112 100644
--- a/flake.lock
+++ b/flake.lock
@@ -5,11 +5,11 @@
         "nixpkgs-lib": "nixpkgs-lib"
       },
       "locked": {
-        "lastModified": 1701473968,
-        "narHash": "sha256-YcVE5emp1qQ8ieHUnxt1wCZCC3ZfAS+SRRWZ2TMda7E=",
+        "lastModified": 1709336216,
+        "narHash": "sha256-Dt/wOWeW6Sqm11Yh+2+t0dfEWxoMxGBvv3JpIocFl9E=",
         "owner": "hercules-ci",
         "repo": "flake-parts",
-        "rev": "34fed993f1674c8d06d58b37ce1e0fe5eebcb9f5",
+        "rev": "f7b3c975cf067e56e7cda6cb098ebe3fb4d74ca2",
         "type": "github"
       },
       "original": {
@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1703637592,
-        "narHash": "sha256-8MXjxU0RfFfzl57Zy3OfXCITS0qWDNLzlBAdwxGZwfY=",
+        "lastModified": 1711703276,
+        "narHash": "sha256-iMUFArF0WCatKK6RzfUJknjem0H9m4KgorO/p3Dopkk=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "cfc3698c31b1fb9cdcf10f36c9643460264d0ca8",
+        "rev": "d8fe5e6c92d0d190646fb9f1056741a229980089",
         "type": "github"
       },
       "original": {
@@ -37,11 +37,11 @@
     "nixpkgs-lib": {
       "locked": {
         "dir": "lib",
-        "lastModified": 1701253981,
-        "narHash": "sha256-ztaDIyZ7HrTAfEEUt9AtTDNoCYxUdSd6NrRHaYOIxtk=",
+        "lastModified": 1709237383,
+        "narHash": "sha256-cy6ArO4k5qTx+l5o+0mL9f5fa86tYUX3ozE1S+Txlds=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "e92039b55bcd58469325ded85d4f58dd5a4eaf58",
+        "rev": "1536926ef5621b09bba54035ae2bb6d806d72ac8",
         "type": "github"
       },
       "original": {
diff --git a/flake.nix b/flake.nix
index 488ed6c59..9cd3756e5 100644
--- a/flake.nix
+++ b/flake.nix
@@ -1,3 +1,17 @@
+# The flake interface to llama.cpp's Nix expressions. The flake is used as a
+# more discoverable entry-point, as well as a way to pin the dependencies and
+# expose default outputs, including the outputs built by the CI.
+
+# For more serious applications involving some kind of customization  you may
+# want to consider consuming the overlay, or instantiating `llamaPackages`
+# directly:
+#
+# ```nix
+# pkgs.callPackage ${llama-cpp-root}/.devops/nix/scope.nix { }`
+# ```
+
+# Cf. https://jade.fyi/blog/flakes-arent-real/ for a more detailed exposition
+# of the relation between Nix and the Nix Flakes.
 {
   description = "Port of Facebook's LLaMA model in C/C++";
 
@@ -6,28 +20,41 @@
     flake-parts.url = "github:hercules-ci/flake-parts";
   };
 
-  # Optional binary cache
-  nixConfig = {
-    extra-substituters = [
-      # Populated by the CI in ggerganov/llama.cpp
-      "https://llama-cpp.cachix.org"
-
-      # A development cache for nixpkgs imported with `config.cudaSupport = true`.
-      # Populated by https://hercules-ci.com/github/SomeoneSerge/nixpkgs-cuda-ci.
-      # This lets one skip building e.g. the CUDA-enabled openmpi.
-      # TODO: Replace once nix-community obtains an official one.
-      "https://cuda-maintainers.cachix.org"
-    ];
-
-    # Verify these are the same keys as published on
-    # - https://app.cachix.org/cache/llama-cpp
-    # - https://app.cachix.org/cache/cuda-maintainers
-    extra-trusted-public-keys = [
-      "llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc="
-      "cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E="
-    ];
-  };
-
+  # There's an optional binary cache available. The details are below, but they're commented out.
+  #
+  # Why? The terrible experience of being prompted to accept them on every single Nix command run.
+  # Plus, there are warnings shown about not being a trusted user on a default Nix install
+  # if you *do* say yes to the prompts.
+  #
+  # This experience makes having `nixConfig` in a flake a persistent UX problem.
+  #
+  # To make use of the binary cache, please add the relevant settings to your `nix.conf`.
+  # It's located at `/etc/nix/nix.conf` on non-NixOS systems. On NixOS, adjust the `nix.settings`
+  # option in your NixOS configuration to add `extra-substituters` and `extra-trusted-public-keys`,
+  # as shown below.
+  #
+  # ```
+  # nixConfig = {
+  #   extra-substituters = [
+  #     # Populated by the CI in ggerganov/llama.cpp
+  #     "https://llama-cpp.cachix.org"
+  #
+  #     # A development cache for nixpkgs imported with `config.cudaSupport = true`.
+  #     # Populated by https://hercules-ci.com/github/SomeoneSerge/nixpkgs-cuda-ci.
+  #     # This lets one skip building e.g. the CUDA-enabled openmpi.
+  #     # TODO: Replace once nix-community obtains an official one.
+  #     "https://cuda-maintainers.cachix.org"
+  #   ];
+  #
+  #   # Verify these are the same keys as published on
+  #   # - https://app.cachix.org/cache/llama-cpp
+  #   # - https://app.cachix.org/cache/cuda-maintainers
+  #   extra-trusted-public-keys = [
+  #     "llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc="
+  #     "cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E="
+  #   ];
+  # };
+  # ```
 
   # For inspection, use `nix flake show github:ggerganov/llama.cpp` or the nix repl:
   #
@@ -80,11 +107,12 @@
         # ```
         #
         # Cf. https://nixos.org/manual/nix/unstable/command-ref/new-cli/nix3-flake.html?highlight=flake#flake-format
-        flake.overlays.default =
-          (final: prev: {
+        flake.overlays.default = (
+          final: prev: {
             llamaPackages = final.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
             inherit (final.llamaPackages) llama-cpp;
-          });
+          }
+        );
 
         systems = [
           "aarch64-darwin"
@@ -104,6 +132,9 @@
             ...
           }:
           {
+            # For standardised reproducible formatting with `nix fmt`
+            formatter = pkgs.nixfmt-rfc-style;
+
             # Unlike `.#packages`, legacyPackages may contain values of
             # arbitrary types (including nested attrsets) and may even throw
             # exceptions. This attribute isn't recursed into by `nix flake
@@ -114,6 +145,7 @@
             # the same path you would with an overlay.
             legacyPackages = {
               llamaPackages = pkgs.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
+              llamaPackagesWindows = pkgs.pkgsCross.mingwW64.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
               llamaPackagesCuda = pkgsCuda.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
               llamaPackagesRocm = pkgsRocm.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
             };
@@ -123,6 +155,8 @@
             packages =
               {
                 default = config.legacyPackages.llamaPackages.llama-cpp;
+                vulkan = config.packages.default.override { useVulkan = true; };
+                windows = config.legacyPackages.llamaPackagesWindows.llama-cpp;
               }
               // lib.optionalAttrs pkgs.stdenv.isLinux {
                 opencl = config.packages.default.override { useOpenCL = true; };
@@ -136,9 +170,14 @@
               };
 
             # Packages exposed in `.#checks` will be built by the CI and by
-            # `nix flake check`. Currently we expose all packages, but we could
-            # make more granular choices
-            checks = config.packages;
+            # `nix flake check`.
+            #
+            # We could test all outputs e.g. as `checks = confg.packages`.
+            #
+            # TODO: Build more once https://github.com/ggerganov/llama.cpp/issues/6346 has been addressed
+            checks = {
+              inherit (config.packages) default vulkan;
+            };
           };
       };
 }
diff --git a/ggml-alloc.c b/ggml-alloc.c
index a27dd54b0..7ceafec30 100644
--- a/ggml-alloc.c
+++ b/ggml-alloc.c
@@ -17,384 +17,11 @@
 //#define AT_PRINTF(...) fprintf(stderr, __VA_ARGS__)
 #define AT_PRINTF(...)
 
-// TODO: GGML_PAD ?
-static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) {
-    assert(alignment && !(alignment & (alignment - 1))); // power of 2
-    size_t align = (alignment - (((uintptr_t)buffer + offset) % alignment)) % alignment;
-    return offset + align;
-}
 
-struct free_block {
-    void * addr;
-    size_t size;
-};
-
-struct ggml_tallocr {
-    struct ggml_backend_buffer * buffer;
-    bool buffer_owned;
-    void * base;
-    size_t alignment;
-
-    int n_free_blocks;
-    struct free_block free_blocks[MAX_FREE_BLOCKS];
-
-    size_t max_size;
-
-    bool measure;
-
-#ifdef GGML_ALLOCATOR_DEBUG
-    struct ggml_tensor * allocated_tensors[1024];
-#endif
-};
-
-#ifdef GGML_ALLOCATOR_DEBUG
-static void add_allocated_tensor(ggml_tallocr_t alloc, struct ggml_tensor * tensor) {
-    for (int i = 0; i < 1024; i++) {
-        if (alloc->allocated_tensors[i] == NULL) {
-            alloc->allocated_tensors[i] = tensor;
-            return;
-        }
-    }
-    GGML_ASSERT(!"out of allocated_tensors");
-}
-static void remove_allocated_tensor(ggml_tallocr_t alloc, struct ggml_tensor * tensor) {
-    for (int i = 0; i < 1024; i++) {
-        if (alloc->allocated_tensors[i] == tensor ||
-            (alloc->allocated_tensors[i] != NULL && alloc->allocated_tensors[i]->data == tensor->data)) {
-            alloc->allocated_tensors[i] = NULL;
-            return;
-        }
-    }
-    printf("tried to free tensor %s not found\n", tensor->name);
-    GGML_ASSERT(!"tensor not found");
-}
-#endif
-
-// check if a tensor is allocated by this buffer
-static bool ggml_tallocr_is_own(ggml_tallocr_t alloc, const struct ggml_tensor * tensor) {
-    return tensor->buffer == alloc->buffer && (!tensor->view_src || tensor->view_src->buffer == alloc->buffer);
-}
-
-static bool ggml_is_view(struct ggml_tensor * t) {
+static bool ggml_is_view(const struct ggml_tensor * t) {
     return t->view_src != NULL;
 }
 
-void ggml_tallocr_alloc(ggml_tallocr_t alloc, struct ggml_tensor * tensor) {
-    GGML_ASSERT(!ggml_is_view(tensor)); // views generally get data pointer from one of their sources
-    GGML_ASSERT(tensor->data == NULL); // avoid allocating tensor which already has memory allocated
-
-    size_t size = ggml_backend_buffer_get_alloc_size(alloc->buffer, tensor);
-    size = aligned_offset(NULL, size, alloc->alignment);
-
-    AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size);
-
-    size_t max_avail = 0;
-
-    // find the best fitting free block besides the last block
-    int best_fit_block = -1;
-    size_t best_fit_size = SIZE_MAX;
-    for (int i = 0; i < alloc->n_free_blocks - 1; i++) {
-        struct free_block * block = &alloc->free_blocks[i];
-        max_avail = MAX(max_avail, block->size);
-        if (block->size >= size && block->size <= best_fit_size) {
-            best_fit_block = i;
-            best_fit_size = block->size;
-        }
-    }
-
-    AT_PRINTF("block %d\n", best_fit_block);
-
-    if (best_fit_block == -1) {
-        // the last block is our last resort
-        struct free_block * block = &alloc->free_blocks[alloc->n_free_blocks - 1];
-        max_avail = MAX(max_avail, block->size);
-        if (block->size >= size) {
-            best_fit_block = alloc->n_free_blocks - 1;
-        } else {
-            fprintf(stderr, "%s: not enough space in the buffer (needed %zu, largest block available %zu)\n",
-                    __func__, size, max_avail);
-            GGML_ASSERT(!"not enough space in the buffer");
-            return;
-        }
-    }
-    struct free_block * block = &alloc->free_blocks[best_fit_block];
-    void * addr = block->addr;
-    block->addr = (char*)block->addr + size;
-    block->size -= size;
-    if (block->size == 0) {
-        // remove block if empty
-        alloc->n_free_blocks--;
-        for (int j = best_fit_block; j < alloc->n_free_blocks; j++) {
-            alloc->free_blocks[j] = alloc->free_blocks[j+1];
-        }
-    }
-
-    tensor->data = addr;
-    tensor->buffer = alloc->buffer;
-    if (!alloc->measure) {
-        ggml_backend_buffer_init_tensor(alloc->buffer, tensor);
-    }
-
-#ifdef GGML_ALLOCATOR_DEBUG
-    add_allocated_tensor(alloc, tensor);
-    size_t cur_max = (char*)addr - (char*)alloc->base + size;
-    if (cur_max > alloc->max_size) {
-        printf("max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
-        for (int i = 0; i < 1024; i++) {
-            if (alloc->allocated_tensors[i]) {
-                printf("%s (%.2f MB) ", alloc->allocated_tensors[i]->name, ggml_nbytes(alloc->allocated_tensors[i]) / 1024.0 / 1024.0);
-            }
-        }
-        printf("\n");
-    }
-#endif
-
-    alloc->max_size = MAX(alloc->max_size, (char*)addr - (char*)alloc->base + size);
-}
-
-// this is a very naive implementation, but for our case the number of free blocks should be very small
-static void ggml_tallocr_free_tensor(ggml_tallocr_t alloc, struct ggml_tensor * tensor) {
-    if (ggml_tallocr_is_own(alloc, tensor) == false) {
-        // the tensor was not allocated in this buffer
-        // this can happen because the graph allocator will try to free weights and other tensors from different buffers
-        // the easiest way to deal with this is just to ignore it
-        // AT_PRINTF("ignoring %s (their buffer: %p, our buffer: %p)\n", tensor->name, (void *)tensor->buffer, (void *)alloc->buffer);
-        return;
-    }
-
-    void * ptr = tensor->data;
-
-    size_t size = ggml_backend_buffer_get_alloc_size(alloc->buffer, tensor);
-    size = aligned_offset(NULL, size, alloc->alignment);
-    AT_PRINTF("%s: freeing %s at %p (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, ptr, size, alloc->n_free_blocks);
-
-#ifdef GGML_ALLOCATOR_DEBUG
-    remove_allocated_tensor(alloc, tensor);
-#endif
-
-    // see if we can merge with an existing block
-    for (int i = 0; i < alloc->n_free_blocks; i++) {
-        struct free_block * block = &alloc->free_blocks[i];
-        // check if ptr is at the end of the block
-        if ((char*)block->addr + block->size == ptr) {
-            block->size += size;
-            // check if we can merge with the next block
-            if (i < alloc->n_free_blocks - 1 && (char*)block->addr + block->size == alloc->free_blocks[i+1].addr) {
-                block->size += alloc->free_blocks[i+1].size;
-                alloc->n_free_blocks--;
-                for (int j = i+1; j < alloc->n_free_blocks; j++) {
-                    alloc->free_blocks[j] = alloc->free_blocks[j+1];
-                }
-            }
-            return;
-        }
-        // check if ptr is at the beginning of the block
-        if ((char*)ptr + size == block->addr) {
-            block->addr = ptr;
-            block->size += size;
-            // check if we can merge with the previous block
-            if (i > 0 && (char*)alloc->free_blocks[i-1].addr + alloc->free_blocks[i-1].size == block->addr) {
-                alloc->free_blocks[i-1].size += block->size;
-                alloc->n_free_blocks--;
-                for (int j = i; j < alloc->n_free_blocks; j++) {
-                    alloc->free_blocks[j] = alloc->free_blocks[j+1];
-                }
-            }
-            return;
-        }
-    }
-    // otherwise, add a new block
-    GGML_ASSERT(alloc->n_free_blocks < MAX_FREE_BLOCKS && "out of free blocks");
-    // insert the new block in the correct position to keep the array sorted by address (to make merging blocks faster)
-    int insert_pos = 0;
-    while (insert_pos < alloc->n_free_blocks && alloc->free_blocks[insert_pos].addr < ptr) {
-        insert_pos++;
-    }
-    // shift all blocks from insert_pos onward to make room for the new block
-    for (int i = alloc->n_free_blocks; i > insert_pos; i--) {
-        alloc->free_blocks[i] = alloc->free_blocks[i-1];
-    }
-    // insert the new block
-    alloc->free_blocks[insert_pos].addr = ptr;
-    alloc->free_blocks[insert_pos].size = size;
-    alloc->n_free_blocks++;
-}
-
-void ggml_tallocr_reset(ggml_tallocr_t alloc) {
-    alloc->n_free_blocks = 1;
-    size_t align_offset = aligned_offset(alloc->base, 0, alloc->alignment);
-    alloc->free_blocks[0].addr = (char *)alloc->base + align_offset;
-
-    if (alloc->measure) {
-        alloc->free_blocks[0].size = SIZE_MAX/2; // restrict maximum size of a measure allocator to half size_t max to avoid overflows
-    } else {
-        alloc->free_blocks[0].size = ggml_backend_buffer_get_size(alloc->buffer) - align_offset;
-    }
-}
-
-ggml_tallocr_t ggml_tallocr_new(void * data, size_t size, size_t alignment) {
-    struct ggml_backend_buffer * buffer = ggml_backend_cpu_buffer_from_ptr(data, size);
-
-    ggml_tallocr_t alloc = (ggml_tallocr_t)malloc(sizeof(struct ggml_tallocr));
-
-    *alloc = (struct ggml_tallocr) {
-        /*.buffer        = */ buffer,
-        /*.buffer_owned  = */ true,
-        /*.base          = */ ggml_backend_buffer_get_base(buffer),
-        /*.alignment     = */ alignment,
-        /*.n_free_blocks = */ 0,
-        /*.free_blocks   = */ {{0}},
-        /*.max_size      = */ 0,
-        /*.measure       = */ false,
-#ifdef GGML_ALLOCATOR_DEBUG
-        /*.allocated_tensors = */ {0},
-#endif
-    };
-
-    ggml_tallocr_reset(alloc);
-
-    return alloc;
-}
-
-ggml_tallocr_t ggml_tallocr_new_measure(size_t alignment) {
-    ggml_tallocr_t alloc = ggml_tallocr_new((void *)0x1000, SIZE_MAX/2, alignment);
-    alloc->measure = true;
-
-    return alloc;
-}
-
-ggml_tallocr_t ggml_tallocr_new_measure_from_backend(struct ggml_backend * backend) {
-    // create a backend buffer to get the correct tensor allocation sizes
-    ggml_backend_buffer_t buffer = ggml_backend_alloc_buffer(backend, 1);
-
-    // TODO: move alloc initialization to a common ggml_tallocr_new_impl function
-    ggml_tallocr_t alloc = ggml_tallocr_new_from_buffer(buffer);
-    alloc->buffer_owned = true;
-    alloc->measure = true;
-    ggml_tallocr_reset(alloc);
-    return alloc;
-}
-
-ggml_tallocr_t ggml_tallocr_new_from_backend(struct ggml_backend * backend, size_t size) {
-    ggml_backend_buffer_t buffer = ggml_backend_alloc_buffer(backend, size);
-    ggml_tallocr_t alloc = ggml_tallocr_new_from_buffer(buffer);
-    alloc->buffer_owned = true;
-    return alloc;
-}
-
-ggml_tallocr_t ggml_tallocr_new_from_buffer(struct ggml_backend_buffer * buffer) {
-    ggml_tallocr_t alloc = (ggml_tallocr_t)malloc(sizeof(struct ggml_tallocr));
-
-    *alloc = (struct ggml_tallocr) {
-        /*.buffer        = */ buffer,
-        /*.buffer_owned  = */ false,
-        /*.base          = */ ggml_backend_buffer_get_base(buffer),
-        /*.alignment     = */ ggml_backend_buffer_get_alignment(buffer),
-        /*.n_free_blocks = */ 0,
-        /*.free_blocks   = */ {{0}},
-        /*.max_size      = */ 0,
-        /*.measure       = */ false,
-#ifdef GGML_ALLOCATOR_DEBUG
-        /*.allocated_tensors = */ {0},
-#endif
-    };
-
-    ggml_tallocr_reset(alloc);
-
-    return alloc;
-}
-
-struct ggml_backend_buffer * ggml_tallocr_get_buffer(ggml_tallocr_t alloc) {
-    return alloc->buffer;
-}
-
-void ggml_tallocr_free(ggml_tallocr_t alloc) {
-    if (alloc == NULL) {
-        return;
-    }
-
-    if (alloc->buffer_owned) {
-        ggml_backend_buffer_free(alloc->buffer);
-    }
-    free(alloc);
-}
-
-bool ggml_tallocr_is_measure(ggml_tallocr_t alloc) {
-    return alloc->measure;
-}
-
-size_t ggml_tallocr_max_size(ggml_tallocr_t alloc) {
-    return alloc->max_size;
-}
-
-// graph allocator
-
-struct hash_node {
-    int n_children;
-    int n_views;
-};
-
-struct ggml_gallocr {
-    ggml_tallocr_t talloc;
-    struct ggml_hash_set hash_set;
-    struct hash_node * hash_values;
-    size_t hash_values_size;
-    ggml_tallocr_t * hash_allocs;
-    int * parse_seq;
-    int parse_seq_len;
-};
-
-ggml_gallocr_t ggml_gallocr_new(void) {
-    ggml_gallocr_t galloc = (ggml_gallocr_t)malloc(sizeof(struct ggml_gallocr));
-
-    *galloc = (struct ggml_gallocr) {
-        /*.talloc           = */ NULL,
-        /*.hash_set         = */ {0},
-        /*.hash_values      = */ NULL,
-        /*.hash_values_size = */ 0,
-        /*.hash_allocs      = */ NULL,
-        /*.parse_seq        = */ NULL,
-        /*.parse_seq_len    = */ 0,
-    };
-
-    return galloc;
-}
-
-void ggml_gallocr_free(ggml_gallocr_t galloc) {
-    if (galloc == NULL) {
-        return;
-    }
-
-    if (galloc->hash_set.keys != NULL) {
-        free(galloc->hash_set.keys);
-    }
-    if (galloc->hash_values != NULL) {
-        free(galloc->hash_values);
-    }
-    if (galloc->hash_allocs != NULL) {
-        free(galloc->hash_allocs);
-    }
-    if (galloc->parse_seq != NULL) {
-        free(galloc->parse_seq);
-    }
-    free(galloc);
-}
-
-void ggml_gallocr_set_parse_seq(ggml_gallocr_t galloc, const int * list, int n) {
-    free(galloc->parse_seq);
-    galloc->parse_seq = malloc(sizeof(int) * n);
-
-    for (int i = 0; i < n; i++) {
-        galloc->parse_seq[i] = list[i];
-    }
-    galloc->parse_seq_len = n;
-}
-
-static struct hash_node * hash_get(ggml_gallocr_t galloc, struct ggml_tensor * t) {
-    size_t i = ggml_hash_find_or_insert(galloc->hash_set, t);
-    return &galloc->hash_values[i];
-}
-
 static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
     if (a->type != b->type) {
         return false;
@@ -434,374 +61,922 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
     }
 }
 
-static ggml_tallocr_t node_tallocr(ggml_gallocr_t galloc, struct ggml_tensor * node) {
-    if (galloc->talloc != NULL) {
-        return galloc->talloc;
-    }
-
-    return galloc->hash_allocs[ggml_hash_find_or_insert(galloc->hash_set, node)];
+static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) {
+    assert(alignment && !(alignment & (alignment - 1))); // power of 2
+    size_t align = (alignment - (((uintptr_t)buffer + offset) % alignment)) % alignment;
+    return offset + align;
 }
 
-static void init_view(ggml_gallocr_t galloc, struct ggml_tensor * view, bool update_backend) {
-    ggml_tallocr_t alloc = node_tallocr(galloc, view);
+// tallocr
 
-    GGML_ASSERT(view->view_src != NULL && view->view_src->data != NULL);
-    if (update_backend) {
-        view->backend = view->view_src->backend;
-    }
-    // views are initialized in the alloc buffer rather than the view_src buffer
-    view->buffer  = alloc->buffer;
-    view->data    = (char *)view->view_src->data + view->view_offs;
+struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer) {
+    void * base = ggml_backend_buffer_get_base(buffer);
+    size_t align = ggml_backend_buffer_get_alignment(buffer);
 
-    assert(ggml_tallocr_is_measure(alloc) || !view->buffer || view->buffer->buft == alloc->buffer->buft);
+    assert(align && !(align & (align - 1))); // power of 2
 
-    if (!alloc->measure) {
-        ggml_backend_buffer_init_tensor(alloc->buffer, view);
-    }
-}
-
-static void allocate_node(ggml_gallocr_t galloc, struct ggml_tensor * node) {
-    ggml_tallocr_t alloc = node_tallocr(galloc, node);
-
-    if (node->data == NULL) {
-        if (ggml_is_view(node)) {
-            init_view(galloc, node, true);
-        } else {
-            // see if we can reuse a parent's buffer (inplace)
-            if (ggml_op_can_inplace(node->op)) {
-                for (int i = 0; i < GGML_MAX_SRC; i++) {
-                    struct ggml_tensor * parent = node->src[i];
-                    if (parent == NULL) {
-                        break;
-                    }
-
-                    // if the node's data is external, then we cannot re-use it
-                    if (ggml_tallocr_is_own(alloc, parent) == false) {
-                        AT_PRINTF("not reusing parent %s for %s as %p is external\n", parent->name, node->name, parent->data);
-                        continue;
-                    }
-
-                    struct hash_node * p_hn = hash_get(galloc, parent);
-                    if (parent->data != NULL && p_hn->n_children == 1 && p_hn->n_views == 0 && ggml_are_same_layout(node, parent)) {
-                        if (ggml_is_view(parent)) {
-                            struct ggml_tensor * view_src = parent->view_src;
-                            struct hash_node * view_src_hn = hash_get(galloc, view_src);
-                            if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) {
-                                // TODO: the offset of the view parent must be kept to ensure that the op doesn't overwrite
-                                // the parent's data that it will need later (same layout requirement). the problem is that then
-                                // we cannot free the tensor because the original address of the allocation is lost.
-                                // adding a view_src pointer to the tensor would solve this and simplify the code dealing with views
-                                // for now, we only reuse the parent's data if the offset is zero (view_src->data == parent->data)
-                                AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name);
-                                node->view_src = view_src;
-                                view_src_hn->n_views += 1;
-                                init_view(galloc, node, false);
-                                return;
-                            }
-                        } else {
-                            AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name);
-                            node->view_src = parent;
-                            p_hn->n_views += 1;
-                            init_view(galloc, node, false);
-                            return;
-                        }
-                    }
-                }
-            }
-            ggml_tallocr_alloc(alloc, node);
-        }
-    }
-}
-
-static void free_node(ggml_gallocr_t galloc, struct ggml_tensor * node) {
-    ggml_tallocr_t alloc = node_tallocr(galloc, node);
-
-    ggml_tallocr_free_tensor(alloc, node);
-}
-
-static void ggml_tallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * gf) {
-    const int * parse_seq     = galloc->parse_seq;
-    int         parse_seq_len = galloc->parse_seq_len;
-
-    // count number of children and views
-    for (int i = 0; i < gf->n_nodes; i++) {
-        struct ggml_tensor * node = gf->nodes[i];
-
-        if (ggml_is_view(node)) {
-            struct ggml_tensor * view_src = node->view_src;
-            hash_get(galloc, view_src)->n_views += 1;
-            if (node->buffer == NULL && node->data != NULL) {
-                // view of a pre-allocated tensor, didn't call init_view() yet
-                init_view(galloc, node, true);
-            }
-        }
-
-        for (int j = 0; j < GGML_MAX_SRC; j++) {
-            struct ggml_tensor * parent = node->src[j];
-            if (parent == NULL) {
-                break;
-            }
-            hash_get(galloc, parent)->n_children += 1;
-            if (ggml_is_view(parent) && parent->buffer == NULL && parent->data != NULL) {
-                init_view(galloc, parent, true);
-            }
-        }
-   }
-
-    // allocate tensors
-    // if we have parse_seq then we allocate nodes following the list, and we only free nodes at barriers
-    int last_barrier_pos = 0;
-    int n_nodes = parse_seq_len ? parse_seq_len : gf->n_nodes;
-
-    for (int ind = 0; ind < n_nodes; ind++) {
-        // allocate a node if there is no parse_seq or this is not a barrier
-        if (parse_seq_len == 0 || parse_seq[ind] != -1) {
-            int i = parse_seq_len ? parse_seq[ind] : ind;
-            struct ggml_tensor * node = gf->nodes[i];
-
-            // allocate parents (leafs)
-            for (int j = 0; j < GGML_MAX_SRC; j++) {
-                struct ggml_tensor * parent = node->src[j];
-                if (parent == NULL) {
-                    break;
-                }
-                allocate_node(galloc, parent);
-            }
-
-            // allocate node
-            allocate_node(galloc, node);
-
-            AT_PRINTF("exec: %s (%s) <= ", ggml_op_name(node->op), node->name);
-            for (int j = 0; j < GGML_MAX_SRC; j++) {
-                struct ggml_tensor * parent = node->src[j];
-                if (parent == NULL) {
-                    break;
-                }
-                AT_PRINTF("%s", parent->name);
-                if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) {
-                    AT_PRINTF(", ");
-                }
-            }
-            AT_PRINTF("\n");
-        }
-
-        // update parents
-        // update immediately if there is no parse_seq
-        // update only at barriers if there is parse_seq
-        if ((parse_seq_len == 0) || parse_seq[ind] == -1) {
-            int update_start = parse_seq_len ? last_barrier_pos : ind;
-            int update_end   = parse_seq_len ? ind              : ind + 1;
-            for (int i = update_start; i < update_end; i++) {
-                int node_i = parse_seq_len ? parse_seq[i] : i;
-                struct ggml_tensor * node = gf->nodes[node_i];
-
-                for (int j = 0; j < GGML_MAX_SRC; j++) {
-                    struct ggml_tensor * parent = node->src[j];
-                    if (parent == NULL) {
-                        break;
-                    }
-                    struct hash_node * p_hn = hash_get(galloc, parent);
-                    p_hn->n_children -= 1;
-
-                    //AT_PRINTF("parent %s: %d children, %d views\n", parent->name, parent->n_children, parent->n_views);
-
-                    if (p_hn->n_children == 0 && p_hn->n_views == 0) {
-                        if (ggml_is_view(parent)) {
-                            struct ggml_tensor * view_src = parent->view_src;
-                            struct hash_node * view_src_hn = hash_get(galloc, view_src);
-                            view_src_hn->n_views -= 1;
-                            AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src_hn->n_children, view_src_hn->n_views);
-                            if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0) {
-                                free_node(galloc, view_src);
-                            }
-                        }
-                        else {
-                            free_node(galloc, parent);
-                        }
-                    }
-                }
-            }
-            AT_PRINTF("\n");
-            if (parse_seq_len) {
-                last_barrier_pos = ind + 1;
-            }
-        }
-    }
-}
-
-size_t ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, ggml_tallocr_t talloc, struct ggml_cgraph * graph) {
-    size_t hash_size = graph->visited_hash_table.size;
-
-    // check if the hash table is initialized and large enough
-    if (galloc->hash_set.size < hash_size) {
-        if (galloc->hash_set.keys != NULL) {
-            free(galloc->hash_set.keys);
-        }
-        if (galloc->hash_values != NULL) {
-            free(galloc->hash_values);
-        }
-        galloc->hash_set.keys = malloc(sizeof(struct ggml_tensor *) * hash_size);
-        galloc->hash_set.size = hash_size;
-        galloc->hash_values = malloc(sizeof(struct hash_node) * hash_size);
-    }
-
-    // reset hash table
-    memset(galloc->hash_set.keys, 0, sizeof(struct ggml_tensor *) * hash_size);
-    memset(galloc->hash_values,   0, sizeof(struct hash_node) * hash_size);
-
-    galloc->talloc = talloc;
-    ggml_tallocr_alloc_graph_impl(galloc, graph);
-    galloc->talloc = NULL;
-
-    size_t max_size = ggml_tallocr_max_size(talloc);
-
-    return max_size;
-}
-
-void ggml_gallocr_alloc_graph_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, struct ggml_hash_set hash_set, ggml_tallocr_t * hash_node_talloc) {
-    const size_t hash_size = hash_set.size;
-
-    GGML_ASSERT(hash_size >= (size_t)(graph->n_nodes + graph->n_leafs));
-
-    galloc->talloc = NULL;
-
-    // alloc hash_values if needed
-    if (galloc->hash_values == NULL || galloc->hash_values_size < hash_size) {
-        free(galloc->hash_values);
-        galloc->hash_values      = malloc(sizeof(struct hash_node) * hash_size);
-        galloc->hash_values_size = hash_size;
-    }
-
-    // free hash_set.keys if needed
-    if (galloc->hash_set.keys != NULL) {
-        free(galloc->hash_set.keys);
-    }
-    galloc->hash_set = hash_set;
-
-    // reset hash values
-    memset(galloc->hash_values, 0, sizeof(struct hash_node) * hash_size);
-
-    galloc->hash_allocs = hash_node_talloc;
-
-    ggml_tallocr_alloc_graph_impl(galloc, graph);
-
-    // remove unowned resources
-    galloc->hash_set.keys = NULL;
-    galloc->hash_allocs = NULL;
-}
-
-// legacy API wrapper
-
-struct ggml_allocr {
-    ggml_tallocr_t talloc;
-    ggml_gallocr_t galloc;
-};
-
-static ggml_allocr_t ggml_allocr_new_impl(ggml_tallocr_t talloc) {
-    ggml_allocr_t alloc = (ggml_allocr_t)malloc(sizeof(struct ggml_allocr));
-    *alloc = (struct ggml_allocr) {
-        /*.talloc = */ talloc,
-        /*.galloc = */ ggml_gallocr_new(),
+    struct ggml_tallocr talloc = (struct ggml_tallocr) {
+        /*.buffer    = */ buffer,
+        /*.base      = */ base,
+        /*.alignment = */ align,
+        /*.offset    = */ aligned_offset(base, 0, align),
     };
-    return alloc;
+    return talloc;
 }
 
-ggml_allocr_t ggml_allocr_new(void * data, size_t size, size_t alignment) {
-    return ggml_allocr_new_impl(ggml_tallocr_new(data, size, alignment));
-}
+void ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor) {
+    size_t size = ggml_backend_buffer_get_alloc_size(talloc->buffer, tensor);
+    size = GGML_PAD(size, talloc->alignment);
 
-ggml_allocr_t ggml_allocr_new_measure(size_t alignment) {
-    return ggml_allocr_new_impl(ggml_tallocr_new_measure(alignment));
-}
-
-ggml_allocr_t ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer) {
-    return ggml_allocr_new_impl(ggml_tallocr_new_from_buffer(buffer));
-}
-
-ggml_allocr_t ggml_allocr_new_from_backend(struct ggml_backend * backend, size_t size) {
-    return ggml_allocr_new_impl(ggml_tallocr_new_from_backend(backend, size));
-}
-
-ggml_allocr_t ggml_allocr_new_measure_from_backend(struct ggml_backend * backend) {
-    return ggml_allocr_new_impl(ggml_tallocr_new_measure_from_backend(backend));
-}
-
-struct ggml_backend_buffer * ggml_allocr_get_buffer(ggml_allocr_t alloc) {
-    return ggml_tallocr_get_buffer(alloc->talloc);
-}
-
-void ggml_allocr_set_parse_seq(ggml_allocr_t alloc, const int * list, int n) {
-    ggml_gallocr_set_parse_seq(alloc->galloc, list, n);
-}
-
-void ggml_allocr_free(ggml_allocr_t alloc) {
-    if (alloc == NULL) {
+    if (talloc->offset + size > ggml_backend_buffer_get_size(talloc->buffer)) {
+        fprintf(stderr, "%s: not enough space in the buffer to allocate %s (needed %zu, available %zu)\n",
+                __func__, tensor->name, size, ggml_backend_buffer_get_size(talloc->buffer) - talloc->offset);
+        GGML_ASSERT(!"not enough space in the buffer");
         return;
     }
 
-    ggml_gallocr_free(alloc->galloc);
-    ggml_tallocr_free(alloc->talloc);
-    free(alloc);
+    void * addr = (char *)ggml_backend_buffer_get_base(talloc->buffer) + talloc->offset;
+    talloc->offset += size;
+
+    assert(((uintptr_t)addr % talloc->alignment) == 0);
+
+    ggml_backend_tensor_alloc(talloc->buffer, tensor, addr);
 }
 
-bool ggml_allocr_is_measure(ggml_allocr_t alloc) {
-    return ggml_tallocr_is_measure(alloc->talloc);
+// dynamic tensor allocator
+
+struct free_block {
+    size_t offset;
+    size_t size;
+};
+
+struct ggml_dyn_tallocr {
+    size_t alignment;
+    int n_free_blocks;
+    struct free_block free_blocks[MAX_FREE_BLOCKS];
+    size_t max_size;
+
+#ifdef GGML_ALLOCATOR_DEBUG
+    struct {
+        const struct ggml_tensor * tensor;
+        size_t offset;
+    } allocated_tensors[1024];
+#endif
+};
+
+#ifdef GGML_ALLOCATOR_DEBUG
+static void add_allocated_tensor(struct ggml_dyn_tallocr * alloc, size_t offset, const struct ggml_tensor * tensor) {
+    for (int i = 0; i < 1024; i++) {
+        if (alloc->allocated_tensors[i].tensor == NULL) {
+            alloc->allocated_tensors[i].tensor = tensor;
+            alloc->allocated_tensors[i].offset = offset;
+            return;
+        }
+    }
+    GGML_ASSERT(!"out of allocated_tensors");
 }
-
-void ggml_allocr_reset(ggml_allocr_t alloc) {
-    ggml_tallocr_reset(alloc->talloc);
+static void remove_allocated_tensor(struct ggml_dyn_tallocr * alloc, size_t offset, const struct ggml_tensor * tensor) {
+    for (int i = 0; i < 1024; i++) {
+        if (alloc->allocated_tensors[i].offset == offset) {
+            alloc->allocated_tensors[i].tensor = NULL;
+            return;
+        }
+    }
+    fprintf(stderr, "tried to free tensor %s not found\n", tensor->name);
+    GGML_ASSERT(!"tensor not found");
 }
+#endif
 
-void ggml_allocr_alloc(ggml_allocr_t alloc, struct ggml_tensor * tensor) {
-    ggml_tallocr_alloc(alloc->talloc, tensor);
-}
+static size_t ggml_dyn_tallocr_alloc(struct ggml_dyn_tallocr * alloc, size_t size, const struct ggml_tensor * tensor) {
+    size = aligned_offset(NULL, size, alloc->alignment);
 
-size_t ggml_allocr_max_size(ggml_allocr_t alloc) {
-    return ggml_tallocr_max_size(alloc->talloc);
-}
+    AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size);
 
-size_t ggml_allocr_alloc_graph(ggml_allocr_t alloc, struct ggml_cgraph * graph) {
-    return ggml_gallocr_alloc_graph(alloc->galloc, alloc->talloc, graph);
-}
+    size_t max_avail = 0;
 
-// utils
-ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) {
-    GGML_ASSERT(ggml_get_no_alloc(ctx) == true);
-
-    size_t alignment = ggml_backend_buft_get_alignment(buft);
-
-    size_t nbytes = 0;
-    for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
-        if (t->data == NULL && t->view_src == NULL) {
-            nbytes += GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), alignment);
+    // find the best fitting free block besides the last block
+    int best_fit_block = -1;
+    size_t best_fit_size = SIZE_MAX;
+    for (int i = 0; i < alloc->n_free_blocks - 1; i++) {
+        struct free_block * block = &alloc->free_blocks[i];
+        max_avail = MAX(max_avail, block->size);
+        if (block->size >= size && block->size <= best_fit_size) {
+            best_fit_block = i;
+            best_fit_size = block->size;
         }
     }
 
-    if (nbytes == 0) {
-        // all the tensors in the context are already allocated
-        return NULL;
+    if (best_fit_block == -1) {
+        // the last block is our last resort
+        struct free_block * block = &alloc->free_blocks[alloc->n_free_blocks - 1];
+        max_avail = MAX(max_avail, block->size);
+        if (block->size >= size) {
+            best_fit_block = alloc->n_free_blocks - 1;
+        } else {
+            // this should never happen
+            fprintf(stderr, "%s: not enough space in the buffer to allocate %zu bytes, largest block available %zu bytes\n",
+                    __func__, size, max_avail);
+            GGML_ASSERT(!"not enough space in the buffer");
+            GGML_UNREACHABLE();
+        }
     }
 
-    ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, nbytes);
-    ggml_tallocr_t tallocr = ggml_tallocr_new_from_buffer(buffer);
+    struct free_block * block = &alloc->free_blocks[best_fit_block];
+    size_t offset = block->offset;
+    block->offset = offset + size;
+    block->size -= size;
+    if (block->size == 0) {
+        // remove block if empty
+        alloc->n_free_blocks--;
+        for (int j = best_fit_block; j < alloc->n_free_blocks; j++) {
+            alloc->free_blocks[j] = alloc->free_blocks[j+1];
+        }
+    }
 
-    for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+    AT_PRINTF("block %d, offset %zu\n", best_fit_block, offset);
+
+#ifdef GGML_ALLOCATOR_DEBUG
+    add_allocated_tensor(alloc, offset, tensor);
+    size_t cur_max = offset + size;
+    if (cur_max > alloc->max_size) {
+        // sort allocated_tensors by offset
+        for (int i = 0; i < 1024; i++) {
+            for (int j = i + 1; j < 1024; j++) {
+                if (alloc->allocated_tensors[i].offset > alloc->allocated_tensors[j].offset) {
+                    const struct ggml_tensor * tmp_tensor = alloc->allocated_tensors[i].tensor;
+                    size_t tmp_offset = alloc->allocated_tensors[i].offset;
+                    alloc->allocated_tensors[i].tensor = alloc->allocated_tensors[j].tensor;
+                    alloc->allocated_tensors[i].offset = alloc->allocated_tensors[j].offset;
+                    alloc->allocated_tensors[j].tensor = tmp_tensor;
+                    alloc->allocated_tensors[j].offset = tmp_offset;
+                }
+            }
+        }
+        fprintf(stderr, "max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
+        for (int i = 0; i < 1024; i++) {
+            if (alloc->allocated_tensors[i].tensor) {
+                fprintf(stderr, "%s [%zx-%zx] (%.2f MB) ", alloc->allocated_tensors[i].tensor->name,
+                    alloc->allocated_tensors[i].offset,
+                    alloc->allocated_tensors[i].offset + ggml_nbytes(alloc->allocated_tensors[i].tensor),
+                    ggml_nbytes(alloc->allocated_tensors[i].tensor) / 1024.0 / 1024.0);
+            }
+        }
+        fprintf(stderr, "\n");
+    }
+#endif
+
+    alloc->max_size = MAX(alloc->max_size, offset + size);
+
+    return offset;
+
+    GGML_UNUSED(tensor);
+}
+
+// this is a very naive implementation, but for our case the number of free blocks should be very small
+static void ggml_dyn_tallocr_free_tensor(struct ggml_dyn_tallocr * alloc, size_t offset, size_t size, const struct ggml_tensor * tensor) {
+    size = aligned_offset(NULL, size, alloc->alignment);
+
+    AT_PRINTF("%s: freeing %s at %zu (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, offset, size, alloc->n_free_blocks);
+
+#ifdef GGML_ALLOCATOR_DEBUG
+    remove_allocated_tensor(alloc, offset, tensor);
+#endif
+
+    // see if we can merge with an existing block
+    for (int i = 0; i < alloc->n_free_blocks; i++) {
+        struct free_block * block = &alloc->free_blocks[i];
+        // check if ptr is at the end of the block
+        if (block->offset + block->size == offset) {
+            block->size += size;
+            // check if we can merge with the next block
+            if (i < alloc->n_free_blocks - 1 && block->offset + block->size == alloc->free_blocks[i+1].offset) {
+                block->size += alloc->free_blocks[i+1].size;
+                alloc->n_free_blocks--;
+                for (int j = i+1; j < alloc->n_free_blocks; j++) {
+                    alloc->free_blocks[j] = alloc->free_blocks[j+1];
+                }
+            }
+            return;
+        }
+        // check if ptr is at the beginning of the block
+        if (offset + size == block->offset) {
+            block->offset = offset;
+            block->size += size;
+            // check if we can merge with the previous block
+            if (i > 0 && alloc->free_blocks[i-1].offset + alloc->free_blocks[i-1].size == block->offset) {
+                alloc->free_blocks[i-1].size += block->size;
+                alloc->n_free_blocks--;
+                for (int j = i; j < alloc->n_free_blocks; j++) {
+                    alloc->free_blocks[j] = alloc->free_blocks[j+1];
+                }
+            }
+            return;
+        }
+    }
+    // otherwise, add a new block
+    GGML_ASSERT(alloc->n_free_blocks < MAX_FREE_BLOCKS && "out of free blocks");
+    // insert the new block in the correct position to keep the array sorted by address (to make merging blocks faster)
+    int insert_pos = 0;
+    while (insert_pos < alloc->n_free_blocks && alloc->free_blocks[insert_pos].offset < offset) {
+        insert_pos++;
+    }
+    // shift all blocks from insert_pos onward to make room for the new block
+    for (int i = alloc->n_free_blocks; i > insert_pos; i--) {
+        alloc->free_blocks[i] = alloc->free_blocks[i-1];
+    }
+    // insert the new block
+    alloc->free_blocks[insert_pos].offset = offset;
+    alloc->free_blocks[insert_pos].size = size;
+    alloc->n_free_blocks++;
+
+    GGML_UNUSED(tensor);
+}
+
+static void ggml_dyn_tallocr_reset(struct ggml_dyn_tallocr * alloc) {
+    alloc->n_free_blocks = 1;
+    alloc->free_blocks[0].offset = 0;
+    alloc->free_blocks[0].size = SIZE_MAX/2; // restrict maximum size of a measure allocator to half size_t max to avoid overflows
+    alloc->max_size = 0;
+}
+
+static struct ggml_dyn_tallocr * ggml_dyn_tallocr_new(size_t alignment) {
+    struct ggml_dyn_tallocr * alloc = (struct ggml_dyn_tallocr *)malloc(sizeof(struct ggml_dyn_tallocr));
+
+    *alloc = (struct ggml_dyn_tallocr) {
+        /*.alignment     = */ alignment,
+        /*.n_free_blocks = */ 0,
+        /*.free_blocks   = */ {{0}},
+        /*.max_size      = */ 0,
+#ifdef GGML_ALLOCATOR_DEBUG
+        /*.allocated_tensors = */ {{0}},
+#endif
+    };
+
+    ggml_dyn_tallocr_reset(alloc);
+
+    return alloc;
+}
+
+static void ggml_dyn_tallocr_free(struct ggml_dyn_tallocr * alloc) {
+    free(alloc);
+}
+
+static size_t ggml_dyn_tallocr_max_size(struct ggml_dyn_tallocr * alloc) {
+    return alloc->max_size;
+}
+
+
+/////////////////////////////////////
+
+// graph allocator
+
+struct hash_node {
+    int n_children;
+    int n_views;
+    int buffer_id;
+    size_t offset; // offset within the buffer
+    bool allocated;
+};
+
+struct tensor_alloc {
+    size_t offset;
+    size_t size_max; // 0 = pre-allocated, unused, or view
+};
+
+struct leaf_alloc {
+    int buffer_id;
+    struct tensor_alloc leaf;
+};
+
+struct node_alloc {
+    int buffer_id;
+    struct tensor_alloc dst;
+    struct tensor_alloc src[GGML_MAX_SRC];
+};
+
+struct ggml_gallocr {
+    ggml_backend_buffer_type_t * bufts; // [n_buffers]
+    ggml_backend_buffer_t * buffers; // [n_buffers]
+    struct ggml_dyn_tallocr ** buf_tallocs; // [n_buffers]
+    int n_buffers;
+
+    struct ggml_hash_set hash_set;
+    struct hash_node * hash_values; // [hash_set.size]
+
+    struct node_alloc * node_allocs; // [n_nodes]
+    int n_nodes;
+
+    struct leaf_alloc * leaf_allocs; // [n_leafs]
+    int n_leafs;
+};
+
+ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs) {
+    ggml_gallocr_t galloc = (ggml_gallocr_t)calloc(sizeof(struct ggml_gallocr), 1);
+    GGML_ASSERT(galloc != NULL);
+
+    galloc->bufts = calloc(sizeof(ggml_backend_buffer_type_t) * n_bufs, 1);
+    GGML_ASSERT(galloc->bufts != NULL);
+
+    galloc->buffers = calloc(sizeof(ggml_backend_buffer_t) * n_bufs, 1);
+    GGML_ASSERT(galloc->buffers != NULL);
+
+    galloc->buf_tallocs = calloc(sizeof(struct ggml_dyn_tallocr *) * n_bufs, 1);
+    GGML_ASSERT(galloc->buf_tallocs != NULL);
+
+    for (int i = 0; i < n_bufs; i++) {
+        galloc->bufts[i] = bufts[i];
+        galloc->buffers[i] = NULL;
+        size_t alignment = ggml_backend_buft_get_alignment(bufts[i]);
+        galloc->buf_tallocs[i] = ggml_dyn_tallocr_new(alignment);
+    }
+    galloc->n_buffers = n_bufs;
+
+    return galloc;
+}
+
+ggml_gallocr_t ggml_gallocr_new(ggml_backend_buffer_type_t buft) {
+    return ggml_gallocr_new_n(&buft, 1);
+}
+
+void ggml_gallocr_free(ggml_gallocr_t galloc) {
+    if (galloc == NULL) {
+        return;
+    }
+
+    for (int i = 0; i < galloc->n_buffers; i++) {
+        if (galloc->buffers != NULL) {
+            ggml_backend_buffer_free(galloc->buffers[i]);
+        }
+        if (galloc->buf_tallocs != NULL) {
+            ggml_dyn_tallocr_free(galloc->buf_tallocs[i]);
+        }
+    }
+
+    free(galloc->hash_set.keys);
+    free(galloc->hash_values);
+    free(galloc->bufts);
+    free(galloc->buffers);
+    free(galloc->buf_tallocs);
+    free(galloc->node_allocs);
+    free(galloc->leaf_allocs);
+    free(galloc);
+}
+
+typedef struct ggml_gallocr * ggml_gallocr_t;
+
+static struct hash_node * ggml_gallocr_hash_get(ggml_gallocr_t galloc, struct ggml_tensor * t) {
+    size_t i = ggml_hash_find_or_insert(galloc->hash_set, t);
+    return &galloc->hash_values[i];
+}
+
+static bool ggml_gallocr_is_own(ggml_gallocr_t galloc, struct ggml_tensor * t) {
+    return ggml_gallocr_hash_get(galloc, t)->allocated;
+}
+
+static void ggml_gallocr_set_node_offset(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id, size_t offset) {
+    struct hash_node * hn = ggml_gallocr_hash_get(galloc, node);
+    hn->buffer_id = buffer_id;
+    hn->offset = offset;
+    hn->allocated = true;
+}
+
+static bool ggml_gallocr_is_allocated(ggml_gallocr_t galloc, struct ggml_tensor * t) {
+    return t->data != NULL || ggml_gallocr_hash_get(galloc, t)->allocated;
+}
+
+static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id) {
+    struct hash_node * hn = ggml_gallocr_hash_get(galloc, node);
+
+    if (!ggml_gallocr_is_allocated(galloc, node) && !ggml_is_view(node)) {
+        hn->allocated = true;
+        assert(hn->offset == 0);
+
+        // try to reuse a parent's buffer (inplace)
+        if (ggml_op_can_inplace(node->op)) {
+            for (int i = 0; i < GGML_MAX_SRC; i++) {
+                struct ggml_tensor * parent = node->src[i];
+                if (parent == NULL) {
+                    continue;
+                }
+
+                // if the node's data is external, then we cannot re-use it
+                if (!ggml_gallocr_is_own(galloc, parent)) {
+                    AT_PRINTF("not reusing parent %s for %s as %p is external\n", parent->name, node->name, parent->data);
+                    continue;
+                }
+
+                // outputs cannot be reused
+                if (parent->flags & GGML_TENSOR_FLAG_OUTPUT || (parent->view_src != NULL && parent->view_src->flags & GGML_TENSOR_FLAG_OUTPUT)) {
+                    AT_PRINTF("not reusing parent %s for %s as it is an output\n", parent->name, node->name);
+                    continue;
+                }
+
+                if (!ggml_are_same_layout(node, parent)) {
+                    AT_PRINTF("not reusing parent %s for %s as layouts are different\n", parent->name, node->name);
+                    continue;
+                }
+
+                struct hash_node * p_hn = ggml_gallocr_hash_get(galloc, parent);
+                if (p_hn->n_children == 1 && p_hn->n_views == 0) {
+                    if (ggml_is_view(parent)) {
+                        struct ggml_tensor * view_src = parent->view_src;
+                        struct hash_node * view_src_hn = ggml_gallocr_hash_get(galloc, view_src);
+                        if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) {
+                            AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name);
+                            assert(view_src_hn->offset == p_hn->offset);
+                            hn->buffer_id = p_hn->buffer_id;
+                            hn->offset = p_hn->offset;
+                            p_hn->allocated = false; // avoid freeing the parent
+                            view_src_hn->allocated = false;
+                            return;
+                        }
+                    } else {
+                        AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name);
+                        hn->buffer_id = p_hn->buffer_id;
+                        hn->offset = p_hn->offset;
+                        p_hn->allocated = false; // avoid freeing the parent
+                        return;
+                    }
+                }
+            }
+        }
+        // allocate tensor from the buffer
+        struct ggml_dyn_tallocr * alloc = galloc->buf_tallocs[buffer_id];
+        ggml_backend_buffer_type_t buft = galloc->bufts[buffer_id];
+        size_t size = ggml_backend_buft_get_alloc_size(buft, node);
+        size_t offset = ggml_dyn_tallocr_alloc(alloc, size, node);
+        hn->buffer_id = buffer_id;
+        hn->offset = offset;
+        return;
+    }
+}
+
+static void ggml_gallocr_free_node(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id) {
+    // graph outputs are never freed
+    if (node->flags & GGML_TENSOR_FLAG_OUTPUT) {
+        AT_PRINTF("not freeing output %s\n", node->name);
+        return;
+    }
+
+    struct ggml_dyn_tallocr * alloc = galloc->buf_tallocs[buffer_id];
+    ggml_backend_buffer_type_t buft = galloc->bufts[buffer_id];
+    struct hash_node * hn = ggml_gallocr_hash_get(galloc, node);
+    size_t offset = hn->offset;
+    size_t size = ggml_backend_buft_get_alloc_size(buft, node);
+    ggml_dyn_tallocr_free_tensor(alloc, offset, size, node);
+    hn->allocated = false;
+}
+
+static int get_node_buffer_id(const int * node_buffer_ids, int i) {
+    return node_buffer_ids ? node_buffer_ids[i] : 0;
+}
+
+static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
+    // clear hash tables
+    memset(galloc->hash_set.keys, 0, galloc->hash_set.size * sizeof(struct ggml_tensor *));
+    memset(galloc->hash_values,   0, galloc->hash_set.size * sizeof(struct hash_node));
+
+    // allocate leafs
+    // these may be tensors that the application is not using in the graph, but may still want to allocate for other purposes
+    for (int i = 0; i < graph->n_leafs; i++) {
+        struct ggml_tensor * leaf = graph->leafs[i];
+        ggml_gallocr_allocate_node(galloc, leaf, get_node_buffer_id(leaf_buffer_ids, i));
+    }
+
+    // count number of children and views
+    // allocate other graph inputs and leafs first to avoid overwriting them
+    for (int i = 0; i < graph->n_nodes; i++) {
+        struct ggml_tensor * node = graph->nodes[i];
+
+        // TODO: better way to add external dependencies
+        // GGML_OP_NONE does not appear normally in the graph nodes, but is used by ggml-backend to add dependencies to
+        // control when some tensors are allocated and freed. in this case, the dependencies are in `src`, but the node
+        // itself is never used and should not be considered a dependency
+        if (ggml_is_view(node) && node->op != GGML_OP_NONE) {
+            struct ggml_tensor * view_src = node->view_src;
+            ggml_gallocr_hash_get(galloc, view_src)->n_views += 1;
+        }
+
+        if (node->flags & GGML_TENSOR_FLAG_INPUT) {
+            ggml_gallocr_allocate_node(galloc, graph->nodes[i], get_node_buffer_id(node_buffer_ids, i));
+        }
+
+        for (int j = 0; j < GGML_MAX_SRC; j++) {
+            struct ggml_tensor * src = node->src[j];
+            if (src == NULL) {
+                continue;
+            }
+
+            ggml_gallocr_hash_get(galloc, src)->n_children += 1;
+
+            // allocate explicit inputs
+            if (src->flags & GGML_TENSOR_FLAG_INPUT) {
+                ggml_gallocr_allocate_node(galloc, src, get_node_buffer_id(node_buffer_ids, i));
+            }
+        }
+    }
+
+    // allocate tensors
+    for (int i = 0; i < graph->n_nodes; i++) {
+        struct ggml_tensor * node = graph->nodes[i];
+        int buffer_id = get_node_buffer_id(node_buffer_ids, i);
+
+        // allocate parents (only leafs need to be allocated at this point)
+        for (int j = 0; j < GGML_MAX_SRC; j++) {
+            struct ggml_tensor * parent = node->src[j];
+            if (parent == NULL) {
+                continue;
+            }
+            ggml_gallocr_allocate_node(galloc, parent, buffer_id);
+        }
+
+        // allocate node
+        ggml_gallocr_allocate_node(galloc, node, buffer_id);
+
+        AT_PRINTF("exec: %s (%s) <= ", ggml_op_desc(node), node->name);
+        for (int j = 0; j < GGML_MAX_SRC; j++) {
+            struct ggml_tensor * parent = node->src[j];
+            if (parent == NULL) {
+                continue;
+            }
+            AT_PRINTF("%s", parent->name);
+            if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) {
+                AT_PRINTF(", ");
+            }
+        }
+        AT_PRINTF("\n");
+
+        // update parents
+        for (int j = 0; j < GGML_MAX_SRC; j++) {
+            struct ggml_tensor * parent = node->src[j];
+            if (parent == NULL) {
+                continue;
+            }
+            struct hash_node * p_hn = ggml_gallocr_hash_get(galloc, parent);
+            p_hn->n_children -= 1;
+
+            AT_PRINTF("parent %s: %d children, %d views, allocated: %d\n",
+                parent->name, p_hn->n_children, p_hn->n_views, p_hn->allocated);
+
+            if (p_hn->n_children == 0 && p_hn->n_views == 0) {
+                if (ggml_is_view(parent)) {
+                    struct ggml_tensor * view_src = parent->view_src;
+                    struct hash_node * view_src_hn = ggml_gallocr_hash_get(galloc, view_src);
+                    view_src_hn->n_views -= 1;
+                    AT_PRINTF("view_src %s: %d children, %d views\n",
+                        view_src->name, view_src_hn->n_children, view_src_hn->n_views);
+                    if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src_hn->allocated) {
+                        ggml_gallocr_free_node(galloc, view_src, buffer_id);
+                    }
+                }
+                else if (p_hn->allocated) {
+                    ggml_gallocr_free_node(galloc, parent, buffer_id);
+                }
+            }
+            AT_PRINTF("\n");
+        }
+    }
+}
+
+bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
+    size_t hash_size = graph->visited_hash_table.size;
+
+    // initialize hash table
+    if (galloc->hash_set.size < hash_size) {
+        free(galloc->hash_set.keys);
+        free(galloc->hash_values);
+        galloc->hash_set.size = hash_size;
+        galloc->hash_set.keys = calloc(sizeof(struct ggml_tensor *), hash_size);
+        galloc->hash_values   = calloc(sizeof(struct hash_node), hash_size);
+        GGML_ASSERT(galloc->hash_set.keys != NULL);
+        GGML_ASSERT(galloc->hash_values != NULL);
+    } else {
+        // reset hash table
+        memset(galloc->hash_set.keys, 0, sizeof(struct ggml_tensor *) * galloc->hash_set.size);
+        memset(galloc->hash_values,   0, sizeof(struct hash_node) * galloc->hash_set.size);
+    }
+
+    // reset allocators
+    for (int i = 0; i < galloc->n_buffers; i++) {
+        ggml_dyn_tallocr_reset(galloc->buf_tallocs[i]);
+    }
+
+    // allocate in hash table
+    ggml_gallocr_alloc_graph_impl(galloc, graph, node_buffer_ids, leaf_buffer_ids);
+
+    // set the node_allocs from the hash table
+    if (galloc->n_nodes < graph->n_nodes) {
+        free(galloc->node_allocs);
+        galloc->node_allocs = calloc(sizeof(struct node_alloc), graph->n_nodes);
+        GGML_ASSERT(galloc->node_allocs != NULL);
+    }
+    galloc->n_nodes = graph->n_nodes;
+    for (int i = 0; i < graph->n_nodes; i++) {
+        struct ggml_tensor * node = graph->nodes[i];
+        struct node_alloc * node_alloc = &galloc->node_allocs[i];
+        node_alloc->buffer_id = get_node_buffer_id(node_buffer_ids, i);
+        if (node->view_src || node->data) {
+            node_alloc->dst.offset = SIZE_MAX;
+            node_alloc->dst.size_max = 0;
+        } else {
+            struct hash_node * hn = ggml_gallocr_hash_get(galloc, node);
+            node_alloc->dst.offset   = hn->offset;
+            node_alloc->dst.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], node);
+        }
+        for (int j = 0; j < GGML_MAX_SRC; j++) {
+            struct ggml_tensor * src = node->src[j];
+            if (!src || src->view_src || src->data) {
+                node_alloc->src[j].offset = SIZE_MAX;
+                node_alloc->src[j].size_max = 0;
+            } else {
+                struct hash_node * hn = ggml_gallocr_hash_get(galloc, src);
+                node_alloc->src[j].offset   = hn->offset;
+                node_alloc->src[j].size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], src);
+            }
+        }
+    }
+    if (galloc->n_leafs < graph->n_leafs) {
+        free(galloc->leaf_allocs);
+        galloc->leaf_allocs = calloc(sizeof(galloc->leaf_allocs[0]), graph->n_leafs);
+        GGML_ASSERT(galloc->leaf_allocs != NULL);
+    }
+    galloc->n_leafs = graph->n_leafs;
+    for (int i = 0; i < graph->n_leafs; i++) {
+        struct ggml_tensor * leaf = graph->leafs[i];
+        struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf);
+        galloc->leaf_allocs[i].buffer_id = hn->buffer_id;
+        if (leaf->view_src || leaf->data) {
+            galloc->leaf_allocs[i].leaf.offset = SIZE_MAX;
+            galloc->leaf_allocs[i].leaf.size_max = 0;
+        } else {
+            galloc->leaf_allocs[i].leaf.offset = hn->offset;
+            galloc->leaf_allocs[i].leaf.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf);
+        }
+    }
+
+    // reallocate buffers if needed
+    for (int i = 0; i < galloc->n_buffers; i++) {
+        size_t cur_size = galloc->buffers[i] ? ggml_backend_buffer_get_size(galloc->buffers[i]) : 0;
+        size_t new_size = ggml_dyn_tallocr_max_size(galloc->buf_tallocs[i]);
+
+        // even if there are no tensors allocated in this buffer, we still need to allocate it to initialize views
+        if (new_size > cur_size || galloc->buffers[i] == NULL) {
+#ifndef NDEBUG
+            fprintf(stderr, "%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
+#endif
+            ggml_backend_buffer_free(galloc->buffers[i]);
+            galloc->buffers[i] = ggml_backend_buft_alloc_buffer(galloc->bufts[i], new_size);
+            if (galloc->buffers[i] == NULL) {
+                fprintf(stderr, "%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size);
+                return false;
+            }
+        }
+    }
+
+    return true;
+}
+
+bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) {
+    return ggml_gallocr_reserve_n(galloc, graph, NULL, NULL);
+}
+
+static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * tensor, int buffer_id, struct tensor_alloc * tensor_alloc) {
+    assert(tensor->data || tensor->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
+
+    if (tensor->view_src != NULL) {
+        if (tensor->buffer == NULL) {
+            assert(tensor_alloc->offset == SIZE_MAX);
+            if (tensor->view_src->buffer == NULL) {
+                // this tensor was allocated without ggml-backend
+                return;
+            }
+            ggml_backend_view_init(galloc->buffers[buffer_id], tensor);
+        }
+    } else {
+        if (tensor->data == NULL) {
+            assert(tensor_alloc->offset != SIZE_MAX);
+            assert(ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
+            void * base = ggml_backend_buffer_get_base(galloc->buffers[buffer_id]);
+            void * addr = (char *)base + tensor_alloc->offset;
+            ggml_backend_tensor_alloc(galloc->buffers[buffer_id], tensor, addr);
+        } else {
+            if (tensor->buffer == NULL) {
+                // this tensor was allocated without ggml-backend
+                return;
+            }
+        }
+    }
+}
+
+static bool ggml_gallocr_node_needs_realloc(ggml_gallocr_t galloc, struct ggml_tensor * node, struct node_alloc * nalloc, struct tensor_alloc * talloc) {
+    ggml_backend_buffer_type_t buft = galloc->bufts[nalloc->buffer_id];
+    size_t node_size = (node->data || node->view_src) ? 0 : ggml_backend_buft_get_alloc_size(buft, node);
+    return talloc->size_max >= node_size;
+}
+
+static bool ggml_gallocr_needs_realloc(ggml_gallocr_t galloc, struct ggml_cgraph * graph) {
+    if (galloc->n_nodes != graph->n_nodes) {
+#ifndef NDEBUG
+        fprintf(stderr, "%s: graph has different number of nodes\n", __func__);
+#endif
+        return true;
+    }
+
+    if (galloc->n_leafs != graph->n_leafs) {
+#ifndef NDEBUG
+        fprintf(stderr, "%s: graph has different number of leafs\n", __func__);
+#endif
+        return true;
+    }
+
+    for (int i = 0; i < graph->n_nodes; i++) {
+        struct ggml_tensor * node = graph->nodes[i];
+        struct node_alloc * node_alloc = &galloc->node_allocs[i];
+
+        if (!ggml_gallocr_node_needs_realloc(galloc, node, node_alloc, &node_alloc->dst)) {
+#ifndef NDEBUG
+            fprintf(stderr, "%s: node %s is not valid\n", __func__, node->name);
+#endif
+            return true;
+        }
+
+        for (int j = 0; j < GGML_MAX_SRC; j++) {
+            struct ggml_tensor * src = node->src[j];
+            if (src == NULL) {
+                continue;
+            }
+            if (!ggml_gallocr_node_needs_realloc(galloc, src, node_alloc, &node_alloc->src[j])) {
+#ifndef NDEBUG
+                fprintf(stderr, "%s: src %d (%s) of node %s is not valid\n", __func__, j, src->name, node->name);
+#endif
+                return true;
+            }
+        }
+    }
+
+    return false;
+}
+
+bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph) {
+    if (ggml_gallocr_needs_realloc(galloc, graph)) {
+        if (galloc->n_buffers == 1) {
+#ifndef NDEBUG
+            fprintf(stderr, "%s: reallocating buffers automatically\n", __func__);
+#endif
+            if (!ggml_gallocr_reserve(galloc, graph)) {
+                return false;
+            }
+        } else {
+#ifndef NDEBUG
+            fprintf(stderr, "%s: cannot reallocate multi buffer graph automatically, call reserve\n", __func__);
+#endif
+            return false;
+        }
+    }
+
+    // reset buffers
+    for (int i = 0; i < galloc->n_buffers; i++) {
+        if (galloc->buffers[i] != NULL) {
+            ggml_backend_buffer_reset(galloc->buffers[i]);
+        }
+    }
+
+    // allocate the graph tensors from the previous assignments
+    // leafs
+    for (int i = 0; i < graph->n_leafs; i++) {
+        struct ggml_tensor * leaf = graph->leafs[i];
+        struct leaf_alloc * leaf_alloc = &galloc->leaf_allocs[i];
+        ggml_gallocr_init_tensor(galloc, leaf, leaf_alloc->buffer_id, &leaf_alloc->leaf);
+    }
+    // nodes
+    for (int i = 0; i < graph->n_nodes; i++) {
+        struct ggml_tensor * node = graph->nodes[i];
+        struct node_alloc * node_alloc = &galloc->node_allocs[i];
+        for (int j = 0; j < GGML_MAX_SRC; j++) {
+            struct ggml_tensor * src = node->src[j];
+            if (src == NULL) {
+                continue;
+            }
+            ggml_gallocr_init_tensor(galloc, src, node_alloc->buffer_id, &node_alloc->src[j]);
+        }
+        ggml_gallocr_init_tensor(galloc, node, node_alloc->buffer_id, &node_alloc->dst);
+    }
+
+    return true;
+}
+
+size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_id) {
+    GGML_ASSERT(buffer_id >= 0 && buffer_id < galloc->n_buffers);
+
+    if (galloc->buffers[buffer_id] == NULL) {
+        return 0;
+    }
+    return ggml_backend_buffer_get_size(galloc->buffers[buffer_id]);
+}
+
+// utils
+
+static bool alloc_tensor_range(struct ggml_context * ctx,
+        struct ggml_tensor * first, struct ggml_tensor * last,
+        ggml_backend_buffer_type_t buft, size_t size,
+        ggml_backend_buffer_t ** buffers, size_t * n_buffers) {
+    ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, size);
+    if (buffer == NULL) {
+#ifndef NDEBUG
+        fprintf(stderr, "%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(buft), size);
+#endif
+        for (size_t i = 0; i < *n_buffers; i++) {
+            ggml_backend_buffer_free(*buffers[i]);
+        }
+        free(*buffers);
+        return false;
+    }
+
+    struct ggml_tallocr tallocr = ggml_tallocr_new(buffer);
+
+    for (struct ggml_tensor * t = first; t != last; t = ggml_get_next_tensor(ctx, t)) {
         if (t->data == NULL) {
             if (t->view_src == NULL) {
-                ggml_tallocr_alloc(tallocr, t);
-            } else {
+                ggml_tallocr_alloc(&tallocr, t);
+            } else if (t->buffer == NULL) {
                 ggml_backend_view_init(buffer, t);
             }
         } else {
-            if (t->view_src != NULL) {
+            if (t->view_src != NULL && t->buffer == NULL) {
                 // view of a pre-allocated tensor
                 ggml_backend_view_init(buffer, t);
             }
         }
     }
 
-    ggml_tallocr_free(tallocr);
+    *buffers = realloc(*buffers, sizeof(ggml_backend_buffer_t) * (*n_buffers + 1));
+    (*buffers)[(*n_buffers)++] = buffer;
 
+    return true;
+}
+
+ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) {
+    GGML_ASSERT(ggml_get_no_alloc(ctx) == true);
+
+    size_t alignment = ggml_backend_buft_get_alignment(buft);
+    size_t max_size = ggml_backend_buft_get_max_size(buft);
+
+    ggml_backend_buffer_t * buffers = NULL;
+    size_t n_buffers = 0;
+
+    size_t cur_buf_size = 0;
+    struct ggml_tensor * first = ggml_get_first_tensor(ctx);
+    for (struct ggml_tensor * t = first; t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+        size_t this_size = 0;
+        if (t->data == NULL && t->view_src == NULL) {
+            this_size = GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), alignment);
+        }
+
+        if (this_size > max_size) {
+            fprintf(stderr, "%s: tensor %s is too large to fit in a %s buffer (tensor size: %zu, max buffer size: %zu)\n",
+                    __func__, t->name,
+                    ggml_backend_buft_name(buft),
+                    this_size, max_size);
+            for (size_t i = 0; i < n_buffers; i++) {
+                ggml_backend_buffer_free(buffers[i]);
+            }
+            free(buffers);
+            return NULL;
+        }
+
+        if ((cur_buf_size + this_size) > max_size) {
+            // allocate tensors in the current buffer
+            if (!alloc_tensor_range(ctx, first, t, buft, cur_buf_size, &buffers, &n_buffers)) {
+                return NULL;
+            }
+            first = t;
+            cur_buf_size = this_size;
+        } else {
+            cur_buf_size += this_size;
+        }
+    }
+
+    // allocate remaining tensors
+    if (cur_buf_size > 0) {
+        if (!alloc_tensor_range(ctx, first, NULL, buft, cur_buf_size, &buffers, &n_buffers)) {
+            return NULL;
+        }
+    }
+
+    if (n_buffers == 0) {
+#ifndef NDEBUG
+        fprintf(stderr, "%s: all tensors in the context are already allocated\n", __func__);
+#endif
+        return NULL;
+    }
+
+    ggml_backend_buffer_t buffer;
+    if (n_buffers == 1) {
+        buffer = buffers[0];
+    } else {
+        buffer = ggml_backend_multi_buffer_alloc_buffer(buffers, n_buffers);
+    }
+    free(buffers);
     return buffer;
 }
 
diff --git a/ggml-alloc.h b/ggml-alloc.h
index 64a412468..434c13b34 100644
--- a/ggml-alloc.h
+++ b/ggml-alloc.h
@@ -6,86 +6,70 @@
 extern "C" {
 #endif
 
-struct ggml_backend;
-struct ggml_backend_buffer;
-struct ggml_backend_buffer_type;
-
-//
-// Legacy API
-//
-
-typedef struct ggml_allocr * ggml_allocr_t;
-
-// initialize allocator for use with CPU backend only
-GGML_API ggml_allocr_t ggml_allocr_new(void * data, size_t size, size_t alignment);
-GGML_API ggml_allocr_t ggml_allocr_new_measure(size_t alignment);
-
-// initialize allocator for use with ggml-backend
-GGML_API ggml_allocr_t ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer);
-GGML_API ggml_allocr_t ggml_allocr_new_from_backend(struct ggml_backend * backend, size_t size); // allocates an owned buffer
-GGML_API ggml_allocr_t ggml_allocr_new_measure_from_backend(struct ggml_backend * backend);
-
-GGML_API struct ggml_backend_buffer * ggml_allocr_get_buffer(ggml_allocr_t alloc);
-
-// tell the allocator to parse nodes following the order described in the list
-// you should call this if your graph are optimized to execute out-of-order
-GGML_API void   ggml_allocr_set_parse_seq(ggml_allocr_t alloc, const int * list, int n);
-
-GGML_API void   ggml_allocr_free       (ggml_allocr_t alloc);
-GGML_API bool   ggml_allocr_is_measure (ggml_allocr_t alloc);
-GGML_API void   ggml_allocr_reset      (ggml_allocr_t alloc);
-GGML_API void   ggml_allocr_alloc      (ggml_allocr_t alloc, struct ggml_tensor * tensor);
-GGML_API size_t ggml_allocr_max_size   (ggml_allocr_t alloc);
-
-GGML_API size_t ggml_allocr_alloc_graph(ggml_allocr_t alloc, struct ggml_cgraph * graph);
-
-//
-// ggml-backend v2 API
-//
-
-// Separate tensor and graph allocator objects
-// This is necessary for multi-backend allocation because the graph allocator needs to use multiple tensor allocators
-// The original API is kept as a wrapper around the new API
+typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
+typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
+typedef struct ggml_backend * ggml_backend_t;
 
 // Tensor allocator
-typedef struct ggml_tallocr * ggml_tallocr_t;
-
-GGML_API ggml_tallocr_t ggml_tallocr_new(void * data, size_t size, size_t alignment);
-GGML_API ggml_tallocr_t ggml_tallocr_new_measure(size_t alignment);
-GGML_API ggml_tallocr_t ggml_tallocr_new_from_buffer(struct ggml_backend_buffer * buffer);
-GGML_API ggml_tallocr_t ggml_tallocr_new_from_backend(struct ggml_backend * backend, size_t size); // allocates an owned buffer
-GGML_API ggml_tallocr_t ggml_tallocr_new_measure_from_backend(struct ggml_backend * backend);
-
-GGML_API struct ggml_backend_buffer * ggml_tallocr_get_buffer(ggml_tallocr_t talloc);
-
-GGML_API void   ggml_tallocr_free       (ggml_tallocr_t talloc);
-GGML_API bool   ggml_tallocr_is_measure (ggml_tallocr_t talloc);
-GGML_API void   ggml_tallocr_reset      (ggml_tallocr_t talloc);
-GGML_API void   ggml_tallocr_alloc      (ggml_tallocr_t talloc, struct ggml_tensor * tensor);
-GGML_API size_t ggml_tallocr_max_size   (ggml_tallocr_t talloc);
+struct ggml_tallocr {
+    ggml_backend_buffer_t buffer;
+    void * base;
+    size_t alignment;
+    size_t offset;
+};
 
+GGML_API struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer);
+GGML_API void                ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor);
 
 // Graph allocator
+/*
+  Example usage:
+    ggml_gallocr_t galloc = ggml_gallocr_new(ggml_bacckend_cpu_buffer_type());
+
+    // optional: create a worst-case graph and reserve the buffers to avoid reallocations
+    ggml_gallocr_reserve(galloc, build_graph(max_batch));
+
+    // allocate the graph
+    struct ggml_cgraph * graph = build_graph(batch);
+    ggml_gallocr_alloc_graph(galloc, graph);
+
+    printf("compute buffer size: %zu bytes\n", ggml_gallocr_get_buffer_size(galloc, 0));
+
+    // evaluate the graph
+    ggml_backend_graph_compute(backend, graph);
+*/
+
+// special tensor flags for use with the graph allocator:
+//   ggml_set_input(): all input tensors are allocated at the beginning of the graph in non-overlapping addresses
+//   ggml_set_output(): output tensors are never freed and never overwritten
+
 typedef struct ggml_gallocr * ggml_gallocr_t;
 
-GGML_API ggml_gallocr_t ggml_gallocr_new(void);
-GGML_API void   ggml_gallocr_free(ggml_gallocr_t galloc);
+GGML_API ggml_gallocr_t ggml_gallocr_new(ggml_backend_buffer_type_t buft);
+GGML_API ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs);
+GGML_API void           ggml_gallocr_free(ggml_gallocr_t galloc);
 
-GGML_API void   ggml_gallocr_set_parse_seq(ggml_gallocr_t galloc, const int * list, int n);
-GGML_API size_t ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, ggml_tallocr_t talloc, struct ggml_cgraph * graph);
+// pre-allocate buffers from a measure graph - does not allocate or modify the graph
+// call with a worst-case graph to avoid buffer reallocations
+// not strictly required for single buffer usage: ggml_gallocr_alloc_graph will reallocate the buffers automatically if needed
+// returns false if the buffer allocation failed
+GGML_API bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph * graph);
+GGML_API bool ggml_gallocr_reserve_n(
+    ggml_gallocr_t galloc,
+    struct ggml_cgraph * graph,
+    const int * node_buffer_ids,
+    const int * leaf_buffer_ids);
 
-// Allocate tensors from the allocators given by the hash table
-GGML_API void   ggml_gallocr_alloc_graph_n(
-                    ggml_gallocr_t galloc,
-                    struct ggml_cgraph * graph,
-                    struct ggml_hash_set hash_set,
-                    ggml_tallocr_t * hash_node_talloc);
+// automatic reallocation if the topology changes when using a single buffer
+// returns false if using multiple buffers and a re-allocation is needed (call ggml_gallocr_reserve_n first to set the node buffers)
+GGML_API bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph);
 
+GGML_API size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_id);
 
 // Utils
 // Create a buffer and allocate all the tensors in a ggml_context
-GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, struct ggml_backend_buffer_type * buft);
-GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, struct ggml_backend * backend);
+GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft);
+GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, ggml_backend_t backend);
 
 #ifdef  __cplusplus
 }
diff --git a/ggml-backend-impl.h b/ggml-backend-impl.h
index ca21b4743..f121e1de4 100644
--- a/ggml-backend-impl.h
+++ b/ggml-backend-impl.h
@@ -16,13 +16,15 @@ extern "C" {
     typedef void * ggml_backend_buffer_type_context_t;
 
     struct ggml_backend_buffer_type_i {
-        ggml_backend_buffer_t (*alloc_buffer)    (ggml_backend_buffer_type_t buft, size_t size);
-        size_t                (*get_alignment)   (ggml_backend_buffer_type_t buft); // tensor alignment
-        size_t                (*get_alloc_size)  (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding
-        bool                  (*supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend
+        const char *          (*GGML_CALL get_name)        (ggml_backend_buffer_type_t buft);
+        ggml_backend_buffer_t (*GGML_CALL alloc_buffer)    (ggml_backend_buffer_type_t buft, size_t size);
+        size_t                (*GGML_CALL get_alignment)   (ggml_backend_buffer_type_t buft); // tensor alignment
+        size_t                (*GGML_CALL get_max_size)    (ggml_backend_buffer_type_t buft); // allocation max size
+        size_t                (*GGML_CALL get_alloc_size)  (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding
+        bool                  (*GGML_CALL supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend
         // check if tensor data is in host memory
         // should be equivalent to supports_backend(buft, ggml_backend_cpu_init())
-        bool                  (*is_host)         (ggml_backend_buffer_type_t buft);
+        bool                  (*GGML_CALL is_host)         (ggml_backend_buffer_type_t buft);
     };
 
     struct ggml_backend_buffer_type {
@@ -34,16 +36,15 @@ extern "C" {
     typedef void * ggml_backend_buffer_context_t;
 
     struct ggml_backend_buffer_i {
-        void   (*free_buffer)    (ggml_backend_buffer_t buffer);
-        //void     (*reset)      (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras
-        void * (*get_base)       (ggml_backend_buffer_t buffer);
-        void   (*init_tensor)    (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
-        void   (*set_tensor)     (ggml_backend_buffer_t buffer,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
-        void   (*get_tensor)     (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
-        // (optional) copy tensor between different buffer-type, allow for single-copy tranfers
-        void   (*cpy_tensor_from)(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst);
-        void   (*cpy_tensor_to)  (ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst);
-        void   (*clear)          (ggml_backend_buffer_t buffer, uint8_t value);
+        const char * (*GGML_CALL get_name)   (ggml_backend_buffer_t buffer);
+        void         (*GGML_CALL free_buffer)(ggml_backend_buffer_t buffer);
+        void *       (*GGML_CALL get_base)   (ggml_backend_buffer_t buffer);
+        void         (*GGML_CALL init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+        void         (*GGML_CALL set_tensor) (ggml_backend_buffer_t buffer,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+        void         (*GGML_CALL get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+        bool         (*GGML_CALL cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst); // dst is in the buffer, src may be in any buffer
+        void         (*GGML_CALL clear)      (ggml_backend_buffer_t buffer, uint8_t value);
+        void         (*GGML_CALL reset)      (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras
     };
 
     struct ggml_backend_buffer {
@@ -51,14 +52,22 @@ extern "C" {
         ggml_backend_buffer_type_t    buft;
         ggml_backend_buffer_context_t context;
         size_t size;
+        enum ggml_backend_buffer_usage usage;
     };
 
-    ggml_backend_buffer_t ggml_backend_buffer_init(
+    GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init(
                    ggml_backend_buffer_type_t      buft,
             struct ggml_backend_buffer_i           iface,
                    ggml_backend_buffer_context_t   context,
                    size_t                          size);
 
+    // do not use directly, use ggml_backend_tensor_copy instead
+    bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst);
+
+    // buffer that contains a collection of buffers
+    GGML_CALL ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer_t * buffers, size_t n_buffers);
+    GGML_CALL bool                  ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer);
+    GGML_CALL void                  ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
 
     //
     // Backend
@@ -67,49 +76,65 @@ extern "C" {
     typedef void * ggml_backend_context_t;
 
     struct ggml_backend_i {
-        const char * (*get_name)(ggml_backend_t backend);
+        const char * (*GGML_CALL get_name)(ggml_backend_t backend);
 
-        void (*free)(ggml_backend_t backend);
+        void (*GGML_CALL free)(ggml_backend_t backend);
 
         // buffer allocation
-        ggml_backend_buffer_type_t (*get_default_buffer_type)(ggml_backend_t backend);
+        ggml_backend_buffer_type_t (*GGML_CALL get_default_buffer_type)(ggml_backend_t backend);
 
-        // (optional) asynchroneous tensor data access
-        void (*set_tensor_async)(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
-        void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+        // (optional) asynchronous tensor data access
+        void (*GGML_CALL set_tensor_async)(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+        void (*GGML_CALL get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+        bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst);
 
-        // (optional) asynchroneous tensor copy
-        void (*cpy_tensor_from_async)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
-        void (*cpy_tensor_to_async)  (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
+        // (optional) complete all pending operations
+        void (*GGML_CALL synchronize)(ggml_backend_t backend);
 
-        void (*synchronize)(ggml_backend_t backend);
+        // compute graph with a plan (not used currently)
+        ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph);
+        void                      (*GGML_CALL graph_plan_free)   (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
 
         // compute graph with a plan
-        ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph);
-        void                      (*graph_plan_free)   (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
-        void                      (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
-
-        // compute graph without a plan
-        bool (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
+        enum ggml_status (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
+        // compute graph without a plan (async)
+        enum ggml_status (*GGML_CALL graph_compute)     (ggml_backend_t backend, struct ggml_cgraph * cgraph);
 
         // check if the backend supports an operation
-        bool (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
+        bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
+
+        // check if the backend wants to run an operation, even if the weights are allocated in a CPU buffer
+        // these should be expensive operations with large batch sizes that may benefit from running on this backend
+        // even if the weight has to be copied from the CPU temporarily
+        bool (*GGML_CALL offload_op)(ggml_backend_t backend, const struct ggml_tensor * op);
+
+        // (optional) event synchronization
+        ggml_backend_event_t (*GGML_CALL event_new)         (ggml_backend_t backend);
+        void                 (*GGML_CALL event_free)        (ggml_backend_event_t event);
+        void                 (*GGML_CALL event_record)      (ggml_backend_event_t event);
+        void                 (*GGML_CALL event_wait)        (ggml_backend_t backend, ggml_backend_event_t event);
+        void                 (*GGML_CALL event_synchronize) (ggml_backend_event_t event);
     };
 
     struct ggml_backend {
-        struct ggml_backend_i iface;
+        ggml_guid_t guid;
 
+        struct ggml_backend_i iface;
         ggml_backend_context_t context;
     };
 
+    struct ggml_backend_event {
+        ggml_backend_t backend;
+        void * context;
+    };
 
     //
     // Backend registry
     //
 
-    typedef ggml_backend_t (*ggml_backend_init_fn)(const char * params, void * user_data);
+    typedef ggml_backend_t (*GGML_CALL ggml_backend_init_fn)(const char * params, void * user_data);
 
-    void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data);
+    GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data);
 
 #ifdef  __cplusplus
 }
diff --git a/ggml-backend.c b/ggml-backend.c
index 53e741cb8..402d86ef3 100644
--- a/ggml-backend.c
+++ b/ggml-backend.c
@@ -12,10 +12,13 @@
 
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
 
-
 // backend buffer type
 
-ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) {
+    return buft->iface.get_name(buft);
+}
+
+GGML_CALL ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
     return buft->iface.alloc_buffer(buft, size);
 }
 
@@ -23,10 +26,20 @@ size_t ggml_backend_buft_get_alignment(ggml_backend_buffer_type_t buft) {
     return buft->iface.get_alignment(buft);
 }
 
-size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor) {
+size_t ggml_backend_buft_get_max_size(ggml_backend_buffer_type_t buft) {
+    // get_max_size is optional, defaults to SIZE_MAX
+    if (buft->iface.get_max_size) {
+        return buft->iface.get_max_size(buft);
+    }
+    return SIZE_MAX;
+}
+
+GGML_CALL size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor) {
     // get_alloc_size is optional, defaults to ggml_nbytes
     if (buft->iface.get_alloc_size) {
-        return buft->iface.get_alloc_size(buft, tensor);
+        size_t size = buft->iface.get_alloc_size(buft, tensor);
+        assert(size >= ggml_nbytes(tensor));
+        return size;
     }
     return ggml_nbytes(tensor);
 }
@@ -44,25 +57,28 @@ bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) {
 
 // backend buffer
 
-ggml_backend_buffer_t ggml_backend_buffer_init(
+GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init(
                ggml_backend_buffer_type_t      buft,
         struct ggml_backend_buffer_i           iface,
                ggml_backend_buffer_context_t   context,
                size_t                          size) {
     ggml_backend_buffer_t buffer = malloc(sizeof(struct ggml_backend_buffer));
 
-    GGML_ASSERT(iface.get_base != NULL);
-
     (*buffer) = (struct ggml_backend_buffer) {
         /* .interface = */ iface,
         /* .buft      = */ buft,
         /* .context   = */ context,
         /* .size      = */ size,
+        /* .usage     = */ GGML_BACKEND_BUFFER_USAGE_ANY
     };
 
     return buffer;
 }
 
+const char * ggml_backend_buffer_name(ggml_backend_buffer_t buffer) {
+    return buffer->iface.get_name(buffer);
+}
+
 void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) {
     if (buffer == NULL) {
         return;
@@ -86,7 +102,7 @@ void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
     return base;
 }
 
-void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
+GGML_CALL void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
     // init_tensor is optional
     if (buffer->iface.init_tensor) {
         buffer->iface.init_tensor(buffer, tensor);
@@ -94,11 +110,15 @@ void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_t
 }
 
 size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer) {
-    return ggml_backend_buft_get_alignment(ggml_backend_buffer_type(buffer));
+    return ggml_backend_buft_get_alignment(ggml_backend_buffer_get_type(buffer));
+}
+
+size_t ggml_backend_buffer_get_max_size(ggml_backend_buffer_t buffer) {
+    return ggml_backend_buft_get_max_size(ggml_backend_buffer_get_type(buffer));
 }
 
 size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
-    return ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type(buffer), tensor);
+    return ggml_backend_buft_get_alloc_size(ggml_backend_buffer_get_type(buffer), tensor);
 }
 
 void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
@@ -106,15 +126,45 @@ void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
 }
 
 bool ggml_backend_buffer_is_host(ggml_backend_buffer_t buffer) {
-    return ggml_backend_buft_is_host(ggml_backend_buffer_type(buffer));
+    return ggml_backend_buft_is_host(ggml_backend_buffer_get_type(buffer));
 }
 
-ggml_backend_buffer_type_t ggml_backend_buffer_type(ggml_backend_buffer_t buffer) {
+void ggml_backend_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) {
+    buffer->usage = usage;
+
+    // FIXME: add a generic callback to the buffer interface
+    if (ggml_backend_buffer_is_multi_buffer(buffer)) {
+        ggml_backend_multi_buffer_set_usage(buffer, usage);
+    }
+}
+
+ggml_backend_buffer_type_t ggml_backend_buffer_get_type(ggml_backend_buffer_t buffer) {
     return buffer->buft;
 }
 
+void ggml_backend_buffer_reset(ggml_backend_buffer_t buffer) {
+    if (buffer->iface.reset) {
+        buffer->iface.reset(buffer);
+    }
+}
+
+bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst) {
+    ggml_backend_buffer_t dst_buf = dst->view_src ? dst->view_src->buffer : dst->buffer;
+    if (dst_buf->iface.cpy_tensor) {
+        return src->buffer->iface.cpy_tensor(dst_buf, src, dst);
+    }
+    return false;
+}
+
 // backend
 
+ggml_guid_t ggml_backend_guid(ggml_backend_t backend) {
+    if (backend == NULL) {
+        return NULL;
+    }
+    return backend->guid;
+}
+
 const char * ggml_backend_name(ggml_backend_t backend) {
     if (backend == NULL) {
         return "NULL";
@@ -142,34 +192,58 @@ size_t ggml_backend_get_alignment(ggml_backend_t backend) {
     return ggml_backend_buft_get_alignment(ggml_backend_get_default_buffer_type(backend));
 }
 
+size_t ggml_backend_get_max_size(ggml_backend_t backend) {
+    return ggml_backend_buft_get_max_size(ggml_backend_get_default_buffer_type(backend));
+}
+
 void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
     GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
 
-    backend->iface.set_tensor_async(backend, tensor, data, offset, size);
+    if (backend->iface.set_tensor_async == NULL) {
+        ggml_backend_tensor_set(tensor, data, offset, size);
+    } else {
+        backend->iface.set_tensor_async(backend, tensor, data, offset, size);
+    }
 }
 
 void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
     GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
 
-    backend->iface.get_tensor_async(backend, tensor, data, offset, size);
+    if (backend->iface.get_tensor_async == NULL) {
+        ggml_backend_tensor_get(tensor, data, offset, size);
+    } else {
+        backend->iface.get_tensor_async(backend, tensor, data, offset, size);
+    }
 }
 
-void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+GGML_CALL void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+
+    GGML_ASSERT(buf != NULL && "tensor buffer not set");
     GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
-    GGML_ASSERT(tensor->buffer != NULL && "tensor buffer not set");
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
 
-    tensor->buffer->iface.set_tensor(tensor->buffer, tensor, data, offset, size);
+    if (!size) {
+        return;
+    }
+
+    buf->iface.set_tensor(buf, tensor, data, offset, size);
 }
 
-void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+
+    GGML_ASSERT(buf != NULL && "tensor buffer not set");
     GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
-    GGML_ASSERT(tensor->buffer != NULL && "tensor buffer not set");
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
 
-    tensor->buffer->iface.get_tensor(tensor->buffer, tensor, data, offset, size);
+    if (!size) {
+        return;
+    }
+
+    buf->iface.get_tensor(buf, tensor, data, offset, size);
 }
 
 void ggml_backend_synchronize(ggml_backend_t backend) {
@@ -181,34 +255,44 @@ void ggml_backend_synchronize(ggml_backend_t backend) {
 }
 
 ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    GGML_ASSERT(backend->iface.graph_plan_create != NULL);
+
     return backend->iface.graph_plan_create(backend, cgraph);
 }
 
 void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+    GGML_ASSERT(backend->iface.graph_plan_free != NULL);
+
     backend->iface.graph_plan_free(backend, plan);
 }
 
-void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
-    backend->iface.graph_plan_compute(backend, plan);
+enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+    GGML_ASSERT(backend->iface.graph_plan_compute != NULL);
 
-    // TODO: optional sync
-    ggml_backend_synchronize(backend);
+    return backend->iface.graph_plan_compute(backend, plan);
 }
 
-bool ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
-    if (!backend->iface.graph_compute(backend, cgraph)) {
-        return false;
-    }
-
-    // TODO: optional sync
+enum ggml_status ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    enum ggml_status err = ggml_backend_graph_compute_async(backend, cgraph);
     ggml_backend_synchronize(backend);
-    return true;
+    return err;
+}
+
+enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    return backend->iface.graph_compute(backend, cgraph);
 }
 
 bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
     return backend->iface.supports_op(backend, op);
 }
 
+bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op) {
+    if (backend->iface.offload_op != NULL) {
+        return backend->iface.offload_op(backend, op);
+    }
+    return false;
+}
+
 // backend copy
 
 static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
@@ -227,28 +311,20 @@ static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml
 }
 
 void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst) {
-    //printf("src: %s ne: [%d %d %d %d] nb: [%d %d %d %d]\n", src->name, (int)src->ne[0], (int)src->ne[1], (int)src->ne[2], (int)src->ne[3], (int)src->nb[0], (int)src->nb[1], (int)src->nb[2], (int)src->nb[3]);
-    //printf("dst: %s ne: [%d %d %d %d] nb: [%d %d %d %d]\n", dst->name, (int)dst->ne[0], (int)dst->ne[1], (int)dst->ne[2], (int)dst->ne[3], (int)dst->nb[0], (int)dst->nb[1], (int)dst->nb[2], (int)dst->nb[3]);
     GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
 
-    // fprintf(stderr, "cpy tensor %s from %s to %s (%lu bytes)\n", src->name, ggml_backend_name(src->backend), ggml_backend_name(dst->backend), ggml_nbytes(src));
-
     if (src == dst) {
         return;
     }
 
-    // TODO: allow backends to support copy to/from same backend
-
-    if (dst->buffer->iface.cpy_tensor_from != NULL) {
-        dst->buffer->iface.cpy_tensor_from(dst->buffer, src, dst);
-    } else if (src->buffer->iface.cpy_tensor_to != NULL) {
-        src->buffer->iface.cpy_tensor_to(src->buffer, src, dst);
-    } else {
-        // shouldn't be hit when copying from/to CPU
-        #ifndef NDEBUG
-        fprintf(stderr, "ggml_backend_tensor_copy: neither cpy_tensor_from nor cpy_tensor_to "
-                        "are implemented for %s and %s, falling back to get/set\n", src->name, dst->name);
-        #endif
+    if (ggml_backend_buffer_is_host(src->buffer)) {
+        ggml_backend_tensor_set(dst, src->data, 0, ggml_nbytes(src));
+    } else if (ggml_backend_buffer_is_host(dst->buffer)) {
+        ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src));
+    } else if (!ggml_backend_buffer_copy_tensor(src, dst)) {
+#ifndef NDEBUG
+        fprintf(stderr, "%s: warning: slow copy from %s to %s\n", __func__, ggml_backend_buffer_name(src->buffer), ggml_backend_buffer_name(dst->buffer));
+#endif
         size_t nbytes = ggml_nbytes(src);
         void * data = malloc(nbytes);
         ggml_backend_tensor_get(src, data, 0, nbytes);
@@ -257,9 +333,68 @@ void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst
     }
 }
 
+void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
+
+    if (src == dst) {
+        return;
+    }
+
+    if (backend_dst->iface.cpy_tensor_async != NULL) {
+        if (backend_dst->iface.cpy_tensor_async(backend_src, backend_dst, src, dst)) {
+            return;
+        }
+    }
+
+    // an async copy would normally happen after all the queued operations on both backends are completed
+    // sync src, set_async dst
+    if (ggml_backend_buffer_is_host(src->buffer)) {
+        ggml_backend_synchronize(backend_src);
+        ggml_backend_tensor_set_async(backend_dst, dst, src->data, 0, ggml_nbytes(src));
+    } else {
+        ggml_backend_synchronize(backend_src);
+        ggml_backend_tensor_copy(src, dst);
+        ggml_backend_synchronize(backend_dst);
+    }
+}
+
+// events
+
+ggml_backend_event_t ggml_backend_event_new(ggml_backend_t backend) {
+    if (backend->iface.event_new == NULL) {
+        return NULL;
+    }
+    return backend->iface.event_new(backend);
+}
+
+void ggml_backend_event_free(ggml_backend_event_t event) {
+    if (event == NULL) {
+        return;
+    }
+    event->backend->iface.event_free(event);
+}
+
+void ggml_backend_event_record(ggml_backend_event_t event) {
+    GGML_ASSERT(event->backend->iface.event_record != NULL);
+
+    event->backend->iface.event_record(event);
+}
+
+void ggml_backend_event_synchronize(ggml_backend_event_t event) {
+    GGML_ASSERT(event->backend->iface.event_synchronize != NULL);
+
+    event->backend->iface.event_synchronize(event);
+}
+
+void ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event) {
+    GGML_ASSERT(backend->iface.event_wait != NULL);
+
+    backend->iface.event_wait(backend, event);
+}
+
 // backend registry
 
-#define GGML_MAX_BACKENDS_REG 16
+#define GGML_REG_MAX_BACKENDS 16
 
 struct ggml_backend_reg {
     char name[128];
@@ -268,12 +403,12 @@ struct ggml_backend_reg {
     void * user_data;
 };
 
-static struct ggml_backend_reg ggml_backend_registry[GGML_MAX_BACKENDS_REG];
+static struct ggml_backend_reg ggml_backend_registry[GGML_REG_MAX_BACKENDS];
 static size_t ggml_backend_registry_count = 0;
 
-static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data);
+GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data);
 
-static void ggml_backend_registry_init(void) {
+GGML_CALL static void ggml_backend_registry_init(void) {
     static bool initialized = false;
 
     if (initialized) {
@@ -285,20 +420,35 @@ static void ggml_backend_registry_init(void) {
     ggml_backend_register("CPU", ggml_backend_reg_cpu_init, ggml_backend_cpu_buffer_type(), NULL);
 
     // add forward decls here to avoid including the backend headers
-#ifdef GGML_USE_CUBLAS
-    extern void ggml_backend_cuda_reg_devices(void);
+#ifdef GGML_USE_CUDA
+    extern GGML_CALL void ggml_backend_cuda_reg_devices(void);
     ggml_backend_cuda_reg_devices();
 #endif
 
+#ifdef GGML_USE_SYCL
+    extern void ggml_backend_sycl_reg_devices(void);
+    ggml_backend_sycl_reg_devices();
+#endif
+
 #ifdef GGML_USE_METAL
-    extern ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data);
-    extern ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
+    extern GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data);
+    extern GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
     ggml_backend_register("Metal", ggml_backend_reg_metal_init, ggml_backend_metal_buffer_type(), NULL);
 #endif
+
+#ifdef GGML_USE_VULKAN
+    extern GGML_CALL int ggml_backend_vk_reg_devices(void);
+    ggml_backend_vk_reg_devices();
+#endif
+
+#ifdef GGML_USE_KOMPUTE
+    extern GGML_CALL void ggml_backend_kompute_reg_devices(void);
+    ggml_backend_kompute_reg_devices();
+#endif
 }
 
-void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) {
-    GGML_ASSERT(ggml_backend_registry_count < GGML_MAX_BACKENDS_REG);
+GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) {
+    GGML_ASSERT(ggml_backend_registry_count < GGML_REG_MAX_BACKENDS);
 
     size_t id = ggml_backend_registry_count;
 
@@ -392,99 +542,122 @@ ggml_backend_buffer_t ggml_backend_reg_alloc_buffer(size_t i, size_t size) {
 
 // backend CPU
 
-static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
-    return (void *)buffer->context;
+static const size_t TENSOR_ALIGNMENT = 32; // required for mmap as gguf only guarantees 32-byte alignment
+
+GGML_CALL static const char * ggml_backend_cpu_buffer_name(ggml_backend_buffer_t buffer) {
+    return "CPU";
+
+    GGML_UNUSED(buffer);
 }
 
-static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+GGML_CALL static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
+    uintptr_t data = (uintptr_t)buffer->context;
+
+    // align the buffer
+    if (data % TENSOR_ALIGNMENT != 0) {
+        data = GGML_PAD(data, TENSOR_ALIGNMENT);
+    }
+
+    return (void *)data;
+}
+
+GGML_CALL static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
     free(buffer->context);
 }
 
-static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+GGML_CALL static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
     memcpy((char *)tensor->data + offset, data, size);
 
     GGML_UNUSED(buffer);
 }
 
-static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+GGML_CALL static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
     memcpy(data, (const char *)tensor->data + offset, size);
 
     GGML_UNUSED(buffer);
 }
 
-static void ggml_backend_cpu_buffer_cpy_tensor_from(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst) {
-    ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src));
+GGML_CALL static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
+    if (ggml_backend_buffer_is_host(src->buffer)) {
+        memcpy(dst->data, src->data, ggml_nbytes(src));
+        return true;
+    }
+    return false;
 
     GGML_UNUSED(buffer);
 }
 
-static void ggml_backend_cpu_buffer_cpy_tensor_to(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst) {
-    ggml_backend_tensor_set(dst, src->data, 0, ggml_nbytes(src));
-
-    GGML_UNUSED(buffer);
-}
-
-static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+GGML_CALL static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
     memset(buffer->context, value, buffer->size);
 }
 
 static struct ggml_backend_buffer_i cpu_backend_buffer_i = {
+    /* .get_name        = */ ggml_backend_cpu_buffer_name,
     /* .free_buffer     = */ ggml_backend_cpu_buffer_free_buffer,
     /* .get_base        = */ ggml_backend_cpu_buffer_get_base,
     /* .init_tensor     = */ NULL, // no initialization required
     /* .set_tensor      = */ ggml_backend_cpu_buffer_set_tensor,
     /* .get_tensor      = */ ggml_backend_cpu_buffer_get_tensor,
-    /* .cpy_tensor_from = */ ggml_backend_cpu_buffer_cpy_tensor_from,
-    /* .cpy_tensor_to   = */ ggml_backend_cpu_buffer_cpy_tensor_to,
+    /* .cpy_tensor      = */ ggml_backend_cpu_buffer_cpy_tensor,
     /* .clear           = */ ggml_backend_cpu_buffer_clear,
+    /* .reset           = */ NULL,
 };
 
 // for buffers from ptr, free is not called
 static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = {
+    /* .get_name        = */ ggml_backend_cpu_buffer_name,
     /* .free_buffer     = */ NULL, // ptr is not owned by the buffer, so it does not need to be freed
     /* .get_base        = */ ggml_backend_cpu_buffer_get_base,
     /* .init_tensor     = */ NULL, // no initialization required
     /* .set_tensor      = */ ggml_backend_cpu_buffer_set_tensor,
     /* .get_tensor      = */ ggml_backend_cpu_buffer_get_tensor,
-    /* .cpy_tensor_from = */ ggml_backend_cpu_buffer_cpy_tensor_from,
-    /* .cpy_tensor_to   = */ ggml_backend_cpu_buffer_cpy_tensor_to,
+    /* .cpy_tensor      = */ ggml_backend_cpu_buffer_cpy_tensor,
     /* .clear           = */ ggml_backend_cpu_buffer_clear,
+    /* .reset           = */ NULL,
 };
 
-static const size_t TENSOR_ALIGNMENT = 64; // should be enough for AVX 512
+GGML_CALL static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
+    return "CPU";
 
-static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    GGML_UNUSED(buft);
+}
+
+GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
     size += TENSOR_ALIGNMENT;   // malloc may return an address that is not aligned
-    void * data = malloc(size); // TODO: maybe use GGML_ALIGNED_MALLOC?
-
-    GGML_ASSERT(data != NULL && "failed to allocate buffer");
+    void * data = malloc(size); // TODO: use GGML_ALIGNED_MALLOC (move to ggml-impl.h)
+    if (data == NULL) {
+        fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size);
+        return NULL;
+    }
 
     return ggml_backend_buffer_init(buft, cpu_backend_buffer_i, data, size);
 }
 
-static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+GGML_CALL static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
     return TENSOR_ALIGNMENT;
 
     GGML_UNUSED(buft);
 }
 
-static bool ggml_backend_cpu_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
+GGML_CALL static bool ggml_backend_cpu_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
     return ggml_backend_is_cpu(backend);
 
     GGML_UNUSED(buft);
 }
 
-static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
+GGML_CALL static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
     return true;
 
     GGML_UNUSED(buft);
 }
 
-ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) {
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) {
     static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = {
         /* .iface = */ {
+            /* .get_name         = */ ggml_backend_cpu_buffer_type_get_name,
             /* .alloc_buffer     = */ ggml_backend_cpu_buffer_type_alloc_buffer,
             /* .get_alignment    = */ ggml_backend_cpu_buffer_type_get_alignment,
+            /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
             /* .get_alloc_size   = */ NULL, // defaults to ggml_nbytes
             /* .supports_backend = */ ggml_backend_cpu_buffer_type_supports_backend,
             /* .is_host          = */ ggml_backend_cpu_buffer_type_is_host,
@@ -501,11 +674,23 @@ ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) {
 
 #include <hbwmalloc.h>
 
-static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
+    return "CPU_HBM";
+
+    GGML_UNUSED(buft);
+}
+
+GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) {
+    return "CPU_HBM";
+
+    GGML_UNUSED(buf);
+}
+
+GGML_CALL static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) {
     hbw_free(buffer->context);
 }
 
-static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
     //void * ptr = hbw_malloc(size);
     void * ptr;
     int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size);
@@ -514,19 +699,21 @@ static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_
         return NULL;
     }
 
-    // FIXME: this is a hack to avoid having to implement a new buffer type
     ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
     buffer->buft = buft;
+    buffer->iface.get_name = ggml_backend_cpu_hbm_buffer_get_name;
     buffer->iface.free_buffer = ggml_backend_cpu_hbm_buffer_free_buffer;
 
     return buffer;
 }
 
-ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type() {
+ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) {
     static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_hbm = {
         /* .iface    = */ {
+            /* .get_name         = */ ggml_backend_cpu_hbm_buffer_type_get_name,
             /* .alloc_buffer     = */ ggml_backend_cpu_hbm_buffer_type_alloc_buffer,
             /* .get_alignment    = */ ggml_backend_cpu_buffer_type_get_alignment,
+            /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
             /* .get_alloc_size   = */ NULL, // defaults to ggml_nbytes
             /* .supports_backend = */ ggml_backend_cpu_buffer_type_supports_backend,
             /* .is_host          = */ ggml_backend_cpu_buffer_type_is_host,
@@ -542,22 +729,25 @@ struct ggml_backend_cpu_context {
     int n_threads;
     void * work_data;
     size_t work_size;
+
+    ggml_abort_callback abort_callback;
+    void *              abort_callback_data;
 };
 
-static const char * ggml_backend_cpu_name(ggml_backend_t backend) {
+GGML_CALL static const char * ggml_backend_cpu_name(ggml_backend_t backend) {
     return "CPU";
 
     GGML_UNUSED(backend);
 }
 
-static void ggml_backend_cpu_free(ggml_backend_t backend) {
+GGML_CALL static void ggml_backend_cpu_free(ggml_backend_t backend) {
     struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
     free(cpu_ctx->work_data);
     free(cpu_ctx);
     free(backend);
 }
 
-static ggml_backend_buffer_type_t ggml_backend_cpu_get_default_buffer_type(ggml_backend_t backend) {
+GGML_CALL static ggml_backend_buffer_type_t ggml_backend_cpu_get_default_buffer_type(ggml_backend_t backend) {
     return ggml_backend_cpu_buffer_type();
 
     GGML_UNUSED(backend);
@@ -568,7 +758,7 @@ struct ggml_backend_plan_cpu {
     struct ggml_cgraph cgraph;
 };
 
-static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+GGML_CALL static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) {
     struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
 
     struct ggml_backend_plan_cpu * cpu_plan = malloc(sizeof(struct ggml_backend_plan_cpu));
@@ -578,12 +768,19 @@ static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend
 
     if (cpu_plan->cplan.work_size > 0) {
         cpu_plan->cplan.work_data = malloc(cpu_plan->cplan.work_size);
+        if (cpu_plan->cplan.work_data == NULL) {
+            free(cpu_plan);
+            return NULL;
+        }
     }
 
+    cpu_plan->cplan.abort_callback      = cpu_ctx->abort_callback;
+    cpu_plan->cplan.abort_callback_data = cpu_ctx->abort_callback_data;
+
     return cpu_plan;
 }
 
-static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+GGML_CALL static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
     struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;
 
     free(cpu_plan->cplan.work_data);
@@ -592,33 +789,40 @@ static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backen
     GGML_UNUSED(backend);
 }
 
-static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+GGML_CALL static enum ggml_status ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
     struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;
 
-    ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan);
+    return ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan);
 
     GGML_UNUSED(backend);
 }
 
-static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
     struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
 
     struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
 
     if (cpu_ctx->work_size < cplan.work_size) {
-        // TODO: may be faster to free and use malloc to avoid the copy
-        cpu_ctx->work_data = realloc(cpu_ctx->work_data, cplan.work_size);
+        free(cpu_ctx->work_data);
+        cpu_ctx->work_data = malloc(cplan.work_size);
+        if (cpu_ctx->work_data == NULL) {
+            cpu_ctx->work_size = 0;
+            return GGML_STATUS_ALLOC_FAILED;
+        }
         cpu_ctx->work_size = cplan.work_size;
     }
-
     cplan.work_data = cpu_ctx->work_data;
 
-    ggml_graph_compute(cgraph, &cplan);
-    return true;
+    cplan.abort_callback      = cpu_ctx->abort_callback;
+    cplan.abort_callback_data = cpu_ctx->abort_callback_data;
+
+    return ggml_graph_compute(cgraph, &cplan);
 }
 
-static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
+GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
     switch (op->op) {
+        case GGML_OP_CPY:
+            return op->type != GGML_TYPE_IQ2_XXS && op->type != GGML_TYPE_IQ2_XS && op->type != GGML_TYPE_IQ1_S; // missing type_traits.from_float
         case GGML_OP_MUL_MAT:
             return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type;
         default:
@@ -634,34 +838,54 @@ static struct ggml_backend_i cpu_backend_i = {
     /* .get_default_buffer_type = */ ggml_backend_cpu_get_default_buffer_type,
     /* .set_tensor_async        = */ NULL,
     /* .get_tensor_async        = */ NULL,
-    /* .cpy_tensor_from_async   = */ NULL,
-    /* .cpy_tensor_to_async     = */ NULL,
+    /* .cpy_tensor_async        = */ NULL,
     /* .synchronize             = */ NULL,
     /* .graph_plan_create       = */ ggml_backend_cpu_graph_plan_create,
     /* .graph_plan_free         = */ ggml_backend_cpu_graph_plan_free,
     /* .graph_plan_compute      = */ ggml_backend_cpu_graph_plan_compute,
     /* .graph_compute           = */ ggml_backend_cpu_graph_compute,
     /* .supports_op             = */ ggml_backend_cpu_supports_op,
+    /* .offload_op              = */ NULL,
+    /* .event_new               = */ NULL,
+    /* .event_free              = */ NULL,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .event_synchronize       = */ NULL,
 };
 
+static ggml_guid_t ggml_backend_cpu_guid(void) {
+    static ggml_guid guid = { 0xaa, 0x67, 0xc7, 0x43, 0x96, 0xe6, 0xa3, 0x8a, 0xe3, 0xaf, 0xea, 0x92, 0x36, 0xbc, 0xfc, 0x89 };
+    return &guid;
+}
+
 ggml_backend_t ggml_backend_cpu_init(void) {
     struct ggml_backend_cpu_context * ctx = malloc(sizeof(struct ggml_backend_cpu_context));
+    if (ctx == NULL) {
+        return NULL;
+    }
 
-    ctx->n_threads = GGML_DEFAULT_N_THREADS;
-    ctx->work_data = NULL;
-    ctx->work_size = 0;
+    ctx->n_threads           = GGML_DEFAULT_N_THREADS;
+    ctx->work_data           = NULL;
+    ctx->work_size           = 0;
+    ctx->abort_callback      = NULL;
+    ctx->abort_callback_data = NULL;
 
     ggml_backend_t cpu_backend = malloc(sizeof(struct ggml_backend));
+    if (cpu_backend == NULL) {
+        free(ctx);
+        return NULL;
+    }
 
     *cpu_backend = (struct ggml_backend) {
+        /* .guid      = */ ggml_backend_cpu_guid(),
         /* .interface = */ cpu_backend_i,
         /* .context   = */ ctx
     };
     return cpu_backend;
 }
 
-bool ggml_backend_is_cpu(ggml_backend_t backend) {
-    return backend->iface.get_name == ggml_backend_cpu_name;
+GGML_CALL bool ggml_backend_is_cpu(ggml_backend_t backend) {
+    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_cpu_guid());
 }
 
 void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) {
@@ -671,113 +895,226 @@ void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) {
     ctx->n_threads = n_threads;
 }
 
-ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) {
+void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data) {
+    GGML_ASSERT(ggml_backend_is_cpu(backend_cpu));
+
+    struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context;
+    ctx->abort_callback = abort_callback;
+    ctx->abort_callback_data = abort_callback_data;
+}
+
+GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) {
+    GGML_ASSERT((uintptr_t)ptr % TENSOR_ALIGNMENT == 0 && "buffer pointer must be aligned");
     return ggml_backend_buffer_init(ggml_backend_cpu_buffer_type(), cpu_backend_buffer_i_from_ptr, ptr, size);
 }
 
-static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data) {
+GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data) {
     return ggml_backend_cpu_init();
 
     GGML_UNUSED(params);
     GGML_UNUSED(user_data);
 }
 
+// multi-buffer buffer
 
-// scheduler
-
-#define GGML_MAX_BACKENDS 4
-#define GGML_MAX_SPLITS 256
-#define GGML_MAX_SPLIT_INPUTS 16
-
-struct ggml_backend_sched_split {
-    ggml_tallocr_t tallocr;
-    int i_start;
-    int i_end;
-    struct ggml_tensor * inputs[GGML_MAX_SPLIT_INPUTS];
-    int n_inputs;
-    struct ggml_cgraph graph;
+struct ggml_backend_multi_buffer_context {
+    ggml_backend_buffer_t * buffers;
+    size_t n_buffers;
 };
 
-struct ggml_backend_sched {
-    int n_backends;
-    ggml_backend_t backends[GGML_MAX_BACKENDS];
-    ggml_tallocr_t  tallocs[GGML_MAX_BACKENDS];
+typedef struct ggml_backend_multi_buffer_context * ggml_backend_multi_buffer_context_t;
 
-    ggml_gallocr_t galloc;
+GGML_CALL static const char * ggml_backend_multi_buffer_get_name(ggml_backend_buffer_t buffer) {
+    ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context;
 
-    struct ggml_hash_set    hash_set;
-    ggml_tallocr_t *        node_talloc;                     // [hash_set.size]
-    struct ggml_tensor * (* node_copies)[GGML_MAX_BACKENDS]; // [hash_set.size][GGML_MAX_BACKENDS]
+    return ctx->buffers[0]->iface.get_name(ctx->buffers[0]);
+}
 
-    struct ggml_cgraph * graph;
-    struct ggml_backend_sched_split splits[GGML_MAX_SPLITS];
-    int n_splits;
+GGML_CALL static void ggml_backend_multi_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context;
+    for (size_t i = 0; i < ctx->n_buffers; i++) {
+        ggml_backend_buffer_free(ctx->buffers[i]);
+    }
 
-    struct ggml_context * ctx;
+    free(ctx->buffers);
+    free(ctx);
+}
 
-    // align context_buffer to GGML_MEM_ALIGN
-    #ifdef _MSC_VER
-    __declspec(align(GGML_MEM_ALIGN))
-    #else
-    __attribute__((aligned(GGML_MEM_ALIGN)))
-    #endif
-    char context_buffer[GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS*sizeof(struct ggml_tensor) + sizeof(struct ggml_cgraph)];
-};
+GGML_CALL static void ggml_backend_multi_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context;
+    for (size_t i = 0; i < ctx->n_buffers; i++) {
+        ggml_backend_buffer_clear(ctx->buffers[i], value);
+    }
+}
 
-#define hash_id(node) ggml_hash_find_or_insert(sched->hash_set, node)
-#define node_allocr(node) sched->node_talloc[hash_id(node)]
+static struct ggml_backend_buffer_i ggml_backend_multi_buffer_context_interface(void) {
+    static struct ggml_backend_buffer_i multi_backend_buffer_i = {
+        /* .get_name        = */ ggml_backend_multi_buffer_get_name,
+        /* .free_buffer     = */ ggml_backend_multi_buffer_free_buffer,
+        /* .get_base        = */ NULL,
+        /* .init_tensor     = */ NULL,
+        /* .set_tensor      = */ NULL,
+        /* .get_tensor      = */ NULL,
+        /* .cpy_tensor      = */ NULL,
+        /* .clear           = */ ggml_backend_multi_buffer_clear,
+        /* .reset           = */ NULL,
+    };
+
+    return multi_backend_buffer_i;
+}
+
+GGML_CALL ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer_t * buffers, size_t n_buffers) {
+    ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) malloc(sizeof(struct ggml_backend_multi_buffer_context));
+    ctx->n_buffers = n_buffers;
+    ctx->buffers = (ggml_backend_buffer_t *) malloc(n_buffers * sizeof(ggml_backend_buffer_t));
+
+    GGML_ASSERT(ctx->buffers != NULL);
+
+    size_t total_size = 0;
+    for (size_t i = 0; i < n_buffers; i++) {
+        ctx->buffers[i] = buffers[i];
+        total_size += ggml_backend_buffer_get_size(buffers[i]);
+    }
+
+    return ggml_backend_buffer_init(buffers[0]->buft, ggml_backend_multi_buffer_context_interface(), ctx, total_size);
+}
+
+GGML_CALL bool ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer) {
+    return buffer->iface.get_name == ggml_backend_multi_buffer_get_name;
+}
+
+GGML_CALL void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) {
+    GGML_ASSERT(ggml_backend_buffer_is_multi_buffer(buffer));
+    ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context;
+    for (size_t i = 0; i < ctx->n_buffers; i++) {
+        ggml_backend_buffer_set_usage(ctx->buffers[i], usage);
+    }
+}
+
+// creates a copy of the tensor with the same memory layout
+static struct ggml_tensor * ggml_dup_tensor_layout(struct ggml_context * ctx, const struct ggml_tensor * tensor) {
+    struct ggml_tensor * dup = ggml_dup_tensor(ctx, tensor);
+    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+        dup->nb[i] = tensor->nb[i];
+    }
+    return dup;
+}
 
 static bool ggml_is_view_op(enum ggml_op op) {
     return op == GGML_OP_VIEW || op == GGML_OP_RESHAPE || op == GGML_OP_PERMUTE || op == GGML_OP_TRANSPOSE;
 }
 
-// returns the priority of the backend, lower is better
-static int sched_backend_prio(ggml_backend_sched_t sched, ggml_backend_t backend) {
+// scheduler
+
+#ifndef GGML_SCHED_MAX_BACKENDS
+#define GGML_SCHED_MAX_BACKENDS 16
+#endif
+
+#ifndef GGML_SCHED_MAX_SPLITS
+#define GGML_SCHED_MAX_SPLITS 2048
+#endif
+
+#ifndef GGML_SCHED_MAX_SPLIT_INPUTS
+#define GGML_SCHED_MAX_SPLIT_INPUTS GGML_MAX_SRC
+#endif
+
+#ifndef GGML_SCHED_MAX_COPIES
+#define GGML_SCHED_MAX_COPIES 4
+#endif
+
+struct ggml_backend_sched_split {
+    int backend_id;
+    int i_start;
+    int i_end;
+    struct ggml_tensor * inputs[GGML_SCHED_MAX_SPLIT_INPUTS];
+    int n_inputs;
+    // graph view of this split
+    struct ggml_cgraph graph;
+};
+
+struct ggml_backend_sched {
+    bool is_reset; // true if the scheduler has been reset since the last graph split
+    bool is_alloc;
+
+    int n_backends;
+
+    ggml_backend_t backends[GGML_SCHED_MAX_BACKENDS];
+    ggml_backend_buffer_type_t bufts[GGML_SCHED_MAX_BACKENDS];
+    ggml_gallocr_t galloc;
+
+    // hash keys of the nodes in the graph
+    struct ggml_hash_set    hash_set;
+    // hash values
+    int * tensor_backend_id;
+    struct ggml_tensor * (* tensor_copies)[GGML_SCHED_MAX_BACKENDS][GGML_SCHED_MAX_COPIES];
+
+    int * node_backend_ids; // [graph_size]
+    int * leaf_backend_ids; // [graph_size]
+
+    // copy of the graph with modified inputs
+    struct ggml_cgraph * graph;
+
+    // graph splits
+    struct ggml_backend_sched_split * splits;
+    int n_splits;
+    int splits_capacity;
+
+    // pipeline parallelism support
+    int n_copies;
+    int cur_copy;
+    ggml_backend_event_t events[GGML_SCHED_MAX_BACKENDS][GGML_SCHED_MAX_COPIES];
+    struct ggml_tensor * graph_inputs[GGML_SCHED_MAX_SPLIT_INPUTS];
+    int n_graph_inputs;
+
+    struct ggml_context * ctx;
+
+    ggml_backend_sched_eval_callback callback_eval;
+    void * callback_eval_user_data;
+
+    // align context_buffer to GGML_MEM_ALIGN
+#ifdef _MSC_VER
+    __declspec(align(GGML_MEM_ALIGN))
+#else
+    __attribute__((aligned(GGML_MEM_ALIGN)))
+#endif
+    char context_buffer[GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2*sizeof(struct ggml_tensor) + sizeof(struct ggml_cgraph)];
+};
+
+#define hash_id(tensor) ggml_hash_find_or_insert(sched->hash_set, tensor)
+#define tensor_backend_id(tensor) sched->tensor_backend_id[hash_id(tensor)]
+
+// returns the priority of the backend, lower id is higher priority
+static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backend_t backend) {
     for (int i = 0; i < sched->n_backends; i++) {
         if (sched->backends[i] == backend) {
             return i;
         }
     }
-    return INT_MAX;
+    return -1;
 }
 
-static int sched_allocr_prio(ggml_backend_sched_t sched, ggml_tallocr_t allocr) {
-    for (int i = 0; i < sched->n_backends; i++) {
-        if (sched->tallocs[i] == allocr) {
-            return i;
-        }
-    }
-    return INT_MAX;
-}
-
-static ggml_backend_t get_buffer_backend(ggml_backend_sched_t sched, ggml_backend_buffer_t buffer) {
+static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, const struct ggml_tensor * tensor) {
+    ggml_backend_buffer_t buffer = tensor->buffer;
     if (buffer == NULL) {
-        return NULL;
+        return -1;
     }
+
     // find highest prio backend that supports the buffer type
     for (int i = 0; i < sched->n_backends; i++) {
         if (ggml_backend_buft_supports_backend(buffer->buft, sched->backends[i])) {
-            return sched->backends[i];
+            return i;
         }
     }
-    GGML_ASSERT(false && "tensor buffer type not supported by any backend");
-}
 
-static ggml_backend_t get_allocr_backend(ggml_backend_sched_t sched, ggml_tallocr_t allocr) {
-    if (allocr == NULL) {
-        return NULL;
-    }
-    // find highest prio backend that supports the buffer type
-    for (int i = 0; i < sched->n_backends; i++) {
-        if (sched->tallocs[i] == allocr) {
-            return sched->backends[i];
-        }
-    }
-    GGML_UNREACHABLE();
+    fprintf(stderr, "%s: error: no backend supports buffer type %s used in tensor %s\n",
+        __func__, ggml_backend_buffer_name(buffer), tensor->name);
+    GGML_ASSERT(false);
+
+    return -1;
 }
 
 #if 0
-static char causes[GGML_DEFAULT_GRAPH_SIZE*8 + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS][128]; // debug, remove
+static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS][128]; // debug only
 #define SET_CAUSE(node, ...) sprintf(causes[hash_id(node)], __VA_ARGS__)
 #define GET_CAUSE(node) causes[hash_id(node)]
 #else
@@ -786,45 +1123,56 @@ static char causes[GGML_DEFAULT_GRAPH_SIZE*8 + GGML_MAX_SPLITS*GGML_MAX_SPLIT_IN
 #endif
 
 // returns the backend that should be used for the node based on the current locations
-static ggml_backend_t sched_backend_from_cur(ggml_backend_sched_t sched, struct ggml_tensor * node) {
-    // if the dst tensor is already allocated in a buffer, we must assume that it is critical to keep it there
-    // ie. kv cache updates
-    // note that this doesn't allow fallback to CPU. need to add output tensors to the splits to copy the data back to the original backend.
-    // dst
-    ggml_backend_t cur_backend = get_buffer_backend(sched, node->buffer);
-    if (cur_backend != NULL) {
-        SET_CAUSE(node, "1.dst");
-        return cur_backend;
+static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, struct ggml_tensor * tensor) {
+    // TODO: use supports_op to check if the backend supports the op
+
+    // assign pre-allocated nodes to their backend
+    int cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor);
+    if (cur_backend_id != -1) {
+        SET_CAUSE(tensor, "1.dst");
+        return cur_backend_id;
     }
 
     // view_src
-    if (node->view_src != NULL && get_buffer_backend(sched, node->view_src->buffer) != NULL) {
-        SET_CAUSE(node, "1.vsrc");
-        return get_buffer_backend(sched, node->view_src->buffer);
+    if (tensor->view_src != NULL) {
+        cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src);
+        if (cur_backend_id != -1) {
+            SET_CAUSE(tensor, "1.vsrc");
+            return cur_backend_id;
+        }
     }
 
-    // src
-    int cur_prio = INT_MAX;
-    size_t cur_size = 0;
+    // graph input
+    if (tensor->flags & GGML_TENSOR_FLAG_INPUT) {
+        cur_backend_id = sched->n_backends - 1; // last backend (assumed CPU)
+        SET_CAUSE(tensor, "1.inp");
+        return cur_backend_id;
+    }
 
+    // assign nodes that use weights to the backend of the weights
+    // operations with weights are preferably run on the same backend as the weights
     for (int i = 0; i < GGML_MAX_SRC; i++) {
-        const struct ggml_tensor * src = node->src[i];
+        const struct ggml_tensor * src = tensor->src[i];
         if (src == NULL) {
-            break;
+            continue;
         }
-        ggml_backend_t src_backend = get_buffer_backend(sched, src->buffer);
-        if (src_backend != NULL) {
-            int src_prio = sched_backend_prio(sched, src_backend);
-            size_t src_size = ggml_nbytes(src);
-            if (src_prio < cur_prio && src_size >= cur_size) {
-                cur_prio = src_prio;
-                cur_size = src_size;
-                cur_backend = src_backend;
-                SET_CAUSE(node, "1.src%d", i);
+        if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
+            int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src);
+            // check if a backend with higher prio wants to offload the op
+            if (src_backend_id == sched->n_backends - 1) {
+                for (int b = 0; b < src_backend_id; b++) {
+                    if (ggml_backend_offload_op(sched->backends[b], tensor)) {
+                        SET_CAUSE(tensor, "1.off");
+                        return b;
+                    }
+                }
             }
+            SET_CAUSE(tensor, "1.wgt%d", i);
+            return src_backend_id;
         }
     }
-    return cur_backend;
+
+    return -1;
 }
 
 static char * fmt_size(size_t size) {
@@ -837,11 +1185,11 @@ static char * fmt_size(size_t size) {
     return buffer;
 }
 
-static void sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
+static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
     int cur_split = 0;
     for (int i = 0; i < graph->n_nodes; i++) {
         if (cur_split < sched->n_splits && i == sched->splits[cur_split].i_start) {
-            ggml_backend_t split_backend = get_allocr_backend(sched, sched->splits[cur_split].tallocr);
+            ggml_backend_t split_backend = sched->backends[sched->splits[cur_split].backend_id];
             fprintf(stderr, "\n## SPLIT #%d: %s # %d inputs: ", cur_split, ggml_backend_name(split_backend),
                 sched->splits[cur_split].n_inputs);
             for (int j = 0; j < sched->splits[cur_split].n_inputs; j++) {
@@ -855,42 +1203,33 @@ static void sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgra
         if (ggml_is_view_op(node->op)) {
             continue;
         }
-        ggml_tallocr_t node_allocr = node_allocr(node);
-        ggml_backend_t node_backend = node_allocr ? get_allocr_backend(sched, node_allocr) : NULL; // FIXME:
-        fprintf(stderr, "node #%3d (%10.10s): %20.20s (%4.4s) [%4.4s %8.8s]:", i, ggml_op_name(node->op), node->name,
-            fmt_size(ggml_nbytes(node)), node_allocr ? ggml_backend_name(node_backend) : "NULL", GET_CAUSE(node));
+        ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node);
+        fprintf(stderr, "node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name,
+            fmt_size(ggml_nbytes(node)), tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", GET_CAUSE(node));
         for (int j = 0; j < GGML_MAX_SRC; j++) {
             struct ggml_tensor * src = node->src[j];
             if (src == NULL) {
-                break;
+                continue;
             }
-            ggml_tallocr_t src_allocr = node_allocr(src);
-            ggml_backend_t src_backend = src_allocr ? get_allocr_backend(sched, src_allocr) : NULL;
-            fprintf(stderr, " %20.20s (%4.4s) [%4.4s %8.8s]", src->name,
+            ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src);
+            fprintf(stderr, " %20.20s (%5.5s) [%5.5s %8.8s]", src->name,
                 fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src));
         }
         fprintf(stderr, "\n");
     }
 }
 
-// creates a copy of the tensor with the same memory layout
-static struct ggml_tensor * ggml_dup_tensor_layout(struct ggml_context * ctx, const struct ggml_tensor * tensor) {
-    struct ggml_tensor * dup = ggml_dup_tensor(ctx, tensor);
-    for (int i = 0; i < GGML_MAX_DIMS; i++) {
-        dup->nb[i] = tensor->nb[i];
-    }
-    return dup;
-}
+//#define DEBUG_PASS1
+//#define DEBUG_PASS2
+//#define DEBUG_PASS3
+//#define DEBUG_PASS4
 
 // assigns backends to ops and splits the graph into subgraphs that can be computed on the same backend
-// TODO: merge passes
-static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
-    // reset state
-    size_t hash_size = sched->hash_set.size;
-    memset(sched->hash_set.keys, 0, sizeof(sched->hash_set.keys[0]) * hash_size);
-    memset(sched->node_talloc,   0, sizeof(sched->node_talloc[0])   * hash_size);
-    memset(sched->node_copies,   0, sizeof(sched->node_copies[0])   * hash_size);
+static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
+    // reset splits
     sched->n_splits = 0;
+    sched->n_graph_inputs = 0;
+    sched->is_reset = false;
 
     struct ggml_init_params params = {
         /* .mem_size =   */ sizeof(sched->context_buffer),
@@ -898,295 +1237,523 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g
         /* .no_alloc =   */ true
     };
 
-    if (sched->ctx != NULL) {
-        ggml_free(sched->ctx);
-    }
+    ggml_free(sched->ctx);
 
     sched->ctx = ggml_init(params);
+    if (sched->ctx == NULL) {
+        fprintf(stderr, "%s: failed to initialize context\n", __func__);
+        GGML_ASSERT(false);
+    }
 
-    // pass 1: assign backends to ops with allocated inputs
+    // pass 1: assign backends to ops with pre-allocated inputs
     for (int i = 0; i < graph->n_leafs; i++) {
         struct ggml_tensor * leaf = graph->leafs[i];
-        if (node_allocr(leaf) != NULL) {
+        int * leaf_backend_id = &tensor_backend_id(leaf);
+        if (*leaf_backend_id != -1) {
             // do not overwrite user assignments
             continue;
         }
-        ggml_backend_t leaf_backend = get_buffer_backend(sched, leaf->buffer);
-        if (leaf_backend == NULL && leaf->view_src != NULL) {
-            leaf_backend = get_buffer_backend(sched, leaf->view_src->buffer);
-        }
-        if (leaf_backend != NULL) {
-            node_allocr(leaf) = ggml_backend_sched_get_tallocr(sched, leaf_backend);
-        }
+        *leaf_backend_id = ggml_backend_sched_backend_id_from_cur(sched, leaf);
     }
 
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
-        if (node_allocr(node) != NULL) {
+        int * node_backend_id = &tensor_backend_id(node);
+        if (*node_backend_id != -1) {
             // do not overwrite user assignments
             continue;
         }
-        ggml_backend_t node_backend = sched_backend_from_cur(sched, node);
-        if (node_backend != NULL) {
-            node_allocr(node) = ggml_backend_sched_get_tallocr(sched, node_backend);
+        *node_backend_id = ggml_backend_sched_backend_id_from_cur(sched, node);
+        // src
+        for (int j = 0; j < GGML_MAX_SRC; j++) {
+            struct ggml_tensor * src = node->src[j];
+            if (src == NULL) {
+                continue;
+            }
+            int * src_backend_id = &tensor_backend_id(src);
+            if (*src_backend_id == -1) {
+                *src_backend_id = ggml_backend_sched_backend_id_from_cur(sched, src);
+            }
         }
     }
-    //printf("PASS 1 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
+#ifdef DEBUG_PASS1
+    fprintf(stderr, "PASS 1 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
+#endif
 
-    // pass 2: assign backends to ops from current assignments
-    // TODO:
-    //  - reuse sched_backend_from_cur
-    for (int i = 0; i < graph->n_nodes; i++) {
-        struct ggml_tensor * node = graph->nodes[i];
-        ggml_tallocr_t node_allocr = node_allocr(node);
-        if (node_allocr == NULL) {
-            int    cur_prio = INT_MAX;
-            size_t cur_size = 0;
-            for (int j = 0; j < GGML_MAX_SRC; j++) {
-                struct ggml_tensor * src = node->src[j];
-                if (src == NULL) {
-                    break;
+    // pass 2: expand current backend assignments
+    // assign the same backend to adjacent nodes
+    // expand gpu backends (i.e. non last prio) up and down, ignoring cpu (the lowest priority backend)
+    // thus, cpu will never be used unless weights are on cpu, or there are no gpu ops between cpu ops
+
+
+    // pass 2.2 expand gpu down
+    {
+        int cur_backend_id = -1;
+        for (int i = 0; i < graph->n_nodes; i++) {
+            struct ggml_tensor * node = graph->nodes[i];
+            if (ggml_is_view_op(node->op)) {
+                continue;
+            }
+            int * node_backend_id = &tensor_backend_id(node);
+            if (*node_backend_id != -1) {
+                if (*node_backend_id == sched->n_backends - 1) {
+                    // skip cpu (lowest prio backend)
+                    cur_backend_id = -1;
+                } else {
+                    cur_backend_id = *node_backend_id;
                 }
-                ggml_tallocr_t src_allocr = node_allocr(src);
-                if (src_allocr != NULL) {
-                    int    src_prio = sched_allocr_prio(sched, src_allocr);
-                    size_t src_size = ggml_nbytes(src);
-                    if (src_prio < cur_prio && src_size >= cur_size) {
-                        cur_prio = src_prio;
-                        cur_size = src_size;
-                        node_allocr = src_allocr;
-                        SET_CAUSE(node, "2.src%d", j);
+            } else {
+                *node_backend_id = cur_backend_id;
+                SET_CAUSE(node, "2.2");
+            }
+        }
+    }
+    // pass 2.1 expand gpu up
+    {
+        int cur_backend_id = -1;
+        for (int i = graph->n_nodes - 1; i >= 0; i--) {
+            struct ggml_tensor * node = graph->nodes[i];
+            if (ggml_is_view_op(node->op)) {
+                continue;
+            }
+            int * node_backend_id = &tensor_backend_id(node);
+            if (*node_backend_id != -1) {
+                if (*node_backend_id == sched->n_backends - 1) {
+                    // skip cpu (lowest prio backend)
+                    cur_backend_id = -1;
+                } else {
+                    cur_backend_id = *node_backend_id;
+                }
+            } else {
+                *node_backend_id = cur_backend_id;
+                SET_CAUSE(node, "2.1");
+            }
+        }
+    }
+    // pass 2.4 expand rest down
+    {
+        int cur_backend_id = -1;
+        for (int i = 0; i < graph->n_nodes; i++) {
+            struct ggml_tensor * node = graph->nodes[i];
+            if (ggml_is_view_op(node->op)) {
+                continue;
+            }
+            int * node_backend_id = &tensor_backend_id(node);
+            if (*node_backend_id != -1) {
+                cur_backend_id = *node_backend_id;
+            } else {
+                *node_backend_id = cur_backend_id;
+                SET_CAUSE(node, "2.4");
+            }
+        }
+    }
+    // pass 2.3 expand rest up
+    {
+        int cur_backend_id = -1;
+        for (int i = graph->n_nodes - 1; i >= 0; i--) {
+            struct ggml_tensor * node = graph->nodes[i];
+            if (ggml_is_view_op(node->op)) {
+                continue;
+            }
+            int * node_backend_id = &tensor_backend_id(node);
+            if (*node_backend_id != -1) {
+                cur_backend_id = *node_backend_id;
+            } else {
+                *node_backend_id = cur_backend_id;
+                SET_CAUSE(node, "2.3");
+            }
+        }
+    }
+
+#ifdef DEBUG_PASS2
+    fprintf(stderr, "PASS 2 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
+#endif
+
+    // pass 3: assign backends to remaining src from dst and view_src
+    for (int i = 0; i < graph->n_nodes; i++) {
+        struct ggml_tensor * node = graph->nodes[i];
+        int * cur_backend_id = &tensor_backend_id(node);
+        if (node->view_src != NULL && *cur_backend_id == -1) {
+            *cur_backend_id = tensor_backend_id(node->view_src);
+            SET_CAUSE(node, "3.vsrc");
+        }
+        for (int j = 0; j < GGML_MAX_SRC; j++) {
+            struct ggml_tensor * src = node->src[j];
+            if (src == NULL) {
+                continue;
+            }
+            int * src_backend_id = &tensor_backend_id(src);
+            if (*src_backend_id == -1) {
+                if (src->view_src != NULL) {
+                    // views are always on the same backend as the source
+                    *src_backend_id = tensor_backend_id(src->view_src);
+                    SET_CAUSE(src, "3.vsrc");
+                } else {
+                    *src_backend_id = *cur_backend_id;
+                    SET_CAUSE(src, "3.cur");
+                }
+            }
+        }
+    }
+#ifdef DEBUG_PASS3
+    fprintf(stderr, "PASS 3 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
+#endif
+
+    // pass 4: split graph, find tensors that need to be copied
+    {
+        int i_split = 0;
+        struct ggml_backend_sched_split * split = &sched->splits[0];
+        // find the backend of the first split, skipping view ops
+        for (int i = 0; i < graph->n_nodes; i++) {
+            struct ggml_tensor * node = graph->nodes[i];
+            if (!ggml_is_view_op(node->op)) {
+                split->backend_id = tensor_backend_id(node);
+                break;
+            }
+        }
+        split->i_start = 0;
+        split->n_inputs = 0;
+        memset(split->inputs, 0, sizeof(split->inputs)); //HACK
+        int cur_backend_id = split->backend_id;
+        for (int i = 0; i < graph->n_nodes; i++) {
+            struct ggml_tensor * node = graph->nodes[i];
+
+            if (ggml_is_view_op(node->op)) {
+                continue;
+            }
+
+            const int node_backend_id = tensor_backend_id(node);
+
+            GGML_ASSERT(node_backend_id != -1); // all nodes should be assigned by now
+
+            // check if we should start a new split based on the sources of the current node
+            bool need_new_split = false;
+            if (node_backend_id == cur_backend_id && split->n_inputs > 0) {
+                for (int j = 0; j < GGML_MAX_SRC; j++) {
+                    struct ggml_tensor * src = node->src[j];
+                    if (src == NULL) {
+                        continue;
+                    }
+                    // check if a weight is on a different backend
+                    // by starting a new split, the memory of the previously offloaded weights can be reused
+                    if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
+                        int src_backend_id = tensor_backend_id(src);
+                        if (src_backend_id != -1 && src_backend_id != cur_backend_id) {
+                            need_new_split = true;
+                            break;
+                        }
+                    }
+                    // check if the split has too many inputs
+                    if (split->n_inputs == GGML_SCHED_MAX_SPLIT_INPUTS) {
+                        const size_t id = hash_id(src);
+                        int src_backend_id = sched->tensor_backend_id[id];
+                        if (src_backend_id != cur_backend_id && sched->tensor_copies[hash_id(src)][cur_backend_id][0] == NULL) {
+                            //printf("starting new split because of too many inputs: node %s, input %s\n", node->name, src->name);
+                            need_new_split = true;
+                            break;
+                        }
                     }
                 }
             }
-            if (node_allocr != NULL) {
-                node_allocr(node) = node_allocr;
-            }
-        }
-    }
-    //printf("PASS 2 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
 
-    // pass 3: assign backends to remaining src from dst (should only be leafs)
-    for (int i = 0; i < graph->n_nodes; i++) {
-        struct ggml_tensor * node = graph->nodes[i];
-        ggml_tallocr_t node_allocr = node_allocr(node);
-        for (int j = 0; j < GGML_MAX_SRC; j++) {
-            struct ggml_tensor * src = node->src[j];
-            if (src == NULL) {
-                break;
-            }
-            ggml_tallocr_t src_allocr = node_allocr(src);
-            if (src_allocr == NULL) {
-                node_allocr(src) = node_allocr;
-            }
-        }
-    }
-    //printf("PASS 3 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
-
-    // pass 4: split graph, find tensors that need to be copied
-    // TODO:
-    //  - when switching from a less preferred backend to a more preferred backend, check if it is possible to move the switch to an earlier point for the same cost
-    // find first backend
-    int cur_split = 0;
-    for (int i = 0; i < graph->n_nodes; i++) {
-        struct ggml_tensor * node = graph->nodes[i];
-        if (node->view_src == NULL) {
-            sched->splits[0].tallocr = node_allocr(node);
-            break;
-        }
-    }
-    sched->splits[0].i_start = 0;
-    sched->splits[0].n_inputs = 0;
-    memset(sched->splits[0].inputs, 0, sizeof(sched->splits[0].inputs)); //HACK
-    ggml_tallocr_t cur_allocr = sched->splits[0].tallocr;
-    size_t cur_backend_id = sched_allocr_prio(sched, cur_allocr);
-    for (int i = 0; i < graph->n_nodes; i++) {
-        struct ggml_tensor * node = graph->nodes[i];
-
-        if (ggml_is_view_op(node->op)) {
-            continue;
-        }
-
-        ggml_tallocr_t node_allocr = node_allocr(node);
-
-        if (node_allocr != cur_allocr) {
-            sched->splits[cur_split].i_end = i;
-            cur_split++;
-            GGML_ASSERT(cur_split < GGML_MAX_SPLITS);
-            sched->splits[cur_split].tallocr = node_allocr;
-            sched->splits[cur_split].i_start = i;
-            sched->splits[cur_split].n_inputs = 0;
-            memset(sched->splits[cur_split].inputs, 0, sizeof(sched->splits[cur_split].inputs)); //HACK
-            cur_allocr = node_allocr;
-            cur_backend_id = sched_allocr_prio(sched, cur_allocr);
-        }
-
-        // find inputs that are not on the same backend
-        for (int j = 0; j < GGML_MAX_SRC; j++) {
-            struct ggml_tensor * src = node->src[j];
-            if (src == NULL) {
-                break;
-            }
-            ggml_tallocr_t src_allocr = node_allocr(src);
-            if (src_allocr != node_allocr) {
-                int n_inputs = sched->splits[cur_split].n_inputs++;
-                GGML_ASSERT(n_inputs < GGML_MAX_SPLIT_INPUTS);
-                sched->splits[cur_split].inputs[n_inputs] = (struct ggml_tensor *)src;
-
-                // create copies
-                size_t id = hash_id(src);
-                if (sched->node_copies[id][cur_backend_id] == NULL) {
-                    struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
-                    sched->node_copies[id][cur_backend_id] = tensor_copy;
-                    node_allocr(tensor_copy) = cur_allocr;
-                    ggml_backend_t backend = get_allocr_backend(sched, cur_allocr);
-                    ggml_format_name(tensor_copy, "%s#%s", ggml_backend_name(backend), src->name);
+            if (node_backend_id != cur_backend_id || need_new_split) {
+                split->i_end = i;
+                i_split++;
+                if (i_split >= sched->splits_capacity) {
+                    sched->splits_capacity *= 2;
+                    sched->splits = realloc(sched->splits, sched->splits_capacity * sizeof(struct ggml_backend_sched_split));
+                    GGML_ASSERT(sched->splits != NULL);
                 }
-                node->src[j] = sched->node_copies[id][cur_backend_id];
+                GGML_ASSERT(i_split < GGML_SCHED_MAX_SPLITS);
+                split = &sched->splits[i_split];
+                split->backend_id = node_backend_id;
+                split->i_start = i;
+                split->n_inputs = 0;
+                cur_backend_id = node_backend_id;
             }
-        }
-    }
-    sched->splits[cur_split].i_end = graph->n_nodes;
-    sched->n_splits = cur_split + 1;
 
-    //fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); fflush(stdout);
+            // find inputs that are not on the same backend
+            for (int j = 0; j < GGML_MAX_SRC; j++) {
+                struct ggml_tensor * src = node->src[j];
+                if (src == NULL) {
+                    continue;
+                }
 
-#if 1
-    // sanity check: all sources should have the same backend as the node
-    for (int i = 0; i < graph->n_nodes; i++) {
-        struct ggml_tensor * node = graph->nodes[i];
-        ggml_tallocr_t node_allocr = node_allocr(node);
-        if (node_allocr == NULL) {
-            fprintf(stderr, "!!!!!!! %s has no backend\n", node->name);
-        }
-        for (int j = 0; j < GGML_MAX_SRC; j++) {
-            struct ggml_tensor * src = node->src[j];
-            if (src == NULL) {
-                break;
-            }
-            ggml_tallocr_t src_allocr = node_allocr(src);
-            if (src_allocr != node_allocr /* && src_backend != NULL */) { // ignore nulls for now
-                fprintf(stderr, "!!!! %s has backend %s, src %d (%s) has backend %s\n",
-                    node->name, node_allocr ? ggml_backend_name(get_allocr_backend(sched, node_allocr)) : "NULL",
-                    j, src->name, src_allocr ? ggml_backend_name(get_allocr_backend(sched, src_allocr)) : "NULL");
+                const int src_backend_id = tensor_backend_id(src);
+                assert(src_backend_id != -1); // all inputs should be assigned by now
+
+                if (src->flags & GGML_TENSOR_FLAG_INPUT && sched->n_copies > 1)  {
+                    size_t id = hash_id(src);
+                    if (sched->tensor_copies[id][src_backend_id][0] == NULL) {
+                        ggml_backend_t backend = sched->backends[src_backend_id];
+                        for (int c = 0; c < sched->n_copies; c++) {
+                            struct ggml_tensor * tensor_copy;
+                            if (c == sched->cur_copy) {
+                                tensor_copy = src; // use the original tensor as the current copy
+                            } else {
+                                tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
+                                ggml_format_name(tensor_copy, "%s#%s#%d", ggml_backend_name(backend), src->name, c);
+                            }
+                            if (sched->n_copies > 1) {
+                                ggml_set_input(tensor_copy);
+                                ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor
+                            }
+                            sched->tensor_copies[id][src_backend_id][c] = tensor_copy;
+                            SET_CAUSE(tensor_copy, "4.cpy");
+                        }
+                        int n_graph_inputs = sched->n_graph_inputs++;
+                        GGML_ASSERT(n_graph_inputs < GGML_SCHED_MAX_SPLIT_INPUTS);
+                        sched->graph_inputs[n_graph_inputs] = src;
+                    }
+                }
+
+                if (src_backend_id != node_backend_id) {
+                    // create a copy of the input in the split's backend
+                    const size_t id = hash_id(src);
+                    if (sched->tensor_copies[id][cur_backend_id][0] == NULL) {
+                        ggml_backend_t backend = sched->backends[cur_backend_id];
+                        for (int c = 0; c < sched->n_copies; c++) {
+                            struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
+                            ggml_format_name(tensor_copy, "%s#%s#%d", ggml_backend_name(backend), src->name, c);
+                            if (sched->n_copies > 1) {
+                                ggml_set_input(tensor_copy);
+                                ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor
+                            }
+                            sched->tensor_copies[id][cur_backend_id][c] = tensor_copy;
+                            SET_CAUSE(tensor_copy, "4.cpy");
+                        }
+                        int n_inputs = split->n_inputs++;
+                        GGML_ASSERT(n_inputs < GGML_SCHED_MAX_SPLIT_INPUTS);
+                        split->inputs[n_inputs] = src;
+                    }
+                    node->src[j] = sched->tensor_copies[id][cur_backend_id][sched->cur_copy];
+                }
             }
         }
+        split->i_end = graph->n_nodes;
+        sched->n_splits = i_split + 1;
     }
+#ifdef DEBUG_PASS4
+    fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
 #endif
 
     // create copies of the graph for each split
-    // FIXME: avoid this copy, pass split inputs to ggml_gallocr_alloc_graph_n in some other way
-    struct ggml_cgraph * graph_copy = ggml_new_graph_custom(sched->ctx, graph->n_nodes + sched->n_splits*GGML_MAX_SPLIT_INPUTS, false);
+    // TODO: avoid this copy
+    struct ggml_cgraph * graph_copy = ggml_new_graph_custom(sched->ctx, graph->n_nodes + sched->n_splits*GGML_SCHED_MAX_SPLIT_INPUTS*2, false);
     for (int i = 0; i < sched->n_splits; i++) {
         struct ggml_backend_sched_split * split = &sched->splits[i];
         split->graph = ggml_graph_view(graph, split->i_start, split->i_end);
 
         // add inputs to the graph copy so that they are allocated by ggml-alloc at the start of the split
         for (int j = 0; j < split->n_inputs; j++) {
+            assert(graph_copy->size > (graph_copy->n_nodes + 1));
+
             struct ggml_tensor * input = split->inputs[j];
-            struct ggml_tensor * input_cpy = sched->node_copies[hash_id(input)][sched_allocr_prio(sched, split->tallocr)];
-            input_cpy->src[0] = input;
+            const size_t input_id = hash_id(input);
+            struct ggml_tensor * input_cpy = sched->tensor_copies[input_id][split->backend_id][sched->cur_copy];
+
+            // add a dependency to the input source so that it is not freed before the copy is done
+            struct ggml_tensor * input_dep = ggml_view_tensor(sched->ctx, input);
+            input_dep->src[0] = input;
+            sched->node_backend_ids[graph_copy->n_nodes] = sched->tensor_backend_id[input_id];
+            graph_copy->nodes[graph_copy->n_nodes++] = input_dep;
+
+            // add a dependency to the input copy so that it is allocated at the start of the split
+            sched->node_backend_ids[graph_copy->n_nodes] = split->backend_id;
             graph_copy->nodes[graph_copy->n_nodes++] = input_cpy;
         }
 
         for (int j = split->i_start; j < split->i_end; j++) {
+            assert(graph_copy->size > graph_copy->n_nodes);
+            sched->node_backend_ids[graph_copy->n_nodes] = tensor_backend_id(graph->nodes[j]);
             graph_copy->nodes[graph_copy->n_nodes++] = graph->nodes[j];
         }
     }
+
+    if (sched->n_copies > 1) {
+        // add input copies as leafs so that they are allocated first
+        for (int i = 0; i < sched->n_graph_inputs; i++) {
+            struct ggml_tensor * input = sched->graph_inputs[i];
+            size_t id = hash_id(input);
+            int backend_id = tensor_backend_id(input);
+            for (int c = 0; c < sched->n_copies; c++) {
+                struct ggml_tensor * input_cpy = sched->tensor_copies[id][backend_id][c];
+                sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id;
+                graph_copy->leafs[graph_copy->n_leafs++] = input_cpy;
+            }
+        }
+
+        for (int i = 0; i < sched->n_splits; i++) {
+            struct ggml_backend_sched_split * split = &sched->splits[i];
+            int backend_id = split->backend_id;
+            for (int j = 0; j < split->n_inputs; j++) {
+                struct ggml_tensor * input = split->inputs[j];
+                size_t id = hash_id(input);
+                for (int c = 0; c < sched->n_copies; c++) {
+                    struct ggml_tensor * input_cpy = sched->tensor_copies[id][backend_id][c];
+                    sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id;
+                    graph_copy->leafs[graph_copy->n_leafs++] = input_cpy;
+                }
+            }
+        }
+    }
+
+    // add leafs from the original graph
+    for (int i = 0; i < graph->n_leafs; i++) {
+        struct ggml_tensor * leaf = graph->leafs[i];
+        sched->leaf_backend_ids[graph_copy->n_leafs] = tensor_backend_id(leaf);
+        graph_copy->leafs[graph_copy->n_leafs++] = leaf;
+    }
+
     sched->graph = graph_copy;
 }
 
-static void sched_alloc_splits(ggml_backend_sched_t sched) {
-    ggml_gallocr_alloc_graph_n(
-        sched->galloc,
-        sched->graph,
-        sched->hash_set,
-        sched->node_talloc);
+static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
+    // allocate graph
+    if (!ggml_gallocr_alloc_graph(sched->galloc, sched->graph)) {
+        // the re-allocation may cause the split inputs to be moved to a different address
+        ggml_backend_sched_synchronize(sched);
+#ifndef NDEBUG
+        fprintf(stderr, "%s: failed to allocate graph, reserving\n", __func__);
+#endif
+        ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids, sched->leaf_backend_ids);
+        if (!ggml_gallocr_alloc_graph(sched->galloc, sched->graph)) {
+            fprintf(stderr, "%s: failed to allocate graph\n", __func__);
+            return false;
+        }
+    }
+
+    return true;
 }
 
-static void sched_compute_splits(ggml_backend_sched_t sched) {
-    uint64_t copy_us[GGML_MAX_BACKENDS] = {0};
-    uint64_t compute_us[GGML_MAX_BACKENDS] = {0};
-
+static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
     struct ggml_backend_sched_split * splits = sched->splits;
 
     for (int i = 0; i < sched->n_splits; i++) {
         struct ggml_backend_sched_split * split = &splits[i];
-        ggml_backend_t split_backend = get_allocr_backend(sched, split->tallocr);
-        int split_backend_id = sched_backend_prio(sched, split_backend);
+        int split_backend_id = split->backend_id;
+        ggml_backend_t split_backend = sched->backends[split_backend_id];
 
         // copy the input tensors to the split backend
-        uint64_t copy_start_us = ggml_time_us();
         for (int j = 0; j < split->n_inputs; j++) {
+            ggml_backend_t input_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[j]);
             struct ggml_tensor * input = split->inputs[j];
-            struct ggml_tensor * input_cpy = sched->node_copies[hash_id(input)][sched_backend_prio(sched, split_backend)];
-            if (input->buffer == NULL) {
-                if (input->view_src == NULL) {
-                    fprintf(stderr, "input %s has no buffer and no view_src\n", input->name);
-                    exit(1);
+            struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split_backend_id][sched->cur_copy];
+
+            if (input->flags & GGML_TENSOR_FLAG_INPUT) {
+                // inputs from the user must be copied immediately to prevent the user overwriting the data before the copy is done
+                if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
+                    ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]);
+                } else {
+                    ggml_backend_synchronize(split_backend);
                 }
-                // FIXME: may need to use the sched buffer instead
-                ggml_backend_view_init(input->view_src->buffer, input);
+                ggml_backend_tensor_copy(input, input_cpy);
+            } else {
+                // wait for the split backend to finish using the input before overwriting it
+                if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
+                    ggml_backend_event_wait(split_backend, sched->events[split_backend_id][sched->cur_copy]);
+                } else {
+                    ggml_backend_synchronize(split_backend);
+                }
+                ggml_backend_tensor_copy_async(input_backend, split_backend, input, input_cpy);
             }
-            if (input_cpy->buffer == NULL) {
-                fprintf(stderr, "input_cpy %s has no buffer\n", input_cpy->name);
-                exit(1);
-            }
-            //GGML_ASSERT(input->buffer->backend != input_cpy->buffer->backend);
-            //GGML_ASSERT(input_cpy->buffer->backend == split_backend);
-            ggml_backend_tensor_copy(input, input_cpy);
         }
-        // ggml_backend_synchronize(split_backend);
-        int64_t copy_end_us = ggml_time_us();
-        copy_us[split_backend_id] += copy_end_us - copy_start_us;
 
-#if 0
-        char split_filename[GGML_MAX_NAME];
-        snprintf(split_filename, GGML_MAX_NAME, "split_%i_%s.dot", i, ggml_backend_name(split_backend));
-        ggml_graph_dump_dot(split->graph, NULL, split_filename);
-#endif
+        if (!sched->callback_eval) {
+            enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &split->graph);
+            if (ec != GGML_STATUS_SUCCESS) {
+                return ec;
+            }
+        } else {
+            // similar to ggml_backend_compare_graph_backend
+            for (int j0 = 0; j0 < split->graph.n_nodes; j0++) {
+                struct ggml_tensor * t = split->graph.nodes[j0];
 
-        uint64_t compute_start_us = ggml_time_us();
-        ggml_backend_graph_compute(split_backend, &split->graph);
-        // ggml_backend_synchronize(split_backend);
-        uint64_t compute_end_us = ggml_time_us();
-        compute_us[split_backend_id] += compute_end_us - compute_start_us;
-    }
+                // check if the user needs data from this node
+                bool need = sched->callback_eval(t, true, sched->callback_eval_user_data);
 
-#if 0
-    // per-backend timings
-    fprintf(stderr, "sched_compute_splits times (%d splits):\n", sched->n_splits);
-    for (int i = 0; i < sched->n_backends; i++) {
-        if (copy_us[i] > 0 || compute_us[i] > 0) {
-            fprintf(stderr, "\t%5.5s: %lu us copy, %lu us compute\n", ggml_backend_name(sched->backends[i]), copy_us[i], compute_us[i]);
+                int j1 = j0;
+
+                // determine the range [j0, j1] of nodes that can be computed together
+                while (!need && j1 < split->graph.n_nodes - 1) {
+                    t = split->graph.nodes[++j1];
+                    need = sched->callback_eval(t, true, sched->callback_eval_user_data);
+                }
+
+                struct ggml_cgraph gv = ggml_graph_view(&split->graph, j0, j1 + 1);
+
+                enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &gv);
+                if (ec != GGML_STATUS_SUCCESS) {
+                    return ec;
+                }
+
+                // TODO: pass backend to the callback, then the user can decide if they want to synchronize
+                ggml_backend_synchronize(split_backend);
+
+                if (need && !sched->callback_eval(t, false, sched->callback_eval_user_data)) {
+                    break;
+                }
+
+                j0 = j1;
+            }
+        }
+
+        // record the event of this copy
+        if (split->n_inputs > 0) {
+            if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
+                ggml_backend_event_record(sched->events[split_backend_id][sched->cur_copy]);
+            }
         }
     }
-#endif
+
+    sched->cur_copy = (sched->cur_copy + 1) % sched->n_copies;
+
+    return GGML_STATUS_SUCCESS;
 }
 
-static void sched_reset(ggml_backend_sched_t sched) {
-    for (int i = 0; i < sched->n_backends; i++) {
-        ggml_tallocr_reset(sched->tallocs[i]);
-    }
-}
+ggml_backend_sched_t ggml_backend_sched_new(
+        ggml_backend_t * backends,
+        ggml_backend_buffer_type_t * bufts,
+        int n_backends,
+        size_t graph_size,
+        bool parallel) {
+    GGML_ASSERT(n_backends > 0);
+    GGML_ASSERT(n_backends <= GGML_SCHED_MAX_BACKENDS);
+    GGML_ASSERT(ggml_backend_is_cpu(backends[n_backends - 1])); // last backend must be CPU
 
-ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, int n_backends) {
-    GGML_ASSERT(n_backends <= GGML_MAX_BACKENDS);
+    struct ggml_backend_sched * sched = calloc(sizeof(struct ggml_backend_sched), 1);
 
-    struct ggml_backend_sched * sched = malloc(sizeof(struct ggml_backend_sched));
-    memset(sched, 0, sizeof(struct ggml_backend_sched));
+    // initialize hash table
+    sched->hash_set          = ggml_hash_set_new(graph_size);
+    sched->tensor_backend_id = calloc(sizeof(sched->tensor_backend_id[0]), sched->hash_set.size);
+    sched->tensor_copies     = calloc(sizeof(sched->tensor_copies[0]), sched->hash_set.size);
+
+    const size_t nodes_size = graph_size + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2;
+    sched->node_backend_ids  = calloc(sizeof(sched->node_backend_ids[0]), nodes_size);
+    sched->leaf_backend_ids  = calloc(sizeof(sched->leaf_backend_ids[0]), nodes_size);
 
     sched->n_backends = n_backends;
-    for (int i = 0; i < n_backends; i++) {
-        sched->backends[i] = backends[i];
+
+    sched->n_copies = parallel ? GGML_SCHED_MAX_COPIES : 1;
+
+    const int initial_splits_capacity = 16;
+    sched->splits = calloc(sizeof(sched->splits[0]), initial_splits_capacity);
+    sched->splits_capacity = initial_splits_capacity;
+
+    for (int b = 0; b < n_backends; b++) {
+        sched->backends[b] = backends[b];
+        sched->bufts[b] = bufts ? bufts[b] : ggml_backend_get_default_buffer_type(backends[b]);
+        GGML_ASSERT(ggml_backend_buft_supports_backend(sched->bufts[b], backends[b]));
+        if (sched->n_copies > 1) {
+            for (int c = 0; c < sched->n_copies; c++) {
+                sched->events[b][c] = ggml_backend_event_new(backends[b]);
+            }
+        }
     }
 
-    sched->galloc = ggml_gallocr_new();
+    sched->galloc = ggml_gallocr_new_n(sched->bufts, n_backends);
 
-    // init measure allocs for each backend
-    for (int i = 0; i < n_backends; i++) {
-        sched->tallocs[i] = ggml_tallocr_new_measure_from_backend(backends[i]);
-    }
+    ggml_backend_sched_reset(sched);
 
     return sched;
 }
@@ -1195,66 +1762,127 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) {
     if (sched == NULL) {
         return;
     }
-    for (int i = 0; i < sched->n_backends; i++) {
-        ggml_tallocr_free(sched->tallocs[i]);
+    for (int b = 0; b < sched->n_backends; b++) {
+        for (int c = 0; c < sched->n_copies; c++) {
+            ggml_backend_event_free(sched->events[b][c]);
+        }
     }
     ggml_gallocr_free(sched->galloc);
+    ggml_free(sched->ctx);
+    free(sched->splits);
     free(sched->hash_set.keys);
-    free(sched->node_talloc);
-    free(sched->node_copies);
+    free(sched->tensor_backend_id);
+    free(sched->tensor_copies);
+    free(sched->node_backend_ids);
+    free(sched->leaf_backend_ids);
     free(sched);
 }
 
-void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) {
-    // initialize hash tables
-    size_t hash_size = measure_graph->visited_hash_table.size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS;
-    sched->hash_set.size = hash_size;
-    sched->hash_set.keys = malloc(sizeof(sched->hash_set.keys[0]) * hash_size);
-    sched->node_talloc   = malloc(sizeof(sched->node_talloc[0])   * hash_size);
-    sched->node_copies   = malloc(sizeof(sched->node_copies[0])   * hash_size);
+void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
+    // reset state for the next run
+    size_t hash_size = sched->hash_set.size;
+    memset(sched->hash_set.keys,      0, sizeof(sched->hash_set.keys[0])     * hash_size); // NOLINT
+    memset(sched->tensor_backend_id, -1, sizeof(sched->tensor_backend_id[0]) * hash_size);
+    memset(sched->tensor_copies,      0, sizeof(sched->tensor_copies[0])     * hash_size);
 
-    sched_split_graph(sched, measure_graph);
-    sched_alloc_splits(sched);
+    sched->is_reset = true;
+    sched->is_alloc = false;
+}
 
-    // allocate buffers and reset allocators
-    for (int i = 0; i < sched->n_backends; i++) {
-        size_t size = ggml_tallocr_max_size(sched->tallocs[i]);
-        ggml_tallocr_free(sched->tallocs[i]);
-        sched->tallocs[i] = ggml_tallocr_new_from_backend(sched->backends[i], size);
+bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) {
+    GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes);
+
+    ggml_backend_sched_split_graph(sched, measure_graph);
+
+    // TODO: extract this to a separate function
+    if (!ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids, sched->leaf_backend_ids)) {
+        return false;
     }
 
-    sched_reset(sched);
+    ggml_backend_sched_reset(sched);
+    ggml_backend_sched_synchronize(sched);
+
+    return true;
 }
 
-void ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
-    GGML_ASSERT(sched->hash_set.size >= graph->visited_hash_table.size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS);
+bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
+    GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes);
 
-    sched_split_graph(sched, graph);
-    sched_alloc_splits(sched);
-    sched_compute_splits(sched);
-    sched_reset(sched);
+    ggml_backend_sched_split_graph(sched, graph);
+
+    if (!ggml_backend_sched_alloc_splits(sched)) {
+        return false;
+    }
+
+    sched->is_alloc = true;
+
+    return true;
 }
 
-ggml_tallocr_t ggml_backend_sched_get_tallocr(ggml_backend_sched_t sched, ggml_backend_t backend) {
-    int backend_index = sched_backend_prio(sched, backend);
-    return sched->tallocs[backend_index];
+enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
+    enum ggml_status err = ggml_backend_sched_graph_compute_async(sched, graph);
+    ggml_backend_sched_synchronize(sched);
+    return err;
 }
 
-ggml_backend_buffer_t ggml_backend_sched_get_buffer(ggml_backend_sched_t sched, ggml_backend_t backend) {
-    int backend_index = sched_backend_prio(sched, backend);
-    return ggml_tallocr_get_buffer(sched->tallocs[backend_index]);
+enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
+    if (!sched->is_reset && !sched->is_alloc) {
+        ggml_backend_sched_reset(sched);
+    }
+
+    if (!sched->is_alloc) {
+        if (!ggml_backend_sched_alloc_graph(sched, graph)) {
+            return GGML_STATUS_ALLOC_FAILED;
+        }
+    }
+
+    return ggml_backend_sched_compute_splits(sched);
 }
 
-void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) {
-    int backend_index = sched_backend_prio(sched, backend);
+void ggml_backend_sched_synchronize(ggml_backend_sched_t sched) {
+    for (int i = 0; i < sched->n_backends; i++) {
+        ggml_backend_synchronize(sched->backends[i]);
+    }
+}
+
+void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) {
+    sched->callback_eval = callback;
+    sched->callback_eval_user_data = user_data;
+}
+
+int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched) {
+    return sched->n_splits;
+}
+
+int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched) {
+    return sched->n_copies;
+}
+
+size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend) {
+    int backend_index = ggml_backend_sched_backend_id(sched, backend);
     GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
-    node_allocr(node) = sched->tallocs[backend_index];
+
+    return ggml_gallocr_get_buffer_size(sched->galloc, backend_index);
+}
+
+void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) {
+    int backend_index = ggml_backend_sched_backend_id(sched, backend);
+    GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
+    tensor_backend_id(node) = backend_index;
+}
+
+ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) {
+    int backend_index = tensor_backend_id(node);
+    if (backend_index == -1) {
+        return NULL;
+    }
+    return sched->backends[backend_index];
 }
 
 // utils
+
 void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
     GGML_ASSERT(tensor->buffer == NULL);
-    //GGML_ASSERT(tensor->data == NULL); // views of pre-allocted tensors may have the data set, but still need to be initialized
     GGML_ASSERT(tensor->view_src != NULL);
     GGML_ASSERT(tensor->view_src->buffer != NULL);
     GGML_ASSERT(tensor->view_src->data != NULL);
@@ -1278,7 +1906,7 @@ void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor
     ggml_backend_buffer_init_tensor(buffer, tensor);
 }
 
-static struct ggml_tensor * graph_dup_tensor(struct ggml_hash_set hash_set, struct ggml_tensor ** node_copies,
+static struct ggml_tensor * graph_copy_dup_tensor(struct ggml_hash_set hash_set, struct ggml_tensor ** node_copies,
     struct ggml_context * ctx_allocated, struct ggml_context * ctx_unallocated, struct ggml_tensor * src) {
 
     GGML_ASSERT(src != NULL);
@@ -1291,7 +1919,7 @@ static struct ggml_tensor * graph_dup_tensor(struct ggml_hash_set hash_set, stru
 
     struct ggml_tensor * dst = ggml_dup_tensor_layout(src->data && !src->view_src ? ctx_allocated : ctx_unallocated, src);
     if (src->view_src != NULL) {
-        dst->view_src = graph_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, src->view_src);
+        dst->view_src = graph_copy_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, src->view_src);
         dst->view_offs = src->view_offs;
     }
     dst->op = src->op;
@@ -1302,16 +1930,16 @@ static struct ggml_tensor * graph_dup_tensor(struct ggml_hash_set hash_set, stru
     for (int i = 0; i < GGML_MAX_SRC; i++) {
         struct ggml_tensor * s = src->src[i];
         if (s == NULL) {
-            break;
+            continue;
         }
-        dst->src[i] = graph_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, s);
+        dst->src[i] = graph_copy_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, s);
     }
 
     node_copies[id] = dst;
     return dst;
 }
 
-static void graph_init_tensor(struct ggml_hash_set hash_set, struct ggml_tensor ** node_copies, bool * node_init, struct ggml_tensor * src) {
+static void graph_copy_init_tensor(struct ggml_hash_set hash_set, struct ggml_tensor ** node_copies, bool * node_init, struct ggml_tensor * src) {
     size_t id = ggml_hash_find(hash_set, src);
     if (node_init[id]) {
         return;
@@ -1320,6 +1948,7 @@ static void graph_init_tensor(struct ggml_hash_set hash_set, struct ggml_tensor
 
     struct ggml_tensor * dst = node_copies[id];
     if (dst->view_src != NULL) {
+        graph_copy_init_tensor(hash_set, node_copies, node_init, src->view_src);
         ggml_backend_view_init(dst->view_src->buffer, dst);
     }
     else {
@@ -1330,19 +1959,19 @@ static void graph_init_tensor(struct ggml_hash_set hash_set, struct ggml_tensor
     for (int i = 0; i < GGML_MAX_SRC; i++) {
         struct ggml_tensor * s = src->src[i];
         if (s == NULL) {
-            break;
+            continue;
         }
-        graph_init_tensor(hash_set, node_copies, node_init, s);
+        graph_copy_init_tensor(hash_set, node_copies, node_init, s);
     }
 }
 
 struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph) {
     struct ggml_hash_set hash_set = {
         /* .size = */ graph->visited_hash_table.size,
-        /* .keys = */ calloc(sizeof(hash_set.keys[0]) * graph->visited_hash_table.size, 1)
+        /* .keys = */ calloc(sizeof(hash_set.keys[0]), graph->visited_hash_table.size) // NOLINT
     };
-    struct ggml_tensor ** node_copies = calloc(sizeof(node_copies[0]) * hash_set.size, 1);
-    bool * node_init = calloc(sizeof(node_init[0]) * hash_set.size, 1);
+    struct ggml_tensor ** node_copies = calloc(sizeof(node_copies[0]), hash_set.size); // NOLINT
+    bool * node_init = calloc(sizeof(node_init[0]), hash_set.size);
 
     struct ggml_init_params params = {
         /* .mem_size   = */ ggml_tensor_overhead()*hash_set.size + ggml_graph_overhead_custom(graph->size, false),
@@ -1353,21 +1982,50 @@ struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, s
     struct ggml_context * ctx_allocated = ggml_init(params);
     struct ggml_context * ctx_unallocated = ggml_init(params);
 
+    if (ctx_allocated == NULL || ctx_unallocated == NULL) {
+        fprintf(stderr, "failed to allocate context for graph copy\n");
+        free(hash_set.keys);
+        free(node_copies);
+        free(node_init);
+        ggml_free(ctx_allocated);
+        ggml_free(ctx_unallocated);
+        return (struct ggml_backend_graph_copy) {
+            /* .buffer           = */ NULL,
+            /* .ctx_allocated    = */ NULL,
+            /* .ctx_unallocated  = */ NULL,
+            /* .graph            = */ NULL,
+        };
+    }
+
     // dup nodes
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
-        graph_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, node);
+        graph_copy_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, node);
     }
 
     // allocate nodes
     ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx_allocated, backend);
+    if (buffer == NULL) {
+        fprintf(stderr, "failed to allocate buffer for graph copy\n");
+        free(hash_set.keys);
+        free(node_copies);
+        free(node_init);
+        ggml_free(ctx_allocated);
+        ggml_free(ctx_unallocated);
+        return (struct ggml_backend_graph_copy) {
+            /* .buffer           = */ NULL,
+            /* .ctx_allocated    = */ NULL,
+            /* .ctx_unallocated  = */ NULL,
+            /* .graph            = */ NULL,
+        };
+    }
 
     //printf("copy buffer size: %zu MB\n", ggml_backend_buffer_get_size(buffer) / 1024 / 1024);
 
     // copy data and init views
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
-        graph_init_tensor(hash_set, node_copies, node_init, node);
+        graph_copy_init_tensor(hash_set, node_copies, node_init, node);
     }
 
     // build graph copy
@@ -1397,8 +2055,12 @@ void ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy) {
     ggml_free(copy.ctx_unallocated);
 }
 
-void ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data) {
+bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data) {
     struct ggml_backend_graph_copy copy = ggml_backend_graph_copy(backend2, graph);
+    if (copy.buffer == NULL) {
+        return false;
+    }
+
     struct ggml_cgraph * g1 = graph;
     struct ggml_cgraph * g2 = copy.graph;
 
@@ -1428,4 +2090,6 @@ void ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t
     }
 
     ggml_backend_graph_copy_free(copy);
+
+    return true;
 }
diff --git a/ggml-backend.h b/ggml-backend.h
index 85ff67b0e..422457ab6 100644
--- a/ggml-backend.h
+++ b/ggml-backend.h
@@ -9,6 +9,7 @@ extern "C" {
 
     typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
     typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
+    typedef struct ggml_backend_event * ggml_backend_event_t;
     typedef struct ggml_backend * ggml_backend_t;
     typedef void * ggml_backend_graph_plan_t;
 
@@ -17,53 +18,79 @@ extern "C" {
     //
 
     // buffer type
-    GGML_API ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size);
-    GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft);
-    GGML_API size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor);
-    GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend);
-    GGML_API bool ggml_backend_buft_is_host         (ggml_backend_buffer_type_t buft);
+    GGML_API           const char *          ggml_backend_buft_name            (ggml_backend_buffer_type_t buft);
+    GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_buft_alloc_buffer    (ggml_backend_buffer_type_t buft, size_t size);
+    GGML_API           size_t                ggml_backend_buft_get_alignment   (ggml_backend_buffer_type_t buft);
+    GGML_API           size_t                ggml_backend_buft_get_max_size    (ggml_backend_buffer_type_t buft);
+    GGML_API GGML_CALL size_t                ggml_backend_buft_get_alloc_size  (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor);
+    GGML_API           bool                  ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend);
+    GGML_API           bool                  ggml_backend_buft_is_host         (ggml_backend_buffer_type_t buft);
 
     // buffer
-    GGML_API void   ggml_backend_buffer_free          (ggml_backend_buffer_t buffer);
-    GGML_API void * ggml_backend_buffer_get_base      (ggml_backend_buffer_t buffer);
-    GGML_API size_t ggml_backend_buffer_get_size      (ggml_backend_buffer_t buffer);
-    GGML_API void   ggml_backend_buffer_init_tensor   (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
-    GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
-    GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
-    GGML_API void   ggml_backend_buffer_clear         (ggml_backend_buffer_t buffer, uint8_t value);
-    GGML_API bool   ggml_backend_buffer_is_host       (ggml_backend_buffer_t buffer);
-    GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_type(ggml_backend_buffer_t buffer);
+    enum ggml_backend_buffer_usage {
+        GGML_BACKEND_BUFFER_USAGE_ANY = 0,
+        GGML_BACKEND_BUFFER_USAGE_WEIGHTS = 1,
+    };
+
+    GGML_API           const char *               ggml_backend_buffer_name          (ggml_backend_buffer_t buffer);
+    GGML_API           void                       ggml_backend_buffer_free          (ggml_backend_buffer_t buffer);
+    GGML_API           void *                     ggml_backend_buffer_get_base      (ggml_backend_buffer_t buffer);
+    GGML_API           size_t                     ggml_backend_buffer_get_size      (ggml_backend_buffer_t buffer);
+    GGML_API GGML_CALL void                       ggml_backend_buffer_init_tensor   (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+    GGML_API           size_t                     ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
+    GGML_API           size_t                     ggml_backend_buffer_get_max_size  (ggml_backend_buffer_t buffer);
+    GGML_API           size_t                     ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+    GGML_API           void                       ggml_backend_buffer_clear         (ggml_backend_buffer_t buffer, uint8_t value);
+    GGML_API           bool                       ggml_backend_buffer_is_host       (ggml_backend_buffer_t buffer);
+    GGML_API           void                       ggml_backend_buffer_set_usage     (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
+    GGML_API           ggml_backend_buffer_type_t ggml_backend_buffer_get_type      (ggml_backend_buffer_t buffer);
+    GGML_API           void                       ggml_backend_buffer_reset         (ggml_backend_buffer_t buffer);
 
     //
     // Backend
     //
 
-
+    GGML_API ggml_guid_t  ggml_backend_guid(ggml_backend_t backend);
     GGML_API const char * ggml_backend_name(ggml_backend_t backend);
     GGML_API void         ggml_backend_free(ggml_backend_t backend);
 
     GGML_API ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend);
     GGML_API ggml_backend_buffer_t      ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size);
     GGML_API size_t                     ggml_backend_get_alignment(ggml_backend_t backend);
+    GGML_API size_t                     ggml_backend_get_max_size(ggml_backend_t backend);
 
     GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
     GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
 
-    GGML_API void ggml_backend_tensor_set(      struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
-    GGML_API void ggml_backend_tensor_get(const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+    GGML_API GGML_CALL void ggml_backend_tensor_set(      struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+    GGML_API GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
 
     GGML_API void ggml_backend_synchronize(ggml_backend_t backend);
 
-    GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create (ggml_backend_t backend, struct ggml_cgraph * cgraph);
+    GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph);
+    GGML_API void                      ggml_backend_graph_plan_free  (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
 
-    GGML_API void ggml_backend_graph_plan_free   (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
-    GGML_API void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
-    GGML_API bool ggml_backend_graph_compute     (ggml_backend_t backend, struct ggml_cgraph * cgraph);
-    GGML_API bool ggml_backend_supports_op       (ggml_backend_t backend, const struct ggml_tensor * op);
+    GGML_API enum ggml_status ggml_backend_graph_plan_compute (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
+    GGML_API enum ggml_status ggml_backend_graph_compute      (ggml_backend_t backend, struct ggml_cgraph * cgraph);
+    GGML_API enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph);
+    GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op);
+    GGML_API bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op);
 
     // tensor copy between different backends
     GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
-    GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); // automatic fallback to sync copy
+
+    // asynchronous copy
+    // the copy is performed after all the currently queued operations in backend_src
+    // backend_dst will wait for the copy to complete before performing other operations
+    // automatic fallback to sync copy if async is not supported
+    GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst);
+
+    // events
+    GGML_API ggml_backend_event_t   ggml_backend_event_new        (ggml_backend_t backend);
+    GGML_API void                   ggml_backend_event_free       (ggml_backend_event_t event);
+    GGML_API void                   ggml_backend_event_record     (ggml_backend_event_t event);
+    GGML_API void                   ggml_backend_event_synchronize(ggml_backend_event_t event);
+    GGML_API void                   ggml_backend_event_wait       (ggml_backend_t backend, ggml_backend_event_t event); // wait async on event
 
     //
     // CPU backend
@@ -71,13 +98,14 @@ extern "C" {
 
     GGML_API ggml_backend_t ggml_backend_cpu_init(void);
 
-    GGML_API bool ggml_backend_is_cpu(ggml_backend_t backend);
-    GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads);
+    GGML_API GGML_CALL bool ggml_backend_is_cpu                (ggml_backend_t backend);
+    GGML_API           void ggml_backend_cpu_set_n_threads     (ggml_backend_t backend_cpu, int n_threads);
+    GGML_API           void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data);
 
     // Create a backend buffer from an existing pointer
-    GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size);
+    GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size);
 
-    GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void);
+    GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void);
 
 #ifdef GGML_USE_CPU_HBM
     GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void);
@@ -109,54 +137,71 @@ extern "C" {
     /*
       Example usage:
 
-        sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, num_backends);
-        // sched is initialized with measure allocators and cannot be used until allocated with a measure graph
+        // operations that use tensors allocated in a buffer with USAGE_WEIGHTS will be asigned
+        // preferrably to run on the same backend as the buffer
+        ggml_backend_buffer_set_usage(buf_weights, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
 
-        // initialize buffers from a measure graph
-        measure_graph = build_graph(sched); // use the allocr to allocate inputs as needed
+        sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false);
 
-        // in build_graph:
-        build_graph(...) {
-            // allocating tensors in a specific backend (optional, recommended: pre-allocate inputs in a different buffer)
-            alloc_cpu = ggml_backend_sched_get_allocr(sched, backend_cpu);
-            ggml_allocr_alloc(alloc_cpu, tensor);
+        // initialize buffers from a max size graph (optional)
+        reserve_graph = build_graph(sched, max_batch_size);
 
-            // manually assigning nodes to a backend (optional, shouldn't be needed in most cases)
-            struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
-            ggml_backend_sched_set_node_backend(sched, node, backend_gpu);
-        }
+        // manually assign nodes to a backend (optional, should not be needed in most cases)
+        struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
+        ggml_backend_sched_set_tensor_backend(sched, node, backend_gpu);
 
-        // allocate backend buffers from measure graph
-        ggml_backend_sched_init_measure(sched, measure_graph);
-
-        // the scheduler is now ready to compute graphs
+        ggml_backend_sched_reserve(sched, reserve_graph);
 
         // compute
         graph = build_graph(sched);
         ggml_backend_sched_graph_compute(sched, graph);
+
+        // if there are graph inputs:
+        ggml_backend_sched_reset(sched);
+        ggml_backend_sched_alloc_graph(sched, graph);
+        ggml_backend_tensor_set(input_tensor, ...);
+        ggml_backend_sched_graph_compute(sched, graph);
+    }
     */
 
     struct ggml_backend_sched;
     typedef struct ggml_backend_sched * ggml_backend_sched_t;
 
-    // Initialize a backend scheduler
-    GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, int n_backends);
+    // when ask == true, the scheduler wants to know if the user wants to observe this node
+    // this allows the scheduler to batch nodes together in order to evaluate them in a single call
+    //
+    // when ask == false, the scheduler is passing the node tensor to the user for observation
+    // if the user returns false, the scheduler will cancel the graph compute
+    //
+    typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
 
-    GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched);
+    // Initialize a backend scheduler
+    GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel);
+    GGML_API void                 ggml_backend_sched_free(ggml_backend_sched_t sched);
 
     // Initialize backend buffers from a measure graph
-    GGML_API void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph);
+    GGML_API bool                 ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph);
 
-    GGML_API ggml_tallocr_t        ggml_backend_sched_get_tallocr(ggml_backend_sched_t sched, ggml_backend_t backend);
-    GGML_API ggml_backend_buffer_t ggml_backend_sched_get_buffer (ggml_backend_sched_t sched, ggml_backend_t backend);
+    // Get the number of splits of the last graph
+    GGML_API int                  ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched);
+    GGML_API int                  ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched);
 
-    GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
+    GGML_API size_t               ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
 
-    // Allocate a graph on the backend scheduler
-    GGML_API void ggml_backend_sched_graph_compute(
-            ggml_backend_sched_t sched,
-            struct ggml_cgraph * graph);
+    GGML_API void                 ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
+    GGML_API ggml_backend_t       ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
 
+    // Allocate and compute graph on the backend scheduler
+    GGML_API bool                 ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
+    GGML_API enum ggml_status     ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
+    GGML_API enum ggml_status     ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
+    GGML_API void                 ggml_backend_sched_synchronize(ggml_backend_sched_t sched);
+
+    // Reset all assignments and allocators - must be called before changing the node backends
+    GGML_API void                 ggml_backend_sched_reset(ggml_backend_sched_t sched);
+
+    // Set a callback to be called for each resulting node during graph compute
+    GGML_API void                 ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data);
 
     //
     // Utils
@@ -173,10 +218,10 @@ extern "C" {
     GGML_API struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph);
     GGML_API void                           ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy);
 
-    typedef bool (*ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data);
+    typedef bool (*GGML_CALL ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data);
 
     // Compare the output of two backends
-    GGML_API void ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data);
+    GGML_API bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data);
 
     // Tensor initialization
     GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
diff --git a/ggml-common.h b/ggml-common.h
new file mode 100644
index 000000000..43c7978a0
--- /dev/null
+++ b/ggml-common.h
@@ -0,0 +1,1853 @@
+#ifndef GGML_COMMON_DECL
+
+#if defined(GGML_COMMON_DECL_C)
+#include <stdint.h>
+
+typedef uint16_t ggml_half;
+typedef uint32_t ggml_half2;
+
+#define GGML_COMMON_AGGR
+
+#define GGML_COMMON_DECL
+#elif defined(GGML_COMMON_DECL_METAL)
+#include <metal_stdlib>
+
+typedef half  ggml_half;
+typedef half2 ggml_half2;
+
+#define GGML_COMMON_AGGR
+
+#define GGML_COMMON_DECL
+#elif defined(GGML_COMMON_DECL_CUDA)
+#include <cuda_fp16.h>
+#include <cstdint>
+
+typedef half  ggml_half;
+typedef half2 ggml_half2;
+
+#define GGML_COMMON_AGGR data
+
+#define GGML_COMMON_DECL
+#elif defined(GGML_COMMON_DECL_HIP)
+#include <hip/hip_fp16.h>
+#include <cstdint>
+
+typedef half  ggml_half;
+typedef half2 ggml_half2;
+
+#define GGML_COMMON_AGGR data
+
+#define GGML_COMMON_DECL
+#elif defined(GGML_COMMON_DECL_SYCL)
+#include <sycl/half_type.hpp>
+#include <cstdint>
+
+typedef sycl::half  ggml_half;
+typedef sycl::half2 ggml_half2;
+
+#define GGML_COMMON_AGGR data
+
+#define GGML_COMMON_DECL
+#endif
+
+#if defined(GGML_COMMON_DECL)
+
+#ifndef __cplusplus
+#ifndef static_assert
+#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
+#define static_assert(cond, msg) _Static_assert(cond, msg)
+#else
+#define static_assert(cond, msg) struct global_scope_noop_trick
+#endif
+#endif
+#endif // __cplusplus
+
+// QK = number of values after dequantization
+// QK_K = super-block size
+
+#ifdef GGML_QKK_64
+#define QK_K 64
+#define K_SCALE_SIZE 4
+#else
+#define QK_K 256
+#define K_SCALE_SIZE 12
+#endif // GGML_QKK_64
+
+#if defined(GGML_COMMON_DECL_CUDA) || defined(GGML_COMMON_DECL_HIP) || defined(GGML_COMMON_DECL_SYCL)
+// QR = QK / number of values before dequantization
+// QI = number of 32 bit integers before dequantization
+
+#define QI4_0 (QK4_0 / (4 * QR4_0))
+#define QR4_0 2
+
+#define QI4_1 (QK4_1 / (4 * QR4_1))
+#define QR4_1 2
+
+#define QI5_0 (QK5_0 / (4 * QR5_0))
+#define QR5_0 2
+
+#define QI5_1 (QK5_1 / (4 * QR5_1))
+#define QR5_1 2
+
+#define QI8_0 (QK8_0 / (4 * QR8_0))
+#define QR8_0 1
+
+#define QI8_1 (QK8_1 / (4 * QR8_1))
+#define QR8_1 1
+
+#define QI2_K (QK_K / (4*QR2_K))
+#define QR2_K 4
+
+#define QI3_K (QK_K / (4*QR3_K))
+#define QR3_K 4
+
+#define QI4_K (QK_K / (4*QR4_K))
+#define QR4_K 2
+
+#define QI5_K (QK_K / (4*QR5_K))
+#define QR5_K 2
+
+#define QI6_K (QK_K / (4*QR6_K))
+#define QR6_K 2
+
+#define QI2_XXS (QK_K / (4*QR2_XXS))
+#define QR2_XXS 8
+
+#define QI2_XS (QK_K / (4*QR2_XS))
+#define QR2_XS 8
+
+#define QI2_S (QK_K / (4*QR2_S))
+#define QR2_S 8
+
+#define QI3_XXS (QK_K / (4*QR3_XXS))
+#define QR3_XXS 8
+
+#define QI3_XS (QK_K / (4*QR3_XS))
+#define QR3_XS 8
+
+#define QI1_S (QK_K / (4*QR1_S))
+#define QR1_S 8
+
+#define QI4_NL (QK4_NL / (4*QR4_NL))
+#define QR4_NL 2
+
+#if QK_K == 64
+#define QI4_XS QI4_NL
+#define QR4_XS QR4_NL
+#else
+#define QI4_XS (QK_K / (4*QR4_XS))
+#define QR4_XS 8
+#endif
+
+#endif // GGML_COMMON_DECL_CUDA || GGML_COMMON_DECL_HIP
+
+#define QK4_0 32
+typedef struct {
+    ggml_half d;           // delta
+    uint8_t qs[QK4_0 / 2]; // nibbles / quants
+} block_q4_0;
+static_assert(sizeof(block_q4_0) == sizeof(ggml_half) + QK4_0 / 2, "wrong q4_0 block size/padding");
+
+#define QK4_1 32
+typedef struct {
+    union {
+        struct {
+            ggml_half d; // delta
+            ggml_half m; // min
+        } GGML_COMMON_AGGR;
+        ggml_half2 dm;
+    };
+    uint8_t qs[QK4_1 / 2]; // nibbles / quants
+} block_q4_1;
+static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_half) + QK4_1 / 2, "wrong q4_1 block size/padding");
+
+#define QK5_0 32
+typedef struct {
+    ggml_half d;           // delta
+    uint8_t qh[4];         // 5-th bit of quants
+    uint8_t qs[QK5_0 / 2]; // nibbles / quants
+} block_q5_0;
+static_assert(sizeof(block_q5_0) == sizeof(ggml_half) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
+
+#define QK5_1 32
+typedef struct {
+    union {
+        struct {
+            ggml_half d; // delta
+            ggml_half m; // min
+        } GGML_COMMON_AGGR;
+        ggml_half2 dm;
+    };
+    uint8_t qh[4];         // 5-th bit of quants
+    uint8_t qs[QK5_1 / 2]; // nibbles / quants
+} block_q5_1;
+static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_half) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
+
+#define QK8_0 32
+typedef struct {
+    ggml_half d;       // delta
+    int8_t  qs[QK8_0]; // quants
+} block_q8_0;
+static_assert(sizeof(block_q8_0) == sizeof(ggml_half) + QK8_0, "wrong q8_0 block size/padding");
+
+#define QK8_1 32
+typedef struct {
+    union {
+        struct {
+            ggml_half d; // delta
+            ggml_half s; // d * sum(qs[i])
+        } GGML_COMMON_AGGR;
+        ggml_half2 ds;
+    };
+    int8_t qs[QK8_1]; // quants
+} block_q8_1;
+static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_half) + QK8_1, "wrong q8_1 block size/padding");
+
+//
+// Super-block quantization structures
+//
+
+// 2-bit quantization
+// weight is represented as x = a * q + b
+// 16 blocks of 16 elements each
+// Effectively 2.625 bits per weight
+typedef struct {
+    uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
+    uint8_t qs[QK_K/4];      // quants
+    union {
+        struct {
+            ggml_half d;    // super-block scale for quantized scales
+            ggml_half dmin; // super-block scale for quantized mins
+        } GGML_COMMON_AGGR;
+        ggml_half2 dm;
+    };
+} block_q2_K;
+static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_half) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
+
+// 3-bit quantization
+// weight is represented as x = a * q
+// 16 blocks of 16 elements each
+// Effectively 3.4375 bits per weight
+#ifdef GGML_QKK_64
+typedef struct {
+    uint8_t hmask[QK_K/8]; // quants - high bit
+    uint8_t qs[QK_K/4];    // quants - low 2 bits
+    uint8_t scales[2];
+    ggml_half d;           // super-block scale
+} block_q3_K;
+static_assert(sizeof(block_q3_K) == sizeof(ggml_half) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding");
+#else
+typedef struct {
+    uint8_t hmask[QK_K/8]; // quants - high bit
+    uint8_t qs[QK_K/4];    // quants - low 2 bits
+    uint8_t scales[12];    // scales, quantized with 6 bits
+    ggml_half d;           // super-block scale
+} block_q3_K;
+static_assert(sizeof(block_q3_K) == sizeof(ggml_half) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding");
+#endif
+
+// 4-bit quantization
+// 8 blocks of 32 elements each
+// weight is represented as x = a * q + b
+// Effectively 4.5 bits per weight
+#ifdef GGML_QKK_64
+typedef struct {
+    ggml_half d[2];     // super-block scales/mins
+    uint8_t scales[2];  // 4-bit block scales/mins
+    uint8_t qs[QK_K/2]; // 4--bit quants
+} block_q4_K;
+static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_half) + QK_K/2 + 2, "wrong q4_K block size/padding");
+#else
+typedef struct {
+    union {
+        struct {
+            ggml_half d;    // super-block scale for quantized scales
+            ggml_half dmin; // super-block scale for quantized mins
+        } GGML_COMMON_AGGR;
+        ggml_half2 dm;
+    };
+    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
+    uint8_t qs[QK_K/2];           // 4--bit quants
+} block_q4_K;
+static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_half) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding");
+#endif
+
+// 5-bit quantization
+// 8 blocks of 32 elements each
+// weight is represented as x = a * q + b
+// Effectively 5.5 bits per weight
+#ifdef GGML_QKK_64
+typedef struct {
+    ggml_half d;             // super-block scale
+    int8_t  scales[QK_K/16]; // 8-bit block scales
+    uint8_t qh[QK_K/8];      // quants, high bit
+    uint8_t qs[QK_K/2];      // quants, low 4 bits
+} block_q5_K;
+static_assert(sizeof(block_q5_K) == sizeof(ggml_half) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
+#else
+typedef struct {
+    union {
+        struct {
+            ggml_half d;    // super-block scale for quantized scales
+            ggml_half dmin; // super-block scale for quantized mins
+        } GGML_COMMON_AGGR;
+        ggml_half2 dm;
+    };
+    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
+    uint8_t qh[QK_K/8];           // quants, high bit
+    uint8_t qs[QK_K/2];           // quants, low 4 bits
+} block_q5_K;
+static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_half) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
+#endif
+
+// 6-bit quantization
+// weight is represented as x = a * q
+// 16 blocks of 16 elements each
+// Effectively 6.5625 bits per weight
+typedef struct {
+    uint8_t ql[QK_K/2];      // quants, lower 4 bits
+    uint8_t qh[QK_K/4];      // quants, upper 2 bits
+    int8_t  scales[QK_K/16]; // scales, quantized with 8 bits
+    ggml_half d;             // super-block scale
+} block_q6_K;
+static_assert(sizeof(block_q6_K) == sizeof(ggml_half) + QK_K / 16 + 3*QK_K/4, "wrong q6_K block size/padding");
+
+// This is only used for intermediate quantization and dot products
+typedef struct {
+    float   d;              // delta
+    int8_t  qs[QK_K];       // quants
+    int16_t bsums[QK_K/16]; // sum of quants in groups of 16
+} block_q8_K;
+static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding");
+
+// (Almost) "true" 2-bit quantization.
+// Due to the need to use blocks as per ggml design, it ends up using
+// 2.0625 bpw because of the 16-bit scale for each block of 256.
+typedef struct {
+    ggml_half d;
+    uint16_t qs[QK_K/8];
+} block_iq2_xxs;
+static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_half) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding");
+
+// 2.3125 bpw quants
+typedef struct {
+    ggml_half d;
+    uint16_t qs[QK_K/8];
+    uint8_t  scales[QK_K/32];
+} block_iq2_xs;
+static_assert(sizeof(block_iq2_xs) == sizeof(ggml_half) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding");
+
+// 2.5625 bpw quants
+typedef struct {
+    ggml_half d;
+    uint8_t qs[QK_K/4];
+    uint8_t qh[QK_K/32];
+    uint8_t scales[QK_K/32];
+} block_iq2_s;
+static_assert(sizeof(block_iq2_s) == sizeof(ggml_half) + QK_K/4 + QK_K/16, "wrong iq2_s block size/padding");
+
+// (Almost) "true" 3-bit quantization.
+// Due to the need to use blocks as per ggml design, it ends up using
+// 3.0625 bpw because of the 16-bit scale for each block of 256.
+typedef struct {
+    ggml_half d;
+    uint8_t qs[3*QK_K/8];
+} block_iq3_xxs;
+static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_half) + 3*(QK_K/8), "wrong iq3_xxs block size/padding");
+
+// 3.4375 bpw
+#if QK_K == 64
+#define IQ3S_N_SCALE 2
+#else
+#define IQ3S_N_SCALE QK_K/64
+#endif
+typedef struct {
+    ggml_half d;
+    uint8_t qs[QK_K/4];
+    uint8_t qh[QK_K/32];
+    uint8_t signs[QK_K/8];
+    uint8_t scales[IQ3S_N_SCALE];
+} block_iq3_s;
+static_assert(sizeof(block_iq3_s) == sizeof(ggml_half) + 13*(QK_K/32) + IQ3S_N_SCALE, "wrong iq3_s block size/padding");
+
+typedef struct {
+    ggml_half d;
+    uint8_t  qs[QK_K/8];
+    uint16_t qh[QK_K/32];
+} block_iq1_s;
+static_assert(sizeof(block_iq1_s) == sizeof(ggml_half) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding");
+
+// 1.75 bpw
+typedef struct {
+    uint8_t  qs[QK_K/8];      // grid index, low 8 bits
+    uint8_t  qh[QK_K/16];     // grid index, high 3 bits + grid shift bit (for two groups of 8)
+#if QK_K == 64
+    ggml_half d;
+#endif
+    uint8_t  scales[QK_K/32]; // 3-bit block scales (4-bit if QK_K == 64)
+} block_iq1_m;
+#if QK_K == 64
+static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32 + sizeof(ggml_half), "wrong iq1_m block size/padding");
+#else
+static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32, "wrong iq1_m block size/padding");
+#endif
+
+// Used by IQ1_M quants
+typedef union {
+    ggml_half f16;
+    uint16_t  u16;
+} iq1m_scale_t;
+
+// Non-linear quants
+#define QK4_NL 32
+typedef struct {
+    ggml_half d;
+    uint8_t qs[QK4_NL/2];
+} block_iq4_nl;
+static_assert(sizeof(block_iq4_nl) == sizeof(ggml_half) + QK4_NL/2, "wrong iq4_nl block size/padding");
+
+#if QK_K == 64
+#define block_iq4_xs block_iq4_nl
+#else
+typedef struct {
+    ggml_half d;
+    uint16_t scales_h;
+    uint8_t  scales_l[QK_K/64];
+    uint8_t  qs[QK_K/2];
+} block_iq4_xs;
+static_assert(sizeof(block_iq4_xs) == sizeof(ggml_half) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding");
+#endif
+
+#endif // GGML_COMMON_DECL
+#endif // GGML_COMMON_DECL
+
+////////////////////////////////////////////////////////////////////////////////
+
+#ifndef GGML_COMMON_IMPL
+
+#if defined(GGML_COMMON_IMPL_C)
+#include <stdint.h>
+
+#define GGML_TABLE_BEGIN(type, name, size) static const type name[size] = {
+#define GGML_TABLE_END() };
+
+#define GGML_COMMON_IMPL
+#elif defined(GGML_COMMON_IMPL_METAL)
+#include <metal_stdlib>
+
+#define GGML_TABLE_BEGIN(type, name, size) static const constant type name[size] = {
+#define GGML_TABLE_END() };
+
+#define GGML_COMMON_IMPL
+#elif defined(GGML_COMMON_IMPL_CUDA) || defined(GGML_COMMON_IMPL_HIP)
+#include <cstdint>
+
+#define GGML_TABLE_BEGIN(type, name, size) static const __device__ type name[size] = {
+#define GGML_TABLE_END() };
+
+#define GGML_COMMON_IMPL
+#elif defined(GGML_COMMON_IMPL_SYCL)
+
+#include <cstdint>
+
+#define GGML_TABLE_BEGIN(type, name, size) static const type name[size] = {
+#define GGML_TABLE_END() };
+
+#define GGML_COMMON_IMPL
+#endif
+
+#if defined(GGML_COMMON_IMPL)
+
+GGML_TABLE_BEGIN(uint8_t, kmask_iq2xs, 8)
+    1, 2, 4, 8, 16, 32, 64, 128
+GGML_TABLE_END()
+
+GGML_TABLE_BEGIN(uint8_t, ksigns_iq2xs, 128)
+      0, 129, 130,   3, 132,   5,   6, 135, 136,   9,  10, 139,  12, 141, 142,  15,
+    144,  17,  18, 147,  20, 149, 150,  23,  24, 153, 154,  27, 156,  29,  30, 159,
+    160,  33,  34, 163,  36, 165, 166,  39,  40, 169, 170,  43, 172,  45,  46, 175,
+     48, 177, 178,  51, 180,  53,  54, 183, 184,  57,  58, 187,  60, 189, 190,  63,
+    192,  65,  66, 195,  68, 197, 198,  71,  72, 201, 202,  75, 204,  77,  78, 207,
+     80, 209, 210,  83, 212,  85,  86, 215, 216,  89,  90, 219,  92, 221, 222,  95,
+     96, 225, 226,  99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111,
+    240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255,
+GGML_TABLE_END()
+
+//#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+GGML_TABLE_BEGIN(uint64_t, ksigns64, 128)
+    0x0000000000000000, 0xff000000000000ff, 0xff0000000000ff00, 0x000000000000ffff,
+    0xff00000000ff0000, 0x0000000000ff00ff, 0x0000000000ffff00, 0xff00000000ffffff,
+    0xff000000ff000000, 0x00000000ff0000ff, 0x00000000ff00ff00, 0xff000000ff00ffff,
+    0x00000000ffff0000, 0xff000000ffff00ff, 0xff000000ffffff00, 0x00000000ffffffff,
+    0xff0000ff00000000, 0x000000ff000000ff, 0x000000ff0000ff00, 0xff0000ff0000ffff,
+    0x000000ff00ff0000, 0xff0000ff00ff00ff, 0xff0000ff00ffff00, 0x000000ff00ffffff,
+    0x000000ffff000000, 0xff0000ffff0000ff, 0xff0000ffff00ff00, 0x000000ffff00ffff,
+    0xff0000ffffff0000, 0x000000ffffff00ff, 0x000000ffffffff00, 0xff0000ffffffffff,
+    0xff00ff0000000000, 0x0000ff00000000ff, 0x0000ff000000ff00, 0xff00ff000000ffff,
+    0x0000ff0000ff0000, 0xff00ff0000ff00ff, 0xff00ff0000ffff00, 0x0000ff0000ffffff,
+    0x0000ff00ff000000, 0xff00ff00ff0000ff, 0xff00ff00ff00ff00, 0x0000ff00ff00ffff,
+    0xff00ff00ffff0000, 0x0000ff00ffff00ff, 0x0000ff00ffffff00, 0xff00ff00ffffffff,
+    0x0000ffff00000000, 0xff00ffff000000ff, 0xff00ffff0000ff00, 0x0000ffff0000ffff,
+    0xff00ffff00ff0000, 0x0000ffff00ff00ff, 0x0000ffff00ffff00, 0xff00ffff00ffffff,
+    0xff00ffffff000000, 0x0000ffffff0000ff, 0x0000ffffff00ff00, 0xff00ffffff00ffff,
+    0x0000ffffffff0000, 0xff00ffffffff00ff, 0xff00ffffffffff00, 0x0000ffffffffffff,
+    0xffff000000000000, 0x00ff0000000000ff, 0x00ff00000000ff00, 0xffff00000000ffff,
+    0x00ff000000ff0000, 0xffff000000ff00ff, 0xffff000000ffff00, 0x00ff000000ffffff,
+    0x00ff0000ff000000, 0xffff0000ff0000ff, 0xffff0000ff00ff00, 0x00ff0000ff00ffff,
+    0xffff0000ffff0000, 0x00ff0000ffff00ff, 0x00ff0000ffffff00, 0xffff0000ffffffff,
+    0x00ff00ff00000000, 0xffff00ff000000ff, 0xffff00ff0000ff00, 0x00ff00ff0000ffff,
+    0xffff00ff00ff0000, 0x00ff00ff00ff00ff, 0x00ff00ff00ffff00, 0xffff00ff00ffffff,
+    0xffff00ffff000000, 0x00ff00ffff0000ff, 0x00ff00ffff00ff00, 0xffff00ffff00ffff,
+    0x00ff00ffffff0000, 0xffff00ffffff00ff, 0xffff00ffffffff00, 0x00ff00ffffffffff,
+    0x00ffff0000000000, 0xffffff00000000ff, 0xffffff000000ff00, 0x00ffff000000ffff,
+    0xffffff0000ff0000, 0x00ffff0000ff00ff, 0x00ffff0000ffff00, 0xffffff0000ffffff,
+    0xffffff00ff000000, 0x00ffff00ff0000ff, 0x00ffff00ff00ff00, 0xffffff00ff00ffff,
+    0x00ffff00ffff0000, 0xffffff00ffff00ff, 0xffffff00ffffff00, 0x00ffff00ffffffff,
+    0xffffffff00000000, 0x00ffffff000000ff, 0x00ffffff0000ff00, 0xffffffff0000ffff,
+    0x00ffffff00ff0000, 0xffffffff00ff00ff, 0xffffffff00ffff00, 0x00ffffff00ffffff,
+    0x00ffffffff000000, 0xffffffffff0000ff, 0xffffffffff00ff00, 0x00ffffffff00ffff,
+    0xffffffffffff0000, 0x00ffffffffff00ff, 0x00ffffffffffff00, 0xffffffffffffffff,
+GGML_TABLE_END()
+//#endif
+
+
+GGML_TABLE_BEGIN(uint64_t, iq2xxs_grid, 256)
+    0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
+    0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808,
+    0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819,
+    0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819,
+    0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b,
+    0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808,
+    0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08,
+    0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b,
+    0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819,
+    0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08,
+    0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808,
+    0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08,
+    0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808,
+    0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808,
+    0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919,
+    0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819,
+    0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08,
+    0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908,
+    0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819,
+    0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808,
+    0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808,
+    0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908,
+    0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808,
+    0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08,
+    0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819,
+    0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819,
+    0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819,
+    0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908,
+    0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19,
+    0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819,
+    0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b,
+    0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808,
+    0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908,
+    0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08,
+    0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08,
+    0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908,
+    0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819,
+    0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808,
+    0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808,
+    0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19,
+    0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819,
+    0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919,
+    0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b,
+    0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08,
+    0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808,
+    0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908,
+    0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b,
+    0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819,
+    0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08,
+    0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08,
+    0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808,
+    0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b,
+    0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b,
+    0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908,
+    0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819,
+    0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808,
+    0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908,
+    0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b,
+    0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808,
+    0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b,
+    0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b,
+    0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808,
+    0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19,
+    0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908,
+GGML_TABLE_END()
+
+GGML_TABLE_BEGIN(uint64_t, iq2xs_grid, 512)
+    0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
+    0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b,
+    0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919,
+    0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b,
+    0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919,
+    0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x080808082b080808,
+    0x080808082b08082b, 0x080808082b081919, 0x080808082b082b08, 0x080808082b190819,
+    0x080808082b191908, 0x080808082b192b19, 0x080808082b2b0808, 0x0808081908080819,
+    0x0808081908081908, 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808,
+    0x080808190819082b, 0x0808081908191919, 0x0808081908192b08, 0x0808081908192b2b,
+    0x08080819082b0819, 0x08080819082b1908, 0x0808081919080808, 0x080808191908082b,
+    0x0808081919081919, 0x0808081919082b08, 0x0808081919190819, 0x0808081919191908,
+    0x08080819192b0808, 0x08080819192b2b08, 0x080808192b080819, 0x080808192b081908,
+    0x080808192b190808, 0x0808082b08080808, 0x0808082b0808082b, 0x0808082b08081919,
+    0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, 0x0808082b082b0808,
+    0x0808082b19080819, 0x0808082b19081908, 0x0808082b19190808, 0x0808082b19191919,
+    0x0808082b2b080808, 0x0808082b2b082b2b, 0x0808190808080819, 0x0808190808081908,
+    0x080819080808192b, 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b,
+    0x0808190808191919, 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908,
+    0x0808190819080808, 0x080819081908082b, 0x0808190819081919, 0x0808190819082b08,
+    0x0808190819190819, 0x0808190819191908, 0x080819081919192b, 0x08081908192b0808,
+    0x080819082b080819, 0x080819082b081908, 0x080819082b190808, 0x0808191908080808,
+    0x080819190808082b, 0x0808191908081919, 0x0808191908082b08, 0x0808191908190819,
+    0x0808191908191908, 0x08081919082b0808, 0x0808191919080819, 0x0808191919081908,
+    0x0808191919190808, 0x08081919192b0819, 0x080819192b080808, 0x0808192b08080819,
+    0x0808192b08081908, 0x0808192b08190808, 0x0808192b082b192b, 0x0808192b19080808,
+    0x0808192b1908082b, 0x0808192b2b081908, 0x08082b0808080808, 0x08082b080808082b,
+    0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808082b2b, 0x08082b0808190819,
+    0x08082b0808191908, 0x08082b08082b0808, 0x08082b08082b1919, 0x08082b0819080819,
+    0x08082b0819081908, 0x08082b0819190808, 0x08082b0819192b08, 0x08082b082b080808,
+    0x08082b082b2b0808, 0x08082b082b2b2b2b, 0x08082b1908080819, 0x08082b1908081908,
+    0x08082b1908190808, 0x08082b1919080808, 0x08082b192b080819, 0x08082b192b082b19,
+    0x08082b2b08080808, 0x08082b2b082b0808, 0x08082b2b082b2b08, 0x08082b2b2b19192b,
+    0x08082b2b2b2b0808, 0x0819080808080819, 0x0819080808081908, 0x081908080808192b,
+    0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, 0x0819080808191919,
+    0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, 0x0819080819080808,
+    0x081908081908082b, 0x0819080819081919, 0x0819080819082b08, 0x0819080819190819,
+    0x0819080819191908, 0x08190808192b0808, 0x08190808192b2b2b, 0x081908082b080819,
+    0x081908082b081908, 0x081908082b190808, 0x0819081908080808, 0x081908190808082b,
+    0x0819081908081919, 0x0819081908082b08, 0x0819081908190819, 0x0819081908191908,
+    0x08190819082b0808, 0x0819081919080819, 0x0819081919081908, 0x0819081919190808,
+    0x081908192b080808, 0x081908192b191908, 0x081908192b19192b, 0x0819082b08080819,
+    0x0819082b08081908, 0x0819082b0808192b, 0x0819082b08190808, 0x0819082b19080808,
+    0x0819082b192b0808, 0x0819190808080808, 0x081919080808082b, 0x0819190808081919,
+    0x0819190808082b08, 0x0819190808190819, 0x0819190808191908, 0x08191908082b0808,
+    0x0819190819080819, 0x0819190819081908, 0x0819190819082b19, 0x0819190819190808,
+    0x08191908192b1908, 0x081919082b080808, 0x0819191908080819, 0x0819191908081908,
+    0x0819191908190808, 0x0819191919080808, 0x0819192b08080808, 0x0819192b08191908,
+    0x0819192b19082b19, 0x08192b0808080819, 0x08192b0808081908, 0x08192b0808190808,
+    0x08192b080819082b, 0x08192b0819080808, 0x08192b0819191908, 0x08192b082b08192b,
+    0x08192b1908080808, 0x08192b1908081919, 0x08192b19192b192b, 0x08192b2b19190819,
+    0x08192b2b2b2b2b19, 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919,
+    0x082b080808082b08, 0x082b080808082b2b, 0x082b080808190819, 0x082b080808191908,
+    0x082b0808082b0808, 0x082b080819080819, 0x082b080819081908, 0x082b080819190808,
+    0x082b08082b080808, 0x082b08082b2b0808, 0x082b081908080819, 0x082b081908081908,
+    0x082b081908190808, 0x082b081919080808, 0x082b081919082b08, 0x082b0819192b1919,
+    0x082b082b08080808, 0x082b082b082b082b, 0x082b082b2b080808, 0x082b082b2b2b2b08,
+    0x082b190808080819, 0x082b190808081908, 0x082b190808190808, 0x082b1908082b2b19,
+    0x082b190819080808, 0x082b191908080808, 0x082b191919080819, 0x082b19191919082b,
+    0x082b19192b192b19, 0x082b192b08080819, 0x082b192b08192b2b, 0x082b192b2b2b192b,
+    0x082b2b0808080808, 0x082b2b0808082b08, 0x082b2b0808082b2b, 0x082b2b08082b0808,
+    0x082b2b0819191919, 0x082b2b082b082b08, 0x082b2b082b2b082b, 0x082b2b19192b2b08,
+    0x082b2b192b190808, 0x082b2b2b08082b08, 0x082b2b2b082b0808, 0x082b2b2b2b08082b,
+    0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, 0x1908080808081908,
+    0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, 0x190808080819082b,
+    0x1908080808191919, 0x1908080808192b08, 0x19080808082b0819, 0x19080808082b1908,
+    0x1908080819080808, 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08,
+    0x1908080819082b2b, 0x1908080819190819, 0x1908080819191908, 0x19080808192b0808,
+    0x19080808192b1919, 0x190808082b080819, 0x190808082b081908, 0x190808082b190808,
+    0x1908081908080808, 0x190808190808082b, 0x1908081908081919, 0x1908081908082b08,
+    0x1908081908190819, 0x1908081908191908, 0x19080819082b0808, 0x1908081919080819,
+    0x1908081919081908, 0x1908081919190808, 0x190808192b080808, 0x190808192b081919,
+    0x190808192b2b082b, 0x1908082b08080819, 0x1908082b08081908, 0x1908082b08190808,
+    0x1908082b0819082b, 0x1908082b082b2b19, 0x1908082b19080808, 0x1908190808080808,
+    0x190819080808082b, 0x1908190808081919, 0x1908190808082b08, 0x1908190808190819,
+    0x1908190808191908, 0x1908190808192b19, 0x19081908082b0808, 0x1908190819080819,
+    0x1908190819081908, 0x1908190819190808, 0x190819082b080808, 0x190819082b191908,
+    0x1908191908080819, 0x1908191908081908, 0x1908191908190808, 0x19081919082b1908,
+    0x1908191919080808, 0x190819192b192b2b, 0x1908192b08080808, 0x1908192b08082b2b,
+    0x1908192b19081908, 0x1908192b19190808, 0x19082b0808080819, 0x19082b0808081908,
+    0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919, 0x19082b0819191908,
+    0x19082b08192b082b, 0x19082b1908080808, 0x19082b1908190819, 0x19082b1919081908,
+    0x19082b1919190808, 0x19082b19192b2b19, 0x19082b2b08081908, 0x1919080808080808,
+    0x191908080808082b, 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819,
+    0x1919080808191908, 0x19190808082b0808, 0x19190808082b2b08, 0x1919080819080819,
+    0x1919080819081908, 0x1919080819190808, 0x191908082b080808, 0x1919081908080819,
+    0x1919081908081908, 0x1919081908190808, 0x1919081908191919, 0x1919081919080808,
+    0x191908191908082b, 0x1919082b08080808, 0x1919082b19081908, 0x1919082b2b2b2b2b,
+    0x1919190808080819, 0x1919190808081908, 0x1919190808190808, 0x19191908082b0819,
+    0x1919190819080808, 0x19191908192b0808, 0x191919082b080819, 0x191919082b2b0819,
+    0x1919191908080808, 0x1919191908082b08, 0x191919192b080808, 0x191919192b082b08,
+    0x1919192b082b0819, 0x1919192b192b2b08, 0x1919192b2b2b0819, 0x19192b0808080808,
+    0x19192b0808191908, 0x19192b0819080819, 0x19192b0819190808, 0x19192b082b192b19,
+    0x19192b1908192b2b, 0x19192b1919080808, 0x19192b191908082b, 0x19192b2b2b081919,
+    0x192b080808080819, 0x192b080808081908, 0x192b080808190808, 0x192b080819080808,
+    0x192b080819191908, 0x192b0808192b082b, 0x192b08082b08192b, 0x192b08082b2b2b19,
+    0x192b081908080808, 0x192b082b082b1908, 0x192b082b19082b2b, 0x192b082b2b19082b,
+    0x192b190808080808, 0x192b19080819192b, 0x192b191908190808, 0x192b191919080808,
+    0x192b191919081919, 0x192b19192b2b1908, 0x192b2b0808080819, 0x192b2b08192b2b2b,
+    0x192b2b19082b1919, 0x192b2b2b0808192b, 0x192b2b2b19191908, 0x192b2b2b192b082b,
+    0x2b08080808080808, 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08,
+    0x2b08080808190819, 0x2b08080808191908, 0x2b080808082b0808, 0x2b080808082b2b2b,
+    0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808082b080808,
+    0x2b0808082b08082b, 0x2b0808082b2b2b08, 0x2b0808082b2b2b2b, 0x2b08081908080819,
+    0x2b08081908081908, 0x2b0808190808192b, 0x2b08081908190808, 0x2b08081919080808,
+    0x2b08081919190819, 0x2b08081919192b19, 0x2b08082b08080808, 0x2b08082b082b0808,
+    0x2b08082b2b080808, 0x2b08082b2b08082b, 0x2b08082b2b2b0808, 0x2b08082b2b2b2b08,
+    0x2b08190808080819, 0x2b08190808081908, 0x2b08190808190808, 0x2b0819080819082b,
+    0x2b08190808191919, 0x2b08190819080808, 0x2b081908192b0808, 0x2b0819082b082b19,
+    0x2b08191908080808, 0x2b08191919081908, 0x2b0819192b2b1919, 0x2b08192b08192b08,
+    0x2b08192b192b2b2b, 0x2b082b0808080808, 0x2b082b0808082b08, 0x2b082b08082b1919,
+    0x2b082b0819192b2b, 0x2b082b082b080808, 0x2b082b082b08082b, 0x2b082b082b2b2b08,
+    0x2b082b190808192b, 0x2b082b2b082b082b, 0x2b082b2b2b080808, 0x2b082b2b2b082b08,
+    0x2b082b2b2b19192b, 0x2b082b2b2b2b2b08, 0x2b19080808080819, 0x2b19080808081908,
+    0x2b19080808190808, 0x2b19080819080808, 0x2b1908081919192b, 0x2b1908082b081908,
+    0x2b19081908080808, 0x2b190819082b082b, 0x2b190819192b1908, 0x2b19082b1919192b,
+    0x2b19082b2b082b19, 0x2b19190808080808, 0x2b19190808081919, 0x2b19190819081908,
+    0x2b19190819190808, 0x2b19190819192b08, 0x2b191919082b2b19, 0x2b1919192b190808,
+    0x2b1919192b19082b, 0x2b19192b19080819, 0x2b192b0819190819, 0x2b192b082b2b192b,
+    0x2b192b1919082b19, 0x2b192b2b08191919, 0x2b192b2b192b0808, 0x2b2b080808080808,
+    0x2b2b08080808082b, 0x2b2b080808082b08, 0x2b2b080808082b2b, 0x2b2b0808082b0808,
+    0x2b2b0808082b2b2b, 0x2b2b08082b2b0808, 0x2b2b081919190819, 0x2b2b081919192b19,
+    0x2b2b08192b2b192b, 0x2b2b082b08080808, 0x2b2b082b0808082b, 0x2b2b082b08082b08,
+    0x2b2b082b082b2b2b, 0x2b2b082b2b080808, 0x2b2b082b2b2b0808, 0x2b2b190819080808,
+    0x2b2b19082b191919, 0x2b2b192b192b1919, 0x2b2b192b2b192b08, 0x2b2b2b0808082b2b,
+    0x2b2b2b08082b0808, 0x2b2b2b08082b082b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b0808,
+    0x2b2b2b082b2b2b08, 0x2b2b2b1908081908, 0x2b2b2b192b081908, 0x2b2b2b192b08192b,
+    0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b,
+GGML_TABLE_END()
+
+GGML_TABLE_BEGIN(uint64_t, iq2s_grid, 1024)
+    0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
+    0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b,
+    0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919,
+    0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b,
+    0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919,
+    0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x08080808192b192b,
+    0x08080808192b2b19, 0x080808082b080808, 0x080808082b08082b, 0x080808082b081919,
+    0x080808082b082b08, 0x080808082b190819, 0x080808082b191908, 0x080808082b2b0808,
+    0x080808082b2b1919, 0x080808082b2b2b2b, 0x0808081908080819, 0x0808081908081908,
+    0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, 0x080808190819082b,
+    0x0808081908191919, 0x0808081908192b08, 0x08080819082b0819, 0x08080819082b1908,
+    0x0808081919080808, 0x080808191908082b, 0x0808081919081919, 0x0808081919082b08,
+    0x0808081919190819, 0x0808081919191908, 0x080808191919192b, 0x0808081919192b19,
+    0x08080819192b0808, 0x08080819192b1919, 0x08080819192b2b08, 0x080808192b080819,
+    0x080808192b081908, 0x080808192b190808, 0x080808192b19082b, 0x080808192b191919,
+    0x080808192b2b0819, 0x080808192b2b1908, 0x0808082b08080808, 0x0808082b0808082b,
+    0x0808082b08081919, 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908,
+    0x0808082b082b0808, 0x0808082b082b2b2b, 0x0808082b19080819, 0x0808082b19081908,
+    0x0808082b1908192b, 0x0808082b19082b19, 0x0808082b19190808, 0x0808082b19191919,
+    0x0808082b2b080808, 0x0808082b2b081919, 0x0808082b2b082b2b, 0x0808082b2b191908,
+    0x0808082b2b2b082b, 0x0808190808080819, 0x0808190808081908, 0x080819080808192b,
+    0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, 0x0808190808191919,
+    0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, 0x08081908082b192b,
+    0x08081908082b2b19, 0x0808190819080808, 0x080819081908082b, 0x0808190819081919,
+    0x0808190819082b08, 0x0808190819082b2b, 0x0808190819190819, 0x0808190819191908,
+    0x080819081919192b, 0x0808190819192b19, 0x08081908192b0808, 0x08081908192b082b,
+    0x08081908192b1919, 0x080819082b080819, 0x080819082b081908, 0x080819082b08192b,
+    0x080819082b082b19, 0x080819082b190808, 0x080819082b191919, 0x080819082b192b08,
+    0x080819082b2b0819, 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b,
+    0x0808191908081919, 0x0808191908082b08, 0x0808191908082b2b, 0x0808191908190819,
+    0x0808191908191908, 0x080819190819192b, 0x0808191908192b19, 0x08081919082b0808,
+    0x08081919082b1919, 0x08081919082b2b08, 0x0808191919080819, 0x0808191919081908,
+    0x080819191908192b, 0x0808191919082b19, 0x0808191919190808, 0x080819191919082b,
+    0x0808191919191919, 0x0808191919192b08, 0x08081919192b0819, 0x08081919192b1908,
+    0x080819192b080808, 0x080819192b08082b, 0x080819192b081919, 0x080819192b082b08,
+    0x080819192b190819, 0x080819192b191908, 0x080819192b2b0808, 0x0808192b08080819,
+    0x0808192b08081908, 0x0808192b0808192b, 0x0808192b08082b19, 0x0808192b08190808,
+    0x0808192b08191919, 0x0808192b19080808, 0x0808192b19081919, 0x0808192b19082b08,
+    0x0808192b19190819, 0x0808192b19191908, 0x0808192b192b0808, 0x0808192b2b080819,
+    0x0808192b2b081908, 0x0808192b2b190808, 0x08082b0808080808, 0x08082b080808082b,
+    0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808190819, 0x08082b0808191908,
+    0x08082b080819192b, 0x08082b0808192b19, 0x08082b08082b0808, 0x08082b08082b1919,
+    0x08082b08082b2b2b, 0x08082b0819080819, 0x08082b0819081908, 0x08082b081908192b,
+    0x08082b0819082b19, 0x08082b0819190808, 0x08082b081919082b, 0x08082b0819191919,
+    0x08082b0819192b08, 0x08082b08192b0819, 0x08082b08192b1908, 0x08082b082b080808,
+    0x08082b082b081919, 0x08082b082b191908, 0x08082b082b2b2b2b, 0x08082b1908080819,
+    0x08082b1908081908, 0x08082b1908190808, 0x08082b190819082b, 0x08082b1908191919,
+    0x08082b1908192b08, 0x08082b19082b0819, 0x08082b1919080808, 0x08082b1919081919,
+    0x08082b1919082b08, 0x08082b1919190819, 0x08082b1919191908, 0x08082b19192b0808,
+    0x08082b192b080819, 0x08082b192b190808, 0x08082b2b08080808, 0x08082b2b08190819,
+    0x08082b2b08191908, 0x08082b2b082b082b, 0x08082b2b082b2b08, 0x08082b2b082b2b2b,
+    0x08082b2b19190808, 0x08082b2b2b192b19, 0x0819080808080819, 0x0819080808081908,
+    0x081908080808192b, 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b,
+    0x0819080808191919, 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908,
+    0x08190808082b192b, 0x0819080819080808, 0x081908081908082b, 0x0819080819081919,
+    0x0819080819082b08, 0x0819080819190819, 0x0819080819191908, 0x081908081919192b,
+    0x0819080819192b19, 0x08190808192b0808, 0x08190808192b082b, 0x08190808192b1919,
+    0x08190808192b2b08, 0x081908082b080819, 0x081908082b081908, 0x081908082b08192b,
+    0x081908082b190808, 0x081908082b191919, 0x081908082b192b08, 0x081908082b2b0819,
+    0x081908082b2b1908, 0x0819081908080808, 0x081908190808082b, 0x0819081908081919,
+    0x0819081908082b08, 0x0819081908082b2b, 0x0819081908190819, 0x0819081908191908,
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+    0x2b19080808081908, 0x2b19080808082b19, 0x2b19080808190808, 0x2b1908080819082b,
+    0x2b19080808191919, 0x2b19080808192b08, 0x2b190808082b1908, 0x2b19080819080808,
+    0x2b1908081908082b, 0x2b19080819081919, 0x2b19080819082b08, 0x2b19080819190819,
+    0x2b19080819191908, 0x2b190808192b0808, 0x2b1908082b080819, 0x2b1908082b081908,
+    0x2b1908082b190808, 0x2b19081908080808, 0x2b19081908081919, 0x2b19081908190819,
+    0x2b19081908191908, 0x2b19081919080819, 0x2b19081919081908, 0x2b19081919190808,
+    0x2b19081919192b2b, 0x2b19082b08080819, 0x2b19082b08081908, 0x2b19082b08190808,
+    0x2b19082b19080808, 0x2b19082b2b2b192b, 0x2b19190808080808, 0x2b1919080808082b,
+    0x2b19190808081919, 0x2b19190808082b08, 0x2b19190808190819, 0x2b19190808191908,
+    0x2b191908082b0808, 0x2b19190819080819, 0x2b19190819081908, 0x2b19190819190808,
+    0x2b1919082b080808, 0x2b1919082b19192b, 0x2b19191908080819, 0x2b19191908081908,
+    0x2b19191908190808, 0x2b19191919080808, 0x2b1919192b192b08, 0x2b1919192b2b0819,
+    0x2b19192b08080808, 0x2b19192b1908192b, 0x2b19192b192b1908, 0x2b192b0808080819,
+    0x2b192b0808081908, 0x2b192b0808190808, 0x2b192b08082b192b, 0x2b192b0819080808,
+    0x2b192b082b2b2b19, 0x2b192b1908080808, 0x2b192b1919082b19, 0x2b192b191919082b,
+    0x2b192b2b2b190808, 0x2b2b080808080808, 0x2b2b080808081919, 0x2b2b080808082b2b,
+    0x2b2b080808191908, 0x2b2b0808082b082b, 0x2b2b0808082b2b2b, 0x2b2b080819080819,
+    0x2b2b080819081908, 0x2b2b080819190808, 0x2b2b08082b2b082b, 0x2b2b08082b2b2b2b,
+    0x2b2b081919080808, 0x2b2b0819192b1919, 0x2b2b082b0808082b, 0x2b2b082b08082b2b,
+    0x2b2b082b082b082b, 0x2b2b082b082b2b08, 0x2b2b082b082b2b2b, 0x2b2b082b2b08082b,
+    0x2b2b082b2b082b08, 0x2b2b082b2b082b2b, 0x2b2b082b2b2b2b08, 0x2b2b190808080819,
+    0x2b2b190808081908, 0x2b2b190808190808, 0x2b2b190819080808, 0x2b2b19082b082b19,
+    0x2b2b19082b2b1908, 0x2b2b191908080808, 0x2b2b191908192b19, 0x2b2b192b19190819,
+    0x2b2b2b0808082b2b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b082b, 0x2b2b2b1919191908,
+    0x2b2b2b192b08192b, 0x2b2b2b2b08082b08, 0x2b2b2b2b08082b2b, 0x2b2b2b2b082b0808,
+    0x2b2b2b2b082b082b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b2b082b08, 0x2b2b2b2b2b2b2b2b,
+GGML_TABLE_END()
+
+GGML_TABLE_BEGIN(uint32_t, iq3xxs_grid, 256)
+    0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414,
+    0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14,
+    0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404,
+    0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e,
+    0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c,
+    0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c,
+    0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34,
+    0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c,
+    0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c,
+    0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04,
+    0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c,
+    0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414,
+    0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434,
+    0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c,
+    0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e,
+    0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24,
+    0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24,
+    0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c,
+    0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c,
+    0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14,
+    0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414,
+    0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e,
+    0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404,
+    0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c,
+    0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c,
+    0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14,
+    0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c,
+    0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c,
+    0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14,
+    0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14,
+    0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c,
+    0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04,
+GGML_TABLE_END()
+
+GGML_TABLE_BEGIN(uint32_t, iq3s_grid, 512)
+    0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305,
+    0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905,
+    0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09,
+    0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b,
+    0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b,
+    0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d,
+    0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03,
+    0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505,
+    0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03,
+    0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901,
+    0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d,
+    0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303,
+    0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501,
+    0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105,
+    0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505,
+    0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101,
+    0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707,
+    0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b,
+    0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01,
+    0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f,
+    0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305,
+    0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103,
+    0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509,
+    0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503,
+    0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b,
+    0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f,
+    0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f,
+    0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f,
+    0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109,
+    0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f,
+    0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509,
+    0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501,
+    0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303,
+    0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f,
+    0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907,
+    0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703,
+    0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03,
+    0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01,
+    0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01,
+    0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903,
+    0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505,
+    0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b,
+    0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107,
+    0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509,
+    0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303,
+    0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103,
+    0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05,
+    0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b,
+    0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f,
+    0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701,
+    0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909,
+    0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305,
+    0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d,
+    0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b,
+    0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d,
+    0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307,
+    0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09,
+    0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309,
+    0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709,
+    0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f,
+    0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303,
+    0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503,
+    0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b,
+    0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101,
+GGML_TABLE_END()
+
+#define NGRID_IQ1S 2048
+#define IQ1S_DELTA 0.125f
+#define IQ1M_DELTA 0.125f
+#if defined(GGML_COMMON_IMPL_C)
+GGML_TABLE_BEGIN(uint64_t, iq1s_grid, NGRID_IQ1S)
+    0xffffffffffffffff, 0xffffffffffffff01, 0xffffffffffff0000, 0xffffffffffff01ff,
+    0xffffffffffff0101, 0xffffffffff00ff00, 0xffffffffff000000, 0xffffffffff01ffff,
+    0xffffffffff01ff01, 0xffffffffff0101ff, 0xffffffffff010101, 0xffffffff00ff0000,
+    0xffffffff0000ff00, 0xffffffff000000ff, 0xffffffff00000001, 0xffffffff00010000,
+    0xffffffff01ffffff, 0xffffffff01ffff01, 0xffffffff01ff01ff, 0xffffffff01ff0101,
+    0xffffffff01000000, 0xffffffff0101ffff, 0xffffffff0101ff01, 0xffffffff010101ff,
+    0xffffffff01010101, 0xffffff00ffff00ff, 0xffffff00ffff0000, 0xffffff00ff00ff00,
+    0xffffff00ff0000ff, 0xffffff00ff000001, 0xffffff00ff000100, 0xffffff00ff000101,
+    0xffffff00ff010000, 0xffffff0000ffff00, 0xffffff0000ff0001, 0xffffff0000ff0100,
+    0xffffff000000ff01, 0xffffff0000000000, 0xffffff0000000101, 0xffffff000001ff00,
+    0xffffff00000100ff, 0xffffff0000010001, 0xffffff00000101ff, 0xffffff0001ff0000,
+    0xffffff000100ff00, 0xffffff00010000ff, 0xffffff0001000001, 0xffffff0001010000,
+    0xffffff01ffffffff, 0xffffff01ffffff01, 0xffffff01ffff01ff, 0xffffff01ffff0101,
+    0xffffff01ff000000, 0xffffff01ff01ffff, 0xffffff01ff01ff01, 0xffffff01ff0101ff,
+    0xffffff01ff010101, 0xffffff0100ff0000, 0xffffff010000ff00, 0xffffff0100000100,
+    0xffffff01000100ff, 0xffffff0100010100, 0xffffff0101ffffff, 0xffffff0101ffff01,
+    0xffffff0101ff01ff, 0xffffff0101ff0101, 0xffffff010100ff00, 0xffffff0101000000,
+    0xffffff0101000100, 0xffffff010101ffff, 0xffffff010101ff01, 0xffffff01010101ff,
+    0xffffff0101010101, 0xffff00ffff00ff00, 0xffff00ffff0000ff, 0xffff00ffff000001,
+    0xffff00ffff010000, 0xffff00ff00ffff00, 0xffff00ff00ff0100, 0xffff00ff00000000,
+    0xffff00ff00000101, 0xffff00ff000100ff, 0xffff00ff00010000, 0xffff00ff0100ff00,
+    0xffff00ff01000100, 0xffff00ff01010000, 0xffff0000ffffff00, 0xffff0000ffff00ff,
+    0xffff0000ffff0000, 0xffff0000ffff0001, 0xffff0000ff000000, 0xffff0000ff0001ff,
+    0xffff0000ff000101, 0xffff0000ff010100, 0xffff000000ffffff, 0xffff000000ff0000,
+    0xffff000000ff0101, 0xffff00000000ffff, 0xffff00000000ff00, 0xffff0000000000ff,
+    0xffff000000000000, 0xffff000000000001, 0xffff000000000100, 0xffff00000001ffff,
+    0xffff00000001ff01, 0xffff000000010000, 0xffff0000000101ff, 0xffff000000010101,
+    0xffff000001ffff00, 0xffff00000100ff00, 0xffff000001000000, 0xffff0000010001ff,
+    0xffff000001000101, 0xffff00000101ff00, 0xffff0000010100ff, 0xffff000001010000,
+    0xffff000001010001, 0xffff000001010100, 0xffff0001ff0000ff, 0xffff0001ff000100,
+    0xffff000100ffff00, 0xffff000100ff00ff, 0xffff00010000ffff, 0xffff00010000ff01,
+    0xffff000100000000, 0xffff0001000001ff, 0xffff00010001ffff, 0xffff00010001ff00,
+    0xffff000100010001, 0xffff000100010100, 0xffff000101ff0000, 0xffff00010100ff00,
+    0xffff0001010000ff, 0xffff000101000100, 0xffff01ffffffffff, 0xffff01ffffffff01,
+    0xffff01ffffff01ff, 0xffff01ffffff0101, 0xffff01ffff000000, 0xffff01ffff01ffff,
+    0xffff01ffff01ff01, 0xffff01ffff0101ff, 0xffff01ffff010101, 0xffff01ff00ff0000,
+    0xffff01ff0000ff00, 0xffff01ff00000001, 0xffff01ff00010000, 0xffff01ff01ffffff,
+    0xffff01ff01ffff01, 0xffff01ff01ff01ff, 0xffff01ff01ff0101, 0xffff01ff01000000,
+    0xffff01ff0101ffff, 0xffff01ff0101ff01, 0xffff01ff010101ff, 0xffff01ff01010101,
+    0xffff0100ffff0000, 0xffff0100ff00ff00, 0xffff0100ff0000ff, 0xffff0100ff000100,
+    0xffff0100ff0100ff, 0xffff0100ff010000, 0xffff010000ffff00, 0xffff01000000ffff,
+    0xffff01000000ff00, 0xffff010000000000, 0xffff01000001ff00, 0xffff0100000100ff,
+    0xffff010000010100, 0xffff01000100ff00, 0xffff0100010000ff, 0xffff010001000001,
+    0xffff010001000100, 0xffff010001010000, 0xffff0101ffffffff, 0xffff0101ffffff01,
+    0xffff0101ffff01ff, 0xffff0101ffff0101, 0xffff0101ff000000, 0xffff0101ff01ffff,
+    0xffff0101ff01ff01, 0xffff0101ff0101ff, 0xffff0101ff010101, 0xffff010100ff0000,
+    0xffff01010000ff00, 0xffff010100000100, 0xffff01010001ff00, 0xffff010100010000,
+    0xffff010101ffffff, 0xffff010101ffff01, 0xffff010101ff0000, 0xffff010101ff01ff,
+    0xffff010101ff0101, 0xffff010101000000, 0xffff01010101ffff, 0xffff01010101ff01,
+    0xffff0101010101ff, 0xffff010101010101, 0xff00ffffff00ffff, 0xff00ffffff00ff00,
+    0xff00ffffff0000ff, 0xff00ffffff000100, 0xff00ffffff0100ff, 0xff00ffffff010000,
+    0xff00ffff00ffff00, 0xff00ffff00ff00ff, 0xff00ffff0000ffff, 0xff00ffff00000000,
+    0xff00ffff000001ff, 0xff00ffff0001ff00, 0xff00ffff000100ff, 0xff00ffff00010000,
+    0xff00ffff00010100, 0xff00ffff0100ff00, 0xff00ffff010000ff, 0xff00ffff01000001,
+    0xff00ffff0101ff00, 0xff00ffff01010000, 0xff00ff00ffffff00, 0xff00ff00ffff00ff,
+    0xff00ff00ffff0001, 0xff00ff00ffff0100, 0xff00ff00ff00ffff, 0xff00ff00ff00ff01,
+    0xff00ff00ff000000, 0xff00ff00ff0001ff, 0xff00ff00ff01ff00, 0xff00ff00ff0100ff,
+    0xff00ff00ff010100, 0xff00ff0000ff0000, 0xff00ff0000ff0101, 0xff00ff000000ffff,
+    0xff00ff000000ff00, 0xff00ff000000ff01, 0xff00ff00000000ff, 0xff00ff0000000000,
+    0xff00ff0000000001, 0xff00ff0000000100, 0xff00ff000001ffff, 0xff00ff0000010000,
+    0xff00ff0001ff00ff, 0xff00ff000100ff01, 0xff00ff0001000000, 0xff00ff000101ff00,
+    0xff00ff00010100ff, 0xff00ff01ff00ff00, 0xff00ff01ff0000ff, 0xff00ff01ff000001,
+    0xff00ff01ff010000, 0xff00ff0100ffffff, 0xff00ff0100ff0001, 0xff00ff0100ff0100,
+    0xff00ff010000ff01, 0xff00ff0100000000, 0xff00ff01000001ff, 0xff00ff0100000101,
+    0xff00ff01000100ff, 0xff00ff0100010001, 0xff00ff0101ff0000, 0xff00ff010100ff00,
+    0xff00ff01010000ff, 0xff00ff0101000001, 0xff00ff0101010000, 0xff0000ffffffff00,
+    0xff0000ffffff0001, 0xff0000ffffff0100, 0xff0000ffff0000ff, 0xff0000ffff000000,
+    0xff0000ffff0001ff, 0xff0000ffff000100, 0xff0000ffff01ff00, 0xff0000ffff010001,
+    0xff0000ff00ffff00, 0xff0000ff00ff0000, 0xff0000ff00ff0001, 0xff0000ff00ff01ff,
+    0xff0000ff00ff0101, 0xff0000ff0000ff00, 0xff0000ff000000ff, 0xff0000ff00000000,
+    0xff0000ff00000001, 0xff0000ff00000100, 0xff0000ff0001ff01, 0xff0000ff00010000,
+    0xff0000ff000101ff, 0xff0000ff01ff00ff, 0xff0000ff01ff0100, 0xff0000ff0100ffff,
+    0xff0000ff010000ff, 0xff0000ff01000000, 0xff0000ff010001ff, 0xff0000ff01000100,
+    0xff0000ff01000101, 0xff0000ff0101ff00, 0xff0000ff010100ff, 0xff0000ff01010000,
+    0xff0000ff01010100, 0xff000000ffffff01, 0xff000000ffff0000, 0xff000000ffff0101,
+    0xff000000ff00ff00, 0xff000000ff0000ff, 0xff000000ff000000, 0xff000000ff000001,
+    0xff000000ff000100, 0xff000000ff01ffff, 0xff000000ff01ff01, 0xff000000ff010000,
+    0xff000000ff0101ff, 0xff000000ff010101, 0xff00000000ffff00, 0xff00000000ff00ff,
+    0xff00000000ff0000, 0xff00000000ff0001, 0xff0000000000ff00, 0xff0000000000ff01,
+    0xff000000000000ff, 0xff00000000000000, 0xff00000000000001, 0xff00000000000100,
+    0xff00000000000101, 0xff0000000001ff00, 0xff000000000100ff, 0xff00000000010000,
+    0xff00000000010001, 0xff00000000010100, 0xff00000001ffffff, 0xff00000001ffff01,
+    0xff00000001ff00ff, 0xff00000001ff0000, 0xff00000001ff01ff, 0xff00000001ff0101,
+    0xff0000000100ffff, 0xff0000000100ff00, 0xff000000010000ff, 0xff00000001000000,
+    0xff00000001000001, 0xff00000001000100, 0xff00000001000101, 0xff0000000101ffff,
+    0xff0000000101ff01, 0xff00000001010000, 0xff000001ffffff00, 0xff000001ffff00ff,
+    0xff000001ffff0000, 0xff000001ffff0001, 0xff000001ff000000, 0xff000001ff000001,
+    0xff000001ff0001ff, 0xff000001ff000101, 0xff000001ff01ff00, 0xff000001ff010001,
+    0xff00000100ffffff, 0xff00000100ffff01, 0xff00000100ff00ff, 0xff00000100ff0000,
+    0xff00000100ff01ff, 0xff00000100ff0101, 0xff0000010000ff00, 0xff00000100000000,
+    0xff00000100000001, 0xff000001000001ff, 0xff00000100000100, 0xff0000010001ff00,
+    0xff000001000100ff, 0xff00000100010000, 0xff000001000101ff, 0xff00000100010100,
+    0xff00000100010101, 0xff00000101ff0001, 0xff00000101ff0101, 0xff0000010100ff01,
+    0xff00000101000000, 0xff000001010100ff, 0xff00000101010100, 0xff0001ffff00ff00,
+    0xff0001ffff000001, 0xff0001ffff010000, 0xff0001ff00ffff00, 0xff0001ff00ff00ff,
+    0xff0001ff00ff0001, 0xff0001ff00ff0100, 0xff0001ff0000ffff, 0xff0001ff00000000,
+    0xff0001ff000001ff, 0xff0001ff00000101, 0xff0001ff0001ffff, 0xff0001ff0001ff00,
+    0xff0001ff000100ff, 0xff0001ff00010001, 0xff0001ff00010100, 0xff0001ff01ff0000,
+    0xff0001ff0100ff00, 0xff0001ff010000ff, 0xff0001ff01010000, 0xff000100ff00ffff,
+    0xff000100ff00ff01, 0xff000100ff000000, 0xff000100ff000101, 0xff000100ff01ff00,
+    0xff000100ff010000, 0xff00010000ffff01, 0xff00010000ff00ff, 0xff00010000ff0000,
+    0xff00010000ff01ff, 0xff0001000000ff00, 0xff000100000000ff, 0xff00010000000000,
+    0xff00010000000001, 0xff00010000000100, 0xff00010000000101, 0xff0001000001ffff,
+    0xff00010000010000, 0xff00010000010101, 0xff00010001ff0100, 0xff0001000100ff00,
+    0xff0001000100ff01, 0xff00010001000000, 0xff000100010001ff, 0xff0001000101ff00,
+    0xff00010001010001, 0xff00010001010100, 0xff000101ffff0100, 0xff000101ff000001,
+    0xff000101ff0100ff, 0xff000101ff010001, 0xff00010100ff00ff, 0xff00010100ff0001,
+    0xff00010100ff0100, 0xff0001010000ffff, 0xff0001010000ff01, 0xff00010100000000,
+    0xff000101000001ff, 0xff0001010001ff00, 0xff00010100010001, 0xff00010100010100,
+    0xff00010101ff0000, 0xff0001010100ff00, 0xff00010101000001, 0xff00010101000101,
+    0xff01ffffffffffff, 0xff01ffffffffff01, 0xff01ffffffff01ff, 0xff01ffffffff0101,
+    0xff01ffffff000000, 0xff01ffffff01ffff, 0xff01ffffff01ff01, 0xff01ffffff010000,
+    0xff01ffffff0101ff, 0xff01ffffff010101, 0xff01ffff00ff0000, 0xff01ffff0000ff00,
+    0xff01ffff00000100, 0xff01ffff0001ff00, 0xff01ffff00010000, 0xff01ffff01ffffff,
+    0xff01ffff01ffff01, 0xff01ffff01ff01ff, 0xff01ffff01ff0101, 0xff01ffff01000000,
+    0xff01ffff0101ffff, 0xff01ffff0101ff01, 0xff01ffff01010000, 0xff01ffff010101ff,
+    0xff01ffff01010101, 0xff01ff00ffff0000, 0xff01ff00ff00ff00, 0xff01ff00ff0000ff,
+    0xff01ff00ff000100, 0xff01ff00ff010000, 0xff01ff0000ffff01, 0xff01ff0000ff00ff,
+    0xff01ff0000ff0100, 0xff01ff0000000000, 0xff01ff00000001ff, 0xff01ff0000000101,
+    0xff01ff000001ff00, 0xff01ff00000100ff, 0xff01ff0000010000, 0xff01ff0000010001,
+    0xff01ff0001ff0000, 0xff01ff000100ffff, 0xff01ff0001000001, 0xff01ff0001000100,
+    0xff01ff0001010000, 0xff01ff01ffffff00, 0xff01ff01ffff01ff, 0xff01ff01ffff0101,
+    0xff01ff01ff00ff00, 0xff01ff01ff000000, 0xff01ff01ff01ffff, 0xff01ff01ff01ff01,
+    0xff01ff01ff0101ff, 0xff01ff01ff010101, 0xff01ff0100ff0000, 0xff01ff010000ff00,
+    0xff01ff0100000001, 0xff01ff0100000100, 0xff01ff0100010000, 0xff01ff0101ffff00,
+    0xff01ff0101ff01ff, 0xff01ff0101ff0101, 0xff01ff010100ff00, 0xff01ff0101000000,
+    0xff01ff010101ffff, 0xff01ff010101ff01, 0xff01ff01010101ff, 0xff01ff0101010101,
+    0xff0100ffffff0000, 0xff0100ffff0000ff, 0xff0100ffff000001, 0xff0100ffff000100,
+    0xff0100ffff010000, 0xff0100ff00ff00ff, 0xff0100ff00ff0000, 0xff0100ff00ff0001,
+    0xff0100ff00ff0100, 0xff0100ff0000ff01, 0xff0100ff00000000, 0xff0100ff000001ff,
+    0xff0100ff00000101, 0xff0100ff00010001, 0xff0100ff01ff0000, 0xff0100ff0100ff00,
+    0xff0100ff010000ff, 0xff0100ff01000100, 0xff0100ff0101ff00, 0xff0100ff01010000,
+    0xff010000ffff0100, 0xff010000ff000000, 0xff010000ff01ff00, 0xff010000ff010100,
+    0xff01000000ffffff, 0xff01000000ff0000, 0xff01000000ff01ff, 0xff0100000000ff00,
+    0xff010000000000ff, 0xff01000000000000, 0xff01000000000100, 0xff0100000001ff01,
+    0xff01000000010000, 0xff010000000101ff, 0xff01000001ff0100, 0xff0100000100ffff,
+    0xff010000010000ff, 0xff01000001000000, 0xff010000010001ff, 0xff01000001000101,
+    0xff0100000101ff00, 0xff010000010100ff, 0xff01000001010001, 0xff01000001010100,
+    0xff010001ffff0000, 0xff010001ff00ffff, 0xff010001ff00ff01, 0xff010001ff000100,
+    0xff010001ff010000, 0xff01000100ffff00, 0xff01000100ff0100, 0xff01000100000000,
+    0xff0100010001ffff, 0xff0100010001ff00, 0xff01000100010100, 0xff01000101ff00ff,
+    0xff01000101ff0001, 0xff0100010100ffff, 0xff01000101000101, 0xff0101ffffffffff,
+    0xff0101ffffffff01, 0xff0101ffffff01ff, 0xff0101ffffff0101, 0xff0101ffff000000,
+    0xff0101ffff01ffff, 0xff0101ffff01ff01, 0xff0101ffff0101ff, 0xff0101ffff010101,
+    0xff0101ff00ff0000, 0xff0101ff0000ff00, 0xff0101ff000000ff, 0xff0101ff00010000,
+    0xff0101ff01ffffff, 0xff0101ff01ffff01, 0xff0101ff01ff01ff, 0xff0101ff01ff0101,
+    0xff0101ff0101ffff, 0xff0101ff0101ff01, 0xff0101ff010101ff, 0xff0101ff01010101,
+    0xff010100ffff0100, 0xff010100ff00ff00, 0xff010100ff0000ff, 0xff010100ff000100,
+    0xff010100ff010000, 0xff01010000ff0001, 0xff01010000ff0100, 0xff0101000000ff01,
+    0xff01010000000000, 0xff0101000001ff00, 0xff010100000100ff, 0xff01010000010001,
+    0xff01010000010100, 0xff01010001ff0000, 0xff0101000100ffff, 0xff01010001000001,
+    0xff01010001000100, 0xff010100010100ff, 0xff01010001010000, 0xff010101ffffffff,
+    0xff010101ffffff01, 0xff010101ffff01ff, 0xff010101ffff0101, 0xff010101ff01ffff,
+    0xff010101ff01ff01, 0xff010101ff0101ff, 0xff010101ff010101, 0xff01010100ff0000,
+    0xff0101010000ff00, 0xff01010100000001, 0xff01010100000100, 0xff01010100010000,
+    0xff01010101ffffff, 0xff01010101ffff01, 0xff01010101ff01ff, 0xff01010101ff0101,
+    0xff01010101000000, 0xff0101010101ffff, 0xff0101010101ff01, 0xff010101010101ff,
+    0xff01010101010101, 0x00ffffffffff0000, 0x00ffffffff00ff00, 0x00ffffffff000001,
+    0x00ffffffff010000, 0x00ffffff00ff0100, 0x00ffffff0000ff01, 0x00ffffff00000000,
+    0x00ffffff000001ff, 0x00ffffff00000101, 0x00ffffff0001ff00, 0x00ffffff000100ff,
+    0x00ffffff00010001, 0x00ffffff010000ff, 0x00ffffff01000100, 0x00ffffff0101ff00,
+    0x00ffffff01010001, 0x00ffff00ffffffff, 0x00ffff00ffffff00, 0x00ffff00ffff00ff,
+    0x00ffff00ffff0001, 0x00ffff00ffff0100, 0x00ffff00ff00ff01, 0x00ffff00ff000000,
+    0x00ffff00ff000001, 0x00ffff00ff0001ff, 0x00ffff00ff000101, 0x00ffff00ff01ff00,
+    0x00ffff00ff010001, 0x00ffff00ff010100, 0x00ffff0000ff0000, 0x00ffff0000ff01ff,
+    0x00ffff0000ff0101, 0x00ffff000000ff00, 0x00ffff00000000ff, 0x00ffff0000000000,
+    0x00ffff0000000001, 0x00ffff0000000100, 0x00ffff0000000101, 0x00ffff0000010000,
+    0x00ffff00000101ff, 0x00ffff0000010101, 0x00ffff0001ffff00, 0x00ffff0001ff00ff,
+    0x00ffff0001ff0001, 0x00ffff000100ffff, 0x00ffff000100ff01, 0x00ffff0001000000,
+    0x00ffff000101ffff, 0x00ffff000101ff00, 0x00ffff000101ff01, 0x00ffff01ffff0000,
+    0x00ffff01ff00ff00, 0x00ffff01ff0000ff, 0x00ffff01ff000001, 0x00ffff01ff010000,
+    0x00ffff0100ffff00, 0x00ffff010000ff01, 0x00ffff0100000000, 0x00ffff0100000101,
+    0x00ffff01000100ff, 0x00ffff0100010100, 0x00ffff0101ff0100, 0x00ffff01010000ff,
+    0x00ffff0101010000, 0x00ff00ffffffff00, 0x00ff00ffff000000, 0x00ff00ffff000100,
+    0x00ff00ffff010100, 0x00ff00ff00ff0000, 0x00ff00ff00ff01ff, 0x00ff00ff00ff0101,
+    0x00ff00ff0000ff00, 0x00ff00ff000000ff, 0x00ff00ff00000000, 0x00ff00ff00000001,
+    0x00ff00ff0001ff00, 0x00ff00ff0001ff01, 0x00ff00ff00010000, 0x00ff00ff000101ff,
+    0x00ff00ff00010101, 0x00ff00ff01ffff00, 0x00ff00ff01ff0001, 0x00ff00ff01ff0100,
+    0x00ff00ff0100ffff, 0x00ff00ff0100ff01, 0x00ff00ff01000000, 0x00ff00ff0101ffff,
+    0x00ff00ff0101ff00, 0x00ff00ff01010100, 0x00ff0000ffffff00, 0x00ff0000ffffff01,
+    0x00ff0000ffff0000, 0x00ff0000ffff0101, 0x00ff0000ff00ff00, 0x00ff0000ff0000ff,
+    0x00ff0000ff000000, 0x00ff0000ff000001, 0x00ff0000ff000100, 0x00ff0000ff01ffff,
+    0x00ff0000ff010000, 0x00ff0000ff010101, 0x00ff000000ffff00, 0x00ff000000ff00ff,
+    0x00ff000000ff0000, 0x00ff000000ff0001, 0x00ff000000ff0100, 0x00ff00000000ffff,
+    0x00ff00000000ff00, 0x00ff0000000000ff, 0x00ff000000000000, 0x00ff000000000001,
+    0x00ff0000000001ff, 0x00ff000000000100, 0x00ff00000001ff00, 0x00ff0000000100ff,
+    0x00ff000000010000, 0x00ff000000010001, 0x00ff000000010100, 0x00ff000001ffff01,
+    0x00ff000001ff00ff, 0x00ff000001ff0000, 0x00ff000001ff01ff, 0x00ff00000100ff00,
+    0x00ff0000010000ff, 0x00ff000001000000, 0x00ff000001000001, 0x00ff000001000100,
+    0x00ff000001000101, 0x00ff000001010000, 0x00ff0000010101ff, 0x00ff000001010101,
+    0x00ff0001ffffff00, 0x00ff0001ffff0000, 0x00ff0001ffff0100, 0x00ff0001ff0000ff,
+    0x00ff0001ff000000, 0x00ff0001ff0001ff, 0x00ff0001ff000101, 0x00ff0001ff01ff00,
+    0x00ff0001ff0100ff, 0x00ff0001ff010100, 0x00ff000100ffffff, 0x00ff000100ffff01,
+    0x00ff000100ff0000, 0x00ff000100ff01ff, 0x00ff00010000ffff, 0x00ff00010000ff00,
+    0x00ff00010000ff01, 0x00ff000100000000, 0x00ff000100000001, 0x00ff000100000100,
+    0x00ff00010001ff01, 0x00ff000100010000, 0x00ff0001000101ff, 0x00ff000101ffff00,
+    0x00ff000101ff0000, 0x00ff000101ff0101, 0x00ff0001010000ff, 0x00ff000101000000,
+    0x00ff00010101ff00, 0x00ff0001010100ff, 0x00ff000101010001, 0x00ff01ffffff0000,
+    0x00ff01ffff00ff00, 0x00ff01ffff000000, 0x00ff01ffff000101, 0x00ff01ffff010000,
+    0x00ff01ff00ffff01, 0x00ff01ff00ff0100, 0x00ff01ff0000ffff, 0x00ff01ff00000000,
+    0x00ff01ff000001ff, 0x00ff01ff0001ff00, 0x00ff01ff000100ff, 0x00ff01ff00010001,
+    0x00ff01ff00010100, 0x00ff01ff01ff0000, 0x00ff01ff0100ff00, 0x00ff01ff010000ff,
+    0x00ff01ff01000001, 0x00ff01ff01000100, 0x00ff01ff01010000, 0x00ff0100ffffff00,
+    0x00ff0100ffff0000, 0x00ff0100ffff0001, 0x00ff0100ffff0101, 0x00ff0100ff00ffff,
+    0x00ff0100ff0000ff, 0x00ff0100ff000000, 0x00ff0100ff0001ff, 0x00ff0100ff01ff00,
+    0x00ff0100ff0100ff, 0x00ff0100ff010001, 0x00ff010000ffffff, 0x00ff010000ff0000,
+    0x00ff010000ff0101, 0x00ff01000000ff00, 0x00ff01000000ff01, 0x00ff0100000000ff,
+    0x00ff010000000000, 0x00ff010000000001, 0x00ff010000000100, 0x00ff01000001ffff,
+    0x00ff01000001ff01, 0x00ff010000010000, 0x00ff010000010001, 0x00ff010000010101,
+    0x00ff010001ff0001, 0x00ff010001ff0100, 0x00ff01000100ff01, 0x00ff010001000000,
+    0x00ff010001000001, 0x00ff0100010001ff, 0x00ff01000101ff00, 0x00ff0100010100ff,
+    0x00ff010001010001, 0x00ff010001010100, 0x00ff0101ff000001, 0x00ff010100ff00ff,
+    0x00ff010100ff0001, 0x00ff010100ff0100, 0x00ff010100000000, 0x00ff0101000001ff,
+    0x00ff010100000101, 0x00ff0101000100ff, 0x00ff010100010100, 0x00ff0101010000ff,
+    0x00ff010101010000, 0x0000ffffffffff00, 0x0000ffffffff00ff, 0x0000ffffffff0000,
+    0x0000ffffffff0001, 0x0000ffffffff0100, 0x0000ffffff00ff01, 0x0000ffffff000000,
+    0x0000ffffff000101, 0x0000ffffff01ff00, 0x0000ffffff0100ff, 0x0000ffffff010100,
+    0x0000ffff00ffffff, 0x0000ffff00ff0000, 0x0000ffff00ff01ff, 0x0000ffff0000ff00,
+    0x0000ffff000000ff, 0x0000ffff00000000, 0x0000ffff00000001, 0x0000ffff00000100,
+    0x0000ffff00010000, 0x0000ffff000101ff, 0x0000ffff01ff0001, 0x0000ffff01ff0100,
+    0x0000ffff01000000, 0x0000ffff010001ff, 0x0000ffff0101ffff, 0x0000ffff0101ff00,
+    0x0000ffff01010001, 0x0000ffff01010100, 0x0000ff00ffff0000, 0x0000ff00ffff01ff,
+    0x0000ff00ffff0100, 0x0000ff00ffff0101, 0x0000ff00ff00ff00, 0x0000ff00ff0000ff,
+    0x0000ff00ff000000, 0x0000ff00ff000001, 0x0000ff00ff0001ff, 0x0000ff00ff000100,
+    0x0000ff00ff01ffff, 0x0000ff00ff010000, 0x0000ff00ff010001, 0x0000ff00ff0101ff,
+    0x0000ff00ff010101, 0x0000ff0000ffff00, 0x0000ff0000ff00ff, 0x0000ff0000ff0000,
+    0x0000ff0000ff0001, 0x0000ff0000ff0100, 0x0000ff000000ffff, 0x0000ff000000ff00,
+    0x0000ff000000ff01, 0x0000ff00000000ff, 0x0000ff0000000000, 0x0000ff0000000001,
+    0x0000ff00000001ff, 0x0000ff0000000100, 0x0000ff0000000101, 0x0000ff000001ff00,
+    0x0000ff00000100ff, 0x0000ff0000010000, 0x0000ff0000010001, 0x0000ff0000010100,
+    0x0000ff0001ffff01, 0x0000ff0001ff0000, 0x0000ff000100ff00, 0x0000ff00010000ff,
+    0x0000ff0001000000, 0x0000ff0001000001, 0x0000ff0001000100, 0x0000ff000101ffff,
+    0x0000ff0001010000, 0x0000ff0001010101, 0x0000ff01ffffff00, 0x0000ff01ffff0001,
+    0x0000ff01ff00ff01, 0x0000ff01ff000000, 0x0000ff01ff000101, 0x0000ff01ff01ff00,
+    0x0000ff01ff0100ff, 0x0000ff0100ffff01, 0x0000ff0100ff0000, 0x0000ff0100ff0101,
+    0x0000ff010000ff00, 0x0000ff01000000ff, 0x0000ff0100000000, 0x0000ff0100000001,
+    0x0000ff0100000100, 0x0000ff010001ff01, 0x0000ff0100010000, 0x0000ff0101ff0000,
+    0x0000ff010100ffff, 0x0000ff010100ff01, 0x0000ff0101000000, 0x0000ff0101000100,
+    0x0000ff0101000101, 0x0000ff01010100ff, 0x000000ffffff00ff, 0x000000ffffff0000,
+    0x000000ffff00ff00, 0x000000ffff0000ff, 0x000000ffff000000, 0x000000ffff000001,
+    0x000000ffff0001ff, 0x000000ffff000100, 0x000000ffff01ff00, 0x000000ffff010000,
+    0x000000ffff0101ff, 0x000000ffff010101, 0x000000ff00ffff00, 0x000000ff00ff00ff,
+    0x000000ff00ff0000, 0x000000ff00ff0001, 0x000000ff00ff0100, 0x000000ff00ff0101,
+    0x000000ff0000ffff, 0x000000ff0000ff00, 0x000000ff000000ff, 0x000000ff00000000,
+    0x000000ff00000001, 0x000000ff000001ff, 0x000000ff00000100, 0x000000ff00000101,
+    0x000000ff0001ff00, 0x000000ff0001ff01, 0x000000ff000100ff, 0x000000ff00010000,
+    0x000000ff00010001, 0x000000ff00010100, 0x000000ff01ffffff, 0x000000ff01ff01ff,
+    0x000000ff01ff0101, 0x000000ff0100ff00, 0x000000ff010000ff, 0x000000ff01000000,
+    0x000000ff01000001, 0x000000ff01000100, 0x000000ff0101ff00, 0x000000ff010100ff,
+    0x000000ff01010000, 0x000000ff01010101, 0x00000000ffffff00, 0x00000000ffffff01,
+    0x00000000ffff00ff, 0x00000000ffff0000, 0x00000000ffff0001, 0x00000000ffff0100,
+    0x00000000ff00ffff, 0x00000000ff00ff00, 0x00000000ff00ff01, 0x00000000ff0000ff,
+    0x00000000ff000000, 0x00000000ff000001, 0x00000000ff000100, 0x00000000ff000101,
+    0x00000000ff01ff00, 0x00000000ff0100ff, 0x00000000ff010000, 0x00000000ff010001,
+    0x00000000ff010100, 0x0000000000ffffff, 0x0000000000ffff00, 0x0000000000ffff01,
+    0x0000000000ff00ff, 0x0000000000ff0000, 0x0000000000ff0001, 0x0000000000ff01ff,
+    0x0000000000ff0100, 0x000000000000ffff, 0x000000000000ff00, 0x000000000000ff01,
+    0x00000000000000ff, 0x0000000000000000, 0x0000000000000001, 0x00000000000001ff,
+    0x0000000000000100, 0x0000000000000101, 0x000000000001ffff, 0x000000000001ff00,
+    0x00000000000100ff, 0x0000000000010000, 0x0000000000010001, 0x00000000000101ff,
+    0x0000000000010100, 0x0000000000010101, 0x0000000001ffff00, 0x0000000001ff00ff,
+    0x0000000001ff0000, 0x0000000001ff0100, 0x0000000001ff0101, 0x000000000100ffff,
+    0x000000000100ff00, 0x00000000010000ff, 0x0000000001000000, 0x0000000001000001,
+    0x00000000010001ff, 0x0000000001000100, 0x000000000101ff00, 0x00000000010100ff,
+    0x0000000001010000, 0x0000000001010001, 0x0000000001010100, 0x00000001ffffffff,
+    0x00000001ffffff00, 0x00000001ffffff01, 0x00000001ffff00ff, 0x00000001ffff0001,
+    0x00000001ffff01ff, 0x00000001ffff0100, 0x00000001ff00ff00, 0x00000001ff0000ff,
+    0x00000001ff000000, 0x00000001ff0001ff, 0x00000001ff000100, 0x00000001ff01ffff,
+    0x00000001ff01ff00, 0x00000001ff01ff01, 0x00000001ff0100ff, 0x00000001ff010000,
+    0x00000001ff010001, 0x00000001ff0101ff, 0x00000001ff010100, 0x0000000100ffff00,
+    0x0000000100ff0000, 0x0000000100ff0001, 0x0000000100ff01ff, 0x0000000100ff0100,
+    0x0000000100ff0101, 0x000000010000ffff, 0x000000010000ff00, 0x000000010000ff01,
+    0x00000001000000ff, 0x0000000100000000, 0x0000000100000001, 0x00000001000001ff,
+    0x0000000100000100, 0x0000000100000101, 0x000000010001ff00, 0x00000001000100ff,
+    0x0000000100010000, 0x0000000100010100, 0x0000000101ffff01, 0x0000000101ff0000,
+    0x0000000101ff0001, 0x0000000101ff01ff, 0x0000000101ff0100, 0x0000000101ff0101,
+    0x000000010100ff00, 0x0000000101000000, 0x0000000101000101, 0x000000010101ff01,
+    0x0000000101010000, 0x0000000101010001, 0x00000001010101ff, 0x0000000101010100,
+    0x000001ffffff00ff, 0x000001ffffff0000, 0x000001ffffff0001, 0x000001ffffff0100,
+    0x000001ffff00ffff, 0x000001ffff000000, 0x000001ffff0001ff, 0x000001ffff01ff00,
+    0x000001ffff010101, 0x000001ff00ff0000, 0x000001ff00ff01ff, 0x000001ff00ff0101,
+    0x000001ff0000ff00, 0x000001ff000000ff, 0x000001ff00000000, 0x000001ff00000001,
+    0x000001ff000001ff, 0x000001ff00000100, 0x000001ff0001ffff, 0x000001ff0001ff01,
+    0x000001ff000100ff, 0x000001ff00010000, 0x000001ff01ffff01, 0x000001ff01ff0100,
+    0x000001ff0100ffff, 0x000001ff0100ff01, 0x000001ff01000000, 0x000001ff010001ff,
+    0x000001ff0101ff00, 0x000001ff01010100, 0x00000100ffffff00, 0x00000100ffffff01,
+    0x00000100ffff0000, 0x00000100ffff0101, 0x00000100ff00ff00, 0x00000100ff0000ff,
+    0x00000100ff000000, 0x00000100ff000001, 0x00000100ff000100, 0x00000100ff010000,
+    0x0000010000ffff00, 0x0000010000ff00ff, 0x0000010000ff0000, 0x0000010000ff0001,
+    0x0000010000ff0100, 0x000001000000ffff, 0x000001000000ff00, 0x000001000000ff01,
+    0x00000100000000ff, 0x0000010000000000, 0x0000010000000001, 0x00000100000001ff,
+    0x0000010000000100, 0x0000010000000101, 0x000001000001ff00, 0x00000100000100ff,
+    0x0000010000010000, 0x0000010000010001, 0x0000010000010100, 0x0000010001ffff00,
+    0x0000010001ff0000, 0x0000010001ff0100, 0x000001000100ff00, 0x00000100010000ff,
+    0x0000010001000000, 0x0000010001000001, 0x00000100010001ff, 0x0000010001000100,
+    0x0000010001010000, 0x00000101ffff00ff, 0x00000101ffff01ff, 0x00000101ff000000,
+    0x00000101ff000101, 0x00000101ff01ffff, 0x00000101ff010000, 0x00000101ff010001,
+    0x00000101ff010100, 0x0000010100ff0000, 0x0000010100ff01ff, 0x0000010100ff0100,
+    0x000001010000ff00, 0x0000010100000000, 0x0000010100000001, 0x00000101000001ff,
+    0x0000010100000100, 0x000001010001ff01, 0x0000010100010000, 0x00000101000101ff,
+    0x0000010100010101, 0x0000010101ffff00, 0x0000010101ff0101, 0x000001010100ff01,
+    0x0000010101000000, 0x0000010101000001, 0x00000101010001ff, 0x0000010101000101,
+    0x000001010101ff00, 0x0001ffffffff0000, 0x0001ffffff0000ff, 0x0001ffffff000001,
+    0x0001ffffff000100, 0x0001ffffff010000, 0x0001ffff00ff00ff, 0x0001ffff0000ffff,
+    0x0001ffff00000000, 0x0001ffff00000001, 0x0001ffff000001ff, 0x0001ffff00000101,
+    0x0001ffff0001ff00, 0x0001ffff000100ff, 0x0001ffff00010001, 0x0001ffff00010100,
+    0x0001ffff01ffff00, 0x0001ffff01000001, 0x0001ffff01010000, 0x0001ff00ffffff00,
+    0x0001ff00ffff00ff, 0x0001ff00ffff0001, 0x0001ff00ffff0100, 0x0001ff00ff00ff01,
+    0x0001ff00ff000000, 0x0001ff00ff01ff00, 0x0001ff00ff01ff01, 0x0001ff00ff010001,
+    0x0001ff00ff010100, 0x0001ff0000ff0000, 0x0001ff0000ff0100, 0x0001ff000000ff00,
+    0x0001ff0000000000, 0x0001ff0000000001, 0x0001ff0000000100, 0x0001ff0000010000,
+    0x0001ff0000010001, 0x0001ff0000010101, 0x0001ff0001ff00ff, 0x0001ff0001ff0101,
+    0x0001ff000100ff01, 0x0001ff0001000000, 0x0001ff000101ff00, 0x0001ff0001010001,
+    0x0001ff0001010100, 0x0001ff01ff00ff00, 0x0001ff01ff000001, 0x0001ff01ff000100,
+    0x0001ff0100ffffff, 0x0001ff0100ffff00, 0x0001ff0100ff0001, 0x0001ff0100000000,
+    0x0001ff0100000001, 0x0001ff01000001ff, 0x0001ff010001ffff, 0x0001ff0101ff0000,
+    0x0001ff010100ff00, 0x0001ff0101000001, 0x0001ff0101010000, 0x000100ffff00ff00,
+    0x000100ffff00ff01, 0x000100ffff000000, 0x000100ffff000001, 0x000100ffff000101,
+    0x000100ffff01ff00, 0x000100ffff010001, 0x000100ffff010100, 0x000100ff00ffffff,
+    0x000100ff00ffff01, 0x000100ff00ff0000, 0x000100ff00ff01ff, 0x000100ff00ff0101,
+    0x000100ff0000ff00, 0x000100ff000000ff, 0x000100ff00000000, 0x000100ff00000001,
+    0x000100ff00000100, 0x000100ff00000101, 0x000100ff0001ffff, 0x000100ff0001ff01,
+    0x000100ff00010000, 0x000100ff01ff00ff, 0x000100ff01ff0000, 0x000100ff01ff0100,
+    0x000100ff0100ffff, 0x000100ff0100ff01, 0x000100ff010000ff, 0x000100ff01000000,
+    0x000100ff01000001, 0x000100ff010001ff, 0x000100ff01000101, 0x000100ff0101ff00,
+    0x000100ff010100ff, 0x000100ff01010100, 0x00010000ffff0000, 0x00010000ffff01ff,
+    0x00010000ffff0101, 0x00010000ff00ff00, 0x00010000ff000000, 0x00010000ff000001,
+    0x00010000ff000100, 0x0001000000ff00ff, 0x0001000000ff0000, 0x0001000000ff0001,
+    0x0001000000ff0100, 0x000100000000ffff, 0x000100000000ff00, 0x00010000000000ff,
+    0x0001000000000000, 0x0001000000000001, 0x0001000000000100, 0x000100000001ff00,
+    0x00010000000100ff, 0x0001000000010000, 0x0001000000010001, 0x0001000000010100,
+    0x0001000001ff0001, 0x0001000001ff0100, 0x0001000001ff0101, 0x000100000100ff00,
+    0x0001000001000000, 0x0001000001000001, 0x0001000001000100, 0x0001000001000101,
+    0x000100000101ff01, 0x0001000001010000, 0x0001000001010001, 0x00010000010101ff,
+    0x00010001ffffff01, 0x00010001ffff0100, 0x00010001ff000000, 0x00010001ff01ffff,
+    0x00010001ff010001, 0x00010001ff0101ff, 0x00010001ff010100, 0x0001000100ffffff,
+    0x0001000100ff0000, 0x0001000100ff01ff, 0x0001000100ff0101, 0x000100010000ff00,
+    0x00010001000000ff, 0x0001000100000000, 0x0001000100000001, 0x00010001000001ff,
+    0x0001000100000101, 0x000100010001ffff, 0x0001000100010000, 0x00010001000101ff,
+    0x0001000101ffffff, 0x0001000101ffff01, 0x0001000101ff0000, 0x0001000101ff0101,
+    0x00010001010000ff, 0x0001000101000001, 0x00010001010001ff, 0x0001000101000100,
+    0x000100010101ffff, 0x00010001010100ff, 0x0001000101010001, 0x0001000101010101,
+    0x000101ffff000001, 0x000101ffff000100, 0x000101ffff010000, 0x000101ff00ffff00,
+    0x000101ff0000ff01, 0x000101ff00000000, 0x000101ff00000101, 0x000101ff0001ff00,
+    0x000101ff00010100, 0x000101ff01ff0000, 0x000101ff0100ff00, 0x000101ff010001ff,
+    0x000101ff01010001, 0x00010100ffffff00, 0x00010100ffff00ff, 0x00010100ff00ffff,
+    0x00010100ff000000, 0x00010100ff01ff00, 0x00010100ff0100ff, 0x00010100ff010001,
+    0x00010100ff010100, 0x0001010000ffffff, 0x0001010000ffff00, 0x0001010000ff0000,
+    0x0001010000ff0001, 0x0001010000ff01ff, 0x000101000000ff00, 0x00010100000000ff,
+    0x0001010000000000, 0x0001010000000001, 0x0001010000000100, 0x000101000001ffff,
+    0x0001010000010000, 0x0001010000010101, 0x0001010001ffff01, 0x0001010001ff00ff,
+    0x0001010001ff0101, 0x0001010001000000, 0x000101000101ff00, 0x00010100010100ff,
+    0x0001010001010000, 0x0001010001010100, 0x00010101ff00ff00, 0x00010101ff000001,
+    0x00010101ff0001ff, 0x0001010100ffff00, 0x0001010100ff00ff, 0x0001010100ff0100,
+    0x000101010000ffff, 0x0001010100000000, 0x00010101000001ff, 0x0001010100000101,
+    0x00010101000100ff, 0x0001010100010000, 0x0001010100010100, 0x0001010101ff0001,
+    0x00010101010000ff, 0x00010101010001ff, 0x0001010101000101, 0x0001010101010001,
+    0x01ffffffffffffff, 0x01ffffffffffff01, 0x01ffffffffff01ff, 0x01ffffffffff0101,
+    0x01ffffffff01ffff, 0x01ffffffff01ff01, 0x01ffffffff0101ff, 0x01ffffffff010101,
+    0x01ffffff00ff0000, 0x01ffffff0000ffff, 0x01ffffff0000ff00, 0x01ffffff000000ff,
+    0x01ffffff00000001, 0x01ffffff00000100, 0x01ffffff00010000, 0x01ffffff01ffffff,
+    0x01ffffff01ffff01, 0x01ffffff01ff01ff, 0x01ffffff01ff0101, 0x01ffffff01000000,
+    0x01ffffff0101ffff, 0x01ffffff0101ff01, 0x01ffffff010101ff, 0x01ffffff01010101,
+    0x01ffff00ffff0000, 0x01ffff00ff00ff00, 0x01ffff00ff0000ff, 0x01ffff00ff000001,
+    0x01ffff00ff000100, 0x01ffff00ff010000, 0x01ffff0000ffff00, 0x01ffff0000ff00ff,
+    0x01ffff0000ff0100, 0x01ffff000000ffff, 0x01ffff000000ff01, 0x01ffff0000000000,
+    0x01ffff0000000001, 0x01ffff00000001ff, 0x01ffff0000000100, 0x01ffff00000100ff,
+    0x01ffff0000010001, 0x01ffff0000010100, 0x01ffff0001ff0000, 0x01ffff0001ff0100,
+    0x01ffff00010000ff, 0x01ffff0001000001, 0x01ffff0001000100, 0x01ffff0001010000,
+    0x01ffff01ffffffff, 0x01ffff01ffffff01, 0x01ffff01ffff01ff, 0x01ffff01ffff0101,
+    0x01ffff01ff000000, 0x01ffff01ff01ffff, 0x01ffff01ff01ff01, 0x01ffff01ff0101ff,
+    0x01ffff01ff010101, 0x01ffff010000ff00, 0x01ffff01000000ff, 0x01ffff0100000100,
+    0x01ffff0100010000, 0x01ffff0101ffffff, 0x01ffff0101ffff01, 0x01ffff0101ff01ff,
+    0x01ffff0101ff0101, 0x01ffff0101000000, 0x01ffff010101ffff, 0x01ffff010101ff01,
+    0x01ffff01010101ff, 0x01ffff0101010101, 0x01ff00ffff0000ff, 0x01ff00ffff000100,
+    0x01ff00ff00ffff00, 0x01ff00ff00ff00ff, 0x01ff00ff0000ff00, 0x01ff00ff00000000,
+    0x01ff00ff00000101, 0x01ff00ff0001ff00, 0x01ff00ff000100ff, 0x01ff00ff00010100,
+    0x01ff00ff010000ff, 0x01ff00ff01000100, 0x01ff0000ffffff00, 0x01ff0000ffff0100,
+    0x01ff0000ff00ff01, 0x01ff0000ff000000, 0x01ff0000ff000101, 0x01ff0000ff010001,
+    0x01ff0000ff010100, 0x01ff000000ffffff, 0x01ff000000ffff00, 0x01ff000000ff0000,
+    0x01ff000000ff01ff, 0x01ff00000000ff00, 0x01ff0000000000ff, 0x01ff000000000000,
+    0x01ff000000000001, 0x01ff000000000100, 0x01ff000000000101, 0x01ff000000010000,
+    0x01ff000000010001, 0x01ff0000000101ff, 0x01ff000000010101, 0x01ff000001ffff00,
+    0x01ff000001ff00ff, 0x01ff000001ff0001, 0x01ff000001ff0100, 0x01ff00000100ffff,
+    0x01ff00000100ff01, 0x01ff000001000000, 0x01ff0000010001ff, 0x01ff000001010001,
+    0x01ff0001ff00ff00, 0x01ff0001ff000001, 0x01ff0001ff000100, 0x01ff0001ff010000,
+    0x01ff000100ffff00, 0x01ff000100ff00ff, 0x01ff000100ff0100, 0x01ff000100ff0101,
+    0x01ff00010000ffff, 0x01ff000100000000, 0x01ff000100000100, 0x01ff000100000101,
+    0x01ff00010001ff00, 0x01ff000100010001, 0x01ff000100010101, 0x01ff000101ff0000,
+    0x01ff00010100ff00, 0x01ff000101000101, 0x01ff0001010100ff, 0x01ff01ffffffffff,
+    0x01ff01ffffffff01, 0x01ff01ffffff01ff, 0x01ff01ffffff0101, 0x01ff01ffff000000,
+    0x01ff01ffff01ffff, 0x01ff01ffff01ff01, 0x01ff01ffff0101ff, 0x01ff01ffff010101,
+    0x01ff01ff00ffff00, 0x01ff01ff00ff0000, 0x01ff01ff0000ff00, 0x01ff01ff000000ff,
+    0x01ff01ff00000100, 0x01ff01ff00010000, 0x01ff01ff00010100, 0x01ff01ff01ffffff,
+    0x01ff01ff01ffff01, 0x01ff01ff01ff01ff, 0x01ff01ff01ff0101, 0x01ff01ff01000000,
+    0x01ff01ff0101ffff, 0x01ff01ff0101ff01, 0x01ff01ff010101ff, 0x01ff01ff01010101,
+    0x01ff0100ffff0000, 0x01ff0100ffff0001, 0x01ff0100ff00ff00, 0x01ff0100ff0000ff,
+    0x01ff0100ff000001, 0x01ff0100ff010000, 0x01ff010000ffff00, 0x01ff010000ff00ff,
+    0x01ff010000ff0001, 0x01ff010000ff0100, 0x01ff01000000ffff, 0x01ff01000000ff01,
+    0x01ff010000000000, 0x01ff010000000101, 0x01ff01000001ff00, 0x01ff0100000100ff,
+    0x01ff010001ff0000, 0x01ff010001000001, 0x01ff010001000100, 0x01ff010001010000,
+    0x01ff0101ffffffff, 0x01ff0101ffffff01, 0x01ff0101ffff01ff, 0x01ff0101ffff0101,
+    0x01ff0101ff000000, 0x01ff0101ff01ffff, 0x01ff0101ff01ff01, 0x01ff0101ff0101ff,
+    0x01ff0101ff010101, 0x01ff010100ff0000, 0x01ff01010000ff00, 0x01ff0101000000ff,
+    0x01ff010100000001, 0x01ff010101ffffff, 0x01ff010101ffff01, 0x01ff010101ff01ff,
+    0x01ff010101ff0101, 0x01ff010101000000, 0x01ff01010101ffff, 0x01ff01010101ff01,
+    0x01ff0101010101ff, 0x01ff010101010101, 0x0100ffffffff0000, 0x0100ffffff00ff00,
+    0x0100ffffff000001, 0x0100ffffff0001ff, 0x0100ffffff000100, 0x0100ffffff010000,
+    0x0100ffff00ffff00, 0x0100ffff00ff0001, 0x0100ffff00ff0100, 0x0100ffff00000000,
+    0x0100ffff000001ff, 0x0100ffff00000101, 0x0100ffff00010100, 0x0100ffff00010101,
+    0x0100ffff01ff0000, 0x0100ffff0100ff00, 0x0100ffff010000ff, 0x0100ffff01000001,
+    0x0100ffff01000100, 0x0100ffff01010000, 0x0100ff00ffffff00, 0x0100ff00ffff00ff,
+    0x0100ff00ffff0001, 0x0100ff00ffff0100, 0x0100ff00ff00ffff, 0x0100ff00ff000000,
+    0x0100ff00ff0001ff, 0x0100ff00ff000101, 0x0100ff00ff01ff00, 0x0100ff00ff0100ff,
+    0x0100ff00ff010001, 0x0100ff00ff010100, 0x0100ff0000ffffff, 0x0100ff0000ff0000,
+    0x0100ff000000ffff, 0x0100ff000000ff00, 0x0100ff00000000ff, 0x0100ff0000000000,
+    0x0100ff0000000001, 0x0100ff0000000100, 0x0100ff000001ff01, 0x0100ff0000010000,
+    0x0100ff0001ff00ff, 0x0100ff0001ff0001, 0x0100ff000100ff01, 0x0100ff0001000000,
+    0x0100ff00010001ff, 0x0100ff000101ff00, 0x0100ff00010100ff, 0x0100ff0001010001,
+    0x0100ff0001010100, 0x0100ff01ffff0000, 0x0100ff01ff00ff00, 0x0100ff01ff0000ff,
+    0x0100ff01ff000100, 0x0100ff01ff010000, 0x0100ff0100ff00ff, 0x0100ff0100ff0001,
+    0x0100ff0100ff0100, 0x0100ff010000ffff, 0x0100ff010000ff01, 0x0100ff0100000000,
+    0x0100ff01000001ff, 0x0100ff0100010001, 0x0100ff0100010100, 0x0100ff0101ff0000,
+    0x0100ff01010000ff, 0x0100ff0101000001, 0x0100ff0101010100, 0x010000ffffffff00,
+    0x010000ffffff00ff, 0x010000ffffff0001, 0x010000ffff00ffff, 0x010000ffff000000,
+    0x010000ffff0001ff, 0x010000ffff010001, 0x010000ff00ffffff, 0x010000ff00ff0101,
+    0x010000ff0000ff00, 0x010000ff000000ff, 0x010000ff00000000, 0x010000ff00000001,
+    0x010000ff000001ff, 0x010000ff00000100, 0x010000ff0001ffff, 0x010000ff0001ff00,
+    0x010000ff0001ff01, 0x010000ff00010000, 0x010000ff01ff00ff, 0x010000ff01ff0001,
+    0x010000ff0100ff01, 0x010000ff010000ff, 0x010000ff01000000, 0x010000ff010001ff,
+    0x010000ff0101ff00, 0x010000ff01010100, 0x01000000ffffffff, 0x01000000ffff0000,
+    0x01000000ffff01ff, 0x01000000ffff0101, 0x01000000ff00ffff, 0x01000000ff00ff00,
+    0x01000000ff0000ff, 0x01000000ff000000, 0x01000000ff000001, 0x01000000ff000100,
+    0x01000000ff01ff00, 0x01000000ff010000, 0x01000000ff010100, 0x01000000ff010101,
+    0x0100000000ffff00, 0x0100000000ff00ff, 0x0100000000ff0000, 0x0100000000ff0001,
+    0x0100000000ff0100, 0x010000000000ffff, 0x010000000000ff00, 0x010000000000ff01,
+    0x01000000000000ff, 0x0100000000000000, 0x0100000000000001, 0x01000000000001ff,
+    0x0100000000000100, 0x0100000000000101, 0x010000000001ff00, 0x01000000000100ff,
+    0x0100000000010000, 0x0100000000010001, 0x0100000000010100, 0x0100000001ffff00,
+    0x0100000001ff0000, 0x0100000001ff01ff, 0x010000000100ff00, 0x010000000100ff01,
+    0x01000000010000ff, 0x0100000001000000, 0x0100000001000001, 0x0100000001000100,
+    0x0100000001000101, 0x010000000101ffff, 0x010000000101ff01, 0x0100000001010000,
+    0x01000000010101ff, 0x0100000001010101, 0x01000001ffffff00, 0x01000001ffff00ff,
+    0x01000001ff00ffff, 0x01000001ff000000, 0x01000001ff000100, 0x01000001ff01ffff,
+    0x01000001ff010001, 0x01000001ff010100, 0x0100000100ff0000, 0x0100000100ff01ff,
+    0x0100000100ff0100, 0x010000010000ff00, 0x010000010000ff01, 0x0100000100000000,
+    0x0100000100000001, 0x0100000100000100, 0x0100000100010000, 0x01000001000101ff,
+    0x0100000101ffff01, 0x0100000101ff00ff, 0x0100000101ff0100, 0x0100000101ff0101,
+    0x010000010100ff01, 0x01000001010000ff, 0x0100000101000000, 0x01000001010100ff,
+    0x0100000101010001, 0x0100000101010100, 0x010001ffffff0000, 0x010001ffff000001,
+    0x010001ffff000100, 0x010001ffff010000, 0x010001ff00ffff00, 0x010001ff00ff0001,
+    0x010001ff0000ffff, 0x010001ff0000ff01, 0x010001ff00000000, 0x010001ff00000001,
+    0x010001ff00000101, 0x010001ff000100ff, 0x010001ff00010000, 0x010001ff01ff0000,
+    0x010001ff0100ff00, 0x010001ff01000001, 0x010001ff01000100, 0x010001ff01010000,
+    0x01000100ffff00ff, 0x01000100ffff0001, 0x01000100ffff0100, 0x01000100ff00ffff,
+    0x01000100ff00ff01, 0x01000100ff000000, 0x01000100ff0001ff, 0x01000100ff000101,
+    0x01000100ff01ffff, 0x01000100ff01ff00, 0x01000100ff0100ff, 0x01000100ff010001,
+    0x0100010000ffffff, 0x0100010000ffff01, 0x0100010000ff0000, 0x0100010000ff01ff,
+    0x0100010000ff0101, 0x010001000000ff00, 0x01000100000000ff, 0x0100010000000000,
+    0x0100010000000001, 0x0100010000000100, 0x010001000001ff01, 0x0100010000010000,
+    0x0100010000010001, 0x0100010000010101, 0x0100010001ffff00, 0x0100010001ff00ff,
+    0x010001000100ffff, 0x010001000100ff01, 0x0100010001000000, 0x0100010001000101,
+    0x010001000101ff00, 0x0100010001010001, 0x01000101ffff0000, 0x01000101ff000000,
+    0x01000101ff010000, 0x0100010100ff00ff, 0x0100010100ff0001, 0x0100010100ff0100,
+    0x010001010000ffff, 0x0100010100000000, 0x01000101000001ff, 0x010001010001ff00,
+    0x0100010101ff0000, 0x010001010100ff00, 0x01000101010000ff, 0x0100010101000000,
+    0x0100010101000001, 0x0101ffffffffffff, 0x0101ffffffffff01, 0x0101ffffffff01ff,
+    0x0101ffffffff0101, 0x0101ffffff000000, 0x0101ffffff01ffff, 0x0101ffffff01ff01,
+    0x0101ffffff0101ff, 0x0101ffffff010101, 0x0101ffff00ff0000, 0x0101ffff0000ff00,
+    0x0101ffff000000ff, 0x0101ffff00000001, 0x0101ffff00000100, 0x0101ffff01ffffff,
+    0x0101ffff01ffff01, 0x0101ffff01ff01ff, 0x0101ffff01ff0101, 0x0101ffff01000000,
+    0x0101ffff0101ffff, 0x0101ffff0101ff01, 0x0101ffff010101ff, 0x0101ffff01010101,
+    0x0101ff00ffff0000, 0x0101ff00ffff0100, 0x0101ff00ff00ff00, 0x0101ff00ff0000ff,
+    0x0101ff00ff000001, 0x0101ff00ff000100, 0x0101ff00ff000101, 0x0101ff0000ff0001,
+    0x0101ff0000ff0100, 0x0101ff000000ff00, 0x0101ff0000000000, 0x0101ff00000001ff,
+    0x0101ff0000000101, 0x0101ff000001ff00, 0x0101ff00000100ff, 0x0101ff0001ff0000,
+    0x0101ff000100ffff, 0x0101ff000100ff01, 0x0101ff0001000001, 0x0101ff0001000100,
+    0x0101ff01ffffff01, 0x0101ff01ffff01ff, 0x0101ff01ffff0101, 0x0101ff01ff00ffff,
+    0x0101ff01ff000100, 0x0101ff01ff01ff01, 0x0101ff01ff0101ff, 0x0101ff01ff010101,
+    0x0101ff0100ff0000, 0x0101ff010000ff00, 0x0101ff0100000001, 0x0101ff0100000100,
+    0x0101ff0100010000, 0x0101ff0101ffffff, 0x0101ff0101ffff01, 0x0101ff0101ff01ff,
+    0x0101ff0101ff0101, 0x0101ff0101000000, 0x0101ff010101ffff, 0x0101ff010101ff01,
+    0x0101ff01010101ff, 0x0101ff0101010101, 0x010100ffff000100, 0x010100ffff010000,
+    0x010100ff00ffff00, 0x010100ff00ff00ff, 0x010100ff0000ffff, 0x010100ff000000ff,
+    0x010100ff00000000, 0x010100ff000001ff, 0x010100ff00000101, 0x010100ff0001ff00,
+    0x010100ff00010000, 0x010100ff00010001, 0x010100ff000101ff, 0x010100ff00010100,
+    0x010100ff01ff0000, 0x01010000ffff0001, 0x01010000ffff0100, 0x01010000ff00ffff,
+    0x01010000ff00ff01, 0x01010000ff000000, 0x01010000ff0001ff, 0x01010000ff010001,
+    0x01010000ff010100, 0x0101000000ffff01, 0x0101000000ff0000, 0x010100000000ff00,
+    0x01010000000000ff, 0x0101000000000000, 0x0101000000000001, 0x0101000000000100,
+    0x0101000000010000, 0x0101000000010101, 0x0101000001ffff00, 0x0101000001ff00ff,
+    0x0101000001ff0000, 0x0101000001ff0001, 0x0101000001ff0100, 0x010100000100ff01,
+    0x0101000001000000, 0x01010000010001ff, 0x01010001ffff0000, 0x01010001ff00ff00,
+    0x01010001ff000001, 0x01010001ff000101, 0x01010001ff01ff00, 0x01010001ff010000,
+    0x0101000100ff00ff, 0x0101000100ff0001, 0x0101000100ff0101, 0x010100010000ff01,
+    0x0101000100000000, 0x0101000100000001, 0x01010001000001ff, 0x010100010001ffff,
+    0x010100010001ff01, 0x0101000101ff0001, 0x010100010100ffff, 0x0101000101000000,
+    0x0101000101000001, 0x0101000101000100, 0x010100010101ff00, 0x01010001010100ff,
+    0x0101000101010001, 0x010101ffffffffff, 0x010101ffffffff01, 0x010101ffffff01ff,
+    0x010101ffffff0101, 0x010101ffff01ffff, 0x010101ffff01ff01, 0x010101ffff0101ff,
+    0x010101ffff010101, 0x010101ff0000ff00, 0x010101ff000000ff, 0x010101ff00000001,
+    0x010101ff00000100, 0x010101ff01ffffff, 0x010101ff01ffff01, 0x010101ff01ff01ff,
+    0x010101ff01ff0101, 0x010101ff01000000, 0x010101ff0101ffff, 0x010101ff0101ff01,
+    0x010101ff010101ff, 0x010101ff01010101, 0x01010100ffff0000, 0x01010100ff0000ff,
+    0x01010100ff000100, 0x01010100ff01ff00, 0x01010100ff010000, 0x0101010000ffff00,
+    0x010101000000ffff, 0x0101010000000000, 0x0101010000000101, 0x010101000001ff00,
+    0x0101010000010001, 0x0101010000010100, 0x010101000100ffff, 0x0101010001000001,
+    0x01010101ffffffff, 0x01010101ffffff01, 0x01010101ffff01ff, 0x01010101ffff0101,
+    0x01010101ff01ffff, 0x01010101ff01ff01, 0x01010101ff0101ff, 0x01010101ff010101,
+    0x010101010000ff00, 0x01010101000000ff, 0x0101010100000001, 0x0101010101ffffff,
+    0x0101010101ffff01, 0x0101010101ff01ff, 0x0101010101ff0101, 0x0101010101000000,
+    0x010101010101ffff, 0x010101010101ff01, 0x01010101010101ff, 0x0101010101010101,
+GGML_TABLE_END()
+#else
+GGML_TABLE_BEGIN(uint32_t, iq1s_grid_gpu, NGRID_IQ1S)
+    0x00000000, 0x00000002, 0x00000101, 0x00000200, 0x00000202, 0x00010001, 0x00010101, 0x00020000,
+    0x00020002, 0x00020200, 0x00020202, 0x01000101, 0x01010001, 0x01010100, 0x01010102, 0x01020101,
+    0x02000000, 0x02000002, 0x02000200, 0x02000202, 0x02010101, 0x02020000, 0x02020002, 0x02020200,
+    0x02020202, 0x00000110, 0x00000111, 0x00010011, 0x00010110, 0x00010112, 0x00010211, 0x00010212,
+    0x00020111, 0x01000011, 0x01000112, 0x01000211, 0x01010012, 0x01010111, 0x01010212, 0x01020011,
+    0x01020110, 0x01020112, 0x01020210, 0x02000111, 0x02010011, 0x02010110, 0x02010112, 0x02020111,
+    0x00000020, 0x00000022, 0x00000220, 0x00000222, 0x00010121, 0x00020020, 0x00020022, 0x00020220,
+    0x00020222, 0x01000121, 0x01010021, 0x01010221, 0x01020120, 0x01020221, 0x02000020, 0x02000022,
+    0x02000220, 0x02000222, 0x02010021, 0x02010121, 0x02010221, 0x02020020, 0x02020022, 0x02020220,
+    0x02020222, 0x00011001, 0x00011100, 0x00011102, 0x00021101, 0x01001001, 0x01001201, 0x01011101,
+    0x01011202, 0x01021100, 0x01021101, 0x02011001, 0x02011201, 0x02021101, 0x00001011, 0x00001110,
+    0x00001111, 0x00001112, 0x00011111, 0x00011210, 0x00011212, 0x00021211, 0x01001010, 0x01001111,
+    0x01001212, 0x01011010, 0x01011011, 0x01011110, 0x01011111, 0x01011112, 0x01011211, 0x01021010,
+    0x01021012, 0x01021111, 0x01021210, 0x01021212, 0x02001011, 0x02011011, 0x02011111, 0x02011210,
+    0x02011212, 0x02021011, 0x02021110, 0x02021111, 0x02021112, 0x02021211, 0x00011120, 0x00011221,
+    0x01001021, 0x01001120, 0x01011020, 0x01011022, 0x01011121, 0x01011220, 0x01021020, 0x01021021,
+    0x01021122, 0x01021221, 0x02001121, 0x02011021, 0x02011120, 0x02011221, 0x00002000, 0x00002002,
+    0x00002200, 0x00002202, 0x00012101, 0x00022000, 0x00022002, 0x00022200, 0x00022202, 0x01002101,
+    0x01012001, 0x01012102, 0x01022101, 0x02002000, 0x02002002, 0x02002200, 0x02002202, 0x02012101,
+    0x02022000, 0x02022002, 0x02022200, 0x02022202, 0x00002111, 0x00012011, 0x00012110, 0x00012211,
+    0x00022110, 0x00022111, 0x01002011, 0x01012010, 0x01012011, 0x01012111, 0x01022011, 0x01022110,
+    0x01022211, 0x02012011, 0x02012110, 0x02012112, 0x02012211, 0x02022111, 0x00002020, 0x00002022,
+    0x00002220, 0x00002222, 0x00012121, 0x00022020, 0x00022022, 0x00022220, 0x00022222, 0x01002121,
+    0x01012021, 0x01012221, 0x01022021, 0x01022121, 0x02002020, 0x02002022, 0x02002121, 0x02002220,
+    0x02002222, 0x02012121, 0x02022020, 0x02022022, 0x02022220, 0x02022222, 0x00110000, 0x00110001,
+    0x00110100, 0x00110201, 0x00120100, 0x00120101, 0x01100001, 0x01100100, 0x01110000, 0x01110101,
+    0x01110200, 0x01120001, 0x01120100, 0x01120101, 0x01120201, 0x02110001, 0x02110100, 0x02110102,
+    0x02120001, 0x02120101, 0x00100011, 0x00100110, 0x00100112, 0x00100211, 0x00110010, 0x00110012,
+    0x00110111, 0x00110210, 0x00120011, 0x00120110, 0x00120211, 0x01100111, 0x01100212, 0x01110010,
+    0x01110011, 0x01110012, 0x01110110, 0x01110111, 0x01110112, 0x01110211, 0x01120010, 0x01120111,
+    0x02100110, 0x02110012, 0x02110111, 0x02120011, 0x02120110, 0x00110021, 0x00110120, 0x00110122,
+    0x00120121, 0x01100020, 0x01100122, 0x01100221, 0x01110022, 0x01110121, 0x01110220, 0x01110222,
+    0x01120120, 0x01120122, 0x02100121, 0x02110021, 0x02110120, 0x02110122, 0x02120121, 0x00101001,
+    0x00101102, 0x00101201, 0x00111100, 0x00111101, 0x00111200, 0x00111201, 0x00121001, 0x00121102,
+    0x01101001, 0x01101101, 0x01101102, 0x01101200, 0x01101202, 0x01111001, 0x01111100, 0x01111101,
+    0x01111102, 0x01111201, 0x01121002, 0x01121101, 0x01121200, 0x02101100, 0x02101201, 0x02111000,
+    0x02111100, 0x02111101, 0x02111200, 0x02111201, 0x02111202, 0x02121001, 0x02121100, 0x02121101,
+    0x02121201, 0x00101012, 0x00101111, 0x00101212, 0x00111011, 0x00111110, 0x00111111, 0x00111112,
+    0x00111211, 0x00121010, 0x00121012, 0x00121111, 0x00121210, 0x00121212, 0x01101011, 0x01101110,
+    0x01101111, 0x01101112, 0x01111011, 0x01111012, 0x01111110, 0x01111111, 0x01111112, 0x01111211,
+    0x01111212, 0x01121011, 0x01121110, 0x01121111, 0x01121112, 0x01121211, 0x02101010, 0x02101012,
+    0x02101110, 0x02101111, 0x02101210, 0x02101212, 0x02111010, 0x02111011, 0x02111110, 0x02111111,
+    0x02111112, 0x02111211, 0x02111212, 0x02121010, 0x02121012, 0x02121111, 0x00101021, 0x00101120,
+    0x00101121, 0x00101122, 0x00111121, 0x00111122, 0x00111220, 0x00111222, 0x00121021, 0x00121122,
+    0x01101020, 0x01101022, 0x01101120, 0x01101121, 0x01101220, 0x01101222, 0x01111021, 0x01111121,
+    0x01111122, 0x01111220, 0x01111221, 0x01121021, 0x01121120, 0x01121121, 0x01121220, 0x01121221,
+    0x01121222, 0x02101122, 0x02101222, 0x02111022, 0x02111121, 0x02121120, 0x02121221, 0x00112001,
+    0x00112102, 0x00122101, 0x01102001, 0x01102100, 0x01102102, 0x01102201, 0x01112000, 0x01112101,
+    0x01112200, 0x01112202, 0x01122000, 0x01122001, 0x01122100, 0x01122102, 0x01122201, 0x02102101,
+    0x02112001, 0x02112100, 0x02122101, 0x00112010, 0x00112012, 0x00112111, 0x00112212, 0x00122011,
+    0x00122111, 0x01102012, 0x01102110, 0x01102111, 0x01102210, 0x01112011, 0x01112110, 0x01112111,
+    0x01112112, 0x01112211, 0x01112212, 0x01122010, 0x01122111, 0x01122212, 0x02102211, 0x02112011,
+    0x02112012, 0x02112111, 0x02112210, 0x02122011, 0x02122112, 0x02122211, 0x00102221, 0x00112122,
+    0x00122120, 0x00122122, 0x01102120, 0x01102122, 0x01102221, 0x01112020, 0x01112022, 0x01112121,
+    0x01112220, 0x01122021, 0x01122122, 0x01122221, 0x02102121, 0x02112021, 0x02112122, 0x02112222,
+    0x00200000, 0x00200002, 0x00200200, 0x00200202, 0x00210101, 0x00220000, 0x00220002, 0x00220101,
+    0x00220200, 0x00220202, 0x01200101, 0x01210001, 0x01210201, 0x01220001, 0x01220101, 0x02200000,
+    0x02200002, 0x02200200, 0x02200202, 0x02210101, 0x02220000, 0x02220002, 0x02220101, 0x02220200,
+    0x02220202, 0x00200111, 0x00210011, 0x00210110, 0x00210211, 0x00220111, 0x01200012, 0x01200110,
+    0x01200211, 0x01210111, 0x01210210, 0x01210212, 0x01220011, 0x01220110, 0x01220111, 0x01220112,
+    0x02200111, 0x02210010, 0x02210112, 0x02210211, 0x02220111, 0x00200021, 0x00200220, 0x00200222,
+    0x00210021, 0x00210121, 0x00220020, 0x00220022, 0x00220220, 0x00220222, 0x01200121, 0x01210021,
+    0x01210122, 0x01210221, 0x01220121, 0x02200021, 0x02200220, 0x02200222, 0x02210021, 0x02210121,
+    0x02220020, 0x02220022, 0x02220220, 0x02220222, 0x00201101, 0x00211100, 0x00211102, 0x00211201,
+    0x00221101, 0x01201100, 0x01201101, 0x01201102, 0x01201201, 0x01211002, 0x01211101, 0x01211200,
+    0x01211202, 0x01221102, 0x02201101, 0x02211001, 0x02211100, 0x02211201, 0x02221001, 0x02221101,
+    0x00201211, 0x00211111, 0x00221011, 0x00221211, 0x01201010, 0x01201111, 0x01201210, 0x01211011,
+    0x01211110, 0x01211111, 0x01211211, 0x01221012, 0x01221111, 0x01221210, 0x02201211, 0x02211010,
+    0x02211110, 0x02211111, 0x02211210, 0x02211212, 0x02221011, 0x02221110, 0x02221112, 0x02221211,
+    0x00201121, 0x00211020, 0x00211022, 0x00211221, 0x00221121, 0x01201021, 0x01201221, 0x01211121,
+    0x01221020, 0x01221021, 0x01221221, 0x02201120, 0x02201122, 0x02211020, 0x02211222, 0x00202000,
+    0x00202002, 0x00202200, 0x00202202, 0x00212101, 0x00222000, 0x00222002, 0x00222200, 0x00222202,
+    0x01202101, 0x01212001, 0x01212100, 0x01222101, 0x02202000, 0x02202002, 0x02202200, 0x02202202,
+    0x02222000, 0x02222002, 0x02222200, 0x02222202, 0x00202211, 0x00212011, 0x00212110, 0x00212211,
+    0x00222111, 0x01202112, 0x01202211, 0x01212012, 0x01212111, 0x01222011, 0x01222110, 0x01222112,
+    0x01222211, 0x02202111, 0x02212010, 0x02212112, 0x02212211, 0x02222110, 0x02222111, 0x00202020,
+    0x00202022, 0x00202220, 0x00202222, 0x00222020, 0x00222022, 0x00222220, 0x00222222, 0x01202121,
+    0x01212021, 0x01212122, 0x01212221, 0x01222121, 0x02202020, 0x02202022, 0x02202220, 0x02202222,
+    0x02212121, 0x02222020, 0x02222022, 0x02222220, 0x02222222, 0x10000101, 0x10010001, 0x10010102,
+    0x10020101, 0x11000201, 0x11010002, 0x11010101, 0x11010200, 0x11010202, 0x11020001, 0x11020100,
+    0x11020102, 0x12010100, 0x12010201, 0x12020001, 0x12020102, 0x10000010, 0x10000011, 0x10000110,
+    0x10000112, 0x10000211, 0x10010012, 0x10010111, 0x10010112, 0x10010210, 0x10010212, 0x10020011,
+    0x10020112, 0x10020211, 0x11000111, 0x11000210, 0x11000212, 0x11010011, 0x11010110, 0x11010111,
+    0x11010112, 0x11010211, 0x11010212, 0x11020111, 0x11020210, 0x11020212, 0x12000011, 0x12000110,
+    0x12000112, 0x12010010, 0x12010012, 0x12010111, 0x12020010, 0x12020011, 0x12020012, 0x10000121,
+    0x10010021, 0x10010120, 0x10010122, 0x10020121, 0x11000021, 0x11010022, 0x11010121, 0x11010222,
+    0x11020120, 0x11020221, 0x12000221, 0x12010120, 0x12020121, 0x10001001, 0x10011101, 0x10011201,
+    0x10021201, 0x11001101, 0x11001200, 0x11001202, 0x11011001, 0x11011100, 0x11011101, 0x11011102,
+    0x11021001, 0x11021002, 0x11021101, 0x11021200, 0x11021202, 0x12001001, 0x12001102, 0x12001201,
+    0x12011000, 0x12011002, 0x12011101, 0x12021000, 0x12021001, 0x12021201, 0x10001011, 0x10001012,
+    0x10001111, 0x10001212, 0x10011011, 0x10011110, 0x10011111, 0x10011112, 0x10011211, 0x10021010,
+    0x10021111, 0x10021212, 0x11001011, 0x11001110, 0x11001111, 0x11001112, 0x11001211, 0x11011010,
+    0x11011011, 0x11011110, 0x11011111, 0x11011112, 0x11011210, 0x11011211, 0x11021011, 0x11021110,
+    0x11021111, 0x11021112, 0x11021211, 0x12001012, 0x12001110, 0x12001111, 0x12001210, 0x12011011,
+    0x12011110, 0x12011111, 0x12011112, 0x12011211, 0x12011212, 0x12021111, 0x12021210, 0x12021212,
+    0x10001021, 0x10001121, 0x10001221, 0x10011120, 0x10011121, 0x10011220, 0x10011222, 0x10021021,
+    0x10021120, 0x10021221, 0x11001020, 0x11001022, 0x11001121, 0x11001220, 0x11011020, 0x11011021,
+    0x11011022, 0x11011121, 0x11011122, 0x11011221, 0x11021022, 0x11021121, 0x11021220, 0x12001021,
+    0x12001121, 0x12001222, 0x12011120, 0x12011121, 0x12021021, 0x12021120, 0x12021122, 0x10002101,
+    0x10012001, 0x10012101, 0x10012202, 0x10022101, 0x11002002, 0x11002201, 0x11012000, 0x11012101,
+    0x11012200, 0x11022001, 0x11022100, 0x11022102, 0x11022201, 0x12002101, 0x12012001, 0x12012100,
+    0x12012102, 0x12012201, 0x12022101, 0x10002011, 0x10002111, 0x10002112, 0x10002212, 0x10012010,
+    0x10012110, 0x10012111, 0x10012210, 0x10022011, 0x10022110, 0x10022112, 0x11002010, 0x11002111,
+    0x11002212, 0x11012011, 0x11012012, 0x11012110, 0x11012111, 0x11012112, 0x11012211, 0x11022010,
+    0x11022012, 0x11022111, 0x11022112, 0x11022212, 0x12002112, 0x12002211, 0x12012012, 0x12012111,
+    0x12012112, 0x12012210, 0x12022011, 0x12022110, 0x12022112, 0x12022211, 0x10012122, 0x11002120,
+    0x11002122, 0x11002221, 0x11012121, 0x11012220, 0x11012222, 0x11022120, 0x11022221, 0x12012120,
+    0x12022121, 0x10100001, 0x10100100, 0x10100101, 0x10100102, 0x10100201, 0x10110002, 0x10110101,
+    0x10110202, 0x10120001, 0x10120100, 0x10120201, 0x11100000, 0x11100101, 0x11100200, 0x11110001,
+    0x11110100, 0x11110101, 0x11110102, 0x11110201, 0x11120101, 0x11120200, 0x12100102, 0x12100201,
+    0x12110101, 0x12110200, 0x12120000, 0x12120001, 0x12120102, 0x12120201, 0x10100111, 0x10100210,
+    0x10100211, 0x10100212, 0x10110011, 0x10110110, 0x10110111, 0x10110112, 0x10110210, 0x10110211,
+    0x10120010, 0x10120111, 0x10120112, 0x10120210, 0x10120212, 0x11100011, 0x11100110, 0x11100111,
+    0x11100112, 0x11100211, 0x11110010, 0x11110011, 0x11110012, 0x11110110, 0x11110111, 0x11110112,
+    0x11110210, 0x11110211, 0x11110212, 0x11120011, 0x11120110, 0x11120111, 0x11120112, 0x11120211,
+    0x12100012, 0x12100111, 0x12110011, 0x12110110, 0x12110111, 0x12110112, 0x12110211, 0x12120010,
+    0x12120111, 0x12120212, 0x10100021, 0x10100122, 0x10110022, 0x10110121, 0x10110222, 0x10120021,
+    0x10120120, 0x11100022, 0x11100121, 0x11100222, 0x11110021, 0x11110120, 0x11110121, 0x11110122,
+    0x11110221, 0x11120022, 0x11120121, 0x12100121, 0x12110020, 0x12110022, 0x12110121, 0x12110221,
+    0x12110222, 0x12120120, 0x10101100, 0x10101101, 0x10111001, 0x10111100, 0x10111101, 0x10111102,
+    0x10111200, 0x10111201, 0x10121001, 0x10121101, 0x10121200, 0x10121202, 0x11101001, 0x11101100,
+    0x11101101, 0x11101102, 0x11101201, 0x11101202, 0x11111000, 0x11111001, 0x11111100, 0x11111101,
+    0x11111102, 0x11111200, 0x11111201, 0x11111202, 0x11121001, 0x11121002, 0x11121100, 0x11121101,
+    0x11121102, 0x11121201, 0x12101000, 0x12101200, 0x12101202, 0x12111001, 0x12111100, 0x12111101,
+    0x12111102, 0x12111201, 0x12121001, 0x12121100, 0x12121101, 0x12121202, 0x10101011, 0x10101012,
+    0x10101110, 0x10101111, 0x10101112, 0x10101211, 0x10111010, 0x10111011, 0x10111012, 0x10111110,
+    0x10111111, 0x10111112, 0x10111211, 0x10111212, 0x10121011, 0x10121110, 0x10121111, 0x10121112,
+    0x10121211, 0x11101010, 0x11101011, 0x11101012, 0x11101110, 0x11101111, 0x11101112, 0x11101210,
+    0x11101211, 0x11111010, 0x11111011, 0x11111012, 0x11111110, 0x11111111, 0x11111112, 0x11111210,
+    0x11111211, 0x11111212, 0x11121010, 0x11121011, 0x11121110, 0x11121111, 0x11121112, 0x11121210,
+    0x11121211, 0x11121212, 0x12101011, 0x12101110, 0x12101111, 0x12101211, 0x12101212, 0x12111010,
+    0x12111011, 0x12111110, 0x12111111, 0x12111112, 0x12111210, 0x12111211, 0x12121011, 0x12121110,
+    0x12121111, 0x12121112, 0x12121211, 0x10101020, 0x10101021, 0x10101022, 0x10101120, 0x10101122,
+    0x10101220, 0x10101221, 0x10111021, 0x10111120, 0x10111121, 0x10111220, 0x10111221, 0x10121020,
+    0x10121021, 0x10121022, 0x10121120, 0x10121121, 0x10121122, 0x10121220, 0x10121221, 0x11101021,
+    0x11101121, 0x11101122, 0x11101220, 0x11101221, 0x11101222, 0x11111020, 0x11111021, 0x11111022,
+    0x11111120, 0x11111121, 0x11111122, 0x11111220, 0x11111221, 0x11111222, 0x11121021, 0x11121120,
+    0x11121121, 0x11121221, 0x12101022, 0x12101121, 0x12101122, 0x12101220, 0x12101221, 0x12101222,
+    0x12111021, 0x12111121, 0x12111222, 0x12121022, 0x12121121, 0x12121122, 0x12121220, 0x12121221,
+    0x10102100, 0x10102101, 0x10102102, 0x10102201, 0x10112000, 0x10112101, 0x10112200, 0x10122001,
+    0x10122202, 0x11102101, 0x11102200, 0x11102202, 0x11112001, 0x11112100, 0x11112101, 0x11112102,
+    0x11112200, 0x11112201, 0x11122000, 0x11122002, 0x11122100, 0x11122101, 0x12102002, 0x12102201,
+    0x12112000, 0x12112002, 0x12112101, 0x12112200, 0x12122001, 0x12122201, 0x10102011, 0x10102012,
+    0x10102111, 0x10102212, 0x10112011, 0x10112110, 0x10112111, 0x10112112, 0x10112211, 0x10122111,
+    0x11102011, 0x11102110, 0x11102111, 0x11102112, 0x11102211, 0x11112010, 0x11112011, 0x11112012,
+    0x11112110, 0x11112111, 0x11112112, 0x11112210, 0x11112211, 0x11112212, 0x11122011, 0x11122110,
+    0x11122111, 0x11122112, 0x11122211, 0x12102011, 0x12102111, 0x12102211, 0x12112011, 0x12112110,
+    0x12112111, 0x12112112, 0x12112210, 0x12112211, 0x12122111, 0x10102120, 0x10102220, 0x10112121,
+    0x10112222, 0x10122020, 0x10122121, 0x10122122, 0x10122221, 0x11102121, 0x11102220, 0x11102221,
+    0x11112021, 0x11112121, 0x11112122, 0x11112220, 0x11112221, 0x11122022, 0x11122121, 0x11122220,
+    0x11122222, 0x12102021, 0x12102222, 0x12112022, 0x12112121, 0x12112122, 0x12112220, 0x12112222,
+    0x12122021, 0x10200101, 0x10210100, 0x10210102, 0x10210201, 0x10220101, 0x11200100, 0x11210000,
+    0x11210101, 0x11210102, 0x11210200, 0x11210202, 0x11220001, 0x11220100, 0x11220102, 0x11220201,
+    0x12200001, 0x12210102, 0x12220101, 0x10200011, 0x10200110, 0x10200112, 0x10200211, 0x10210012,
+    0x10210111, 0x10220011, 0x10220012, 0x10220112, 0x10220211, 0x11200111, 0x11200211, 0x11210011,
+    0x11210111, 0x11210112, 0x11210211, 0x11220111, 0x11220112, 0x11220212, 0x12200110, 0x12200212,
+    0x12210012, 0x12210111, 0x12220011, 0x12220112, 0x12220211, 0x10210021, 0x10210122, 0x10210221,
+    0x11200020, 0x11200021, 0x11200122, 0x11210121, 0x11210122, 0x11210220, 0x11220020, 0x12200121,
+    0x12210021, 0x12210122, 0x12220121, 0x10211001, 0x10211002, 0x10211101, 0x10211102, 0x10211202,
+    0x10221001, 0x10221102, 0x10221201, 0x11201000, 0x11201002, 0x11201101, 0x11201200, 0x11201202,
+    0x11211001, 0x11211100, 0x11211101, 0x11211102, 0x11211201, 0x11211202, 0x11221000, 0x11221002,
+    0x11221101, 0x12201100, 0x12201101, 0x12201201, 0x12211000, 0x12211002, 0x12211100, 0x12211101,
+    0x12211102, 0x12211200, 0x12211202, 0x12221001, 0x12221100, 0x12221201, 0x10201111, 0x10201210,
+    0x10201212, 0x10211011, 0x10211111, 0x10211112, 0x10211211, 0x11201110, 0x11201111, 0x11201112,
+    0x11201211, 0x11211010, 0x11211011, 0x11211110, 0x11211111, 0x11211112, 0x11211211, 0x11221011,
+    0x11221110, 0x11221111, 0x11221112, 0x11221211, 0x12201112, 0x12201211, 0x12201212, 0x12211011,
+    0x12211111, 0x12211112, 0x12211211, 0x12211212, 0x12221012, 0x12221111, 0x12221112, 0x12221210,
+    0x10201022, 0x10201221, 0x10211121, 0x10221020, 0x10221122, 0x10221220, 0x10221221, 0x11201020,
+    0x11201121, 0x11201220, 0x11201222, 0x11211021, 0x11211120, 0x11211121, 0x11211122, 0x11211220,
+    0x11211222, 0x11221020, 0x11221121, 0x11221220, 0x12201020, 0x12201022, 0x12201121, 0x12201222,
+    0x12211120, 0x12211122, 0x12211220, 0x12211221, 0x12221020, 0x12221120, 0x12221122, 0x12221222,
+    0x10212102, 0x10212201, 0x10222101, 0x11202001, 0x11212002, 0x11212101, 0x11212202, 0x11222001,
+    0x11222201, 0x12202101, 0x12212001, 0x12212200, 0x12222102, 0x10202011, 0x10202110, 0x10212010,
+    0x10212111, 0x10222011, 0x10222110, 0x10222112, 0x10222211, 0x11202010, 0x11202011, 0x11202111,
+    0x11202112, 0x11202210, 0x11212011, 0x11212110, 0x11212111, 0x11212112, 0x11212211, 0x11222010,
+    0x11222111, 0x11222212, 0x12202012, 0x12202110, 0x12202212, 0x12212111, 0x12222011, 0x12222110,
+    0x12222111, 0x12222211, 0x10212021, 0x10212122, 0x10212220, 0x11202021, 0x11202120, 0x11202221,
+    0x11212020, 0x11212121, 0x11212220, 0x11212222, 0x11222120, 0x11222121, 0x11222221, 0x12202122,
+    0x12212120, 0x12212220, 0x12212222, 0x12222122, 0x20000000, 0x20000002, 0x20000200, 0x20000202,
+    0x20020000, 0x20020002, 0x20020200, 0x20020202, 0x21000101, 0x21010000, 0x21010001, 0x21010100,
+    0x21010102, 0x21010201, 0x21020101, 0x22000000, 0x22000002, 0x22000200, 0x22000202, 0x22010101,
+    0x22020000, 0x22020002, 0x22020200, 0x22020202, 0x20000111, 0x20010011, 0x20010110, 0x20010112,
+    0x20010211, 0x20020111, 0x21000011, 0x21000110, 0x21000211, 0x21010010, 0x21010012, 0x21010111,
+    0x21010112, 0x21010210, 0x21010211, 0x21020110, 0x21020112, 0x21020211, 0x22000111, 0x22000211,
+    0x22010110, 0x22010112, 0x22010211, 0x22020111, 0x20000020, 0x20000022, 0x20000220, 0x20000222,
+    0x20010121, 0x20020020, 0x20020022, 0x20020220, 0x20020222, 0x21010021, 0x21010120, 0x21010221,
+    0x21020121, 0x22000020, 0x22000022, 0x22000220, 0x22000222, 0x22010121, 0x22020020, 0x22020022,
+    0x22020220, 0x22020222, 0x20011100, 0x20011201, 0x21001001, 0x21001100, 0x21011001, 0x21011101,
+    0x21011202, 0x21021001, 0x21021100, 0x21021201, 0x22011100, 0x22011201, 0x20001011, 0x20001211,
+    0x20011012, 0x20011111, 0x20011212, 0x20021112, 0x20021211, 0x21001010, 0x21001011, 0x21001111,
+    0x21001210, 0x21011011, 0x21011110, 0x21011111, 0x21011112, 0x21011211, 0x21011212, 0x21021111,
+    0x21021112, 0x21021210, 0x21021212, 0x22001011, 0x22001110, 0x22001112, 0x22001211, 0x22011010,
+    0x22011012, 0x22011111, 0x22011210, 0x22021112, 0x20011021, 0x20011122, 0x20011221, 0x20021121,
+    0x21001021, 0x21001120, 0x21001221, 0x21001222, 0x21011020, 0x21011121, 0x21011221, 0x21011222,
+    0x21021021, 0x21021122, 0x21021222, 0x22001121, 0x22011021, 0x22011222, 0x22021120, 0x20002000,
+    0x20002002, 0x20002200, 0x20002202, 0x20012101, 0x20022000, 0x20022002, 0x20022200, 0x20022202,
+    0x21002001, 0x21002101, 0x21012001, 0x21012100, 0x21012201, 0x21022101, 0x21022201, 0x22002000,
+    0x22002002, 0x22002200, 0x22002202, 0x22012101, 0x22022000, 0x22022002, 0x22022200, 0x22022202,
+    0x20002111, 0x20002112, 0x20012011, 0x20012110, 0x20012112, 0x20022111, 0x21002011, 0x21002110,
+    0x21002112, 0x21002211, 0x21012010, 0x21012012, 0x21012111, 0x21012212, 0x21022011, 0x21022110,
+    0x22002111, 0x22012112, 0x22012211, 0x22022111, 0x20002020, 0x20002022, 0x20002220, 0x20002222,
+    0x20012121, 0x20022020, 0x20022022, 0x20022220, 0x20022222, 0x21002121, 0x21012021, 0x21012120,
+    0x21012122, 0x22002020, 0x22002022, 0x22002220, 0x22002222, 0x22012121, 0x22022020, 0x22022022,
+    0x22022220, 0x22022222, 0x20100101, 0x20110001, 0x20110102, 0x20110200, 0x20110201, 0x20120101,
+    0x21100001, 0x21100102, 0x21100201, 0x21110101, 0x21110200, 0x21110202, 0x21120201, 0x21120202,
+    0x22100101, 0x22110001, 0x22110100, 0x22110102, 0x22110201, 0x22120101, 0x20100011, 0x20100110,
+    0x20100112, 0x20100211, 0x20110010, 0x20110111, 0x20110210, 0x20110212, 0x20120011, 0x20120110,
+    0x20120112, 0x20120211, 0x21100010, 0x21100111, 0x21110010, 0x21110011, 0x21110110, 0x21110111,
+    0x21110112, 0x21110211, 0x21120012, 0x21120111, 0x22100110, 0x22100112, 0x22110012, 0x22110111,
+    0x22110210, 0x22120011, 0x22120110, 0x22120112, 0x22120211, 0x20100121, 0x20110021, 0x20110120,
+    0x20110221, 0x20120121, 0x21100120, 0x21100122, 0x21100221, 0x21110020, 0x21110022, 0x21110121,
+    0x21110220, 0x21120122, 0x21120221, 0x22100121, 0x22110120, 0x22110122, 0x22120221, 0x20101001,
+    0x20101100, 0x20101102, 0x20111000, 0x20111101, 0x20111200, 0x20121102, 0x21101000, 0x21101202,
+    0x21111001, 0x21111100, 0x21111101, 0x21111102, 0x21111200, 0x21111201, 0x21121000, 0x21121001,
+    0x21121002, 0x21121101, 0x22101100, 0x22101102, 0x22111002, 0x22111100, 0x22111101, 0x22111200,
+    0x22121001, 0x22121201, 0x20101010, 0x20101111, 0x20101210, 0x20101212, 0x20111010, 0x20111011,
+    0x20111110, 0x20111111, 0x20111112, 0x20111211, 0x20121011, 0x20121111, 0x20121211, 0x20121212,
+    0x21101011, 0x21101110, 0x21101111, 0x21101112, 0x21101211, 0x21111010, 0x21111011, 0x21111012,
+    0x21111110, 0x21111111, 0x21111112, 0x21111210, 0x21111211, 0x21111212, 0x21121011, 0x21121110,
+    0x21121111, 0x21121112, 0x21121211, 0x22101011, 0x22101111, 0x22101210, 0x22111011, 0x22111012,
+    0x22111110, 0x22111111, 0x22111112, 0x22111211, 0x22111212, 0x22121010, 0x22121012, 0x22121111,
+    0x22121210, 0x22121212, 0x20101021, 0x20101120, 0x20111020, 0x20111121, 0x20111221, 0x20121020,
+    0x20121122, 0x20121221, 0x21101121, 0x21101220, 0x21101221, 0x21111021, 0x21111022, 0x21111121,
+    0x21111122, 0x21111221, 0x21121121, 0x21121220, 0x22101022, 0x22101120, 0x22101221, 0x22101222,
+    0x22111022, 0x22111120, 0x22111121, 0x22121120, 0x22121122, 0x22121221, 0x20102101, 0x20112102,
+    0x20112201, 0x20122101, 0x21102001, 0x21102102, 0x21112000, 0x21112002, 0x21112101, 0x21112102,
+    0x21112202, 0x21122100, 0x21122101, 0x22102101, 0x22112001, 0x22112102, 0x22112201, 0x22122101,
+    0x20102110, 0x20102112, 0x20102211, 0x20112010, 0x20112012, 0x20112111, 0x20112210, 0x20112212,
+    0x20122010, 0x20122011, 0x20122110, 0x20122112, 0x21102010, 0x21102012, 0x21102111, 0x21102210,
+    0x21102212, 0x21112011, 0x21112110, 0x21112111, 0x21112112, 0x21112211, 0x21122012, 0x21122111,
+    0x21122112, 0x21122212, 0x22102011, 0x22102110, 0x22112010, 0x22112012, 0x22112111, 0x22112212,
+    0x22122011, 0x22122112, 0x20102121, 0x20112121, 0x20122121, 0x21102120, 0x21102122, 0x21102221,
+    0x21112020, 0x21112121, 0x21112220, 0x21122021, 0x22102121, 0x22112021, 0x22112120, 0x22112121,
+    0x22112122, 0x20200000, 0x20200002, 0x20200200, 0x20200202, 0x20210101, 0x20220000, 0x20220002,
+    0x20220200, 0x20220202, 0x21200101, 0x21210001, 0x21210100, 0x21210102, 0x21210201, 0x22200000,
+    0x22200002, 0x22200200, 0x22200202, 0x22210101, 0x22220000, 0x22220002, 0x22220200, 0x22220202,
+    0x20200111, 0x20200211, 0x20210011, 0x20210110, 0x20210112, 0x20210211, 0x20210212, 0x21200112,
+    0x21200211, 0x21210011, 0x21210111, 0x21210210, 0x21210212, 0x21220011, 0x21220110, 0x22200111,
+    0x22210010, 0x22210012, 0x22210112, 0x22210211, 0x20200022, 0x20200220, 0x20200222, 0x20210020,
+    0x20210221, 0x20220022, 0x20220220, 0x20220222, 0x21200121, 0x21210021, 0x21210122, 0x21210221,
+    0x21220121, 0x22200020, 0x22200022, 0x22200220, 0x22200222, 0x22210121, 0x22220020, 0x22220022,
+    0x22220220, 0x22220222, 0x20211201, 0x20221101, 0x21201001, 0x21201100, 0x21211000, 0x21211100,
+    0x21211101, 0x21211200, 0x21211202, 0x21221001, 0x21221101, 0x21221102, 0x21221200, 0x21221201,
+    0x22201101, 0x20201112, 0x20201211, 0x20211010, 0x20211012, 0x20211111, 0x20211210, 0x20221112,
+    0x20221211, 0x21201012, 0x21201111, 0x21211011, 0x21211110, 0x21211111, 0x21211112, 0x21211211,
+    0x21221111, 0x21221212, 0x22201011, 0x22201110, 0x22201111, 0x22201112, 0x22201211, 0x22211012,
+    0x22211111, 0x22211210, 0x20201121, 0x20211021, 0x20211122, 0x20211222, 0x20221021, 0x20221121,
+    0x21201120, 0x21201122, 0x21201222, 0x21211022, 0x21211121, 0x21211122, 0x21211220, 0x21221020,
+    0x21221022, 0x22201122, 0x22211020, 0x22211121, 0x22211122, 0x22211221, 0x22221021, 0x22221120,
+    0x22221122, 0x20202000, 0x20202002, 0x20202200, 0x20202202, 0x20222000, 0x20222002, 0x20222200,
+    0x20222202, 0x21212001, 0x21212100, 0x21212102, 0x21212201, 0x22202000, 0x22202002, 0x22202200,
+    0x22202202, 0x22212101, 0x22222000, 0x22222002, 0x22222200, 0x22222202, 0x20202111, 0x20212110,
+    0x20212211, 0x20222011, 0x20222111, 0x21202011, 0x21212010, 0x21212111, 0x21212212, 0x21222011,
+    0x21222112, 0x21222211, 0x22212010, 0x22212112, 0x20202020, 0x20202022, 0x20202220, 0x20202222,
+    0x20222020, 0x20222022, 0x20222220, 0x20222222, 0x21212021, 0x21212120, 0x21212122, 0x22202020,
+    0x22202022, 0x22202220, 0x22202222, 0x22212121, 0x22222020, 0x22222022, 0x22222220, 0x22222222,
+GGML_TABLE_END()
+#endif
+
+#endif // GGML_COMMON_IMPL
+#endif // GGML_COMMON_IMPL
diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index e26260a35..f51b2042d 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -1,222 +1,55 @@
+#include "ggml-cuda.h"
+#include "ggml.h"
+#include "ggml-backend-impl.h"
+
+#include "ggml-cuda/common.cuh"
+#include "ggml-cuda/acc.cuh"
+#include "ggml-cuda/alibi.cuh"
+#include "ggml-cuda/arange.cuh"
+#include "ggml-cuda/argsort.cuh"
+#include "ggml-cuda/binbcast.cuh"
+#include "ggml-cuda/clamp.cuh"
+#include "ggml-cuda/concat.cuh"
+#include "ggml-cuda/convert.cuh"
+#include "ggml-cuda/cpy.cuh"
+#include "ggml-cuda/diagmask.cuh"
+#include "ggml-cuda/dmmv.cuh"
+#include "ggml-cuda/getrows.cuh"
+#include "ggml-cuda/im2col.cuh"
+#include "ggml-cuda/mmq.cuh"
+#include "ggml-cuda/mmvq.cuh"
+#include "ggml-cuda/norm.cuh"
+#include "ggml-cuda/pad.cuh"
+#include "ggml-cuda/pool2d.cuh"
+#include "ggml-cuda/quantize.cuh"
+#include "ggml-cuda/rope.cuh"
+#include "ggml-cuda/scale.cuh"
+#include "ggml-cuda/softmax.cuh"
+#include "ggml-cuda/sumrows.cuh"
+#include "ggml-cuda/tsembd.cuh"
+#include "ggml-cuda/unary.cuh"
+#include "ggml-cuda/upscale.cuh"
+
 #include <algorithm>
-#include <assert.h>
+#include <array>
 #include <atomic>
 #include <cinttypes>
 #include <cstddef>
 #include <cstdint>
 #include <float.h>
 #include <limits>
+#include <map>
+#include <memory>
+#include <mutex>
 #include <stdint.h>
 #include <stdio.h>
+#include <string>
 #include <vector>
 
-
-#if defined(GGML_USE_HIPBLAS)
-#include <hip/hip_runtime.h>
-#include <hipblas/hipblas.h>
-#include <hip/hip_fp16.h>
-#ifdef __HIP_PLATFORM_AMD__
-// for rocblas_initialize()
-#include "rocblas/rocblas.h"
-#endif // __HIP_PLATFORM_AMD__
-#define CUBLAS_COMPUTE_16F HIPBLAS_R_16F
-#define CUBLAS_COMPUTE_32F HIPBLAS_R_32F
-#define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_R_32F
-#define CUBLAS_GEMM_DEFAULT HIPBLAS_GEMM_DEFAULT
-#define CUBLAS_GEMM_DEFAULT_TENSOR_OP HIPBLAS_GEMM_DEFAULT
-#define CUBLAS_OP_N HIPBLAS_OP_N
-#define CUBLAS_OP_T HIPBLAS_OP_T
-#define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
-#define CUBLAS_TF32_TENSOR_OP_MATH 0
-#define CUDA_R_16F  HIPBLAS_R_16F
-#define CUDA_R_32F  HIPBLAS_R_32F
-#define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
-#define cublasComputeType_t hipblasDatatype_t //deprecated, new hipblasComputeType_t not in 5.6
-#define cublasCreate hipblasCreate
-#define cublasGemmEx hipblasGemmEx
-#define cublasGemmBatchedEx hipblasGemmBatchedEx
-#define cublasGemmStridedBatchedEx hipblasGemmStridedBatchedEx
-#define cublasHandle_t hipblasHandle_t
-#define cublasSetMathMode(handle, mode) CUBLAS_STATUS_SUCCESS
-#define cublasSetStream hipblasSetStream
-#define cublasSgemm hipblasSgemm
-#define cublasStatus_t hipblasStatus_t
-#define cudaDataType_t hipblasDatatype_t //deprecated, new hipblasDatatype not in 5.6
-#define cudaDeviceCanAccessPeer hipDeviceCanAccessPeer
-#define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess
-#define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess
-#define cudaDeviceProp hipDeviceProp_t
-#define cudaDeviceSynchronize hipDeviceSynchronize
-#define cudaError_t hipError_t
-#define cudaEventCreateWithFlags hipEventCreateWithFlags
-#define cudaEventDisableTiming hipEventDisableTiming
-#define cudaEventRecord hipEventRecord
-#define cudaEvent_t hipEvent_t
-#define cudaEventDestroy hipEventDestroy
-#define cudaFree hipFree
-#define cudaFreeHost hipHostFree
-#define cudaGetDevice hipGetDevice
-#define cudaGetDeviceCount hipGetDeviceCount
-#define cudaGetDeviceProperties hipGetDeviceProperties
-#define cudaGetErrorString hipGetErrorString
-#define cudaGetLastError hipGetLastError
-#ifdef GGML_HIP_UMA
-#define cudaMalloc hipMallocManaged
-#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size)
-#else
-#define cudaMalloc hipMalloc
-#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault)
-#endif
-#define cudaMemcpy hipMemcpy
-#define cudaMemcpyAsync hipMemcpyAsync
-#define cudaMemcpyPeerAsync hipMemcpyPeerAsync
-#define cudaMemcpy2DAsync hipMemcpy2DAsync
-#define cudaMemcpyDeviceToDevice hipMemcpyDeviceToDevice
-#define cudaMemcpyDeviceToHost hipMemcpyDeviceToHost
-#define cudaMemcpyHostToDevice hipMemcpyHostToDevice
-#define cudaMemcpyKind hipMemcpyKind
-#define cudaMemset hipMemset
-#define cudaMemsetAsync hipMemsetAsync
-#define cudaOccupancyMaxPotentialBlockSize hipOccupancyMaxPotentialBlockSize
-#define cudaSetDevice hipSetDevice
-#define cudaStreamCreateWithFlags hipStreamCreateWithFlags
-#define cudaStreamFireAndForget hipStreamFireAndForget
-#define cudaStreamNonBlocking hipStreamNonBlocking
-#define cudaStreamSynchronize hipStreamSynchronize
-#define cudaStreamWaitEvent(stream, event, flags) hipStreamWaitEvent(stream, event, flags)
-#define cudaStream_t hipStream_t
-#define cudaSuccess hipSuccess
-#define __trap abort
-#define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
-#define CUBLAS_STATUS_NOT_INITIALIZED HIPBLAS_STATUS_NOT_INITIALIZED
-#define CUBLAS_STATUS_ALLOC_FAILED HIPBLAS_STATUS_ALLOC_FAILED
-#define CUBLAS_STATUS_INVALID_VALUE HIPBLAS_STATUS_INVALID_VALUE
-#define CUBLAS_STATUS_ARCH_MISMATCH HIPBLAS_STATUS_ARCH_MISMATCH
-#define CUBLAS_STATUS_MAPPING_ERROR HIPBLAS_STATUS_MAPPING_ERROR
-#define CUBLAS_STATUS_EXECUTION_FAILED HIPBLAS_STATUS_EXECUTION_FAILED
-#define CUBLAS_STATUS_INTERNAL_ERROR HIPBLAS_STATUS_INTERNAL_ERROR
-#define CUBLAS_STATUS_NOT_SUPPORTED HIPBLAS_STATUS_NOT_SUPPORTED
-#else
-#include <cuda_runtime.h>
-#include <cuda.h>
-#include <cublas_v2.h>
-#include <cuda_fp16.h>
-
-#if CUDART_VERSION < 11020
-#define CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED CU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED
-#define CUBLAS_TF32_TENSOR_OP_MATH CUBLAS_TENSOR_OP_MATH
-#define CUBLAS_COMPUTE_16F CUDA_R_16F
-#define CUBLAS_COMPUTE_32F CUDA_R_32F
-#define cublasComputeType_t cudaDataType_t
-#endif // CUDART_VERSION < 11020
-
-#endif // defined(GGML_USE_HIPBLAS)
-
-#include "ggml-cuda.h"
-#include "ggml.h"
-#include "ggml-backend-impl.h"
-
-#define CC_PASCAL     600
-#define MIN_CC_DP4A   610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
-#define CC_VOLTA      700
-#define CC_OFFSET_AMD 1000000
-#define CC_RDNA1      (CC_OFFSET_AMD + 1010)
-#define CC_RDNA2      (CC_OFFSET_AMD + 1030)
-#define CC_RDNA3      (CC_OFFSET_AMD + 1100)
-
-#define GGML_CUDA_MAX_NODES 8192
-
-// define this if you want to always fallback to MMQ kernels and not use cuBLAS for matrix multiplication
-// on modern hardware, using cuBLAS is recommended as it utilizes F16 tensor cores which are very performant
-// for large computational tasks. the drawback is that this requires some extra amount of VRAM:
-// -  7B quantum model: +100-200 MB
-// - 13B quantum model: +200-400 MB
-//
-//#define GGML_CUDA_FORCE_MMQ
-
-// TODO: improve this to be correct for more hardware
-//       for example, currently fails for GeForce GTX 1660 which is TURING arch (> VOLTA) but does not have tensor cores
-#if !defined(GGML_CUDA_FORCE_MMQ)
-#define CUDA_USE_TENSOR_CORES
-#endif
-
-// max batch size to use MMQ kernels when tensor cores are available
-#define MMQ_MAX_BATCH_SIZE 32
-
-#if defined(GGML_USE_HIPBLAS)
-#define __CUDA_ARCH__ 1300
-
-#if defined(__gfx1100__) || defined(__gfx1101__) || defined(__gfx1102__) || defined(__gfx1103__) || \
-    defined(__gfx1150__) || defined(__gfx1151__)
-#define RDNA3
-#endif
-
-#if defined(__gfx1030__) || defined(__gfx1031__) || defined(__gfx1032__) || defined(__gfx1033__) || \
-    defined(__gfx1034__) || defined(__gfx1035__) || defined(__gfx1036__) || defined(__gfx1037__)
-#define RDNA2
-#endif
-
-#ifndef __has_builtin
-    #define __has_builtin(x) 0
-#endif
-
-typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
-static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
-    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
-    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
-#if __has_builtin(__builtin_elementwise_sub_sat)
-    const int8x4_t c = __builtin_elementwise_sub_sat(va, vb);
-    return reinterpret_cast<const int &>(c);
-#else
-    int8x4_t c;
-    int16_t tmp;
-#pragma unroll
-    for (int i = 0; i < 4; i++) {
-        tmp = va[i] - vb[i];
-        if(tmp > std::numeric_limits<int8_t>::max()) tmp = std::numeric_limits<int8_t>::max();
-        if(tmp < std::numeric_limits<int8_t>::min()) tmp = std::numeric_limits<int8_t>::min();
-        c[i] = tmp;
-    }
-    return reinterpret_cast<int &>(c);
-#endif // __has_builtin(__builtin_elementwise_sub_sat)
-}
-
-static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
-#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
-    c = __builtin_amdgcn_sdot4(a, b, c, false);
-#elif defined(RDNA3)
-    c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
-#elif defined(__gfx1010__) || defined(__gfx900__)
-    int tmp1;
-    int tmp2;
-    asm("\n \
-        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \
-        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \
-        v_add3_u32 %0, %1, %2, %0 \n \
-        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \
-        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \
-        v_add3_u32 %0, %1, %2, %0 \n \
-        "
-        : "+v"(c), "=&v"(tmp1), "=&v"(tmp2)
-        : "v"(a), "v"(b)
-    );
-#else
-    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
-    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
-    c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3];
-#endif
-    return c;
-}
-#endif // defined(GGML_USE_HIPBLAS)
-
-#if defined(_MSC_VER)
-#pragma warning(disable: 4244 4267) // possible loss of data
-#endif
-
 static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
 
 [[noreturn]]
-static void ggml_cuda_error(const char * stmt, const char * func, const char * file, const int line, const char * msg) {
+void ggml_cuda_error(const char * stmt, const char * func, const char * file, int line, const char * msg) {
     int id = -1; // in case cudaGetDevice fails
     cudaGetDevice(&id);
 
@@ -227,320 +60,9 @@ static void ggml_cuda_error(const char * stmt, const char * func, const char * f
     GGML_ASSERT(!"CUDA error");
 }
 
-#define CUDA_CHECK_GEN(err, success, error_fn)                                      \
-     do {                                                                           \
-        auto err_ = (err);                                                          \
-        if (err_ != (success)) {                                                    \
-            ggml_cuda_error(#err, __func__, __FILE__, __LINE__, error_fn(err_));    \
-        }                                                                           \
-    } while (0)
-
-#define CUDA_CHECK(err) CUDA_CHECK_GEN(err, cudaSuccess, cudaGetErrorString)
-
-#if CUDART_VERSION >= 12000
-    static const char * cublas_get_error_str(const cublasStatus_t err) {
-        return cublasGetStatusString(err);
-    }
-#else
-    static const char * cublas_get_error_str(const cublasStatus_t err) {
-        switch (err) {
-            case CUBLAS_STATUS_SUCCESS: return "CUBLAS_STATUS_SUCCESS";
-            case CUBLAS_STATUS_NOT_INITIALIZED: return "CUBLAS_STATUS_NOT_INITIALIZED";
-            case CUBLAS_STATUS_ALLOC_FAILED: return "CUBLAS_STATUS_ALLOC_FAILED";
-            case CUBLAS_STATUS_INVALID_VALUE: return "CUBLAS_STATUS_INVALID_VALUE";
-            case CUBLAS_STATUS_ARCH_MISMATCH: return "CUBLAS_STATUS_ARCH_MISMATCH";
-            case CUBLAS_STATUS_MAPPING_ERROR: return "CUBLAS_STATUS_MAPPING_ERROR";
-            case CUBLAS_STATUS_EXECUTION_FAILED: return "CUBLAS_STATUS_EXECUTION_FAILED";
-            case CUBLAS_STATUS_INTERNAL_ERROR: return "CUBLAS_STATUS_INTERNAL_ERROR";
-            case CUBLAS_STATUS_NOT_SUPPORTED: return "CUBLAS_STATUS_NOT_SUPPORTED";
-            default: return "unknown error";
-        }
-    }
-#endif // CUDART_VERSION >= 12000
-
-#define CUBLAS_CHECK(err) CUDA_CHECK_GEN(err, CUBLAS_STATUS_SUCCESS, cublas_get_error_str)
-
-#if !defined(GGML_USE_HIPBLAS)
-static const char * cu_get_error_str(CUresult err) {
-    const char * err_str;
-    cuGetErrorString(err, &err_str);
-    return err_str;
-}
-#define CU_CHECK(err) CUDA_CHECK_GEN(err, CUDA_SUCCESS, cu_get_error_str)
-#endif
-
-#if CUDART_VERSION >= 11100
-#define GGML_CUDA_ASSUME(x) __builtin_assume(x)
-#else
-#define GGML_CUDA_ASSUME(x)
-#endif // CUDART_VERSION >= 11100
-
-#ifdef GGML_CUDA_F16
-typedef half dfloat; // dequantize float
-typedef half2 dfloat2;
-#else
-typedef float dfloat; // dequantize float
-typedef float2 dfloat2;
-#endif //GGML_CUDA_F16
-
-static __device__ __forceinline__ int get_int_from_int8(const int8_t * x8, const int & i32) {
-    const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
-
-    int x32 = 0;
-    x32 |= x16[0] <<  0;
-    x32 |= x16[1] << 16;
-
-    return x32;
-}
-
-static __device__ __forceinline__ int get_int_from_uint8(const uint8_t * x8, const int & i32) {
-    const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
-
-    int x32 = 0;
-    x32 |= x16[0] <<  0;
-    x32 |= x16[1] << 16;
-
-    return x32;
-}
-
-static __device__ __forceinline__ int get_int_from_int8_aligned(const int8_t * x8, const int & i32) {
-    return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
-}
-
-static __device__ __forceinline__ int get_int_from_uint8_aligned(const uint8_t * x8, const int & i32) {
-    return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
-}
-
-template<typename T>
-using to_t_cuda_t = void (*)(const void * __restrict__ x, T * __restrict__ y, int k, cudaStream_t stream);
-typedef to_t_cuda_t<float> to_fp32_cuda_t;
-typedef to_t_cuda_t<half> to_fp16_cuda_t;
-
-typedef void (*dequantize_kernel_t)(const void * vx, const int ib, const int iqs, dfloat2 & v);
-typedef void (*dot_kernel_k_t)(const void * __restrict__ vx, const int ib, const int iqs, const float * __restrict__ y, float & v);
-typedef void (*cpy_kernel_t)(const char * cx, char * cdst);
-typedef void (*ggml_cuda_func_t)(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
-typedef void (*ggml_cuda_op_mul_mat_t)(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
-    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
-    const int64_t src1_padded_row_size, cudaStream_t stream);
-typedef void (*ggml_cuda_op_flatten_t)(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream);
-
-// QK = number of values after dequantization
-// QR = QK / number of values before dequantization
-// QI = number of 32 bit integers before dequantization
-
-#define QK4_0 32
-#define QR4_0 2
-#define QI4_0 (QK4_0 / (4 * QR4_0))
-typedef struct {
-    half    d;              // delta
-    uint8_t qs[QK4_0 / 2];  // nibbles / quants
-} block_q4_0;
-static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
-
-#define QK4_1 32
-#define QR4_1 2
-#define QI4_1 (QK4_1 / (4 * QR4_1))
-typedef struct {
-    half2   dm;             // dm.x = delta, dm.y = min
-    uint8_t qs[QK4_1 / 2];  // nibbles / quants
-} block_q4_1;
-static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding");
-
-#define QK5_0 32
-#define QR5_0 2
-#define QI5_0 (QK5_0 / (4 * QR5_0))
-typedef struct {
-    half d;                 // delta
-    uint8_t qh[4];          // 5-th bit of quants
-    uint8_t qs[QK5_0 / 2];  // nibbles / quants
-} block_q5_0;
-static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
-
-#define QK5_1 32
-#define QR5_1 2
-#define QI5_1 (QK5_1 / (4 * QR5_1))
-typedef struct {
-    half2 dm;               // dm.x = delta, dm.y = min
-    uint8_t qh[4];          // 5-th bit of quants
-    uint8_t qs[QK5_1 / 2];  // nibbles / quants
-} block_q5_1;
-static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
-
-#define QK8_0 32
-#define QR8_0 1
-#define QI8_0 (QK8_0 / (4 * QR8_0))
-typedef struct {
-    half    d;              // delta
-    int8_t  qs[QK8_0];      // quants
-} block_q8_0;
-static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
-
-#define QK8_1 32
-#define QR8_1 1
-#define QI8_1 (QK8_1 / (4 * QR8_1))
-typedef struct {
-    half2   ds;             // ds.x = delta, ds.y = sum
-    int8_t  qs[QK8_0];      // quants
-} block_q8_1;
-static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_fp16_t) + QK8_0, "wrong q8_1 block size/padding");
-
-typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs);
-typedef void (*allocate_tiles_cuda_t)(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc);
-typedef void (*load_tiles_cuda_t)(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row);
-typedef float (*vec_dot_q_mul_mat_cuda_t)(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ms, const int & i, const int & j, const int & k);
-
-//================================= k-quants
-
-#ifdef GGML_QKK_64
-#define QK_K 64
-#define K_SCALE_SIZE 4
-#else
-#define QK_K 256
-#define K_SCALE_SIZE 12
-#endif
-
-#define QR2_K 4
-#define QI2_K (QK_K / (4*QR2_K))
-typedef struct {
-    uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
-    uint8_t qs[QK_K/4];      // quants
-    half2 dm;                // super-block scale for quantized scales/mins
-} block_q2_K;
-static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
-
-#define QR3_K 4
-#define QI3_K (QK_K / (4*QR3_K))
-typedef struct {
-    uint8_t hmask[QK_K/8];     // quants - high bit
-    uint8_t qs[QK_K/4];        // quants - low 2 bits
-#ifdef GGML_QKK_64
-    uint8_t scales[2]; // scales, quantized with 8 bits
-#else
-    uint8_t scales[K_SCALE_SIZE]; // scales, quantized with 6 bits
-#endif
-    half d;             // super-block scale
-} block_q3_K;
-//static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + K_SCALE_SIZE, "wrong q3_K block size/padding");
-
-#define QR4_K 2
-#define QI4_K (QK_K / (4*QR4_K))
-#ifdef GGML_QKK_64
-typedef struct {
-    half    dm[2];             // super-block scales/mins
-    uint8_t scales[2];         // 4-bit block scales/mins
-    uint8_t qs[QK_K/2];        // 4--bit quants
-} block_q4_K;
-static_assert(sizeof(block_q4_K) == sizeof(half2) + QK_K/2 + 2, "wrong q4_K block size/padding");
-#else
-typedef struct {
-    half2 dm;                  // super-block scale for quantized scales/mins
-    uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits
-    uint8_t qs[QK_K/2];        // 4--bit quants
-} block_q4_K;
-static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2, "wrong q4_K block size/padding");
-#endif
-
-#define QR5_K 2
-#define QI5_K (QK_K / (4*QR5_K))
-#ifdef GGML_QKK_64
-typedef struct {
-    half d;                  // super-block scale
-    int8_t scales[QK_K/16];  // block scales
-    uint8_t qh[QK_K/8];      // quants, high bit
-    uint8_t qs[QK_K/2];      // quants, low 4 bits
-} block_q5_K;
-static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
-#else
-typedef struct {
-    half2 dm;                     // super-block scale for quantized scales/mins
-    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
-    uint8_t qh[QK_K/8];           // quants, high bit
-    uint8_t qs[QK_K/2];           // quants, low 4 bits
-} block_q5_K;
-static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
-#endif
-
-#define QR6_K 2
-#define QI6_K (QK_K / (4*QR6_K))
-typedef struct {
-    uint8_t ql[QK_K/2];   // quants, lower 4 bits
-    uint8_t qh[QK_K/4];   // quants, upper 2 bits
-    int8_t  scales[QK_K/16]; // scales
-    half    d;         // delta
-} block_q6_K;
-static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_K block size/padding");
-
-#define QR2_XXS 8
-#define QI2_XXS (QK_K / (4*QR2_XXS))
-typedef struct {
-    half d;
-    uint16_t qs[QK_K/8];
-} block_iq2_xxs;
-static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding");
-
-#define WARP_SIZE 32
-#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
-
-#define CUDA_GELU_BLOCK_SIZE 256
-#define CUDA_SILU_BLOCK_SIZE 256
-#define CUDA_TANH_BLOCK_SIZE 256
-#define CUDA_RELU_BLOCK_SIZE 256
-#define CUDA_SQR_BLOCK_SIZE 256
-#define CUDA_CPY_BLOCK_SIZE 32
-#define CUDA_SCALE_BLOCK_SIZE 256
-#define CUDA_CLAMP_BLOCK_SIZE 256
-#define CUDA_ROPE_BLOCK_SIZE 256
-#define CUDA_SOFT_MAX_BLOCK_SIZE 1024
-#define CUDA_ALIBI_BLOCK_SIZE 32
-#define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32
-#define CUDA_QUANTIZE_BLOCK_SIZE 256
-#define CUDA_DEQUANTIZE_BLOCK_SIZE 256
-#define CUDA_GET_ROWS_BLOCK_SIZE 256
-#define CUDA_UPSCALE_BLOCK_SIZE 256
-#define CUDA_CONCAT_BLOCK_SIZE 256
-#define CUDA_PAD_BLOCK_SIZE 256
-#define CUDA_ACC_BLOCK_SIZE 256
-#define CUDA_IM2COL_BLOCK_SIZE 256
-
-// dmmv = dequantize_mul_mat_vec
-#ifndef GGML_CUDA_DMMV_X
-#define GGML_CUDA_DMMV_X 32
-#endif
-#ifndef GGML_CUDA_MMV_Y
-#define GGML_CUDA_MMV_Y 1
-#endif
-
-#ifndef K_QUANTS_PER_ITERATION
-#define K_QUANTS_PER_ITERATION 2
-#else
-static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
-#endif
-
-#ifndef GGML_CUDA_PEER_MAX_BATCH_SIZE
-#define GGML_CUDA_PEER_MAX_BATCH_SIZE 128
-#endif // GGML_CUDA_PEER_MAX_BATCH_SIZE
-
-#define MUL_MAT_SRC1_COL_STRIDE 128
-
-#define MAX_STREAMS 8
-static cudaStream_t g_cudaStreams[GGML_CUDA_MAX_DEVICES][MAX_STREAMS] = { { nullptr } };
-
-struct ggml_tensor_extra_gpu {
-    void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors
-    cudaEvent_t events[GGML_CUDA_MAX_DEVICES][MAX_STREAMS]; // events for synchronizing multiple GPUs
-};
-
 // this is faster on Windows
 // probably because the Windows CUDA libraries forget to make this check before invoking the drivers
-static void ggml_cuda_set_device(const int device) {
+void ggml_cuda_set_device(int device) {
     int current_device;
     CUDA_CHECK(cudaGetDevice(&current_device));
 
@@ -551,4381 +73,993 @@ static void ggml_cuda_set_device(const int device) {
     CUDA_CHECK(cudaSetDevice(device));
 }
 
-static int g_device_count = -1;
-static int g_main_device = 0;
-static float g_tensor_split[GGML_CUDA_MAX_DEVICES] = {0};
+int ggml_cuda_get_device() {
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    return id;
+}
 
-struct cuda_device_capabilities {
-    int     cc;                 // compute capability
-    size_t  smpb;               // max. shared memory per block
-    bool    vmm;                // virtual memory support
-    size_t  vmm_granularity;    // granularity of virtual memory
+static ggml_cuda_device_info ggml_cuda_init() {
+#ifdef __HIP_PLATFORM_AMD__
+    // Workaround for a rocBLAS bug when using multiple graphics cards:
+    // https://github.com/ROCmSoftwarePlatform/rocBLAS/issues/1346
+    rocblas_initialize();
+    CUDA_CHECK(cudaDeviceSynchronize());
+#endif
+
+    ggml_cuda_device_info info = {};
+
+    cudaError_t err = cudaGetDeviceCount(&info.device_count);
+    if (err != cudaSuccess) {
+        fprintf(stderr, "%s: failed to initialize " GGML_CUDA_NAME ": %s\n", __func__, cudaGetErrorString(err));
+        return info;
+    }
+
+    GGML_ASSERT(info.device_count <= GGML_CUDA_MAX_DEVICES);
+
+    int64_t total_vram = 0;
+#if defined(GGML_CUDA_FORCE_MMQ)
+    fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ:   yes\n", __func__);
+#else
+    fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ:   no\n", __func__);
+#endif
+#if defined(CUDA_USE_TENSOR_CORES)
+    fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: yes\n", __func__);
+#else
+    fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: no\n", __func__);
+#endif
+    fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count);
+    for (int id = 0; id < info.device_count; ++id) {
+        int device_vmm = 0;
+
+#if !defined(GGML_USE_HIPBLAS)
+        CUdevice device;
+        CU_CHECK(cuDeviceGet(&device, id));
+        CU_CHECK(cuDeviceGetAttribute(&device_vmm, CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED, device));
+
+        if (device_vmm) {
+            CUmemAllocationProp alloc_prop = {};
+            alloc_prop.type = CU_MEM_ALLOCATION_TYPE_PINNED;
+            alloc_prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
+            alloc_prop.location.id = id;
+            CU_CHECK(cuMemGetAllocationGranularity(&info.devices[id].vmm_granularity, &alloc_prop, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED));
+        }
+#endif // !defined(GGML_USE_HIPBLAS)
+        info.devices[id].vmm = !!device_vmm;
+
+        cudaDeviceProp prop;
+        CUDA_CHECK(cudaGetDeviceProperties(&prop, id));
+        fprintf(stderr, "  Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no");
+
+        info.default_tensor_split[id] = total_vram;
+        total_vram += prop.totalGlobalMem;
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+        info.devices[id].cc = 100*prop.major + 10*prop.minor + CC_OFFSET_AMD;
+#else
+        info.devices[id].cc = 100*prop.major + 10*prop.minor;
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+        info.devices[id].smpb = prop.sharedMemPerBlock;
+    }
+
+    for (int id = 0; id < info.device_count; ++id) {
+        info.default_tensor_split[id] /= total_vram;
+    }
+
+    // configure logging to stdout
+    // CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr));
+
+    return info;
+}
+
+const ggml_cuda_device_info & ggml_cuda_info() {
+    static ggml_cuda_device_info info = ggml_cuda_init();
+    return info;
+}
+
+// #define DEBUG_CUDA_MALLOC
+
+// buffer pool for cuda (legacy)
+struct ggml_cuda_pool_leg : public ggml_cuda_pool {
+    static const int MAX_BUFFERS = 256;
+
+    int device;
+    struct ggml_cuda_buffer {
+        void * ptr = nullptr;
+        size_t size = 0;
+    };
+
+    ggml_cuda_buffer buffer_pool[MAX_BUFFERS] = {};
+    size_t pool_size = 0;
+
+    explicit ggml_cuda_pool_leg(int device) :
+        device(device) {
+    }
+
+    ~ggml_cuda_pool_leg() {
+        ggml_cuda_set_device(device);
+        for (int i = 0; i < MAX_BUFFERS; ++i) {
+            ggml_cuda_buffer & b = buffer_pool[i];
+            if (b.ptr != nullptr) {
+                CUDA_CHECK(cudaFree(b.ptr));
+                pool_size -= b.size;
+            }
+        }
+        GGML_ASSERT(pool_size == 0);
+    }
+
+    void * alloc(size_t size, size_t * actual_size) override {
+#ifdef DEBUG_CUDA_MALLOC
+        int nnz = 0;
+        size_t max_size = 0;
+#endif
+        size_t best_diff = 1ull << 36;
+        int ibest = -1;
+        for (int i = 0; i < MAX_BUFFERS; ++i) {
+            ggml_cuda_buffer& b = buffer_pool[i];
+            if (b.ptr != nullptr) {
+#ifdef DEBUG_CUDA_MALLOC
+                ++nnz;
+                if (b.size > max_size) max_size = b.size;
+#endif
+                if (b.size >= size) {
+                    size_t diff = b.size - size;
+                    if (diff < best_diff) {
+                        best_diff = diff;
+                        ibest = i;
+                        if (!best_diff) {
+                            void * ptr = b.ptr;
+                            *actual_size = b.size;
+                            b.ptr = nullptr;
+                            b.size = 0;
+                            return ptr;
+                        }
+                    }
+                }
+            }
+        }
+        if (ibest >= 0) {
+            ggml_cuda_buffer& b = buffer_pool[ibest];
+            void * ptr = b.ptr;
+            *actual_size = b.size;
+            b.ptr = nullptr;
+            b.size = 0;
+            return ptr;
+        }
+        void * ptr;
+        size_t look_ahead_size = (size_t) (1.05 * size);
+        look_ahead_size = 256 * ((look_ahead_size + 255)/256);
+        ggml_cuda_set_device(device);
+        CUDA_CHECK(cudaMalloc((void **) &ptr, look_ahead_size));
+        *actual_size = look_ahead_size;
+        pool_size += look_ahead_size;
+#ifdef DEBUG_CUDA_MALLOC
+        fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, device, nnz,
+                (uint32_t)(max_size/1024/1024), (uint32_t)(pool_size/1024/1024), (uint32_t)(size/1024/1024));
+#endif
+        return ptr;
+    }
+
+    void free(void * ptr, size_t size) override {
+        for (int i = 0; i < MAX_BUFFERS; ++i) {
+            ggml_cuda_buffer& b = buffer_pool[i];
+            if (b.ptr == nullptr) {
+                b.ptr = ptr;
+                b.size = size;
+                return;
+            }
+        }
+        fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n");
+        ggml_cuda_set_device(device);
+        CUDA_CHECK(cudaFree(ptr));
+        pool_size -= size;
+    }
 };
 
-static cuda_device_capabilities g_device_caps[GGML_CUDA_MAX_DEVICES] = { {0, 0, false, 0} };
+// pool with virtual memory
+#if !defined(GGML_USE_HIPBLAS)
+struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
+    static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB
 
-static void * g_scratch_buffer = nullptr;
-static size_t g_scratch_size = 0; // disabled by default
-static size_t g_scratch_offset = 0;
+    int device;
+    CUdeviceptr pool_addr = 0;
+    size_t pool_used = 0;
+    size_t pool_size = 0;
+    size_t granularity;
 
-static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};
-
-[[noreturn]]
-static __device__ void bad_arch() {
-    printf("ERROR: ggml-cuda was compiled without support for the current GPU architecture.\n");
-    __trap();
-
-    (void) bad_arch; // suppress unused function warning
-}
-
-static __device__ __forceinline__ float warp_reduce_sum(float x) {
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        x += __shfl_xor_sync(0xffffffff, x, mask, 32);
-    }
-    return x;
-}
-
-static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32);
-        a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32);
-    }
-    return a;
-}
-
-static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
-#if __CUDA_ARCH__ < CC_PASCAL || (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
-    (void) a;
-    bad_arch();
-#else
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32));
-    }
-    return a;
-#endif // __CUDA_ARCH__ < CC_PASCAL || (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
-}
-
-static __device__ __forceinline__ float warp_reduce_max(float x) {
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
-    }
-    return x;
-}
-
-static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
-#if __CUDA_ARCH__ < CC_PASCAL || (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
-    (void) x;
-    bad_arch();
-#else
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        x = __hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
-    }
-    return x;
-#endif // __CUDA_ARCH__ < CC_PASCAL || (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
-}
-
-static __device__ __forceinline__ float op_repeat(const float a, const float b) {
-    return b;
-    GGML_UNUSED(a);
-}
-
-static __device__ __forceinline__ float op_add(const float a, const float b) {
-    return a + b;
-}
-
-static __device__ __forceinline__ float op_mul(const float a, const float b) {
-    return a * b;
-}
-
-static __device__ __forceinline__ float op_div(const float a, const float b) {
-    return a / b;
-}
-
-template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
-static __global__ void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst,
-        int ne0, int ne1, int ne2, int ne3,
-        int ne10, int ne11, int ne12, int ne13,
-        /*int s0, */ int s1,  int s2,  int s3,
-        /*int s10,*/ int s11, int s12, int s13) {
-    const int i0s = blockDim.x*blockIdx.x + threadIdx.x;
-    const int i1 = (blockDim.y*blockIdx.y + threadIdx.y);
-    const int i2 = (blockDim.z*blockIdx.z + threadIdx.z) / ne3;
-    const int i3 = (blockDim.z*blockIdx.z + threadIdx.z) % ne3;
-
-    if (i0s >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
-        return;
+    explicit ggml_cuda_pool_vmm(int device) :
+        device(device),
+        granularity(ggml_cuda_info().devices[device].vmm_granularity) {
     }
 
-    const int i11 = i1 % ne11;
-    const int i12 = i2 % ne12;
-    const int i13 = i3 % ne13;
-
-    const size_t i_src0 = i3*s3 + i2*s2 + i1*s1;
-    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
-    const size_t i_dst  = i_src0;
-
-    const src0_t * src0_row = src0 + i_src0;
-    const src1_t * src1_row = src1 + i_src1;
-    dst_t * dst_row = dst + i_dst;
-
-    for (int i0 = i0s; i0 < ne0; i0 += blockDim.x*gridDim.x) {
-        const int i10 = i0 % ne10;
-        dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
-    }
-}
-
-template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
-static __global__ void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t * dst,
-        int ne0, int ne1, int ne2, int ne3,
-        int ne10, int ne11, int ne12, int ne13,
-        /*int s0, */ int s1,  int s2,  int s3,
-        /*int s10,*/ int s11, int s12, int s13) {
-
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    const int i3 = i/(ne2*ne1*ne0);
-    const int i2 = (i/(ne1*ne0)) % ne2;
-    const int i1 = (i/ne0) % ne1;
-    const int i0 = i % ne0;
-
-    if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
-        return;
-    }
-
-    const int i11 = i1 % ne11;
-    const int i12 = i2 % ne12;
-    const int i13 = i3 % ne13;
-
-    const size_t i_src0 = i3*s3 + i2*s2 + i1*s1;
-    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
-    const size_t i_dst  = i_src0;
-
-    const src0_t * src0_row = src0 + i_src0;
-    const src1_t * src1_row = src1 + i_src1;
-    dst_t * dst_row = dst + i_dst;
-
-    const int i10 = i0 % ne10;
-    dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
-}
-
-static __global__ void acc_f32(const float * x, const float * y, float * dst, const int ne,
-    const int ne10, const int ne11, const int ne12,
-    const int nb1, const int nb2, int offset) {
-    const int i = blockDim.x * blockIdx.x + threadIdx.x;
-    if (i >= ne) {
-        return;
-    }
-    int src1_idx = i - offset;
-    int oz = src1_idx / nb2;
-    int oy = (src1_idx - (oz * nb2)) / nb1;
-    int ox = src1_idx % nb1;
-    if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) {
-        dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11];
-    } else {
-        dst[i] = x[i];
-    }
-}
-
-static __global__ void gelu_f32(const float * x, float * dst, const int k) {
-    const float GELU_COEF_A    = 0.044715f;
-    const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (i >= k) {
-        return;
-    }
-
-    float xi = x[i];
-    dst[i] = 0.5f*xi*(1.0f + tanhf(SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi)));
-}
-
-static __global__ void silu_f32(const float * x, float * dst, const int k) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (i >= k) {
-        return;
-    }
-    dst[i] = x[i] / (1.0f + expf(-x[i]));
-}
-
-static __global__ void gelu_quick_f32(const float * x, float * dst, int k) {
-    const float GELU_QUICK_COEF = -1.702f;
-    const int i  = blockDim.x*blockIdx.x + threadIdx.x;
-    if (i >= k) {
-        return;
-    }
-    dst[i] = x[i] * (1.0f / (1.0f + expf(GELU_QUICK_COEF * x[i])));
-}
-
-static __global__ void tanh_f32(const float * x, float * dst, int k) {
-    const int i  = blockDim.x*blockIdx.x + threadIdx.x;
-    if (i >= k) {
-        return;
-    }
-    dst[i] = tanhf(x[i]);
-}
-
-static __global__ void relu_f32(const float * x, float * dst, const int k) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (i >= k) {
-        return;
-    }
-    dst[i] = fmaxf(x[i], 0);
-}
-
-static __global__ void leaky_relu_f32(const float * x, float * dst, const int k, const float negative_slope) {
-    const int i  = blockDim.x*blockIdx.x + threadIdx.x;
-    if (i >= k) {
-        return;
-    }
-    dst[i] = fmaxf(x[i], 0) + fminf(x[i], 0.0f) * negative_slope;
-}
-
-static __global__ void sqr_f32(const float * x, float * dst, const int k) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (i >= k) {
-        return;
-    }
-    dst[i] = x[i] * x[i];
-}
-
-template <int block_size>
-static __global__ void norm_f32(const float * x, float * dst, const int ncols, const float eps) {
-    const int row = blockIdx.x*blockDim.y + threadIdx.y;
-    const int tid = threadIdx.x;
-
-    float2 mean_var = make_float2(0.f, 0.f);
-
-    for (int col = tid; col < ncols; col += block_size) {
-        const float xi = x[row*ncols + col];
-        mean_var.x += xi;
-        mean_var.y += xi * xi;
-    }
-
-    // sum up partial sums
-    mean_var = warp_reduce_sum(mean_var);
-    if (block_size > WARP_SIZE) {
-        __shared__ float2 s_sum[32];
-        int warp_id = threadIdx.x / WARP_SIZE;
-        int lane_id = threadIdx.x % WARP_SIZE;
-        if (lane_id == 0) {
-            s_sum[warp_id] = mean_var;
+    ~ggml_cuda_pool_vmm() {
+        if (pool_addr != 0) {
+            CU_CHECK(cuMemUnmap(pool_addr, pool_size));
+            CU_CHECK(cuMemAddressFree(pool_addr, CUDA_POOL_VMM_MAX_SIZE));
         }
-        __syncthreads();
-        mean_var = s_sum[lane_id];
-        mean_var = warp_reduce_sum(mean_var);
     }
 
-    const float mean = mean_var.x / ncols;
-    const float var = mean_var.y / ncols - mean * mean;
-    const float inv_std = rsqrtf(var + eps);
+    void * alloc(size_t size, size_t * actual_size) override {
+        // round up the allocation size to the alignment to ensure that all allocations are aligned for all data types
+        const size_t alignment = 128;
+        size = alignment * ((size + alignment - 1) / alignment);
 
-    for (int col = tid; col < ncols; col += block_size) {
-        dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_std;
-    }
-}
+        size_t avail = pool_size - pool_used;
 
-static __global__ void concat_f32(const float * x,const float * y, float * dst, const int ne0, const int ne02) {
-    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
-    if (nidx >= ne0) {
-        return;
-    }
-    // operation
-    int offset_dst =
-        nidx +
-        blockIdx.y * ne0 +
-        blockIdx.z * ne0 * gridDim.y;
-    if (blockIdx.z < ne02) { // src0
-        int offset_src =
-            nidx +
-            blockIdx.y * ne0 +
-            blockIdx.z * ne0 * gridDim.y;
-            dst[offset_dst] = x[offset_src];
-    } else {
-        int offset_src =
-            nidx +
-            blockIdx.y * ne0 +
-            (blockIdx.z - ne02) * ne0 *  gridDim.y;
-            dst[offset_dst] = y[offset_src];
-    }
-}
+        if (size > avail) {
+            // round up to the next multiple of the granularity
+            size_t reserve_size = size - avail;
+            reserve_size = granularity * ((reserve_size + granularity - 1) / granularity);
 
-static __global__ void upscale_f32(const float * x, float * dst, const int ne00, const int nb02, const int scale_factor) {
-    int ne0 = ne00 * scale_factor;
-    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
-    if (nidx >= ne0) {
-        return;
-    }
-    // operation
-    int i00 = nidx / scale_factor;
-    int i01 = blockIdx.y / scale_factor;
-    int offset_src =
-        i00 +
-        i01 * ne00 +
-        blockIdx.z * nb02;
-    int offset_dst =
-        nidx +
-        blockIdx.y * ne0 +
-        blockIdx.z * ne0 * gridDim.y;
-    dst[offset_dst] = x[offset_src];
-}
+            GGML_ASSERT(pool_size + reserve_size <= CUDA_POOL_VMM_MAX_SIZE);
 
-static __global__ void pad_f32(const float * x, float * dst, const int ne0, const int ne00, const int ne01, const int ne02) {
-    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
-    if (nidx >= ne0) {
-        return;
-    }
+            // allocate more physical memory
+            CUmemAllocationProp prop = {};
+            prop.type = CU_MEM_ALLOCATION_TYPE_PINNED;
+            prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
+            prop.location.id = device;
+            CUmemGenericAllocationHandle handle;
+            CU_CHECK(cuMemCreate(&handle, reserve_size, &prop, 0));
 
-    // operation
-    int offset_dst =
-        nidx +
-        blockIdx.y * ne0 +
-        blockIdx.z * ne0 * gridDim.y;
-    if (nidx < ne00 && blockIdx.y < ne01 && blockIdx.z < ne02) {
-        int offset_src =
-            nidx +
-            blockIdx.y * ne00 +
-            blockIdx.z * ne00 * ne01;
-            dst[offset_dst] = x[offset_src];
-    } else {
-        dst[offset_dst] = 0.0f;
-    }
-}
+            // reserve virtual address space (if not already reserved)
+            if (pool_addr == 0) {
+                CU_CHECK(cuMemAddressReserve(&pool_addr, CUDA_POOL_VMM_MAX_SIZE, 0, 0, 0));
+            }
 
-template <int block_size>
-static __global__ void group_norm_f32(const float * x, float * dst, const int group_size, const int ne_elements, const float eps) {
-    int start = blockIdx.x * group_size;
-    int end = start + group_size;
+            // map at the end of the pool
+            CU_CHECK(cuMemMap(pool_addr + pool_size, reserve_size, 0, handle, 0));
 
-    start += threadIdx.x;
+            // the memory allocation handle is no longer needed after mapping
+            CU_CHECK(cuMemRelease(handle));
 
-    if (end >= ne_elements) {
-        end = ne_elements;
-    }
+            // set access
+            CUmemAccessDesc access = {};
+            access.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
+            access.location.id = device;
+            access.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE;
+            CU_CHECK(cuMemSetAccess(pool_addr + pool_size, reserve_size, &access, 1));
 
-    float tmp = 0.0f; // partial sum for thread in warp
+            // add to the pool
+            pool_size += reserve_size;
 
-    for (int j = start; j < end; j += block_size) {
-        tmp += x[j];
-    }
-
-    tmp = warp_reduce_sum(tmp);
-    if (block_size > WARP_SIZE) {
-        __shared__ float s_sum[32];
-        int warp_id = threadIdx.x / WARP_SIZE;
-        int lane_id = threadIdx.x % WARP_SIZE;
-        if (lane_id == 0) {
-            s_sum[warp_id] = tmp;
+            //printf("cuda pool[%d]: size increased to %llu MB (reserved %llu MB)\n",
+            //       device, (unsigned long long) (pool_size/1024/1024),
+            //       (unsigned long long) (reserve_size/1024/1024));
         }
-        __syncthreads();
-        tmp = s_sum[lane_id];
-        tmp = warp_reduce_sum(tmp);
-    }
 
-    float mean = tmp / group_size;
-    tmp = 0.0f;
+        GGML_ASSERT(pool_addr != 0);
 
-    for (int j = start; j < end; j += block_size) {
-        float xi = x[j] - mean;
-        dst[j] = xi;
-        tmp += xi * xi;
-    }
+        void * ptr = (void *) (pool_addr + pool_used);
+        *actual_size = size;
+        pool_used += size;
 
-    tmp = warp_reduce_sum(tmp);
-    if (block_size > WARP_SIZE) {
-        __shared__ float s_sum[32];
-        int warp_id = threadIdx.x / WARP_SIZE;
-        int lane_id = threadIdx.x % WARP_SIZE;
-        if (lane_id == 0) {
-            s_sum[warp_id] = tmp;
-        }
-        __syncthreads();
-        tmp = s_sum[lane_id];
-        tmp = warp_reduce_sum(tmp);
-    }
-
-    float variance = tmp / group_size;
-    float scale = rsqrtf(variance + eps);
-    for (int j = start; j < end; j += block_size) {
-        dst[j] *= scale;
-    }
-}
-
-template <int block_size>
-static __global__ void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps) {
-    const int row = blockIdx.x*blockDim.y + threadIdx.y;
-    const int tid = threadIdx.x;
-
-    float tmp = 0.0f; // partial sum for thread in warp
-
-    for (int col = tid; col < ncols; col += block_size) {
-        const float xi = x[row*ncols + col];
-        tmp += xi * xi;
-    }
-
-    // sum up partial sums
-    tmp = warp_reduce_sum(tmp);
-    if (block_size > WARP_SIZE) {
-        __shared__ float s_sum[32];
-        int warp_id = threadIdx.x / WARP_SIZE;
-        int lane_id = threadIdx.x % WARP_SIZE;
-        if (lane_id == 0) {
-            s_sum[warp_id] = tmp;
-        }
-        __syncthreads();
-        tmp = s_sum[lane_id];
-        tmp = warp_reduce_sum(tmp);
-    }
-
-    const float mean = tmp / ncols;
-    const float scale = rsqrtf(mean + eps);
-
-    for (int col = tid; col < ncols; col += block_size) {
-        dst[row*ncols + col] = scale * x[row*ncols + col];
-    }
-}
-
-static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
-    const block_q4_0 * x = (const block_q4_0 *) vx;
-
-    const dfloat d = x[ib].d;
-
-    const int vui = x[ib].qs[iqs];
-
-    v.x = vui & 0xF;
-    v.y = vui >> 4;
-
-#ifdef GGML_CUDA_F16
-    v = __hsub2(v, {8.0f, 8.0f});
-    v = __hmul2(v, {d, d});
-#else
-    v.x = (v.x - 8.0f) * d;
-    v.y = (v.y - 8.0f) * d;
-#endif // GGML_CUDA_F16
-}
-
-static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
-    const block_q4_1 * x = (const block_q4_1 *) vx;
-
-    const dfloat d = __low2half(x[ib].dm);
-    const dfloat m = __high2half(x[ib].dm);
-
-    const int vui = x[ib].qs[iqs];
-
-    v.x = vui & 0xF;
-    v.y = vui >> 4;
-
-#ifdef GGML_CUDA_F16
-    v = __hmul2(v, {d, d});
-    v = __hadd2(v, {m, m});
-#else
-    v.x = (v.x * d) + m;
-    v.y = (v.y * d) + m;
-#endif // GGML_CUDA_F16
-}
-
-static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
-    const block_q5_0 * x = (const block_q5_0 *) vx;
-
-    const dfloat d = x[ib].d;
-
-    uint32_t qh;
-    memcpy(&qh, x[ib].qh, sizeof(qh));
-
-    const int xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
-    const int xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
-
-    v.x = ((x[ib].qs[iqs] & 0xf) | xh_0);
-    v.y = ((x[ib].qs[iqs] >>  4) | xh_1);
-
-#ifdef GGML_CUDA_F16
-    v = __hsub2(v, {16.0f, 16.0f});
-    v = __hmul2(v, {d, d});
-#else
-    v.x = (v.x - 16.0f) * d;
-    v.y = (v.y - 16.0f) * d;
-#endif // GGML_CUDA_F16
-}
-
-static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
-    const block_q5_1 * x = (const block_q5_1 *) vx;
-
-    const dfloat d = __low2half(x[ib].dm);
-    const dfloat m = __high2half(x[ib].dm);
-
-    uint32_t qh;
-    memcpy(&qh, x[ib].qh, sizeof(qh));
-
-    const int xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
-    const int xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
-
-    v.x = ((x[ib].qs[iqs] & 0xf) | xh_0);
-    v.y = ((x[ib].qs[iqs] >>  4) | xh_1);
-
-#ifdef GGML_CUDA_F16
-    v = __hmul2(v, {d, d});
-    v = __hadd2(v, {m, m});
-#else
-    v.x = (v.x * d) + m;
-    v.y = (v.y * d) + m;
-#endif // GGML_CUDA_F16
-}
-
-static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
-    const block_q8_0 * x = (const block_q8_0 *) vx;
-
-    const dfloat d = x[ib].d;
-
-    v.x = x[ib].qs[iqs + 0];
-    v.y = x[ib].qs[iqs + 1];
-
-#ifdef GGML_CUDA_F16
-    v = __hmul2(v, {d, d});
-#else
-    v.x *= d;
-    v.y *= d;
-#endif // GGML_CUDA_F16
-}
-
-//================================== k-quants
-
-template<typename dst_t>
-static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-
-    const int i   = blockIdx.x;
-    const block_q2_K * x = (const block_q2_K *) vx;
-
-    const int tid = threadIdx.x;
-#if QK_K == 256
-    const int n   = tid/32;
-    const int l   = tid - 32*n;
-    const int is  = 8*n + l/16;
-
-    const uint8_t q = x[i].qs[32*n + l];
-    dst_t * y = yy + i*QK_K + 128*n;
-
-    float dall = __low2half(x[i].dm);
-    float dmin = __high2half(x[i].dm);
-    y[l+ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
-    y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4);
-    y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4);
-    y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4);
-#else
-    const int is = tid/16;  // 0 or 1
-    const int il = tid%16;  // 0...15
-    const uint8_t q = x[i].qs[il] >> (2*is);
-    dst_t * y = yy + i*QK_K + 16*is + il;
-    float dall = __low2half(x[i].dm);
-    float dmin = __high2half(x[i].dm);
-    y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
-    y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4);
+#ifdef DEBUG_CUDA_MALLOC
+        printf("cuda pool[%d]: allocated %llu bytes at %llx\n", device, (unsigned long long) size, ptr);
 #endif
 
-}
+        return ptr;
+    }
 
-template<typename dst_t>
-static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    void free(void * ptr, size_t size) override {
+#ifdef DEBUG_CUDA_MALLOC
+        printf("cuda pool[%d]: freed %llu bytes at %llx\n", device, (unsigned long long) size, ptr);
+#endif
 
-    const int i = blockIdx.x;
-    const block_q3_K * x = (const block_q3_K *) vx;
+        pool_used -= size;
 
-#if QK_K == 256
-    const int r = threadIdx.x/4;
-    const int tid = r/2;
-    const int is0 = r%2;
-    const int l0 = 16*is0 + 4*(threadIdx.x%4);
-    const int n = tid / 4;
-    const int j = tid - 4*n;
+        // all deallocations must be in reverse order of the allocations
+        GGML_ASSERT(ptr == (void *) (pool_addr + pool_used));
+    }
+};
+#endif // !defined(GGML_USE_HIPBLAS)
 
-    uint8_t m = 1 << (4*n + j);
-    int is = 8*n + 2*j + is0;
-    int shift = 2*j;
-
-    int8_t us = is <  4 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+8] >> 0) & 3) << 4) :
-                is <  8 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+4] >> 2) & 3) << 4) :
-                is < 12 ? (x[i].scales[is-8] >>  4) | (((x[i].scales[is+0] >> 4) & 3) << 4) :
-                          (x[i].scales[is-8] >>  4) | (((x[i].scales[is-4] >> 6) & 3) << 4);
-    float d_all = x[i].d;
-    float dl = d_all * (us - 32);
-
-    dst_t * y = yy + i*QK_K + 128*n + 32*j;
-    const uint8_t * q = x[i].qs + 32*n;
-    const uint8_t * hm = x[i].hmask;
-
-    for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
-#else
-    const int tid = threadIdx.x;
-    const int is  = tid/16;  // 0 or 1
-    const int il  = tid%16;  // 0...15
-    const int im  = il/8;    // 0...1
-    const int in  = il%8;    // 0...7
-
-    dst_t * y = yy + i*QK_K + 16*is + il;
-
-    const uint8_t q = x[i].qs[il] >> (2*is);
-    const uint8_t h = x[i].hmask[in] >> (2*is + im);
-    const float   d = (float)x[i].d;
-
-    if (is == 0) {
-        y[ 0] = d * ((x[i].scales[0] & 0xF) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
-        y[32] = d * ((x[i].scales[1] & 0xF) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
-    } else {
-        y[ 0] = d * ((x[i].scales[0] >>  4) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
-        y[32] = d * ((x[i].scales[1] >>  4) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
+std::unique_ptr<ggml_cuda_pool> ggml_backend_cuda_context::new_pool_for_device(int device) {
+#if !defined(GGML_USE_HIPBLAS)
+    if (ggml_cuda_info().devices[device].vmm) {
+        return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_vmm(device));
     }
 #endif
-
+    return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_leg(device));
 }
 
-#if QK_K == 256
-static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
-    if (j < 4) {
-        d = q[j] & 63; m = q[j + 4] & 63;
-    } else {
-        d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
-        m = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
+// cuda buffer
+
+struct ggml_backend_cuda_buffer_context {
+    int device;
+    void * dev_ptr = nullptr;
+    std::string name;
+
+    ggml_backend_cuda_buffer_context(int device, void * dev_ptr) :
+        device(device), dev_ptr(dev_ptr),
+        name(GGML_CUDA_NAME + std::to_string(device)) {
     }
-}
-#endif
 
-template<typename dst_t>
-static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-    const block_q4_K * x = (const block_q4_K *) vx;
-
-    const int i = blockIdx.x;
-
-#if QK_K == 256
-    // assume 32 threads
-    const int tid = threadIdx.x;
-    const int il  = tid/8;
-    const int ir  = tid%8;
-    const int is  = 2*il;
-    const int n   = 4;
-
-    dst_t * y = yy + i*QK_K + 64*il + n*ir;
-
-    const float dall = __low2half(x[i].dm);
-    const float dmin = __high2half(x[i].dm);
-
-    const uint8_t * q = x[i].qs + 32*il + n*ir;
-
-    uint8_t sc, m;
-    get_scale_min_k4(is + 0, x[i].scales, sc, m);
-    const float d1 = dall * sc; const float m1 = dmin * m;
-    get_scale_min_k4(is + 1, x[i].scales, sc, m);
-    const float d2 = dall * sc; const float m2 = dmin * m;
-    for (int l = 0; l < n; ++l) {
-        y[l + 0] = d1 * (q[l] & 0xF) - m1;
-        y[l +32] = d2 * (q[l] >>  4) - m2;
+    ~ggml_backend_cuda_buffer_context() {
+        CUDA_CHECK(cudaFree(dev_ptr));
     }
-#else
-    const int tid = threadIdx.x;
-    const uint8_t * q = x[i].qs;
-    dst_t * y = yy + i*QK_K;
-    const float d = (float)x[i].dm[0];
-    const float m = (float)x[i].dm[1];
-    y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4);
-    y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >>  4) - m * (x[i].scales[1] >> 4);
-#endif
-}
-
-template<typename dst_t>
-static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-    const block_q5_K * x = (const block_q5_K *) vx;
-
-    const int i = blockIdx.x;
-
-#if QK_K == 256
-    // assume 64 threads - this is very slightly better than the one below
-    const int tid = threadIdx.x;
-    const int il  = tid/16;   // il is in 0...3
-    const int ir  = tid%16;   // ir is in 0...15
-    const int is  = 2*il;     // is is in 0...6
-
-    dst_t * y = yy + i*QK_K + 64*il + 2*ir;
-
-    const float dall = __low2half(x[i].dm);
-    const float dmin = __high2half(x[i].dm);
-
-    const uint8_t * ql = x[i].qs + 32*il + 2*ir;
-    const uint8_t * qh = x[i].qh + 2*ir;
-
-    uint8_t sc, m;
-    get_scale_min_k4(is + 0, x[i].scales, sc, m);
-    const float d1 = dall * sc; const float m1 = dmin * m;
-    get_scale_min_k4(is + 1, x[i].scales, sc, m);
-    const float d2 = dall * sc; const float m2 = dmin * m;
-
-    uint8_t   hm  = 1 << (2*il);
-    y[ 0] = d1 * ((ql[ 0] & 0xF) + (qh[ 0] & hm ? 16 : 0)) - m1;
-    y[ 1] = d1 * ((ql[ 1] & 0xF) + (qh[ 1] & hm ? 16 : 0)) - m1;
-    hm <<= 1;
-    y[32] = d2 * ((ql[ 0] >>  4) + (qh[ 0] & hm ? 16 : 0)) - m2;
-    y[33] = d2 * ((ql[ 1] >>  4) + (qh[ 1] & hm ? 16 : 0)) - m2;
-#else
-    const int tid = threadIdx.x;
-    const uint8_t q = x[i].qs[tid];
-    const int im = tid/8;  // 0...3
-    const int in = tid%8;  // 0...7
-    const int is = tid/16; // 0 or 1
-    const uint8_t h = x[i].qh[in] >> im;
-    const float d = x[i].d;
-    dst_t * y = yy + i*QK_K + tid;
-    y[ 0] = d * x[i].scales[is+0] * ((q & 0xF) - ((h >> 0) & 1 ? 0 : 16));
-    y[32] = d * x[i].scales[is+2] * ((q >>  4) - ((h >> 4) & 1 ? 0 : 16));
-#endif
-}
-
-template<typename dst_t>
-static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-    const block_q6_K * x = (const block_q6_K *) vx;
-
-    const int i = blockIdx.x;
-#if QK_K == 256
-
-    // assume 64 threads - this is very slightly better than the one below
-    const int tid = threadIdx.x;
-    const int ip  = tid/32;   // ip is 0 or 1
-    const int il  = tid - 32*ip; // 0...32
-    const int is  = 8*ip + il/16;
-
-    dst_t * y = yy + i*QK_K + 128*ip + il;
-
-    const float d = x[i].d;
-
-    const uint8_t * ql = x[i].ql + 64*ip + il;
-    const uint8_t   qh = x[i].qh[32*ip + il];
-    const int8_t  * sc = x[i].scales + is;
-
-    y[ 0] = d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
-    y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32);
-    y[64] = d * sc[4] * ((int8_t)((ql[ 0]  >> 4) | (((qh >> 4) & 3) << 4)) - 32);
-    y[96] = d * sc[6] * ((int8_t)((ql[32]  >> 4) | (((qh >> 6) & 3) << 4)) - 32);
-#else
-
-    // assume 32 threads
-    const int tid = threadIdx.x;
-    const int ip  = tid/16;         // 0 or 1
-    const int il  = tid - 16*ip;    // 0...15
-
-    dst_t * y = yy + i*QK_K + 16*ip + il;
-
-    const float d = x[i].d;
-
-    const uint8_t   ql = x[i].ql[16*ip + il];
-    const uint8_t   qh = x[i].qh[il] >> (2*ip);
-    const int8_t  * sc = x[i].scales;
-
-    y[ 0] = d * sc[ip+0] * ((int8_t)((ql & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
-    y[32] = d * sc[ip+2] * ((int8_t)((ql  >> 4) | (((qh >> 4) & 3) << 4)) - 32);
-#endif
-}
-
-static const __device__ uint64_t kgrid_iq2xxs[256] = {
-    0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
-    0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808,
-    0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819,
-    0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819,
-    0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b,
-    0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808,
-    0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08,
-    0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b,
-    0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819,
-    0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08,
-    0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808,
-    0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08,
-    0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808,
-    0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808,
-    0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919,
-    0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819,
-    0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08,
-    0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908,
-    0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819,
-    0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808,
-    0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808,
-    0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908,
-    0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808,
-    0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08,
-    0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819,
-    0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819,
-    0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819,
-    0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908,
-    0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19,
-    0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819,
-    0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b,
-    0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808,
-    0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908,
-    0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08,
-    0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08,
-    0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908,
-    0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819,
-    0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808,
-    0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808,
-    0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19,
-    0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819,
-    0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919,
-    0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b,
-    0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08,
-    0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808,
-    0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908,
-    0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b,
-    0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819,
-    0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08,
-    0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08,
-    0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808,
-    0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b,
-    0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b,
-    0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908,
-    0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819,
-    0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808,
-    0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908,
-    0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b,
-    0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808,
-    0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b,
-    0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b,
-    0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808,
-    0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19,
-    0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908,
 };
 
-static const __device__ uint8_t ksigns_iq2xs[128] = {
-      0, 129, 130,   3, 132,   5,   6, 135, 136,   9,  10, 139,  12, 141, 142,  15,
-    144,  17,  18, 147,  20, 149, 150,  23,  24, 153, 154,  27, 156,  29,  30, 159,
-    160,  33,  34, 163,  36, 165, 166,  39,  40, 169, 170,  43, 172,  45,  46, 175,
-     48, 177, 178,  51, 180,  53,  54, 183, 184,  57,  58, 187,  60, 189, 190,  63,
-    192,  65,  66, 195,  68, 197, 198,  71,  72, 201, 202,  75, 204,  77,  78, 207,
-     80, 209, 210,  83, 212,  85,  86, 215, 216,  89,  90, 219,  92, 221, 222,  95,
-     96, 225, 226,  99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111,
-    240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255,
+GGML_CALL static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) {
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
+    return ctx->name.c_str();
+}
+
+GGML_CALL static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) {
+    return buffer->iface.get_name == ggml_backend_cuda_buffer_get_name;
+}
+
+GGML_CALL static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
+    delete ctx;
+}
+
+GGML_CALL static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) {
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
+    return ctx->dev_ptr;
+}
+
+GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
+
+    if (tensor->view_src != NULL) {
+        assert(tensor->view_src->buffer->buft == buffer->buft);
+        return;
+    }
+
+    if (ggml_is_quantized(tensor->type)) {
+        // initialize padding to 0 to avoid possible NaN values
+        size_t original_size = ggml_nbytes(tensor);
+        size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor);
+
+        if (padded_size > original_size && tensor->view_src == nullptr) {
+            ggml_cuda_set_device(ctx->device);
+            CUDA_CHECK(cudaMemset((char *)tensor->data + original_size, 0, padded_size - original_size));
+        }
+    }
+}
+
+GGML_CALL static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
+
+    ggml_cuda_set_device(ctx->device);
+    CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, cudaStreamPerThread));
+    CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
+}
+
+GGML_CALL static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
+
+    ggml_cuda_set_device(ctx->device);
+    CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, cudaStreamPerThread));
+    CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
+}
+
+GGML_CALL static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
+    if (ggml_backend_buffer_is_cuda(src->buffer)) {
+        ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src->buffer->context;
+        ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)dst->buffer->context;
+        if (src_ctx->device == dst_ctx->device) {
+            CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(src), cudaMemcpyDeviceToDevice, cudaStreamPerThread));
+        } else {
+#ifdef GGML_CUDA_NO_PEER_COPY
+            return false;
+#else
+            CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, dst_ctx->device, src->data, src_ctx->device, ggml_nbytes(src), cudaStreamPerThread));
+#endif
+        }
+        CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
+        return true;
+    }
+    return false;
+
+    GGML_UNUSED(buffer);
+}
+
+GGML_CALL static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
+
+    ggml_cuda_set_device(ctx->device);
+    CUDA_CHECK(cudaDeviceSynchronize());
+    CUDA_CHECK(cudaMemset(ctx->dev_ptr, value, buffer->size));
+    CUDA_CHECK(cudaDeviceSynchronize());
+}
+
+static ggml_backend_buffer_i ggml_backend_cuda_buffer_interface = {
+    /* .get_name        = */ ggml_backend_cuda_buffer_get_name,
+    /* .free_buffer     = */ ggml_backend_cuda_buffer_free_buffer,
+    /* .get_base        = */ ggml_backend_cuda_buffer_get_base,
+    /* .init_tensor     = */ ggml_backend_cuda_buffer_init_tensor,
+    /* .set_tensor      = */ ggml_backend_cuda_buffer_set_tensor,
+    /* .get_tensor      = */ ggml_backend_cuda_buffer_get_tensor,
+    /* .cpy_tensor      = */ ggml_backend_cuda_buffer_cpy_tensor,
+    /* .clear           = */ ggml_backend_cuda_buffer_clear,
+    /* .reset           = */ NULL,
 };
 
-static const __device__ uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128};
+// cuda buffer type
+struct ggml_backend_cuda_buffer_type_context {
+    int device;
+    std::string name;
+};
 
-inline bool ggml_cuda_supports_mmq(enum ggml_type type) {
-    switch (type) {
+GGML_CALL static const char * ggml_backend_cuda_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    ggml_backend_cuda_buffer_type_context * ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
+
+    return ctx->name.c_str();
+}
+
+GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
+
+    ggml_cuda_set_device(buft_ctx->device);
+
+    size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0
+
+    void * dev_ptr;
+    cudaError_t err = cudaMalloc(&dev_ptr, size);
+    if (err != cudaSuccess) {
+        fprintf(stderr, "%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size/1024.0/1024.0, buft_ctx->device, cudaGetErrorString(err));
+        return nullptr;
+    }
+
+    ggml_backend_cuda_buffer_context * ctx = new ggml_backend_cuda_buffer_context(buft_ctx->device, dev_ptr);
+
+    return ggml_backend_buffer_init(buft, ggml_backend_cuda_buffer_interface, ctx, size);
+}
+
+GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+    return 128;
+
+    GGML_UNUSED(buft);
+}
+
+GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
+    size_t size = ggml_nbytes(tensor);
+    int64_t ne0 = tensor->ne[0];
+
+    if (ggml_is_quantized(tensor->type)) {
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
+    }
+
+    return size;
+
+    GGML_UNUSED(buft);
+}
+
+GGML_CALL static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
+    if (!ggml_backend_is_cuda(backend)) {
+        return false;
+    }
+
+    ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+
+    return buft_ctx->device == cuda_ctx->device;
+}
+
+static ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface = {
+    /* .get_name         = */ ggml_backend_cuda_buffer_type_name,
+    /* .alloc_buffer     = */ ggml_backend_cuda_buffer_type_alloc_buffer,
+    /* .get_alignment    = */ ggml_backend_cuda_buffer_type_get_alignment,
+    /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
+    /* .get_alloc_size   = */ ggml_backend_cuda_buffer_type_get_alloc_size,
+    /* .supports_backend = */ ggml_backend_cuda_buffer_type_supports_backend,
+    /* .is_host          = */ NULL,
+};
+
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) {
+    static std::mutex mutex;
+    std::lock_guard<std::mutex> lock(mutex);
+
+    if (device >= ggml_backend_cuda_get_device_count()) {
+        return nullptr;
+    }
+
+    static ggml_backend_buffer_type ggml_backend_cuda_buffer_types[GGML_CUDA_MAX_DEVICES];
+
+    static bool ggml_backend_cuda_buffer_type_initialized = false;
+
+    if (!ggml_backend_cuda_buffer_type_initialized) {
+        for (int i = 0; i < GGML_CUDA_MAX_DEVICES; i++) {
+            ggml_backend_cuda_buffer_types[i] = {
+                /* .iface    = */ ggml_backend_cuda_buffer_type_interface,
+                /* .context  = */ new ggml_backend_cuda_buffer_type_context{i, GGML_CUDA_NAME + std::to_string(i)},
+            };
+        }
+        ggml_backend_cuda_buffer_type_initialized = true;
+    }
+
+    return &ggml_backend_cuda_buffer_types[device];
+}
+
+// cuda split buffer
+
+static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_CUDA_MAX_DEVICES> & tensor_split) {
+    int64_t min_compute_capability = INT_MAX;
+    int64_t max_compute_capability = INT_MIN;
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+        if (tensor_split[id] < (id + 1 < ggml_backend_cuda_get_device_count() ? tensor_split[id + 1] : 1.0f)) {
+            if (min_compute_capability > ggml_cuda_info().devices[id].cc) {
+                min_compute_capability = ggml_cuda_info().devices[id].cc;
+            }
+            if (max_compute_capability < ggml_cuda_info().devices[id].cc) {
+                max_compute_capability = ggml_cuda_info().devices[id].cc;
+            }
+        }
+    }
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+    switch(type) {
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
+            return max_compute_capability >= CC_RDNA2 ? 128 : 64;
+        case GGML_TYPE_F16:
+        case GGML_TYPE_F32:
+            return 1;
+        case GGML_TYPE_Q2_K:
+            return max_compute_capability >= CC_RDNA2 ? 128 : 32;
+        case GGML_TYPE_Q3_K:
+            return min_compute_capability < CC_RDNA2 ? 128 : 64;
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ3_S:
+            return max_compute_capability >= CC_RDNA2 ? 128 : 64;
+        default:
+            GGML_ASSERT(false);
+    }
+#else
+    switch(type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+            return max_compute_capability >= CC_VOLTA ? 128 : 64;
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+            return 64;
+        case GGML_TYPE_F16:
+        case GGML_TYPE_F32:
+            return 1;
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q5_K:
+        case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ3_S:
+            return max_compute_capability >= CC_VOLTA ? 128 : 64;
         case GGML_TYPE_Q6_K:
-            return true;
+            return 64;
         default:
-            return false;
+            GGML_ASSERT(false);
     }
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
 }
 
-template<typename dst_t>
-static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array<float, GGML_CUDA_MAX_DEVICES> & tensor_split, int id) {
+    const int64_t nrows = ggml_nrows(tensor);
+    const int64_t rounding = get_row_rounding(tensor->type, tensor_split);
 
-    const int i   = blockIdx.x;
-    const block_iq2_xxs * x = (const block_iq2_xxs  *) vx;
+    *row_low = id == 0 ? 0 : nrows*tensor_split[id];
+    *row_low -= *row_low % rounding;
 
-    const int tid = threadIdx.x;
-#if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const uint16_t * q2 = x[i].qs + 4*ib;
-    const uint8_t  * aux8 = (const uint8_t *)q2;
-    const uint8_t  * grid = (const uint8_t *)(kgrid_iq2xxs + aux8[il]);
-    const uint32_t aux32 = q2[2] | (q2[3] << 16);
-    const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f;
-    const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
-    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
-#else
-    assert(false);
-#endif
-
-}
-
-static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
-
-    static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
-
-    const int row = blockIdx.x*blockDim.y + threadIdx.y;
-    if (row > nrows) return;
-
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
-
-    const block_q2_K * x = (const block_q2_K *)vx + ib0;
-
-    float tmp = 0; // partial sum for thread in warp
-
-#if QK_K == 256
-    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...15
-    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0,1
-
-    const int step = 16/K_QUANTS_PER_ITERATION;
-
-    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
-    const int in = tid - step*im;                        // 0...15 or 0...7
-
-    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15 or 0...14 in steps of 2
-    const int q_offset = 32*im + l0;
-    const int s_offset = 8*im;
-    const int y_offset = 128*im + l0;
-
-    uint32_t aux[4];
-    const uint8_t * d = (const uint8_t *)aux;
-    const uint8_t * m = (const uint8_t *)(aux + 2);
-
-    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
-        const float   * y = yy + i * QK_K + y_offset;
-        const uint8_t * q = x[i].qs + q_offset;
-
-        const float dall = __low2half(x[i].dm);
-        const float dmin = __high2half(x[i].dm);
-
-        const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset);
-        aux[0] = a[0] & 0x0f0f0f0f;
-        aux[1] = a[1] & 0x0f0f0f0f;
-        aux[2] = (a[0] >> 4) & 0x0f0f0f0f;
-        aux[3] = (a[1] >> 4) & 0x0f0f0f0f;
-
-        float sum1 = 0, sum2 = 0;
-        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
-            sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3)
-                  + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3)
-                  + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3)
-                  + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3)
-                  + y[l+16] * d[1] * ((q[l+16] >> 0) & 3)
-                  + y[l+48] * d[3] * ((q[l+16] >> 2) & 3)
-                  + y[l+80] * d[5] * ((q[l+16] >> 4) & 3)
-                  +y[l+112] * d[7] * ((q[l+16] >> 6) & 3);
-            sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6]
-                  + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7];
-
-        }
-        tmp += dall * sum1 - dmin * sum2;
-
-    }
-#else
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15 or 0...7
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0....1 or 0...3
-    const int offset = tid * K_QUANTS_PER_ITERATION;
-
-    uint32_t uaux[2];
-    const uint8_t * d = (const uint8_t *)uaux;
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-
-        const float   * y = yy + i * QK_K + offset;
-        const uint8_t * q = x[i].qs + offset;
-        const uint32_t * s = (const uint32_t *)x[i].scales;
-
-        uaux[0] = s[0] & 0x0f0f0f0f;
-        uaux[1] = (s[0] >> 4) & 0x0f0f0f0f;
-
-        const float2 dall = __half22float2(x[i].dm);
-
-        float sum1 = 0, sum2 = 0;
-        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
-            const uint8_t ql = q[l];
-            sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3)
-                  + y[l+16] * d[1] * ((ql >> 2) & 3)
-                  + y[l+32] * d[2] * ((ql >> 4) & 3)
-                  + y[l+48] * d[3] * ((ql >> 6) & 3);
-            sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7];
-        }
-        tmp += dall.x * sum1 - dall.y * sum2;
-    }
-#endif
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
-    }
-
-    if (threadIdx.x == 0) {
-        dst[row] = tmp;
-    }
-}
-
-static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
-
-    const int row = blockIdx.x*blockDim.y + threadIdx.y;
-    if (row > nrows) return;
-
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
-
-    const block_q3_K * x = (const block_q3_K *)vx + ib0;
-
-    float tmp = 0; // partial sum for thread in warp
-
-#if QK_K == 256
-
-    const uint16_t kmask1 = 0x0303;
-    const uint16_t kmask2 = 0x0f0f;
-
-    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
-    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0,1
-
-    const int n  = K_QUANTS_PER_ITERATION;               // iterations in the inner loop
-    const int step = 16/K_QUANTS_PER_ITERATION;
-    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
-    const int in = tid - step*im;                        // 0....15 or 0...7
-
-    const uint8_t m = 1 << (4*im);
-
-    const int l0 = n*in;                                 // 0...15 or 0...14 in steps of 2
-    const int q_offset =  32*im + l0;
-    const int y_offset = 128*im + l0;
-
-    uint16_t utmp[4];
-    const int8_t * s = (const int8_t *)utmp;
-
-    const uint16_t s_shift = 4*im;
-
-    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
-        const float   * y  = yy + i * QK_K + y_offset;
-        const uint8_t * q = x[i].qs + q_offset;
-        const uint8_t * h = x[i].hmask + l0;
-
-        const uint16_t * a = (const uint16_t *)x[i].scales;
-        utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4);
-        utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4);
-        utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4);
-        utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4);
-
-        const float d = x[i].d;
-
-        float sum = 0;
-        for (int l = 0; l < n; ++l) {
-            sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4))
-                 + y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4))
-                 + y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4))
-                 + y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4));
-            sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4))
-                 + y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4))
-                 + y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4))
-                + y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4));
-        }
-        tmp += d * sum;
-
-    }
-#else
-
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15 or 0...7
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0....1 or 0...3
-    const int offset = tid * K_QUANTS_PER_ITERATION;         // 0...15 or 0...14
-    const int in = offset/8;                                 // 0 or 1
-    const int im = offset%8;                                 // 0...7
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-
-        const float   * y = yy + i * QK_K + offset;
-        const uint8_t * q = x[i].qs + offset;
-        const uint8_t * s = x[i].scales;
-
-        const float dall = (float)x[i].d;
-
-        float sum = 0;
-        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
-            const uint8_t hl = x[i].hmask[im+l] >> in;
-            const uint8_t ql = q[l];
-            sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4))
-                 + y[l+16] * dall * ((s[0] >>  4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4))
-                 + y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4))
-                 + y[l+48] * dall * ((s[1] >>  4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4));
-        }
-        tmp += sum;
-    }
-#endif
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
-    }
-
-    if (threadIdx.x == 0) {
-        dst[row] = tmp;
-    }
-}
-
-static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
-
-    const int row = blockIdx.x*blockDim.y + threadIdx.y;
-    if (row > nrows) return;
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
-
-    const block_q4_K * x = (const block_q4_K *)vx + ib0;
-
-#if QK_K == 256
-    const uint16_t kmask1 = 0x3f3f;
-    const uint16_t kmask2 = 0x0f0f;
-    const uint16_t kmask3 = 0xc0c0;
-
-    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
-    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0,1
-
-    const int step = 8/K_QUANTS_PER_ITERATION;           // 8 or 4
-
-    const int il  = tid/step;                            // 0...3
-    const int ir  = tid - step*il;                       // 0...7 or 0...3
-    const int n   = 2 * K_QUANTS_PER_ITERATION;          // 2 or 4
-
-    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
-    const int in = il%2;
-
-    const int l0 = n*(2*ir + in);
-    const int q_offset = 32*im + l0;
-    const int y_offset = 64*im + l0;
-
-    uint16_t aux[4];
-    const uint8_t * sc = (const uint8_t *)aux;
-
-#if K_QUANTS_PER_ITERATION == 2
-    uint32_t q32[4];
-    const uint8_t * q4 = (const uint8_t *)q32;
-#else
-    uint16_t q16[4];
-    const uint8_t * q4 = (const uint8_t *)q16;
-#endif
-
-    float tmp = 0; // partial sum for thread in warp
-
-    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
-        const float   * y1 = yy + i*QK_K + y_offset;
-        const float   * y2 = y1 + 128;
-
-        const float dall = __low2half(x[i].dm);
-        const float dmin = __high2half(x[i].dm);
-
-        const uint16_t * a = (const uint16_t *)x[i].scales;
-        aux[0] = a[im+0] & kmask1;
-        aux[1] = a[im+2] & kmask1;
-        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
-        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
-
-#if K_QUANTS_PER_ITERATION == 2
-        const uint32_t * q1 = (const uint32_t *)(x[i].qs + q_offset);
-        const uint32_t * q2 = q1 + 16;
-
-        q32[0] = q1[0] & 0x0f0f0f0f;
-        q32[1] = q1[0] & 0xf0f0f0f0;
-        q32[2] = q2[0] & 0x0f0f0f0f;
-        q32[3] = q2[0] & 0xf0f0f0f0;
-
-        float4 s = {0.f, 0.f, 0.f, 0.f};
-        float smin = 0;
-        for (int l = 0; l < 4; ++l) {
-            s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+ 4];
-            s.z += y2[l] * q4[l+8]; s.w += y2[l+32] * q4[l+12];
-            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
-        }
-        tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
-#else
-        const uint16_t * q1 = (const uint16_t *)(x[i].qs + q_offset);
-        const uint16_t * q2 = q1 + 32;
-
-        q16[0] = q1[0] & 0x0f0f;
-        q16[1] = q1[0] & 0xf0f0;
-        q16[2] = q2[0] & 0x0f0f;
-        q16[3] = q2[0] & 0xf0f0;
-
-        float4 s = {0.f, 0.f, 0.f, 0.f};
-        float smin = 0;
-        for (int l = 0; l < 2; ++l) {
-            s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2];
-            s.z += y2[l] * q4[l+4]; s.w += y2[l+32] * q4[l+6];
-            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
-        }
-        tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
-#endif
-
-    }
-#else
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
-
-    const int step = tid * K_QUANTS_PER_ITERATION;
-
-    uint16_t aux16[2];
-    const uint8_t * s = (const uint8_t *)aux16;
-
-    float tmp = 0;
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-        const uint8_t * q = x[i].qs + step;
-        const float   * y = yy + i*QK_K + step;
-        const uint16_t * a = (const uint16_t *)x[i].scales;
-        aux16[0] = a[0] & 0x0f0f;
-        aux16[1] = (a[0] >> 4) & 0x0f0f;
-        const float d = (float)x[i].dm[0];
-        const float m = (float)x[i].dm[1];
-        float sum = 0.f;
-        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
-            sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2])
-                 + y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2])
-                 + y[j+32] * (d * s[1] * (q[j+ 0] >>  4) - m * s[3])
-                 + y[j+48] * (d * s[1] * (q[j+16] >>  4) - m * s[3]);
-        }
-        tmp += sum;
-    }
-
-#endif
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
-    }
-
-    if (tid == 0) {
-        dst[row] = tmp;
-    }
-}
-
-static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols) {
-
-    const int row = blockIdx.x;
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
-
-    const block_q5_K * x = (const block_q5_K *)vx + ib0;
-
-    float tmp = 0; // partial sum for thread in warp
-
-#if QK_K == 256
-    const uint16_t kmask1 = 0x3f3f;
-    const uint16_t kmask2 = 0x0f0f;
-    const uint16_t kmask3 = 0xc0c0;
-
-    const int tid = threadIdx.x/2;  // 0...15
-    const int ix  = threadIdx.x%2;
-
-    const int il  = tid/4;     // 0...3
-    const int ir  = tid - 4*il;// 0...3
-    const int n   = 2;
-
-    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
-    const int in = il%2;
-
-    const int l0 = n*(2*ir + in);
-    const int q_offset = 32*im + l0;
-    const int y_offset = 64*im + l0;
-
-    const uint8_t hm1  = 1 << (2*im);
-    const uint8_t hm2  = hm1 << 4;
-
-    uint16_t aux[4];
-    const uint8_t * sc = (const uint8_t *)aux;
-
-    uint16_t q16[8];
-    const uint8_t * q4 = (const uint8_t *)q16;
-
-    for (int i = ix; i < num_blocks_per_row; i += 2) {
-
-        const uint8_t * ql1 = x[i].qs + q_offset;
-        const uint8_t * qh  = x[i].qh + l0;
-        const float   * y1  = yy + i*QK_K + y_offset;
-        const float   * y2  = y1 + 128;
-
-        const float dall = __low2half(x[i].dm);
-        const float dmin = __high2half(x[i].dm);
-
-        const uint16_t * a = (const uint16_t *)x[i].scales;
-        aux[0] = a[im+0] & kmask1;
-        aux[1] = a[im+2] & kmask1;
-        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
-        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
-
-        float4 sum = {0.f, 0.f, 0.f, 0.f};
-        float smin = 0;
-        const uint16_t * q1 = (const uint16_t *)ql1;
-        const uint16_t * q2 = q1 + 32;
-        q16[0] = q1[0] & 0x0f0f;
-        q16[1] = q1[8] & 0x0f0f;
-        q16[2] = (q1[0] >> 4) & 0x0f0f;
-        q16[3] = (q1[8] >> 4) & 0x0f0f;
-        q16[4] = q2[0] & 0x0f0f;
-        q16[5] = q2[8] & 0x0f0f;
-        q16[6] = (q2[0] >> 4) & 0x0f0f;
-        q16[7] = (q2[8] >> 4) & 0x0f0f;
-        for (int l = 0; l < n; ++l) {
-            sum.x += y1[l+ 0] * (q4[l +0] + (qh[l+ 0] & (hm1 << 0) ? 16 : 0))
-                   + y1[l+16] * (q4[l +2] + (qh[l+16] & (hm1 << 0) ? 16 : 0));
-            sum.y += y1[l+32] * (q4[l +4] + (qh[l+ 0] & (hm1 << 1) ? 16 : 0))
-                   + y1[l+48] * (q4[l +6] + (qh[l+16] & (hm1 << 1) ? 16 : 0));
-            sum.z += y2[l+ 0] * (q4[l +8] + (qh[l+ 0] & (hm2 << 0) ? 16 : 0))
-                   + y2[l+16] * (q4[l+10] + (qh[l+16] & (hm2 << 0) ? 16 : 0));
-            sum.w += y2[l+32] * (q4[l+12] + (qh[l+ 0] & (hm2 << 1) ? 16 : 0))
-                   + y2[l+48] * (q4[l+14] + (qh[l+16] & (hm2 << 1) ? 16 : 0));
-            smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3]
-                  + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7];
-        }
-        tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin;
-    }
-
-#else
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
-    const int step = tid * K_QUANTS_PER_ITERATION;
-    const int im = step/8;
-    const int in = step%8;
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-        const uint8_t * q = x[i].qs + step;
-        const int8_t  * s = x[i].scales;
-        const float   * y = yy + i*QK_K + step;
-        const float     d = x[i].d;
-        float sum = 0.f;
-        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
-            const uint8_t h = x[i].qh[in+j] >> im;
-            sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16))
-                 + y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16))
-                 + y[j+32] * d * s[2] * ((q[j+ 0] >>  4) - ((h >> 4) & 1 ? 0 : 16))
-                 + y[j+48] * d * s[3] * ((q[j+16] >>  4) - ((h >> 6) & 1 ? 0 : 16));
-        }
-        tmp += sum;
-    }
-#endif
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
-    }
-
-    if (threadIdx.x == 0) {
-        dst[row] = tmp;
-    }
-}
-
-static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
-
-    static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
-
-    const int row = blockIdx.x*blockDim.y + threadIdx.y;
-    if (row > nrows) return;
-
-    const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
-
-    const block_q6_K * x = (const block_q6_K *)vx + ib0;
-
-#if QK_K == 256
-
-    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
-    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
-
-    const int step = 16/K_QUANTS_PER_ITERATION;          // 16 or 8
-
-    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
-    const int in = tid - step*im;                        // 0...15 or 0...7
-
-#if K_QUANTS_PER_ITERATION == 1
-    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15
-    const int is = 0;
-#else
-    const int l0 = 4 * in;                               // 0, 4, 8, ..., 28
-    const int is = in / 4;
-#endif
-    const int ql_offset = 64*im + l0;
-    const int qh_offset = 32*im + l0;
-    const int s_offset  =  8*im + is;
-    const int y_offset = 128*im + l0;
-
-    float tmp = 0; // partial sum for thread in warp
-
-    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
-
-        const float   * y  = yy + i * QK_K + y_offset;
-        const uint8_t * ql = x[i].ql + ql_offset;
-        const uint8_t * qh = x[i].qh + qh_offset;
-        const int8_t  * s  = x[i].scales + s_offset;
-
-        const float d = x[i].d;
-
-#if K_QUANTS_PER_ITERATION == 1
-        float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
-                  + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
-                  + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
-                  + y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
-                  + y[64] * s[4] * d * ((int8_t)((ql[ 0]  >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
-                  + y[80] * s[5] * d * ((int8_t)((ql[16]  >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
-                  + y[96] * s[6] * d * ((int8_t)((ql[32]  >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
-                  +y[112] * s[7] * d * ((int8_t)((ql[48]  >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
-        tmp += sum;
-#else
-        float sum = 0;
-        for (int l = 0; l < 4; ++l) {
-            sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
-                 + y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
-                 + y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
-                 + y[l+96] * s[6] * d * ((int8_t)((ql[l+32]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
-        }
-        tmp += sum;
-#endif
-
-    }
-
-#else
-
-    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...7
-    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0...3
-
-    const int step = tid * K_QUANTS_PER_ITERATION;
-
-    float tmp = 0; // partial sum for thread in warp
-
-    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
-
-        const float   * y  = yy + i * QK_K + step;
-        const uint8_t * ql = x[i].ql + step;
-        const uint8_t * qh = x[i].qh + step;
-        const int8_t  * s  = x[i].scales;
-
-        const float d = x[i+0].d;
-
-        float sum = 0;
-        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
-            sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32)
-                 + y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32)
-                 + y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >>  4) | ((qh[j] & 0x30) >> 0)) - 32)
-                 + y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >>  4) | ((qh[j] & 0xc0) >> 2)) - 32);
-        }
-        tmp += sum;
-
-    }
-
-#endif
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
-    }
-
-    if (tid == 0) {
-        dst[row] = tmp;
-    }
-}
-
-static __device__ void convert_f16(const void * vx, const int ib, const int iqs, dfloat2 & v){
-    const half * x = (const half *) vx;
-
-    // automatic half -> float type cast if dfloat == float
-    v.x = x[ib + iqs + 0];
-    v.y = x[ib + iqs + 1];
-}
-
-static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded) {
-    const int ix = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (ix >= kx_padded) {
-        return;
-    }
-
-    const int iy = blockDim.y*blockIdx.y + threadIdx.y;
-
-    const int i_padded = iy*kx_padded + ix;
-
-    block_q8_1 * y = (block_q8_1 *) vy;
-
-    const int ib = i_padded / QK8_1; // block index
-    const int iqs = i_padded % QK8_1; // quant index
-
-    const float xi = ix < kx ? x[iy*kx + ix] : 0.0f;
-    float amax = fabsf(xi);
-    float sum = xi;
-
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        amax = fmaxf(amax, __shfl_xor_sync(0xffffffff, amax, mask, 32));
-        sum += __shfl_xor_sync(0xffffffff, sum, mask, 32);
-    }
-
-    const float d = amax / 127;
-    const int8_t q = amax == 0.0f ? 0 : roundf(xi / d);
-
-    y[ib].qs[iqs] = q;
-
-    if (iqs > 0) {
-        return;
-    }
-
-    reinterpret_cast<half&>(y[ib].ds.x) = d;
-    reinterpret_cast<half&>(y[ib].ds.y) = sum;
-}
-
-template<int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
-static __global__ void k_get_rows(
-            const void * src0, const int32_t * src1, dst_t * dst,
-            int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
-            /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
-            /*size_t s0,*/ size_t s1, size_t s2, size_t s3,
-            /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
-            size_t s10, size_t s11, size_t s12/*, size_t s13*/) {
-
-    const int i00 = (blockIdx.x*blockDim.x + threadIdx.x)*2;
-    const int i10 = blockDim.y*blockIdx.y + threadIdx.y;
-    const int i11 = (blockIdx.z*blockDim.z + threadIdx.z)/ne12;
-    const int i12 = (blockIdx.z*blockDim.z + threadIdx.z)%ne12;
-
-    if (i00 >= ne00) {
-        return;
-    }
-
-    const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
-
-    dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
-    const void * src0_row = (const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03;
-
-    const int ib = i00/qk; // block index
-    const int iqs = (i00%qk)/qr; // quant index
-    const int iybs = i00 - i00%qk; // dst block start index
-    const int y_offset = qr == 1 ? 1 : qk/2;
-
-    // dequantize
-    dfloat2 v;
-    dequantize_kernel(src0_row, ib, iqs, v);
-
-    dst_row[iybs + iqs + 0]        = v.x;
-    dst_row[iybs + iqs + y_offset] = v.y;
-}
-
-template<typename src0_t, typename dst_t>
-static __global__ void k_get_rows_float(
-            const src0_t * src0, const int32_t * src1, dst_t * dst,
-            int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
-            /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
-            /*size_t s0,*/ size_t s1, size_t s2, size_t s3,
-            /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
-            size_t s10, size_t s11, size_t s12/*, size_t s13*/) {
-
-    const int i00 = blockIdx.x*blockDim.x + threadIdx.x;
-    const int i10 = blockDim.y*blockIdx.y + threadIdx.y;
-    const int i11 = (blockIdx.z*blockDim.z + threadIdx.z)/ne12;
-    const int i12 = (blockIdx.z*blockDim.z + threadIdx.z)%ne12;
-
-    if (i00 >= ne00) {
-        return;
-    }
-
-    const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
-
-    dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
-    const src0_t * src0_row = (const src0_t *)((const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03);
-
-    dst_row[i00] = src0_row[i00];
-}
-
-template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
-static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) {
-    const int i = 2*(blockDim.x*blockIdx.x + threadIdx.x);
-
-    if (i >= k) {
-        return;
-    }
-
-    const int ib = i/qk; // block index
-    const int iqs = (i%qk)/qr; // quant index
-    const int iybs = i - i%qk; // y block start index
-    const int y_offset = qr == 1 ? 1 : qk/2;
-
-    // dequantize
-    dfloat2 v;
-    dequantize_kernel(vx, ib, iqs, v);
-
-    y[iybs + iqs + 0]        = v.x;
-    y[iybs + iqs + y_offset] = v.y;
-}
-
-template <typename src_t, typename dst_t>
-static __global__ void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (i >= k) {
-        return;
-    }
-
-    const src_t * x = (src_t *) vx;
-
-    y[i] = x[i];
-}
-
-// VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
-// MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
-
-#define VDR_Q4_0_Q8_1_MMVQ 2
-#define VDR_Q4_0_Q8_1_MMQ  4
-
-template <int vdr> static __device__ __forceinline__ float vec_dot_q4_0_q8_1_impl(
-    const int * v, const int * u, const float & d4, const half2 & ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
-        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
-
-        // SIMD dot product of quantized values
-        sumi = __dp4a(vi0, u[2*i+0], sumi);
-        sumi = __dp4a(vi1, u[2*i+1], sumi);
-    }
-
-    const float2 ds8f = __half22float2(ds8);
-
-    // second part effectively subtracts 8 from each quant value
-    return d4 * (sumi * ds8f.x - (8*vdr/QI4_0) * ds8f.y);
-#else
-    bad_arch();
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q4_1_Q8_1_MMVQ 2
-#define VDR_Q4_1_Q8_1_MMQ  4
-
-template <int vdr> static __device__ __forceinline__ float vec_dot_q4_1_q8_1_impl(
-    const int * v, const int * u, const half2 & dm4, const half2 & ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
-        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
-
-        // SIMD dot product of quantized values
-        sumi = __dp4a(vi0, u[2*i+0], sumi);
-        sumi = __dp4a(vi1, u[2*i+1], sumi);
-    }
-
-#ifdef GGML_CUDA_F16
-    const float2 tmp = __half22float2(__hmul2(dm4, ds8));
-    const float d4d8 = tmp.x;
-    const float m4s8 = tmp.y;
-#else
-    const float2 dm4f = __half22float2(dm4);
-    const float2 ds8f = __half22float2(ds8);
-    const float d4d8 = dm4f.x * ds8f.x;
-    const float m4s8 = dm4f.y * ds8f.y;
-#endif // GGML_CUDA_F16
-
-    // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it
-    return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1));
-#else
-    bad_arch();
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q5_0_Q8_1_MMVQ 2
-#define VDR_Q5_0_Q8_1_MMQ  4
-
-template <int vdr> static __device__ __forceinline__ float vec_dot_q5_0_q8_1_impl(
-    const int * vl, const int * vh, const int * u, const float & d5, const half2 & ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
-        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
-        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
-        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
-        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
-        sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
-
-        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
-        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
-        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
-        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
-        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
-        sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
-    }
-
-    const float2 ds8f = __half22float2(ds8);
-
-    // second part effectively subtracts 16 from each quant value
-    return d5 * (sumi * ds8f.x - (16*vdr/QI5_0) * ds8f.y);
-#else
-    bad_arch();
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q5_1_Q8_1_MMVQ 2
-#define VDR_Q5_1_Q8_1_MMQ  4
-
-template <int vdr> static __device__ __forceinline__ float vec_dot_q5_1_q8_1_impl(
-    const int * vl, const int * vh, const int * u, const half2 & dm5, const half2 & ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
-        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
-        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
-        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
-        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
-        sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
-
-        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
-        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
-        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
-        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
-        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
-        sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
-    }
-
-#ifdef GGML_CUDA_F16
-    const float2 tmp = __half22float2(__hmul2(dm5, ds8));
-    const float d5d8 = tmp.x;
-    const float m5s8 = tmp.y;
-#else
-    const float2 dm5f = __half22float2(dm5);
-    const float2 ds8f = __half22float2(ds8);
-    const float d5d8 = dm5f.x * ds8f.x;
-    const float m5s8 = dm5f.y * ds8f.y;
-#endif // GGML_CUDA_F16
-
-    // scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it
-    return sumi*d5d8 + m5s8 / (QI5_1 / vdr);
-
-#else
-    bad_arch();
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q8_0_Q8_1_MMVQ 2
-#define VDR_Q8_0_Q8_1_MMQ 8
-
-template <int vdr> static __device__ __forceinline__ float vec_dot_q8_0_q8_1_impl(
-    const int * v, const int * u, const float & d8_0, const float & d8_1) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        // SIMD dot product of quantized values
-        sumi = __dp4a(v[i], u[i], sumi);
-    }
-
-    return d8_0*d8_1 * sumi;
-#else
-    bad_arch();
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-template <int vdr> static __device__ __forceinline__ float vec_dot_q8_1_q8_1_impl(
-    const int * v, const int * u, const half2 & dm8, const half2 & ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i = 0; i < vdr; ++i) {
-        // SIMD dot product of quantized values
-        sumi = __dp4a(v[i], u[i], sumi);
-    }
-
-#ifdef GGML_CUDA_F16
-    const float2 tmp = __half22float2(__hmul2(dm8, ds8));
-    const float d8d8 = tmp.x;
-    const float m8s8 = tmp.y;
-#else
-    const float2 dm8f = __half22float2(dm8);
-    const float2 ds8f = __half22float2(ds8);
-    const float d8d8 = dm8f.x * ds8f.x;
-    const float m8s8 = dm8f.y * ds8f.y;
-#endif // GGML_CUDA_F16
-
-    // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it
-    return sumi*d8d8 + m8s8 / (QI8_1 / vdr);
-#else
-    bad_arch();
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q2_K_Q8_1_MMVQ 1
-#define VDR_Q2_K_Q8_1_MMQ  2
-
-// contiguous v/x values
-static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq(
-    const int & v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
-    const half2 & dm2, const float * __restrict__ d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR2_K; ++i) {
-        const int sc = scales[2*i];
-
-        const int vi = (v >> (2*i)) & 0x03030303;
-
-        sumf_d += d8[i] * (__dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product
-
-        // fill int with 4x m
-        int m = sc >> 4;
-        m |= m <<  8;
-        m |= m << 16;
-        sumf_m += d8[i] * __dp4a(m, u[i], 0); // multiply constant q2_K part with sum of q8_1 values
-    }
-
-    const float2 dm2f = __half22float2(dm2);
-
-    return dm2f.x*sumf_d - dm2f.y*sumf_m;
-#else
-    bad_arch();
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-// contiguous u/y values
-static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq(
-    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
-    const half2 & dm2, const float & d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi_d = 0;
-    int sumi_m = 0;
-
-#pragma unroll
-    for (int i0 = 0; i0 < QI8_1; i0 += QI8_1/2) {
-        int sumi_d_sc = 0;
-
-        const int sc = scales[i0 / (QI8_1/2)];
-
-        // fill int with 4x m
-        int m = sc >> 4;
-        m |= m <<  8;
-        m |= m << 16;
-
-#pragma unroll
-        for (int i = i0; i < i0 + QI8_1/2; ++i) {
-            sumi_d_sc = __dp4a(v[i], u[i], sumi_d_sc); // SIMD dot product
-            sumi_m    = __dp4a(m,    u[i], sumi_m); // multiply sum of q8_1 values with m
-        }
-
-        sumi_d += sumi_d_sc * (sc & 0xF);
-    }
-
-    const float2 dm2f = __half22float2(dm2);
-
-    return d8 * (dm2f.x*sumi_d - dm2f.y*sumi_m);
-#else
-    bad_arch();
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q3_K_Q8_1_MMVQ 1
-#define VDR_Q3_K_Q8_1_MMQ  2
-
-// contiguous v/x values
-static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq(
-    const int & vl, const int & vh, const int * __restrict__ u, const uint8_t * __restrict__ scales,
-    const int & scale_offset, const float & d3, const float * __restrict__ d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR3_K; ++i) {
-        const int isc = scale_offset + 2*i;
-
-        const int isc_low = isc % (QK_K/32);
-        const int sc_shift_low = 4 * (isc / (QK_K/32));
-        const int sc_low  = (scales[isc_low] >> sc_shift_low) & 0xF;
-
-        const int isc_high = isc % (QK_K/64);
-        const int sc_shift_high = 2 * (isc / (QK_K/64));
-        const int sc_high = ((scales[(QK_K/32) + isc_high] >> sc_shift_high) & 3) << 4;
-
-        const int sc = (sc_low | sc_high) - 32;
-
-        const int vil = (vl >> (2*i)) & 0x03030303;
-
-        const int vih = ((vh >> i) << 2) & 0x04040404;
-
-        const int vi = __vsubss4(vil, vih);
-
-        sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
-    }
-
-    return d3 * sumf;
-#else
-    bad_arch();
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-// contiguous u/y values
-static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq(
-    const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ scales,
-    const float & d3, const float & d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    int sumi = 0;
-
-#pragma unroll
-    for (int i0 = 0; i0 < QR3_K*VDR_Q3_K_Q8_1_MMQ; i0 += QI8_1/2) {
-        int sumi_sc = 0;
-
-        for (int i = i0; i < i0 + QI8_1/2; ++i) {
-            sumi_sc = __dp4a(v[i], u[i], sumi_sc); // SIMD dot product
-        }
-
-        sumi += sumi_sc * scales[i0 / (QI8_1/2)];
-    }
-
-    return d3*d8 * sumi;
-#else
-    bad_arch();
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q4_K_Q8_1_MMVQ 2
-#define VDR_Q4_K_Q8_1_MMQ  8
-
-// contiguous v/x values
-static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq(
-    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
-    const uint8_t * __restrict__ m, const half2 & dm4, const float * __restrict__ d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR4_K; ++i) {
-        const int v0i = (v[0] >> (4*i)) & 0x0F0F0F0F;
-        const int v1i = (v[1] >> (4*i)) & 0x0F0F0F0F;
-
-        const int dot1 = __dp4a(v1i, u[2*i+1], __dp4a(v0i, u[2*i+0], 0)); // SIMD dot product
-        const int dot2 = __dp4a(0x01010101, u[2*i+1], __dp4a(0x01010101, u[2*i+0], 0)); // sum of u
-
-        sumf_d += d8[i] * (dot1 * sc[i]);
-        sumf_m += d8[i] * (dot2 * m[i]);  // multiply constant part of q4_K with sum of q8_1 values
-    }
-
-    const float2 dm4f = __half22float2(dm4);
-
-    return dm4f.x*sumf_d - dm4f.y*sumf_m;
-
-#else
-    bad_arch();
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-// contiguous u/y values
-static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq(
-    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
-    const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR4_K*VDR_Q4_K_Q8_1_MMQ/QI8_1; ++i) {
-        int sumi_d = 0;
-
-#pragma unroll
-        for (int j = 0; j < QI8_1; ++j) {
-            sumi_d = __dp4a((v[j] >> (4*i)) & 0x0F0F0F0F, u[i*QI8_1 + j], sumi_d); // SIMD dot product
-        }
-
-        const float2 ds8f = __half22float2(ds8[i]);
-
-        sumf_d += ds8f.x * (sc[i] * sumi_d);
-        sumf_m += ds8f.y *   m[i]; // sum of q8_1 block * q4_K min val
-    }
-
-    const float2 dm4f = __half22float2(dm4);
-
-    return dm4f.x*sumf_d - dm4f.y*sumf_m;
-
-#else
-    bad_arch();
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q5_K_Q8_1_MMVQ 2
-#define VDR_Q5_K_Q8_1_MMQ  8
-
-// contiguous v/x values
-static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq(
-    const int * __restrict__ vl, const int * __restrict__ vh, const int * __restrict__ u, const uint8_t * __restrict__ sc,
-    const uint8_t * __restrict__ m, const half2 & dm5, const float * __restrict__ d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR5_K; ++i) {
-        const int vl0i = (vl[0] >> (4*i)) & 0x0F0F0F0F;
-        const int vl1i = (vl[1] >> (4*i)) & 0x0F0F0F0F;
-
-        const int vh0i = ((vh[0] >> i) << 4) & 0x10101010;
-        const int vh1i = ((vh[1] >> i) << 4) & 0x10101010;
-
-        const int v0i = vl0i | vh0i;
-        const int v1i = vl1i | vh1i;
-
-        const int dot1 = __dp4a(v0i, u[2*i+0], __dp4a(v1i, u[2*i+1], 0)); // SIMD dot product
-        const int dot2 = __dp4a(0x01010101, u[2*i+0], __dp4a(0x01010101, u[2*i+1], 0)); // sum of u
-
-        sumf_d += d8[i] * (dot1 * sc[i]);
-        sumf_m += d8[i] * (dot2 * m[i]);
-
-    }
-
-    const float2 dm5f = __half22float2(dm5);
-
-    return dm5f.x*sumf_d - dm5f.y*sumf_m;
-
-#else
-    bad_arch();
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-// contiguous u/y values
-static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq(
-    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
-    const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR5_K*VDR_Q5_K_Q8_1_MMQ/QI8_1; ++i) {
-        int sumi_d = 0;
-
-#pragma unroll
-        for (int j = 0; j < QI8_1; ++j) {
-            sumi_d = __dp4a(v[i*QI8_1 + j], u[i*QI8_1 + j], sumi_d); // SIMD dot product
-        }
-
-        const float2 ds8f = __half22float2(ds8[i]);
-
-        sumf_d += ds8f.x * (sc[i] * sumi_d);
-        sumf_m += ds8f.y *   m[i]; // sum of q8_1 block * q4_K min val
-    }
-
-    const float2 dm4f = __half22float2(dm4);
-
-    return dm4f.x*sumf_d - dm4f.y*sumf_m;
-
-#else
-    bad_arch();
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-#define VDR_Q6_K_Q8_1_MMVQ 1
-#define VDR_Q6_K_Q8_1_MMQ  8
-
-// contiguous v/x values
-static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq(
-    const int & vl, const int & vh, const int * __restrict__ u, const int8_t * __restrict__ scales,
-    const float & d, const float * __restrict__ d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < QR6_K; ++i) {
-        const int sc = scales[4*i];
-
-        const int vil = (vl >> (4*i)) & 0x0F0F0F0F;
-
-        const int vih = ((vh >> (4*i)) << 4) & 0x30303030;
-
-        const int vi = __vsubss4((vil | vih), 0x20202020); // vi = (vil | vih) - 32
-
-        sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
-    }
-
-    return d*sumf;
-#else
-    bad_arch();
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-// contiguous u/y values
-static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq(
-    const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ sc,
-    const float & d6, const float * __restrict__ d8) {
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    float sumf_d = 0.0f;
-
-#pragma unroll
-    for (int i0 = 0; i0 < VDR_Q6_K_Q8_1_MMQ; i0 += 4) {
-        int2 sumi_d = {0, 0}; // 2 q6_K scales per q8_1 scale
-
-#pragma unroll
-        for (int i = i0; i < i0 + 2; ++i) {
-            sumi_d.x = __dp4a(v[2*i+0], u[2*i+0], sumi_d.x); // SIMD dot product
-            sumi_d.x = __dp4a(v[2*i+1], u[2*i+1], sumi_d.x); // SIMD dot product
-
-            sumi_d.y = __dp4a(v[2*i+4], u[2*i+4], sumi_d.y); // SIMD dot product
-            sumi_d.y = __dp4a(v[2*i+5], u[2*i+5], sumi_d.y); // SIMD dot product
-        }
-
-        sumf_d += d8[i0/4] * (sc[i0/2+0]*sumi_d.x + sc[i0/2+1]*sumi_d.y);
-    }
-
-    return d6 * sumf_d;
-
-#else
-    bad_arch();
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-}
-
-static __device__ __forceinline__ float vec_dot_q4_0_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq;
-
-    int v[VDR_Q4_0_Q8_1_MMVQ];
-    int u[2*VDR_Q4_0_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q4_0_Q8_1_MMVQ; ++i) {
-        v[i]     = get_int_from_uint8(bq4_0->qs, iqs + i);
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_0);
-    }
-
-    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMVQ>(v, u, bq4_0->d, bq8_1->ds);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    (void)x_qh; (void)x_sc;
-
-    __shared__ int  tile_x_qs[mmq_y * (WARP_SIZE)       + mmq_y];
-    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI4_0) + mmq_y/QI4_0];
-
-    *x_ql = tile_x_qs;
-    *x_dm = (half2 *) tile_x_d;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_0(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    (void)x_qh; (void)x_sc;
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI4_0;
-    const int kqsx = k % QI4_0;
-
-    const block_q4_0 * bx0 = (const block_q4_0 *) vx;
-
-    float * x_dmf = (float *) x_dm;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
-        // x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = bxi->d;
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI4_0;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_0) {
-        int i = i0 + i_offset * QI4_0 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = bxi->d;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q4_0_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    (void)x_qh; (void)x_sc;
-
-    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
-    const float * x_dmf = (const float *) x_dm;
-
-    int u[2*VDR_Q4_0_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) {
-        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
-        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_0) % WARP_SIZE];
-    }
-
-    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
-        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dmf[i * (WARP_SIZE/QI4_0) + i/QI4_0 + k/QI4_0],
-         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
-}
-
-static __device__ __forceinline__ float vec_dot_q4_1_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq;
-
-    int v[VDR_Q4_1_Q8_1_MMVQ];
-    int u[2*VDR_Q4_1_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) {
-        v[i]    = get_int_from_uint8_aligned(bq4_1->qs, iqs + i);
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_1);
-    }
-
-    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, bq4_1->dm, bq8_1->ds);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    (void)x_qh; (void)x_sc;
-
-    __shared__ int   tile_x_qs[mmq_y * (WARP_SIZE) +     + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_1) + mmq_y/QI4_1];
-
-    *x_ql = tile_x_qs;
-    *x_dm = tile_x_dm;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_1(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    (void)x_qh; (void)x_sc;
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI4_1;
-    const int kqsx = k % QI4_1;
-
-    const block_q4_1 * bx0 = (const block_q4_1 *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI4_1;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_1) {
-        int i = i0 + i_offset * QI4_1 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q4_1_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    (void)x_qh; (void)x_sc;
-
-    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
-
-    int u[2*VDR_Q4_1_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) {
-        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
-        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_1) % WARP_SIZE];
-    }
-
-    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMQ>
-        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dm[i * (WARP_SIZE/QI4_1) + i/QI4_1 + k/QI4_1],
-         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
-}
-
-static __device__ __forceinline__ float vec_dot_q5_0_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq;
-
-    int vl[VDR_Q5_0_Q8_1_MMVQ];
-    int vh[VDR_Q5_0_Q8_1_MMVQ];
-    int  u[2*VDR_Q5_0_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q5_0_Q8_1_MMVQ; ++i) {
-        vl[i]    = get_int_from_uint8(bq5_0->qs, iqs + i);
-        vh[i]    = get_int_from_uint8(bq5_0->qh, 0) >> (4 * (iqs + i));
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_0);
-    }
-
-    return vec_dot_q5_0_q8_1_impl<VDR_Q5_0_Q8_1_MMVQ>(vl, vh, u, bq5_0->d, bq8_1->ds);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    (void)x_qh; (void)x_sc;
-
-    __shared__ int  tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
-    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI5_0) + mmq_y/QI5_0];
-
-    *x_ql = tile_x_ql;
-    *x_dm = (half2 *) tile_x_d;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_0(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    (void)x_qh; (void)x_sc;
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI5_0;
-    const int kqsx = k % QI5_0;
-
-    const block_q5_0 * bx0 = (const block_q5_0 *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        const int ql = get_int_from_uint8(bxi->qs, kqsx);
-        const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (k % QI5_0));
-
-        int qs0 = (ql >>  0)   & 0x0F0F0F0F;
-        qs0    |= (qh <<  4)   & 0x00000010;  // 0 ->  4
-        qs0    |= (qh << 11)   & 0x00001000;  // 1 -> 12
-        qs0    |= (qh << 18)   & 0x00100000;  // 2 -> 20
-        qs0    |= (qh << 25)   & 0x10000000;  // 3 -> 28
-        qs0     = __vsubss4(qs0, 0x10101010); // subtract 16
-
-        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
-
-        int qs1 = (ql >>  4)   & 0x0F0F0F0F;
-        qs1    |= (qh >> 12)   & 0x00000010;  // 16 ->  4
-        qs1    |= (qh >>  5)   & 0x00001000;  // 17 -> 12
-        qs1    |= (qh <<  2)   & 0x00100000;  // 18 -> 20
-        qs1    |= (qh <<  9)   & 0x10000000;  // 19 -> 28
-        qs1     = __vsubss4(qs1, 0x10101010); // subtract 16
-
-        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI5_0;
-    const int kbxd = k % blocks_per_tile_x_row;
-    float * x_dmf = (float *) x_dm;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_0) {
-        int i = i0 + i_offset * QI5_0 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = bxi->d;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q5_0_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    (void)x_qh; (void)x_sc;
-
-    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
-    const int index_bx = i * (WARP_SIZE/QI5_0) + i/QI5_0 + k/QI5_0;
-    const float * x_dmf = (const float *) x_dm;
-    const float * y_df  = (const float *) y_ds;
-
-    int u[2*VDR_Q5_0_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) {
-        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
-        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE];
-    }
-
-    return vec_dot_q8_0_q8_1_impl<QR5_0*VDR_Q5_0_Q8_1_MMQ>
-        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_df[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
-}
-
-static __device__ __forceinline__ float vec_dot_q5_1_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq;
-
-    int vl[VDR_Q5_1_Q8_1_MMVQ];
-    int vh[VDR_Q5_1_Q8_1_MMVQ];
-    int  u[2*VDR_Q5_1_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) {
-        vl[i]   = get_int_from_uint8_aligned(bq5_1->qs, iqs + i);
-        vh[i]   = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * (iqs + i));
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_1);
-    }
-
-    return vec_dot_q5_1_q8_1_impl<VDR_Q5_1_Q8_1_MMVQ>(vl, vh, u, bq5_1->dm, bq8_1->ds);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    (void)x_qh; (void)x_sc;
-
-    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_1) + mmq_y/QI5_1];
-
-    *x_ql = tile_x_ql;
-    *x_dm = tile_x_dm;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_1(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    (void)x_qh; (void)x_sc;
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset < nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI5_1;
-    const int kqsx = k % QI5_1;
-
-    const block_q5_1 * bx0 = (const block_q5_1 *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
-        const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (k % QI5_1));
-
-        int qs0 = (ql >>  0) & 0x0F0F0F0F;
-        qs0    |= (qh <<  4) & 0x00000010; // 0 ->  4
-        qs0    |= (qh << 11) & 0x00001000; // 1 -> 12
-        qs0    |= (qh << 18) & 0x00100000; // 2 -> 20
-        qs0    |= (qh << 25) & 0x10000000; // 3 -> 28
-
-        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
-
-        int qs1 = (ql >>  4) & 0x0F0F0F0F;
-        qs1    |= (qh >> 12) & 0x00000010; // 16 ->  4
-        qs1    |= (qh >>  5) & 0x00001000; // 17 -> 12
-        qs1    |= (qh <<  2) & 0x00100000; // 18 -> 20
-        qs1    |= (qh <<  9) & 0x10000000; // 19 -> 28
-
-        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI5_1;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_1) {
-        int i = i0 + i_offset * QI5_1 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q5_1_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    (void)x_qh; (void)x_sc;
-
-    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
-    const int index_bx = i * (WARP_SIZE/QI5_1) + + i/QI5_1 + k/QI5_1;
-
-    int u[2*VDR_Q5_1_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) {
-        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
-        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_1) % WARP_SIZE];
-    }
-
-    return vec_dot_q8_1_q8_1_impl<QR5_1*VDR_Q5_1_Q8_1_MMQ>
-        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dm[index_bx], y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
-}
-
-static __device__ __forceinline__ float vec_dot_q8_0_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq;
-
-    int v[VDR_Q8_0_Q8_1_MMVQ];
-    int u[VDR_Q8_0_Q8_1_MMVQ];
-
-#pragma unroll
-    for (int i = 0; i < VDR_Q8_0_Q8_1_MMVQ; ++i) {
-        v[i] = get_int_from_int8(bq8_0->qs, iqs + i);
-        u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-    }
-
-    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d, __low2half(bq8_1->ds));
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q8_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    (void)x_qh; (void)x_sc;
-
-    __shared__ int  tile_x_qs[mmq_y * (WARP_SIZE)       + mmq_y];
-    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI8_0) + mmq_y/QI8_0];
-
-    *x_ql = tile_x_qs;
-    *x_dm = (half2 *) tile_x_d;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q8_0(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    (void)x_qh; (void)x_sc;
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI8_0;
-    const int kqsx = k % QI8_0;
-    float * x_dmf = (float *) x_dm;
-
-    const block_q8_0 * bx0 = (const block_q8_0 *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_int8(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI8_0;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0) {
-        int i = i0 + i_offset * QI8_0 + k / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbxd] = bxi->d;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q8_0_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    (void)x_qh; (void)x_sc;
-
-    const float * x_dmf = (const float *) x_dm;
-    const float * y_df  = (const float *) y_ds;
-
-    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMQ>
-        (&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0],
-         y_df[j * (WARP_SIZE/QI8_1) + k/QI8_1]);
-}
-
-static __device__ __forceinline__ float vec_dot_q2_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q2_K * bq2_K = (const block_q2_K *) vbq;
-
-    const int bq8_offset = QR2_K * (iqs / QI8_1);
-    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
-
-    const uint8_t * scales = bq2_K->scales + scale_offset;
-
-    const int v = get_int_from_uint8_aligned(bq2_K->qs, iqs);
-    int    u[QR2_K];
-    float d8[QR2_K];
-
-#pragma unroll
-    for (int i = 0; i < QR2_K; ++ i) {
-        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
-        d8[i] = __low2half(bq8_1[bq8_offset + i].ds);
-    }
-
-    return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q2_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    (void)x_qh;
-
-    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI2_K) + mmq_y/QI2_K];
-    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/4)     + mmq_y/4];
-
-    *x_ql = tile_x_ql;
-    *x_dm = tile_x_dm;
-    *x_sc = tile_x_sc;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q2_K(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    (void)x_qh;
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI2_K;
-    const int kqsx = k % QI2_K;
-
-    const block_q2_K * bx0 = (const block_q2_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI2_K;
-    const int kbxd = k % blocks_per_tile_x_row;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI2_K) {
-        int i = (i0 + i_offset * QI2_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dm[i * (WARP_SIZE/QI2_K) + i / QI2_K + kbxd] = bxi->dm;
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
-        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q2_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI2_K/4);
-
-        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = get_int_from_uint8_aligned(bxi->scales, k % (QI2_K/4));
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q2_K_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    (void)x_qh;
-
-    const int kbx = k / QI2_K;
-    const int ky  = (k % QI2_K) * QR2_K;
-    const float * y_df = (const float *) y_ds;
-
-    int v[QR2_K*VDR_Q2_K_Q8_1_MMQ];
-
-    const int kqsx = i * (WARP_SIZE + 1) + kbx*QI2_K + (QI2_K/2) * (ky/(2*QI2_K)) + ky % (QI2_K/2);
-    const int shift = 2 * ((ky % (2*QI2_K)) / (QI2_K/2));
-
-#pragma unroll
-    for (int l = 0; l < QR2_K*VDR_Q2_K_Q8_1_MMQ; ++l) {
-        v[l] = (x_ql[kqsx + l] >> shift) & 0x03030303;
-    }
-
-    const uint8_t * scales = ((const uint8_t *) &x_sc[i * (WARP_SIZE/4) + i/4 + kbx*4]) + ky/4;
-
-    const int index_y = j * WARP_SIZE + (QR2_K*k) % WARP_SIZE;
-    return vec_dot_q2_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dm[i * (WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[index_y/QI8_1]);
-}
-
-static __device__ __forceinline__ float vec_dot_q3_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q3_K * bq3_K = (const block_q3_K *) vbq;
-
-    const int bq8_offset = QR3_K * (iqs / (QI3_K/2));
-    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
-
-    const float d = bq3_K->d;
-
-    const int vl = get_int_from_uint8(bq3_K->qs, iqs);
-
-    // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
-    const int vh = ~get_int_from_uint8(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset;
-
-    int    u[QR3_K];
-    float d8[QR3_K];
-
-#pragma unroll
-    for (int i = 0; i < QR3_K; ++i) {
-        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
-        d8[i] = __low2half(bq8_1[bq8_offset + i].ds);
-    }
-
-    return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q3_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-
-    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI3_K) + mmq_y/QI3_K];
-    __shared__ int   tile_x_qh[mmq_y * (WARP_SIZE/2)     + mmq_y/2];
-    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/4)     + mmq_y/4];
-
-    *x_ql = tile_x_ql;
-    *x_dm = tile_x_dm;
-    *x_qh = tile_x_qh;
-    *x_sc = tile_x_sc;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q3_K(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI3_K;
-    const int kqsx = k % QI3_K;
-
-    const block_q3_K * bx0 = (const block_q3_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI3_K;
-    const int kbxd = k % blocks_per_tile_x_row;
-    float * x_dmf = (float *) x_dm;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI3_K) {
-        int i = (i0 + i_offset * QI3_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI3_K) + i / QI3_K + kbxd] = bxi->d;
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 2) {
-        int i = i0 + i_offset * 2 + k / (WARP_SIZE/2);
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/2)) / (QI3_K/2);
-
-        // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
-        x_qh[i * (WARP_SIZE/2) + i / 2 + k % (WARP_SIZE/2)] = ~get_int_from_uint8(bxi->hmask, k % (QI3_K/2));
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
-        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI3_K/4);
-
-        const int ksc = k % (QI3_K/4);
-
-        const int ksc_low = ksc % (QI3_K/8);
-        const int shift_low = 4 * (ksc / (QI3_K/8));
-        const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F;
-
-        const int ksc_high = QI3_K/8;
-        const int shift_high = 2 * ksc;
-        const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030;
-
-        const int sc = __vsubss4(sc_low | sc_high, 0x20202020);
-
-        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = sc;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q3_K_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-
-    const int kbx  = k / QI3_K;
-    const int ky  = (k % QI3_K) * QR3_K;
-    const float * x_dmf = (const float *) x_dm;
-    const float * y_df  = (const float *) y_ds;
-
-    const int8_t * scales = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4;
-
-    int v[QR3_K*VDR_Q3_K_Q8_1_MMQ];
-
-#pragma unroll
-    for (int l = 0; l < QR3_K*VDR_Q3_K_Q8_1_MMQ; ++l) {
-        const int kqsx = i * (WARP_SIZE + 1) + kbx*QI3_K + (QI3_K/2) * (ky/(2*QI3_K)) + ky % (QI3_K/2);
-        const int shift = 2 * ((ky % 32) / 8);
-        const int vll = (x_ql[kqsx + l] >> shift) & 0x03030303;
-
-        const int vh = x_qh[i * (WARP_SIZE/2) + i/2 + kbx * (QI3_K/2) + (ky+l)%8] >> ((ky+l) / 8);
-        const int vlh = (vh << 2) & 0x04040404;
-
-        v[l] = __vsubss4(vll, vlh);
-    }
-
-    const int index_y = j * WARP_SIZE + (k*QR3_K) % WARP_SIZE;
-    return vec_dot_q3_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dmf[i * (WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[index_y/QI8_1]);
-}
-
-static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-#ifndef GGML_QKK_64
-    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
-
-    int    v[2];
-    int    u[2*QR4_K];
-    float d8[QR4_K];
-
-    // iqs is in 0,2..30. bq8_offset = iqs/4 -> bq8_offset = 0, 2, 4, 6
-    const int bq8_offset = QR4_K * ((iqs/2) / (QI8_1/2));
-
-    // iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12
-    // iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44
-    // iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76
-    // iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108
-
-    const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
-    v[0] = q4[0];
-    v[1] = q4[4];
-
-    const uint16_t * scales = (const uint16_t *)bq4_K->scales;
-    uint16_t aux[2];
-    const int j = bq8_offset/2;
-    if (j < 2) {
-        aux[0] = scales[j+0] & 0x3f3f;
-        aux[1] = scales[j+2] & 0x3f3f;
+    if (id == ggml_backend_cuda_get_device_count() - 1) {
+        *row_high = nrows;
     } else {
-        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
-        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
-    }
-    const uint8_t * sc = (const uint8_t *)aux;
-    const uint8_t * m  = sc + 2;
-
-    for (int i = 0; i < QR4_K; ++i) {
-        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
-        d8[i] = __low2half(bq8i->ds);
-
-        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
-        u[2*i+0] = q8[0];
-        u[2*i+1] = q8[4];
-    }
-
-    return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8);
-
-#else
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
-
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
-
-    uint16_t aux16[2];
-    const uint8_t * s = (const uint8_t *)aux16;
-
-    const uint16_t * a = (const uint16_t *)bq4_K->scales;
-    aux16[0] = a[0] & 0x0f0f;
-    aux16[1] = (a[0] >> 4) & 0x0f0f;
-
-    const float dall = bq4_K->dm[0];
-    const float dmin = bq4_K->dm[1];
-
-    const float d8_1 = __low2float(bq8_1[0].ds);
-    const float d8_2 = __low2float(bq8_1[1].ds);
-
-    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
-    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
-    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
-    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
-
-    const int * q4 = (const int *)bq4_K->qs + (iqs/2);
-    const int v1 = q4[0];
-    const int v2 = q4[4];
-
-    const int dot1 = __dp4a(ui2, v2 & 0x0f0f0f0f, __dp4a(ui1, v1 & 0x0f0f0f0f, 0));
-    const int dot2 = __dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, __dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0));
-    const int dot3 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0));
-    const int dot4 = __dp4a(0x01010101, ui4, __dp4a(0x01010101, ui3, 0));
-
-    sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]);
-    sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]);
-
-    return dall * sumf_d - dmin * sumf_m;
-
-#else
-    bad_arch();
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-
-#endif
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    (void)x_qh;
-
-    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_K) + mmq_y/QI4_K];
-    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];
-
-    *x_ql = tile_x_ql;
-    *x_dm = tile_x_dm;
-    *x_sc = tile_x_sc;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_K(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    (void)x_qh;
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI4_K; // == 0 if QK_K == 256
-    const int kqsx = k % QI4_K; // == k if QK_K == 256
-
-    const block_q4_K * bx0 = (const block_q4_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbx;
-
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256
-    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_K) {
-        int i = (i0 + i_offset * QI4_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-#if QK_K == 256
-        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm;
-#else
-        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]};
-#endif
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
-        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q4_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI4_K/8);
-
-        const int * scales = (const int *) bxi->scales;
-
-        const int ksc = k % (WARP_SIZE/8);
-
-        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
-        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
-        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
-
-        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
+        *row_high = nrows*tensor_split[id + 1];
+        *row_high -= *row_high % rounding;
     }
 }
 
-static __device__ __forceinline__ float vec_dot_q4_K_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    (void)x_qh;
+static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
-    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2*((k % 16) / 8);
-
-    const int index_y = j * WARP_SIZE + (QR4_K*k) % WARP_SIZE;
-    return vec_dot_q4_K_q8_1_impl_mmq(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[index_y], sc, sc+8,
-                                      x_dm[i * (WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[index_y/QI8_1]);
+    return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]);
 }
 
-static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-#ifndef GGML_QKK_64
-    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
-
-    int   vl[2];
-    int   vh[2];
-    int    u[2*QR5_K];
-    float d8[QR5_K];
-
-    const int bq8_offset = QR5_K * ((iqs/2) / (QI8_1/2));
-    const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
-    const int * qh = (const int *)(bq5_K->qh + 4 * ((iqs/2)%4));
-
-    vl[0] = ql[0];
-    vl[1] = ql[4];
-
-    vh[0] = qh[0] >> bq8_offset;
-    vh[1] = qh[4] >> bq8_offset;
-
-    const uint16_t * scales = (const uint16_t *)bq5_K->scales;
-    uint16_t aux[2];
-    const int j = bq8_offset/2;
-    if (j < 2) {
-        aux[0] = scales[j+0] & 0x3f3f;
-        aux[1] = scales[j+2] & 0x3f3f;
-    } else {
-        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
-        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
-    }
-    const uint8_t * sc = (const uint8_t *)aux;
-    const uint8_t * m  = sc + 2;
-
-#pragma unroll
-    for (int i = 0; i < QR5_K; ++i) {
-        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
-        d8[i] = __low2float(bq8i->ds);
-
-        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
-        u[2*i+0] = q8[0];
-        u[2*i+1] = q8[4];
-    }
-
-    return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8);
-
-#else
-
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
-
-    const int8_t * s = bq5_K->scales;
-
-    const float d = bq5_K->d;
-
-    const float d8_1 = __low2half(bq8_1[0].ds);
-    const float d8_2 = __low2half(bq8_1[1].ds);
-
-    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
-    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
-    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
-    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
-
-    const int * ql = (const int *)bq5_K->qs + (iqs/2);
-    const int vl1 = ql[0];
-    const int vl2 = ql[4];
-
-    const int step = 4 * (iqs/2); // 0, 4, 8, 12
-    const int im = step/8; // = 0 for iqs = 0, 2, = 1 for iqs = 4, 6
-    const int in = step%8; // 0, 4, 0, 4
-    const int vh = (*((const int *)(bq5_K->qh + in))) >> im;
-
-    const int v1 = (((vh << 4) & 0x10101010) ^ 0x10101010) | ((vl1 >> 0) & 0x0f0f0f0f);
-    const int v2 = (((vh << 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 0) & 0x0f0f0f0f);
-    const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f);
-    const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f);
-
-    const float sumf_d = d8_1 * (__dp4a(ui1, v1, 0) * s[0] + __dp4a(ui2, v2, 0) * s[1])
-                       + d8_2 * (__dp4a(ui3, v3, 0) * s[2] + __dp4a(ui4, v4, 0) * s[3]);
-
-    return d * sumf_d;
-
-#else
-    bad_arch();
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
-
-#endif
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    (void)x_qh;
-
-    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_K) + mmq_y/QI5_K];
-    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];
-
-    *x_ql = tile_x_ql;
-    *x_dm = tile_x_dm;
-    *x_sc = tile_x_sc;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_K(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    (void)x_qh;
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI5_K; // == 0 if QK_K == 256
-    const int kqsx = k % QI5_K; // == k if QK_K == 256
-
-    const block_q5_K * bx0 = (const block_q5_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbx;
-        const int ky = QR5_K*kqsx;
-
-        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
-        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
-        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
-
-        const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4));
-        const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010;
-        const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010;
-
-        const int kq0 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + 0;
-        const int kq1 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + (QI5_K/4);
-
-        x_ql[i * (2*WARP_SIZE + 1) + kq0] = ql0 | qh0;
-        x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1;
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256
-    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_K) {
-        int i = (i0 + i_offset * QI5_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-#if QK_K == 256
-        x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm;
-#endif
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
-        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q5_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI5_K/8);
-
-        const int * scales = (const int *) bxi->scales;
-
-        const int ksc = k % (WARP_SIZE/8);
-
-        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
-        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
-        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
-
-        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q5_K_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    (void)x_qh;
-
-    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2 * ((k % 16) / 8);
-
-    const int index_x = i * (QR5_K*WARP_SIZE + 1) +  QR5_K*k;
-    const int index_y = j * WARP_SIZE             + (QR5_K*k) % WARP_SIZE;
-    return vec_dot_q5_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, sc+8,
-                                      x_dm[i * (WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[index_y/QI8_1]);
-}
-
-static __device__ __forceinline__ float vec_dot_q6_K_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-
-    const block_q6_K * bq6_K = (const block_q6_K *) vbq;
-
-    const int bq8_offset = 2 * QR6_K * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/4);
-    const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8);
-    const int vh_shift = 2 * ((iqs % (QI6_K/2)) / (QI6_K/4));
-
-    const int vl = get_int_from_uint8(bq6_K->ql, iqs);
-    const int vh = get_int_from_uint8(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift;
-
-    const int8_t * scales = bq6_K->scales + scale_offset;
-
-    int    u[QR6_K];
-    float d8[QR6_K];
-
-#pragma unroll
-    for (int i = 0; i < QR6_K; ++i) {
-        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1);
-        d8[i] = __low2half(bq8_1[bq8_offset + 2*i].ds);
-    }
-
-    return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8);
-}
-
-template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q6_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
-    (void)x_qh;
-
-    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
-    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI6_K) + mmq_y/QI6_K];
-    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];
-
-    *x_ql = tile_x_ql;
-    *x_dm = tile_x_dm;
-    *x_sc = tile_x_sc;
-}
-
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q6_K(
-    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
-    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
-    (void)x_qh;
-
-    GGML_CUDA_ASSUME(i_offset >= 0);
-    GGML_CUDA_ASSUME(i_offset <  nwarps);
-    GGML_CUDA_ASSUME(k >= 0);
-    GGML_CUDA_ASSUME(k <  WARP_SIZE);
-
-    const int kbx  = k / QI6_K; // == 0 if QK_K == 256
-    const int kqsx = k % QI6_K; // == k if QK_K == 256
-
-    const block_q6_K * bx0 = (const block_q6_K *) vx;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + i_offset;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbx;
-        const int ky = QR6_K*kqsx;
-
-        const int ql = get_int_from_uint8(bxi->ql, kqsx);
-        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
-        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
-
-        const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4));
-        const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030;
-        const int qh1 =  (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4))))       & 0x30303030;
-
-        const int kq0 = ky - ky % QI6_K + k % (QI6_K/2) + 0;
-        const int kq1 = ky - ky % QI6_K + k % (QI6_K/2) + (QI6_K/2);
-
-        x_ql[i * (2*WARP_SIZE + 1) + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
-        x_ql[i * (2*WARP_SIZE + 1) + kq1] = __vsubss4(ql1 | qh1, 0x20202020);
-    }
-
-    const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256
-    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
-    float * x_dmf = (float *) x_dm;
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI6_K) {
-        int i = (i0 + i_offset * QI6_K + k / blocks_per_tile_x_row) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbxd;
-
-        x_dmf[i * (WARP_SIZE/QI6_K) + i / QI6_K + kbxd] = bxi->d;
-    }
-
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
-        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q6_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / 4;
-
-        x_sc[i * (WARP_SIZE/8) + i / 8 + k % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, k % (QI6_K/8));
-    }
-}
-
-static __device__ __forceinline__ float vec_dot_q6_K_q8_1_mul_mat(
-    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
-    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
-    (void)x_qh;
-
-    const float * x_dmf = (const float *) x_dm;
-    const float * y_df  = (const float *) y_ds;
-
-    const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/8]);
-
-    const int index_x = i * (QR6_K*WARP_SIZE + 1) +  QR6_K*k;
-    const int index_y = j * WARP_SIZE             + (QR6_K*k) % WARP_SIZE;
-    return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]);
-}
-
-static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
-#if QK_K == 256
-    const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq;
-
-#if QR2_XXS == 8
-    const int ib32 = iqs;
-    const uint16_t * q2 = bq2->qs + 4*ib32;
-    const uint8_t  * aux8 = (const uint8_t *)q2;
-    const int8_t   * q8 = bq8_1[ib32].qs;
-    uint32_t aux32 = q2[2] | (q2[3] << 16);
-    int sumi = 0;
-    for (int l = 0; l < 4; ++l) {
-        const uint8_t * grid = (const uint8_t *)(kgrid_iq2xxs + aux8[l]);
-        const uint8_t  signs = ksigns_iq2xs[aux32 & 127];
-        for (int j = 0; j < 8; ++j) {
-            sumi += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
-        }
-        q8 += 8;
-        aux32 >>= 7;
-    }
-    const float d = (float)bq2->d * (0.5f + aux32) * (float)bq8_1[ib32].ds.x * 0.25f;
-    return d * sumi;
-#else
-    // iqs is 0...15
-    const int ib32 = iqs/2;
-    const int il = iqs%2;
-    const uint16_t * q2 = bq2->qs + 4*ib32;
-    const uint8_t  * aux8 = (const uint8_t *)q2;
-    const uint8_t  * grid1 = (const uint8_t *)(kgrid_iq2xxs + aux8[2*il+0]);
-    const uint8_t  * grid2 = (const uint8_t *)(kgrid_iq2xxs + aux8[2*il+1]);
-    const uint32_t aux32 = q2[2] | (q2[3] << 16);
-    const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * (float)bq8_1[ib32].ds.x * 0.25f;
-    const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127];
-    const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127];
-    const int8_t * q8 = bq8_1[ib32].qs + 16*il;
-    int sumi1 = 0, sumi2 = 0;
-    for (int j = 0; j < 8; ++j) {
-        sumi1 += q8[j+0] * grid1[j] * (signs1 & kmask_iq2xs[j] ? -1 : 1);
-        sumi2 += q8[j+8] * grid2[j] * (signs2 & kmask_iq2xs[j] ? -1 : 1);
-    }
-    return d * (sumi1 + sumi2);
-#endif
-#else
-    assert(false);
-    return 0.f;
-#endif
-}
-
-template <int qk, int qr, int qi, bool need_sum, typename block_q_t, int mmq_x, int mmq_y, int nwarps,
-              allocate_tiles_cuda_t allocate_tiles, load_tiles_cuda_t load_tiles, int vdr, vec_dot_q_mul_mat_cuda_t vec_dot>
-static __device__ __forceinline__ void mul_mat_q(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-    const block_q_t  * x = (const block_q_t  *) vx;
-    const block_q8_1 * y = (const block_q8_1 *) vy;
-
-    const int blocks_per_row_x = ncols_x / qk;
-    const int blocks_per_col_y = nrows_y / QK8_1;
-    const int blocks_per_warp = WARP_SIZE / qi;
-
-    const int & ncols_dst = ncols_y;
-
-    const int row_dst_0 = blockIdx.x*mmq_y;
-    const int & row_x_0 = row_dst_0;
-
-    const int col_dst_0 = blockIdx.y*mmq_x;
-    const int & col_y_0 = col_dst_0;
-
-    int   * tile_x_ql = nullptr;
-    half2 * tile_x_dm = nullptr;
-    int   * tile_x_qh = nullptr;
-    int   * tile_x_sc = nullptr;
-
-    allocate_tiles(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc);
-
-    __shared__ int    tile_y_qs[mmq_x * WARP_SIZE];
-    __shared__ half2  tile_y_ds[mmq_x * WARP_SIZE/QI8_1];
-
-    float sum[mmq_y/WARP_SIZE][mmq_x/nwarps] = {{0.0f}};
-
-    for (int ib0 = 0; ib0 < blocks_per_row_x; ib0 += blocks_per_warp) {
-
-        load_tiles(x + row_x_0*blocks_per_row_x + ib0, tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc,
-                   threadIdx.y, nrows_x-row_x_0-1, threadIdx.x, blocks_per_row_x);
-
-#pragma unroll
-        for (int ir = 0; ir < qr; ++ir) {
-            const int kqs = ir*WARP_SIZE + threadIdx.x;
-            const int kbxd = kqs / QI8_1;
-
-#pragma unroll
-            for (int i = 0; i < mmq_x; i += nwarps) {
-                const int col_y_eff = min(col_y_0 + threadIdx.y + i, ncols_y-1); // to prevent out-of-bounds memory accesses
-
-                const block_q8_1 * by0 = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + kbxd];
-
-                const int index_y = (threadIdx.y + i) * WARP_SIZE + kqs % WARP_SIZE;
-                tile_y_qs[index_y] = get_int_from_int8_aligned(by0->qs, threadIdx.x % QI8_1);
-            }
-
-#pragma unroll
-            for (int ids0 = 0; ids0 < mmq_x; ids0 += nwarps * QI8_1) {
-                const int ids = (ids0 + threadIdx.y * QI8_1 + threadIdx.x / (WARP_SIZE/QI8_1)) % mmq_x;
-                const int kby = threadIdx.x % (WARP_SIZE/QI8_1);
-                const int col_y_eff = min(col_y_0 + ids, ncols_y-1);
-
-                // if the sum is not needed it's faster to transform the scale to f32 ahead of time
-                const half2 * dsi_src = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + ir*(WARP_SIZE/QI8_1) + kby].ds;
-                half2       * dsi_dst = &tile_y_ds[ids * (WARP_SIZE/QI8_1) + kby];
-                if (need_sum) {
-                    *dsi_dst = *dsi_src;
-                } else {
-                    float * dfi_dst = (float *) dsi_dst;
-                    *dfi_dst = __low2half(*dsi_src);
-                }
-            }
-
-            __syncthreads();
-
-// #pragma unroll // unrolling this loop causes too much register pressure
-            for (int k = ir*WARP_SIZE/qr; k < (ir+1)*WARP_SIZE/qr; k += vdr) {
-#pragma unroll
-                for (int j = 0; j < mmq_x; j += nwarps) {
-#pragma unroll
-                    for (int i = 0; i < mmq_y; i += WARP_SIZE) {
-                        sum[i/WARP_SIZE][j/nwarps] += vec_dot(
-                            tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, tile_y_qs, tile_y_ds,
-                            threadIdx.x + i, threadIdx.y + j, k);
+struct ggml_backend_cuda_split_buffer_type_context {
+    std::array<float, GGML_CUDA_MAX_DEVICES> tensor_split;
+};
+
+struct ggml_backend_cuda_split_buffer_context {
+    ~ggml_backend_cuda_split_buffer_context() {
+        for (ggml_tensor_extra_gpu * extra : tensor_extras) {
+            for (int id = 0; id < GGML_CUDA_MAX_DEVICES; ++id) {
+                for (int64_t is = 0; is < GGML_CUDA_MAX_STREAMS; ++is) {
+                    if (extra->events[id][is] != nullptr) {
+                        CUDA_CHECK(cudaEventDestroy(extra->events[id][is]));
                     }
                 }
+                if (extra->data_device[id] != nullptr) {
+                    CUDA_CHECK(cudaFree(extra->data_device[id]));
+                }
             }
-
-            __syncthreads();
+            delete extra;
         }
     }
 
-#pragma unroll
-    for (int j = 0; j < mmq_x; j += nwarps) {
-        const int col_dst = col_dst_0 + j + threadIdx.y;
+    std::vector<ggml_tensor_extra_gpu *> tensor_extras;
+};
 
-        if (col_dst >= ncols_dst) {
-            return;
+GGML_CALL static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) {
+    return GGML_CUDA_NAME "_Split";
+
+    GGML_UNUSED(buffer);
+}
+
+static bool ggml_backend_buffer_is_cuda_split(ggml_backend_buffer_t buffer) {
+    return buffer->iface.get_name == ggml_backend_cuda_split_buffer_get_name;
+    GGML_UNUSED(ggml_backend_buffer_is_cuda_split); // only used in debug builds currently, avoid unused function warning in release builds
+}
+
+GGML_CALL static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context;
+    delete ctx;
+}
+
+GGML_CALL static void * ggml_backend_cuda_split_buffer_get_base(ggml_backend_buffer_t buffer) {
+    // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced
+    return (void *)0x1000;
+
+    GGML_UNUSED(buffer);
+}
+
+GGML_CALL static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
+    GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported
+
+    ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context;
+    ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context;
+
+    const int64_t ne0 = tensor->ne[0];
+
+    ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{};
+    ctx->tensor_extras.push_back(extra);
+
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id);
+
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
         }
 
-#pragma unroll
-        for (int i = 0; i < mmq_y; i += WARP_SIZE) {
-            const int row_dst = row_dst_0 + threadIdx.x + i;
+        size_t size = ggml_nbytes_split(tensor, nrows_split);
+        const size_t original_size = size;
 
-            if (row_dst >= nrows_dst) {
-                continue;
-            }
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
 
-            dst[col_dst*nrows_dst + row_dst] = sum[i/WARP_SIZE][j/nwarps];
+        // FIXME: do not crash if cudaMalloc fails
+        // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first
+        ggml_cuda_set_device(id);
+        char * buf;
+        CUDA_CHECK(cudaMalloc(&buf, size));
+
+        // set padding to 0 to avoid possible NaN values
+        if (size > original_size) {
+            CUDA_CHECK(cudaMemset(buf + original_size, 0, size - original_size));
+        }
+
+        extra->data_device[id] = buf;
+
+        for (int64_t is = 0; is < GGML_CUDA_MAX_STREAMS; ++is) {
+            CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id][is], cudaEventDisableTiming));
         }
     }
+    tensor->extra = extra;
 }
 
-#define  MMQ_X_Q4_0_RDNA2  64
-#define  MMQ_Y_Q4_0_RDNA2  128
-#define NWARPS_Q4_0_RDNA2  8
-#define  MMQ_X_Q4_0_RDNA1  64
-#define  MMQ_Y_Q4_0_RDNA1  64
-#define NWARPS_Q4_0_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q4_0_AMPERE 4
-#define  MMQ_Y_Q4_0_AMPERE 32
-#define NWARPS_Q4_0_AMPERE 4
-#else
-#define  MMQ_X_Q4_0_AMPERE 64
-#define  MMQ_Y_Q4_0_AMPERE 128
-#define NWARPS_Q4_0_AMPERE 4
-#endif
-#define  MMQ_X_Q4_0_PASCAL 64
-#define  MMQ_Y_Q4_0_PASCAL 64
-#define NWARPS_Q4_0_PASCAL 8
+GGML_CALL static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    // split tensors must always be set in their entirety at once
+    GGML_ASSERT(offset == 0);
+    GGML_ASSERT(size == ggml_nbytes(tensor));
 
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q4_0_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-    mul_mat_q4_0(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+    ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context;
 
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q4_0_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q4_0_RDNA2;
-    const int nwarps = NWARPS_Q4_0_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q4_0_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q4_0_RDNA1;
-    const int nwarps = NWARPS_Q4_0_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
+    const int64_t ne0 = tensor->ne[0];
+    const size_t nb1 = tensor->nb[1];
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra;
 
-    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
-        load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id);
 
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q4_0_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q4_0_AMPERE;
-    const int nwarps = NWARPS_Q4_0_AMPERE;
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
+        }
 
-    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
-        load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+        const size_t offset_split = row_low*nb1;
+        size_t size = ggml_nbytes_split(tensor, nrows_split);
+        const size_t original_size = size;
 
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q4_0_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q4_0_PASCAL;
-    const int nwarps = NWARPS_Q4_0_PASCAL;
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
 
-    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
-        load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q4_0_q8_1_mul_mat;
-    bad_arch();
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q4_1_RDNA2  64
-#define  MMQ_Y_Q4_1_RDNA2  128
-#define NWARPS_Q4_1_RDNA2  8
-#define  MMQ_X_Q4_1_RDNA1  64
-#define  MMQ_Y_Q4_1_RDNA1  64
-#define NWARPS_Q4_1_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q4_1_AMPERE 4
-#define  MMQ_Y_Q4_1_AMPERE 32
-#define NWARPS_Q4_1_AMPERE 4
-#else
-#define  MMQ_X_Q4_1_AMPERE 64
-#define  MMQ_Y_Q4_1_AMPERE 128
-#define NWARPS_Q4_1_AMPERE 4
-#endif
-#define  MMQ_X_Q4_1_PASCAL 64
-#define  MMQ_Y_Q4_1_PASCAL 64
-#define NWARPS_Q4_1_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#elif __CUDA_ARCH__ < CC_VOLTA
-    __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_PASCAL, 2)
-#endif // __CUDA_ARCH__ < CC_VOLTA
-    mul_mat_q4_1(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q4_1_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q4_1_RDNA2;
-    const int nwarps = NWARPS_Q4_1_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q4_1_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q4_1_RDNA1;
-    const int nwarps = NWARPS_Q4_1_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
-        load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q4_1_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q4_1_AMPERE;
-    const int nwarps = NWARPS_Q4_1_AMPERE;
-
-    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
-        load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q4_1_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q4_1_PASCAL;
-    const int nwarps = NWARPS_Q4_1_PASCAL;
-
-    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
-        load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q4_1_q8_1_mul_mat;
-    bad_arch();
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q5_0_RDNA2  64
-#define  MMQ_Y_Q5_0_RDNA2  128
-#define NWARPS_Q5_0_RDNA2  8
-#define  MMQ_X_Q5_0_RDNA1  64
-#define  MMQ_Y_Q5_0_RDNA1  64
-#define NWARPS_Q5_0_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q5_0_AMPERE 4
-#define  MMQ_Y_Q5_0_AMPERE 32
-#define NWARPS_Q5_0_AMPERE 4
-#else
-#define  MMQ_X_Q5_0_AMPERE 128
-#define  MMQ_Y_Q5_0_AMPERE 64
-#define NWARPS_Q5_0_AMPERE 4
-#endif
-#define  MMQ_X_Q5_0_PASCAL 64
-#define  MMQ_Y_Q5_0_PASCAL 64
-#define NWARPS_Q5_0_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q5_0_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-    mul_mat_q5_0(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q5_0_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q5_0_RDNA2;
-    const int nwarps = NWARPS_Q5_0_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q5_0_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q5_0_RDNA1;
-    const int nwarps = NWARPS_Q5_0_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
-        load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q5_0_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q5_0_AMPERE;
-    const int nwarps = NWARPS_Q5_0_AMPERE;
-
-    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
-        load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q5_0_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q5_0_PASCAL;
-    const int nwarps = NWARPS_Q5_0_PASCAL;
-
-    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
-        load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q5_0_q8_1_mul_mat;
-    bad_arch();
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q5_1_RDNA2  64
-#define  MMQ_Y_Q5_1_RDNA2  128
-#define NWARPS_Q5_1_RDNA2  8
-#define  MMQ_X_Q5_1_RDNA1  64
-#define  MMQ_Y_Q5_1_RDNA1  64
-#define NWARPS_Q5_1_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q5_1_AMPERE 4
-#define  MMQ_Y_Q5_1_AMPERE 32
-#define NWARPS_Q5_1_AMPERE 4
-#else
-#define  MMQ_X_Q5_1_AMPERE 128
-#define  MMQ_Y_Q5_1_AMPERE 64
-#define NWARPS_Q5_1_AMPERE 4
-#endif
-#define  MMQ_X_Q5_1_PASCAL 64
-#define  MMQ_Y_Q5_1_PASCAL 64
-#define NWARPS_Q5_1_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q5_1_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-mul_mat_q5_1(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q5_1_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q5_1_RDNA2;
-    const int nwarps = NWARPS_Q5_1_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q5_1_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q5_1_RDNA1;
-    const int nwarps = NWARPS_Q5_1_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
-        load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q5_1_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q5_1_AMPERE;
-    const int nwarps = NWARPS_Q5_1_AMPERE;
-
-    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
-        load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q5_1_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q5_1_PASCAL;
-    const int nwarps = NWARPS_Q5_1_PASCAL;
-
-    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
-        load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q5_1_q8_1_mul_mat;
-    bad_arch();
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q8_0_RDNA2  64
-#define  MMQ_Y_Q8_0_RDNA2  128
-#define NWARPS_Q8_0_RDNA2  8
-#define  MMQ_X_Q8_0_RDNA1  64
-#define  MMQ_Y_Q8_0_RDNA1  64
-#define NWARPS_Q8_0_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q8_0_AMPERE 4
-#define  MMQ_Y_Q8_0_AMPERE 32
-#define NWARPS_Q8_0_AMPERE 4
-#else
-#define  MMQ_X_Q8_0_AMPERE 128
-#define  MMQ_Y_Q8_0_AMPERE 64
-#define NWARPS_Q8_0_AMPERE 4
-#endif
-#define  MMQ_X_Q8_0_PASCAL 64
-#define  MMQ_Y_Q8_0_PASCAL 64
-#define NWARPS_Q8_0_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q8_0_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-    mul_mat_q8_0(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q8_0_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q8_0_RDNA2;
-    const int nwarps = NWARPS_Q8_0_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q8_0_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q8_0_RDNA1;
-    const int nwarps = NWARPS_Q8_0_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
-        load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q8_0_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q8_0_AMPERE;
-    const int nwarps = NWARPS_Q8_0_AMPERE;
-
-    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
-        load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q8_0_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q8_0_PASCAL;
-    const int nwarps = NWARPS_Q8_0_PASCAL;
-
-    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
-        load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q8_0_q8_1_mul_mat;
-    bad_arch();
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q2_K_RDNA2  64
-#define  MMQ_Y_Q2_K_RDNA2  128
-#define NWARPS_Q2_K_RDNA2  8
-#define  MMQ_X_Q2_K_RDNA1  128
-#define  MMQ_Y_Q2_K_RDNA1  32
-#define NWARPS_Q2_K_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q2_K_AMPERE 4
-#define  MMQ_Y_Q2_K_AMPERE 32
-#define NWARPS_Q2_K_AMPERE 4
-#else
-#define  MMQ_X_Q2_K_AMPERE 64
-#define  MMQ_Y_Q2_K_AMPERE 128
-#define NWARPS_Q2_K_AMPERE 4
-#endif
-#define  MMQ_X_Q2_K_PASCAL 64
-#define  MMQ_Y_Q2_K_PASCAL 64
-#define NWARPS_Q2_K_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q2_K_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-mul_mat_q2_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q2_K_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q2_K_RDNA2;
-    const int nwarps = NWARPS_Q2_K_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q2_K_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q2_K_RDNA1;
-    const int nwarps = NWARPS_Q2_K_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
-        load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q2_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q2_K_AMPERE;
-    const int nwarps = NWARPS_Q2_K_AMPERE;
-
-    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
-        load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q2_K_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q2_K_PASCAL;
-    const int nwarps = NWARPS_Q2_K_PASCAL;
-
-    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
-        load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q2_K_q8_1_mul_mat;
-    bad_arch();
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q3_K_RDNA2  128
-#define  MMQ_Y_Q3_K_RDNA2  64
-#define NWARPS_Q3_K_RDNA2  8
-#define  MMQ_X_Q3_K_RDNA1  32
-#define  MMQ_Y_Q3_K_RDNA1  128
-#define NWARPS_Q3_K_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q3_K_AMPERE 4
-#define  MMQ_Y_Q3_K_AMPERE 32
-#define NWARPS_Q3_K_AMPERE 4
-#else
-#define  MMQ_X_Q3_K_AMPERE 128
-#define  MMQ_Y_Q3_K_AMPERE 128
-#define NWARPS_Q3_K_AMPERE 4
-#endif
-#define  MMQ_X_Q3_K_PASCAL 64
-#define  MMQ_Y_Q3_K_PASCAL 64
-#define NWARPS_Q3_K_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#elif __CUDA_ARCH__ < CC_VOLTA
-    __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_PASCAL, 2)
-#endif // __CUDA_ARCH__ < CC_VOLTA
-    mul_mat_q3_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q3_K_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q3_K_RDNA2;
-    const int nwarps = NWARPS_Q3_K_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q3_K_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q3_K_RDNA1;
-    const int nwarps = NWARPS_Q3_K_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
-        load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q3_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q3_K_AMPERE;
-    const int nwarps = NWARPS_Q3_K_AMPERE;
-
-    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
-        load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q3_K_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q3_K_PASCAL;
-    const int nwarps = NWARPS_Q3_K_PASCAL;
-
-    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
-        load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q3_K_q8_1_mul_mat;
-    bad_arch();
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q4_K_RDNA2  64
-#define  MMQ_Y_Q4_K_RDNA2  128
-#define NWARPS_Q4_K_RDNA2  8
-#define  MMQ_X_Q4_K_RDNA1  32
-#define  MMQ_Y_Q4_K_RDNA1  64
-#define NWARPS_Q4_K_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q4_K_AMPERE 4
-#define  MMQ_Y_Q4_K_AMPERE 32
-#define NWARPS_Q4_K_AMPERE 4
-#else
-#define  MMQ_X_Q4_K_AMPERE 64
-#define  MMQ_Y_Q4_K_AMPERE 128
-#define NWARPS_Q4_K_AMPERE 4
-#endif
-#define  MMQ_X_Q4_K_PASCAL 64
-#define  MMQ_Y_Q4_K_PASCAL 64
-#define NWARPS_Q4_K_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#elif __CUDA_ARCH__ < CC_VOLTA
-    __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_PASCAL, 2)
-#endif // __CUDA_ARCH__ < CC_VOLTA
-    mul_mat_q4_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q4_K_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q4_K_RDNA2;
-    const int nwarps = NWARPS_Q4_K_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q4_K_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q4_K_RDNA1;
-    const int nwarps = NWARPS_Q4_K_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
-        load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q4_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q4_K_AMPERE;
-    const int nwarps = NWARPS_Q4_K_AMPERE;
-
-    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
-        load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q4_K_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q4_K_PASCAL;
-    const int nwarps = NWARPS_Q4_K_PASCAL;
-
-    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
-        load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q4_K_q8_1_mul_mat;
-    bad_arch();
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q5_K_RDNA2  64
-#define  MMQ_Y_Q5_K_RDNA2  128
-#define NWARPS_Q5_K_RDNA2  8
-#define  MMQ_X_Q5_K_RDNA1  32
-#define  MMQ_Y_Q5_K_RDNA1  64
-#define NWARPS_Q5_K_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q5_K_AMPERE 4
-#define  MMQ_Y_Q5_K_AMPERE 32
-#define NWARPS_Q5_K_AMPERE 4
-#else
-#define  MMQ_X_Q5_K_AMPERE 64
-#define  MMQ_Y_Q5_K_AMPERE 128
-#define NWARPS_Q5_K_AMPERE 4
-#endif
-#define  MMQ_X_Q5_K_PASCAL 64
-#define  MMQ_Y_Q5_K_PASCAL 64
-#define NWARPS_Q5_K_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q5_K_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-mul_mat_q5_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q5_K_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q5_K_RDNA2;
-    const int nwarps = NWARPS_Q5_K_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q5_K_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q5_K_RDNA1;
-    const int nwarps = NWARPS_Q5_K_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
-        load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q5_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q5_K_AMPERE;
-    const int nwarps = NWARPS_Q5_K_AMPERE;
-
-    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
-        load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q5_K_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q5_K_PASCAL;
-    const int nwarps = NWARPS_Q5_K_PASCAL;
-
-    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
-        load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q5_K_q8_1_mul_mat;
-    bad_arch();
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-#define  MMQ_X_Q6_K_RDNA2  64
-#define  MMQ_Y_Q6_K_RDNA2  128
-#define NWARPS_Q6_K_RDNA2  8
-#define  MMQ_X_Q6_K_RDNA1  32
-#define  MMQ_Y_Q6_K_RDNA1  64
-#define NWARPS_Q6_K_RDNA1  8
-#if defined(CUDA_USE_TENSOR_CORES)
-#define  MMQ_X_Q6_K_AMPERE 4
-#define  MMQ_Y_Q6_K_AMPERE 32
-#define NWARPS_Q6_K_AMPERE 4
-#else
-#define  MMQ_X_Q6_K_AMPERE 64
-#define  MMQ_Y_Q6_K_AMPERE 64
-#define NWARPS_Q6_K_AMPERE 4
-#endif
-#define  MMQ_X_Q6_K_PASCAL 64
-#define  MMQ_Y_Q6_K_PASCAL 64
-#define NWARPS_Q6_K_PASCAL 8
-
-template <bool need_check> static __global__ void
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_RDNA2, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
-#elif __CUDA_ARCH__ < CC_VOLTA
-    __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_PASCAL, 2)
-#endif // __CUDA_ARCH__ < CC_VOLTA
-    mul_mat_q6_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
-    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
-    const int mmq_x  =  MMQ_X_Q6_K_RDNA2;
-    const int mmq_y  =  MMQ_Y_Q6_K_RDNA2;
-    const int nwarps = NWARPS_Q6_K_RDNA2;
-#else
-    const int mmq_x  =  MMQ_X_Q6_K_RDNA1;
-    const int mmq_y  =  MMQ_Y_Q6_K_RDNA1;
-    const int nwarps = NWARPS_Q6_K_RDNA1;
-#endif // defined(RDNA3) || defined(RDNA2)
-
-    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
-        load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= CC_VOLTA
-    const int mmq_x  =  MMQ_X_Q6_K_AMPERE;
-    const int mmq_y  =  MMQ_Y_Q6_K_AMPERE;
-    const int nwarps = NWARPS_Q6_K_AMPERE;
-
-    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
-        load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-
-#elif __CUDA_ARCH__ >= MIN_CC_DP4A
-    const int mmq_x  =  MMQ_X_Q6_K_PASCAL;
-    const int mmq_y  =  MMQ_Y_Q6_K_PASCAL;
-    const int nwarps = NWARPS_Q6_K_PASCAL;
-
-    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
-        load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
-        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-#else
-    (void) vec_dot_q6_K_q8_1_mul_mat;
-    bad_arch();
-#endif // __CUDA_ARCH__ >= CC_VOLTA
-}
-
-template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
-static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows) {
-    const int row = blockIdx.x*blockDim.y + threadIdx.y;
-
-    if (row >= nrows) {
-        return;
+        const char * buf_host = (const char *)data + offset_split;
+        CUDA_CHECK(cudaMemcpyAsync(extra->data_device[id], buf_host, original_size, cudaMemcpyHostToDevice, cudaStreamPerThread));
     }
 
-    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * WARP_SIZE / qi;
-
-// partial sum for each thread
-    float tmp = 0.0f;
-
-    const block_q_t  * x = (const block_q_t  *) vx;
-    const block_q8_1 * y = (const block_q8_1 *) vy;
-
-    for (int i = 0; i < blocks_per_row; i += blocks_per_warp) {
-        const int ibx = row*blocks_per_row + i + threadIdx.x / (qi/vdr); // x block index
-
-        const int iby = (i + threadIdx.x / (qi/vdr)) * (qk/QK8_1); // y block index that aligns with ibx
-
-        const int iqs  = vdr * (threadIdx.x % (qi/vdr)); // x block quant index when casting the quants to int
-
-        tmp += vec_dot_q_cuda(&x[ibx], &y[iby], iqs);
-    }
-
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
-    }
-
-    if (threadIdx.x == 0) {
-        dst[row] = tmp;
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+        CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
     }
 }
 
-template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
-static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows) {
-    // qk = quantized weights per x block
-    // qr = number of quantized weights per data value in x block
-    const int row = blockIdx.x*blockDim.y + threadIdx.y;
+GGML_CALL static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    // split tensors must always be set in their entirety at once
+    GGML_ASSERT(offset == 0);
+    GGML_ASSERT(size == ggml_nbytes(tensor));
 
-    if (row >= nrows) {
-        return;
+    ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context;
+
+    const int64_t ne0 = tensor->ne[0];
+    const size_t nb1 = tensor->nb[1];
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra;
+
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id);
+
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
+        }
+
+        const size_t offset_split = row_low*nb1;
+        size_t size = ggml_nbytes_split(tensor, nrows_split);
+        const size_t original_size = size;
+
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
+
+        char * buf_host = (char *)data + offset_split;
+        CUDA_CHECK(cudaMemcpyAsync(buf_host, extra->data_device[id], original_size, cudaMemcpyDeviceToHost, cudaStreamPerThread));
     }
 
-    const int tid = threadIdx.x;
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+        CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
+    }
+}
 
-    const int iter_stride = 2*GGML_CUDA_DMMV_X;
-    const int vals_per_iter = iter_stride / WARP_SIZE; // num quantized vals per thread and i iter
-    const int y_offset = qr == 1 ? 1 : qk/2;
+GGML_CALL static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    GGML_UNUSED(buffer);
+    GGML_UNUSED(value);
+}
 
-// partial sum for each thread
-#ifdef GGML_CUDA_F16
-    half2 tmp = {0.0f, 0.0f}; // two sums for f16 to take advantage of half2 intrinsics
-#else
-    float tmp = 0.0f;
-#endif // GGML_CUDA_F16
+static struct ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = {
+    /* .get_name        = */ ggml_backend_cuda_split_buffer_get_name,
+    /* .free_buffer     = */ ggml_backend_cuda_split_buffer_free_buffer,
+    /* .get_base        = */ ggml_backend_cuda_split_buffer_get_base,
+    /* .init_tensor     = */ ggml_backend_cuda_split_buffer_init_tensor,
+    /* .set_tensor      = */ ggml_backend_cuda_split_buffer_set_tensor,
+    /* .get_tensor      = */ ggml_backend_cuda_split_buffer_get_tensor,
+    /* .cpy_tensor      = */ NULL,
+    /* .clear           = */ ggml_backend_cuda_split_buffer_clear,
+    /* .reset           = */ NULL,
+};
 
-    for (int i = 0; i < ncols; i += iter_stride) {
-        const int col = i + vals_per_iter*tid;
-        const int ib = (row*ncols + col)/qk; // x block index
-        const int iqs = (col%qk)/qr; // x quant index
-        const int iybs = col - col%qk; // y block start index
+// cuda split buffer type
 
-// processing >2 values per i iter is faster for fast GPUs
-#pragma unroll
-        for (int j = 0; j < vals_per_iter; j += 2) {
-            // process 2 vals per j iter
+GGML_CALL static const char * ggml_backend_cuda_split_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    return GGML_CUDA_NAME "_Split";
 
-            // dequantize
-            // for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val
-            dfloat2 v;
-            dequantize_kernel(vx, ib, iqs + j/qr, v);
+    GGML_UNUSED(buft);
+}
 
-            // matrix multiplication
-            // for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2
-#ifdef GGML_CUDA_F16
-            tmp += __hmul2(v, {
-                y[iybs + iqs + j/qr + 0],
-                y[iybs + iqs + j/qr + y_offset]
-            });
-#else
-            tmp += v.x * y[iybs + iqs + j/qr + 0];
-            tmp += v.y * y[iybs + iqs + j/qr + y_offset];
-#endif // GGML_CUDA_F16
+GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point
+    // instead, we allocate them for each tensor separately in init_tensor
+    // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated,
+    // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct.
+    ggml_backend_cuda_split_buffer_context * ctx = new ggml_backend_cuda_split_buffer_context();
+
+    return ggml_backend_buffer_init(buft, ggml_backend_cuda_split_buffer_interface, ctx, size);
+}
+
+GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+    return 128;
+
+    GGML_UNUSED(buft);
+}
+
+GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
+    ggml_backend_cuda_split_buffer_type_context * ctx = (ggml_backend_cuda_split_buffer_type_context *)buft->context;
+
+    size_t total_size = 0;
+
+    const int64_t ne0 = tensor->ne[0];
+
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, id);
+
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
+        }
+
+        total_size += ggml_nbytes_split(tensor, nrows_split);
+
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
         }
     }
 
-    // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
+    return total_size;
+}
+
+GGML_CALL static bool ggml_backend_cuda_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
+    return ggml_backend_is_cuda(backend);
+
+    GGML_UNUSED(buft);
+}
+
+GGML_CALL static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
+    return false;
+
+    GGML_UNUSED(buft);
+}
+
+static ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_interface = {
+    /* .get_name         = */ ggml_backend_cuda_split_buffer_type_name,
+    /* .alloc_buffer     = */ ggml_backend_cuda_split_buffer_type_alloc_buffer,
+    /* .get_alignment    = */ ggml_backend_cuda_split_buffer_type_get_alignment,
+    /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
+    /* .get_alloc_size   = */ ggml_backend_cuda_split_buffer_type_get_alloc_size,
+    /* .supports_backend = */ ggml_backend_cuda_split_buffer_type_supports_backend,
+    /* .is_host          = */ ggml_backend_cuda_split_buffer_type_is_host,
+};
+
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) {
+    static std::mutex mutex;
+    std::lock_guard<std::mutex> lock(mutex);
+
+    static std::map<std::array<float, GGML_CUDA_MAX_DEVICES>, struct ggml_backend_buffer_type> buft_map;
+
+    std::array<float, GGML_CUDA_MAX_DEVICES> tensor_split_arr = {};
+
+    bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_CUDA_MAX_DEVICES, [](float x) { return x == 0.0f; });
+    if (all_zero) {
+        tensor_split_arr = ggml_cuda_info().default_tensor_split;
+    } else {
+        float split_sum = 0.0f;
+        for (int i = 0; i < ggml_backend_cuda_get_device_count(); ++i) {
+            tensor_split_arr[i] = split_sum;
+            split_sum += tensor_split[i];
+        }
+        for (int i = 0; i < ggml_backend_cuda_get_device_count(); ++i) {
+            tensor_split_arr[i] /= split_sum;
+        }
     }
 
-    if (tid == 0) {
-#ifdef GGML_CUDA_F16
-        dst[row] = tmp.x + tmp.y;
-#else
-        dst[row] = tmp;
-#endif // GGML_CUDA_F16
+    auto it = buft_map.find(tensor_split_arr);
+    if (it != buft_map.end()) {
+        return &it->second;
     }
+
+    struct ggml_backend_buffer_type buft {
+        /* .iface   = */ ggml_backend_cuda_split_buffer_type_interface,
+        /* .context = */ new ggml_backend_cuda_split_buffer_type_context{tensor_split_arr},
+    };
+
+    auto result = buft_map.emplace(tensor_split_arr, buft);
+    return &result.first->second;
 }
 
+// host buffer type
+
+GGML_CALL static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    return GGML_CUDA_NAME "_Host";
+
+    GGML_UNUSED(buft);
+}
+
+GGML_CALL static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) {
+    return GGML_CUDA_NAME "_Host";
+
+    GGML_UNUSED(buffer);
+}
+
+GGML_CALL static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    CUDA_CHECK(cudaFreeHost(buffer->context));
+}
+
+static void * ggml_cuda_host_malloc(size_t size) {
+    if (getenv("GGML_CUDA_NO_PINNED") != nullptr) {
+        return nullptr;
+    }
+
+    void * ptr = nullptr;
+    cudaError_t err = cudaMallocHost((void **) &ptr, size);
+    if (err != cudaSuccess) {
+        // clear the error
+        cudaGetLastError();
+        fprintf(stderr, "%s: warning: failed to allocate %.2f MiB of pinned memory: %s\n", __func__,
+            size/1024.0/1024.0, cudaGetErrorString(err));
+        return nullptr;
+    }
+
+    return ptr;
+}
+
+GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    void * ptr = ggml_cuda_host_malloc(size);
+
+    if (ptr == nullptr) {
+        // fallback to cpu buffer
+        return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size);
+    }
+
+    ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
+    buffer->buft = buft;
+    buffer->iface.get_name = ggml_backend_cuda_host_buffer_name;
+    buffer->iface.free_buffer = ggml_backend_cuda_host_buffer_free_buffer;
+
+    return buffer;
+}
+
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() {
+    static struct ggml_backend_buffer_type ggml_backend_cuda_buffer_type_host = {
+        /* .iface    = */ {
+            /* .get_name         = */ ggml_backend_cuda_host_buffer_type_name,
+            /* .alloc_buffer     = */ ggml_backend_cuda_host_buffer_type_alloc_buffer,
+            /* .get_alignment    = */ ggml_backend_cpu_buffer_type()->iface.get_alignment,
+            /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
+            /* .get_alloc_size   = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
+            /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend,
+            /* .is_host          = */ ggml_backend_cpu_buffer_type()->iface.is_host,
+        },
+        /* .context  = */ nullptr,
+    };
+
+    return &ggml_backend_cuda_buffer_type_host;
+}
+
+//static bool ggml_backend_buffer_is_cuda_host(ggml_backend_buffer_t buffer) {
+//    return buffer->buft->iface.get_name == ggml_backend_cuda_host_buffer_type_name;
+//}
+
+/// kernels
+
+typedef void (*ggml_cuda_op_mul_mat_t)(
+    ggml_backend_cuda_context & ctx,
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, cudaStream_t stream);
+
+#ifndef GGML_CUDA_PEER_MAX_BATCH_SIZE
+#define GGML_CUDA_PEER_MAX_BATCH_SIZE 128
+#endif // GGML_CUDA_PEER_MAX_BATCH_SIZE
+
+#define MUL_MAT_SRC1_COL_STRIDE 128
+
 static __global__ void mul_mat_p021_f16_f32(
     const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst,
     const int ncols_x, const int nrows_x, const int nchannels_x, const int nchannels_y) {
@@ -4965,10 +1099,7 @@ static __global__ void mul_mat_p021_f16_f32(
     const int idst = channel*nrows_dst + row_dst;
 
     // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
-    }
+    tmp = warp_reduce_sum(tmp);
 
     if (threadIdx.x == 0) {
         dst[idst] = tmp;
@@ -5011,1747 +1142,13 @@ static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous
     }
 
     // sum up partial sums and write back result
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
-    }
+    tmp = warp_reduce_sum(tmp);
 
     if (threadIdx.x == 0) {
         dst[idst] = tmp;
     }
 }
 
-static __device__ void cpy_1_f32_f32(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    float * dsti = (float *) cdsti;
-
-    *dsti = *xi;
-}
-
-static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    half * dsti = (half *) cdsti;
-
-    *dsti = __float2half(*xi);
-}
-
-static __device__ void cpy_1_f16_f16(const char * cxi, char * cdsti) {
-    const half * xi = (const half *) cxi;
-    half * dsti = (half *) cdsti;
-
-    *dsti = *xi;
-}
-
-template <cpy_kernel_t cpy_1>
-static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne,
-                                   const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
-                                   const int ne10, const int ne11, const int nb10, const int nb11, const int nb12) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (i >= ne) {
-        return;
-    }
-
-    // determine indices i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor
-    // then combine those indices with the corresponding byte offsets to get the total offsets
-    const int i02 = i / (ne00*ne01);
-    const int i01 = (i - i02*ne01*ne00) / ne00;
-    const int i00 = i - i02*ne01*ne00 - i01*ne00;
-    const int x_offset = i00*nb00 + i01*nb01 + i02*nb02;
-
-    const int i12 = i / (ne10*ne11);
-    const int i11 = (i - i12*ne10*ne11) / ne10;
-    const int i10 = i - i12*ne10*ne11 - i11*ne10;
-    const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12;
-
-    cpy_1(cx + x_offset, cdst + dst_offset);
-}
-
-static __device__ void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    block_q8_0 * dsti = (block_q8_0 *) cdsti;
-
-    float amax = 0.0f; // absolute max
-
-    for (int j = 0; j < QK8_0; j++) {
-        const float v = xi[j];
-        amax = fmaxf(amax, fabsf(v));
-    }
-
-    const float d = amax / ((1 << 7) - 1);
-    const float id = d ? 1.0f/d : 0.0f;
-
-    dsti->d = d;
-
-    for (int j = 0; j < QK8_0; ++j) {
-        const float x0 = xi[j]*id;
-
-        dsti->qs[j] = roundf(x0);
-    }
-}
-
-static __device__ void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    block_q4_0 * dsti = (block_q4_0 *) cdsti;
-
-    float amax = 0.0f;
-    float vmax = 0.0f;
-
-    for (int j = 0; j < QK4_0; ++j) {
-        const float v = xi[j];
-        if (amax < fabsf(v)) {
-            amax = fabsf(v);
-            vmax = v;
-        }
-    }
-
-    const float d  = vmax / -8;
-    const float id = d ? 1.0f/d : 0.0f;
-
-    dsti->d = d;
-
-    for (int j = 0; j < QK4_0/2; ++j) {
-        const float x0 = xi[0       + j]*id;
-        const float x1 = xi[QK4_0/2 + j]*id;
-
-        const uint8_t xi0 = min(15, (int8_t)(x0 + 8.5f));
-        const uint8_t xi1 = min(15, (int8_t)(x1 + 8.5f));
-
-        dsti->qs[j]  = xi0;
-        dsti->qs[j] |= xi1 << 4;
-    }
-}
-
-static __device__ void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) {
-    const float * xi = (const float *) cxi;
-    block_q4_1 * dsti = (block_q4_1 *) cdsti;
-
-    float vmin = FLT_MAX;
-    float vmax = -FLT_MAX;
-
-    for (int j = 0; j < QK4_1; ++j) {
-        const float v = xi[j];
-
-        if (v < vmin) vmin = v;
-        if (v > vmax) vmax = v;
-    }
-
-    const float d  = (vmax - vmin) / ((1 << 4) - 1);
-    const float id = d ? 1.0f/d : 0.0f;
-
-    dsti->dm.x = d;
-    dsti->dm.y = vmin;
-
-    for (int j = 0; j < QK4_1/2; ++j) {
-        const float x0 = (xi[0       + j] - vmin)*id;
-        const float x1 = (xi[QK4_1/2 + j] - vmin)*id;
-
-        const uint8_t xi0 = min(15, (int8_t)(x0 + 0.5f));
-        const uint8_t xi1 = min(15, (int8_t)(x1 + 0.5f));
-
-        dsti->qs[j]  = xi0;
-        dsti->qs[j] |= xi1 << 4;
-    }
-}
-
-template <cpy_kernel_t cpy_blck, int qk>
-static __global__ void cpy_f32_q(const char * cx, char * cdst, const int ne,
-                                 const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
-                                 const int ne10, const int ne11, const int nb10, const int nb11, const int nb12) {
-    const int i = (blockDim.x*blockIdx.x + threadIdx.x)*qk;
-
-    if (i >= ne) {
-        return;
-    }
-
-    const int i02 = i / (ne00*ne01);
-    const int i01 = (i - i02*ne01*ne00) / ne00;
-    const int i00 = (i - i02*ne01*ne00 - i01*ne00);
-    const int x_offset = i00*nb00 + i01*nb01 + i02*nb02;
-
-    const int i12 = i / (ne10*ne11);
-    const int i11 = (i - i12*ne10*ne11) / ne10;
-    const int i10 = (i - i12*ne10*ne11 - i11*ne10)/qk;
-    const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12;
-
-    cpy_blck(cx + x_offset, cdst + dst_offset);
-}
-
-static __device__ float rope_yarn_ramp(const float low, const float high, const int i0) {
-    const float y = (i0 / 2 - low) / max(0.001f, high - low);
-    return 1.0f - min(1.0f, max(0.0f, y));
-}
-
-struct rope_corr_dims {
-    float v[4];
-};
-
-// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
-// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
-static __device__ void rope_yarn(
-    float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale,
-    float * cos_theta, float * sin_theta
-) {
-    // Get n-d rotational scaling corrected for extrapolation
-    float theta_interp = freq_scale * theta_extrap;
-    float theta = theta_interp;
-    if (ext_factor != 0.0f) {
-        float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor;
-        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
-
-        // Get n-d magnitude scaling corrected for interpolation
-        mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
-    }
-    *cos_theta = cosf(theta) * mscale;
-    *sin_theta = sinf(theta) * mscale;
-}
-
-// rope == RoPE == rotary positional embedding
-template<typename T, bool has_pos>
-static __global__ void rope(
-    const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
-    float ext_factor, float attn_factor, rope_corr_dims corr_dims
-) {
-    const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
-
-    if (col >= ncols) {
-        return;
-    }
-
-    const int row = blockDim.x*blockIdx.x + threadIdx.x;
-    const int i = row*ncols + col;
-    const int i2 = row/p_delta_rows;
-
-    const int p = has_pos ? pos[i2] : 0;
-    const float theta_base = p*powf(freq_base, -float(col)/ncols);
-
-    float cos_theta, sin_theta;
-    rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta);
-
-    const float x0 = x[i + 0];
-    const float x1 = x[i + 1];
-
-    dst[i + 0] = x0*cos_theta - x1*sin_theta;
-    dst[i + 1] = x0*sin_theta + x1*cos_theta;
-}
-
-template<typename T, bool has_pos>
-static __global__ void rope_neox(
-    const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims
-) {
-    const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
-
-    if (col >= ncols) {
-        return;
-    }
-
-    const int row = blockDim.x*blockIdx.x + threadIdx.x;
-    const int ib = col / n_dims;
-    const int ic = col % n_dims;
-
-    if (ib > 0) {
-        const int i = row*ncols + ib*n_dims + ic;
-
-        dst[i + 0] = x[i + 0];
-        dst[i + 1] = x[i + 1];
-
-        return;
-    }
-
-    const int i  = row*ncols + ib*n_dims + ic/2;
-    const int i2 = row/p_delta_rows;
-
-    float cur_rot = inv_ndims * ic - ib;
-
-    const int p = has_pos ? pos[i2] : 0;
-    const float theta_base = p*freq_scale*powf(theta_scale, col/2.0f);
-
-    float cos_theta, sin_theta;
-    rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta);
-
-    const float x0 = x[i + 0];
-    const float x1 = x[i + n_dims/2];
-
-    dst[i + 0]        = x0*cos_theta - x1*sin_theta;
-    dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
-}
-
-static __global__ void rope_glm_f32(
-    const float * x, float * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
-    int n_ctx
-) {
-    const int col = blockDim.x*blockIdx.x + threadIdx.x;
-    const int half_n_dims = ncols/4;
-
-    if (col >= half_n_dims) {
-        return;
-    }
-
-    const int row = blockDim.y*blockIdx.y + threadIdx.y;
-    const int i = row*ncols + col;
-    const int i2 = row/p_delta_rows;
-
-    const float col_theta_scale = powf(freq_base, -2.0f*col/ncols);
-     // FIXME: this is likely wrong
-    const int p = pos != nullptr ? pos[i2] : 0;
-
-    const float theta = min(p, n_ctx - 2)*freq_scale*col_theta_scale;
-    const float sin_theta = sinf(theta);
-    const float cos_theta = cosf(theta);
-
-    const float x0 = x[i + 0];
-    const float x1 = x[i + half_n_dims];
-
-    dst[i + 0]           = x0*cos_theta - x1*sin_theta;
-    dst[i + half_n_dims] = x0*sin_theta + x1*cos_theta;
-
-    const float block_theta = ((float)max(p - n_ctx - 2, 0))*col_theta_scale;
-    const float sin_block_theta = sinf(block_theta);
-    const float cos_block_theta = cosf(block_theta);
-
-    const float x2 = x[i + half_n_dims * 2];
-    const float x3 = x[i + half_n_dims * 3];
-
-    dst[i + half_n_dims * 2] = x2*cos_block_theta - x3*sin_block_theta;
-    dst[i + half_n_dims * 3] = x2*sin_block_theta + x3*cos_block_theta;
-}
-
-static __global__ void alibi_f32(const float * x, float * dst, const int ncols, const int k_rows,
-                                 const int n_heads_log2_floor, const float m0, const float m1) {
-    const int col = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (col >= ncols) {
-        return;
-    }
-
-    const int row = blockDim.y*blockIdx.y + threadIdx.y;
-    const int i = row*ncols + col;
-
-    const int k = row/k_rows;
-
-    float m_k;
-    if (k < n_heads_log2_floor) {
-        m_k = powf(m0, k + 1);
-    } else {
-        m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
-    }
-
-    dst[i] = col * m_k + x[i];
-}
-
-static __global__ void k_sum_rows_f32(const float * x, float * dst, const int ncols) {
-    const int row = blockIdx.y;
-    const int col = threadIdx.x;
-
-    float sum = 0.0f;
-    for (int i = col; i < ncols; i += blockDim.x) {
-        sum += x[row * ncols + i];
-    }
-
-    sum = warp_reduce_sum(sum);
-
-    if (col == 0) {
-        dst[row] = sum;
-    }
-}
-
-template<typename T>
-static inline __device__ void swap(T & a, T & b) {
-    T tmp = a;
-    a = b;
-    b = tmp;
-}
-
-template<ggml_sort_order order>
-static __global__ void k_argsort_f32_i32(const float * x, int * dst, const int ncols) {
-    // bitonic sort
-    int col = threadIdx.x;
-    int row = blockIdx.y;
-
-    if (col >= ncols) return;
-
-    const float * x_row = x + row * ncols;
-    int * dst_row = dst + row * ncols;
-
-    // initialize indices
-    if (col < ncols) {
-        dst_row[col] = col;
-    }
-    __syncthreads();
-
-    for (int k = 2; k <= ncols; k *= 2) {
-        for (int j = k / 2; j > 0; j /= 2) {
-            int ixj = col ^ j;
-            if (ixj > col) {
-                if ((col & k) == 0) {
-                    if (order == GGML_SORT_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) {
-                        swap(dst_row[col], dst_row[ixj]);
-                    }
-                } else {
-                    if (order == GGML_SORT_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) {
-                        swap(dst_row[col], dst_row[ixj]);
-                    }
-                }
-            }
-            __syncthreads();
-        }
-    }
-}
-
-static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past) {
-    const int col = blockDim.y*blockIdx.y + threadIdx.y;
-    const int row = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (col >= ncols) {
-        return;
-    }
-
-    const int i = row*ncols + col;
-    //dst[i] = col > (n_past + row % rows_per_channel) ? -INFINITY : x[i];
-    //dst[i] = x[i] - (col > n_past + row % rows_per_channel) * INT_MAX; // equivalent within rounding error but slightly faster on GPU
-    dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX;
-}
-
-template <bool vals_smem, int ncols_template, int block_size_template, bool need_check>
-static __global__ void soft_max_f16(const float * x, const float * y, float * dst, const int ncols_par, const int nrows_y, const float scale) {
-#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
-    const int ncols_data = ncols_template == 0 ? ncols_par : ncols_template;
-    const int ncols_smem = GGML_PAD(ncols_data, 2*WARP_SIZE)/2;
-
-    const int tid  = threadIdx.x;
-    const int rowx = blockIdx.x;
-    const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension
-
-    const int block_size = block_size_template == 0 ? blockDim.x : block_size_template;
-
-    const int warp_id = threadIdx.x / WARP_SIZE;
-    const int lane_id = threadIdx.x % WARP_SIZE;
-
-    extern __shared__ half data_soft_max_f16[];
-    half  * buf_iw = data_soft_max_f16 + 0; // shared memory buffer for inter-warp communication
-    // (shared memory) buffer to cache values between iterations:
-    half2 * vals = vals_smem ? (half2 *) (buf_iw + WARP_SIZE) : (half2 *) (dst + rowx*ncols_data);
-    // if the buffer is larger than max. shared memory per block, use dst as temp. buffer instead
-    // in that case col_smem == col_data must be enforced to avoid race conditions
-
-    half2 max_val = make_half2(-INFINITY, -INFINITY);
-
-#pragma unroll
-    for (int col0 = 0; col0 < ncols_smem; col0 += block_size) {
-        const int col_data = 2*col0 + 2*WARP_SIZE*warp_id + lane_id;
-        const int col_smem = vals_smem ? col0 + tid : col_data;
-
-        const int ix = rowx*ncols_data + col_data;
-        const int iy = rowy*ncols_data + col_data;
-
-        half2 val;
-        if (need_check && col_data + 0 >= ncols_data) {
-            val.x = -INFINITY;
-        } else {
-            val.x = x[ix + 0]*scale + (y ? y[iy + 0] : 0.0f);
-        }
-        if (need_check && col_data + WARP_SIZE >= ncols_data) {
-            val.y = -INFINITY;
-        } else {
-            val.y = x[ix + WARP_SIZE]*scale + (y ? y[iy + WARP_SIZE] : 0.0f);
-        }
-        if (!need_check || col_smem < (vals_smem ? ncols_smem : ncols_data)) {
-            vals[col_smem] = val;
-        }
-        max_val = __hmax2(max_val, val);
-    }
-
-    // find the max value in the block
-    max_val = warp_reduce_max(max_val);
-    if (block_size > WARP_SIZE) {
-        if (warp_id == 0) {
-            buf_iw[lane_id] = -INFINITY;
-        }
-        __syncthreads();
-
-        if (lane_id == 0) {
-            buf_iw[warp_id] = __hmax(max_val.x, max_val.y);
-        }
-        __syncthreads();
-
-        max_val = __half2half2(buf_iw[lane_id]);
-        max_val = warp_reduce_max(max_val);
-    } else {
-        max_val = __half2half2(__hmax(max_val.x, max_val.y));
-    }
-
-    half2 tmp = make_half2(0.0f, 0.0f); // partial sums
-
-#pragma unroll
-    for (int col0 = 0; col0 < ncols_smem; col0 += block_size) {
-        const int col_smem = vals_smem ? col0 + tid : 2*col0 + 2*warp_id*WARP_SIZE + lane_id;
-
-        if (ncols_template == 0 && col_smem >= (vals_smem ? ncols_smem : ncols_data)) {
-            break;
-        }
-
-        const half2 val = h2exp(vals[col_smem] - max_val);
-
-        tmp += val;
-        vals[col_smem] = val;
-    }
-
-    // find the sum of exps in the block
-    tmp = warp_reduce_sum(tmp);
-    if (block_size > WARP_SIZE) {
-        if (warp_id == 0) {
-            buf_iw[lane_id] = 0.0f;
-        }
-        __syncthreads();
-
-        if (lane_id == 0) {
-            buf_iw[warp_id] = tmp.x + tmp.y;
-        }
-        __syncthreads();
-
-        tmp = __half2half2(buf_iw[lane_id]);
-        tmp = warp_reduce_sum(tmp);
-    } else {
-        tmp = __half2half2(tmp.x + tmp.y);
-    }
-
-    const half2 inv_sum = make_half2(1.0f, 1.0f) / tmp;
-
-#pragma unroll
-    for (int col0 = 0; col0 < ncols_smem; col0 += block_size) {
-        const int col_data = 2*col0 + 2*WARP_SIZE*warp_id + lane_id;
-        const int col_smem = vals_smem ? col0 + tid : col_data;
-
-        const int idst = rowx*ncols_data + col_data;
-        const half2 result = vals[col_smem] * inv_sum;
-
-        if (need_check && col_data + 0 >= ncols_data) {
-            return;
-        }
-        dst[idst] = result.x;
-
-        if (need_check && col_data + WARP_SIZE >= ncols_data) {
-            return;
-        }
-
-        dst[idst + WARP_SIZE] = result.y;
-    }
-#else
-    (void) x; (void) y; (void) dst; (void) ncols_par; (void) nrows_y; (void) scale;
-    bad_arch();
-#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
-}
-
-template <bool vals_smem, int ncols_template, int block_size_template>
-static __global__ void soft_max_f32(const float * x, const float * y, float * dst, const int ncols_par, const int nrows_y, const float scale) {
-    const int ncols = ncols_template == 0 ? ncols_par : ncols_template;
-
-    const int tid  = threadIdx.x;
-    const int rowx = blockIdx.x;
-    const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension
-
-    const int block_size = block_size_template == 0 ? blockDim.x : block_size_template;
-
-    const int warp_id = threadIdx.x / WARP_SIZE;
-    const int lane_id = threadIdx.x % WARP_SIZE;
-
-    extern __shared__ float data_soft_max_f32[];
-    float * buf_iw = data_soft_max_f32; // shared memory buffer for inter-warp communication
-    // shared memory buffer to cache values between iterations:
-    float * vals = vals_smem ? buf_iw + WARP_SIZE : dst + rowx*ncols;
-
-    float max_val = -INFINITY;
-
-#pragma unroll
-    for (int col0 = 0; col0 < ncols; col0 += block_size) {
-        const int col = col0 + tid;
-
-        if (ncols_template == 0 && col >= ncols) {
-            break;
-        }
-
-        const int ix = rowx*ncols + col;
-        const int iy = rowy*ncols + col;
-
-        const float val = x[ix]*scale + (y ? y[iy] : 0.0f);
-        vals[col] = val;
-        max_val = max(max_val, val);
-    }
-
-    // find the max value in the block
-    max_val = warp_reduce_max(max_val);
-    if (block_size > WARP_SIZE) {
-        if (warp_id == 0) {
-            buf_iw[lane_id] = -INFINITY;
-        }
-        __syncthreads();
-
-        if (lane_id == 0) {
-            buf_iw[warp_id] = max_val;
-        }
-        __syncthreads();
-
-        max_val = buf_iw[lane_id];
-        max_val = warp_reduce_max(max_val);
-    }
-
-    float tmp = 0.0f; // partial sum
-
-#pragma unroll
-    for (int col0 = 0; col0 < ncols; col0 += block_size) {
-        const int col = col0 + tid;
-
-        if (ncols_template == 0 && col >= ncols) {
-            break;
-        }
-
-        const float val = expf(vals[col] - max_val);
-        tmp += val;
-        vals[col] = val;
-    }
-
-    // find the sum of exps in the block
-    tmp = warp_reduce_sum(tmp);
-    if (block_size > WARP_SIZE) {
-        if (warp_id == 0) {
-            buf_iw[lane_id] = 0.0f;
-        }
-        __syncthreads();
-
-        if (lane_id == 0) {
-            buf_iw[warp_id] = tmp;
-        }
-        __syncthreads();
-
-        tmp = buf_iw[lane_id];
-        tmp = warp_reduce_sum(tmp);
-    }
-
-    const float inv_sum = 1.0f / tmp;
-
-#pragma unroll
-    for (int col0 = 0; col0 < ncols; col0 += block_size) {
-        const int col = col0 + tid;
-
-        if (ncols_template == 0 && col >= ncols) {
-            return;
-        }
-
-        const int idst = rowx*ncols + col;
-        dst[idst] = vals[col] * inv_sum;
-    }
-}
-
-static __global__ void scale_f32(const float * x, float * dst, const float scale, const int k) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (i >= k) {
-        return;
-    }
-
-    dst[i] = scale * x[i];
-}
-
-static __global__ void clamp_f32(const float * x, float * dst, const float min, const float max, const int k) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (i >= k) {
-        return;
-    }
-
-    dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]);
-}
-
-static  __global__ void im2col_f32_f16(
-        const float * x, half * dst,
-        int offset_delta, int IW, int IH, int OW, int KW, int KH, int pelements, int CHW,
-        int s0, int s1, int p0, int p1, int d0, int d1) {
-    const int i = threadIdx.x + blockIdx.x * blockDim.x;
-    if (i >= pelements) {
-        return;
-    }
-
-    const int ksize = OW * (KH > 1 ? KW : 1);
-    const int kx = i / ksize;
-    const int kd = kx * ksize;
-    const int ky = (i - kd) / OW;
-    const int ix = i % OW;
-
-    const int64_t iiw = ix * s0 + kx * d0 - p0;
-    const int64_t iih = blockIdx.y * s1 + ky * d1 - p1;
-
-    const int64_t offset_dst =
-        (blockIdx.y * OW + ix) * CHW +
-        (blockIdx.z * (KW * KH) + ky * KW + kx);
-
-    if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
-        dst[offset_dst] = __float2half(0.0f);
-    } else {
-        const int64_t offset_src = blockIdx.z * offset_delta;
-        dst[offset_dst] = __float2half(x[offset_src + iih * IW + iiw]);
-    }
-}
-
-template<int qk, int qr, dequantize_kernel_t dq>
-static void get_rows_cuda(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-                            const void * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) {
-
-    GGML_TENSOR_BINARY_OP_LOCALS
-
-    const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
-    const int block_num_x = (ne00 + 2*CUDA_GET_ROWS_BLOCK_SIZE - 1) / (2*CUDA_GET_ROWS_BLOCK_SIZE);
-    const dim3 block_nums(block_num_x, ne10, ne11*ne12);
-
-    // strides in elements
-    //const size_t s0 = nb0 / ggml_element_size(dst);
-    const size_t s1 = nb1 / ggml_element_size(dst);
-    const size_t s2 = nb2 / ggml_element_size(dst);
-    const size_t s3 = nb3 / ggml_element_size(dst);
-
-    const size_t s10 = nb10 / ggml_element_size(src1);
-    const size_t s11 = nb11 / ggml_element_size(src1);
-    const size_t s12 = nb12 / ggml_element_size(src1);
-    //const size_t s13 = nb13 / ggml_element_size(src1);
-
-    GGML_ASSERT(ne00 % 2 == 0);
-
-    k_get_rows<qk, qr, dq><<<block_nums, block_dims, 0, stream>>>(
-            src0_dd, src1_dd, dst_dd,
-            ne00, /*ne01, ne02, ne03,*/
-            /*ne10, ne11,*/ ne12, /*ne13,*/
-            /* s0,*/ s1, s2, s3,
-            /* nb00,*/ nb01, nb02, nb03,
-            s10, s11, s12/*, s13*/);
-
-    (void) dst;
-}
-
-template<typename src0_t>
-static void get_rows_cuda_float(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-                                const src0_t * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) {
-
-    GGML_TENSOR_BINARY_OP_LOCALS
-
-    const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
-    const int block_num_x = (ne00 + CUDA_GET_ROWS_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BLOCK_SIZE;
-    const dim3 block_nums(block_num_x, ne10, ne11*ne12);
-
-    // strides in elements
-    //const size_t s0 = nb0 / ggml_element_size(dst);
-    const size_t s1 = nb1 / ggml_element_size(dst);
-    const size_t s2 = nb2 / ggml_element_size(dst);
-    const size_t s3 = nb3 / ggml_element_size(dst);
-
-    const size_t s10 = nb10 / ggml_element_size(src1);
-    const size_t s11 = nb11 / ggml_element_size(src1);
-    const size_t s12 = nb12 / ggml_element_size(src1);
-    //const size_t s13 = nb13 / ggml_element_size(src1);
-
-    k_get_rows_float<<<block_nums, block_dims, 0, stream>>>(
-            src0_dd, src1_dd, dst_dd,
-            ne00, /*ne01, ne02, ne03,*/
-            /*ne10, ne11,*/ ne12, /*ne13,*/
-            /* s0,*/ s1, s2, s3,
-            /* nb00,*/ nb01, nb02, nb03,
-            s10, s11, s12/*, s13*/);
-
-    (void) dst;
-}
-
-template<float (*bin_op)(const float, const float)>
-struct bin_bcast_cuda {
-    template<typename src0_t, typename src1_t, typename dst_t>
-    void operator()(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst,
-            const src0_t * src0_dd, const src1_t * src1_dd, dst_t * dst_dd,
-            cudaStream_t stream) {
-
-        GGML_TENSOR_BINARY_OP_LOCALS
-
-        int nr0 = ne10/ne0;
-        int nr1 = ne11/ne1;
-        int nr2 = ne12/ne2;
-        int nr3 = ne13/ne3;
-
-        int nr[4] = { nr0, nr1, nr2, nr3 };
-
-        // collapse dimensions until first broadcast dimension
-        int64_t cne0[] = {ne0, ne1, ne2, ne3};
-        int64_t cne1[] = {ne10, ne11, ne12, ne13};
-        size_t cnb0[] = {nb0, nb1, nb2, nb3};
-        size_t cnb1[] = {nb10, nb11, nb12, nb13};
-        auto collapse = [](int64_t cne[]) {
-            cne[0] *= cne[1];
-            cne[1] = cne[2];
-            cne[2] = cne[3];
-            cne[3] = 1;
-        };
-
-        auto collapse_nb = [](size_t cnb[], const int64_t cne[]) {
-            cnb[1] *= cne[1];
-            cnb[2] *= cne[2];
-            cnb[3] *= cne[3];
-        };
-
-        for (int i = 0; i < 4; i++) {
-            if (nr[i] != 1) {
-                break;
-            }
-            if (i > 0) {
-                collapse_nb(cnb0, cne0);
-                collapse_nb(cnb1, cne1);
-                collapse(cne0);
-                collapse(cne1);
-            }
-        }
-        {
-            int64_t ne0 = cne0[0];
-            int64_t ne1 = cne0[1];
-            int64_t ne2 = cne0[2];
-            int64_t ne3 = cne0[3];
-
-            int64_t ne10 = cne1[0];
-            int64_t ne11 = cne1[1];
-            int64_t ne12 = cne1[2];
-            int64_t ne13 = cne1[3];
-
-            size_t nb0 = cnb0[0];
-            size_t nb1 = cnb0[1];
-            size_t nb2 = cnb0[2];
-            size_t nb3 = cnb0[3];
-
-            size_t nb10 = cnb1[0];
-            size_t nb11 = cnb1[1];
-            size_t nb12 = cnb1[2];
-            size_t nb13 = cnb1[3];
-
-            size_t s0 = nb0 / sizeof(dst_t);
-            size_t s1 = nb1 / sizeof(dst_t);
-            size_t s2 = nb2 / sizeof(dst_t);
-            size_t s3 = nb3 / sizeof(dst_t);
-
-            size_t s10 = nb10 / sizeof(src1_t);
-            size_t s11 = nb11 / sizeof(src1_t);
-            size_t s12 = nb12 / sizeof(src1_t);
-            size_t s13 = nb13 / sizeof(src1_t);
-
-            GGML_ASSERT(s0 == 1);
-            GGML_ASSERT(s10 == 1);
-
-            const int block_size = 128;
-
-            int64_t hne0 = std::max(ne0/2LL, 1LL);
-
-            dim3 block_dims;
-            block_dims.x = std::min<unsigned int>(hne0, block_size);
-            block_dims.y = std::min<unsigned int>(ne1, block_size / block_dims.x);
-            block_dims.z = std::min(std::min<unsigned int>(ne2*ne3, block_size / block_dims.x / block_dims.y), 64U);
-
-            dim3 block_nums(
-                (hne0 + block_dims.x - 1) / block_dims.x,
-                (ne1 + block_dims.y - 1) / block_dims.y,
-                (ne2*ne3 + block_dims.z - 1) / block_dims.z
-            );
-
-            if (block_nums.z > 65535) {
-                // this is the maximum number of blocks in z direction, fallback to 1D grid kernel
-                int block_num = (ne0*ne1*ne2*ne3 + block_size - 1) / block_size;
-                k_bin_bcast_unravel<bin_op><<<block_num, block_size, 0, stream>>>(
-                    src0_dd, src1_dd, dst_dd,
-                    ne0, ne1, ne2, ne3,
-                    ne10, ne11, ne12, ne13,
-                    /* s0, */ s1, s2, s3,
-                    /* s10, */ s11, s12, s13);
-            } else {
-                k_bin_bcast<bin_op><<<block_nums, block_dims, 0, stream>>>(
-                    src0_dd, src1_dd, dst_dd,
-                    ne0, ne1, ne2, ne3,
-                    ne10, ne11, ne12, ne13,
-                    /* s0, */ s1, s2, s3,
-                    /* s10, */ s11, s12, s13);
-            }
-        }
-    }
-};
-
-static void acc_f32_cuda(const float * x, const float * y, float * dst, const int n_elements,
-    const int ne10, const int ne11, const int ne12,
-    const int nb1, const int nb2, const int offset, cudaStream_t stream) {
-    int num_blocks = (n_elements + CUDA_ACC_BLOCK_SIZE - 1) / CUDA_ACC_BLOCK_SIZE;
-    acc_f32<<<num_blocks, CUDA_ACC_BLOCK_SIZE, 0, stream>>>(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset);
-}
-
-static void gelu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE;
-    gelu_f32<<<num_blocks, CUDA_GELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
-}
-
-static void silu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_SILU_BLOCK_SIZE - 1) / CUDA_SILU_BLOCK_SIZE;
-    silu_f32<<<num_blocks, CUDA_SILU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
-}
-
-static void gelu_quick_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE;
-    gelu_quick_f32<<<num_blocks, CUDA_GELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
-}
-
-static void tanh_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_TANH_BLOCK_SIZE - 1) / CUDA_TANH_BLOCK_SIZE;
-    tanh_f32<<<num_blocks, CUDA_TANH_BLOCK_SIZE, 0, stream>>>(x, dst, k);
-}
-
-static void relu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE;
-    relu_f32<<<num_blocks, CUDA_RELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
-}
-
-static void leaky_relu_f32_cuda(const float * x, float * dst, const int k, const float negative_slope, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE;
-    leaky_relu_f32<<<num_blocks, CUDA_RELU_BLOCK_SIZE, 0, stream>>>(x, dst, k, negative_slope);
-}
-
-static void sqr_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_SQR_BLOCK_SIZE - 1) / CUDA_SQR_BLOCK_SIZE;
-    sqr_f32<<<num_blocks, CUDA_SQR_BLOCK_SIZE, 0, stream>>>(x, dst, k);
-}
-
-static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
-    GGML_ASSERT(ncols % WARP_SIZE == 0);
-    if (ncols < 1024) {
-        const dim3 block_dims(WARP_SIZE, 1, 1);
-        norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
-    } else {
-        const dim3 block_dims(1024, 1, 1);
-        norm_f32<1024><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
-    }
-}
-
-static void group_norm_f32_cuda(const float * x, float * dst, const int num_groups, const int group_size, const int ne_elements, cudaStream_t stream) {
-    static const float eps = 1e-6f;
-    if (group_size < 1024) {
-        const dim3 block_dims(WARP_SIZE, 1, 1);
-        group_norm_f32<WARP_SIZE><<<num_groups, block_dims, 0, stream>>>(x, dst, group_size, ne_elements, eps);
-    } else {
-        const dim3 block_dims(1024, 1, 1);
-        group_norm_f32<1024><<<num_groups, block_dims, 0, stream>>>(x, dst, group_size, ne_elements, eps);
-    }
-}
-
-static void concat_f32_cuda(const float * x, const float * y, float * dst, const int ne0, int ne1, int ne2, int ne02, cudaStream_t stream) {
-    int num_blocks = (ne0 + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE;
-    dim3 gridDim(num_blocks, ne1, ne2);
-    concat_f32<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne02);
-}
-
-static void upscale_f32_cuda(const float * x, float * dst, const int ne00, const int ne01, const int ne02, const int scale_factor, cudaStream_t stream) {
-    int ne0 = (ne00 * scale_factor);
-    int num_blocks = (ne0 + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE;
-    dim3 gridDim(num_blocks, (ne01 * scale_factor), ne02);
-    upscale_f32<<<gridDim, CUDA_UPSCALE_BLOCK_SIZE, 0, stream>>>(x, dst, ne00, ne00 * ne01, scale_factor);
-}
-
-static void pad_f32_cuda(const float * x, float * dst,
-    const int ne00, const int ne01, const int ne02,
-    const int ne0, const int ne1, const int ne2, cudaStream_t stream) {
-    int num_blocks = (ne0 + CUDA_PAD_BLOCK_SIZE - 1) / CUDA_PAD_BLOCK_SIZE;
-    dim3 gridDim(num_blocks, ne1, ne2);
-    pad_f32<<<gridDim, CUDA_PAD_BLOCK_SIZE, 0, stream>>>(x, dst, ne0, ne00, ne01, ne02);
-}
-
-static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
-    GGML_ASSERT(ncols % WARP_SIZE == 0);
-    if (ncols < 1024) {
-        const dim3 block_dims(WARP_SIZE, 1, 1);
-        rms_norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
-    } else {
-        const dim3 block_dims(1024, 1, 1);
-        rms_norm_f32<1024><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
-    }
-}
-
-static void quantize_row_q8_1_cuda(const float * x, void * vy, const int kx, const int ky, const int kx_padded, cudaStream_t stream) {
-    const int block_num_x = (kx_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
-    const dim3 num_blocks(block_num_x, ky, 1);
-    const dim3 block_size(CUDA_DEQUANTIZE_BLOCK_SIZE, 1, 1);
-    quantize_q8_1<<<num_blocks, block_size, 0, stream>>>(x, vy, kx, kx_padded);
-}
-
-template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
-static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE);
-    dequantize_block<qk, qr, dequantize_kernel><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
-}
-
-template<typename dst_t>
-static void dequantize_row_q2_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
-    const int nb = k / QK_K;
-#if QK_K == 256
-    dequantize_block_q2_K<<<nb, 64, 0, stream>>>(vx, y);
-#else
-    dequantize_block_q2_K<<<nb, 32, 0, stream>>>(vx, y);
-#endif
-}
-
-template<typename dst_t>
-static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
-    const int nb = k / QK_K;
-#if QK_K == 256
-    dequantize_block_q3_K<<<nb, 64, 0, stream>>>(vx, y);
-#else
-    dequantize_block_q3_K<<<nb, 32, 0, stream>>>(vx, y);
-#endif
-}
-
-template<typename dst_t>
-static void dequantize_row_q4_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
-    const int nb = k / QK_K;
-    dequantize_block_q4_K<<<nb, 32, 0, stream>>>(vx, y);
-}
-
-template<typename dst_t>
-static void dequantize_row_q5_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
-    const int nb = k / QK_K;
-#if QK_K == 256
-    dequantize_block_q5_K<<<nb, 64, 0, stream>>>(vx, y);
-#else
-    dequantize_block_q5_K<<<nb, 32, 0, stream>>>(vx, y);
-#endif
-}
-
-template<typename dst_t>
-static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
-    const int nb = k / QK_K;
-#if QK_K == 256
-    dequantize_block_q6_K<<<nb, 64, 0, stream>>>(vx, y);
-#else
-    dequantize_block_q6_K<<<nb, 32, 0, stream>>>(vx, y);
-#endif
-}
-
-template<typename dst_t>
-static void dequantize_row_iq2_xxs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
-    const int nb = k / QK_K;
-    dequantize_block_iq2_xxs<<<nb, 32, 0, stream>>>(vx, y);
-}
-
-template <typename src_t, typename dst_t>
-static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
-    convert_unary<src_t><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
-}
-
-static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
-    switch (type) {
-        case GGML_TYPE_Q4_0:
-            return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;
-        case GGML_TYPE_Q4_1:
-            return dequantize_block_cuda<QK4_1, QR4_1, dequantize_q4_1>;
-        case GGML_TYPE_Q5_0:
-            return dequantize_block_cuda<QK5_0, QR5_0, dequantize_q5_0>;
-        case GGML_TYPE_Q5_1:
-            return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>;
-        case GGML_TYPE_Q8_0:
-            return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>;
-        case GGML_TYPE_Q2_K:
-            return dequantize_row_q2_K_cuda;
-        case GGML_TYPE_Q3_K:
-            return dequantize_row_q3_K_cuda;
-        case GGML_TYPE_Q4_K:
-            return dequantize_row_q4_K_cuda;
-        case GGML_TYPE_Q5_K:
-            return dequantize_row_q5_K_cuda;
-        case GGML_TYPE_Q6_K:
-            return dequantize_row_q6_K_cuda;
-        case GGML_TYPE_IQ2_XXS:
-            return dequantize_row_iq2_xxs_cuda;
-        case GGML_TYPE_F32:
-            return convert_unary_cuda<float>;
-        default:
-            return nullptr;
-    }
-}
-
-static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
-    switch (type) {
-        case GGML_TYPE_Q4_0:
-            return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;
-        case GGML_TYPE_Q4_1:
-            return dequantize_block_cuda<QK4_1, QR4_1, dequantize_q4_1>;
-        case GGML_TYPE_Q5_0:
-            return dequantize_block_cuda<QK5_0, QR5_0, dequantize_q5_0>;
-        case GGML_TYPE_Q5_1:
-            return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>;
-        case GGML_TYPE_Q8_0:
-            return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>;
-        case GGML_TYPE_Q2_K:
-            return dequantize_row_q2_K_cuda;
-        case GGML_TYPE_Q3_K:
-            return dequantize_row_q3_K_cuda;
-        case GGML_TYPE_Q4_K:
-            return dequantize_row_q4_K_cuda;
-        case GGML_TYPE_Q5_K:
-            return dequantize_row_q5_K_cuda;
-        case GGML_TYPE_Q6_K:
-            return dequantize_row_q6_K_cuda;
-        case GGML_TYPE_IQ2_XXS:
-            return dequantize_row_iq2_xxs_cuda;
-        case GGML_TYPE_F16:
-            return convert_unary_cuda<half>;
-        default:
-            return nullptr;
-    }
-}
-
-static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>
-        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>
-        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>
-        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-static void dequantize_mul_mat_vec_q2_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2
-    const int block_num_y = (nrows + ny - 1) / ny;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(32, ny, 1);
-    dequantize_mul_mat_vec_q2_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-static void dequantize_mul_mat_vec_q3_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int ny = 2 / K_QUANTS_PER_ITERATION;
-    const int block_num_y = (nrows + ny - 1) / ny;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(32, ny, 1);
-    dequantize_mul_mat_vec_q3_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-static void dequantize_mul_mat_vec_q4_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int ny = 2 / K_QUANTS_PER_ITERATION;
-    const int block_num_y = (nrows + ny - 1) / ny;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(32, ny, 1);
-    dequantize_mul_mat_vec_q4_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-static void dequantize_mul_mat_vec_q5_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const dim3 block_dims(32, 1, 1);
-    dequantize_mul_mat_vec_q5_k<<<nrows, block_dims, 0, stream>>>(vx, y, dst, ncols);
-}
-
-static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int ny = 2 / K_QUANTS_PER_ITERATION;
-    const int block_num_y = (nrows + ny - 1) / ny;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(32, ny, 1);
-    dequantize_mul_mat_vec_q6_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    dequantize_mul_mat_vec<1, 1, convert_f16>
-        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK4_0 == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK4_1 == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK5_0 == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK5_1 == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK8_0 == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_q6_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void mul_mat_vec_iq2_xxs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    GGML_ASSERT(ncols % QK_K == 0);
-    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    const dim3 block_nums(block_num_y, 1, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI2_XXS, block_iq2_xxs, 1, vec_dot_iq2_xxs_q8_1>
-        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
-}
-
-static void ggml_mul_mat_q4_0_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q4_0_RDNA2;
-        mmq_y  =  MMQ_Y_Q4_0_RDNA2;
-        nwarps = NWARPS_Q4_0_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q4_0_RDNA1;
-        mmq_y  =  MMQ_Y_Q4_0_RDNA1;
-        nwarps = NWARPS_Q4_0_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q4_0_AMPERE;
-        mmq_y  =  MMQ_Y_Q4_0_AMPERE;
-        nwarps = NWARPS_Q4_0_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q4_0_PASCAL;
-        mmq_y  =  MMQ_Y_Q4_0_PASCAL;
-        nwarps = NWARPS_Q4_0_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q4_0<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q4_0<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q4_1_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q4_1_RDNA2;
-        mmq_y  =  MMQ_Y_Q4_1_RDNA2;
-        nwarps = NWARPS_Q4_1_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q4_1_RDNA1;
-        mmq_y  =  MMQ_Y_Q4_1_RDNA1;
-        nwarps = NWARPS_Q4_1_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q4_1_AMPERE;
-        mmq_y  =  MMQ_Y_Q4_1_AMPERE;
-        nwarps = NWARPS_Q4_1_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q4_1_PASCAL;
-        mmq_y  =  MMQ_Y_Q4_1_PASCAL;
-        nwarps = NWARPS_Q4_1_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q4_1<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q4_1<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q5_0_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q5_0_RDNA2;
-        mmq_y  =  MMQ_Y_Q5_0_RDNA2;
-        nwarps = NWARPS_Q5_0_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q5_0_RDNA1;
-        mmq_y  =  MMQ_Y_Q5_0_RDNA1;
-        nwarps = NWARPS_Q5_0_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q5_0_AMPERE;
-        mmq_y  =  MMQ_Y_Q5_0_AMPERE;
-        nwarps = NWARPS_Q5_0_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q5_0_PASCAL;
-        mmq_y  =  MMQ_Y_Q5_0_PASCAL;
-        nwarps = NWARPS_Q5_0_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q5_0<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q5_0<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q5_1_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q5_1_RDNA2;
-        mmq_y  =  MMQ_Y_Q5_1_RDNA2;
-        nwarps = NWARPS_Q5_1_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q5_1_RDNA1;
-        mmq_y  =  MMQ_Y_Q5_1_RDNA1;
-        nwarps = NWARPS_Q5_1_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q5_1_AMPERE;
-        mmq_y  =  MMQ_Y_Q5_1_AMPERE;
-        nwarps = NWARPS_Q5_1_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q5_1_PASCAL;
-        mmq_y  =  MMQ_Y_Q5_1_PASCAL;
-        nwarps = NWARPS_Q5_1_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q5_1<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q5_1<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q8_0_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q8_0_RDNA2;
-        mmq_y  =  MMQ_Y_Q8_0_RDNA2;
-        nwarps = NWARPS_Q8_0_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q8_0_RDNA1;
-        mmq_y  =  MMQ_Y_Q8_0_RDNA1;
-        nwarps = NWARPS_Q8_0_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q8_0_AMPERE;
-        mmq_y  =  MMQ_Y_Q8_0_AMPERE;
-        nwarps = NWARPS_Q8_0_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q8_0_PASCAL;
-        mmq_y  =  MMQ_Y_Q8_0_PASCAL;
-        nwarps = NWARPS_Q8_0_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q8_0<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q8_0<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q2_K_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q2_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q2_K_RDNA2;
-        nwarps = NWARPS_Q2_K_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q2_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q2_K_RDNA1;
-        nwarps = NWARPS_Q2_K_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q2_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q2_K_AMPERE;
-        nwarps = NWARPS_Q2_K_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q2_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q2_K_PASCAL;
-        nwarps = NWARPS_Q2_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q2_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q2_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q3_K_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-#if QK_K == 256
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q3_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q3_K_RDNA2;
-        nwarps = NWARPS_Q3_K_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q3_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q3_K_RDNA1;
-        nwarps = NWARPS_Q3_K_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q3_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q3_K_AMPERE;
-        nwarps = NWARPS_Q3_K_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q3_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q3_K_PASCAL;
-        nwarps = NWARPS_Q3_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-#endif
-}
-
-static void ggml_mul_mat_q4_K_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q4_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q4_K_RDNA2;
-        nwarps = NWARPS_Q4_K_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q4_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q4_K_RDNA1;
-        nwarps = NWARPS_Q4_K_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q4_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q4_K_AMPERE;
-        nwarps = NWARPS_Q4_K_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q4_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q4_K_PASCAL;
-        nwarps = NWARPS_Q4_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q4_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q4_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q5_K_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q5_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q5_K_RDNA2;
-        nwarps = NWARPS_Q5_K_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q5_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q5_K_RDNA1;
-        nwarps = NWARPS_Q5_K_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q5_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q5_K_AMPERE;
-        nwarps = NWARPS_Q5_K_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q5_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q5_K_PASCAL;
-        nwarps = NWARPS_Q5_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q5_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q5_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
-static void ggml_mul_mat_q6_K_q8_1_cuda(
-    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
-    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-    const int compute_capability = g_device_caps[id].cc;
-
-    int mmq_x, mmq_y, nwarps;
-    if (compute_capability >= CC_RDNA2) {
-        mmq_x  =  MMQ_X_Q6_K_RDNA2;
-        mmq_y  =  MMQ_Y_Q6_K_RDNA2;
-        nwarps = NWARPS_Q6_K_RDNA2;
-    } else if (compute_capability >= CC_OFFSET_AMD) {
-        mmq_x  =  MMQ_X_Q6_K_RDNA1;
-        mmq_y  =  MMQ_Y_Q6_K_RDNA1;
-        nwarps = NWARPS_Q6_K_RDNA1;
-    } else if (compute_capability >= CC_VOLTA) {
-        mmq_x  =  MMQ_X_Q6_K_AMPERE;
-        mmq_y  =  MMQ_Y_Q6_K_AMPERE;
-        nwarps = NWARPS_Q6_K_AMPERE;
-    } else if (compute_capability >= MIN_CC_DP4A) {
-        mmq_x  =  MMQ_X_Q6_K_PASCAL;
-        mmq_y  =  MMQ_Y_Q6_K_PASCAL;
-        nwarps = NWARPS_Q6_K_PASCAL;
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
-    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
-    const dim3 block_nums(block_num_x, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
-
-    if (nrows_x % mmq_y == 0) {
-        const bool need_check = false;
-        mul_mat_q6_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    } else {
-        const bool need_check = true;
-        mul_mat_q6_K<need_check><<<block_nums, block_dims, 0, stream>>>
-            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
-    }
-}
-
 static void ggml_mul_mat_p021_f16_f32_cuda(
     const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x,
     const int nchannels_x, const int nchannels_y, cudaStream_t stream) {
@@ -6771,662 +1168,11 @@ static void ggml_mul_mat_vec_nc_f16_f32_cuda(
         (vx, y, dst, ncols_x, nrows_x, row_stride_x, channel_stride_x, nchannels_y/nchannels_x);
 }
 
-static void ggml_cpy_f32_f32_cuda(
-    const char * cx, char * cdst, const int ne,
-    const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
-    const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) {
-
-    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
-    cpy_f32_f16<cpy_1_f32_f32><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
-        (cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12);
-}
-
-static void ggml_cpy_f32_f16_cuda(
-    const char * cx, char * cdst, const int ne,
-    const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
-    const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) {
-
-    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
-    cpy_f32_f16<cpy_1_f32_f16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
-        (cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12);
-}
-
-static void ggml_cpy_f32_q8_0_cuda(
-    const char * cx, char * cdst, const int ne,
-    const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
-    const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) {
-
-    GGML_ASSERT(ne % QK8_0 == 0);
-    const int num_blocks = ne / QK8_0;
-    cpy_f32_q<cpy_blck_f32_q8_0, QK8_0><<<num_blocks, 1, 0, stream>>>
-        (cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12);
-}
-
-static void ggml_cpy_f32_q4_0_cuda(
-    const char * cx, char * cdst, const int ne,
-    const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
-    const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) {
-
-    GGML_ASSERT(ne % QK4_0 == 0);
-    const int num_blocks = ne / QK4_0;
-    cpy_f32_q<cpy_blck_f32_q4_0, QK4_0><<<num_blocks, 1, 0, stream>>>
-        (cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12);
-}
-
-static void ggml_cpy_f32_q4_1_cuda(
-    const char * cx, char * cdst, const int ne,
-    const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
-    const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) {
-
-    GGML_ASSERT(ne % QK4_1 == 0);
-    const int num_blocks = ne / QK4_1;
-    cpy_f32_q<cpy_blck_f32_q4_1, QK4_1><<<num_blocks, 1, 0, stream>>>
-        (cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12);
-}
-
-static void ggml_cpy_f16_f16_cuda(
-    const char * cx, char * cdst, const int ne,
-    const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
-    const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) {
-
-    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
-    cpy_f32_f16<cpy_1_f16_f16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
-        (cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12);
-}
-
-static void scale_f32_cuda(const float * x, float * dst, const float scale, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE;
-    scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, k);
-}
-
-static void clamp_f32_cuda(const float * x, float * dst, const float min, const float max, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_CLAMP_BLOCK_SIZE - 1) / CUDA_CLAMP_BLOCK_SIZE;
-    clamp_f32<<<num_blocks, CUDA_CLAMP_BLOCK_SIZE, 0, stream>>>(x, dst, min, max, k);
-}
-
-template<typename T>
-static void rope_cuda(
-    const T * x, T * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream
-) {
-    GGML_ASSERT(ncols % 2 == 0);
-    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
-    const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
-    const dim3 block_nums(nrows, num_blocks_x, 1);
-    if (pos == nullptr) {
-        rope<T, false><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
-        );
-    } else {
-        rope<T, true><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
-        );
-    }
-}
-
-template<typename T>
-static void rope_neox_cuda(
-    const T * x, T * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream
-) {
-    GGML_ASSERT(ncols % 2 == 0);
-    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
-    const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
-    const dim3 block_nums(nrows, num_blocks_x, 1);
-
-    const float theta_scale = powf(freq_base, -2.0f/n_dims);
-    const float inv_ndims = -1.0f / n_dims;
-
-    if (pos == nullptr) {
-        rope_neox<T, false><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
-            theta_scale, inv_ndims
-        );
-    } else {
-        rope_neox<T, true><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
-            theta_scale, inv_ndims
-        );
-    }
-}
-
-static void rope_glm_f32_cuda(
-    const float * x, float * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float freq_base, int n_ctx, cudaStream_t stream
-) {
-    GGML_ASSERT(ncols % 4 == 0);
-    const dim3 block_dims(CUDA_ROPE_BLOCK_SIZE/4, 1, 1);
-    const int num_blocks_x = (ncols + CUDA_ROPE_BLOCK_SIZE - 1) / CUDA_ROPE_BLOCK_SIZE;
-    const dim3 block_nums(num_blocks_x, nrows, 1);
-    rope_glm_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, n_ctx);
-}
-
-static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const int nrows,
-                           const int k_rows, const int n_heads_log2_floor, const float m0,
-                           const float m1, cudaStream_t stream) {
-    const dim3 block_dims(CUDA_ALIBI_BLOCK_SIZE, 1, 1);
-    const int num_blocks_x = (ncols + CUDA_ALIBI_BLOCK_SIZE - 1) / (CUDA_ALIBI_BLOCK_SIZE);
-    const dim3 block_nums(num_blocks_x, nrows, 1);
-    alibi_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, k_rows, n_heads_log2_floor, m0, m1);
-}
-
-static void sum_rows_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
-    const dim3 block_dims(WARP_SIZE, 1, 1);
-    const dim3 block_nums(1, nrows, 1);
-    k_sum_rows_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
-}
-
-static void argsort_f32_i32_cuda(const float * x, int * dst, const int ncols, const int nrows, ggml_sort_order order, cudaStream_t stream) {
-    // bitonic sort requires ncols to be power of 2
-    GGML_ASSERT((ncols & (ncols - 1)) == 0);
-
-    const dim3 block_dims(ncols, 1, 1);
-    const dim3 block_nums(1, nrows, 1);
-    if (order == GGML_SORT_ASC) {
-        k_argsort_f32_i32<GGML_SORT_ASC><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
-    } else if (order == GGML_SORT_DESC) {
-        k_argsort_f32_i32<GGML_SORT_DESC><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
-    } else {
-        GGML_ASSERT(false);
-    }
-}
-
-static void diag_mask_inf_f32_cuda(const float * x, float * dst, const int ncols_x, const int nrows_x, const int rows_per_channel, const int n_past, cudaStream_t stream) {
-    const dim3 block_dims(1, CUDA_DIAG_MASK_INF_BLOCK_SIZE, 1);
-    const int block_num_x = (ncols_x + CUDA_DIAG_MASK_INF_BLOCK_SIZE - 1) / CUDA_DIAG_MASK_INF_BLOCK_SIZE;
-    const dim3 block_nums(nrows_x, block_num_x, 1);
-    diag_mask_inf_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols_x, rows_per_channel, n_past);
-}
-
-static void soft_max_f16_cuda(const float * x, const float * y, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, cudaStream_t stream) {
-    int nth = WARP_SIZE;
-    while (nth < ncols_x/2 && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2;
-    const dim3 block_dims(nth,     1, 1);
-    const dim3 block_nums(nrows_x, 1, 1);
-    const size_t shmem = (GGML_PAD(ncols_x, 2*WARP_SIZE) + WARP_SIZE)*sizeof(half);
-    static_assert(CUDA_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted.");
-    if (shmem <= g_device_caps[g_main_device].smpb) {
-        switch (ncols_x) {
-            case 32:
-                soft_max_f16<true, 32, 32, true><<<block_nums, block_dims, shmem, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-                break;
-            case 64:
-                soft_max_f16<true, 64, 32, false><<<block_nums, block_dims, shmem, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-                break;
-            case 128:
-                soft_max_f16<true, 128, 64, false><<<block_nums, block_dims, shmem, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-                break;
-            case 256:
-                soft_max_f16<true, 256, 128, false><<<block_nums, block_dims, shmem, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-                break;
-            case 512:
-                soft_max_f16<true, 512, 256, false><<<block_nums, block_dims, shmem, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-                break;
-            case 1024:
-                soft_max_f16<true, 1024, 512, false><<<block_nums, block_dims, shmem, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-                break;
-            case 2048:
-                soft_max_f16<true, 2048, 1024, false><<<block_nums, block_dims, shmem, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-                break;
-            case 4096:
-                soft_max_f16<true, 4096, 1024, false><<<block_nums, block_dims, shmem, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-                break;
-            default:
-                soft_max_f16<true, 0, 0, true><<<block_nums, block_dims, shmem, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-                break;
-        }
-    } else {
-        const size_t shmem_low = WARP_SIZE*sizeof(half);
-        soft_max_f16<false, 0, 0, true><<<block_nums, block_dims, shmem_low, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-    }
-}
-
-static void soft_max_f32_cuda(const float * x, const float * y, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, cudaStream_t stream) {
-    int nth = WARP_SIZE;
-    while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2;
-    const dim3 block_dims(nth,     1, 1);
-    const dim3 block_nums(nrows_x, 1, 1);
-    const size_t shmem = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE)*sizeof(float);
-    static_assert(CUDA_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted.");
-    if (shmem < g_device_caps[g_main_device].smpb) {
-        switch (ncols_x) {
-            case 32:
-                soft_max_f32<true, 32, 32><<<block_nums, block_dims, shmem, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-                break;
-            case 64:
-                soft_max_f32<true, 64, 64><<<block_nums, block_dims, shmem, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-                break;
-            case 128:
-                soft_max_f32<true, 128, 128><<<block_nums, block_dims, shmem, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-                break;
-            case 256:
-                soft_max_f32<true, 256, 256><<<block_nums, block_dims, shmem, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-                break;
-            case 512:
-                soft_max_f32<true, 512, 512><<<block_nums, block_dims, shmem, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-                break;
-            case 1024:
-                soft_max_f32<true, 1024, 1024><<<block_nums, block_dims, shmem, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-                break;
-            case 2048:
-                soft_max_f32<true, 2048, 1024><<<block_nums, block_dims, shmem, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-                break;
-            case 4096:
-                soft_max_f32<true, 4096, 1024><<<block_nums, block_dims, shmem, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-                break;
-            default:
-                soft_max_f32<true, 0, 0><<<block_nums, block_dims, shmem, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-                break;
-        }
-    } else {
-        const size_t shmem_low = WARP_SIZE*sizeof(float);
-        soft_max_f32<false, 0, 0><<<block_nums, block_dims, shmem_low, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
-    }
-}
-
-static void im2col_f32_f16_cuda(const float* x, half* dst,
-    int IW, int IH, int OW, int OH, int KW, int KH, int IC,
-    int offset_delta,
-    int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {
-    const int parallel_elements = OW * KW * KH;
-    const int num_blocks = (parallel_elements + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE;
-    dim3 block_nums(num_blocks, OH, IC);
-    im2col_f32_f16<<<block_nums, CUDA_IM2COL_BLOCK_SIZE, 0, stream>>>(x, dst, offset_delta, IW, IH, OW, KW, KH, parallel_elements, (IC * KH * KW), s0, s1, p0, p1, d0, d1);
-}
-
-// buffer pool for cuda
-#define MAX_CUDA_BUFFERS 256
-
-struct scoped_spin_lock {
-    std::atomic_flag& lock;
-    scoped_spin_lock(std::atomic_flag& lock) : lock(lock) {
-        while (lock.test_and_set(std::memory_order_acquire)) {
-            ; // spin
-        }
-    }
-    ~scoped_spin_lock() {
-        lock.clear(std::memory_order_release);
-    }
-    scoped_spin_lock(const scoped_spin_lock&) = delete;
-    scoped_spin_lock& operator=(const scoped_spin_lock&) = delete;
-};
-
-static std::atomic_flag g_cuda_pool_lock = ATOMIC_FLAG_INIT;
-
-// #define DEBUG_CUDA_MALLOC
-struct ggml_cuda_buffer {
-    void * ptr = nullptr;
-    size_t size = 0;
-};
-
-static ggml_cuda_buffer g_cuda_buffer_pool[GGML_CUDA_MAX_DEVICES][MAX_CUDA_BUFFERS];
-static size_t g_cuda_pool_size[GGML_CUDA_MAX_DEVICES] = {0};
-
-static void * ggml_cuda_pool_malloc_leg(int device, size_t size, size_t * actual_size) {
-    scoped_spin_lock lock(g_cuda_pool_lock);
-#ifdef DEBUG_CUDA_MALLOC
-    int nnz = 0;
-    size_t max_size = 0;
-#endif
-    size_t best_diff = 1ull << 36;
-    int ibest = -1;
-    for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
-        ggml_cuda_buffer& b = g_cuda_buffer_pool[device][i];
-        if (b.ptr != nullptr) {
-#ifdef DEBUG_CUDA_MALLOC
-            ++nnz;
-            if (b.size > max_size) max_size = b.size;
-#endif
-            if (b.size >= size) {
-                size_t diff = b.size - size;
-                if (diff < best_diff) {
-                    best_diff = diff;
-                    ibest = i;
-                    if (!best_diff) {
-                        void * ptr = b.ptr;
-                        *actual_size = b.size;
-                        b.ptr = nullptr;
-                        b.size = 0;
-                        return ptr;
-                    }
-                }
-            }
-        }
-    }
-    if (ibest >= 0) {
-        ggml_cuda_buffer& b = g_cuda_buffer_pool[device][ibest];
-        void * ptr = b.ptr;
-        *actual_size = b.size;
-        b.ptr = nullptr;
-        b.size = 0;
-        return ptr;
-    }
-    void * ptr;
-    size_t look_ahead_size = (size_t) (1.05 * size);
-    look_ahead_size = 256 * ((look_ahead_size + 255)/256);
-    ggml_cuda_set_device(device);
-    CUDA_CHECK(cudaMalloc((void **) &ptr, look_ahead_size));
-    *actual_size = look_ahead_size;
-    g_cuda_pool_size[device] += look_ahead_size;
-#ifdef DEBUG_CUDA_MALLOC
-    fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz,
-            (uint32_t)(max_size/1024/1024), (uint32_t)(g_cuda_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024));
-#endif
-    return ptr;
-}
-
-static void ggml_cuda_pool_free_leg(int device, void * ptr, size_t size) {
-    scoped_spin_lock lock(g_cuda_pool_lock);
-
-    for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
-        ggml_cuda_buffer& b = g_cuda_buffer_pool[device][i];
-        if (b.ptr == nullptr) {
-            b.ptr = ptr;
-            b.size = size;
-            return;
-        }
-    }
-    fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n");
-    ggml_cuda_set_device(device);
-    CUDA_CHECK(cudaFree(ptr));
-    g_cuda_pool_size[device] -= size;
-}
-
-#if !defined(GGML_USE_HIPBLAS)
-// pool with virtual memory
-static CUdeviceptr g_cuda_pool_addr[GGML_CUDA_MAX_DEVICES] = {0};
-static size_t g_cuda_pool_used[GGML_CUDA_MAX_DEVICES] = {0};
-static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB
-
-static void * ggml_cuda_pool_malloc_vmm(int device, size_t size, size_t * actual_size) {
-    scoped_spin_lock lock(g_cuda_pool_lock);
-
-    // round up the allocation size to the alignment to ensure that all allocations are aligned for all data types
-    const size_t alignment = 128;
-    size = alignment * ((size + alignment - 1) / alignment);
-
-    size_t avail = g_cuda_pool_size[device] - g_cuda_pool_used[device];
-
-    if (size > avail) {
-        // round up to the next multiple of the granularity
-        size_t reserve_size = size - avail;
-        const size_t granularity = g_device_caps[device].vmm_granularity;
-        reserve_size = granularity * ((reserve_size + granularity - 1) / granularity);
-
-        GGML_ASSERT(g_cuda_pool_size[device] + reserve_size <= CUDA_POOL_VMM_MAX_SIZE);
-
-        // allocate more physical memory
-        CUmemAllocationProp prop = {};
-        prop.type = CU_MEM_ALLOCATION_TYPE_PINNED;
-        prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
-        prop.location.id = device;
-        CUmemGenericAllocationHandle handle;
-        CU_CHECK(cuMemCreate(&handle, reserve_size, &prop, 0));
-
-        // reserve virtual address space (if not already reserved)
-        if (g_cuda_pool_addr[device] == 0) {
-            CU_CHECK(cuMemAddressReserve(&g_cuda_pool_addr[device], CUDA_POOL_VMM_MAX_SIZE, 0, 0, 0));
-        }
-
-        // map at the end of the pool
-        CU_CHECK(cuMemMap(g_cuda_pool_addr[device] + g_cuda_pool_size[device], reserve_size, 0, handle, 0));
-
-        // the memory allocation handle is no longer needed after mapping
-        CU_CHECK(cuMemRelease(handle));
-
-        // set access
-        CUmemAccessDesc access = {};
-        access.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
-        access.location.id = device;
-        access.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE;
-        CU_CHECK(cuMemSetAccess(g_cuda_pool_addr[device] + g_cuda_pool_size[device], reserve_size, &access, 1));
-
-        // add to the pool
-        g_cuda_pool_size[device] += reserve_size;
-
-        //printf("cuda pool[%d]: size increased to %llu MB (reserved %llu MB)\n",
-        //       id, (unsigned long long) (g_cuda_pool_size[id]/1024/1024),
-        //       (unsigned long long) (reserve_size/1024/1024));
-    }
-
-    GGML_ASSERT(g_cuda_pool_addr[device] != 0);
-
-    void * ptr = (void *) (g_cuda_pool_addr[device] + g_cuda_pool_used[device]);
-    *actual_size = size;
-    g_cuda_pool_used[device] += size;
-
-#ifdef DEBUG_CUDA_MALLOC
-    printf("cuda pool[%d]: allocated %llu bytes at %llx [%s]\n", id, (unsigned long long) size, ptr);
-#endif
-
-    return ptr;
-}
-
-static void ggml_cuda_pool_free_vmm(int device, void * ptr, size_t size) {
-    scoped_spin_lock lock(g_cuda_pool_lock);
-
-#ifdef DEBUG_CUDA_MALLOC
-    printf("cuda pool[%d]: freed %llu bytes at %llx\n", id, (unsigned long long) size, ptr);
-#endif
-
-    g_cuda_pool_used[device] -= size;
-
-    // all deallocations must be in reverse order of the allocations
-    GGML_ASSERT(ptr == (void *) (g_cuda_pool_addr[device] + g_cuda_pool_used[device]));
-}
-
-static void * ggml_cuda_pool_malloc(int device, size_t size, size_t * actual_size) {
-    if (g_device_caps[device].vmm) {
-        return ggml_cuda_pool_malloc_vmm(device, size, actual_size);
-    } else {
-        return ggml_cuda_pool_malloc_leg(device, size, actual_size);
-    }
-}
-
-static void ggml_cuda_pool_free(int device, void * ptr, size_t size) {
-    if (g_device_caps[device].vmm) {
-        ggml_cuda_pool_free_vmm(device, ptr, size);
-    } else {
-        ggml_cuda_pool_free_leg(device, ptr, size);
-    }
-}
-#else
-#define ggml_cuda_pool_malloc ggml_cuda_pool_malloc_leg
-#define ggml_cuda_pool_free ggml_cuda_pool_free_leg
-#endif // !defined(GGML_USE_HIPBLAS)
-
-template<typename T>
-struct cuda_pool_alloc {
-    int device = -1;
-    T * ptr = nullptr;
-    size_t actual_size = 0;
-
-    // size is in number of elements
-    T * alloc(size_t size) {
-        GGML_ASSERT(ptr == nullptr);
-        CUDA_CHECK(cudaGetDevice(&device));
-        ptr = (T *) ggml_cuda_pool_malloc(device, size * sizeof(T), &this->actual_size);
-        return ptr;
-    }
-
-    cuda_pool_alloc(size_t size) {
-        alloc(size);
-    }
-
-    ~cuda_pool_alloc() {
-        if (ptr != nullptr) {
-            ggml_cuda_pool_free(device, ptr, actual_size);
-        }
-    }
-
-    T * get() {
-        return ptr;
-    }
-
-    cuda_pool_alloc() = default;
-    cuda_pool_alloc(const cuda_pool_alloc &) = delete;
-    cuda_pool_alloc(cuda_pool_alloc &&) = delete;
-    cuda_pool_alloc& operator=(const cuda_pool_alloc &) = delete;
-    cuda_pool_alloc& operator=(cuda_pool_alloc &&) = delete;
-};
-
-static bool g_cublas_loaded = false;
-
-bool ggml_cublas_loaded(void) {
-    return g_cublas_loaded;
-}
-
-void ggml_init_cublas() {
-    static bool initialized = false;
-
-    if (!initialized) {
-
-#ifdef __HIP_PLATFORM_AMD__
-        // Workaround for a rocBLAS bug when using multiple graphics cards:
-        // https://github.com/ROCmSoftwarePlatform/rocBLAS/issues/1346
-        rocblas_initialize();
-        CUDA_CHECK(cudaDeviceSynchronize());
-#endif
-
-        if (cudaGetDeviceCount(&g_device_count) != cudaSuccess) {
-            initialized = true;
-            g_cublas_loaded = false;
-            return;
-        }
-
-        GGML_ASSERT(g_device_count <= GGML_CUDA_MAX_DEVICES);
-        int64_t total_vram = 0;
-#if defined(GGML_CUDA_FORCE_MMQ)
-        fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ:   yes\n", __func__);
-#else
-        fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ:   no\n", __func__);
-#endif
-#if defined(CUDA_USE_TENSOR_CORES)
-        fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: yes\n", __func__);
-#else
-        fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: no\n", __func__);
-#endif
-        fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, g_device_count);
-        for (int id = 0; id < g_device_count; ++id) {
-            int device_vmm = 0;
-
-#if !defined(GGML_USE_HIPBLAS)
-            CUdevice device;
-            CU_CHECK(cuDeviceGet(&device, id));
-            CU_CHECK(cuDeviceGetAttribute(&device_vmm, CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED, device));
-
-            if (device_vmm) {
-                CUmemAllocationProp alloc_prop = {};
-                alloc_prop.type = CU_MEM_ALLOCATION_TYPE_PINNED;
-                alloc_prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
-                alloc_prop.location.id = id;
-                CU_CHECK(cuMemGetAllocationGranularity(&g_device_caps[id].vmm_granularity, &alloc_prop, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED));
-            }
-#endif // !defined(GGML_USE_HIPBLAS)
-            g_device_caps[id].vmm = !!device_vmm;
-
-            cudaDeviceProp prop;
-            CUDA_CHECK(cudaGetDeviceProperties(&prop, id));
-            fprintf(stderr, "  Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no");
-
-            g_tensor_split[id] = total_vram;
-            total_vram += prop.totalGlobalMem;
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-            g_device_caps[id].cc = 100*prop.major + 10*prop.minor + CC_OFFSET_AMD;
-#else
-            g_device_caps[id].cc = 100*prop.major + 10*prop.minor;
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-            g_device_caps[id].smpb = prop.sharedMemPerBlock;
-        }
-        for (int id = 0; id < g_device_count; ++id) {
-            g_tensor_split[id] /= total_vram;
-        }
-
-        for (int id = 0; id < g_device_count; ++id) {
-            ggml_cuda_set_device(id);
-
-            // create cuda streams
-            for (int is = 0; is < MAX_STREAMS; ++is) {
-                CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStreams[id][is], cudaStreamNonBlocking));
-            }
-
-            // create cublas handle
-            CUBLAS_CHECK(cublasCreate(&g_cublas_handles[id]));
-            CUBLAS_CHECK(cublasSetMathMode(g_cublas_handles[id], CUBLAS_TF32_TENSOR_OP_MATH));
-        }
-
-        // configure logging to stdout
-        // CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr));
-
-        initialized = true;
-        g_cublas_loaded = true;
-    }
-}
-
-void ggml_cuda_set_tensor_split(const float * tensor_split) {
-    if (tensor_split == nullptr) {
-        return;
-    }
-    bool all_zero = true;
-    for (int i = 0; i < g_device_count; ++i) {
-        if (tensor_split[i] != 0.0f) {
-            all_zero = false;
-            break;
-        }
-    }
-    if (all_zero) {
-        return;
-    }
-    float split_sum = 0.0f;
-    for (int i = 0; i < g_device_count; ++i) {
-        g_tensor_split[i] = split_sum;
-        split_sum += tensor_split[i];
-    }
-    for (int i = 0; i < g_device_count; ++i) {
-        g_tensor_split[i] /= split_sum;
-    }
-}
-
-void * ggml_cuda_host_malloc(size_t size) {
-    if (getenv("GGML_CUDA_NO_PINNED") != nullptr) {
-        return nullptr;
-    }
-
-    void * ptr = nullptr;
-    cudaError_t err = cudaMallocHost((void **) &ptr, size);
-    if (err != cudaSuccess) {
-        // clear the error
-        cudaGetLastError();
-        fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory: %s\n",
-            size/1024.0/1024.0, cudaGetErrorString(err));
-        return nullptr;
-    }
-
-    return ptr;
-}
-
-void ggml_cuda_host_free(void * ptr) {
-    CUDA_CHECK(cudaFreeHost(ptr));
-}
-
 static cudaError_t ggml_cuda_cpy_tensor_2d(
     void * dst, const struct ggml_tensor * src, int64_t i3, int64_t i2, int64_t i1_low, int64_t i1_high, cudaStream_t stream) {
 
-    cudaMemcpyKind kind;
-    char * src_ptr;
-    if (src->backend == GGML_BACKEND_CPU) {
-        kind = cudaMemcpyHostToDevice;
-        src_ptr = (char *) src->data;
-    } else if (src->backend == GGML_BACKEND_GPU || src->backend == GGML_BACKEND_GPU_SPLIT) {
-        GGML_ASSERT(src->backend != GGML_BACKEND_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1]));
-        kind = cudaMemcpyDeviceToDevice;
-        ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra;
-        int id;
-        CUDA_CHECK(cudaGetDevice(&id));
-        src_ptr = (char *) extra->data_device[id];
-    } else {
-        GGML_ASSERT(false);
-    }
+    GGML_ASSERT(ggml_backend_buffer_is_cuda(src->buffer));
+    char * src_ptr = (char *) src->data;
     char * dst_ptr = (char *) dst;
 
     const int64_t ne0 = src->ne[0];
@@ -7441,599 +1187,25 @@ static cudaError_t ggml_cuda_cpy_tensor_2d(
 
     const char * x = src_ptr + i1_low*nb1 + i2*nb2 + i3*nb3;
     if (nb0 == ts && nb1 == ts*ne0/bs) {
-        return cudaMemcpyAsync(dst_ptr, x, i1_diff*nb1, kind, stream);
+        return cudaMemcpyAsync(dst_ptr, x, i1_diff*nb1, cudaMemcpyDeviceToDevice, stream);
     } else if (nb0 == ts) {
-        return cudaMemcpy2DAsync(dst_ptr, ts*ne0/bs, x, nb1, ts*ne0/bs, i1_diff, kind, stream);
+        return cudaMemcpy2DAsync(dst_ptr, ts*ne0/bs, x, nb1, ts*ne0/bs, i1_diff, cudaMemcpyDeviceToDevice, stream);
     } else {
         for (int64_t i1 = 0; i1 < i1_diff; i1++) {
             const void * rx = (const void *) ((const char *) x + i1*nb1);
             void * rd = (void *) (dst_ptr + i1*ts*ne0/bs);
             // pretend the row is a matrix with cols=1
-            cudaError_t r = cudaMemcpy2DAsync(rd, ts/bs, rx, nb0, ts/bs, ne0, kind, stream);
-            if (r != cudaSuccess) return r;
+            cudaError_t r = cudaMemcpy2DAsync(rd, ts/bs, rx, nb0, ts/bs, ne0, cudaMemcpyDeviceToDevice, stream);
+            if (r != cudaSuccess) {
+                return r;
+            }
         }
         return cudaSuccess;
     }
 }
 
-static void ggml_cuda_op_get_rows(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_d, const float * src1_d, float * dst_d, cudaStream_t stream) {
-
-    GGML_ASSERT(src1->type == GGML_TYPE_I32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-
-    GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
-    GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type));
-    GGML_ASSERT(dst->nb[0] == ggml_type_size(dst->type));
-
-    const int32_t * src1_i32 = (const int32_t *) src1_d;
-
-    switch (src0->type) {
-        case GGML_TYPE_F16:
-            get_rows_cuda_float(src0, src1, dst, (const half *)src0_d, src1_i32, dst_d, stream);
-            break;
-        case GGML_TYPE_F32:
-            get_rows_cuda_float(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
-            break;
-        case GGML_TYPE_Q4_0:
-            get_rows_cuda<QK4_0, QR4_0, dequantize_q4_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
-            break;
-        case GGML_TYPE_Q4_1:
-            get_rows_cuda<QK4_1, QR4_1, dequantize_q4_1>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
-            break;
-        case GGML_TYPE_Q5_0:
-            get_rows_cuda<QK5_0, QR5_0, dequantize_q5_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
-            break;
-        case GGML_TYPE_Q5_1:
-            get_rows_cuda<QK5_1, QR5_1, dequantize_q5_1>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
-            break;
-        case GGML_TYPE_Q8_0:
-            get_rows_cuda<QK8_0, QR8_0, dequantize_q8_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
-            break;
-        default:
-            // TODO: k-quants
-            fprintf(stderr, "%s: unsupported type: %s\n", __func__, ggml_type_name(src0->type));
-            GGML_ASSERT(false);
-            break;
-    }
-}
-
-template<class op>
-static void ggml_cuda_op_bin_bcast(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-
-    if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-        op()(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
-    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
-        op()(src0, src1, dst, (const half *) src0_dd, src1_dd, (half *) dst_dd, main_stream);
-    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
-        op()(src0, src1, dst, (const half *) src0_dd, src1_dd, dst_dd, main_stream);
-    } else {
-        fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__,
-            ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type));
-        GGML_ASSERT(false);
-    }
-}
-
-static void ggml_cuda_op_repeat(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_d, const float * src1_d, float * dst_d, cudaStream_t main_stream) {
-
-    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_repeat>>(dst, src0, dst, nullptr, src0_d, dst_d, main_stream);
-
-    (void) src1;
-    (void) src1_d;
-}
-
-static void ggml_cuda_op_add(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_add>>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
-}
-
-static void ggml_cuda_op_acc(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported
-
-    int nb1 = dst->op_params[0] / 4; // 4 bytes of float32
-    int nb2 = dst->op_params[1] / 4; // 4 bytes of float32
-    // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused
-    int offset = dst->op_params[3] / 4; // offset in bytes
-
-    acc_f32_cuda(src0_dd, src1_dd, dst_dd, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, main_stream);
-
-    (void) dst;
-}
-
-static void ggml_cuda_op_mul(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_mul>>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
-}
-
-static void ggml_cuda_op_div(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_div>>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
-}
-
-static void ggml_cuda_op_gelu(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    gelu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_silu(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    silu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_gelu_quick(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    gelu_quick_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_tanh(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    tanh_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_relu(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    relu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_leaky_relu(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    float negative_slope;
-    memcpy(&negative_slope, dst->op_params, sizeof(float));
-
-    leaky_relu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), negative_slope, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_sqr(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    sqr_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_norm(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t nrows = ggml_nrows(src0);
-
-    float eps;
-    memcpy(&eps, dst->op_params, sizeof(float));
-
-    norm_f32_cuda(src0_dd, dst_dd, ne00, nrows, eps, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_group_norm(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    int num_groups = dst->op_params[0];
-    int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups);
-    group_norm_f32_cuda(src0_dd, dst_dd, num_groups, group_size, src0->ne[0] * src0->ne[1] * src0->ne[2], main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_concat(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-
-    for (int i3 = 0; i3 < dst->ne[3]; i3++) {
-        concat_f32_cuda(src0_dd + i3 * (src0->nb[3] / 4), src1_dd + i3 * (src1->nb[3] / 4), dst_dd + i3 * (dst->nb[3] / 4), dst->ne[0], dst->ne[1], dst->ne[2], src0->ne[2], main_stream);
-    }
-
-    (void) src1;
-    (void) dst;
-}
-
-static void ggml_cuda_op_upscale(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
-
-    const int scale_factor = dst->op_params[0];
-
-    upscale_f32_cuda(src0_dd, dst_dd, src0->ne[0], src0->ne[1], src0->ne[2], scale_factor, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_pad(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
-
-    pad_f32_cuda(src0_dd, dst_dd,
-        src0->ne[0], src0->ne[1], src0->ne[2],
-        dst->ne[0], dst->ne[1], dst->ne[2], main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_rms_norm(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t nrows = ggml_nrows(src0);
-
-    float eps;
-    memcpy(&eps, dst->op_params, sizeof(float));
-
-    rms_norm_f32_cuda(src0_dd, dst_dd, ne00, nrows, eps, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_mul_mat_q(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
-    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
-    const int64_t src1_padded_row_size, cudaStream_t stream) {
-
-    const int64_t ne00 = src0->ne[0];
-
-    const int64_t ne10 = src1->ne[0];
-    GGML_ASSERT(ne10 % QK8_1 == 0);
-
-    const int64_t ne0 = dst->ne[0];
-
-    const int64_t row_diff = row_high - row_low;
-
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
-
-    // the main device has a larger memory buffer to hold the results from all GPUs
-    // nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into
-    const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff;
-
-    switch (src0->type) {
-        case GGML_TYPE_Q4_0:
-            ggml_mul_mat_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q4_1:
-            ggml_mul_mat_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q5_0:
-            ggml_mul_mat_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q5_1:
-            ggml_mul_mat_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q8_0:
-            ggml_mul_mat_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q2_K:
-            ggml_mul_mat_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q3_K:
-            ggml_mul_mat_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q4_K:
-            ggml_mul_mat_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q5_K:
-            ggml_mul_mat_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        case GGML_TYPE_Q6_K:
-            ggml_mul_mat_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
-            break;
-        default:
-            GGML_ASSERT(false);
-            break;
-    }
-
-    (void) src1;
-    (void) dst;
-    (void) src1_ddf_i;
-}
-
-static int64_t get_row_rounding(ggml_type type) {
-    int64_t min_compute_capability = INT_MAX;
-    int64_t max_compute_capability = INT_MIN;
-    for (int id = 0; id < g_device_count; ++id) {
-        if (g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) {
-            if (min_compute_capability > g_device_caps[id].cc) {
-                min_compute_capability = g_device_caps[id].cc;
-            }
-            if (max_compute_capability < g_device_caps[id].cc) {
-                max_compute_capability = g_device_caps[id].cc;
-            }
-        }
-    }
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-    switch(type) {
-        case GGML_TYPE_Q4_0:
-        case GGML_TYPE_Q4_1:
-        case GGML_TYPE_Q5_0:
-        case GGML_TYPE_Q5_1:
-        case GGML_TYPE_Q8_0:
-            return max_compute_capability >= CC_RDNA2 ? 128 : 64;
-        case GGML_TYPE_F16:
-        case GGML_TYPE_F32:
-            return 1;
-        case GGML_TYPE_Q2_K:
-            return max_compute_capability >= CC_RDNA2 ? 128 : 32;
-        case GGML_TYPE_Q3_K:
-            return min_compute_capability < CC_RDNA2 ? 128 : 64;
-        case GGML_TYPE_Q4_K:
-        case GGML_TYPE_Q5_K:
-        case GGML_TYPE_Q6_K:
-        case GGML_TYPE_IQ2_XXS:
-            return max_compute_capability >= CC_RDNA2 ? 128 : 64;
-        default:
-            GGML_ASSERT(false);
-    }
-#else
-    switch(type) {
-        case GGML_TYPE_Q4_0:
-        case GGML_TYPE_Q4_1:
-            return max_compute_capability >= CC_VOLTA ? 128 : 64;
-        case GGML_TYPE_Q5_0:
-        case GGML_TYPE_Q5_1:
-        case GGML_TYPE_Q8_0:
-            return 64;
-        case GGML_TYPE_F16:
-        case GGML_TYPE_F32:
-            return 1;
-        case GGML_TYPE_Q2_K:
-        case GGML_TYPE_Q3_K:
-        case GGML_TYPE_Q4_K:
-        case GGML_TYPE_Q5_K:
-        case GGML_TYPE_IQ2_XXS:
-            return max_compute_capability >= CC_VOLTA ? 128 : 64;
-        case GGML_TYPE_Q6_K:
-            return 64;
-        default:
-            GGML_ASSERT(false);
-    }
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-}
-
-static void ggml_cuda_op_mul_mat_vec_q(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
-    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
-    const int64_t src1_padded_row_size, cudaStream_t stream) {
-
-    GGML_ASSERT(ggml_nrows(src1) == 1);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t row_diff = row_high - row_low;
-
-    switch (src0->type) {
-        case GGML_TYPE_Q4_0:
-            mul_mat_vec_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q4_1:
-            mul_mat_vec_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q5_0:
-            mul_mat_vec_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q5_1:
-            mul_mat_vec_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q8_0:
-            mul_mat_vec_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q2_K:
-            mul_mat_vec_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q3_K:
-            mul_mat_vec_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q4_K:
-            mul_mat_vec_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q5_K:
-            mul_mat_vec_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q6_K:
-            mul_mat_vec_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_IQ2_XXS:
-            mul_mat_vec_iq2_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        default:
-            GGML_ASSERT(false);
-            break;
-    }
-
-    (void) src1;
-    (void) dst;
-    (void) src1_ddf_i;
-    (void) src1_ncols;
-    (void) src1_padded_row_size;
-}
-
-static void ggml_cuda_op_dequantize_mul_mat_vec(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
-    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
-    const int64_t src1_padded_row_size, cudaStream_t stream) {
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t row_diff = row_high - row_low;
-
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-
-    // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
-#ifdef GGML_CUDA_F16
-    cuda_pool_alloc<half> src1_dfloat_a;
-    half * src1_dfloat = nullptr; // dfloat == half
-
-    bool src1_convert_f16 =
-        src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 ||
-        src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 ||
-        src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16;
-
-    if (src1_convert_f16) {
-        src1_dfloat = src1_dfloat_a.alloc(ne00);
-        ggml_cpy_f32_f16_cuda((const char *) src1_ddf_i, (char *) src1_dfloat, ne00,
-                                ne00, 1, sizeof(float), 0, 0,
-                                ne00, 1, sizeof(half),  0, 0, stream);
-    }
-#else
-    const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion
-#endif // GGML_CUDA_F16
-
-    switch (src0->type) {
-        case GGML_TYPE_Q4_0:
-            dequantize_mul_mat_vec_q4_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q4_1:
-            dequantize_mul_mat_vec_q4_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q5_0:
-            dequantize_mul_mat_vec_q5_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q5_1:
-            dequantize_mul_mat_vec_q5_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q8_0:
-            dequantize_mul_mat_vec_q8_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q2_K:
-            dequantize_mul_mat_vec_q2_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q3_K:
-            dequantize_mul_mat_vec_q3_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q4_K:
-            dequantize_mul_mat_vec_q4_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q5_K:
-            dequantize_mul_mat_vec_q5_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_Q6_K:
-            dequantize_mul_mat_vec_q6_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
-            break;
-        case GGML_TYPE_F16:
-            convert_mul_mat_vec_f16_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
-            break;
-        default:
-            GGML_ASSERT(false);
-            break;
-    }
-
-    (void) src1;
-    (void) dst;
-    (void) src1_ddq_i;
-    (void) src1_ncols;
-    (void) src1_padded_row_size;
-}
-
 static void ggml_cuda_op_mul_mat_cublas(
+    ggml_backend_cuda_context & ctx,
     const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
     const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
     const int64_t src1_padded_row_size, cudaStream_t stream) {
@@ -8049,19 +1221,17 @@ static void ggml_cuda_op_mul_mat_cublas(
 
     const int64_t row_diff = row_high - row_low;
 
-    int id;
-    CUDA_CHECK(cudaGetDevice(&id));
+    int id = ggml_cuda_get_device();
 
     // the main device has a larger memory buffer to hold the results from all GPUs
     // ldc == nrows of the matrix that cuBLAS writes into
-    int ldc = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff;
+    int ldc = id == ctx.device ? ne0 : row_diff;
 
-    const int compute_capability = g_device_caps[id].cc;
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
 
     if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) {
-        //printf("this branch\n");
         // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32
-        cuda_pool_alloc<half> src0_as_f16;
+        ggml_cuda_pool_alloc<half> src0_as_f16(ctx.pool());
         if (src0->type != GGML_TYPE_F16) {
             const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src0->type);
             GGML_ASSERT(to_fp16_cuda != nullptr);
@@ -8071,7 +1241,7 @@ static void ggml_cuda_op_mul_mat_cublas(
         }
         const half * src0_ptr = src0->type == GGML_TYPE_F16 ? (const half *) src0_dd_i : src0_as_f16.get();
 
-        cuda_pool_alloc<half> src1_as_f16;
+        ggml_cuda_pool_alloc<half> src1_as_f16(ctx.pool());
         if (src1->type != GGML_TYPE_F16) {
             const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
             GGML_ASSERT(to_fp16_cuda != nullptr);
@@ -8080,14 +1250,14 @@ static void ggml_cuda_op_mul_mat_cublas(
             to_fp16_cuda(src1_ddf_i, src1_as_f16.get(), ne, stream);
         }
         const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16.get();
-        cuda_pool_alloc<half> dst_f16(row_diff*src1_ncols);
+        ggml_cuda_pool_alloc<half> dst_f16(ctx.pool(), row_diff*src1_ncols);
 
         const half alpha_f16 = 1.0f;
         const half beta_f16 = 0.0f;
 
-        CUBLAS_CHECK(cublasSetStream(g_cublas_handles[id], stream));
+        CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream));
         CUBLAS_CHECK(
-            cublasGemmEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
+            cublasGemmEx(ctx.cublas_handle(id), CUBLAS_OP_T, CUBLAS_OP_N,
                     row_diff, src1_ncols, ne10,
                     &alpha_f16, src0_ptr,       CUDA_R_16F, ne00,
                                 src1_ptr,       CUDA_R_16F, ne10,
@@ -8098,8 +1268,8 @@ static void ggml_cuda_op_mul_mat_cublas(
         const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
         to_fp32_cuda(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream);
     } else {
-        cuda_pool_alloc<float> src0_ddq_as_f32;
-        cuda_pool_alloc<float> src1_ddq_as_f32;
+        ggml_cuda_pool_alloc<float> src0_ddq_as_f32(ctx.pool(id));
+        ggml_cuda_pool_alloc<float> src1_ddq_as_f32(ctx.pool(id));
 
         if (src0->type != GGML_TYPE_F32) {
             const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src0->type);
@@ -8120,360 +1290,21 @@ static void ggml_cuda_op_mul_mat_cublas(
         const float alpha = 1.0f;
         const float beta = 0.0f;
 
-        CUBLAS_CHECK(cublasSetStream(g_cublas_handles[id], stream));
+        CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream));
         CUBLAS_CHECK(
-            cublasSgemm(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
+            cublasSgemm(ctx.cublas_handle(id), CUBLAS_OP_T, CUBLAS_OP_N,
                     row_diff, src1_ncols, ne10,
                     &alpha, src0_ddf_i,  ne00,
                             src1_ddf1_i, ne10,
                     &beta,  dst_dd_i,    ldc));
     }
 
-    (void) dst;
-    (void) src1_ddq_i;
-    (void) src1_padded_row_size;
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_ddq_i);
+    GGML_UNUSED(src1_padded_row_size);
 }
 
-static void ggml_cuda_op_rope(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32 ||  dst->type == GGML_TYPE_F16);
-    GGML_ASSERT(src0->type == dst->type);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne2 = dst->ne[2];
-    const int64_t nrows = ggml_nrows(src0);
-
-    //const int n_past      = ((int32_t *) dst->op_params)[0];
-    const int n_dims      = ((int32_t *) dst->op_params)[1];
-    const int mode        = ((int32_t *) dst->op_params)[2];
-    const int n_ctx       = ((int32_t *) dst->op_params)[3];
-    const int n_orig_ctx  = ((int32_t *) dst->op_params)[4];
-
-    // RoPE alteration for extended context
-    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
-    memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
-    memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
-    memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
-    memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
-    memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
-    memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
-
-    const int32_t * pos = nullptr;
-    if ((mode & 1) == 0) {
-        GGML_ASSERT(src1->type == GGML_TYPE_I32);
-        GGML_ASSERT(src1->ne[0] == ne2);
-        pos = (const int32_t *) src1_dd;
-    }
-
-    const bool is_neox = mode & 2;
-    const bool is_glm  = mode & 4;
-
-    rope_corr_dims corr_dims;
-    ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v);
-
-    // compute
-    if (is_glm) {
-        GGML_ASSERT(false);
-        rope_glm_f32_cuda(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, n_ctx, main_stream);
-    } else if (is_neox) {
-        if (src0->type == GGML_TYPE_F32) {
-            rope_neox_cuda(
-                (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, main_stream
-            );
-        } else if (src0->type == GGML_TYPE_F16) {
-            rope_neox_cuda(
-                (const half *)src0_dd, (half *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, main_stream
-            );
-        } else {
-            GGML_ASSERT(false);
-        }
-    } else {
-        if (src0->type == GGML_TYPE_F32) {
-            rope_cuda(
-                (const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, main_stream
-            );
-        } else if (src0->type == GGML_TYPE_F16) {
-            rope_cuda(
-                (const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, main_stream
-            );
-        } else {
-            GGML_ASSERT(false);
-        }
-    }
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_alibi(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t nrows = ggml_nrows(src0);
-
-    //const int n_past = ((int32_t *) dst->op_params)[0];
-    const int n_head = ((int32_t *) dst->op_params)[1];
-    float max_bias;
-    memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
-
-    //GGML_ASSERT(ne01 + n_past == ne00);
-    GGML_ASSERT(n_head == ne02);
-
-    const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
-
-    const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
-    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
-
-    alibi_f32_cuda(src0_dd, dst_dd, ne00, nrows, ne01, n_heads_log2_floor, m0, m1, main_stream);
-
-    (void) src1;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_im2col(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F16);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F16);
-
-    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
-    const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
-    const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
-    const int32_t p1 = ((const int32_t*)(dst->op_params))[3];
-    const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
-    const int32_t d1 = ((const int32_t*)(dst->op_params))[5];
-
-    const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1;
-
-    const int64_t IC = src1->ne[is_2D ? 2 : 1];
-    const int64_t IH = is_2D ? src1->ne[1] : 1;
-    const int64_t IW =         src1->ne[0];
-
-    const int64_t KH = is_2D ? src0->ne[1] : 1;
-    const int64_t KW =         src0->ne[0];
-
-    const int64_t OH = is_2D ? dst->ne[2] : 1;
-    const int64_t OW =         dst->ne[1];
-
-    const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
-
-    im2col_f32_f16_cuda(src1_dd, (half*) dst_dd, IW, IH, OW, OH, KW, KH, IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream);
-
-    (void) src0;
-    (void) src0_dd;
-}
-
-static void ggml_cuda_op_sum_rows(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    const int64_t ncols = src0->ne[0];
-    const int64_t nrows = ggml_nrows(src0);
-
-    sum_rows_f32_cuda(src0_dd, dst_dd, ncols, nrows, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_argsort(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_I32);
-
-    const int64_t ncols = src0->ne[0];
-    const int64_t nrows = ggml_nrows(src0);
-
-    enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0];
-
-    argsort_f32_i32_cuda(src0_dd, (int *)dst_dd, ncols, nrows, order, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_diag_mask_inf(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int nrows0 = ggml_nrows(src0);
-
-    const int n_past = ((int32_t *) dst->op_params)[0];
-
-    diag_mask_inf_f32_cuda(src0_dd, dst_dd, ne00, nrows0, ne01, n_past, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_soft_max(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t nrows_x = ggml_nrows(src0);
-    const int64_t nrows_y = src1 ? ggml_nrows(src1) : 1;
-
-    float scale = 1.0f;
-    memcpy(&scale, dst->op_params, sizeof(float));
-
-#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-    const bool use_f16_soft_max = false;
-#else
-#ifdef GGML_CUDA_F16
-    const bool use_f16_soft_max = true;
-#else
-    const bool use_f16_soft_max = false;
-#endif // GGML_CUDA_F16
-#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-
-    if (use_f16_soft_max) {
-        soft_max_f16_cuda(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, main_stream);
-    } else {
-        soft_max_f32_cuda(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, main_stream);
-    }
-
-    (void) dst;
-}
-
-static void ggml_cuda_op_scale(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    float scale;
-    memcpy(&scale, dst->op_params, sizeof(float));
-
-    scale_f32_cuda(src0_dd, dst_dd, scale, ggml_nelements(src0), main_stream);
-    CUDA_CHECK(cudaGetLastError());
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_clamp(
-    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-    const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    float min;
-    float max;
-    memcpy(&min, dst->op_params, sizeof(float));
-    memcpy(&max, (float *) dst->op_params + 1, sizeof(float));
-
-    clamp_f32_cuda(src0_dd, dst_dd, min, max, ggml_nelements(src0), main_stream);
-    CUDA_CHECK(cudaGetLastError());
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const ggml_cuda_op_flatten_t op) {
-    const int64_t nrows0 = ggml_nrows(src0);
-
-    const bool use_src1 = src1 != nullptr;
-    const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1;
-
-    GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_GPU_SPLIT);
-    GGML_ASSERT(              dst->backend != GGML_BACKEND_GPU_SPLIT);
-
-    ggml_tensor_extra_gpu * src0_extra =            (ggml_tensor_extra_gpu *) src0->extra;
-    ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
-    ggml_tensor_extra_gpu * dst_extra  =            (ggml_tensor_extra_gpu *)  dst->extra;
-
-    const bool src0_on_device =             src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
-    const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_GPU;
-    const bool  dst_on_device =              dst->backend == GGML_BACKEND_GPU;
-
-    // dd = data device
-    float * src0_ddf = nullptr;
-    float * src1_ddf = nullptr;
-    float *  dst_ddf = nullptr;
-
-    cuda_pool_alloc<float> src0_f;
-    cuda_pool_alloc<float> src1_f;
-    cuda_pool_alloc<float>  dst_f;
-
-    ggml_cuda_set_device(g_main_device);
-    cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
-
-    if (src0_on_device) {
-        src0_ddf = (float *) src0_extra->data_device[g_main_device];
-    } else {
-        src0_ddf = src0_f.alloc(ggml_nelements(src0));
-        CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_ddf, src0, 0, 0, 0, nrows0, main_stream));
-    }
-
-    if (use_src1) {
-        if (src1_on_device) {
-            src1_ddf = (float *) src1_extra->data_device[g_main_device];
-        } else {
-            src1_ddf = src1_f.alloc(ggml_nelements(src1));
-            CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src1_ddf, src1, 0, 0, 0, nrows1, main_stream));
-        }
-    }
-    if (dst_on_device) {
-        dst_ddf = (float *) dst_extra->data_device[g_main_device];
-    } else {
-        dst_ddf = dst_f.alloc(ggml_nelements(dst));
-    }
-
-    // do the computation
-    op(src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream);
-    CUDA_CHECK(cudaGetLastError());
-
-    // copy dst to host if necessary
-    if (!dst_on_device) {
-        CUDA_CHECK(cudaMemcpyAsync(dst->data, dst_ddf, ggml_nbytes(dst), cudaMemcpyDeviceToHost, main_stream));
-    }
-
-    if (dst->backend == GGML_BACKEND_CPU) {
-        CUDA_CHECK(cudaDeviceSynchronize());
-    }
-}
-
-static void ggml_cuda_set_peer_access(const int n_tokens) {
+static void ggml_cuda_set_peer_access(const int n_tokens, int main_device) {
     static bool peer_access_enabled = false;
 
     const bool enable_peer_access = n_tokens <= GGML_CUDA_PEER_MAX_BATCH_SIZE;
@@ -8483,19 +1314,19 @@ static void ggml_cuda_set_peer_access(const int n_tokens) {
     }
 
 #ifdef NDEBUG
-    for (int id = 0; id < g_device_count; ++id) {
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
         ggml_cuda_set_device(id);
         CUDA_CHECK(cudaDeviceSynchronize());
     }
 
-    for (int id = 0; id < g_device_count; ++id) {
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
         ggml_cuda_set_device(id);
 
-        for (int id_other = 0; id_other < g_device_count; ++id_other) {
+        for (int id_other = 0; id_other < ggml_backend_cuda_get_device_count(); ++id_other) {
             if (id == id_other) {
                 continue;
             }
-            if (id != g_main_device && id_other != g_main_device) {
+            if (id != main_device && id_other != main_device) {
                 continue;
             }
 
@@ -8503,19 +1334,30 @@ static void ggml_cuda_set_peer_access(const int n_tokens) {
             CUDA_CHECK(cudaDeviceCanAccessPeer(&can_access_peer, id, id_other));
             if (can_access_peer) {
                 if (enable_peer_access) {
-                    CUDA_CHECK(cudaDeviceEnablePeerAccess(id_other, 0));
+                    cudaError_t err = cudaDeviceEnablePeerAccess(id_other, 0);
+                    if (err != cudaErrorPeerAccessAlreadyEnabled) {
+                        CUDA_CHECK(err);
+                    }
                 } else {
-                    CUDA_CHECK(cudaDeviceDisablePeerAccess(id_other));
+                    cudaError_t err = cudaDeviceDisablePeerAccess(id_other);
+                    if (err != cudaErrorPeerAccessNotEnabled) {
+                        CUDA_CHECK(err);
+                    }
                 }
             }
         }
     }
+
+    ggml_cuda_set_device(main_device);
 #endif // NDEBUG
 
     peer_access_enabled = enable_peer_access;
+
+    GGML_UNUSED(main_device);
 }
 
 static void ggml_cuda_op_mul_mat(
+    ggml_backend_cuda_context & ctx,
     const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_cuda_op_mul_mat_t op,
     const bool convert_src1_to_q8_1) {
 
@@ -8538,8 +1380,11 @@ static void ggml_cuda_op_mul_mat(
     const int nb2 = dst->nb[2];
     const int nb3 = dst->nb[3];
 
-    GGML_ASSERT(dst->backend != GGML_BACKEND_GPU_SPLIT);
-    GGML_ASSERT(src1->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(ggml_backend_buffer_is_cuda(dst->buffer));
+    GGML_ASSERT(ggml_backend_buffer_is_cuda(src1->buffer));
+    ggml_backend_cuda_buffer_context * src1_ctx = (ggml_backend_cuda_buffer_context *) src1->buffer->context;
+    ggml_backend_cuda_buffer_context * dst_ctx  = (ggml_backend_cuda_buffer_context *) dst->buffer->context;
+
     GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1));
 
     GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0);
@@ -8551,26 +1396,30 @@ static void ggml_cuda_op_mul_mat(
     const size_t q8_1_ts = sizeof(block_q8_1);
     const size_t q8_1_bs = QK8_1;
 
-    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    ggml_tensor_extra_gpu *  dst_extra = (ggml_tensor_extra_gpu *)  dst->extra;
-
-    const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
     const bool src0_is_contiguous = ggml_is_contiguous(src0);
     const bool src1_is_contiguous = ggml_is_contiguous(src1);
 
     const int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING);
 
-    const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
+    const bool split = ggml_backend_buffer_is_cuda_split(src0->buffer);
     GGML_ASSERT(!(split && ne02 > 1));
     GGML_ASSERT(!(split && ne03 > 1));
     GGML_ASSERT(!(split && ne02 < ne12));
 
+    ggml_tensor_extra_gpu * src0_extra = split ? (ggml_tensor_extra_gpu *) src0->extra : nullptr;
+
+
+    std::array<float, GGML_CUDA_MAX_DEVICES> tensor_split;
+    if (split) {
+        ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context;
+        tensor_split = buft_ctx->tensor_split;
+    }
+
     struct dev_data {
-        cuda_pool_alloc<char>  src0_dd_alloc;
-        cuda_pool_alloc<float> src1_ddf_alloc;
-        cuda_pool_alloc<char>  src1_ddq_alloc;
-        cuda_pool_alloc<float>   dst_dd_alloc;
+        ggml_cuda_pool_alloc<char>  src0_dd_alloc;
+        ggml_cuda_pool_alloc<float> src1_ddf_alloc;
+        ggml_cuda_pool_alloc<char>  src1_ddq_alloc;
+        ggml_cuda_pool_alloc<float>   dst_dd_alloc;
 
         char  *  src0_dd = nullptr;
         float * src1_ddf = nullptr; // float
@@ -8585,7 +1434,7 @@ static void ggml_cuda_op_mul_mat(
 
     int used_devices = 0;
 
-    for (int id = 0; id < g_device_count; ++id) {
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
         // by default, use all rows
         dev[id].row_low  = 0;
         dev[id].row_high = ne01;
@@ -8593,17 +1442,17 @@ static void ggml_cuda_op_mul_mat(
         // for multi GPU, get the row boundaries from tensor split
         // and round to mul_mat_q tile sizes
         if (split) {
-            const int64_t rounding = get_row_rounding(src0->type);
+            const int64_t rounding = get_row_rounding(src0->type, tensor_split);
 
             if (id != 0) {
-                dev[id].row_low  = ne01*g_tensor_split[id];
+                dev[id].row_low  = ne01*tensor_split[id];
                 if (dev[id].row_low < ne01) {
                     dev[id].row_low -= dev[id].row_low % rounding;
                 }
             }
 
-            if (id != g_device_count - 1) {
-                dev[id].row_high  = ne01*g_tensor_split[id + 1];
+            if (id != ggml_backend_cuda_get_device_count() - 1) {
+                dev[id].row_high  = ne01*tensor_split[id + 1];
                 if (dev[id].row_high < ne01) {
                     dev[id].row_high -= dev[id].row_high % rounding;
                 }
@@ -8611,33 +1460,33 @@ static void ggml_cuda_op_mul_mat(
         }
     }
 
-    for (int id = 0; id < g_device_count; ++id) {
-        if ((!split && id != g_main_device) || dev[id].row_low == dev[id].row_high) {
+    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+        if ((!split && id != ctx.device) || dev[id].row_low == dev[id].row_high) {
             continue;
         }
 
         used_devices++;
 
-        const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device;
-        const bool  dst_on_device =  dst->backend == GGML_BACKEND_GPU && id == g_main_device;
+        const bool src1_on_device = id == src1_ctx->device;
+        const bool  dst_on_device = id == dst_ctx->device;
 
         ggml_cuda_set_device(id);
-        cudaStream_t stream = g_cudaStreams[id][0];
+        cudaStream_t stream = ctx.stream(id, 0);
 
-        if (src0_on_device && src0_is_contiguous) {
-            dev[id].src0_dd = (char *) src0_extra->data_device[id];
+        if (src0_is_contiguous) {
+            dev[id].src0_dd = split ? (char *) src0_extra->data_device[id] : (char *) src0->data;
         } else {
-            dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ggml_nbytes(src0));
+            dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ctx.pool(id), ggml_nbytes(src0));
         }
 
         if (src1_on_device && src1_is_contiguous) {
-            dev[id].src1_ddf = (float *) src1_extra->data_device[id];
+            dev[id].src1_ddf = (float *) src1->data;
         } else {
-            dev[id].src1_ddf = dev[id].src1_ddf_alloc.alloc(ggml_nelements(src1));
+            dev[id].src1_ddf = dev[id].src1_ddf_alloc.alloc(ctx.pool(id), ggml_nelements(src1));
         }
 
         if (convert_src1_to_q8_1) {
-            dev[id].src1_ddq = dev[id].src1_ddq_alloc.alloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs);
+            dev[id].src1_ddq = dev[id].src1_ddq_alloc.alloc(ctx.pool(id), nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs);
 
             if (src1_on_device && src1_is_contiguous) {
                 quantize_row_q8_1_cuda(dev[id].src1_ddf, dev[id].src1_ddq, ne10, nrows1, src1_padded_col_size, stream);
@@ -8646,40 +1495,40 @@ static void ggml_cuda_op_mul_mat(
         }
 
         if (dst_on_device) {
-            dev[id].dst_dd = (float *) dst_extra->data_device[id];
+            dev[id].dst_dd = (float *) dst->data;
         } else {
             const size_t size_dst_ddf = split ? (dev[id].row_high - dev[id].row_low)*ne1 : ggml_nelements(dst);
-            dev[id].dst_dd = dev[id].dst_dd_alloc.alloc(size_dst_ddf);
+            dev[id].dst_dd = dev[id].dst_dd_alloc.alloc(ctx.pool(id), size_dst_ddf);
         }
     }
 
     // if multiple devices are used they need to wait for the main device
     // here an event is recorded that signals that the main device has finished calculating the input data
     if (split && used_devices > 1) {
-        ggml_cuda_set_device(g_main_device);
-        CUDA_CHECK(cudaEventRecord(src0_extra->events[g_main_device][0], g_cudaStreams[g_main_device][0]));
+        ggml_cuda_set_device(ctx.device);
+        CUDA_CHECK(cudaEventRecord(src0_extra->events[ctx.device][0], ctx.stream()));
     }
 
     const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11;
     for (int64_t src1_col_0 = 0; src1_col_0 < ne11; src1_col_0 += src1_col_stride) {
-        const int64_t is = split ? (src1_col_0/src1_col_stride) % MAX_STREAMS : 0;
+        const int64_t is = split ? (src1_col_0/src1_col_stride) % GGML_CUDA_MAX_STREAMS : 0;
         const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride;
 
-        for (int id = 0; id < g_device_count; ++id) {
-            if ((!split && id != g_main_device) || dev[id].row_low == dev[id].row_high) {
+        for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+            if ((!split && id != ctx.device) || dev[id].row_low == dev[id].row_high) {
                 continue;
             }
 
-            const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device;
-            const bool  dst_on_device =  dst->backend == GGML_BACKEND_GPU && id == g_main_device;
+            const bool src1_on_device = id == src1_ctx->device;
+            const bool  dst_on_device = id == dst_ctx->device;
             const int64_t row_diff = dev[id].row_high - dev[id].row_low;
 
             ggml_cuda_set_device(id);
-            cudaStream_t stream = g_cudaStreams[id][is];
+            cudaStream_t stream = ctx.stream(id, is);
 
             // wait for main GPU data if necessary
-            if (split && (id != g_main_device || is != 0)) {
-                CUDA_CHECK(cudaStreamWaitEvent(stream, src0_extra->events[g_main_device][0], 0));
+            if (split && (id != ctx.device || is != 0)) {
+                CUDA_CHECK(cudaStreamWaitEvent(stream, src0_extra->events[ctx.device][0], 0));
             }
 
             for (int64_t i0 = 0; i0 < ne13*ne12; ++i0) {
@@ -8696,58 +1545,48 @@ static void ggml_cuda_op_mul_mat(
 
                 // the main device memory buffer can be on VRAM scratch, with space for all partial results
                 // in that case an offset on dst_ddf_i is needed
-                if (dst->backend == GGML_BACKEND_GPU && id == g_main_device) {
+                if (id == ctx.device) {
                     dst_dd_i += dev[id].row_low; // offset is 0 if no tensor split
                 }
 
                 // copy src0, src1 to device if necessary
-                if (src1->backend == GGML_BACKEND_GPU && src1_is_contiguous) {
-                    if (id != g_main_device) {
+                if (src1_is_contiguous) {
+                    if (id != ctx.device) {
                         if (convert_src1_to_q8_1) {
-                            char * src1_ddq_i_source = dev[g_main_device].src1_ddq + src1_ddq_i_offset;
-                            CUDA_CHECK(cudaMemcpyPeerAsync(src1_ddq_i, id, src1_ddq_i_source, g_main_device,
+                            char * src1_ddq_i_source = dev[ctx.device].src1_ddq + src1_ddq_i_offset;
+                            CUDA_CHECK(cudaMemcpyPeerAsync(src1_ddq_i, id, src1_ddq_i_source, ctx.device,
                                                             src1_ncols*src1_padded_col_size*q8_1_ts/q8_1_bs, stream));
                         } else {
-                            float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device];
+                            float * src1_ddf_i_source = (float *) src1->data;
                             src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10;
-                            CUDA_CHECK(cudaMemcpyPeerAsync(src1_ddf_i, id, src1_ddf_i_source, g_main_device,
+                            CUDA_CHECK(cudaMemcpyPeerAsync(src1_ddf_i, id, src1_ddf_i_source, ctx.device,
                                                             src1_ncols*ne10*sizeof(float), stream));
                         }
                     }
-                } else if (src1->backend == GGML_BACKEND_CPU || (src1_on_device && !src1_is_contiguous)) {
+                } else if (src1_on_device && !src1_is_contiguous) {
                     CUDA_CHECK(ggml_cuda_cpy_tensor_2d(
                                 src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream));
                 } else {
                     GGML_ASSERT(false);
                 }
 
-                if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_CPU || !src1_is_contiguous)) {
+                if (convert_src1_to_q8_1 && !src1_is_contiguous) {
                     quantize_row_q8_1_cuda(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream);
                     CUDA_CHECK(cudaGetLastError());
                 }
 
-                if (src1_col_0 == 0 && (!src0_on_device || !src0_is_contiguous) && i02 % i02_divisor == 0) {
+                if (src1_col_0 == 0 && !src0_is_contiguous && i02 % i02_divisor == 0) {
                     CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, dev[id].row_low, dev[id].row_high, stream));
                 }
 
                 // do the computation
-                op(src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i,
+                op(ctx, src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i,
                     dev[id].row_low, dev[id].row_high, src1_ncols, src1_padded_col_size, stream);
                 CUDA_CHECK(cudaGetLastError());
 
                 // copy dst to host or other device if necessary
                 if (!dst_on_device) {
-                    void * dst_off_device;
-                    cudaMemcpyKind kind;
-                    if (dst->backend == GGML_BACKEND_CPU) {
-                        dst_off_device = dst->data;
-                        kind = cudaMemcpyDeviceToHost;
-                    } else if (dst->backend == GGML_BACKEND_GPU) {
-                        dst_off_device = dst_extra->data_device[g_main_device];
-                        kind = cudaMemcpyDeviceToDevice;
-                    } else {
-                        GGML_ASSERT(false);
-                    }
+                    void * dst_off_device = dst->data;
                     if (split) {
                         // src0 = weight matrix is saved as a transposed matrix for better memory layout.
                         // dst is NOT transposed.
@@ -8758,33 +1597,31 @@ static void ggml_cuda_op_mul_mat(
                         GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
                         dhf_dst_i += src1_col_0*ne0 + dev[id].row_low;
 #if !defined(GGML_USE_HIPBLAS)
-                        if (kind == cudaMemcpyDeviceToDevice) {
-                            // cudaMemcpy2DAsync may fail with copies between vmm pools of different devices
-                            cudaMemcpy3DPeerParms p = {};
-                            p.dstDevice = g_main_device;
-                            p.dstPtr = make_cudaPitchedPtr(dhf_dst_i, ne0*sizeof(float), row_diff, src1_ncols);
-                            p.srcDevice = id;
-                            p.srcPtr = make_cudaPitchedPtr(dst_dd_i, row_diff*sizeof(float), row_diff, src1_ncols);
-                            p.extent = make_cudaExtent(row_diff*sizeof(float), src1_ncols, 1);
-                            CUDA_CHECK(cudaMemcpy3DPeerAsync(&p, stream));
-                        } else
+                        // cudaMemcpy2DAsync may fail with copies between vmm pools of different devices
+                        cudaMemcpy3DPeerParms p = {};
+                        p.dstDevice = ctx.device;
+                        p.dstPtr = make_cudaPitchedPtr(dhf_dst_i, ne0*sizeof(float), row_diff, src1_ncols);
+                        p.srcDevice = id;
+                        p.srcPtr = make_cudaPitchedPtr(dst_dd_i, row_diff*sizeof(float), row_diff, src1_ncols);
+                        p.extent = make_cudaExtent(row_diff*sizeof(float), src1_ncols, 1);
+                        CUDA_CHECK(cudaMemcpy3DPeerAsync(&p, stream));
+#else
+                        // HIP does not support cudaMemcpy3DPeerAsync or vmm pools
+                        CUDA_CHECK(cudaMemcpy2DAsync(dhf_dst_i, ne0*sizeof(float),
+                                                        dst_dd_i, row_diff*sizeof(float),
+                                                        row_diff*sizeof(float), src1_ncols,
+                                                        cudaMemcpyDeviceToDevice, stream));
 #endif
-                        {
-                            CUDA_CHECK(cudaMemcpy2DAsync(dhf_dst_i, ne0*sizeof(float),
-                                                            dst_dd_i, row_diff*sizeof(float),
-                                                            row_diff*sizeof(float), src1_ncols,
-                                                            kind, stream));
-                        }
                     } else {
                         float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
                         GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
                         dhf_dst_i += src1_col_0*ne0;
-                        CUDA_CHECK(cudaMemcpyAsync(dhf_dst_i, dst_dd_i, src1_ncols*ne0*sizeof(float), kind, stream));
+                        CUDA_CHECK(cudaMemcpyAsync(dhf_dst_i, dst_dd_i, src1_ncols*ne0*sizeof(float), cudaMemcpyDeviceToDevice, stream));
                     }
                 }
 
                 // add event for the main device to wait on until other device is done
-                if (split && (id != g_main_device || is != 0)) {
+                if (split && (id != ctx.device || is != 0)) {
                     CUDA_CHECK(cudaEventRecord(src0_extra->events[id][is], stream));
                 }
             }
@@ -8792,121 +1629,25 @@ static void ggml_cuda_op_mul_mat(
     }
 
     // main device waits for all other devices to be finished
-    if (split && g_device_count > 1) {
+    if (split && ggml_backend_cuda_get_device_count() > 1) {
         int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE;
-        is_max = is_max <= MAX_STREAMS ? is_max : MAX_STREAMS;
+        is_max = is_max <= GGML_CUDA_MAX_STREAMS ? is_max : GGML_CUDA_MAX_STREAMS;
 
-        ggml_cuda_set_device(g_main_device);
-        for (int id = 0; id < g_device_count; ++id) {
+        ggml_cuda_set_device(ctx.device);
+        for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
             if (dev[id].row_low == dev[id].row_high) {
                 continue;
             }
             for (int64_t is = 0; is < is_max; ++is) {
-                CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[g_main_device][0], src0_extra->events[id][is], 0));
+                CUDA_CHECK(cudaStreamWaitEvent(ctx.stream(), src0_extra->events[id][is], 0));
             }
         }
     }
-
-    if (dst->backend == GGML_BACKEND_CPU) {
-        ggml_cuda_set_device(g_main_device);
-        CUDA_CHECK(cudaDeviceSynchronize());
-    }
 }
 
-static void ggml_cuda_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_repeat);
-}
-
-static void ggml_cuda_get_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_get_rows);
-}
-
-static void ggml_cuda_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_add);
-}
-
-static void ggml_cuda_acc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_acc);
-}
-
-static void ggml_cuda_mul(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_mul);
-}
-
-static void ggml_cuda_div(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_div);
-}
-
-static void ggml_cuda_gelu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_gelu);
-}
-
-static void ggml_cuda_silu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_silu);
-}
-
-static void ggml_cuda_gelu_quick(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_gelu_quick);
-}
-
-static void ggml_cuda_tanh(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_tanh);
-}
-
-static void ggml_cuda_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_relu);
-}
-
-static void ggml_cuda_leaky_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_leaky_relu);
-}
-
-static void ggml_cuda_sqr(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_sqr);
-}
-
-static void ggml_cuda_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_norm);
-}
-
-static void ggml_cuda_group_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_group_norm);
-}
-
-static void ggml_cuda_concat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_concat);
-}
-
-static void ggml_cuda_upscale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_upscale);
-}
-
-static void ggml_cuda_pad(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_pad);
-}
-
-static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rms_norm);
-}
-
-bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
-    if (!g_cublas_loaded) return false;
-
-    const int64_t ne10 = src1->ne[0];
-
-    const int64_t ne0 = dst->ne[0];
-    const int64_t ne1 = dst->ne[1];
-
-    // TODO: find the optimal values for these
-    return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
-            src1->type == GGML_TYPE_F32 &&
-             dst->type == GGML_TYPE_F32 &&
-            (ne0 >= 32 && ne1 >= 32 && ne10 >= 32);
-}
-
-static void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
+static void ggml_cuda_mul_mat_vec_p021(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
     GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
-    GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer));
     GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation
     GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
@@ -8918,26 +1659,20 @@ static void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tens
 
     const int64_t ne12 = src1->ne[2];
 
-    ggml_cuda_set_device(g_main_device);
-    cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
+    cudaStream_t main_stream = ctx.stream();
 
-    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    void * src0_ddq = src0_extra->data_device[g_main_device];
-
-    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
-
-    ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
-    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
+    void  * src0_ddq = src0->data;
+    float * src1_ddf = (float *) src1->data;
+    float * dst_ddf  = (float *) dst->data;
 
     ggml_mul_mat_p021_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream);
 }
 
-static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
+static void ggml_cuda_mul_mat_vec_nc(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
     GGML_ASSERT(!ggml_is_transposed(src0));
     GGML_ASSERT(!ggml_is_transposed(src1));
     GGML_ASSERT(!ggml_is_permuted(src0));
-    GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer));
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
 
@@ -8950,17 +1685,11 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor
 
     const int64_t ne12 = src1->ne[2];
 
-    ggml_cuda_set_device(g_main_device);
-    cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
+    cudaStream_t main_stream = ctx.stream();
 
-    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    void * src0_ddq = src0_extra->data_device[g_main_device];
-
-    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
-
-    ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
-    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
+    void  * src0_ddq = src0->data;
+    float * src1_ddf = (float *) src1->data;
+    float * dst_ddf  = (float *) dst->data;
 
     const int64_t row_stride_x = nb01 / sizeof(half);
     const int64_t channel_stride_x = nb02 / sizeof(half);
@@ -8992,34 +1721,28 @@ static __global__ void k_compute_batched_ptrs(
     ptrs_dst[0*ne23 + i12 + i13*ne12] = (      char *)         dst + i12*nbd2 + i13*nbd3;
 }
 
-static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_ASSERT(!ggml_is_transposed(src0));
     GGML_ASSERT(!ggml_is_transposed(src1));
 
-    GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer));
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
 
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int64_t ne_dst = ggml_nelements(dst);
 
-    ggml_cuda_set_device(g_main_device);
-    cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
+    cudaStream_t main_stream = ctx.stream();
 
-    CUBLAS_CHECK(cublasSetStream(g_cublas_handles[g_main_device], main_stream));
+    CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(), main_stream));
 
-    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    void * src0_ddq = src0_extra->data_device[g_main_device];
+    void * src0_ddq = src0->data;
     half * src0_f16 = (half *) src0_ddq;
-
-    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
-
-    ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
-    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
+    float * src1_ddf = (float *) src1->data;
+    float * dst_ddf  = (float *) dst->data;
 
     // convert src1 to fp16
-    cuda_pool_alloc<half> src1_f16_alloc;
+    ggml_cuda_pool_alloc<half> src1_f16_alloc(ctx.pool());
     if (src1->type != GGML_TYPE_F16) {
         const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
         const int64_t ne_src1 = ggml_nelements(src1);
@@ -9029,7 +1752,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
     }
     half * src1_f16 = src1->type == GGML_TYPE_F16 ? (half *) src1_ddf : src1_f16_alloc.get();
 
-    cuda_pool_alloc<half> dst_f16;
+    ggml_cuda_pool_alloc<half> dst_f16(ctx.pool());
     char * dst_t;
 
     cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F;
@@ -9094,7 +1817,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
         // there is no broadcast and src0, src1 are contiguous across dims 2, 3
         // use cublasGemmStridedBatchedEx
         CUBLAS_CHECK(
-        cublasGemmStridedBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
+        cublasGemmStridedBatchedEx(ctx.cublas_handle(), CUBLAS_OP_T, CUBLAS_OP_N,
                 ne01, ne11, ne10,
                 alpha, (const char *) src0_f16, CUDA_R_16F,   nb01/nb00, nb02/nb00,  // strideA
                        (const char *) src1_f16, CUDA_R_16F,   nb11/nb10, nb12/nb10,  // strideB
@@ -9106,8 +1829,8 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
         // use cublasGemmBatchedEx
         const int ne23 = ne12*ne13;
 
-        cuda_pool_alloc<const void *> ptrs_src(2*ne23);
-        cuda_pool_alloc<      void *> ptrs_dst(1*ne23);
+        ggml_cuda_pool_alloc<const void *> ptrs_src(ctx.pool(), 2*ne23);
+        ggml_cuda_pool_alloc<      void *> ptrs_dst(ctx.pool(), 1*ne23);
 
         dim3 block_dims(ne13, ne12);
         k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>(
@@ -9123,7 +1846,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
         CUDA_CHECK(cudaGetLastError());
 
         CUBLAS_CHECK(
-        cublasGemmBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
+        cublasGemmBatchedEx(ctx.cublas_handle(), CUBLAS_OP_T, CUBLAS_OP_N,
                 ne01, ne11, ne10,
                 alpha, (const void **) (ptrs_src.get() + 0*ne23), CUDA_R_16F,   nb01/nb00,
                        (const void **) (ptrs_src.get() + 1*ne23), CUDA_R_16F,   nb11/nb10,
@@ -9140,42 +1863,74 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
     }
 }
 
-static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    const bool all_on_device =
-        (src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) &&
-        (src1->backend == GGML_BACKEND_GPU) &&
-        ( dst->backend == GGML_BACKEND_GPU);
-
-    const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
+static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    const bool split = ggml_backend_buffer_is_cuda_split(src0->buffer);
 
     int64_t min_compute_capability = INT_MAX;
-    for (int id = 0; id < g_device_count; ++id) {
-        if (min_compute_capability > g_device_caps[id].cc && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) {
-            min_compute_capability = g_device_caps[id].cc;
+
+    bool any_pascal_with_slow_fp16 = false;
+    if (split) {
+        ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context;
+        auto & tensor_split = buft_ctx->tensor_split;
+        for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
+            // skip devices that are not going to do any work:
+            if (tensor_split[id] >= (id + 1 < ggml_backend_cuda_get_device_count() ? tensor_split[id + 1] : 1.0f)) {
+                continue;
+            }
+
+            if (min_compute_capability > ggml_cuda_info().devices[id].cc) {
+                min_compute_capability = ggml_cuda_info().devices[id].cc;
+            }
+            if (ggml_cuda_info().devices[id].cc == 610) {
+                any_pascal_with_slow_fp16 = true;
+            }
         }
+    } else {
+        min_compute_capability    = ggml_cuda_info().devices[ctx.device].cc;
+        any_pascal_with_slow_fp16 = ggml_cuda_info().devices[ctx.device].cc == 610;
     }
 
+    // check data types and tensor shapes for custom matrix multiplication kernels:
+    bool use_dequantize_mul_mat_vec = (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16)
+        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
+        && src0->ne[0] % GGML_CUDA_DMMV_X == 0 && src1->ne[1] == 1;
+
+    bool          use_mul_mat_vec_q =  ggml_is_quantized(src0->type)
+        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
+        && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE;
+
+    bool              use_mul_mat_q =  ggml_cuda_supports_mmq(src0->type)
+        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
+
 #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
 
     const bool fp16_performance_good = min_compute_capability >= CC_RDNA1;
-    bool               use_mul_mat_q = ggml_is_quantized(src0->type);
+
 #ifdef CUDA_USE_TENSOR_CORES
     use_mul_mat_q = use_mul_mat_q && min_compute_capability < CC_RDNA3;
 #endif // CUDA_USE_TENSOR_CORES
 
 #else
 
-    const bool fp16_performance_good = min_compute_capability >= CC_VOLTA;
-    bool               use_mul_mat_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type);
+    // fp16 performance is good on Volta or newer and on P100 (compute capability 6.0)
+    const bool fp16_performance_good = min_compute_capability >= CC_PASCAL && !any_pascal_with_slow_fp16;
+
+    // mmvq and mmq need the __dp4a instruction which on NVIDIA is only available for CC >= 6.1
+    use_mul_mat_vec_q = use_mul_mat_vec_q && min_compute_capability >= MIN_CC_DP4A;
+    use_mul_mat_q     = use_mul_mat_q     && min_compute_capability >= MIN_CC_DP4A;
+
 #ifdef CUDA_USE_TENSOR_CORES
     // when tensor cores are available, use them for large batch size
     // ref: https://github.com/ggerganov/llama.cpp/pull/3776
-    use_mul_mat_q = use_mul_mat_q && !(fp16_performance_good && src1->ne[1] > MMQ_MAX_BATCH_SIZE);
+    use_mul_mat_q     = use_mul_mat_q     && (!fp16_performance_good || src1->ne[1] <= MMQ_MAX_BATCH_SIZE);
 #endif // CUDA_USE_TENSOR_CORES
 
 #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
 
-    use_mul_mat_q = use_mul_mat_q && ggml_cuda_supports_mmq(src0->type);
+    // if mmvq is available it's a better choice than dmmv:
+#ifndef GGML_CUDA_FORCE_DMMV
+    use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q;
+#endif // GGML_CUDA_FORCE_DMMV
 
     // debug helpers
     //printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]);
@@ -9185,307 +1940,78 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
     //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
     //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
 
-    if (!split && all_on_device && !fp16_performance_good && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
+    if (!split && !fp16_performance_good && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
         // KQ single-batch
-        ggml_cuda_mul_mat_vec_p021(src0, src1, dst);
-    } else if (!split && all_on_device && !fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
+        ggml_cuda_mul_mat_vec_p021(ctx, src0, src1, dst);
+    } else if (!split && !fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
         // KQV single-batch
-        ggml_cuda_mul_mat_vec_nc(src0, src1, dst);
-    } else if (!split && all_on_device && fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
+        ggml_cuda_mul_mat_vec_nc(ctx, src0, src1, dst);
+    } else if (!split && fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
         // KQ + KQV multi-batch
-        ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst);
-    } else if (src0->type == GGML_TYPE_F32) {
-        ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);
-    } else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) {
-        if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0 && src1->type == GGML_TYPE_F32) {
-#ifdef GGML_CUDA_FORCE_DMMV
-            const bool use_mul_mat_vec_q = false;
-#else
-            const bool use_mul_mat_vec_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type) && ggml_nrows(src1) == 1;
-#endif // GGML_CUDA_FORCE_DMMV
-
-            if (use_mul_mat_vec_q) {
-                // NOTE: this kernel does not support ggml_nrows(src1) > 1
-                ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true);
-            } else {
-                ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false);
-            }
-        } else {
-            if (use_mul_mat_q) {
-                ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true);
-            } else {
-                ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);
-            }
-        }
+        ggml_cuda_mul_mat_batched_cublas(ctx, src0, src1, dst);
+    } else if (use_dequantize_mul_mat_vec) {
+        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false);
+    } else if (use_mul_mat_vec_q) {
+        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true);
+    } else if (use_mul_mat_q) {
+        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_q, true);
     } else {
-        GGML_ASSERT(false);
+        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);
     }
 }
 
-#if 0
-template<typename ... Srcs>
-static __global__ void k_compute_batched_ptrs_id(
-        const void ** ptrs_src, void ** ptrs_dst,
-        int ne12, int ne13,
-        int ne23,
-        int nb02, int nb03,
-        int nb12, int nb13,
-        int nb2, int nb3,
-        int r2, int r3,
-        ggml_type src0_type, half * src0_as_f16, int64_t src0_ne,
-        const half * src1_f16, half * dst_f16,
-        const int32_t * ids, const int id,
-        Srcs... src0s) {
+static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const ggml_tensor * ids  = dst->src[2];
 
-    int i = ids[id];
+    GGML_ASSERT(!ggml_backend_buffer_is_cuda_split(src0->buffer) && "mul_mat_id does not support split buffers");
 
-    half * src0_f16;
-    const void * srcs_ar[] = { (const half *) src0s... };
-    if (src0_type == GGML_TYPE_F16) {
-        src0_f16 = (half *) srcs_ar[i];
-    } else {
-        src0_f16 = src0_as_f16;
-        if (threadIdx.x == 0 && threadIdx.y == 0) {
-            const to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(src0_type);
-            to_fp16(srcs_ar[i], src0_f16, src0_ne, cudaStreamFireAndForget);
-        }
-    }
+    cudaStream_t stream = ctx.stream();
 
-    int i13 = blockIdx.x * blockDim.x + threadIdx.x;
-    int i12 = blockIdx.y * blockDim.y + threadIdx.y;
+    const size_t nb11 = src1->nb[1];
+    const size_t nb1  =  dst->nb[1];
 
-    if (i13 >= ne13 || i12 >= ne12) {
-        return;
-    }
-
-    int i03 = i13 / r3;
-    int i02 = i12 / r2;
-
-    ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_f16 + i02*nb02   + i03*nb03;
-    ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_f16 + i12*nb12/2 + i13*nb13/2;
-    ptrs_dst[0*ne23 + i12 + i13*ne12] = (      char *)  dst_f16 + i12* nb2/2 + i13* nb3/2;
-}
-
-static void ggml_cuda_mul_mat_id_cublas(ggml_tensor * dst) {
-    const struct ggml_tensor * ids = dst->src[0];
-    const struct ggml_tensor * src1 = dst->src[1];
-    const struct ggml_tensor * src00 = dst->src[2];
-
-    const int id = dst->op_params[0];
-
-    GGML_ASSERT(!ggml_is_transposed(src00));
-    GGML_ASSERT(!ggml_is_transposed(src1));
-
-    GGML_ASSERT(src00->backend != GGML_BACKEND_GPU_SPLIT);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-
-    const int64_t ne00 = src00->ne[0]; GGML_UNUSED(ne00);
-    const int64_t ne01 = src00->ne[1];
-    const int64_t ne02 = src00->ne[2];
-    const int64_t ne03 = src00->ne[3];
-
-    //const int64_t nb01 = src00->nb[1];
-    const int64_t nb02 = src00->nb[2]; GGML_UNUSED(nb02);
-    const int64_t nb03 = src00->nb[3]; GGML_UNUSED(nb03);
-
-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    const int64_t ne12 = src1->ne[2];
-    const int64_t ne13 = src1->ne[3];
-
-    //const int64_t nb11 = src1->nb[1];
-    const int64_t nb12 = src1->nb[2]; GGML_UNUSED(nb12);
-    const int64_t nb13 = src1->nb[3]; GGML_UNUSED(nb13);
-
-    const int64_t ne1 = ggml_nelements(src1);
-    const int64_t ne  = ggml_nelements(dst);
-
-    ggml_cuda_set_device(g_main_device);
-    cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
-
-    CUBLAS_CHECK(cublasSetStream(g_cublas_handles[g_main_device], main_stream));
-
-    //ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    //void * src0_ddq = src0_extra->data_device[g_main_device];
-    //half * src0_as_f16 = (half *) src0_ddq;
-
-    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
-
-    ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
-    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
-
-    // convert src1 to fp16
-    const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
-    GGML_ASSERT(to_fp16_cuda != nullptr);
-
-    size_t src1_as = 0;
-    half * src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne1 * sizeof(half), &src1_as);
-    to_fp16_cuda(src1_ddf, src1_as_f16, ne1, main_stream);
-
-    size_t dst_as = 0;
-    half * dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as);
-
-    GGML_ASSERT(ne12 % ne02 == 0);
-    GGML_ASSERT(ne13 % ne03 == 0);
-
-    // broadcast factors
-    const int64_t r2 = ne12/ne02;
-    const int64_t r3 = ne13/ne03;
-
-    const half alpha_f16 = 1.0f;
-    const half beta_f16  = 0.0f;
-
-    // use cublasGemmBatchedEx
-    const int ne23 = ne12*ne13;
-
-    const void ** ptrs_src = nullptr;
-          void ** ptrs_dst = nullptr;
-
-    size_t ptrs_src_s = 0;
-    size_t ptrs_dst_s = 0;
-
-    ptrs_src = (const void **) ggml_cuda_pool_malloc(2*ne23*sizeof(void *), &ptrs_src_s);
-    ptrs_dst = (      void **) ggml_cuda_pool_malloc(1*ne23*sizeof(void *), &ptrs_dst_s);
-
-    int64_t src0_ne = ggml_nelements(src00);
-    half * src0_as_f16 = nullptr;
-    size_t src0_as = 0;
-    if (src00->type != GGML_TYPE_F16) {
-        src0_as_f16 = (half *) ggml_cuda_pool_malloc(src0_ne * sizeof(half), &src0_as);
-    }
-
-    static_assert(GGML_MAX_SRC == 6, "GGML_MAX_SRC == 6");
-    dim3 block_dims(ne13, ne12);
-    k_compute_batched_ptrs_id<<<1, block_dims, 0, main_stream>>>(
-            ptrs_src, ptrs_dst,
-            ne12, ne13,
-            ne23,
-            ne00*ne01*sizeof(half), ne00*ne01*ne02*sizeof(half),
-            nb12, nb13,
-            dst->nb[2], dst->nb[3],
-            r2, r3,
-            src00->type, src0_as_f16, src0_ne,
-            src1_as_f16, dst_f16,
-            (const int *)((ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device], id,
-            dst->src[2] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[2]->extra)->data_device[g_main_device] : nullptr,
-            dst->src[3] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[3]->extra)->data_device[g_main_device] : nullptr,
-            dst->src[4] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[4]->extra)->data_device[g_main_device] : nullptr,
-            dst->src[5] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[5]->extra)->data_device[g_main_device] : nullptr
-    );
-    CUDA_CHECK(cudaGetLastError());
-
-    CUBLAS_CHECK(
-    cublasGemmBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
-            ne01, ne11, ne10,
-            &alpha_f16, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, ne00,
-                        (const void **) (ptrs_src + 1*ne23), CUDA_R_16F, ne10,
-            &beta_f16,  (      void **) (ptrs_dst + 0*ne23), CUDA_R_16F, ne01,
-            ne23,
-            CUBLAS_COMPUTE_16F,
-            CUBLAS_GEMM_DEFAULT_TENSOR_OP));
-
-    if (src0_as != 0) {
-        ggml_cuda_pool_free(src0_as_f16, src0_as);
-    }
-    if (ptrs_src_s != 0) {
-        ggml_cuda_pool_free(ptrs_src, ptrs_src_s);
-    }
-    if (ptrs_dst_s != 0) {
-        ggml_cuda_pool_free(ptrs_dst, ptrs_dst_s);
-    }
-
-    const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
-    to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream);
-
-    ggml_cuda_pool_free(src1_as_f16, src1_as);
-    ggml_cuda_pool_free(dst_f16, dst_as);
-}
-#endif
-
-static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-#if 0
-    ggml_cuda_mul_mat_id_cublas(dst);
-    // TODO: mmq/mmv support
-#endif
-
-    const int64_t nb11 = src1->nb[1];
-    const int64_t nb1  =  dst->nb[1];
-
-    const struct ggml_tensor * ids = src0;
     const int32_t id = ((int32_t *) dst->op_params)[0];
-    const int32_t n_as = ((int32_t *) dst->op_params)[1];
+    const int32_t n_as = src0->ne[2];
 
     std::vector<char> ids_host(ggml_nbytes(ids));
+    const char * ids_dev = (const char *) ids->data;
+    CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream));
+    CUDA_CHECK(cudaStreamSynchronize(stream));
 
-    cudaStream_t stream = g_cudaStreams[g_main_device][0];
-
-    if (ids->backend == GGML_BACKEND_GPU) {
-        const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device];
-        CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream));
-        CUDA_CHECK(cudaStreamSynchronize(stream));
-    } else {
-        memcpy(ids_host.data(), ids->data, ggml_nbytes(ids));
-    }
-
-    const ggml_tensor_extra_gpu * src1_extra = (const ggml_tensor_extra_gpu *) src1->extra;
-    const ggml_tensor_extra_gpu * dst_extra = (const ggml_tensor_extra_gpu *) dst->extra;
-
-    ggml_tensor_extra_gpu src1_row_extra;
-    ggml_tensor_extra_gpu dst_row_extra;
-
+    ggml_tensor src0_row = *src0;
     ggml_tensor src1_row = *src1;
     ggml_tensor dst_row = *dst;
 
-    src1_row.backend = GGML_BACKEND_GPU;
-    dst_row.backend  = GGML_BACKEND_GPU;
+    char * src0_original = (char *) src0->data;
+    char * src1_original = (char *) src1->data;
+    char * dst_original  = (char *)  dst->data;
 
-    src1_row.extra = &src1_row_extra;
-    dst_row.extra = &dst_row_extra;
-
-    char * src1_original = src1->backend == GGML_BACKEND_CPU ?
-        (char *) src1->data : (char *) src1_extra->data_device[g_main_device];
-    char * dst_original  =  dst->backend == GGML_BACKEND_CPU ?
-        (char *)  dst->data : (char *)  dst_extra->data_device[g_main_device];
+    src0_row.ne[2] = 1;
+    src0_row.ne[3] = 1;
+    src0_row.nb[3] = src0->nb[2];
 
     if (src1->ne[1] == 1) {
-        GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
-        GGML_ASSERT(dst->backend  == GGML_BACKEND_GPU);
-
         for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
-            //int32_t row_id;
-            //CUDA_CHECK(cudaMemcpyAsync(&row_id, ids_dev + i01*ids->nb[1] + id*ids->nb[0], sizeof(int32_t), cudaMemcpyDeviceToHost, g_cudaStreams[g_main_device][0]));
-            //CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[g_main_device][0]));
-
             const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
 
             GGML_ASSERT(row_id >= 0 && row_id < n_as);
 
-            const struct ggml_tensor * src0_row = dst->src[row_id + 2];
+            src0_row.data = src0_original + row_id*src0->nb[2];
+            src1_row.data = src1_original + i01*src1->nb[1];
+            dst_row.data  =  dst_original + i01*dst->nb[1];
 
-            src1_row_extra.data_device[g_main_device] = src1_original + i01*src1->nb[1];
-            src1_row.data = (char *) src1->data + i01*src1->nb[1]; // TODO why is this set?
-
-            dst_row_extra.data_device[g_main_device] = dst_original + i01*dst->nb[1];
-            dst_row.data = (char *) dst->data + i01*dst->nb[1]; // TODO why is this set?
-
-            ggml_cuda_mul_mat(src0_row, &src1_row, &dst_row);
+            ggml_cuda_mul_mat(ctx, &src0_row, &src1_row, &dst_row);
         }
     } else {
-        cuda_pool_alloc<char> src1_contiguous(sizeof(float)*ggml_nelements(src1));
-        cuda_pool_alloc<char>  dst_contiguous(sizeof(float)*ggml_nelements(dst));
+        ggml_cuda_pool_alloc<char> src1_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(src1));
+        ggml_cuda_pool_alloc<char>  dst_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(dst));
 
-        src1_row_extra.data_device[g_main_device] = src1_contiguous.get();
-        dst_row_extra.data_device[g_main_device]  =  dst_contiguous.get();
-
-        const cudaMemcpyKind src1_kind = src1->backend == GGML_BACKEND_CPU ?
-            cudaMemcpyHostToDevice : cudaMemcpyDeviceToDevice;
-        const cudaMemcpyKind dst_kind  =  dst->backend == GGML_BACKEND_CPU ?
-            cudaMemcpyDeviceToHost : cudaMemcpyDeviceToDevice;
+        src1_row.data = src1_contiguous.get();
+        dst_row.data  =  dst_contiguous.get();
 
         for (int32_t row_id = 0; row_id < n_as; ++row_id) {
-            const struct ggml_tensor * src0_row = dst->src[row_id + 2];
-
             int64_t num_src1_rows = 0;
             for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
                 const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
@@ -9497,7 +2023,7 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s
                 GGML_ASSERT(row_id >= 0 && row_id < n_as);
 
                 CUDA_CHECK(cudaMemcpyAsync(src1_contiguous.get() + num_src1_rows*nb11, src1_original + i01*nb11,
-                                        nb11, src1_kind, stream));
+                                        nb11, cudaMemcpyDeviceToDevice, stream));
                 num_src1_rows++;
             }
 
@@ -9505,6 +2031,8 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s
                 continue;
             }
 
+            src0_row.data = src0_original + row_id*src0->nb[2];
+
             src1_row.ne[1] = num_src1_rows;
             dst_row.ne[1] = num_src1_rows;
 
@@ -9516,7 +2044,7 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s
             dst_row.nb[2] = num_src1_rows*nb1;
             dst_row.nb[3] = num_src1_rows*nb1;
 
-            ggml_cuda_mul_mat(src0_row, &src1_row, &dst_row);
+            ggml_cuda_mul_mat(ctx, &src0_row, &src1_row, &dst_row);
 
             num_src1_rows = 0;
             for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
@@ -9529,950 +2057,306 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s
                 GGML_ASSERT(row_id >= 0 && row_id < n_as);
 
                 CUDA_CHECK(cudaMemcpyAsync(dst_original + i01*nb1, dst_contiguous.get() + num_src1_rows*nb1,
-                                        nb1, dst_kind, stream));
+                                        nb1, cudaMemcpyDeviceToDevice, stream));
                 num_src1_rows++;
             }
         }
     }
-
-    if (dst->backend == GGML_BACKEND_CPU) {
-        CUDA_CHECK(cudaStreamSynchronize(stream));
-    }
 }
 
-static void ggml_cuda_scale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_scale);
-}
-
-static void ggml_cuda_clamp(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_clamp);
-}
-
-static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    const int64_t ne = ggml_nelements(src0);
-    GGML_ASSERT(ne == ggml_nelements(src1));
-
-    GGML_ASSERT(src0->backend == GGML_BACKEND_GPU);
-    GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
-
-    GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
-    GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    GGML_ASSERT(src0->ne[3] == 1);
-
-    const int64_t nb00 = src0->nb[0];
-    const int64_t nb01 = src0->nb[1];
-    const int64_t nb02 = src0->nb[2];
-
-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    GGML_ASSERT(src1->ne[3] == 1);
-
-    const int64_t nb10 = src1->nb[0];
-    const int64_t nb11 = src1->nb[1];
-    const int64_t nb12 = src1->nb[2];
-
-    ggml_cuda_set_device(g_main_device);
-    cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
-
-    const ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
-    const ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-
-    char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
-    char * src1_ddc = (char *) src1_extra->data_device[g_main_device];
-
-    if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
-        ggml_cpy_f32_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
-    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
-        ggml_cpy_f32_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
-    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
-        ggml_cpy_f32_q8_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
-    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
-        ggml_cpy_f32_q4_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
-    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
-        ggml_cpy_f32_q4_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
-    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
-        ggml_cpy_f16_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
-    } else {
-        fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
-                ggml_type_name(src0->type), ggml_type_name(src1->type));
-        GGML_ASSERT(false);
+static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct ggml_tensor * dst) {
+    // why is this here instead of mul_mat?
+    if (dst->src[0] != nullptr && ggml_backend_buffer_is_cuda_split(dst->src[0]->buffer)) {
+        ggml_cuda_set_peer_access(dst->src[1]->ne[1], ctx.device);
     }
 
-    (void) dst;
-}
-
-static void ggml_cuda_dup(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    // TODO: why do we pass dst as src1 here?
-    ggml_cuda_cpy(src0, dst, nullptr);
-    (void) src1;
-}
-
-static void ggml_cuda_diag_mask_inf(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_diag_mask_inf);
-}
-
-static void ggml_cuda_soft_max(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_soft_max);
-}
-
-static void ggml_cuda_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rope);
-}
-
-static void ggml_cuda_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_alibi);
-}
-
-static void ggml_cuda_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_im2col);
-}
-
-static void ggml_cuda_sum_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(ggml_is_contiguous(src0));
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_sum_rows);
-}
-
-static void ggml_cuda_argsort(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(ggml_is_contiguous(src0));
-    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_argsort);
-}
-
-static void ggml_cuda_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    (void) src0;
-    (void) src1;
-    (void) dst;
-}
-
-static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) {
-    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
-
-    return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]);
-}
-
-void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) {
-    const int64_t nrows = ggml_nrows(tensor);
-
-    const int64_t ne0 = tensor->ne[0];
-
-    const size_t nb1 = tensor->nb[1];
-
-    ggml_backend_type backend = tensor->backend;
-    ggml_tensor_extra_gpu * extra = new struct ggml_tensor_extra_gpu;
-    memset(extra, 0, sizeof(*extra));
-
-    for (int id = 0; id < g_device_count; ++id) {
-        if (backend == GGML_BACKEND_GPU && id != g_main_device) {
-            continue;
-        }
-
-        ggml_cuda_set_device(id);
-
-        int64_t row_low, row_high;
-        if (backend == GGML_BACKEND_GPU) {
-            row_low = 0;
-            row_high = nrows;
-        } else if (backend == GGML_BACKEND_GPU_SPLIT) {
-            const int64_t rounding = get_row_rounding(tensor->type);
-
-            row_low = id == 0 ? 0 : nrows*g_tensor_split[id];
-            row_low -= row_low % rounding;
-
-            if (id == g_device_count - 1) {
-                row_high = nrows;
-            } else {
-                row_high = nrows*g_tensor_split[id + 1];
-                row_high -= row_high % rounding;
-            }
-        } else {
-            GGML_ASSERT(false);
-        }
-        if (row_low == row_high) {
-            continue;
-        }
-
-        int64_t nrows_split = row_high - row_low;
-
-        const size_t offset_split = row_low*nb1;
-        size_t size = ggml_nbytes_split(tensor, nrows_split);
-        const size_t original_size = size;
-
-        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
-        if (ne0 % MATRIX_ROW_PADDING != 0) {
-            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
-        }
-
-        char * buf;
-        CUDA_CHECK(cudaMalloc(&buf, size));
-        char * buf_host = (char *)data + offset_split;
-
-        // set padding to 0 to avoid possible NaN values
-        if (size > original_size) {
-            CUDA_CHECK(cudaMemset(buf + original_size, 0, size - original_size));
-        }
-
-        CUDA_CHECK(cudaMemcpy(buf, buf_host, original_size, cudaMemcpyHostToDevice));
-
-        extra->data_device[id] = buf;
-
-        if (backend == GGML_BACKEND_GPU_SPLIT) {
-            for (int64_t is = 0; is < MAX_STREAMS; ++is) {
-                CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id][is], cudaEventDisableTiming));
-            }
-        }
-    }
-
-    tensor->extra = extra;
-}
-
-void ggml_cuda_free_data(struct ggml_tensor * tensor) {
-    if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) ) {
-        return;
-    }
-
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
-
-    for (int id = 0; id < g_device_count; ++id) {
-        ggml_cuda_set_device(id);
-        if (extra->data_device[id] != nullptr) {
-            CUDA_CHECK(cudaFree(extra->data_device[id]));
-        }
-
-        for (int64_t is = 0; is < MAX_STREAMS; ++is) {
-            if (extra->events[id][is] != nullptr) {
-                CUDA_CHECK(cudaEventDestroy(extra->events[id][is]));
-            }
-        }
-    }
-
-    delete extra;
-}
-
-static ggml_tensor_extra_gpu * g_temp_tensor_extras = nullptr;
-static size_t g_temp_tensor_extra_index = 0;
-
-static ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() {
-    if (g_temp_tensor_extras == nullptr) {
-        g_temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_CUDA_MAX_NODES];
-    }
-
-    size_t alloc_index = g_temp_tensor_extra_index;
-    g_temp_tensor_extra_index = (g_temp_tensor_extra_index + 1) % GGML_CUDA_MAX_NODES;
-    ggml_tensor_extra_gpu * extra = &g_temp_tensor_extras[alloc_index];
-    memset(extra, 0, sizeof(*extra));
-
-    return extra;
-}
-
-static void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bool force_inplace, bool no_alloc) {
-    if (scratch && g_scratch_size == 0) {
-        return;
-    }
-
-    tensor->backend = GGML_BACKEND_GPU;
-
-    // recursively assign CUDA buffers until a compute tensor is found
-    if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_CPU) {
-        const ggml_op src0_op = tensor->src[0]->op;
-        if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW || src0_op == GGML_OP_PERMUTE) {
-            ggml_cuda_assign_buffers_impl(tensor->src[0], scratch, force_inplace, no_alloc);
-        }
-    }
-    if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_CPU) {
-        ggml_cuda_assign_buffers_impl(tensor->src[1], scratch, force_inplace, no_alloc);
-    }
-
-    if (scratch && no_alloc) {
-        return;
-    }
-
-    ggml_tensor_extra_gpu * extra;
-
-    const bool inplace = (tensor->src[0] != nullptr && tensor->src[0]->data == tensor->data) ||
-        tensor->op == GGML_OP_VIEW ||
-        force_inplace;
-    const size_t size = ggml_nbytes(tensor);
-
-    ggml_cuda_set_device(g_main_device);
-    if (inplace && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) {
-        ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra;
-        char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
-        size_t offset = 0;
-        if (tensor->op == GGML_OP_VIEW) {
-            memcpy(&offset, tensor->op_params, sizeof(size_t));
-        }
-        extra = ggml_cuda_alloc_temp_tensor_extra();
-        extra->data_device[g_main_device] = src0_ddc + offset;
-    } else if (tensor->op == GGML_OP_CPY) {
-        ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu * ) tensor->src[1]->extra;
-        void * src1_ddv = src1_extra->data_device[g_main_device];
-        extra = ggml_cuda_alloc_temp_tensor_extra();
-        extra->data_device[g_main_device] = src1_ddv;
-    } else if (scratch) {
-        GGML_ASSERT(size <= g_scratch_size);
-        if (g_scratch_offset + size > g_scratch_size) {
-            g_scratch_offset = 0;
-        }
-
-        char * data = (char *) g_scratch_buffer;
-        if (data == nullptr) {
-            CUDA_CHECK(cudaMalloc(&data, g_scratch_size));
-            g_scratch_buffer = data;
-        }
-        extra = ggml_cuda_alloc_temp_tensor_extra();
-        extra->data_device[g_main_device] = data + g_scratch_offset;
-
-        g_scratch_offset += size;
-
-        GGML_ASSERT(g_scratch_offset <= g_scratch_size);
-    } else { // allocate new buffers outside of scratch
-        void * data;
-        CUDA_CHECK(cudaMalloc(&data, size));
-        CUDA_CHECK(cudaMemset(data, 0, size));
-        extra = new ggml_tensor_extra_gpu;
-        memset(extra, 0, sizeof(*extra));
-        extra->data_device[g_main_device] = data;
-    }
-
-    tensor->extra = extra;
-}
-
-void ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset) {
-    if (g_scratch_size == 0) {
-        return;
-    }
-    if (g_scratch_buffer == nullptr) {
-        ggml_cuda_set_device(g_main_device);
-        CUDA_CHECK(cudaMalloc(&g_scratch_buffer, g_scratch_size));
-    }
-
-    ggml_tensor_extra_gpu * extra = ggml_cuda_alloc_temp_tensor_extra();
-
-    const bool inplace = tensor->view_src != nullptr;
-
-    if (inplace && (tensor->view_src->backend == GGML_BACKEND_GPU || tensor->view_src->backend == GGML_BACKEND_GPU_SPLIT)) {
-        ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->view_src->extra;
-        char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
-        size_t view_offset = 0;
-        if (tensor->op == GGML_OP_VIEW) {
-            memcpy(&view_offset, tensor->op_params, sizeof(size_t));
-        }
-        extra->data_device[g_main_device] = src0_ddc + view_offset;
-    } else {
-        extra->data_device[g_main_device] = (char *) g_scratch_buffer + offset;
-    }
-
-    tensor->extra = extra;
-}
-
-void ggml_cuda_copy_to_device(struct ggml_tensor * tensor) {
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
-    GGML_ASSERT(ggml_is_contiguous(tensor));
-
-    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
-    ggml_cuda_set_device(g_main_device);
-    CUDA_CHECK(cudaMemcpy(extra->data_device[g_main_device], tensor->data, ggml_nbytes(tensor), cudaMemcpyHostToDevice));
-}
-
-void ggml_cuda_assign_buffers(struct ggml_tensor * tensor) {
-    ggml_cuda_assign_buffers_impl(tensor, true, false, false);
-}
-
-void ggml_cuda_assign_buffers_no_alloc(struct ggml_tensor * tensor) {
-    ggml_cuda_assign_buffers_impl(tensor, true, false, true);
-}
-
-void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor) {
-    ggml_cuda_assign_buffers_impl(tensor, false, false, false);
-}
-
-void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor) {
-    ggml_cuda_assign_buffers_impl(tensor, false, true, false);
-}
-
-void ggml_cuda_set_main_device(const int main_device) {
-    if (main_device >= g_device_count) {
-        fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n",
-                main_device, g_device_count, g_main_device);
-        return;
-    }
-
-    if (g_main_device != main_device && g_device_count > 1) {
-        g_main_device = main_device;
-        cudaDeviceProp prop;
-        CUDA_CHECK(cudaGetDeviceProperties(&prop, g_main_device));
-        fprintf(stderr, "%s: using device %d (%s) as main device\n", __func__, g_main_device, prop.name);
-    }
-}
-
-void ggml_cuda_set_scratch_size(const size_t scratch_size) {
-    // this is a hack to not completely break llama.cpp when using multiple models or contexts simultaneously
-    // it still won't always work as expected, but it's better than nothing
-    if (scratch_size > g_scratch_size) {
-        ggml_cuda_free_scratch();
-    }
-    g_scratch_size = std::max(g_scratch_size, scratch_size);
-}
-
-void ggml_cuda_free_scratch() {
-    if (g_scratch_buffer == nullptr) {
-        return;
-    }
-
-    CUDA_CHECK(cudaFree(g_scratch_buffer));
-    g_scratch_buffer = nullptr;
-}
-
-bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
-    if (!g_cublas_loaded) return false;
-
-    ggml_cuda_func_t func;
-    const bool any_on_device = tensor->backend == GGML_BACKEND_GPU
-        || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT))
-        || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU);
-
-    if (!any_on_device && tensor->op != GGML_OP_MUL_MAT && tensor->op != GGML_OP_MUL_MAT_ID) {
-        return false;
-    }
-
-    if (tensor->op == GGML_OP_MUL_MAT) {
-        if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) {
-#ifndef NDEBUG
-            fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, tensor->name, tensor->src[0]->ne[3], tensor->src[1]->ne[3]);
-#endif
-            return false;
-        }
-    }
-
-    switch (tensor->op) {
+    switch (dst->op) {
         case GGML_OP_REPEAT:
-            func = ggml_cuda_repeat;
+            ggml_cuda_op_repeat(ctx, dst);
             break;
         case GGML_OP_GET_ROWS:
-            func = ggml_cuda_get_rows;
+            ggml_cuda_op_get_rows(ctx, dst);
             break;
         case GGML_OP_DUP:
-            func = ggml_cuda_dup;
+            ggml_cuda_dup(ctx, dst);
+            break;
+        case GGML_OP_CPY:
+            ggml_cuda_cpy(ctx, dst->src[0], dst->src[1]);
+            break;
+        case GGML_OP_CONT:
+            ggml_cuda_dup(ctx, dst);
             break;
         case GGML_OP_ADD:
-            func = ggml_cuda_add;
+            ggml_cuda_op_add(ctx, dst);
             break;
         case GGML_OP_ACC:
-            func = ggml_cuda_acc;
+            ggml_cuda_op_acc(ctx, dst);
             break;
         case GGML_OP_MUL:
-            func = ggml_cuda_mul;
+            ggml_cuda_op_mul(ctx, dst);
             break;
         case GGML_OP_DIV:
-            func = ggml_cuda_div;
+            ggml_cuda_op_div(ctx, dst);
             break;
         case GGML_OP_UNARY:
-            switch (ggml_get_unary_op(tensor)) {
+            switch (ggml_get_unary_op(dst)) {
                 case GGML_UNARY_OP_GELU:
-                    func = ggml_cuda_gelu;
+                    ggml_cuda_op_gelu(ctx, dst);
                     break;
                 case GGML_UNARY_OP_SILU:
-                    func = ggml_cuda_silu;
+                    ggml_cuda_op_silu(ctx, dst);
                     break;
                 case GGML_UNARY_OP_GELU_QUICK:
-                    func = ggml_cuda_gelu_quick;
+                    ggml_cuda_op_gelu_quick(ctx, dst);
                     break;
                 case GGML_UNARY_OP_TANH:
-                    func = ggml_cuda_tanh;
+                    ggml_cuda_op_tanh(ctx, dst);
                     break;
                 case GGML_UNARY_OP_RELU:
-                    func = ggml_cuda_relu;
+                    ggml_cuda_op_relu(ctx, dst);
+                    break;
+                case GGML_UNARY_OP_HARDSIGMOID:
+                    ggml_cuda_op_hardsigmoid(ctx, dst);
+                    break;
+                case GGML_UNARY_OP_HARDSWISH:
+                    ggml_cuda_op_hardswish(ctx, dst);
                     break;
                 default:
                     return false;
             }
             break;
         case GGML_OP_NORM:
-            func = ggml_cuda_norm;
+            ggml_cuda_op_norm(ctx, dst);
             break;
         case GGML_OP_GROUP_NORM:
-            func = ggml_cuda_group_norm;
+            ggml_cuda_op_group_norm(ctx, dst);
             break;
         case GGML_OP_CONCAT:
-            func = ggml_cuda_concat;
+            ggml_cuda_op_concat(ctx, dst);
             break;
         case GGML_OP_UPSCALE:
-            func = ggml_cuda_upscale;
+            ggml_cuda_op_upscale(ctx, dst);
             break;
         case GGML_OP_PAD:
-            func = ggml_cuda_pad;
+            ggml_cuda_op_pad(ctx, dst);
+            break;
+        case GGML_OP_ARANGE:
+            ggml_cuda_op_arange(ctx, dst);
+            break;
+        case GGML_OP_TIMESTEP_EMBEDDING:
+            ggml_cuda_op_timestep_embedding(ctx, dst);
             break;
         case GGML_OP_LEAKY_RELU:
-            func = ggml_cuda_leaky_relu;
+            ggml_cuda_op_leaky_relu(ctx, dst);
             break;
         case GGML_OP_RMS_NORM:
-            func = ggml_cuda_rms_norm;
+            ggml_cuda_op_rms_norm(ctx, dst);
             break;
         case GGML_OP_MUL_MAT:
-            if (!any_on_device && !ggml_cuda_can_mul_mat(tensor->src[0], tensor->src[1], tensor)) {
+            if (dst->src[0]->ne[3] != dst->src[1]->ne[3]) {
+                fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, dst->name, dst->src[0]->ne[3], dst->src[1]->ne[3]);
                 return false;
+            } else {
+                ggml_cuda_mul_mat(ctx, dst->src[0], dst->src[1], dst);
             }
-            func = ggml_cuda_mul_mat;
             break;
         case GGML_OP_MUL_MAT_ID:
-            if (!any_on_device && !ggml_cuda_can_mul_mat(tensor->src[2], tensor->src[1], tensor)) {
-                return false;
-            }
-            func = ggml_cuda_mul_mat_id;
+            ggml_cuda_mul_mat_id(ctx, dst);
             break;
         case GGML_OP_SCALE:
-            func = ggml_cuda_scale;
+            ggml_cuda_op_scale(ctx, dst);
             break;
         case GGML_OP_SQR:
-            func = ggml_cuda_sqr;
+            ggml_cuda_op_sqr(ctx, dst);
             break;
         case GGML_OP_CLAMP:
-            func = ggml_cuda_clamp;
-            break;
-        case GGML_OP_CPY:
-            func = ggml_cuda_cpy;
-            break;
-        case GGML_OP_CONT:
-            func = ggml_cuda_dup;
+            ggml_cuda_op_clamp(ctx, dst);
             break;
         case GGML_OP_NONE:
         case GGML_OP_RESHAPE:
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
         case GGML_OP_TRANSPOSE:
-            func = ggml_cuda_nop;
-            break;
+                break;
         case GGML_OP_DIAG_MASK_INF:
-            func = ggml_cuda_diag_mask_inf;
+            ggml_cuda_op_diag_mask_inf(ctx, dst);
             break;
         case GGML_OP_SOFT_MAX:
-            func = ggml_cuda_soft_max;
+            ggml_cuda_op_soft_max(ctx, dst);
             break;
         case GGML_OP_ROPE:
-            func = ggml_cuda_rope;
+            ggml_cuda_op_rope(ctx, dst);
             break;
         case GGML_OP_ALIBI:
-            func = ggml_cuda_alibi;
+            ggml_cuda_op_alibi(ctx, dst);
             break;
         case GGML_OP_IM2COL:
-            func = ggml_cuda_im2col;
+            ggml_cuda_op_im2col(ctx, dst);
+            break;
+        case GGML_OP_POOL_2D:
+            ggml_cuda_op_pool2d(ctx, dst);
             break;
         case GGML_OP_SUM_ROWS:
-            func = ggml_cuda_sum_rows;
+            ggml_cuda_op_sum_rows(ctx, dst);
             break;
         case GGML_OP_ARGSORT:
-            func = ggml_cuda_argsort;
+            ggml_cuda_op_argsort(ctx, dst);
             break;
         default:
             return false;
     }
 
-    if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT) {
-        ggml_cuda_set_peer_access(tensor->src[1]->ne[1]);
+    cudaError_t err = cudaGetLastError();
+    if (err != cudaSuccess) {
+        fprintf(stderr, "%s: %s failed\n", __func__, ggml_op_desc(dst));
+        CUDA_CHECK(err);
     }
 
-    if (params->ith != 0) {
-        return true;
-    }
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
-        return true;
-    }
-    func(tensor->src[0], tensor->src[1], tensor);
     return true;
 }
 
-int ggml_cuda_get_device_count() {
-    int device_count;
-    if (cudaGetDeviceCount(&device_count) != cudaSuccess) {
-        return 0;
-    }
-    return device_count;
-}
-
-void ggml_cuda_get_device_description(int device, char * description, size_t description_size) {
-    cudaDeviceProp prop;
-    CUDA_CHECK(cudaGetDeviceProperties(&prop, device));
-    snprintf(description, description_size, "%s", prop.name);
-}
-
 ////////////////////////////////////////////////////////////////////////////////
 
-// backend interface
-
-#define UNUSED GGML_UNUSED
-
-// cuda buffer
-
-struct ggml_backend_buffer_context_cuda {
-    int device;
-    void * dev_ptr = nullptr;
-    ggml_tensor_extra_gpu * temp_tensor_extras = nullptr;
-    size_t temp_tensor_extra_index = 0;
-
-    ggml_backend_buffer_context_cuda(int device, void * dev_ptr) : device(device), dev_ptr(dev_ptr) {}
-
-    ~ggml_backend_buffer_context_cuda() {
-        delete[] temp_tensor_extras;
-    }
-
-    ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() {
-        if (temp_tensor_extras == nullptr) {
-            temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_CUDA_MAX_NODES];
-        }
-
-        size_t alloc_index = temp_tensor_extra_index;
-        temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_CUDA_MAX_NODES;
-        ggml_tensor_extra_gpu * extra = &temp_tensor_extras[alloc_index];
-        memset(extra, 0, sizeof(*extra));
-
-        return extra;
-    }
-};
-
-static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) {
-    ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context;
-    CUDA_CHECK(cudaFree(ctx->dev_ptr));
-    delete ctx;
-}
-
-static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) {
-    ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context;
-    return ctx->dev_ptr;
-}
-
-static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
-    ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context;
-
-    if (tensor->view_src != NULL && tensor->view_offs == 0) {
-        assert(tensor->view_src->buffer->buft == buffer->buft);
-        tensor->backend = tensor->view_src->backend;
-        tensor->extra = tensor->view_src->extra;
-        return;
-    }
-
-    ggml_tensor_extra_gpu * extra = ctx->ggml_cuda_alloc_temp_tensor_extra();
-
-    extra->data_device[ctx->device] = tensor->data;
-
-    tensor->backend = GGML_BACKEND_GPU;
-    tensor->extra = extra;
-
-    if (ggml_is_quantized(tensor->type)) {
-        // initialize padding to 0 to avoid possible NaN values
-        int64_t row_low = 0;
-        int64_t row_high = ggml_nrows(tensor);
-        int64_t nrows_split = row_high - row_low;
-
-        size_t original_size = ggml_nbytes_split(tensor, nrows_split);
-        size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor);
-
-        if (padded_size > original_size && tensor->view_src == nullptr) {
-            CUDA_CHECK(cudaMemsetAsync((char *)tensor->data + original_size, 0, padded_size - original_size, g_cudaStreams[ctx->device][0]));
-        }
-    }
-
-    UNUSED(buffer);
-}
-
-static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
-
-    ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context;
-
-    ggml_cuda_set_device(ctx->device);
-    CUDA_CHECK(cudaDeviceSynchronize());
-
-    CUDA_CHECK(cudaMemcpy((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice));
-}
-
-static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
-
-    ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context;
-
-    ggml_cuda_set_device(ctx->device);
-    CUDA_CHECK(cudaDeviceSynchronize());
-
-    CUDA_CHECK(cudaMemcpy(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost));
-}
-
-static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
-    ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context;
-
-    ggml_cuda_set_device(ctx->device);
-    CUDA_CHECK(cudaDeviceSynchronize());
-
-    CUDA_CHECK(cudaMemset(ctx->dev_ptr, value, buffer->size));
-}
-
-static struct ggml_backend_buffer_i cuda_backend_buffer_interface = {
-    /* .free_buffer     = */ ggml_backend_cuda_buffer_free_buffer,
-    /* .get_base        = */ ggml_backend_cuda_buffer_get_base,
-    /* .init_tensor     = */ ggml_backend_cuda_buffer_init_tensor,
-    /* .set_tensor      = */ ggml_backend_cuda_buffer_set_tensor,
-    /* .get_tensor      = */ ggml_backend_cuda_buffer_get_tensor,
-    /* .cpy_tensor_from = */ NULL,
-    /* .cpy_tensor_to   = */ NULL,
-    /* .clear           = */ ggml_backend_cuda_buffer_clear,
-};
-
-// cuda buffer type
-
-static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
-    int device = (int) (intptr_t) buft->context;
-
-    ggml_cuda_set_device(device);
-
-    size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0
-
-    void * dev_ptr;
-    CUDA_CHECK(cudaMalloc(&dev_ptr, size));
-
-    ggml_backend_buffer_context_cuda * ctx = new ggml_backend_buffer_context_cuda(device, dev_ptr);
-
-    return ggml_backend_buffer_init(buft, cuda_backend_buffer_interface, ctx, size);
-}
-
-static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
-    return 128;
-
-    UNUSED(buft);
-}
-
-static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, ggml_tensor * tensor) {
-    int64_t row_low = 0;
-    int64_t row_high = ggml_nrows(tensor);
-    int64_t nrows_split = row_high - row_low;
-
-    size_t size = ggml_nbytes_split(tensor, nrows_split);
-
-    int64_t ne0 = tensor->ne[0];
-
-    if (ggml_is_quantized(tensor->type)) {
-        if (ne0 % MATRIX_ROW_PADDING != 0) {
-            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
-        }
-    }
-
-    return size;
-
-    UNUSED(buft);
-}
-
-static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
-    return ggml_backend_is_cuda(backend);
-
-    UNUSED(buft);
-}
-
-static ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface = {
-    /* .alloc_buffer     = */ ggml_backend_cuda_buffer_type_alloc_buffer,
-    /* .get_alignment    = */ ggml_backend_cuda_buffer_type_get_alignment,
-    /* .get_alloc_size   = */ ggml_backend_cuda_buffer_type_get_alloc_size,
-    /* .supports_backend = */ ggml_backend_cuda_buffer_type_supports_backend,
-    /* .is_host          = */ nullptr,
-};
-
-ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) {
-    static struct ggml_backend_buffer_type ggml_backend_cuda_buffer_types[GGML_CUDA_MAX_DEVICES];
-
-    static bool ggml_backend_cuda_buffer_type_initialized = false;
-
-    if (!ggml_backend_cuda_buffer_type_initialized) {
-        for (int i = 0; i < GGML_CUDA_MAX_DEVICES; i++) {
-            ggml_backend_cuda_buffer_types[i] = {
-                /* .iface    = */ ggml_backend_cuda_buffer_type_interface,
-                /* .context  = */ (ggml_backend_buffer_type_context_t) (intptr_t) i,
-            };
-        }
-        ggml_backend_cuda_buffer_type_initialized = true;
-    }
-
-    return &ggml_backend_cuda_buffer_types[device];
-}
-
-// host buffer type
-
-static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
-    ggml_cuda_host_free(buffer->context);
-}
-
-static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
-    void * ptr = ggml_cuda_host_malloc(size);
-
-    if (ptr == nullptr) {
-        // fallback to cpu buffer
-        return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size);
-    }
-
-    // FIXME: this is a hack to avoid having to implement a new buffer type
-    ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
-    buffer->buft = buft;
-    buffer->iface.free_buffer = ggml_backend_cuda_host_buffer_free_buffer;
-
-    return buffer;
-}
-
-ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() {
-    static struct ggml_backend_buffer_type ggml_backend_cuda_buffer_type_host = {
-        /* .iface    = */ {
-            /* .alloc_buffer     = */ ggml_backend_cuda_host_buffer_type_alloc_buffer,
-            /* .get_alignment    = */ ggml_backend_cpu_buffer_type()->iface.get_alignment,
-            /* .get_alloc_size   = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
-            /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend,
-            /* .is_host          = */ ggml_backend_cpu_buffer_type()->iface.is_host,
-        },
-        /* .context  = */ nullptr,
-    };
-
-    return &ggml_backend_cuda_buffer_type_host;
-}
-
 // backend
 
-struct ggml_backend_context_cuda {
-    int device;
-};
+GGML_CALL static const char * ggml_backend_cuda_name(ggml_backend_t backend) {
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
 
-static const char * ggml_backend_cuda_name(ggml_backend_t backend) {
-    return GGML_CUDA_NAME;
-
-    UNUSED(backend);
+    return cuda_ctx->name.c_str();
 }
 
-static void ggml_backend_cuda_free(ggml_backend_t backend) {
-    ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context;
+GGML_CALL static void ggml_backend_cuda_free(ggml_backend_t backend) {
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
 
     delete cuda_ctx;
     delete backend;
 }
 
-static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer_type(ggml_backend_t backend) {
-    ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context;
+GGML_CALL static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer_type(ggml_backend_t backend) {
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
 
     return ggml_backend_cuda_buffer_type(cuda_ctx->device);
 }
 
-static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
-    ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context;
+GGML_CALL static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
 
-    GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
 
-    CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, g_cudaStreams[cuda_ctx->device][0]));
+    CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, cuda_ctx->stream()));
 }
 
-static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
-    ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context;
+GGML_CALL static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
 
-    GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
 
-    CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[cuda_ctx->device][0]));
+    CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, cuda_ctx->stream()));
 }
 
-static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
-    ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context;
+GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const ggml_tensor * src, ggml_tensor * dst) {
+    GGML_ASSERT(ggml_backend_is_cuda(backend_src) || ggml_backend_is_cuda(backend_dst));
 
-    CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[cuda_ctx->device][0]));
+    ggml_backend_buffer_t buf_src = src->view_src ? src->view_src->buffer : src->buffer;
+    ggml_backend_buffer_t buf_dst = dst->view_src ? dst->view_src->buffer : dst->buffer;
 
-    UNUSED(backend);
+    if (!ggml_backend_buffer_is_cuda(src->buffer)) {
+        return false;
+    }
+
+    if (!ggml_backend_buffer_is_cuda(dst->buffer)) {
+        return false;
+    }
+
+    // device -> device
+    ggml_backend_cuda_context * cuda_ctx_src = (ggml_backend_cuda_context *)backend_src->context;
+    ggml_backend_cuda_context * cuda_ctx_dst = (ggml_backend_cuda_context *)backend_dst->context;
+
+    if (backend_src != backend_dst) {
+        ggml_backend_cuda_buffer_context * buf_ctx_src = (ggml_backend_cuda_buffer_context *)buf_src->context;
+        ggml_backend_cuda_buffer_context * buf_ctx_dst = (ggml_backend_cuda_buffer_context *)buf_dst->context;
+
+        GGML_ASSERT(cuda_ctx_src->device == buf_ctx_src->device);
+        GGML_ASSERT(cuda_ctx_dst->device == buf_ctx_dst->device);
+
+        // copy on src stream
+        if (cuda_ctx_src->device == cuda_ctx_dst->device) {
+            CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, cuda_ctx_dst->stream()));
+        } else {
+#ifdef GGML_CUDA_NO_PEER_COPY
+            return false;
+#else
+            CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, cuda_ctx_dst->device, src->data, cuda_ctx_src->device, ggml_nbytes(dst), cuda_ctx_src->stream()));
+#endif
+        }
+
+        // record event on src stream
+        if (!cuda_ctx_src->copy_event) {
+            ggml_cuda_set_device(cuda_ctx_src->device);
+            CUDA_CHECK(cudaEventCreateWithFlags(&cuda_ctx_src->copy_event, cudaEventDisableTiming));
+        }
+
+        CUDA_CHECK(cudaEventRecord(cuda_ctx_src->copy_event, cuda_ctx_src->stream()));
+
+        // wait on dst stream for the copy to complete
+        CUDA_CHECK(cudaStreamWaitEvent(cuda_ctx_dst->stream(), cuda_ctx_src->copy_event, 0));
+    } else {
+        // src and dst are on the same backend
+        CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, cuda_ctx_dst->stream()));
+    }
+    return true;
 }
 
-static ggml_backend_graph_plan_t ggml_backend_cuda_graph_plan_create(ggml_backend_t backend, ggml_cgraph * cgraph) {
-    GGML_ASSERT(!"not implemented");
+GGML_CALL static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
 
-    return nullptr;
+    CUDA_CHECK(cudaStreamSynchronize(cuda_ctx->stream()));
 
-    UNUSED(backend);
-    UNUSED(cgraph);
+    GGML_UNUSED(backend);
 }
 
-static void ggml_backend_cuda_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
-    GGML_ASSERT(!"not implemented");
+GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
 
-    UNUSED(backend);
-    UNUSED(plan);
-}
+    ggml_cuda_set_device(cuda_ctx->device);
 
-static void ggml_backend_cuda_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
-    GGML_ASSERT(!"not implemented");
-
-    UNUSED(backend);
-    UNUSED(plan);
-}
-
-static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
-    ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context;
-
-    ggml_cuda_set_main_device(cuda_ctx->device);
-
-    ggml_compute_params params = {};
-    params.type = GGML_TASK_COMPUTE;
-    params.ith = 0;
     for (int i = 0; i < cgraph->n_nodes; i++) {
         ggml_tensor * node = cgraph->nodes[i];
 
-        if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE)
+        if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
             continue;
-
-        assert(node->backend == GGML_BACKEND_GPU);
-        assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
-        assert(node->extra != nullptr);
-
-        for (int j = 0; j < GGML_MAX_SRC; j++) {
-            if (node->src[j] != nullptr) {
-                assert(node->src[j]->backend == GGML_BACKEND_GPU);
-                assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
-                assert(node->src[j]->extra != nullptr);
-            }
         }
 
-        bool ok = ggml_cuda_compute_forward(&params, node);
+#ifndef NDEBUG
+        assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
+        for (int j = 0; j < GGML_MAX_SRC; j++) {
+            if (node->src[j] != nullptr) {
+                assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || ggml_backend_buffer_is_cuda_split(node->src[j]->buffer));
+            }
+        }
+#endif
+
+        bool ok = ggml_cuda_compute_forward(*cuda_ctx, node);
         if (!ok) {
             fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
         }
         GGML_ASSERT(ok);
-
-#if 0
-        if (node->type == GGML_TYPE_F32) {
-            cudaDeviceSynchronize();
-            std::vector<float> tmp(ggml_nelements(node), 0.0f);
-            cudaMemcpy(tmp.data(), node->data, ggml_nelements(node)*sizeof(float), cudaMemcpyDeviceToHost);
-            printf("\n%s (%s) (%s %s) (%s %s): ", node->name, ggml_op_name(node->op),
-                ggml_type_name(node->src[0]->type),
-                node->src[1] ? ggml_type_name(node->src[1]->type) : "none",
-                node->src[0]->name,
-                node->src[1] ? node->src[1]->name : "none");
-            double sum = 0.0;
-            double sq_sum = 0.0;
-            for (int i = 0; i < ggml_nelements(node); i++) {
-                printf("%f ", tmp[i]);
-                sum += tmp[i];
-                sq_sum += tmp[i]*tmp[i];
-            }
-            printf("\n");
-            printf("sum: %f, ", sum);
-            printf("sq_sum: %f\n", sq_sum);
-        }
-#endif
     }
 
-    UNUSED(backend);
-
-    return true;
+    return GGML_STATUS_SUCCESS;
 }
 
-static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
+GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
     switch (op->op) {
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(op)) {
                 case GGML_UNARY_OP_GELU:
                 case GGML_UNARY_OP_SILU:
                 case GGML_UNARY_OP_RELU:
+                case GGML_UNARY_OP_HARDSIGMOID:
+                case GGML_UNARY_OP_HARDSWISH:
                 case GGML_UNARY_OP_GELU_QUICK:
                 case GGML_UNARY_OP_TANH:
                     return true;
@@ -10495,6 +2379,14 @@ static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_ten
                 if (a->ne[3] != b->ne[3]) {
                     return false;
                 }
+                ggml_type a_type = a->type;
+                if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS ||
+                    a_type == GGML_TYPE_IQ1_S   || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S   ||
+                    a_type == GGML_TYPE_IQ1_M   || a_type == GGML_TYPE_IQ2_S  || a_type == GGML_TYPE_IQ4_XS) {
+                    if (b->ne[1] == 1 && ggml_nrows(b) > 1) {
+                        return false;
+                    }
+                }
                 return true;
             } break;
         case GGML_OP_GET_ROWS:
@@ -10531,9 +2423,21 @@ static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_ten
                 if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_1) {
                     return true;
                 }
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q5_0) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q5_1) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_IQ4_NL) {
+                    return true;
+                }
                 if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
                     return true;
                 }
+                if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
+                    return true;
+                }
                 return false;
             } break;
         case GGML_OP_DUP:
@@ -10562,75 +2466,193 @@ static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_ten
         case GGML_OP_ROPE:
         case GGML_OP_ALIBI:
         case GGML_OP_IM2COL:
+        case GGML_OP_POOL_2D:
         case GGML_OP_SUM_ROWS:
         case GGML_OP_ARGSORT:
         case GGML_OP_ACC:
         case GGML_OP_GROUP_NORM:
         case GGML_OP_UPSCALE:
         case GGML_OP_PAD:
+        case GGML_OP_ARANGE:
+        case GGML_OP_TIMESTEP_EMBEDDING:
         case GGML_OP_LEAKY_RELU:
             return true;
         default:
             return false;
     }
 
-    UNUSED(backend);
+    GGML_UNUSED(backend);
 }
 
-static ggml_backend_i cuda_backend_i = {
+GGML_CALL static bool ggml_backend_cuda_offload_op(ggml_backend_t backend, const ggml_tensor * op) {
+    const int min_batch_size = 32;
+
+    return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS;
+
+    GGML_UNUSED(backend);
+}
+
+static ggml_backend_event_t ggml_backend_cuda_event_new(ggml_backend_t backend) {
+#ifdef GGML_CUDA_NO_PEER_COPY
+    return nullptr;
+#else
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+
+    ggml_cuda_set_device(cuda_ctx->device);
+
+    cudaEvent_t event;
+    CUDA_CHECK(cudaEventCreateWithFlags(&event, cudaEventDisableTiming));
+
+    return new ggml_backend_event {
+        /* .backend = */ backend,
+        /* .context = */ event,
+    };
+#endif
+}
+
+static void ggml_backend_cuda_event_free(ggml_backend_event_t event) {
+    CUDA_CHECK(cudaEventDestroy((cudaEvent_t)event->context));
+
+    delete event;
+}
+
+static void ggml_backend_cuda_event_record(ggml_backend_event_t event) {
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)event->backend->context;
+
+    CUDA_CHECK(cudaEventRecord((cudaEvent_t)event->context, cuda_ctx->stream()));
+}
+
+static void ggml_backend_cuda_event_wait(ggml_backend_t backend, ggml_backend_event_t event) {
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+
+    if (ggml_backend_is_cuda(event->backend)) {
+        CUDA_CHECK(cudaStreamWaitEvent(cuda_ctx->stream(), (cudaEvent_t)event->context, 0));
+    } else {
+#if 0
+        // untested
+        auto wait_fn = [](void * user_data) {
+            ggml_backend_event_t event = (ggml_backend_event_t)user_data;
+            ggml_backend_event_synchronize(event);
+        };
+
+        CUDA_CHECK(cudaLaunchHostFunc(cuda_ctx->stream(), wait_fn, event));
+#endif
+        GGML_ASSERT(false);
+    }
+}
+
+static void ggml_backend_cuda_event_synchronize(ggml_backend_event_t event) {
+    CUDA_CHECK(cudaEventSynchronize((cudaEvent_t)event->context));
+}
+
+static ggml_backend_i ggml_backend_cuda_interface = {
     /* .get_name                = */ ggml_backend_cuda_name,
     /* .free                    = */ ggml_backend_cuda_free,
     /* .get_default_buffer_type = */ ggml_backend_cuda_get_default_buffer_type,
     /* .set_tensor_async        = */ ggml_backend_cuda_set_tensor_async,
     /* .get_tensor_async        = */ ggml_backend_cuda_get_tensor_async,
-    /* .cpy_tensor_from_async   = */ NULL,
-    /* .cpy_tensor_to_async     = */ NULL,
+    /* .cpy_tensor_async        = */ ggml_backend_cuda_cpy_tensor_async,
     /* .synchronize             = */ ggml_backend_cuda_synchronize,
-    /* .graph_plan_create       = */ ggml_backend_cuda_graph_plan_create,
-    /* .graph_plan_free         = */ ggml_backend_cuda_graph_plan_free,
-    /* .graph_plan_compute      = */ ggml_backend_cuda_graph_plan_compute,
+    /* .graph_plan_create       = */ NULL,
+    /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_compute      = */ NULL,
     /* .graph_compute           = */ ggml_backend_cuda_graph_compute,
     /* .supports_op             = */ ggml_backend_cuda_supports_op,
+    /* .offload_op              = */ ggml_backend_cuda_offload_op,
+    /* .event_new               = */ ggml_backend_cuda_event_new,
+    /* .event_free              = */ ggml_backend_cuda_event_free,
+    /* .event_record            = */ ggml_backend_cuda_event_record,
+    /* .event_wait              = */ ggml_backend_cuda_event_wait,
+    /* .event_synchronize       = */ ggml_backend_cuda_event_synchronize,
 };
 
-ggml_backend_t ggml_backend_cuda_init(int device) {
-    ggml_init_cublas(); // TODO: remove from ggml.c
+static ggml_guid_t ggml_backend_cuda_guid() {
+    static ggml_guid guid = { 0x2c, 0xdd, 0xe8, 0x1c, 0x65, 0xb3, 0x65, 0x73, 0x6a, 0x12, 0x88, 0x61, 0x1c, 0xc9, 0xdc, 0x25 };
+    return &guid;
+}
 
-    if (device < 0 || device >= ggml_cuda_get_device_count()) {
+GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) {
+    if (device < 0 || device >= ggml_backend_cuda_get_device_count()) {
         fprintf(stderr, "%s: error: invalid device %d\n", __func__, device);
         return nullptr;
     }
 
-    // not strictly necessary, but it may reduce the overhead of the first graph_compute
-    ggml_cuda_set_main_device(device);
-
-    ggml_backend_context_cuda * ctx = new ggml_backend_context_cuda {
-        /* .device = */ device
-    };
+    ggml_backend_cuda_context * ctx = new ggml_backend_cuda_context(device);
+    if (ctx == nullptr) {
+        fprintf(stderr, "%s: error: failed to allocate context\n", __func__);
+        return nullptr;
+    }
 
     ggml_backend_t cuda_backend = new ggml_backend {
-        /* .interface = */ cuda_backend_i,
+        /* .guid      = */ ggml_backend_cuda_guid(),
+        /* .interface = */ ggml_backend_cuda_interface,
         /* .context   = */ ctx
     };
 
     return cuda_backend;
 }
 
-bool ggml_backend_is_cuda(ggml_backend_t backend) {
-    return backend->iface.get_name == ggml_backend_cuda_name;
+GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend) {
+    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_cuda_guid());
 }
 
-static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, void * user_data) {
+GGML_CALL int ggml_backend_cuda_get_device_count() {
+    return ggml_cuda_info().device_count;
+}
+
+GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) {
+    cudaDeviceProp prop;
+    CUDA_CHECK(cudaGetDeviceProperties(&prop, device));
+    snprintf(description, description_size, "%s", prop.name);
+}
+
+GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total) {
+    ggml_cuda_set_device(device);
+
+    CUDA_CHECK(cudaMemGetInfo(free, total));
+}
+
+GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size) {
+    if (getenv("GGML_CUDA_REGISTER_HOST") == nullptr) {
+        return false;
+    }
+
+    cudaError_t err = cudaHostRegister(buffer, size, cudaHostRegisterPortable | cudaHostRegisterReadOnly);
+    if (err != cudaSuccess) {
+        // clear the error
+        cudaGetLastError();
+
+        fprintf(stderr, "%s: warning: failed to register %.2f MiB of pinned memory: %s\n", __func__,
+                size/1024.0/1024.0, cudaGetErrorString(err));
+        return false;
+    }
+    return true;
+}
+
+GGML_CALL void ggml_backend_cuda_unregister_host_buffer(void * buffer) {
+    if (getenv("GGML_CUDA_REGISTER_HOST") == nullptr) {
+        return;
+    }
+
+    cudaError_t err = cudaHostUnregister(buffer);
+    if (err != cudaSuccess) {
+        // clear the error
+        cudaGetLastError();
+    }
+}
+
+// backend registry
+GGML_CALL static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, void * user_data) {
     ggml_backend_t cuda_backend = ggml_backend_cuda_init((int) (intptr_t) user_data);
     return cuda_backend;
 
-    UNUSED(params);
+    GGML_UNUSED(params);
 }
 
-extern "C" int ggml_backend_cuda_reg_devices();
+extern "C" GGML_CALL int ggml_backend_cuda_reg_devices();
 
-int ggml_backend_cuda_reg_devices() {
-    int device_count = ggml_cuda_get_device_count();
+GGML_CALL int ggml_backend_cuda_reg_devices() {
+    int device_count = ggml_backend_cuda_get_device_count();
     //int device_count = 1; // DEBUG: some tools require delaying CUDA initialization
     for (int i = 0; i < device_count; i++) {
         char name[128];
diff --git a/ggml-cuda.h b/ggml-cuda.h
index cdb0c0c41..5eb4af40f 100644
--- a/ggml-cuda.h
+++ b/ggml-cuda.h
@@ -17,47 +17,26 @@ extern "C" {
 
 #define GGML_CUDA_MAX_DEVICES       16
 
-// Always success. To check if CUDA is actually loaded, use `ggml_cublas_loaded`.
-GGML_API void   ggml_init_cublas(void);
-
-// Returns `true` if there are available CUDA devices and cublas loads successfully; otherwise, it returns `false`.
-GGML_API bool   ggml_cublas_loaded(void);
-
-GGML_API void * ggml_cuda_host_malloc(size_t size);
-GGML_API void   ggml_cuda_host_free(void * ptr);
-
-GGML_API bool   ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-GGML_API void   ggml_cuda_set_tensor_split(const float * tensor_split);
-GGML_API void   ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
-GGML_API void   ggml_cuda_free_data(struct ggml_tensor * tensor);
-
-GGML_API void   ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
-GGML_API void   ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
-GGML_API void   ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
-
-GGML_API void   ggml_cuda_assign_buffers_no_alloc(struct ggml_tensor * tensor);
-GGML_API void   ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset);
-GGML_API void   ggml_cuda_copy_to_device(struct ggml_tensor * tensor);
-
-GGML_API void   ggml_cuda_set_main_device(int main_device);
-GGML_API void   ggml_cuda_set_mul_mat_q(bool mul_mat_q);
-GGML_API void   ggml_cuda_set_scratch_size(size_t scratch_size);
-GGML_API void   ggml_cuda_free_scratch(void);
-GGML_API bool   ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
-
-GGML_API int    ggml_cuda_get_device_count(void);
-GGML_API void   ggml_cuda_get_device_description(int device, char * description, size_t description_size);
-
 // backend API
-GGML_API ggml_backend_t ggml_backend_cuda_init(int device);
+GGML_API GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device);
 
-GGML_API bool ggml_backend_is_cuda(ggml_backend_t backend);
-GGML_API int  ggml_backend_cuda_get_device(ggml_backend_t backend);
+GGML_API GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend);
 
-GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
+// device buffer
+GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
 
-// pinned host buffer for use with CPU backend for faster copies between CPU and GPU
-GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
+// split tensor buffer that splits matrices by rows across multiple devices
+GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split);
+
+// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
+GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
+
+GGML_API GGML_CALL int  ggml_backend_cuda_get_device_count(void);
+GGML_API GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size);
+GGML_API GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total);
+
+GGML_API GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size);
+GGML_API GGML_CALL void ggml_backend_cuda_unregister_host_buffer(void * buffer);
 
 #ifdef  __cplusplus
 }
diff --git a/ggml-cuda/acc.cu b/ggml-cuda/acc.cu
new file mode 100644
index 000000000..96bfe1c9d
--- /dev/null
+++ b/ggml-cuda/acc.cu
@@ -0,0 +1,47 @@
+#include "acc.cuh"
+
+static __global__ void acc_f32(const float * x, const float * y, float * dst, const int ne,
+    const int ne10, const int ne11, const int ne12,
+    const int nb1, const int nb2, int offset) {
+    const int i = blockDim.x * blockIdx.x + threadIdx.x;
+    if (i >= ne) {
+        return;
+    }
+    int src1_idx = i - offset;
+    int oz = src1_idx / nb2;
+    int oy = (src1_idx - (oz * nb2)) / nb1;
+    int ox = src1_idx % nb1;
+    if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) {
+        dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11];
+    } else {
+        dst[i] = x[i];
+    }
+}
+
+static void acc_f32_cuda(const float * x, const float * y, float * dst, const int n_elements,
+    const int ne10, const int ne11, const int ne12,
+    const int nb1, const int nb2, const int offset, cudaStream_t stream) {
+    int num_blocks = (n_elements + CUDA_ACC_BLOCK_SIZE - 1) / CUDA_ACC_BLOCK_SIZE;
+    acc_f32<<<num_blocks, CUDA_ACC_BLOCK_SIZE, 0, stream>>>(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset);
+}
+
+void ggml_cuda_op_acc(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const float * src0_d = (const float *)src0->data;
+    const float * src1_d = (const float *)src1->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported
+
+    int nb1 = dst->op_params[0] / 4; // 4 bytes of float32
+    int nb2 = dst->op_params[1] / 4; // 4 bytes of float32
+    // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused
+    int offset = dst->op_params[3] / 4; // offset in bytes
+
+    acc_f32_cuda(src0_d, src1_d, dst_d, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, stream);
+}
diff --git a/ggml-cuda/acc.cuh b/ggml-cuda/acc.cuh
new file mode 100644
index 000000000..1168ea1b2
--- /dev/null
+++ b/ggml-cuda/acc.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_ACC_BLOCK_SIZE 256
+
+void ggml_cuda_op_acc(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/alibi.cu b/ggml-cuda/alibi.cu
new file mode 100644
index 000000000..6c7f1fd95
--- /dev/null
+++ b/ggml-cuda/alibi.cu
@@ -0,0 +1,63 @@
+#include "alibi.cuh"
+
+static __global__ void alibi_f32(const float * x, float * dst, const int ncols, const int k_rows,
+                                 const int n_heads_log2_floor, const float m0, const float m1) {
+    const int col = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int row = blockDim.y*blockIdx.y + threadIdx.y;
+    const int i = row*ncols + col;
+
+    const int k = row/k_rows;
+
+    float m_k;
+    if (k < n_heads_log2_floor) {
+        m_k = powf(m0, k + 1);
+    } else {
+        m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
+    }
+
+    dst[i] = col * m_k + x[i];
+}
+
+static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const int nrows,
+                           const int k_rows, const int n_heads_log2_floor, const float m0,
+                           const float m1, cudaStream_t stream) {
+    const dim3 block_dims(CUDA_ALIBI_BLOCK_SIZE, 1, 1);
+    const int num_blocks_x = (ncols + CUDA_ALIBI_BLOCK_SIZE - 1) / (CUDA_ALIBI_BLOCK_SIZE);
+    const dim3 block_nums(num_blocks_x, nrows, 1);
+    alibi_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, k_rows, n_heads_log2_floor, m0, m1);
+}
+
+void ggml_cuda_op_alibi(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+    const int64_t nrows = ggml_nrows(src0);
+
+    //const int n_past = ((int32_t *) dst->op_params)[0];
+    const int n_head = ((int32_t *) dst->op_params)[1];
+    float max_bias;
+    memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
+
+    //GGML_ASSERT(ne01 + n_past == ne00);
+    GGML_ASSERT(n_head == ne02);
+
+    const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
+
+    const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
+
+    alibi_f32_cuda(src0_d, dst_d, ne00, nrows, ne01, n_heads_log2_floor, m0, m1, stream);
+}
diff --git a/ggml-cuda/alibi.cuh b/ggml-cuda/alibi.cuh
new file mode 100644
index 000000000..630adfc7f
--- /dev/null
+++ b/ggml-cuda/alibi.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_ALIBI_BLOCK_SIZE 32
+
+void ggml_cuda_op_alibi(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/arange.cu b/ggml-cuda/arange.cu
new file mode 100644
index 000000000..b5e495a24
--- /dev/null
+++ b/ggml-cuda/arange.cu
@@ -0,0 +1,34 @@
+#include "arange.cuh"
+
+static __global__ void arange_f32(float * dst, const int ne0, const float start, const float step) {
+    // blockIDx.x: idx of ne0 / BLOCK_SIZE
+    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
+    if (nidx >= ne0) {
+        return;
+    }
+    dst[nidx] = start + step * nidx;
+}
+
+static void arange_f32_cuda(float * dst, const int ne0, const float start, const float step, cudaStream_t stream) {
+    int num_blocks = (ne0 + CUDA_ARANGE_BLOCK_SIZE - 1) / CUDA_ARANGE_BLOCK_SIZE;
+    arange_f32<<<num_blocks, CUDA_ARANGE_BLOCK_SIZE, 0, stream>>>(dst, ne0, start,  step);
+}
+
+void ggml_cuda_op_arange(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    float start;
+    float stop;
+    float step;
+    memcpy(&start, (float *)dst->op_params + 0, sizeof(float));
+    memcpy(&stop,  (float *)dst->op_params + 1, sizeof(float));
+    memcpy(&step,  (float *)dst->op_params + 2, sizeof(float));
+
+    int64_t steps = (int64_t)ceil((stop - start) / step);
+    GGML_ASSERT(ggml_nelements(dst) == steps);
+
+    arange_f32_cuda(dst_d, dst->ne[0], start, step, stream);
+}
diff --git a/ggml-cuda/arange.cuh b/ggml-cuda/arange.cuh
new file mode 100644
index 000000000..41e74fdfc
--- /dev/null
+++ b/ggml-cuda/arange.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_ARANGE_BLOCK_SIZE 256
+
+void ggml_cuda_op_arange(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/argsort.cu b/ggml-cuda/argsort.cu
new file mode 100644
index 000000000..164144061
--- /dev/null
+++ b/ggml-cuda/argsort.cu
@@ -0,0 +1,103 @@
+#include "argsort.cuh"
+
+template<typename T>
+static inline __device__ void ggml_cuda_swap(T & a, T & b) {
+    T tmp = a;
+    a = b;
+    b = tmp;
+}
+
+template<ggml_sort_order order>
+static __global__ void k_argsort_f32_i32(const float * x, int * dst, const int ncols, int ncols_pad) {
+    // bitonic sort
+    int col = threadIdx.x;
+    int row = blockIdx.y;
+
+    if (col >= ncols_pad) {
+        return;
+    }
+
+    const float * x_row = x + row * ncols;
+    extern __shared__ int dst_row[];
+
+    // initialize indices
+    dst_row[col] = col;
+
+    __syncthreads();
+
+    for (int k = 2; k <= ncols_pad; k *= 2) {
+        for (int j = k / 2; j > 0; j /= 2) {
+            int ixj = col ^ j;
+            if (ixj > col) {
+                if ((col & k) == 0) {
+                    if (dst_row[col] >= ncols ||
+                        (dst_row[ixj] < ncols && (order == GGML_SORT_ORDER_ASC ?
+                            x_row[dst_row[col]] > x_row[dst_row[ixj]] :
+                            x_row[dst_row[col]] < x_row[dst_row[ixj]]))
+                    ) {
+                        ggml_cuda_swap(dst_row[col], dst_row[ixj]);
+                    }
+                } else {
+                    if (dst_row[ixj] >= ncols ||
+                        (dst_row[col] < ncols && (order == GGML_SORT_ORDER_ASC ?
+                            x_row[dst_row[col]] < x_row[dst_row[ixj]] :
+                            x_row[dst_row[col]] > x_row[dst_row[ixj]]))
+                    ) {
+                        ggml_cuda_swap(dst_row[col], dst_row[ixj]);
+                    }
+                }
+            }
+            __syncthreads();
+        }
+    }
+
+    // copy the result to dst without the padding
+    if (col < ncols) {
+        dst[row * ncols + col] = dst_row[col];
+    }
+}
+
+static int next_power_of_2(int x) {
+    int n = 1;
+    while (n < x) {
+        n *= 2;
+    }
+    return n;
+}
+
+static void argsort_f32_i32_cuda(const float * x, int * dst, const int ncols, const int nrows, ggml_sort_order order, cudaStream_t stream) {
+    // bitonic sort requires ncols to be power of 2
+    const int ncols_pad = next_power_of_2(ncols);
+
+    const dim3 block_dims(ncols_pad, 1, 1);
+    const dim3 block_nums(1, nrows, 1);
+    const size_t shared_mem = ncols_pad * sizeof(int);
+
+    GGML_ASSERT(shared_mem <= ggml_cuda_info().devices[ggml_cuda_get_device()].smpb);
+
+    if (order == GGML_SORT_ORDER_ASC) {
+        k_argsort_f32_i32<GGML_SORT_ORDER_ASC><<<block_nums, block_dims, shared_mem, stream>>>(x, dst, ncols, ncols_pad);
+    } else if (order == GGML_SORT_ORDER_DESC) {
+        k_argsort_f32_i32<GGML_SORT_ORDER_DESC><<<block_nums, block_dims, shared_mem, stream>>>(x, dst, ncols, ncols_pad);
+    } else {
+        GGML_ASSERT(false);
+    }
+}
+
+void ggml_cuda_op_argsort(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_I32);
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
+    const int64_t ncols = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0];
+
+    argsort_f32_i32_cuda(src0_d, (int *)dst_d, ncols, nrows, order, stream);
+}
diff --git a/ggml-cuda/argsort.cuh b/ggml-cuda/argsort.cuh
new file mode 100644
index 000000000..68a001547
--- /dev/null
+++ b/ggml-cuda/argsort.cuh
@@ -0,0 +1,3 @@
+#include "common.cuh"
+
+void ggml_cuda_op_argsort(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/binbcast.cu b/ggml-cuda/binbcast.cu
new file mode 100644
index 000000000..959eaed95
--- /dev/null
+++ b/ggml-cuda/binbcast.cu
@@ -0,0 +1,236 @@
+#include "binbcast.cuh"
+
+static __device__ __forceinline__ float op_repeat(const float a, const float b) {
+    return b;
+    GGML_UNUSED(a);
+}
+
+static __device__ __forceinline__ float op_add(const float a, const float b) {
+    return a + b;
+}
+
+static __device__ __forceinline__ float op_mul(const float a, const float b) {
+    return a * b;
+}
+
+static __device__ __forceinline__ float op_div(const float a, const float b) {
+    return a / b;
+}
+
+template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
+static __global__ void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst,
+        int ne0, int ne1, int ne2, int ne3,
+        int ne10, int ne11, int ne12, int ne13,
+        /*int s0, */ int s1,  int s2,  int s3,
+        /*int s10,*/ int s11, int s12, int s13) {
+    const int i0s = blockDim.x*blockIdx.x + threadIdx.x;
+    const int i1 = (blockDim.y*blockIdx.y + threadIdx.y);
+    const int i2 = (blockDim.z*blockIdx.z + threadIdx.z) / ne3;
+    const int i3 = (blockDim.z*blockIdx.z + threadIdx.z) % ne3;
+
+    if (i0s >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
+        return;
+    }
+
+    const int i11 = i1 % ne11;
+    const int i12 = i2 % ne12;
+    const int i13 = i3 % ne13;
+
+    const size_t i_src0 = i3*s3 + i2*s2 + i1*s1;
+    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
+    const size_t i_dst  = i_src0;
+
+    const src0_t * src0_row = src0 + i_src0;
+    const src1_t * src1_row = src1 + i_src1;
+    dst_t * dst_row = dst + i_dst;
+
+    for (int i0 = i0s; i0 < ne0; i0 += blockDim.x*gridDim.x) {
+        const int i10 = i0 % ne10;
+        dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
+    }
+}
+
+template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
+static __global__ void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t * dst,
+        int ne0, int ne1, int ne2, int ne3,
+        int ne10, int ne11, int ne12, int ne13,
+        /*int s0, */ int s1,  int s2,  int s3,
+        /*int s10,*/ int s11, int s12, int s13) {
+
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    const int i3 = i/(ne2*ne1*ne0);
+    const int i2 = (i/(ne1*ne0)) % ne2;
+    const int i1 = (i/ne0) % ne1;
+    const int i0 = i % ne0;
+
+    if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
+        return;
+    }
+
+    const int i11 = i1 % ne11;
+    const int i12 = i2 % ne12;
+    const int i13 = i3 % ne13;
+
+    const size_t i_src0 = i3*s3 + i2*s2 + i1*s1;
+    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
+    const size_t i_dst  = i_src0;
+
+    const src0_t * src0_row = src0 + i_src0;
+    const src1_t * src1_row = src1 + i_src1;
+    dst_t * dst_row = dst + i_dst;
+
+    const int i10 = i0 % ne10;
+    dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
+}
+
+template<float (*bin_op)(const float, const float)>
+struct bin_bcast_cuda {
+    template<typename src0_t, typename src1_t, typename dst_t>
+    void operator()(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst,
+            const src0_t * src0_dd, const src1_t * src1_dd, dst_t * dst_dd,
+            cudaStream_t stream) {
+
+        GGML_TENSOR_BINARY_OP_LOCALS
+
+        int nr0 = ne10/ne0;
+        int nr1 = ne11/ne1;
+        int nr2 = ne12/ne2;
+        int nr3 = ne13/ne3;
+
+        int nr[4] = { nr0, nr1, nr2, nr3 };
+
+        // collapse dimensions until first broadcast dimension
+        int64_t cne0[] = {ne0, ne1, ne2, ne3};
+        int64_t cne1[] = {ne10, ne11, ne12, ne13};
+        size_t cnb0[] = {nb0, nb1, nb2, nb3};
+        size_t cnb1[] = {nb10, nb11, nb12, nb13};
+        auto collapse = [](int64_t cne[]) {
+            cne[0] *= cne[1];
+            cne[1] = cne[2];
+            cne[2] = cne[3];
+            cne[3] = 1;
+        };
+
+        auto collapse_nb = [](size_t cnb[], const int64_t cne[]) {
+            cnb[1] *= cne[1];
+            cnb[2] *= cne[2];
+            cnb[3] *= cne[3];
+        };
+
+        for (int i = 0; i < 4; i++) {
+            if (nr[i] != 1) {
+                break;
+            }
+            if (i > 0) {
+                collapse_nb(cnb0, cne0);
+                collapse_nb(cnb1, cne1);
+                collapse(cne0);
+                collapse(cne1);
+            }
+        }
+        {
+            int64_t ne0 = cne0[0];
+            int64_t ne1 = cne0[1];
+            int64_t ne2 = cne0[2];
+            int64_t ne3 = cne0[3];
+
+            int64_t ne10 = cne1[0];
+            int64_t ne11 = cne1[1];
+            int64_t ne12 = cne1[2];
+            int64_t ne13 = cne1[3];
+
+            size_t nb0 = cnb0[0];
+            size_t nb1 = cnb0[1];
+            size_t nb2 = cnb0[2];
+            size_t nb3 = cnb0[3];
+
+            size_t nb10 = cnb1[0];
+            size_t nb11 = cnb1[1];
+            size_t nb12 = cnb1[2];
+            size_t nb13 = cnb1[3];
+
+            size_t s0 = nb0 / sizeof(dst_t);
+            size_t s1 = nb1 / sizeof(dst_t);
+            size_t s2 = nb2 / sizeof(dst_t);
+            size_t s3 = nb3 / sizeof(dst_t);
+
+            size_t s10 = nb10 / sizeof(src1_t);
+            size_t s11 = nb11 / sizeof(src1_t);
+            size_t s12 = nb12 / sizeof(src1_t);
+            size_t s13 = nb13 / sizeof(src1_t);
+
+            GGML_ASSERT(s0 == 1);
+            GGML_ASSERT(s10 == 1);
+
+            const int block_size = 128;
+
+            int64_t hne0 = std::max(ne0/2LL, 1LL);
+
+            dim3 block_dims;
+            block_dims.x = std::min<unsigned int>(hne0, block_size);
+            block_dims.y = std::min<unsigned int>(ne1, block_size / block_dims.x);
+            block_dims.z = std::min(std::min<unsigned int>(ne2*ne3, block_size / block_dims.x / block_dims.y), 64U);
+
+            dim3 block_nums(
+                (hne0 + block_dims.x - 1) / block_dims.x,
+                (ne1 + block_dims.y - 1) / block_dims.y,
+                (ne2*ne3 + block_dims.z - 1) / block_dims.z
+            );
+
+            if (block_nums.z > 65535) {
+                // this is the maximum number of blocks in z direction, fallback to 1D grid kernel
+                int block_num = (ne0*ne1*ne2*ne3 + block_size - 1) / block_size;
+                k_bin_bcast_unravel<bin_op><<<block_num, block_size, 0, stream>>>(
+                    src0_dd, src1_dd, dst_dd,
+                    ne0, ne1, ne2, ne3,
+                    ne10, ne11, ne12, ne13,
+                    /* s0, */ s1, s2, s3,
+                    /* s10, */ s11, s12, s13);
+            } else {
+                k_bin_bcast<bin_op><<<block_nums, block_dims, 0, stream>>>(
+                    src0_dd, src1_dd, dst_dd,
+                    ne0, ne1, ne2, ne3,
+                    ne10, ne11, ne12, ne13,
+                    /* s0, */ s1, s2, s3,
+                    /* s10, */ s11, s12, s13);
+            }
+        }
+    }
+};
+
+template<class op>
+static void ggml_cuda_op_bin_bcast(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+    const void * src0_dd, const void * src1_dd, void * dst_dd, cudaStream_t stream) {
+
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+        op()(src0, src1, dst, (const float *)src0_dd, (const float *)src1_dd, (float *)dst_dd, stream);
+    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
+        op()(src0, src1, dst, (const half *) src0_dd, (const float *)src1_dd, (half *) dst_dd, stream);
+    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
+        op()(src0, src1, dst, (const half *) src0_dd, (const float *)src1_dd, (float *)dst_dd, stream);
+    } else {
+        fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__,
+            ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type));
+        GGML_ASSERT(false);
+    }
+}
+
+void ggml_cuda_op_repeat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_repeat>>(dst, dst->src[0], dst, nullptr, dst->src[0]->data, dst->data, ctx.stream());
+}
+
+void ggml_cuda_op_add(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_add>>(dst->src[0], dst->src[1], dst, dst->src[0]->data, dst->src[1]->data, dst->data, ctx.stream());
+}
+
+void ggml_cuda_op_mul(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_mul>>(dst->src[0], dst->src[1], dst, dst->src[0]->data, dst->src[1]->data, dst->data, ctx.stream());
+}
+
+void ggml_cuda_op_div(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_div>>(dst->src[0], dst->src[1], dst, dst->src[0]->data, dst->src[1]->data, dst->data, ctx.stream());
+}
diff --git a/ggml-cuda/binbcast.cuh b/ggml-cuda/binbcast.cuh
new file mode 100644
index 000000000..4f63d6372
--- /dev/null
+++ b/ggml-cuda/binbcast.cuh
@@ -0,0 +1,6 @@
+#include "common.cuh"
+
+void ggml_cuda_op_repeat(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+void ggml_cuda_op_add(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+void ggml_cuda_op_mul(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+void ggml_cuda_op_div(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/clamp.cu b/ggml-cuda/clamp.cu
new file mode 100644
index 000000000..379ded042
--- /dev/null
+++ b/ggml-cuda/clamp.cu
@@ -0,0 +1,35 @@
+#include "clamp.cuh"
+
+static __global__ void clamp_f32(const float * x, float * dst, const float min, const float max, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]);
+}
+
+static void clamp_f32_cuda(const float * x, float * dst, const float min, const float max, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_CLAMP_BLOCK_SIZE - 1) / CUDA_CLAMP_BLOCK_SIZE;
+    clamp_f32<<<num_blocks, CUDA_CLAMP_BLOCK_SIZE, 0, stream>>>(x, dst, min, max, k);
+}
+
+
+void ggml_cuda_op_clamp(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    float min;
+    float max;
+    memcpy(&min, dst->op_params, sizeof(float));
+    memcpy(&max, (float *) dst->op_params + 1, sizeof(float));
+
+    clamp_f32_cuda(src0_d, dst_d, min, max, ggml_nelements(src0), stream);
+    CUDA_CHECK(cudaGetLastError());
+}
diff --git a/ggml-cuda/clamp.cuh b/ggml-cuda/clamp.cuh
new file mode 100644
index 000000000..7f9559dd1
--- /dev/null
+++ b/ggml-cuda/clamp.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_CLAMP_BLOCK_SIZE 256
+
+void ggml_cuda_op_clamp(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/common.cuh b/ggml-cuda/common.cuh
new file mode 100644
index 000000000..b98d7cbd0
--- /dev/null
+++ b/ggml-cuda/common.cuh
@@ -0,0 +1,551 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-cuda.h"
+
+#include <memory>
+
+#if defined(GGML_USE_HIPBLAS)
+#define GGML_COMMON_DECL_HIP
+#define GGML_COMMON_IMPL_HIP
+#else
+#define GGML_COMMON_DECL_CUDA
+#define GGML_COMMON_IMPL_CUDA
+#endif
+#include "ggml-common.h"
+
+#include <cstdio>
+#include <array>
+#include <cassert>
+#include <cfloat>
+#include <string>
+
+#if defined(GGML_USE_HIPBLAS)
+#include <hip/hip_runtime.h>
+#include <hipblas/hipblas.h>
+#include <hip/hip_fp16.h>
+#ifdef __HIP_PLATFORM_AMD__
+// for rocblas_initialize()
+#include "rocblas/rocblas.h"
+#endif // __HIP_PLATFORM_AMD__
+#define CUBLAS_COMPUTE_16F HIPBLAS_R_16F
+#define CUBLAS_COMPUTE_32F HIPBLAS_R_32F
+#define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_R_32F
+#define CUBLAS_GEMM_DEFAULT HIPBLAS_GEMM_DEFAULT
+#define CUBLAS_GEMM_DEFAULT_TENSOR_OP HIPBLAS_GEMM_DEFAULT
+#define CUBLAS_OP_N HIPBLAS_OP_N
+#define CUBLAS_OP_T HIPBLAS_OP_T
+#define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
+#define CUBLAS_TF32_TENSOR_OP_MATH 0
+#define CUDA_R_16F  HIPBLAS_R_16F
+#define CUDA_R_32F  HIPBLAS_R_32F
+#define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
+#define cublasComputeType_t hipblasDatatype_t //deprecated, new hipblasComputeType_t not in 5.6
+#define cublasCreate hipblasCreate
+#define cublasDestroy hipblasDestroy
+#define cublasGemmEx hipblasGemmEx
+#define cublasGemmBatchedEx hipblasGemmBatchedEx
+#define cublasGemmStridedBatchedEx hipblasGemmStridedBatchedEx
+#define cublasHandle_t hipblasHandle_t
+#define cublasSetMathMode(handle, mode) CUBLAS_STATUS_SUCCESS
+#define cublasSetStream hipblasSetStream
+#define cublasSgemm hipblasSgemm
+#define cublasStatus_t hipblasStatus_t
+#define cudaDataType_t hipblasDatatype_t //deprecated, new hipblasDatatype not in 5.6
+#define cudaDeviceCanAccessPeer hipDeviceCanAccessPeer
+#define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess
+#define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess
+#define cudaDeviceProp hipDeviceProp_t
+#define cudaDeviceSynchronize hipDeviceSynchronize
+#define cudaError_t hipError_t
+#define cudaErrorPeerAccessAlreadyEnabled hipErrorPeerAccessAlreadyEnabled
+#define cudaErrorPeerAccessNotEnabled hipErrorPeerAccessNotEnabled
+#define cudaEventCreateWithFlags hipEventCreateWithFlags
+#define cudaEventDisableTiming hipEventDisableTiming
+#define cudaEventRecord hipEventRecord
+#define cudaEventSynchronize hipEventSynchronize
+#define cudaEvent_t hipEvent_t
+#define cudaEventDestroy hipEventDestroy
+#define cudaFree hipFree
+#define cudaFreeHost hipHostFree
+#define cudaGetDevice hipGetDevice
+#define cudaGetDeviceCount hipGetDeviceCount
+#define cudaGetDeviceProperties hipGetDeviceProperties
+#define cudaGetErrorString hipGetErrorString
+#define cudaGetLastError hipGetLastError
+#define cudaHostRegister hipHostRegister
+#define cudaHostRegisterPortable hipHostRegisterPortable
+#define cudaHostRegisterReadOnly hipHostRegisterReadOnly
+#define cudaHostUnregister hipHostUnregister
+#define cudaLaunchHostFunc hipLaunchHostFunc
+#ifdef GGML_HIP_UMA
+#define cudaMalloc hipMallocManaged
+#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size)
+#else
+#define cudaMalloc hipMalloc
+#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault)
+#endif
+#define cudaMemcpy hipMemcpy
+#define cudaMemcpyAsync hipMemcpyAsync
+#define cudaMemcpyPeerAsync hipMemcpyPeerAsync
+#define cudaMemcpy2DAsync hipMemcpy2DAsync
+#define cudaMemcpyDeviceToDevice hipMemcpyDeviceToDevice
+#define cudaMemcpyDeviceToHost hipMemcpyDeviceToHost
+#define cudaMemcpyHostToDevice hipMemcpyHostToDevice
+#define cudaMemcpyKind hipMemcpyKind
+#define cudaMemset hipMemset
+#define cudaMemsetAsync hipMemsetAsync
+#define cudaMemGetInfo hipMemGetInfo
+#define cudaOccupancyMaxPotentialBlockSize hipOccupancyMaxPotentialBlockSize
+#define cudaSetDevice hipSetDevice
+#define cudaStreamCreateWithFlags hipStreamCreateWithFlags
+#define cudaStreamDestroy hipStreamDestroy
+#define cudaStreamFireAndForget hipStreamFireAndForget
+#define cudaStreamNonBlocking hipStreamNonBlocking
+#define cudaStreamPerThread hipStreamPerThread
+#define cudaStreamSynchronize hipStreamSynchronize
+#define cudaStreamWaitEvent(stream, event, flags) hipStreamWaitEvent(stream, event, flags)
+#define cudaStream_t hipStream_t
+#define cudaSuccess hipSuccess
+#define __trap abort
+#define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
+#define CUBLAS_STATUS_NOT_INITIALIZED HIPBLAS_STATUS_NOT_INITIALIZED
+#define CUBLAS_STATUS_ALLOC_FAILED HIPBLAS_STATUS_ALLOC_FAILED
+#define CUBLAS_STATUS_INVALID_VALUE HIPBLAS_STATUS_INVALID_VALUE
+#define CUBLAS_STATUS_ARCH_MISMATCH HIPBLAS_STATUS_ARCH_MISMATCH
+#define CUBLAS_STATUS_MAPPING_ERROR HIPBLAS_STATUS_MAPPING_ERROR
+#define CUBLAS_STATUS_EXECUTION_FAILED HIPBLAS_STATUS_EXECUTION_FAILED
+#define CUBLAS_STATUS_INTERNAL_ERROR HIPBLAS_STATUS_INTERNAL_ERROR
+#define CUBLAS_STATUS_NOT_SUPPORTED HIPBLAS_STATUS_NOT_SUPPORTED
+#else
+#include <cuda_runtime.h>
+#include <cuda.h>
+#include <cublas_v2.h>
+#include <cuda_fp16.h>
+
+#if CUDART_VERSION < 11020
+#define CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED CU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED
+#define CUBLAS_TF32_TENSOR_OP_MATH CUBLAS_TENSOR_OP_MATH
+#define CUBLAS_COMPUTE_16F CUDA_R_16F
+#define CUBLAS_COMPUTE_32F CUDA_R_32F
+#define cublasComputeType_t cudaDataType_t
+#endif // CUDART_VERSION < 11020
+
+#endif // defined(GGML_USE_HIPBLAS)
+
+#define STRINGIZE_IMPL(...) #__VA_ARGS__
+#define STRINGIZE(...) STRINGIZE_IMPL(__VA_ARGS__)
+
+#define WARP_SIZE 32
+#define CUDART_HMAX     11070 // CUDA 11.7, min. ver. for which __hmax and __hmax2 are known to work (may be higher than needed)
+
+#define CC_PASCAL     600
+#define MIN_CC_DP4A   610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
+#define CC_VOLTA      700
+#define CC_OFFSET_AMD 1000000
+#define CC_RDNA1      (CC_OFFSET_AMD + 1010)
+#define CC_RDNA2      (CC_OFFSET_AMD + 1030)
+#define CC_RDNA3      (CC_OFFSET_AMD + 1100)
+
+// define this if you want to always fallback to MMQ kernels and not use cuBLAS for matrix multiplication
+// on modern hardware, using cuBLAS is recommended as it utilizes F16 tensor cores which are very performant
+// for large computational tasks. the drawback is that this requires some extra amount of VRAM:
+// -  7B quantum model: +100-200 MB
+// - 13B quantum model: +200-400 MB
+//
+//#define GGML_CUDA_FORCE_MMQ
+
+// TODO: improve this to be correct for more hardware
+//       for example, currently fails for GeForce GTX 1660 which is TURING arch (> VOLTA) but does not have tensor cores
+#if !defined(GGML_CUDA_FORCE_MMQ)
+#define CUDA_USE_TENSOR_CORES
+#endif
+
+#define MMVQ_MAX_BATCH_SIZE  8 // max batch size to use MMVQ kernels
+#define  MMQ_MAX_BATCH_SIZE 32 // max batch size to use MMQ kernels when tensor cores are available
+
+#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+#define GGML_CUDA_MAX_STREAMS 8
+
+[[noreturn]]
+void ggml_cuda_error(const char * stmt, const char * func, const char * file, int line, const char * msg);
+
+#define CUDA_CHECK_GEN(err, success, error_fn)                                      \
+     do {                                                                           \
+        auto err_ = (err);                                                          \
+        if (err_ != (success)) {                                                    \
+            ggml_cuda_error(#err, __func__, __FILE__, __LINE__, error_fn(err_));    \
+        }                                                                           \
+    } while (0)
+
+#define CUDA_CHECK(err) CUDA_CHECK_GEN(err, cudaSuccess, cudaGetErrorString)
+
+#if CUDART_VERSION >= 12000
+    static const char * cublas_get_error_str(const cublasStatus_t err) {
+        return cublasGetStatusString(err);
+    }
+#else
+    static const char * cublas_get_error_str(const cublasStatus_t err) {
+        switch (err) {
+            case CUBLAS_STATUS_SUCCESS: return "CUBLAS_STATUS_SUCCESS";
+            case CUBLAS_STATUS_NOT_INITIALIZED: return "CUBLAS_STATUS_NOT_INITIALIZED";
+            case CUBLAS_STATUS_ALLOC_FAILED: return "CUBLAS_STATUS_ALLOC_FAILED";
+            case CUBLAS_STATUS_INVALID_VALUE: return "CUBLAS_STATUS_INVALID_VALUE";
+            case CUBLAS_STATUS_ARCH_MISMATCH: return "CUBLAS_STATUS_ARCH_MISMATCH";
+            case CUBLAS_STATUS_MAPPING_ERROR: return "CUBLAS_STATUS_MAPPING_ERROR";
+            case CUBLAS_STATUS_EXECUTION_FAILED: return "CUBLAS_STATUS_EXECUTION_FAILED";
+            case CUBLAS_STATUS_INTERNAL_ERROR: return "CUBLAS_STATUS_INTERNAL_ERROR";
+            case CUBLAS_STATUS_NOT_SUPPORTED: return "CUBLAS_STATUS_NOT_SUPPORTED";
+            default: return "unknown error";
+        }
+    }
+#endif // CUDART_VERSION >= 12000
+
+#define CUBLAS_CHECK(err) CUDA_CHECK_GEN(err, CUBLAS_STATUS_SUCCESS, cublas_get_error_str)
+
+#if !defined(GGML_USE_HIPBLAS)
+static const char * cu_get_error_str(CUresult err) {
+    const char * err_str;
+    cuGetErrorString(err, &err_str);
+    return err_str;
+}
+#define CU_CHECK(err) CUDA_CHECK_GEN(err, CUDA_SUCCESS, cu_get_error_str)
+#endif
+
+#if CUDART_VERSION >= 11100
+#define GGML_CUDA_ASSUME(x) __builtin_assume(x)
+#else
+#define GGML_CUDA_ASSUME(x)
+#endif // CUDART_VERSION >= 11100
+
+#ifdef GGML_CUDA_F16
+typedef half dfloat; // dequantize float
+typedef half2 dfloat2;
+#else
+typedef float dfloat; // dequantize float
+typedef float2 dfloat2;
+#endif //GGML_CUDA_F16
+
+[[noreturn]]
+static __device__ void no_device_code(
+    const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+    printf("%s:%d: ERROR: HIP kernel %s has no device code compatible with HIP arch %d.\n",
+           file_name, line, function_name, arch);
+    GGML_UNUSED(arch_list);
+#else
+    printf("%s:%d: ERROR: CUDA kernel %s has no device code compatible with CUDA arch %d. ggml-cuda.cu was compiled for: %s\n",
+           file_name, line, function_name, arch, arch_list);
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+    __trap();
+
+    GGML_UNUSED(no_device_code); // suppress unused function warning
+}
+
+#ifdef __CUDA_ARCH__
+#define NO_DEVICE_CODE no_device_code(__FILE__, __LINE__, __FUNCTION__, __CUDA_ARCH__, STRINGIZE(__CUDA_ARCH_LIST__))
+#else
+#define NO_DEVICE_CODE //GGML_ASSERT(false && "NO_DEVICE_CODE not valid in host code.")
+#endif // __CUDA_ARCH__
+
+static __device__ __forceinline__ float warp_reduce_sum(float x) {
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        x += __shfl_xor_sync(0xffffffff, x, mask, 32);
+    }
+    return x;
+}
+
+static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32);
+        a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32);
+    }
+    return a;
+}
+
+#ifdef GGML_CUDA_F16
+static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
+#pragma unroll
+   for (int mask = 16; mask > 0; mask >>= 1) {
+       a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32));
+   }
+   return a;
+#else
+   GGML_UNUSED(a);
+   NO_DEVICE_CODE;
+#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
+}
+#endif // GGML_CUDA_F16
+
+static __device__ __forceinline__ float warp_reduce_max(float x) {
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
+    }
+    return x;
+}
+
+//static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
+//#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX
+//#pragma unroll
+//    for (int mask = 16; mask > 0; mask >>= 1) {
+//        x = __hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
+//    }
+//    return x;
+//#else
+//    GGML_UNUSED(x);
+//    NO_DEVICE_CODE;
+//#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX
+//}
+
+
+#if defined(GGML_USE_HIPBLAS)
+#define __CUDA_ARCH__ 1300
+
+#if defined(__gfx1100__) || defined(__gfx1101__) || defined(__gfx1102__) || defined(__gfx1103__) || \
+    defined(__gfx1150__) || defined(__gfx1151__)
+#define RDNA3
+#endif
+
+#if defined(__gfx1030__) || defined(__gfx1031__) || defined(__gfx1032__) || defined(__gfx1033__) || \
+    defined(__gfx1034__) || defined(__gfx1035__) || defined(__gfx1036__) || defined(__gfx1037__)
+#define RDNA2
+#endif
+
+#ifndef __has_builtin
+    #define __has_builtin(x) 0
+#endif
+
+typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
+typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4)));
+static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
+    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
+    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
+#if __has_builtin(__builtin_elementwise_sub_sat)
+    const int8x4_t c = __builtin_elementwise_sub_sat(va, vb);
+    return reinterpret_cast<const int &>(c);
+#else
+    int8x4_t c;
+    int16_t tmp;
+#pragma unroll
+    for (int i = 0; i < 4; i++) {
+        tmp = va[i] - vb[i];
+        if(tmp > std::numeric_limits<int8_t>::max()) tmp = std::numeric_limits<int8_t>::max();
+        if(tmp < std::numeric_limits<int8_t>::min()) tmp = std::numeric_limits<int8_t>::min();
+        c[i] = tmp;
+    }
+    return reinterpret_cast<int &>(c);
+#endif // __has_builtin(__builtin_elementwise_sub_sat)
+}
+
+static __device__ __forceinline__ int __vsub4(const int a, const int b) {
+    return __vsubss4(a, b);
+}
+
+static __device__ __forceinline__ unsigned int __vcmpeq4(unsigned int a, unsigned int b) {
+    const uint8x4_t& va = reinterpret_cast<const uint8x4_t&>(a);
+    const uint8x4_t& vb = reinterpret_cast<const uint8x4_t&>(b);
+    unsigned int c;
+    uint8x4_t& vc = reinterpret_cast<uint8x4_t&>(c);
+#pragma unroll
+    for (int i = 0; i < 4; ++i) {
+        vc[i] = va[i] == vb[i] ? 0xff : 0x00;
+    }
+    return c;
+}
+
+static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
+#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
+    c = __builtin_amdgcn_sdot4(a, b, c, false);
+#elif defined(RDNA3)
+    c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
+#elif defined(__gfx1010__) || defined(__gfx900__)
+    int tmp1;
+    int tmp2;
+    asm("\n \
+        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \
+        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \
+        v_add3_u32 %0, %1, %2, %0 \n \
+        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \
+        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \
+        v_add3_u32 %0, %1, %2, %0 \n \
+        "
+        : "+v"(c), "=&v"(tmp1), "=&v"(tmp2)
+        : "v"(a), "v"(b)
+    );
+#else
+    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
+    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
+    c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3];
+#endif
+    return c;
+}
+#endif // defined(GGML_USE_HIPBLAS)
+
+// TODO: move to ggml-common.h
+static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
+
+typedef void (*dequantize_kernel_t)(const void * vx, const int ib, const int iqs, dfloat2 & v);
+
+
+//////////////////////
+
+struct ggml_cuda_device_info {
+    int device_count;
+
+    struct cuda_device_info {
+        int     cc;                 // compute capability
+        size_t  smpb;               // max. shared memory per block
+        bool    vmm;                // virtual memory support
+        size_t  vmm_granularity;    // granularity of virtual memory
+        size_t  total_vram;
+    };
+
+    cuda_device_info devices[GGML_CUDA_MAX_DEVICES] = {};
+
+    std::array<float, GGML_CUDA_MAX_DEVICES> default_tensor_split = {};
+};
+
+const ggml_cuda_device_info & ggml_cuda_info();
+
+void ggml_cuda_set_device(int device);
+int ggml_cuda_get_device();
+
+struct ggml_cuda_pool {
+    virtual ~ggml_cuda_pool() = default;
+
+    virtual void * alloc(size_t size, size_t * actual_size) = 0;
+    virtual void free(void * ptr, size_t size) = 0;
+};
+
+template<typename T>
+struct ggml_cuda_pool_alloc {
+    ggml_cuda_pool * pool = nullptr;
+    T * ptr = nullptr;
+    size_t actual_size = 0;
+
+    ggml_cuda_pool_alloc() = default;
+
+    explicit ggml_cuda_pool_alloc(ggml_cuda_pool & pool) : pool(&pool) {
+    }
+
+    ggml_cuda_pool_alloc(ggml_cuda_pool & pool, size_t size) : pool(&pool) {
+        alloc(size);
+    }
+
+    ~ggml_cuda_pool_alloc() {
+        if (ptr != nullptr) {
+            pool->free(ptr, actual_size);
+        }
+    }
+
+    // size is in number of elements
+    T * alloc(size_t size) {
+        GGML_ASSERT(pool != nullptr);
+        GGML_ASSERT(ptr == nullptr);
+        ptr = (T *) pool->alloc(size * sizeof(T), &this->actual_size);
+        return ptr;
+    }
+
+    T * alloc(ggml_cuda_pool & pool, size_t size) {
+        this->pool = &pool;
+        return alloc(size);
+    }
+
+    T * get() {
+        return ptr;
+    }
+
+    ggml_cuda_pool_alloc(const ggml_cuda_pool_alloc &) = delete;
+    ggml_cuda_pool_alloc(ggml_cuda_pool_alloc &&) = delete;
+    ggml_cuda_pool_alloc& operator=(const ggml_cuda_pool_alloc &) = delete;
+    ggml_cuda_pool_alloc& operator=(ggml_cuda_pool_alloc &&) = delete;
+};
+
+
+// backend interface
+
+struct ggml_tensor_extra_gpu {
+    void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors
+    cudaEvent_t events[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS]; // events for synchronizing multiple GPUs
+};
+
+struct ggml_backend_cuda_context {
+    int device;
+    std::string name;
+    cudaEvent_t copy_event = nullptr;
+
+    cudaStream_t streams[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS] = { { nullptr } };
+    cublasHandle_t cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};
+
+    explicit ggml_backend_cuda_context(int device) :
+        device(device),
+        name(GGML_CUDA_NAME + std::to_string(device)) {
+    }
+
+    ~ggml_backend_cuda_context() {
+        if (copy_event != nullptr) {
+            CUDA_CHECK(cudaEventDestroy(copy_event));
+        }
+        for (int i = 0; i < GGML_CUDA_MAX_DEVICES; ++i) {
+            for (int j = 0; j < GGML_CUDA_MAX_STREAMS; ++j) {
+                if (streams[i][j] != nullptr) {
+                    CUDA_CHECK(cudaStreamDestroy(streams[i][j]));
+                }
+            }
+            if (cublas_handles[i] != nullptr) {
+                CUBLAS_CHECK(cublasDestroy(cublas_handles[i]));
+            }
+        }
+    }
+
+    cudaStream_t stream(int device, int stream) {
+        if (streams[device][stream] == nullptr) {
+            ggml_cuda_set_device(device);
+            CUDA_CHECK(cudaStreamCreateWithFlags(&streams[device][stream], cudaStreamNonBlocking));
+        }
+        return streams[device][stream];
+    }
+
+    cudaStream_t stream() {
+        return stream(device, 0);
+    }
+
+    cublasHandle_t cublas_handle(int device) {
+        if (cublas_handles[device] == nullptr) {
+            ggml_cuda_set_device(device);
+            CUBLAS_CHECK(cublasCreate(&cublas_handles[device]));
+            CUBLAS_CHECK(cublasSetMathMode(cublas_handles[device], CUBLAS_TF32_TENSOR_OP_MATH));
+        }
+        return cublas_handles[device];
+    }
+
+    cublasHandle_t cublas_handle() {
+        return cublas_handle(device);
+    }
+
+    // pool
+    std::unique_ptr<ggml_cuda_pool> pools[GGML_CUDA_MAX_DEVICES];
+
+    static std::unique_ptr<ggml_cuda_pool> new_pool_for_device(int device);
+
+    ggml_cuda_pool & pool(int device) {
+        if (pools[device] == nullptr) {
+            pools[device] = new_pool_for_device(device);
+        }
+        return *pools[device];
+    }
+
+    ggml_cuda_pool & pool() {
+        return pool(device);
+    }
+};
diff --git a/ggml-cuda/concat.cu b/ggml-cuda/concat.cu
new file mode 100644
index 000000000..2941d2f17
--- /dev/null
+++ b/ggml-cuda/concat.cu
@@ -0,0 +1,49 @@
+#include "concat.cuh"
+
+static __global__ void concat_f32(const float * x,const float * y, float * dst, const int ne0, const int ne02) {
+    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
+    if (nidx >= ne0) {
+        return;
+    }
+    // operation
+    int offset_dst =
+        nidx +
+        blockIdx.y * ne0 +
+        blockIdx.z * ne0 * gridDim.y;
+    if (blockIdx.z < ne02) { // src0
+        int offset_src =
+            nidx +
+            blockIdx.y * ne0 +
+            blockIdx.z * ne0 * gridDim.y;
+        dst[offset_dst] = x[offset_src];
+    } else {
+        int offset_src =
+            nidx +
+            blockIdx.y * ne0 +
+            (blockIdx.z - ne02) * ne0 *  gridDim.y;
+        dst[offset_dst] = y[offset_src];
+    }
+}
+
+static void concat_f32_cuda(const float * x, const float * y, float * dst, const int ne0, int ne1, int ne2, int ne02, cudaStream_t stream) {
+    int num_blocks = (ne0 + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE;
+    dim3 gridDim(num_blocks, ne1, ne2);
+    concat_f32<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne02);
+}
+
+void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const float * src0_d = (const float *)src0->data;
+    const float * src1_d = (const float *)src1->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    for (int i3 = 0; i3 < dst->ne[3]; i3++) {
+        concat_f32_cuda(src0_d + i3 * (src0->nb[3] / 4), src1_d + i3 * (src1->nb[3] / 4), dst_d + i3 * (dst->nb[3] / 4), dst->ne[0], dst->ne[1], dst->ne[2], src0->ne[2], stream);
+    }
+}
diff --git a/ggml-cuda/concat.cuh b/ggml-cuda/concat.cuh
new file mode 100644
index 000000000..aa506a05f
--- /dev/null
+++ b/ggml-cuda/concat.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_CONCAT_BLOCK_SIZE 256
+
+void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/convert.cu b/ggml-cuda/convert.cu
new file mode 100644
index 000000000..18a31edc3
--- /dev/null
+++ b/ggml-cuda/convert.cu
@@ -0,0 +1,824 @@
+#include "convert.cuh"
+#include "dequantize.cuh"
+
+#define CUDA_Q8_0_NE_ALIGN 2048
+
+template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
+static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) {
+    const int i = 2*(blockDim.x*blockIdx.x + threadIdx.x);
+
+    if (i >= k) {
+        return;
+    }
+
+    const int ib = i/qk; // block index
+    const int iqs = (i%qk)/qr; // quant index
+    const int iybs = i - i%qk; // y block start index
+    const int y_offset = qr == 1 ? 1 : qk/2;
+
+    // dequantize
+    dfloat2 v;
+    dequantize_kernel(vx, ib, iqs, v);
+
+    y[iybs + iqs + 0]        = v.x;
+    y[iybs + iqs + y_offset] = v.y;
+}
+
+template <bool need_check>
+static __global__ void dequantize_block_q8_0_f16(const void * __restrict__ vx, half * __restrict__ y, const int k) {
+#if __CUDA_ARCH__ >= CC_PASCAL
+    constexpr int nint = CUDA_Q8_0_NE_ALIGN/sizeof(int) + WARP_SIZE;
+
+    const int   i0 = CUDA_Q8_0_NE_ALIGN*blockIdx.x;
+    const int * x0 = ((int *) vx) + blockIdx.x * nint;
+    half2 * y2 = (half2 *) (y + i0);
+
+    __shared__ int vals[nint];
+
+#pragma unroll
+    for (int ix0 = 0; ix0 < nint; ix0 += WARP_SIZE) {
+        if (need_check && i0*sizeof(block_q8_0)/QK8_0 + sizeof(int)*(ix0 + threadIdx.x) >= k*sizeof(block_q8_0)/QK8_0) {
+            break;
+        }
+
+        const int ix = ix0 + threadIdx.x;
+        vals[ix] = x0[ix];
+    }
+
+#pragma unroll
+    for (int iy = 0; iy < CUDA_Q8_0_NE_ALIGN; iy += 2*WARP_SIZE) {
+        if (need_check && i0 + iy + 2*threadIdx.x >= k) {
+            return;
+        }
+
+        const half * b0 = ((const half  *) vals) + (sizeof(block_q8_0)/sizeof(half)) * ((iy + 2*threadIdx.x)/QK8_0);
+        const half    d = *b0;
+        const char2  qs = ((const char2 *) (b0 + 1))[threadIdx.x % (QK8_0/2)];
+
+        y2[iy/2 + threadIdx.x] = __hmul2(make_half2(qs.x, qs.y), __half2half2(d));
+    }
+#else
+    GGML_UNUSED(vx);
+    GGML_UNUSED(y);
+    GGML_UNUSED(k);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_PASCAL
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_q4_0(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32) {
+
+    const int i = blockIdx.x;
+
+    // assume 32 threads
+    const int tid = threadIdx.x;
+    const int il  = tid/8;
+    const int ir  = tid%8;
+    const int ib = 8*i + ir;
+    if (ib >= nb32) {
+        return;
+    }
+
+    dst_t * y = yy + 256*i + 32*ir + 4*il;
+
+    const block_q4_0 * x = (const block_q4_0 *)vx + ib;
+    const float d = __half2float(x->d);
+    const float dm = -8*d;
+
+    const uint8_t * q = x->qs + 4*il;
+
+    for (int l = 0; l < 4; ++l) {
+        y[l+ 0] = d * (q[l] & 0xF) + dm;
+        y[l+16] = d * (q[l] >>  4) + dm;
+    }
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_q4_1(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32) {
+
+    const int i = blockIdx.x;
+
+    // assume 32 threads
+    const int tid = threadIdx.x;
+    const int il  = tid/8;
+    const int ir  = tid%8;
+    const int ib = 8*i + ir;
+    if (ib >= nb32) {
+        return;
+    }
+
+    dst_t * y = yy + 256*i + 32*ir + 4*il;
+
+    const block_q4_1 * x = (const block_q4_1 *)vx + ib;
+    const float2 d = __half22float2(x->dm);
+
+    const uint8_t * q = x->qs + 4*il;
+
+    for (int l = 0; l < 4; ++l) {
+        y[l+ 0] = d.x * (q[l] & 0xF) + d.y;
+        y[l+16] = d.x * (q[l] >>  4) + d.y;
+    }
+}
+
+//================================== k-quants
+
+template<typename dst_t>
+static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int i   = blockIdx.x;
+    const block_q2_K * x = (const block_q2_K *) vx;
+
+    const int tid = threadIdx.x;
+#if QK_K == 256
+    const int n   = tid/32;
+    const int l   = tid - 32*n;
+    const int is  = 8*n + l/16;
+
+    const uint8_t q = x[i].qs[32*n + l];
+    dst_t * y = yy + i*QK_K + 128*n;
+
+    float dall = __low2half(x[i].dm);
+    float dmin = __high2half(x[i].dm);
+    y[l+ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
+    y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4);
+    y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4);
+    y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4);
+#else
+    const int is = tid/16;  // 0 or 1
+    const int il = tid%16;  // 0...15
+    const uint8_t q = x[i].qs[il] >> (2*is);
+    dst_t * y = yy + i*QK_K + 16*is + il;
+    float dall = __low2half(x[i].dm);
+    float dmin = __high2half(x[i].dm);
+    y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
+    y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4);
+#endif
+
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int i = blockIdx.x;
+    const block_q3_K * x = (const block_q3_K *) vx;
+
+#if QK_K == 256
+    const int r = threadIdx.x/4;
+    const int tid = r/2;
+    const int is0 = r%2;
+    const int l0 = 16*is0 + 4*(threadIdx.x%4);
+    const int n = tid / 4;
+    const int j = tid - 4*n;
+
+    uint8_t m = 1 << (4*n + j);
+    int is = 8*n + 2*j + is0;
+    int shift = 2*j;
+
+    int8_t us = is <  4 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+8] >> 0) & 3) << 4) :
+                is <  8 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+4] >> 2) & 3) << 4) :
+                is < 12 ? (x[i].scales[is-8] >>  4) | (((x[i].scales[is+0] >> 4) & 3) << 4) :
+                          (x[i].scales[is-8] >>  4) | (((x[i].scales[is-4] >> 6) & 3) << 4);
+    float d_all = x[i].d;
+    float dl = d_all * (us - 32);
+
+    dst_t * y = yy + i*QK_K + 128*n + 32*j;
+    const uint8_t * q = x[i].qs + 32*n;
+    const uint8_t * hm = x[i].hmask;
+
+    for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
+#else
+    const int tid = threadIdx.x;
+    const int is  = tid/16;  // 0 or 1
+    const int il  = tid%16;  // 0...15
+    const int im  = il/8;    // 0...1
+    const int in  = il%8;    // 0...7
+
+    dst_t * y = yy + i*QK_K + 16*is + il;
+
+    const uint8_t q = x[i].qs[il] >> (2*is);
+    const uint8_t h = x[i].hmask[in] >> (2*is + im);
+    const float   d = (float)x[i].d;
+
+    if (is == 0) {
+        y[ 0] = d * ((x[i].scales[0] & 0xF) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
+        y[32] = d * ((x[i].scales[1] & 0xF) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
+    } else {
+        y[ 0] = d * ((x[i].scales[0] >>  4) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
+        y[32] = d * ((x[i].scales[1] >>  4) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
+    }
+#endif
+
+}
+
+#if QK_K == 256
+static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
+    if (j < 4) {
+        d = q[j] & 63; m = q[j + 4] & 63;
+    } else {
+        d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
+        m = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
+    }
+}
+#endif
+
+template<typename dst_t>
+static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const block_q4_K * x = (const block_q4_K *) vx;
+
+    const int i = blockIdx.x;
+
+#if QK_K == 256
+    // assume 32 threads
+    const int tid = threadIdx.x;
+    const int il  = tid/8;
+    const int ir  = tid%8;
+    const int is  = 2*il;
+    const int n   = 4;
+
+    dst_t * y = yy + i*QK_K + 64*il + n*ir;
+
+    const float dall = __low2half(x[i].dm);
+    const float dmin = __high2half(x[i].dm);
+
+    const uint8_t * q = x[i].qs + 32*il + n*ir;
+
+    uint8_t sc, m;
+    get_scale_min_k4(is + 0, x[i].scales, sc, m);
+    const float d1 = dall * sc; const float m1 = dmin * m;
+    get_scale_min_k4(is + 1, x[i].scales, sc, m);
+    const float d2 = dall * sc; const float m2 = dmin * m;
+    for (int l = 0; l < n; ++l) {
+        y[l + 0] = d1 * (q[l] & 0xF) - m1;
+        y[l +32] = d2 * (q[l] >>  4) - m2;
+    }
+#else
+    const int tid = threadIdx.x;
+    const uint8_t * q = x[i].qs;
+    dst_t * y = yy + i*QK_K;
+    const float d = (float)x[i].dm[0];
+    const float m = (float)x[i].dm[1];
+    y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4);
+    y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >>  4) - m * (x[i].scales[1] >> 4);
+#endif
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const block_q5_K * x = (const block_q5_K *) vx;
+
+    const int i = blockIdx.x;
+
+#if QK_K == 256
+    // assume 64 threads - this is very slightly better than the one below
+    const int tid = threadIdx.x;
+    const int il  = tid/16;   // il is in 0...3
+    const int ir  = tid%16;   // ir is in 0...15
+    const int is  = 2*il;     // is is in 0...6
+
+    dst_t * y = yy + i*QK_K + 64*il + 2*ir;
+
+    const float dall = __low2half(x[i].dm);
+    const float dmin = __high2half(x[i].dm);
+
+    const uint8_t * ql = x[i].qs + 32*il + 2*ir;
+    const uint8_t * qh = x[i].qh + 2*ir;
+
+    uint8_t sc, m;
+    get_scale_min_k4(is + 0, x[i].scales, sc, m);
+    const float d1 = dall * sc; const float m1 = dmin * m;
+    get_scale_min_k4(is + 1, x[i].scales, sc, m);
+    const float d2 = dall * sc; const float m2 = dmin * m;
+
+    uint8_t   hm  = 1 << (2*il);
+    y[ 0] = d1 * ((ql[ 0] & 0xF) + (qh[ 0] & hm ? 16 : 0)) - m1;
+    y[ 1] = d1 * ((ql[ 1] & 0xF) + (qh[ 1] & hm ? 16 : 0)) - m1;
+    hm <<= 1;
+    y[32] = d2 * ((ql[ 0] >>  4) + (qh[ 0] & hm ? 16 : 0)) - m2;
+    y[33] = d2 * ((ql[ 1] >>  4) + (qh[ 1] & hm ? 16 : 0)) - m2;
+#else
+    const int tid = threadIdx.x;
+    const uint8_t q = x[i].qs[tid];
+    const int im = tid/8;  // 0...3
+    const int in = tid%8;  // 0...7
+    const int is = tid/16; // 0 or 1
+    const uint8_t h = x[i].qh[in] >> im;
+    const float d = x[i].d;
+    dst_t * y = yy + i*QK_K + tid;
+    y[ 0] = d * x[i].scales[is+0] * ((q & 0xF) - ((h >> 0) & 1 ? 0 : 16));
+    y[32] = d * x[i].scales[is+2] * ((q >>  4) - ((h >> 4) & 1 ? 0 : 16));
+#endif
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const block_q6_K * x = (const block_q6_K *) vx;
+
+    const int i = blockIdx.x;
+#if QK_K == 256
+
+    // assume 64 threads - this is very slightly better than the one below
+    const int tid = threadIdx.x;
+    const int ip  = tid/32;   // ip is 0 or 1
+    const int il  = tid - 32*ip; // 0...32
+    const int is  = 8*ip + il/16;
+
+    dst_t * y = yy + i*QK_K + 128*ip + il;
+
+    const float d = x[i].d;
+
+    const uint8_t * ql = x[i].ql + 64*ip + il;
+    const uint8_t   qh = x[i].qh[32*ip + il];
+    const int8_t  * sc = x[i].scales + is;
+
+    y[ 0] = d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
+    y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32);
+    y[64] = d * sc[4] * ((int8_t)((ql[ 0]  >> 4) | (((qh >> 4) & 3) << 4)) - 32);
+    y[96] = d * sc[6] * ((int8_t)((ql[32]  >> 4) | (((qh >> 6) & 3) << 4)) - 32);
+#else
+
+    // assume 32 threads
+    const int tid = threadIdx.x;
+    const int ip  = tid/16;         // 0 or 1
+    const int il  = tid - 16*ip;    // 0...15
+
+    dst_t * y = yy + i*QK_K + 16*ip + il;
+
+    const float d = x[i].d;
+
+    const uint8_t   ql = x[i].ql[16*ip + il];
+    const uint8_t   qh = x[i].qh[il] >> (2*ip);
+    const int8_t  * sc = x[i].scales;
+
+    y[ 0] = d * sc[ip+0] * ((int8_t)((ql & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
+    y[32] = d * sc[ip+2] * ((int8_t)((ql  >> 4) | (((qh >> 4) & 3) << 4)) - 32);
+#endif
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int i   = blockIdx.x;
+    const block_iq2_xxs * x = (const block_iq2_xxs  *) vx;
+
+    const int tid = threadIdx.x;
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint16_t * q2 = x[i].qs + 4*ib;
+    const uint8_t  * aux8 = (const uint8_t *)q2;
+    const uint8_t  * grid = (const uint8_t *)(iq2xxs_grid + aux8[il]);
+    const uint32_t aux32 = q2[2] | (q2[3] << 16);
+    const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f;
+    const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
+    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
+#else
+    NO_DEVICE_CODE;
+#endif
+
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int i   = blockIdx.x;
+    const block_iq2_xs * x = (const block_iq2_xs *) vx;
+
+    const int tid = threadIdx.x;
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint16_t * q2 = x[i].qs + 4*ib;
+    const uint8_t  * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511));
+    const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
+    const uint8_t signs = ksigns_iq2xs[q2[il] >> 9];
+    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
+#else
+    NO_DEVICE_CODE;
+#endif
+
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int i   = blockIdx.x;
+    const block_iq2_s * x = (const block_iq2_s *) vx;
+
+    const int tid = threadIdx.x;
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[i].qs[4*ib+il] | ((x[i].qh[ib] << (8-2*il)) & 0x300)));
+    const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
+    const uint8_t signs = x[i].qs[QK_K/8+4*ib+il];
+    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
+#else
+    NO_DEVICE_CODE;
+#endif
+
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int i   = blockIdx.x;
+    const block_iq3_xxs * x = (const block_iq3_xxs  *) vx;
+
+    const int tid = threadIdx.x;
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint8_t  * q3 = x[i].qs + 8*ib;
+    const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib;
+    const uint8_t  * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]);
+    const uint8_t  * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]);
+    const uint32_t aux32 = gas[0] | (gas[1] << 16);
+    const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.5f;
+    const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
+    for (int j = 0; j < 4; ++j) {
+        y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
+        y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
+    }
+#else
+    NO_DEVICE_CODE;
+#endif
+
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int i   = blockIdx.x;
+    const block_iq3_s * x = (const block_iq3_s *) vx;
+
+    const int tid = threadIdx.x;
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint8_t * qs = x[i].qs + 8*ib;
+    const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256)));
+    const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256)));
+    const float d = (float)x[i].d * (1 + 2*((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf));
+    const uint8_t signs = x[i].signs[4*ib + il];
+    for (int j = 0; j < 4; ++j) {
+        y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
+        y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
+    }
+#else
+    NO_DEVICE_CODE;
+#endif
+
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int i   = blockIdx.x;
+    const block_iq1_s * x = (const block_iq1_s  *) vx;
+
+    const int tid = threadIdx.x;
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const float delta = x[i].qh[ib] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA;
+    const float d = (float)x[i].d * (2*((x[i].qh[ib] >> 12) & 7) + 1);
+    uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
+    grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[ib] >> 3*il) & 7) << 8)];
+    grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
+    grid32[0] &= 0x0f0f0f0f;
+    for (int j = 0; j < 8; ++j) {
+        y[j] = d * (q[j] + delta);
+    }
+#else
+    NO_DEVICE_CODE;
+#endif
+
+}
+
+template<typename dst_t>
+static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int i   = blockIdx.x;
+    const block_iq1_m * x = (const block_iq1_m  *) vx;
+
+    const int tid = threadIdx.x;
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint16_t * sc = (const uint16_t *)x[i].scales;
+    iq1m_scale_t scale;
+    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+    const int ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4);
+    const float d = (float)scale.f16 * (2*((sc[ib16/4] >> 3*(ib16%4)) & 0x7) + 1);
+    const float delta = x[i].qh[2*ib+il/2] & (0x08 << 4*(il%2)) ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA;
+    uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
+    grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[2*ib+il/2] >> 4*(il%2)) & 7) << 8)];
+    grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
+    grid32[0] &= 0x0f0f0f0f;
+    for (int j = 0; j < 8; ++j) {
+        y[j] = d * (q[j] + delta);
+    }
+#else
+    NO_DEVICE_CODE;
+#endif
+
+}
+
+
+template<typename dst_t>
+static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int i   = blockIdx.x;
+    const block_iq4_nl * x = (const block_iq4_nl *) vx + i*(QK_K/QK4_NL);
+
+    const int tid = threadIdx.x;
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
+    const uint8_t  * q4 = x[ib].qs + 4*il;
+    const float d = (float)x[ib].d;
+    for (int j = 0; j < 4; ++j) {
+        y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf];
+        y[j+16] = d * kvalues_iq4nl[q4[j] >>  4];
+    }
+
+}
+
+#if QK_K != 64
+template<typename dst_t>
+static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const int i   = blockIdx.x;
+    const block_iq4_xs * x = (const block_iq4_xs *)vx;
+
+    const int tid = threadIdx.x;
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
+    const uint8_t  * q4 = x[i].qs + 16*ib + 4*il;
+    const float d = (float)x[i].d * ((((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4)) - 32);
+    for (int j = 0; j < 4; ++j) {
+        y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf];
+        y[j+16] = d * kvalues_iq4nl[q4[j] >>  4];
+    }
+}
+#endif
+
+template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
+static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + 2*CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / (2*CUDA_DEQUANTIZE_BLOCK_SIZE);
+    dequantize_block<qk, qr, dequantize_kernel><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
+}
+
+static void dequantize_block_q8_0_f16_cuda(const void * __restrict__ vx, half * __restrict__ y, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_Q8_0_NE_ALIGN - 1) / CUDA_Q8_0_NE_ALIGN;
+    if (k % CUDA_Q8_0_NE_ALIGN == 0) {
+        const bool need_check = false;
+        dequantize_block_q8_0_f16<need_check><<<num_blocks, WARP_SIZE, 0, stream>>>(vx, y, k);
+    } else {
+        const bool need_check = true;
+        dequantize_block_q8_0_f16<need_check><<<num_blocks, WARP_SIZE, 0, stream>>>(vx, y, k);
+    }
+}
+
+template<typename dst_t>
+static void dequantize_row_q2_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    dequantize_block_q2_K<<<nb, 64, 0, stream>>>(vx, y);
+#else
+    dequantize_block_q2_K<<<nb, 32, 0, stream>>>(vx, y);
+#endif
+}
+
+template<typename dst_t>
+static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    dequantize_block_q3_K<<<nb, 64, 0, stream>>>(vx, y);
+#else
+    dequantize_block_q3_K<<<nb, 32, 0, stream>>>(vx, y);
+#endif
+}
+
+template<typename dst_t>
+static void dequantize_row_q4_0_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb32 = k / 32;
+    const int nb = (k + 255) / 256;
+    dequantize_block_q4_0<<<nb, 32, 0, stream>>>(vx, y, nb32);
+}
+
+template<typename dst_t>
+static void dequantize_row_q4_1_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb32 = k / 32;
+    const int nb = (k + 255) / 256;
+    dequantize_block_q4_1<<<nb, 32, 0, stream>>>(vx, y, nb32);
+}
+
+template<typename dst_t>
+static void dequantize_row_q4_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+    dequantize_block_q4_K<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_q5_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    dequantize_block_q5_K<<<nb, 64, 0, stream>>>(vx, y);
+#else
+    dequantize_block_q5_K<<<nb, 32, 0, stream>>>(vx, y);
+#endif
+}
+
+template<typename dst_t>
+static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    dequantize_block_q6_K<<<nb, 64, 0, stream>>>(vx, y);
+#else
+    dequantize_block_q6_K<<<nb, 32, 0, stream>>>(vx, y);
+#endif
+}
+
+template<typename dst_t>
+static void dequantize_row_iq2_xxs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+    dequantize_block_iq2_xxs<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_iq2_xs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+    dequantize_block_iq2_xs<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_iq2_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+    dequantize_block_iq2_s<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_iq3_xxs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+    dequantize_block_iq3_xxs<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_iq3_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+    dequantize_block_iq3_s<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_iq1_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+    dequantize_block_iq1_s<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_iq4_nl_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = (k + QK_K - 1) / QK_K;
+    dequantize_block_iq4_nl<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_iq1_m_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = k / QK_K;
+    dequantize_block_iq1_m<<<nb, 32, 0, stream>>>(vx, y);
+}
+
+template<typename dst_t>
+static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
+    const int nb = (k + QK_K - 1) / QK_K;
+#if QK_K == 64
+    dequantize_block_iq4_nl<<<nb, 32, 0, stream>>>(vx, y);
+#else
+    dequantize_block_iq4_xs<<<nb, 32, 0, stream>>>(vx, y);
+#endif
+}
+
+template <typename src_t, typename dst_t>
+static __global__ void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    const src_t * x = (src_t *) vx;
+
+    y[i] = x[i];
+}
+
+template <typename src_t, typename dst_t>
+static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
+    convert_unary<src_t><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
+}
+
+to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
+    int id;
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+            return dequantize_row_q4_0_cuda;
+        case GGML_TYPE_Q4_1:
+            return dequantize_row_q4_1_cuda;
+        case GGML_TYPE_Q5_0:
+            return dequantize_block_cuda<QK5_0, QR5_0, dequantize_q5_0>;
+        case GGML_TYPE_Q5_1:
+            return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>;
+        case GGML_TYPE_Q8_0:
+            CUDA_CHECK(cudaGetDevice(&id));
+            if (ggml_cuda_info().devices[id].cc >= CC_PASCAL) {
+                return dequantize_block_q8_0_f16_cuda;
+            }
+            return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>;
+        case GGML_TYPE_Q2_K:
+            return dequantize_row_q2_K_cuda;
+        case GGML_TYPE_Q3_K:
+            return dequantize_row_q3_K_cuda;
+        case GGML_TYPE_Q4_K:
+            return dequantize_row_q4_K_cuda;
+        case GGML_TYPE_Q5_K:
+            return dequantize_row_q5_K_cuda;
+        case GGML_TYPE_Q6_K:
+            return dequantize_row_q6_K_cuda;
+        case GGML_TYPE_IQ2_XXS:
+            return dequantize_row_iq2_xxs_cuda;
+        case GGML_TYPE_IQ2_XS:
+            return dequantize_row_iq2_xs_cuda;
+        case GGML_TYPE_IQ2_S:
+            return dequantize_row_iq2_s_cuda;
+        case GGML_TYPE_IQ3_XXS:
+            return dequantize_row_iq3_xxs_cuda;
+        case GGML_TYPE_IQ1_S:
+            return dequantize_row_iq1_s_cuda;
+        case GGML_TYPE_IQ1_M:
+            return dequantize_row_iq1_m_cuda;
+        case GGML_TYPE_IQ4_NL:
+            return dequantize_row_iq4_nl_cuda;
+        case GGML_TYPE_IQ4_XS:
+            return dequantize_row_iq4_xs_cuda;
+        case GGML_TYPE_IQ3_S:
+            return dequantize_row_iq3_s_cuda;
+        case GGML_TYPE_F32:
+            return convert_unary_cuda<float>;
+        default:
+            return nullptr;
+    }
+}
+
+to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+            return dequantize_row_q4_0_cuda;
+        case GGML_TYPE_Q4_1:
+            return dequantize_row_q4_1_cuda;
+        case GGML_TYPE_Q5_0:
+            return dequantize_block_cuda<QK5_0, QR5_0, dequantize_q5_0>;
+        case GGML_TYPE_Q5_1:
+            return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>;
+        case GGML_TYPE_Q8_0:
+            return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>;
+        case GGML_TYPE_Q2_K:
+            return dequantize_row_q2_K_cuda;
+        case GGML_TYPE_Q3_K:
+            return dequantize_row_q3_K_cuda;
+        case GGML_TYPE_Q4_K:
+            return dequantize_row_q4_K_cuda;
+        case GGML_TYPE_Q5_K:
+            return dequantize_row_q5_K_cuda;
+        case GGML_TYPE_Q6_K:
+            return dequantize_row_q6_K_cuda;
+        case GGML_TYPE_IQ2_XXS:
+            return dequantize_row_iq2_xxs_cuda;
+        case GGML_TYPE_IQ2_XS:
+            return dequantize_row_iq2_xs_cuda;
+        case GGML_TYPE_IQ2_S:
+            return dequantize_row_iq2_s_cuda;
+        case GGML_TYPE_IQ3_XXS:
+            return dequantize_row_iq3_xxs_cuda;
+        case GGML_TYPE_IQ1_S:
+            return dequantize_row_iq1_s_cuda;
+        case GGML_TYPE_IQ1_M:
+            return dequantize_row_iq1_m_cuda;
+        case GGML_TYPE_IQ4_NL:
+            return dequantize_row_iq4_nl_cuda;
+        case GGML_TYPE_IQ4_XS:
+            return dequantize_row_iq4_xs_cuda;
+        case GGML_TYPE_IQ3_S:
+            return dequantize_row_iq3_s_cuda;
+        case GGML_TYPE_F16:
+            return convert_unary_cuda<half>;
+        default:
+            return nullptr;
+    }
+}
diff --git a/ggml-cuda/convert.cuh b/ggml-cuda/convert.cuh
new file mode 100644
index 000000000..db34c0be9
--- /dev/null
+++ b/ggml-cuda/convert.cuh
@@ -0,0 +1,13 @@
+#include "common.cuh"
+
+#define CUDA_DEQUANTIZE_BLOCK_SIZE 256
+
+template<typename T>
+using to_t_cuda_t = void (*)(const void * __restrict__ x, T * __restrict__ y, int k, cudaStream_t stream);
+
+typedef to_t_cuda_t<float> to_fp32_cuda_t;
+typedef to_t_cuda_t<half> to_fp16_cuda_t;
+
+to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type);
+
+to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type);
diff --git a/ggml-cuda/cpy.cu b/ggml-cuda/cpy.cu
new file mode 100644
index 000000000..16d9c8fff
--- /dev/null
+++ b/ggml-cuda/cpy.cu
@@ -0,0 +1,461 @@
+#include "cpy.cuh"
+
+typedef void (*cpy_kernel_t)(const char * cx, char * cdst);
+
+static __device__ void cpy_1_f32_f32(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    float * dsti = (float *) cdsti;
+
+    *dsti = *xi;
+}
+
+static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    half * dsti = (half *) cdsti;
+
+    *dsti = __float2half(*xi);
+}
+
+static __device__ void cpy_1_f16_f16(const char * cxi, char * cdsti) {
+    const half * xi = (const half *) cxi;
+    half * dsti = (half *) cdsti;
+
+    *dsti = *xi;
+}
+
+static __device__ void cpy_1_f16_f32(const char * cxi, char * cdsti) {
+    const half * xi = (const half *) cxi;
+    float * dsti = (float *) cdsti;
+
+    *dsti = *xi;
+}
+
+template <cpy_kernel_t cpy_1>
+static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne,
+                                   const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+                                   const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13) {
+    const int64_t i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= ne) {
+        return;
+    }
+
+    // determine indices i03/i13, i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor
+    // then combine those indices with the corresponding byte offsets to get the total offsets
+    const int64_t i03 = i/(ne00 * ne01 * ne02);
+    const int64_t i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
+    const int64_t i01 = (i - i03*ne00*ne01*ne02  -  i02*ne01*ne00) / ne00;
+    const int64_t i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
+    const int64_t x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
+
+    const int64_t i13 = i/(ne10 * ne11 * ne12);
+    const int64_t i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
+    const int64_t i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
+    const int64_t i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
+    const int64_t dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13;
+
+    cpy_1(cx + x_offset, cdst + dst_offset);
+}
+
+static __device__ void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    block_q8_0 * dsti = (block_q8_0 *) cdsti;
+
+    float amax = 0.0f; // absolute max
+
+    for (int j = 0; j < QK8_0; j++) {
+        const float v = xi[j];
+        amax = fmaxf(amax, fabsf(v));
+    }
+
+    const float d = amax / ((1 << 7) - 1);
+    const float id = d ? 1.0f/d : 0.0f;
+
+    dsti->d = d;
+
+    for (int j = 0; j < QK8_0; ++j) {
+        const float x0 = xi[j]*id;
+
+        dsti->qs[j] = roundf(x0);
+    }
+}
+
+static __device__ void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    block_q4_0 * dsti = (block_q4_0 *) cdsti;
+
+    float amax = 0.0f;
+    float vmax = 0.0f;
+
+    for (int j = 0; j < QK4_0; ++j) {
+        const float v = xi[j];
+        if (amax < fabsf(v)) {
+            amax = fabsf(v);
+            vmax = v;
+        }
+    }
+
+    const float d  = vmax / -8;
+    const float id = d ? 1.0f/d : 0.0f;
+
+    dsti->d = d;
+
+    for (int j = 0; j < QK4_0/2; ++j) {
+        const float x0 = xi[0       + j]*id;
+        const float x1 = xi[QK4_0/2 + j]*id;
+
+        const uint8_t xi0 = min(15, (int8_t)(x0 + 8.5f));
+        const uint8_t xi1 = min(15, (int8_t)(x1 + 8.5f));
+
+        dsti->qs[j]  = xi0;
+        dsti->qs[j] |= xi1 << 4;
+    }
+}
+
+static __device__ void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    block_q4_1 * dsti = (block_q4_1 *) cdsti;
+
+    float vmin = FLT_MAX;
+    float vmax = -FLT_MAX;
+
+    for (int j = 0; j < QK4_1; ++j) {
+        const float v = xi[j];
+
+        if (v < vmin) vmin = v;
+        if (v > vmax) vmax = v;
+    }
+
+    const float d  = (vmax - vmin) / ((1 << 4) - 1);
+    const float id = d ? 1.0f/d : 0.0f;
+
+    dsti->dm.x = d;
+    dsti->dm.y = vmin;
+
+    for (int j = 0; j < QK4_1/2; ++j) {
+        const float x0 = (xi[0       + j] - vmin)*id;
+        const float x1 = (xi[QK4_1/2 + j] - vmin)*id;
+
+        const uint8_t xi0 = min(15, (int8_t)(x0 + 0.5f));
+        const uint8_t xi1 = min(15, (int8_t)(x1 + 0.5f));
+
+        dsti->qs[j]  = xi0;
+        dsti->qs[j] |= xi1 << 4;
+    }
+}
+
+static __device__ void cpy_blck_f32_q5_0(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    block_q5_0 * dsti = (block_q5_0 *) cdsti;
+
+    float amax = 0.0f;
+    float vmax = 0.0f;
+
+    for (int j = 0; j < QK5_0; ++j) {
+        const float v = xi[j];
+        if (amax < fabsf(v)) {
+            amax = fabsf(v);
+            vmax = v;
+        }
+    }
+
+    const float d  = vmax / -16;
+    const float id = d ? 1.0f/d : 0.0f;
+
+    dsti->d = d;
+
+    uint32_t qh = 0;
+    for (int j = 0; j < QK5_0/2; ++j) {
+        const float x0 = xi[0       + j]*id;
+        const float x1 = xi[QK5_0/2 + j]*id;
+
+        const uint8_t xi0 = min(31, (int8_t)(x0 + 16.5f));
+        const uint8_t xi1 = min(31, (int8_t)(x1 + 16.5f));
+
+        dsti->qs[j]  = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
+        qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
+        qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2);
+    }
+    memcpy(dsti->qh, &qh, sizeof(qh));
+}
+
+static __device__ void cpy_blck_f32_q5_1(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    block_q5_1 * dsti = (block_q5_1 *) cdsti;
+
+    float min = xi[0];
+    float max = xi[0];
+
+    for (int j = 1; j < QK5_1; ++j) {
+        const float v = xi[j];
+        min = v < min ? v : min;
+        max = v > max ? v : max;
+    }
+
+    const float d  = (max - min) / 31;
+    const float id = d ? 1.0f/d : 0.0f;
+
+    dsti->dm.x = d;
+    dsti->dm.y = min;
+
+    uint32_t qh = 0;
+    for (int j = 0; j < QK5_1/2; ++j) {
+        const float x0 = (xi[0       + j] - min)*id;
+        const float x1 = (xi[QK5_1/2 + j] - min)*id;
+
+        const uint8_t xi0 = (uint8_t)(x0 + 0.5f);
+        const uint8_t xi1 = (uint8_t)(x1 + 0.5f);
+
+        dsti->qs[j]  = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
+        qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
+        qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_1/2);
+    }
+    memcpy(dsti->qh, &qh, sizeof(qh));
+}
+
+
+static __device__ __forceinline__ int best_index_int8(int n, const int8_t * val, float x) {
+    if (x <= val[0]) return 0;
+    if (x >= val[n-1]) return n-1;
+    int ml = 0, mu = n-1;
+    while (mu-ml > 1) {
+        int mav = (ml+mu)/2;
+        if (x < val[mav]) mu = mav; else ml = mav;
+    }
+    return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
+}
+
+static __device__ void cpy_blck_f32_iq4_nl(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    block_iq4_nl * dsti = (block_iq4_nl *) cdsti;
+
+    float amax = 0.0f;
+    float vmax = 0.0f;
+
+    for (int j = 0; j < QK4_NL; ++j) {
+        const float v = xi[j];
+        if (amax < fabsf(v)) {
+            amax = fabsf(v);
+            vmax = v;
+        }
+    }
+
+    float d = vmax / kvalues_iq4nl[0];
+    const float id = d ? 1.0f/d : 0.0f;
+
+    float sumqx = 0, sumq2 = 0;
+    for (int j = 0; j < QK4_NL/2; ++j) {
+        const float x0 = xi[0        + j]*id;
+        const float x1 = xi[QK4_NL/2 + j]*id;
+        const uint8_t xi0 = best_index_int8(16, kvalues_iq4nl, x0);
+        const uint8_t xi1 = best_index_int8(16, kvalues_iq4nl, x1);
+        dsti->qs[j] = xi0 | (xi1 << 4);
+        const float v0 = kvalues_iq4nl[xi0];
+        const float v1 = kvalues_iq4nl[xi1];
+        const float w0 = xi[0        + j]*xi[0        + j];
+        const float w1 = xi[QK4_NL/2 + j]*xi[QK4_NL/2 + j];
+        sumqx += w0*v0*xi[j] + w1*v1*xi[QK4_NL/2 + j];
+        sumq2 += w0*v0*v0 + w1*v1*v1;
+    }
+
+    dsti->d = sumq2 > 0 ? sumqx/sumq2 : d;
+}
+
+template <cpy_kernel_t cpy_blck, int qk>
+static __global__ void cpy_f32_q(const char * cx, char * cdst, const int ne,
+                                 const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+                                 const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                 const int nb12, const int nb13) {
+    const int i = (blockDim.x*blockIdx.x + threadIdx.x)*qk;
+
+    if (i >= ne) {
+        return;
+    }
+
+    const int i03 = i/(ne00 * ne01 * ne02);
+    const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
+    const int i01 = (i - i03*ne00*ne01*ne02  -  i02*ne01*ne00) / ne00;
+    const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
+    const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
+
+    const int i13 = i/(ne10 * ne11 * ne12);
+    const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
+    const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
+    const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
+    const int dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13;
+
+    cpy_blck(cx + x_offset, cdst + dst_offset);
+}
+
+static void ggml_cpy_f16_f32_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
+    cpy_f32_f16<cpy_1_f16_f32><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+static void ggml_cpy_f32_f32_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
+    cpy_f32_f16<cpy_1_f32_f32><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+static void ggml_cpy_f32_f16_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
+    cpy_f32_f16<cpy_1_f32_f16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+static void ggml_cpy_f32_q8_0_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    GGML_ASSERT(ne % QK8_0 == 0);
+    const int num_blocks = ne / QK8_0;
+    cpy_f32_q<cpy_blck_f32_q8_0, QK8_0><<<num_blocks, 1, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+static void ggml_cpy_f32_q4_0_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    GGML_ASSERT(ne % QK4_0 == 0);
+    const int num_blocks = ne / QK4_0;
+    cpy_f32_q<cpy_blck_f32_q4_0, QK4_0><<<num_blocks, 1, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+static void ggml_cpy_f32_q4_1_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    GGML_ASSERT(ne % QK4_1 == 0);
+    const int num_blocks = ne / QK4_1;
+    cpy_f32_q<cpy_blck_f32_q4_1, QK4_1><<<num_blocks, 1, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+static void ggml_cpy_f32_q5_0_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    GGML_ASSERT(ne % QK5_0 == 0);
+    const int num_blocks = ne / QK5_0;
+    cpy_f32_q<cpy_blck_f32_q5_0, QK5_0><<<num_blocks, 1, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+static void ggml_cpy_f32_q5_1_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    GGML_ASSERT(ne % QK5_1 == 0);
+    const int num_blocks = ne / QK5_1;
+    cpy_f32_q<cpy_blck_f32_q5_1, QK5_1><<<num_blocks, 1, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+static void ggml_cpy_f32_iq4_nl_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    GGML_ASSERT(ne % QK4_NL == 0);
+    const int num_blocks = ne / QK4_NL;
+    cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL><<<num_blocks, 1, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+static void ggml_cpy_f16_f16_cuda(
+    const char * cx, char * cdst, const int ne,
+    const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+    const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
+
+    const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
+    cpy_f32_f16<cpy_1_f16_f16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
+        (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+}
+
+void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1) {
+    const int64_t ne = ggml_nelements(src0);
+    GGML_ASSERT(ne == ggml_nelements(src1));
+
+    GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
+    GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+
+    //GGML_ASSERT(src0->ne[3] == 1);
+
+    const int64_t nb00 = src0->nb[0];
+    const int64_t nb01 = src0->nb[1];
+    const int64_t nb02 = src0->nb[2];
+    const int64_t nb03 = src0->nb[3];
+
+    const int64_t ne10 = src1->ne[0];
+    const int64_t ne11 = src1->ne[1];
+    const int64_t ne12 = src1->ne[2];
+
+    //GGML_ASSERT(src1->ne[3] == 1);
+
+    const int64_t nb10 = src1->nb[0];
+    const int64_t nb11 = src1->nb[1];
+    const int64_t nb12 = src1->nb[2];
+    const int64_t nb13 = src1->nb[3];
+
+    cudaStream_t main_stream = ctx.stream();
+
+    char * src0_ddc = (char *) src0->data;
+    char * src1_ddc = (char *) src1->data;
+
+    if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
+        ggml_cpy_f32_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
+        ggml_cpy_f32_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
+        ggml_cpy_f32_q8_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
+        ggml_cpy_f32_q4_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
+        ggml_cpy_f32_q4_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_0) {
+        ggml_cpy_f32_q5_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_IQ4_NL) {
+        ggml_cpy_f32_iq4_nl_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_1) {
+        ggml_cpy_f32_q5_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
+        ggml_cpy_f16_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
+        ggml_cpy_f16_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else {
+        fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
+                ggml_type_name(src0->type), ggml_type_name(src1->type));
+        GGML_ASSERT(false);
+    }
+}
+
+void ggml_cuda_dup(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    ggml_cuda_cpy(ctx, src0, dst);
+}
diff --git a/ggml-cuda/cpy.cuh b/ggml-cuda/cpy.cuh
new file mode 100644
index 000000000..f0b2c453b
--- /dev/null
+++ b/ggml-cuda/cpy.cuh
@@ -0,0 +1,7 @@
+#include "common.cuh"
+
+#define CUDA_CPY_BLOCK_SIZE 32
+
+void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1);
+
+void ggml_cuda_dup(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/dequantize.cuh b/ggml-cuda/dequantize.cuh
new file mode 100644
index 000000000..b54400632
--- /dev/null
+++ b/ggml-cuda/dequantize.cuh
@@ -0,0 +1,103 @@
+#include "common.cuh"
+
+static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
+    const block_q4_0 * x = (const block_q4_0 *) vx;
+
+    const dfloat d = x[ib].d;
+
+    const int vui = x[ib].qs[iqs];
+
+    v.x = vui & 0xF;
+    v.y = vui >> 4;
+
+#ifdef GGML_CUDA_F16
+    v = __hsub2(v, {8.0f, 8.0f});
+    v = __hmul2(v, {d, d});
+#else
+    v.x = (v.x - 8.0f) * d;
+    v.y = (v.y - 8.0f) * d;
+#endif // GGML_CUDA_F16
+}
+
+static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
+    const block_q4_1 * x = (const block_q4_1 *) vx;
+
+    const dfloat d = __low2half(x[ib].dm);
+    const dfloat m = __high2half(x[ib].dm);
+
+    const int vui = x[ib].qs[iqs];
+
+    v.x = vui & 0xF;
+    v.y = vui >> 4;
+
+#ifdef GGML_CUDA_F16
+    v = __hmul2(v, {d, d});
+    v = __hadd2(v, {m, m});
+#else
+    v.x = (v.x * d) + m;
+    v.y = (v.y * d) + m;
+#endif // GGML_CUDA_F16
+}
+
+static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
+    const block_q5_0 * x = (const block_q5_0 *) vx;
+
+    const dfloat d = x[ib].d;
+
+    uint32_t qh;
+    memcpy(&qh, x[ib].qh, sizeof(qh));
+
+    const int xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
+    const int xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
+
+    v.x = ((x[ib].qs[iqs] & 0xf) | xh_0);
+    v.y = ((x[ib].qs[iqs] >>  4) | xh_1);
+
+#ifdef GGML_CUDA_F16
+    v = __hsub2(v, {16.0f, 16.0f});
+    v = __hmul2(v, {d, d});
+#else
+    v.x = (v.x - 16.0f) * d;
+    v.y = (v.y - 16.0f) * d;
+#endif // GGML_CUDA_F16
+}
+
+static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
+    const block_q5_1 * x = (const block_q5_1 *) vx;
+
+    const dfloat d = __low2half(x[ib].dm);
+    const dfloat m = __high2half(x[ib].dm);
+
+    uint32_t qh;
+    memcpy(&qh, x[ib].qh, sizeof(qh));
+
+    const int xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
+    const int xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
+
+    v.x = ((x[ib].qs[iqs] & 0xf) | xh_0);
+    v.y = ((x[ib].qs[iqs] >>  4) | xh_1);
+
+#ifdef GGML_CUDA_F16
+    v = __hmul2(v, {d, d});
+    v = __hadd2(v, {m, m});
+#else
+    v.x = (v.x * d) + m;
+    v.y = (v.y * d) + m;
+#endif // GGML_CUDA_F16
+}
+
+static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
+    const block_q8_0 * x = (const block_q8_0 *) vx;
+
+    const dfloat d = x[ib].d;
+
+    v.x = x[ib].qs[iqs + 0];
+    v.y = x[ib].qs[iqs + 1];
+
+#ifdef GGML_CUDA_F16
+    v = __hmul2(v, {d, d});
+#else
+    v.x *= d;
+    v.y *= d;
+#endif // GGML_CUDA_F16
+}
diff --git a/ggml-cuda/diagmask.cu b/ggml-cuda/diagmask.cu
new file mode 100644
index 000000000..4b713ba22
--- /dev/null
+++ b/ggml-cuda/diagmask.cu
@@ -0,0 +1,40 @@
+#include "diagmask.cuh"
+
+static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past) {
+    const int col = blockDim.y*blockIdx.y + threadIdx.y;
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int i = row*ncols + col;
+    //dst[i] = col > (n_past + row % rows_per_channel) ? -INFINITY : x[i];
+    //dst[i] = x[i] - (col > n_past + row % rows_per_channel) * INT_MAX; // equivalent within rounding error but slightly faster on GPU
+    dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX;
+}
+
+static void diag_mask_inf_f32_cuda(const float * x, float * dst, const int ncols_x, const int nrows_x, const int rows_per_channel, const int n_past, cudaStream_t stream) {
+    const dim3 block_dims(1, CUDA_DIAG_MASK_INF_BLOCK_SIZE, 1);
+    const int block_num_x = (ncols_x + CUDA_DIAG_MASK_INF_BLOCK_SIZE - 1) / CUDA_DIAG_MASK_INF_BLOCK_SIZE;
+    const dim3 block_nums(nrows_x, block_num_x, 1);
+    diag_mask_inf_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols_x, rows_per_channel, n_past);
+}
+
+void ggml_cuda_op_diag_mask_inf(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int nrows0 = ggml_nrows(src0);
+
+    const int n_past = ((int32_t *) dst->op_params)[0];
+
+    diag_mask_inf_f32_cuda(src0_d, dst_d, ne00, nrows0, ne01, n_past, stream);
+}
diff --git a/ggml-cuda/diagmask.cuh b/ggml-cuda/diagmask.cuh
new file mode 100644
index 000000000..6cdbef17e
--- /dev/null
+++ b/ggml-cuda/diagmask.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32
+
+void ggml_cuda_op_diag_mask_inf(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/dmmv.cu b/ggml-cuda/dmmv.cu
new file mode 100644
index 000000000..0b17e3cb9
--- /dev/null
+++ b/ggml-cuda/dmmv.cu
@@ -0,0 +1,813 @@
+#include "dmmv.cuh"
+#include "dequantize.cuh"
+#include "convert.cuh"
+
+#ifndef K_QUANTS_PER_ITERATION
+#define K_QUANTS_PER_ITERATION 2
+#else
+static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
+#endif
+
+static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
+
+    static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
+
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
+    if (row > nrows) return;
+
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q2_K * x = (const block_q2_K *)vx + ib0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+#if QK_K == 256
+    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...15
+    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0,1
+
+    const int step = 16/K_QUANTS_PER_ITERATION;
+
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0...15 or 0...7
+
+    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15 or 0...14 in steps of 2
+    const int q_offset = 32*im + l0;
+    const int s_offset = 8*im;
+    const int y_offset = 128*im + l0;
+
+    uint32_t aux[4];
+    const uint8_t * d = (const uint8_t *)aux;
+    const uint8_t * m = (const uint8_t *)(aux + 2);
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y = yy + i * QK_K + y_offset;
+        const uint8_t * q = x[i].qs + q_offset;
+
+        const float dall = __low2half(x[i].dm);
+        const float dmin = __high2half(x[i].dm);
+
+        const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset);
+        aux[0] = a[0] & 0x0f0f0f0f;
+        aux[1] = a[1] & 0x0f0f0f0f;
+        aux[2] = (a[0] >> 4) & 0x0f0f0f0f;
+        aux[3] = (a[1] >> 4) & 0x0f0f0f0f;
+
+        float sum1 = 0, sum2 = 0;
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3)
+                  + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3)
+                  + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3)
+                  + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3)
+                  + y[l+16] * d[1] * ((q[l+16] >> 0) & 3)
+                  + y[l+48] * d[3] * ((q[l+16] >> 2) & 3)
+                  + y[l+80] * d[5] * ((q[l+16] >> 4) & 3)
+                  +y[l+112] * d[7] * ((q[l+16] >> 6) & 3);
+            sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6]
+                  + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7];
+
+        }
+        tmp += dall * sum1 - dmin * sum2;
+
+    }
+#else
+    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15 or 0...7
+    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0....1 or 0...3
+    const int offset = tid * K_QUANTS_PER_ITERATION;
+
+    uint32_t uaux[2];
+    const uint8_t * d = (const uint8_t *)uaux;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+
+        const float   * y = yy + i * QK_K + offset;
+        const uint8_t * q = x[i].qs + offset;
+        const uint32_t * s = (const uint32_t *)x[i].scales;
+
+        uaux[0] = s[0] & 0x0f0f0f0f;
+        uaux[1] = (s[0] >> 4) & 0x0f0f0f0f;
+
+        const float2 dall = __half22float2(x[i].dm);
+
+        float sum1 = 0, sum2 = 0;
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            const uint8_t ql = q[l];
+            sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3)
+                  + y[l+16] * d[1] * ((ql >> 2) & 3)
+                  + y[l+32] * d[2] * ((ql >> 4) & 3)
+                  + y[l+48] * d[3] * ((ql >> 6) & 3);
+            sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7];
+        }
+        tmp += dall.x * sum1 - dall.y * sum2;
+    }
+#endif
+
+    // sum up partial sums and write back result
+    tmp = warp_reduce_sum(tmp);
+
+    if (threadIdx.x == 0) {
+        dst[row] = tmp;
+    }
+}
+
+static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
+
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
+    if (row > nrows) return;
+
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q3_K * x = (const block_q3_K *)vx + ib0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+#if QK_K == 256
+
+    const uint16_t kmask1 = 0x0303;
+    const uint16_t kmask2 = 0x0f0f;
+
+    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0,1
+
+    const int n  = K_QUANTS_PER_ITERATION;               // iterations in the inner loop
+    const int step = 16/K_QUANTS_PER_ITERATION;
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0....15 or 0...7
+
+    const uint8_t m = 1 << (4*im);
+
+    const int l0 = n*in;                                 // 0...15 or 0...14 in steps of 2
+    const int q_offset =  32*im + l0;
+    const int y_offset = 128*im + l0;
+
+    uint16_t utmp[4];
+    const int8_t * s = (const int8_t *)utmp;
+
+    const uint16_t s_shift = 4*im;
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y  = yy + i * QK_K + y_offset;
+        const uint8_t * q = x[i].qs + q_offset;
+        const uint8_t * h = x[i].hmask + l0;
+
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4);
+        utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4);
+        utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4);
+        utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4);
+
+        const float d = x[i].d;
+
+        float sum = 0;
+        for (int l = 0; l < n; ++l) {
+            sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4))
+                 + y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4))
+                 + y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4))
+                 + y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4));
+            sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4))
+                 + y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4))
+                 + y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4))
+                + y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4));
+        }
+        tmp += d * sum;
+
+    }
+#else
+
+    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15 or 0...7
+    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0....1 or 0...3
+    const int offset = tid * K_QUANTS_PER_ITERATION;         // 0...15 or 0...14
+    const int in = offset/8;                                 // 0 or 1
+    const int im = offset%8;                                 // 0...7
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+
+        const float   * y = yy + i * QK_K + offset;
+        const uint8_t * q = x[i].qs + offset;
+        const uint8_t * s = x[i].scales;
+
+        const float dall = (float)x[i].d;
+
+        float sum = 0;
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            const uint8_t hl = x[i].hmask[im+l] >> in;
+            const uint8_t ql = q[l];
+            sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4))
+                 + y[l+16] * dall * ((s[0] >>  4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4))
+                 + y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4))
+                 + y[l+48] * dall * ((s[1] >>  4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4));
+        }
+        tmp += sum;
+    }
+#endif
+
+    // sum up partial sums and write back result
+    tmp = warp_reduce_sum(tmp);
+
+    if (threadIdx.x == 0) {
+        dst[row] = tmp;
+    }
+}
+
+static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
+
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
+    if (row > nrows) return;
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q4_K * x = (const block_q4_K *)vx + ib0;
+
+#if QK_K == 256
+    const uint16_t kmask1 = 0x3f3f;
+    const uint16_t kmask2 = 0x0f0f;
+    const uint16_t kmask3 = 0xc0c0;
+
+    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0,1
+
+    const int step = 8/K_QUANTS_PER_ITERATION;           // 8 or 4
+
+    const int il  = tid/step;                            // 0...3
+    const int ir  = tid - step*il;                       // 0...7 or 0...3
+    const int n   = 2 * K_QUANTS_PER_ITERATION;          // 2 or 4
+
+    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const int in = il%2;
+
+    const int l0 = n*(2*ir + in);
+    const int q_offset = 32*im + l0;
+    const int y_offset = 64*im + l0;
+
+    uint16_t aux[4];
+    const uint8_t * sc = (const uint8_t *)aux;
+
+#if K_QUANTS_PER_ITERATION == 2
+    uint32_t q32[4];
+    const uint8_t * q4 = (const uint8_t *)q32;
+#else
+    uint16_t q16[4];
+    const uint8_t * q4 = (const uint8_t *)q16;
+#endif
+
+    float tmp = 0; // partial sum for thread in warp
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y1 = yy + i*QK_K + y_offset;
+        const float   * y2 = y1 + 128;
+
+        const float dall = __low2half(x[i].dm);
+        const float dmin = __high2half(x[i].dm);
+
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        aux[0] = a[im+0] & kmask1;
+        aux[1] = a[im+2] & kmask1;
+        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
+        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
+
+#if K_QUANTS_PER_ITERATION == 2
+        const uint32_t * q1 = (const uint32_t *)(x[i].qs + q_offset);
+        const uint32_t * q2 = q1 + 16;
+
+        q32[0] = q1[0] & 0x0f0f0f0f;
+        q32[1] = q1[0] & 0xf0f0f0f0;
+        q32[2] = q2[0] & 0x0f0f0f0f;
+        q32[3] = q2[0] & 0xf0f0f0f0;
+
+        float4 s = {0.f, 0.f, 0.f, 0.f};
+        float smin = 0;
+        for (int l = 0; l < 4; ++l) {
+            s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+ 4];
+            s.z += y2[l] * q4[l+8]; s.w += y2[l+32] * q4[l+12];
+            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
+        }
+        tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
+#else
+        const uint16_t * q1 = (const uint16_t *)(x[i].qs + q_offset);
+        const uint16_t * q2 = q1 + 32;
+
+        q16[0] = q1[0] & 0x0f0f;
+        q16[1] = q1[0] & 0xf0f0;
+        q16[2] = q2[0] & 0x0f0f;
+        q16[3] = q2[0] & 0xf0f0;
+
+        float4 s = {0.f, 0.f, 0.f, 0.f};
+        float smin = 0;
+        for (int l = 0; l < 2; ++l) {
+            s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2];
+            s.z += y2[l] * q4[l+4]; s.w += y2[l+32] * q4[l+6];
+            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
+        }
+        tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
+#endif
+
+    }
+#else
+    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15
+    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
+
+    const int step = tid * K_QUANTS_PER_ITERATION;
+
+    uint16_t aux16[2];
+    const uint8_t * s = (const uint8_t *)aux16;
+
+    float tmp = 0;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+        const uint8_t * q = x[i].qs + step;
+        const float   * y = yy + i*QK_K + step;
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        aux16[0] = a[0] & 0x0f0f;
+        aux16[1] = (a[0] >> 4) & 0x0f0f;
+        const float d = (float)x[i].dm[0];
+        const float m = (float)x[i].dm[1];
+        float sum = 0.f;
+        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
+            sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2])
+                 + y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2])
+                 + y[j+32] * (d * s[1] * (q[j+ 0] >>  4) - m * s[3])
+                 + y[j+48] * (d * s[1] * (q[j+16] >>  4) - m * s[3]);
+        }
+        tmp += sum;
+    }
+
+#endif
+
+    // sum up partial sums and write back result
+    tmp = warp_reduce_sum(tmp);
+
+    if (tid == 0) {
+        dst[row] = tmp;
+    }
+}
+
+static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols) {
+
+    const int row = blockIdx.x;
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q5_K * x = (const block_q5_K *)vx + ib0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+#if QK_K == 256
+    const uint16_t kmask1 = 0x3f3f;
+    const uint16_t kmask2 = 0x0f0f;
+    const uint16_t kmask3 = 0xc0c0;
+
+    const int tid = threadIdx.x/2;  // 0...15
+    const int ix  = threadIdx.x%2;
+
+    const int il  = tid/4;     // 0...3
+    const int ir  = tid - 4*il;// 0...3
+    const int n   = 2;
+
+    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const int in = il%2;
+
+    const int l0 = n*(2*ir + in);
+    const int q_offset = 32*im + l0;
+    const int y_offset = 64*im + l0;
+
+    const uint8_t hm1  = 1 << (2*im);
+    const uint8_t hm2  = hm1 << 4;
+
+    uint16_t aux[4];
+    const uint8_t * sc = (const uint8_t *)aux;
+
+    uint16_t q16[8];
+    const uint8_t * q4 = (const uint8_t *)q16;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2) {
+
+        const uint8_t * ql1 = x[i].qs + q_offset;
+        const uint8_t * qh  = x[i].qh + l0;
+        const float   * y1  = yy + i*QK_K + y_offset;
+        const float   * y2  = y1 + 128;
+
+        const float dall = __low2half(x[i].dm);
+        const float dmin = __high2half(x[i].dm);
+
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        aux[0] = a[im+0] & kmask1;
+        aux[1] = a[im+2] & kmask1;
+        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
+        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
+
+        float4 sum = {0.f, 0.f, 0.f, 0.f};
+        float smin = 0;
+        const uint16_t * q1 = (const uint16_t *)ql1;
+        const uint16_t * q2 = q1 + 32;
+        q16[0] = q1[0] & 0x0f0f;
+        q16[1] = q1[8] & 0x0f0f;
+        q16[2] = (q1[0] >> 4) & 0x0f0f;
+        q16[3] = (q1[8] >> 4) & 0x0f0f;
+        q16[4] = q2[0] & 0x0f0f;
+        q16[5] = q2[8] & 0x0f0f;
+        q16[6] = (q2[0] >> 4) & 0x0f0f;
+        q16[7] = (q2[8] >> 4) & 0x0f0f;
+        for (int l = 0; l < n; ++l) {
+            sum.x += y1[l+ 0] * (q4[l +0] + (qh[l+ 0] & (hm1 << 0) ? 16 : 0))
+                   + y1[l+16] * (q4[l +2] + (qh[l+16] & (hm1 << 0) ? 16 : 0));
+            sum.y += y1[l+32] * (q4[l +4] + (qh[l+ 0] & (hm1 << 1) ? 16 : 0))
+                   + y1[l+48] * (q4[l +6] + (qh[l+16] & (hm1 << 1) ? 16 : 0));
+            sum.z += y2[l+ 0] * (q4[l +8] + (qh[l+ 0] & (hm2 << 0) ? 16 : 0))
+                   + y2[l+16] * (q4[l+10] + (qh[l+16] & (hm2 << 0) ? 16 : 0));
+            sum.w += y2[l+32] * (q4[l+12] + (qh[l+ 0] & (hm2 << 1) ? 16 : 0))
+                   + y2[l+48] * (q4[l+14] + (qh[l+16] & (hm2 << 1) ? 16 : 0));
+            smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3]
+                  + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7];
+        }
+        tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin;
+    }
+
+#else
+    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...15
+    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
+    const int step = tid * K_QUANTS_PER_ITERATION;
+    const int im = step/8;
+    const int in = step%8;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+        const uint8_t * q = x[i].qs + step;
+        const int8_t  * s = x[i].scales;
+        const float   * y = yy + i*QK_K + step;
+        const float     d = x[i].d;
+        float sum = 0.f;
+        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
+            const uint8_t h = x[i].qh[in+j] >> im;
+            sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16))
+                 + y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16))
+                 + y[j+32] * d * s[2] * ((q[j+ 0] >>  4) - ((h >> 4) & 1 ? 0 : 16))
+                 + y[j+48] * d * s[3] * ((q[j+16] >>  4) - ((h >> 6) & 1 ? 0 : 16));
+        }
+        tmp += sum;
+    }
+#endif
+
+    // sum up partial sums and write back result
+    tmp = warp_reduce_sum(tmp);
+
+    if (threadIdx.x == 0) {
+        dst[row] = tmp;
+    }
+}
+
+static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
+
+    static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
+
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
+    if (row > nrows) return;
+
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q6_K * x = (const block_q6_K *)vx + ib0;
+
+#if QK_K == 256
+
+    const int tid = threadIdx.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const int ix  = threadIdx.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const int step = 16/K_QUANTS_PER_ITERATION;          // 16 or 8
+
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0...15 or 0...7
+
+#if K_QUANTS_PER_ITERATION == 1
+    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15
+    const int is = 0;
+#else
+    const int l0 = 4 * in;                               // 0, 4, 8, ..., 28
+    const int is = in / 4;
+#endif
+    const int ql_offset = 64*im + l0;
+    const int qh_offset = 32*im + l0;
+    const int s_offset  =  8*im + is;
+    const int y_offset = 128*im + l0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y  = yy + i * QK_K + y_offset;
+        const uint8_t * ql = x[i].ql + ql_offset;
+        const uint8_t * qh = x[i].qh + qh_offset;
+        const int8_t  * s  = x[i].scales + s_offset;
+
+        const float d = x[i].d;
+
+#if K_QUANTS_PER_ITERATION == 1
+        float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
+                  + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
+                  + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
+                  + y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
+                  + y[64] * s[4] * d * ((int8_t)((ql[ 0]  >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
+                  + y[80] * s[5] * d * ((int8_t)((ql[16]  >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
+                  + y[96] * s[6] * d * ((int8_t)((ql[32]  >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
+                  +y[112] * s[7] * d * ((int8_t)((ql[48]  >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
+        tmp += sum;
+#else
+        float sum = 0;
+        for (int l = 0; l < 4; ++l) {
+            sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
+                 + y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
+                 + y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
+                 + y[l+96] * s[6] * d * ((int8_t)((ql[l+32]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
+        }
+        tmp += sum;
+#endif
+
+    }
+
+#else
+
+    const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION);  // 0...7
+    const int ix  = threadIdx.x%(2*K_QUANTS_PER_ITERATION);  // 0...3
+
+    const int step = tid * K_QUANTS_PER_ITERATION;
+
+    float tmp = 0; // partial sum for thread in warp
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+
+        const float   * y  = yy + i * QK_K + step;
+        const uint8_t * ql = x[i].ql + step;
+        const uint8_t * qh = x[i].qh + step;
+        const int8_t  * s  = x[i].scales;
+
+        const float d = x[i+0].d;
+
+        float sum = 0;
+        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
+            sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32)
+                 + y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32)
+                 + y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >>  4) | ((qh[j] & 0x30) >> 0)) - 32)
+                 + y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >>  4) | ((qh[j] & 0xc0) >> 2)) - 32);
+        }
+        tmp += sum;
+
+    }
+
+#endif
+
+    // sum up partial sums and write back result
+    tmp = warp_reduce_sum(tmp);
+
+    if (tid == 0) {
+        dst[row] = tmp;
+    }
+}
+
+static __device__ void convert_f16(const void * vx, const int ib, const int iqs, dfloat2 & v){
+    const half * x = (const half *) vx;
+
+    // automatic half -> float type cast if dfloat == float
+    v.x = x[ib + iqs + 0];
+    v.y = x[ib + iqs + 1];
+}
+
+template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
+static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows) {
+    // qk = quantized weights per x block
+    // qr = number of quantized weights per data value in x block
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int tid = threadIdx.x;
+
+    const int iter_stride = 2*GGML_CUDA_DMMV_X;
+    const int vals_per_iter = iter_stride / WARP_SIZE; // num quantized vals per thread and i iter
+    const int y_offset = qr == 1 ? 1 : qk/2;
+
+// partial sum for each thread
+#ifdef GGML_CUDA_F16
+    half2 tmp = {0.0f, 0.0f}; // two sums for f16 to take advantage of half2 intrinsics
+#else
+    float tmp = 0.0f;
+#endif // GGML_CUDA_F16
+
+    for (int i = 0; i < ncols; i += iter_stride) {
+        const int col = i + vals_per_iter*tid;
+        const int ib = (row*ncols + col)/qk; // x block index
+        const int iqs = (col%qk)/qr; // x quant index
+        const int iybs = col - col%qk; // y block start index
+
+// processing >2 values per i iter is faster for fast GPUs
+#pragma unroll
+        for (int j = 0; j < vals_per_iter; j += 2) {
+            // process 2 vals per j iter
+
+            // dequantize
+            // for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val
+            dfloat2 v;
+            dequantize_kernel(vx, ib, iqs + j/qr, v);
+
+            // matrix multiplication
+            // for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2
+#ifdef GGML_CUDA_F16
+            tmp += __hmul2(v, {
+                y[iybs + iqs + j/qr + 0],
+                y[iybs + iqs + j/qr + y_offset]
+            });
+#else
+            tmp += v.x * y[iybs + iqs + j/qr + 0];
+            tmp += v.y * y[iybs + iqs + j/qr + y_offset];
+#endif // GGML_CUDA_F16
+        }
+    }
+
+    // sum up partial sums and write back result
+    tmp = warp_reduce_sum(tmp);
+
+    if (tid == 0) {
+#ifdef GGML_CUDA_F16
+        dst[row] = tmp.x + tmp.y;
+#else
+        dst[row] = tmp;
+#endif // GGML_CUDA_F16
+    }
+}
+
+static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>
+        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>
+        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>
+        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q2_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(32, ny, 1);
+    dequantize_mul_mat_vec_q2_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q3_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2 / K_QUANTS_PER_ITERATION;
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(32, ny, 1);
+    dequantize_mul_mat_vec_q3_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q4_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2 / K_QUANTS_PER_ITERATION;
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(32, ny, 1);
+    dequantize_mul_mat_vec_q4_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void dequantize_mul_mat_vec_q5_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const dim3 block_dims(32, 1, 1);
+    dequantize_mul_mat_vec_q5_k<<<nrows, block_dims, 0, stream>>>(vx, y, dst, ncols);
+}
+
+static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2 / K_QUANTS_PER_ITERATION;
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(32, ny, 1);
+    dequantize_mul_mat_vec_q6_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(block_num_y, 1, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    dequantize_mul_mat_vec<1, 1, convert_f16>
+        <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
+}
+
+void ggml_cuda_op_dequantize_mul_mat_vec(
+    ggml_backend_cuda_context & ctx,
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, cudaStream_t stream) {
+    GGML_UNUSED(ctx);
+    const int64_t ne00 = src0->ne[0];
+    const int64_t row_diff = row_high - row_low;
+
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
+#ifdef GGML_CUDA_F16
+    ggml_cuda_pool_alloc<half> src1_dfloat_a(ctx.pool());
+    half * src1_dfloat = nullptr; // dfloat == half
+
+    bool src1_convert_f16 =
+        src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 ||
+        src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 ||
+        src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16;
+
+    if (src1_convert_f16) {
+        src1_dfloat = src1_dfloat_a.alloc(ne00);
+        const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
+        GGML_ASSERT(to_fp16_cuda != nullptr);
+        to_fp16_cuda(src1_ddf_i, src1_dfloat, ne00, stream);
+    }
+#else
+    const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion
+#endif // GGML_CUDA_F16
+
+    switch (src0->type) {
+        case GGML_TYPE_Q4_0:
+            dequantize_mul_mat_vec_q4_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            dequantize_mul_mat_vec_q4_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            dequantize_mul_mat_vec_q5_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            dequantize_mul_mat_vec_q5_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            dequantize_mul_mat_vec_q8_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q2_K:
+            dequantize_mul_mat_vec_q2_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q3_K:
+            dequantize_mul_mat_vec_q3_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q4_K:
+            dequantize_mul_mat_vec_q4_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_K:
+            dequantize_mul_mat_vec_q5_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q6_K:
+            dequantize_mul_mat_vec_q6_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_F16:
+            convert_mul_mat_vec_f16_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_ddq_i);
+    GGML_UNUSED(src1_ncols);
+    GGML_UNUSED(src1_padded_row_size);
+}
diff --git a/ggml-cuda/dmmv.cuh b/ggml-cuda/dmmv.cuh
new file mode 100644
index 000000000..4c5ebd475
--- /dev/null
+++ b/ggml-cuda/dmmv.cuh
@@ -0,0 +1,18 @@
+#include "common.cuh"
+
+// dmmv = dequantize_mul_mat_vec
+
+// TODO: remove this?
+#ifndef GGML_CUDA_DMMV_X
+#define GGML_CUDA_DMMV_X 32
+#endif
+
+#ifndef GGML_CUDA_MMV_Y
+#define GGML_CUDA_MMV_Y 1
+#endif
+
+void ggml_cuda_op_dequantize_mul_mat_vec(
+    ggml_backend_cuda_context & ctx,
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, cudaStream_t stream);
diff --git a/ggml-cuda/getrows.cu b/ggml-cuda/getrows.cu
new file mode 100644
index 000000000..55af195fd
--- /dev/null
+++ b/ggml-cuda/getrows.cu
@@ -0,0 +1,178 @@
+#include "getrows.cuh"
+#include "dequantize.cuh"
+
+template<int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
+static __global__ void k_get_rows(
+            const void * src0, const int32_t * src1, dst_t * dst,
+            int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
+            /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
+            /*size_t s0,*/ size_t s1, size_t s2, size_t s3,
+            /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
+            size_t s10, size_t s11, size_t s12/*, size_t s13*/) {
+
+    const int i00 = (blockIdx.x*blockDim.x + threadIdx.x)*2;
+    const int i10 = blockDim.y*blockIdx.y + threadIdx.y;
+    const int i11 = (blockIdx.z*blockDim.z + threadIdx.z)/ne12;
+    const int i12 = (blockIdx.z*blockDim.z + threadIdx.z)%ne12;
+
+    if (i00 >= ne00) {
+        return;
+    }
+
+    const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
+
+    dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
+    const void * src0_row = (const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03;
+
+    const int ib = i00/qk; // block index
+    const int iqs = (i00%qk)/qr; // quant index
+    const int iybs = i00 - i00%qk; // dst block start index
+    const int y_offset = qr == 1 ? 1 : qk/2;
+
+    // dequantize
+    dfloat2 v;
+    dequantize_kernel(src0_row, ib, iqs, v);
+
+    dst_row[iybs + iqs + 0]        = v.x;
+    dst_row[iybs + iqs + y_offset] = v.y;
+}
+
+template<typename src0_t, typename dst_t>
+static __global__ void k_get_rows_float(
+            const src0_t * src0, const int32_t * src1, dst_t * dst,
+            int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
+            /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
+            /*size_t s0,*/ size_t s1, size_t s2, size_t s3,
+            /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
+            size_t s10, size_t s11, size_t s12/*, size_t s13*/) {
+
+    const int i00 = blockIdx.x*blockDim.x + threadIdx.x;
+    const int i10 = blockDim.y*blockIdx.y + threadIdx.y;
+    const int i11 = (blockIdx.z*blockDim.z + threadIdx.z)/ne12;
+    const int i12 = (blockIdx.z*blockDim.z + threadIdx.z)%ne12;
+
+    if (i00 >= ne00) {
+        return;
+    }
+
+    const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
+
+    dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
+    const src0_t * src0_row = (const src0_t *)((const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03);
+
+    dst_row[i00] = src0_row[i00];
+}
+
+template<int qk, int qr, dequantize_kernel_t dq>
+static void get_rows_cuda(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+                            const void * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) {
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
+    const int block_num_x = (ne00 + 2*CUDA_GET_ROWS_BLOCK_SIZE - 1) / (2*CUDA_GET_ROWS_BLOCK_SIZE);
+    const dim3 block_nums(block_num_x, ne10, ne11*ne12);
+
+    // strides in elements
+    //const size_t s0 = nb0 / ggml_element_size(dst);
+    const size_t s1 = nb1 / ggml_element_size(dst);
+    const size_t s2 = nb2 / ggml_element_size(dst);
+    const size_t s3 = nb3 / ggml_element_size(dst);
+
+    const size_t s10 = nb10 / ggml_element_size(src1);
+    const size_t s11 = nb11 / ggml_element_size(src1);
+    const size_t s12 = nb12 / ggml_element_size(src1);
+    //const size_t s13 = nb13 / ggml_element_size(src1);
+
+    GGML_ASSERT(ne00 % 2 == 0);
+
+    k_get_rows<qk, qr, dq><<<block_nums, block_dims, 0, stream>>>(
+            src0_dd, src1_dd, dst_dd,
+            ne00, /*ne01, ne02, ne03,*/
+            /*ne10, ne11,*/ ne12, /*ne13,*/
+            /* s0,*/ s1, s2, s3,
+            /* nb00,*/ nb01, nb02, nb03,
+            s10, s11, s12/*, s13*/);
+
+    GGML_UNUSED(dst);
+}
+
+template<typename src0_t>
+static void get_rows_cuda_float(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+                                const src0_t * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) {
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
+    const int block_num_x = (ne00 + CUDA_GET_ROWS_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BLOCK_SIZE;
+    const dim3 block_nums(block_num_x, ne10, ne11*ne12);
+
+    // strides in elements
+    //const size_t s0 = nb0 / ggml_element_size(dst);
+    const size_t s1 = nb1 / ggml_element_size(dst);
+    const size_t s2 = nb2 / ggml_element_size(dst);
+    const size_t s3 = nb3 / ggml_element_size(dst);
+
+    const size_t s10 = nb10 / ggml_element_size(src1);
+    const size_t s11 = nb11 / ggml_element_size(src1);
+    const size_t s12 = nb12 / ggml_element_size(src1);
+    //const size_t s13 = nb13 / ggml_element_size(src1);
+
+    k_get_rows_float<<<block_nums, block_dims, 0, stream>>>(
+            src0_dd, src1_dd, dst_dd,
+            ne00, /*ne01, ne02, ne03,*/
+            /*ne10, ne11,*/ ne12, /*ne13,*/
+            /* s0,*/ s1, s2, s3,
+            /* nb00,*/ nb01, nb02, nb03,
+            s10, s11, s12/*, s13*/);
+
+    GGML_UNUSED(dst);
+}
+
+void ggml_cuda_op_get_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const float * src0_d = (const float *)src0->data;
+    const float * src1_d = (const float *)src1->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+
+    GGML_ASSERT(src1->type == GGML_TYPE_I32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
+    GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type));
+    GGML_ASSERT(dst->nb[0] == ggml_type_size(dst->type));
+
+    const int32_t * src1_i32 = (const int32_t *) src1_d;
+
+    switch (src0->type) {
+        case GGML_TYPE_F16:
+            get_rows_cuda_float(src0, src1, dst, (const half *)src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_F32:
+            get_rows_cuda_float(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q4_0:
+            get_rows_cuda<QK4_0, QR4_0, dequantize_q4_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            get_rows_cuda<QK4_1, QR4_1, dequantize_q4_1>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            get_rows_cuda<QK5_0, QR5_0, dequantize_q5_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            get_rows_cuda<QK5_1, QR5_1, dequantize_q5_1>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            get_rows_cuda<QK8_0, QR8_0, dequantize_q8_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        default:
+            // TODO: k-quants
+            fprintf(stderr, "%s: unsupported type: %s\n", __func__, ggml_type_name(src0->type));
+            GGML_ASSERT(false);
+            break;
+    }
+}
diff --git a/ggml-cuda/getrows.cuh b/ggml-cuda/getrows.cuh
new file mode 100644
index 000000000..bbf130232
--- /dev/null
+++ b/ggml-cuda/getrows.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_GET_ROWS_BLOCK_SIZE 256
+
+void ggml_cuda_op_get_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/im2col.cu b/ggml-cuda/im2col.cu
new file mode 100644
index 000000000..3d0d8d4e6
--- /dev/null
+++ b/ggml-cuda/im2col.cu
@@ -0,0 +1,104 @@
+#include "im2col.cuh"
+
+template <typename T>
+static  __global__ void im2col_kernel(
+        const float * x, T * dst, int64_t batch_offset,
+        int64_t offset_delta, int64_t IC, int64_t IW, int64_t IH, int64_t OH, int64_t OW, int64_t KW, int64_t KH, int64_t pelements, int64_t CHW,
+        int s0, int s1, int p0, int p1, int d0, int d1) {
+    const int64_t i = threadIdx.x + blockIdx.x * blockDim.x;
+    if (i >= pelements) {
+        return;
+    }
+
+    const int64_t  ksize = OW * (KH > 1 ? KW : 1);
+    const int64_t  kx = i / ksize;
+    const int64_t  kd = kx * ksize;
+    const int64_t  ky = (i - kd) / OW;
+    const int64_t  ix = i % OW;
+
+    const int64_t  oh = blockIdx.y;
+    const int64_t  batch = blockIdx.z / IC;
+    const int64_t  ic = blockIdx.z % IC;
+
+    const int64_t iiw = ix * s0 + kx * d0 - p0;
+    const int64_t iih = oh * s1 + ky * d1 - p1;
+
+    const int64_t offset_dst =
+        ((batch * OH + oh) * OW + ix) * CHW +
+        (ic * (KW * KH) + ky * KW + kx);
+
+    if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
+        dst[offset_dst] = 0.0f;
+    } else {
+        const int64_t offset_src = ic * offset_delta + batch * batch_offset;
+        dst[offset_dst] = x[offset_src + iih * IW + iiw];
+    }
+}
+
+template <typename T>
+static void im2col_cuda(const float * x, T* dst,
+    int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC,
+    int64_t batch, int64_t batch_offset, int64_t offset_delta,
+    int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {
+    const int parallel_elements = OW * KW * KH;
+    const int num_blocks = (parallel_elements + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE;
+    dim3 block_nums(num_blocks, OH, batch * IC);
+    im2col_kernel<<<block_nums, CUDA_IM2COL_BLOCK_SIZE, 0, stream>>>(x, dst, batch_offset, offset_delta, IC, IW, IH, OH, OW, KW, KH, parallel_elements, (IC * KH * KW), s0, s1, p0, p1, d0, d1);
+}
+
+static void im2col_cuda_f16(const float * x, half * dst,
+    int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC,
+    int64_t batch, int64_t batch_offset, int64_t offset_delta,
+    int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {
+
+    im2col_cuda<half>(x, dst, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, offset_delta, s0, s1, p0, p1, d0, d1, stream);
+}
+
+static void im2col_cuda_f32(const float * x, float * dst,
+    int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC,
+    int64_t batch, int64_t batch_offset, int64_t offset_delta,
+    int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {
+
+    im2col_cuda<float>(x, dst, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, offset_delta, s0, s1, p0, p1, d0, d1, stream);
+}
+
+void ggml_cuda_op_im2col(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const float * src1_d = (const float *)src1->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
+
+    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
+    const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
+    const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
+    const int32_t p1 = ((const int32_t*)(dst->op_params))[3];
+    const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
+    const int32_t d1 = ((const int32_t*)(dst->op_params))[5];
+
+    const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1;
+
+    const int64_t IC = src1->ne[is_2D ? 2 : 1];
+    const int64_t IH = is_2D ? src1->ne[1] : 1;
+    const int64_t IW =         src1->ne[0];
+
+    const int64_t KH = is_2D ? src0->ne[1] : 1;
+    const int64_t KW =         src0->ne[0];
+
+    const int64_t OH = is_2D ? dst->ne[2] : 1;
+    const int64_t OW =         dst->ne[1];
+
+    const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
+    const int64_t batch = src1->ne[3];
+    const size_t batch_offset = src1->nb[3] / 4; // nb is byte offset, src is type float32
+
+    if(dst->type == GGML_TYPE_F16) {
+        im2col_cuda_f16(src1_d, (half *) dst_d, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, stream);
+    } else {
+        im2col_cuda_f32(src1_d, (float *) dst_d, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, stream);
+    }
+}
diff --git a/ggml-cuda/im2col.cuh b/ggml-cuda/im2col.cuh
new file mode 100644
index 000000000..1ce8fae4d
--- /dev/null
+++ b/ggml-cuda/im2col.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_IM2COL_BLOCK_SIZE 256
+
+void ggml_cuda_op_im2col(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/mmq.cu b/ggml-cuda/mmq.cu
new file mode 100644
index 000000000..60d6616a8
--- /dev/null
+++ b/ggml-cuda/mmq.cu
@@ -0,0 +1,2265 @@
+#include "mmq.cuh"
+#include "vecdotq.cuh"
+
+typedef void (*allocate_tiles_cuda_t)(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc);
+typedef void (*load_tiles_cuda_t)(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row);
+typedef float (*vec_dot_q_mul_mat_cuda_t)(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ms, const int & i, const int & j, const int & k);
+typedef void (*dot_kernel_k_t)(const void * __restrict__ vx, const int ib, const int iqs, const float * __restrict__ y, float & v);
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+    GGML_UNUSED(x_qh);
+    GGML_UNUSED(x_sc);
+
+    __shared__ int  tile_x_qs[mmq_y * (WARP_SIZE)       + mmq_y];
+    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI4_0) + mmq_y/QI4_0];
+
+    *x_ql = tile_x_qs;
+    *x_dm = (half2 *) tile_x_d;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_0(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI4_0;
+    const int kqsx = k % QI4_0;
+
+    const block_q4_0 * bx0 = (const block_q4_0 *) vx;
+
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
+        // x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = bxi->d;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_0;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_0) {
+        int i = i0 + i_offset * QI4_0 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = bxi->d;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q4_0_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+    const float * x_dmf = (const float *) x_dm;
+
+    int u[2*VDR_Q4_0_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_0) % WARP_SIZE];
+    }
+
+    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
+        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dmf[i * (WARP_SIZE/QI4_0) + i/QI4_0 + k/QI4_0],
+         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    __shared__ int   tile_x_qs[mmq_y * (WARP_SIZE) +     + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_1) + mmq_y/QI4_1];
+
+    *x_ql = tile_x_qs;
+    *x_dm = tile_x_dm;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_1(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI4_1;
+    const int kqsx = k % QI4_1;
+
+    const block_q4_1 * bx0 = (const block_q4_1 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_1;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_1) {
+        int i = i0 + i_offset * QI4_1 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q4_1_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+
+    int u[2*VDR_Q4_1_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_1) % WARP_SIZE];
+    }
+
+    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMQ>
+        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dm[i * (WARP_SIZE/QI4_1) + i/QI4_1 + k/QI4_1],
+         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    __shared__ int  tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
+    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI5_0) + mmq_y/QI5_0];
+
+    *x_ql = tile_x_ql;
+    *x_dm = (half2 *) tile_x_d;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_0(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI5_0;
+    const int kqsx = k % QI5_0;
+
+    const block_q5_0 * bx0 = (const block_q5_0 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        const int ql = get_int_from_uint8(bxi->qs, kqsx);
+        const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (k % QI5_0));
+
+        int qs0 = (ql >>  0)   & 0x0F0F0F0F;
+        qs0    |= (qh <<  4)   & 0x00000010;  // 0 ->  4
+        qs0    |= (qh << 11)   & 0x00001000;  // 1 -> 12
+        qs0    |= (qh << 18)   & 0x00100000;  // 2 -> 20
+        qs0    |= (qh << 25)   & 0x10000000;  // 3 -> 28
+        qs0     = __vsubss4(qs0, 0x10101010); // subtract 16
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
+
+        int qs1 = (ql >>  4)   & 0x0F0F0F0F;
+        qs1    |= (qh >> 12)   & 0x00000010;  // 16 ->  4
+        qs1    |= (qh >>  5)   & 0x00001000;  // 17 -> 12
+        qs1    |= (qh <<  2)   & 0x00100000;  // 18 -> 20
+        qs1    |= (qh <<  9)   & 0x10000000;  // 19 -> 28
+        qs1     = __vsubss4(qs1, 0x10101010); // subtract 16
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_0;
+    const int kbxd = k % blocks_per_tile_x_row;
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_0) {
+        int i = i0 + i_offset * QI5_0 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = bxi->d;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q5_0_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+    const int index_bx = i * (WARP_SIZE/QI5_0) + i/QI5_0 + k/QI5_0;
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    int u[2*VDR_Q5_0_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE];
+    }
+
+    return vec_dot_q8_0_q8_1_impl<QR5_0*VDR_Q5_0_Q8_1_MMQ>
+        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_df[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_1) + mmq_y/QI5_1];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_1(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset < nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI5_1;
+    const int kqsx = k % QI5_1;
+
+    const block_q5_1 * bx0 = (const block_q5_1 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
+        const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (k % QI5_1));
+
+        int qs0 = (ql >>  0) & 0x0F0F0F0F;
+        qs0    |= (qh <<  4) & 0x00000010; // 0 ->  4
+        qs0    |= (qh << 11) & 0x00001000; // 1 -> 12
+        qs0    |= (qh << 18) & 0x00100000; // 2 -> 20
+        qs0    |= (qh << 25) & 0x10000000; // 3 -> 28
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
+
+        int qs1 = (ql >>  4) & 0x0F0F0F0F;
+        qs1    |= (qh >> 12) & 0x00000010; // 16 ->  4
+        qs1    |= (qh >>  5) & 0x00001000; // 17 -> 12
+        qs1    |= (qh <<  2) & 0x00100000; // 18 -> 20
+        qs1    |= (qh <<  9) & 0x10000000; // 19 -> 28
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_1;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_1) {
+        int i = i0 + i_offset * QI5_1 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q5_1_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+    const int index_bx = i * (WARP_SIZE/QI5_1) + + i/QI5_1 + k/QI5_1;
+
+    int u[2*VDR_Q5_1_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_1) % WARP_SIZE];
+    }
+
+    return vec_dot_q8_1_q8_1_impl<QR5_1*VDR_Q5_1_Q8_1_MMQ>
+        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dm[index_bx], y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q8_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    __shared__ int  tile_x_qs[mmq_y * (WARP_SIZE)       + mmq_y];
+    __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI8_0) + mmq_y/QI8_0];
+
+    *x_ql = tile_x_qs;
+    *x_dm = (half2 *) tile_x_d;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q8_0(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI8_0;
+    const int kqsx = k % QI8_0;
+    float * x_dmf = (float *) x_dm;
+
+    const block_q8_0 * bx0 = (const block_q8_0 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_int8(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI8_0;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0) {
+        int i = i0 + i_offset * QI8_0 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbxd] = bxi->d;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q8_0_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+    GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
+
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMQ>
+        (&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0],
+         y_df[j * (WARP_SIZE/QI8_1) + k/QI8_1]);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q2_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+    GGML_UNUSED(x_qh);
+
+    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI2_K) + mmq_y/QI2_K];
+    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/4)     + mmq_y/4];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_sc = tile_x_sc;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q2_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+    GGML_UNUSED(x_qh);
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI2_K;
+    const int kqsx = k % QI2_K;
+
+    const block_q2_K * bx0 = (const block_q2_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI2_K;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI2_K) {
+        int i = (i0 + i_offset * QI2_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI2_K) + i / QI2_K + kbxd] = bxi->dm;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
+        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q2_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI2_K/4);
+
+        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = get_int_from_uint8_aligned(bxi->scales, k % (QI2_K/4));
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q2_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+    GGML_UNUSED(x_qh);
+
+    const int kbx = k / QI2_K;
+    const int ky  = (k % QI2_K) * QR2_K;
+    const float * y_df = (const float *) y_ds;
+
+    int v[QR2_K*VDR_Q2_K_Q8_1_MMQ];
+
+    const int kqsx = i * (WARP_SIZE + 1) + kbx*QI2_K + (QI2_K/2) * (ky/(2*QI2_K)) + ky % (QI2_K/2);
+    const int shift = 2 * ((ky % (2*QI2_K)) / (QI2_K/2));
+
+#pragma unroll
+    for (int l = 0; l < QR2_K*VDR_Q2_K_Q8_1_MMQ; ++l) {
+        v[l] = (x_ql[kqsx + l] >> shift) & 0x03030303;
+    }
+
+    const uint8_t * scales = ((const uint8_t *) &x_sc[i * (WARP_SIZE/4) + i/4 + kbx*4]) + ky/4;
+
+    const int index_y = j * WARP_SIZE + (QR2_K*k) % WARP_SIZE;
+    return vec_dot_q2_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dm[i * (WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[index_y/QI8_1]);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q3_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+
+    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI3_K) + mmq_y/QI3_K];
+    __shared__ int   tile_x_qh[mmq_y * (WARP_SIZE/2)     + mmq_y/2];
+    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/4)     + mmq_y/4];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_qh = tile_x_qh;
+    *x_sc = tile_x_sc;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q3_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI3_K;
+    const int kqsx = k % QI3_K;
+
+    const block_q3_K * bx0 = (const block_q3_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI3_K;
+    const int kbxd = k % blocks_per_tile_x_row;
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI3_K) {
+        int i = (i0 + i_offset * QI3_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI3_K) + i / QI3_K + kbxd] = bxi->d;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 2) {
+        int i = i0 + i_offset * 2 + k / (WARP_SIZE/2);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/2)) / (QI3_K/2);
+
+        // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
+        x_qh[i * (WARP_SIZE/2) + i / 2 + k % (WARP_SIZE/2)] = ~get_int_from_uint8(bxi->hmask, k % (QI3_K/2));
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
+        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI3_K/4);
+
+        const int ksc = k % (QI3_K/4);
+
+        const int ksc_low = ksc % (QI3_K/8);
+        const int shift_low = 4 * (ksc / (QI3_K/8));
+        const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F;
+
+        const int ksc_high = QI3_K/8;
+        const int shift_high = 2 * ksc;
+        const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030;
+
+        const int sc = __vsubss4(sc_low | sc_high, 0x20202020);
+
+        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = sc;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q3_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+
+    const int kbx  = k / QI3_K;
+    const int ky  = (k % QI3_K) * QR3_K;
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    const int8_t * scales = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4;
+
+    int v[QR3_K*VDR_Q3_K_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < QR3_K*VDR_Q3_K_Q8_1_MMQ; ++l) {
+        const int kqsx = i * (WARP_SIZE + 1) + kbx*QI3_K + (QI3_K/2) * (ky/(2*QI3_K)) + ky % (QI3_K/2);
+        const int shift = 2 * ((ky % 32) / 8);
+        const int vll = (x_ql[kqsx + l] >> shift) & 0x03030303;
+
+        const int vh = x_qh[i * (WARP_SIZE/2) + i/2 + kbx * (QI3_K/2) + (ky+l)%8] >> ((ky+l) / 8);
+        const int vlh = (vh << 2) & 0x04040404;
+
+        v[l] = __vsubss4(vll, vlh);
+    }
+
+    const int index_y = j * WARP_SIZE + (k*QR3_K) % WARP_SIZE;
+    return vec_dot_q3_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dmf[i * (WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[index_y/QI8_1]);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+    GGML_UNUSED(x_qh);
+
+    __shared__ int   tile_x_ql[mmq_y * (WARP_SIZE)       + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_K) + mmq_y/QI4_K];
+    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_sc = tile_x_sc;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+    GGML_UNUSED(x_qh);
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI4_K; // == 0 if QK_K == 256
+    const int kqsx = k % QI4_K; // == k if QK_K == 256
+
+    const block_q4_K * bx0 = (const block_q4_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256
+    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_K) {
+        int i = (i0 + i_offset * QI4_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+#if QK_K == 256
+        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm;
+#else
+        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]};
+#endif
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
+        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI4_K/8);
+
+        const int * scales = (const int *) bxi->scales;
+
+        const int ksc = k % (WARP_SIZE/8);
+
+        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
+        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
+        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
+
+        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q4_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+    GGML_UNUSED(x_qh);
+
+    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2*((k % 16) / 8);
+
+    const int index_y = j * WARP_SIZE + (QR4_K*k) % WARP_SIZE;
+    return vec_dot_q4_K_q8_1_impl_mmq(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[index_y], sc, sc+8,
+                                      x_dm[i * (WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[index_y/QI8_1]);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+    GGML_UNUSED(x_qh);
+
+    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_K) + mmq_y/QI5_K];
+    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_sc = tile_x_sc;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+    GGML_UNUSED(x_qh);
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI5_K; // == 0 if QK_K == 256
+    const int kqsx = k % QI5_K; // == k if QK_K == 256
+
+    const block_q5_K * bx0 = (const block_q5_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbx;
+        const int ky = QR5_K*kqsx;
+
+        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
+        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
+        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
+
+        const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4));
+        const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010;
+        const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010;
+
+        const int kq0 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + 0;
+        const int kq1 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + (QI5_K/4);
+
+        x_ql[i * (2*WARP_SIZE + 1) + kq0] = ql0 | qh0;
+        x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256
+    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_K) {
+        int i = (i0 + i_offset * QI5_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+#if QK_K == 256
+        x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm;
+#endif
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
+        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI5_K/8);
+
+        const int * scales = (const int *) bxi->scales;
+
+        const int ksc = k % (WARP_SIZE/8);
+
+        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
+        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
+        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
+
+        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q5_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+    GGML_UNUSED(x_qh);
+
+    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2 * ((k % 16) / 8);
+
+    const int index_x = i * (QR5_K*WARP_SIZE + 1) +  QR5_K*k;
+    const int index_y = j * WARP_SIZE             + (QR5_K*k) % WARP_SIZE;
+    return vec_dot_q5_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, sc+8,
+                                      x_dm[i * (WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[index_y/QI8_1]);
+}
+
+template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q6_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
+    GGML_UNUSED(x_qh);
+
+    __shared__ int   tile_x_ql[mmq_y * (2*WARP_SIZE)     + mmq_y];
+    __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI6_K) + mmq_y/QI6_K];
+    __shared__ int   tile_x_sc[mmq_y * (WARP_SIZE/8)     + mmq_y/8];
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_sc = tile_x_sc;
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q6_K(
+    const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
+    int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
+    GGML_UNUSED(x_qh);
+
+    GGML_CUDA_ASSUME(i_offset >= 0);
+    GGML_CUDA_ASSUME(i_offset <  nwarps);
+    GGML_CUDA_ASSUME(k >= 0);
+    GGML_CUDA_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI6_K; // == 0 if QK_K == 256
+    const int kqsx = k % QI6_K; // == k if QK_K == 256
+
+    const block_q6_K * bx0 = (const block_q6_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbx;
+        const int ky = QR6_K*kqsx;
+
+        const int ql = get_int_from_uint8(bxi->ql, kqsx);
+        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
+        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
+
+        const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4));
+        const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030;
+        const int qh1 =  (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4))))       & 0x30303030;
+
+        const int kq0 = ky - ky % QI6_K + k % (QI6_K/2) + 0;
+        const int kq1 = ky - ky % QI6_K + k % (QI6_K/2) + (QI6_K/2);
+
+        x_ql[i * (2*WARP_SIZE + 1) + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
+        x_ql[i * (2*WARP_SIZE + 1) + kq1] = __vsubss4(ql1 | qh1, 0x20202020);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256
+    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI6_K) {
+        int i = (i0 + i_offset * QI6_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI6_K) + i / QI6_K + kbxd] = bxi->d;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
+        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q6_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / 4;
+
+        x_sc[i * (WARP_SIZE/8) + i / 8 + k % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, k % (QI6_K/8));
+    }
+}
+
+static __device__ __forceinline__ float vec_dot_q6_K_q8_1_mul_mat(
+    const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
+    const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
+    GGML_UNUSED(x_qh);
+
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/8]);
+
+    const int index_x = i * (QR6_K*WARP_SIZE + 1) +  QR6_K*k;
+    const int index_y = j * WARP_SIZE             + (QR6_K*k) % WARP_SIZE;
+    return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]);
+}
+
+#define  MMQ_X_Q4_0_RDNA2  64
+#define  MMQ_Y_Q4_0_RDNA2  128
+#define NWARPS_Q4_0_RDNA2  8
+#define  MMQ_X_Q4_0_RDNA1  64
+#define  MMQ_Y_Q4_0_RDNA1  64
+#define NWARPS_Q4_0_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q4_0_AMPERE 4
+#define  MMQ_Y_Q4_0_AMPERE 32
+#define NWARPS_Q4_0_AMPERE 4
+#else
+#define  MMQ_X_Q4_0_AMPERE 64
+#define  MMQ_Y_Q4_0_AMPERE 128
+#define NWARPS_Q4_0_AMPERE 4
+#endif
+#define  MMQ_X_Q4_0_PASCAL 64
+#define  MMQ_Y_Q4_0_PASCAL 64
+#define NWARPS_Q4_0_PASCAL 8
+
+template <int qk, int qr, int qi, bool need_sum, typename block_q_t, int mmq_x, int mmq_y, int nwarps,
+              allocate_tiles_cuda_t allocate_tiles, load_tiles_cuda_t load_tiles, int vdr, vec_dot_q_mul_mat_cuda_t vec_dot>
+static __device__ __forceinline__ void mul_mat_q(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    const int blocks_per_row_x = ncols_x / qk;
+    const int blocks_per_col_y = nrows_y / QK8_1;
+    const int blocks_per_warp = WARP_SIZE / qi;
+
+    const int & ncols_dst = ncols_y;
+
+    const int row_dst_0 = blockIdx.x*mmq_y;
+    const int & row_x_0 = row_dst_0;
+
+    const int col_dst_0 = blockIdx.y*mmq_x;
+    const int & col_y_0 = col_dst_0;
+
+    int   * tile_x_ql = nullptr;
+    half2 * tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+    allocate_tiles(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc);
+
+    __shared__ int    tile_y_qs[mmq_x * WARP_SIZE];
+    __shared__ half2  tile_y_ds[mmq_x * WARP_SIZE/QI8_1];
+
+    float sum[mmq_y/WARP_SIZE][mmq_x/nwarps] = {{0.0f}};
+
+    for (int ib0 = 0; ib0 < blocks_per_row_x; ib0 += blocks_per_warp) {
+
+        load_tiles(x + row_x_0*blocks_per_row_x + ib0, tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc,
+                   threadIdx.y, nrows_x-row_x_0-1, threadIdx.x, blocks_per_row_x);
+
+#pragma unroll
+        for (int ir = 0; ir < qr; ++ir) {
+            const int kqs = ir*WARP_SIZE + threadIdx.x;
+            const int kbxd = kqs / QI8_1;
+
+#pragma unroll
+            for (int i = 0; i < mmq_x; i += nwarps) {
+                const int col_y_eff = min(col_y_0 + threadIdx.y + i, ncols_y-1); // to prevent out-of-bounds memory accesses
+
+                const block_q8_1 * by0 = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + kbxd];
+
+                const int index_y = (threadIdx.y + i) * WARP_SIZE + kqs % WARP_SIZE;
+                tile_y_qs[index_y] = get_int_from_int8_aligned(by0->qs, threadIdx.x % QI8_1);
+            }
+
+#pragma unroll
+            for (int ids0 = 0; ids0 < mmq_x; ids0 += nwarps * QI8_1) {
+                const int ids = (ids0 + threadIdx.y * QI8_1 + threadIdx.x / (WARP_SIZE/QI8_1)) % mmq_x;
+                const int kby = threadIdx.x % (WARP_SIZE/QI8_1);
+                const int col_y_eff = min(col_y_0 + ids, ncols_y-1);
+
+                // if the sum is not needed it's faster to transform the scale to f32 ahead of time
+                const half2 * dsi_src = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + ir*(WARP_SIZE/QI8_1) + kby].ds;
+                half2       * dsi_dst = &tile_y_ds[ids * (WARP_SIZE/QI8_1) + kby];
+                if (need_sum) {
+                    *dsi_dst = *dsi_src;
+                } else {
+                    float * dfi_dst = (float *) dsi_dst;
+                    *dfi_dst = __low2float(*dsi_src);
+                }
+            }
+
+            __syncthreads();
+
+// #pragma unroll // unrolling this loop causes too much register pressure
+            for (int k = ir*WARP_SIZE/qr; k < (ir+1)*WARP_SIZE/qr; k += vdr) {
+#pragma unroll
+                for (int j = 0; j < mmq_x; j += nwarps) {
+#pragma unroll
+                    for (int i = 0; i < mmq_y; i += WARP_SIZE) {
+                        sum[i/WARP_SIZE][j/nwarps] += vec_dot(
+                            tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, tile_y_qs, tile_y_ds,
+                            threadIdx.x + i, threadIdx.y + j, k);
+                    }
+                }
+            }
+
+            __syncthreads();
+        }
+    }
+
+#pragma unroll
+    for (int j = 0; j < mmq_x; j += nwarps) {
+        const int col_dst = col_dst_0 + j + threadIdx.y;
+
+        if (col_dst >= ncols_dst) {
+            return;
+        }
+
+#pragma unroll
+        for (int i = 0; i < mmq_y; i += WARP_SIZE) {
+            const int row_dst = row_dst_0 + threadIdx.x + i;
+
+            if (row_dst >= nrows_dst) {
+                continue;
+            }
+
+            dst[col_dst*nrows_dst + row_dst] = sum[i/WARP_SIZE][j/nwarps];
+        }
+    }
+}
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q4_0_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+    mul_mat_q4_0(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q4_0_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q4_0_RDNA2;
+    const int nwarps = NWARPS_Q4_0_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q4_0_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q4_0_RDNA1;
+    const int nwarps = NWARPS_Q4_0_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
+        load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q4_0_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q4_0_AMPERE;
+    const int nwarps = NWARPS_Q4_0_AMPERE;
+
+    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
+        load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q4_0_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q4_0_PASCAL;
+    const int nwarps = NWARPS_Q4_0_PASCAL;
+
+    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
+        load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q4_0_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q4_1_RDNA2  64
+#define  MMQ_Y_Q4_1_RDNA2  128
+#define NWARPS_Q4_1_RDNA2  8
+#define  MMQ_X_Q4_1_RDNA1  64
+#define  MMQ_Y_Q4_1_RDNA1  64
+#define NWARPS_Q4_1_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q4_1_AMPERE 4
+#define  MMQ_Y_Q4_1_AMPERE 32
+#define NWARPS_Q4_1_AMPERE 4
+#else
+#define  MMQ_X_Q4_1_AMPERE 64
+#define  MMQ_Y_Q4_1_AMPERE 128
+#define NWARPS_Q4_1_AMPERE 4
+#endif
+#define  MMQ_X_Q4_1_PASCAL 64
+#define  MMQ_Y_Q4_1_PASCAL 64
+#define NWARPS_Q4_1_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#elif __CUDA_ARCH__ < CC_VOLTA
+    __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_PASCAL, 2)
+#endif // __CUDA_ARCH__ < CC_VOLTA
+    mul_mat_q4_1(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q4_1_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q4_1_RDNA2;
+    const int nwarps = NWARPS_Q4_1_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q4_1_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q4_1_RDNA1;
+    const int nwarps = NWARPS_Q4_1_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
+        load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q4_1_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q4_1_AMPERE;
+    const int nwarps = NWARPS_Q4_1_AMPERE;
+
+    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
+        load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q4_1_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q4_1_PASCAL;
+    const int nwarps = NWARPS_Q4_1_PASCAL;
+
+    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
+        load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q4_1_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q5_0_RDNA2  64
+#define  MMQ_Y_Q5_0_RDNA2  128
+#define NWARPS_Q5_0_RDNA2  8
+#define  MMQ_X_Q5_0_RDNA1  64
+#define  MMQ_Y_Q5_0_RDNA1  64
+#define NWARPS_Q5_0_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q5_0_AMPERE 4
+#define  MMQ_Y_Q5_0_AMPERE 32
+#define NWARPS_Q5_0_AMPERE 4
+#else
+#define  MMQ_X_Q5_0_AMPERE 128
+#define  MMQ_Y_Q5_0_AMPERE 64
+#define NWARPS_Q5_0_AMPERE 4
+#endif
+#define  MMQ_X_Q5_0_PASCAL 64
+#define  MMQ_Y_Q5_0_PASCAL 64
+#define NWARPS_Q5_0_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q5_0_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+    mul_mat_q5_0(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q5_0_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q5_0_RDNA2;
+    const int nwarps = NWARPS_Q5_0_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q5_0_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q5_0_RDNA1;
+    const int nwarps = NWARPS_Q5_0_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
+        load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q5_0_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q5_0_AMPERE;
+    const int nwarps = NWARPS_Q5_0_AMPERE;
+
+    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
+        load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q5_0_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q5_0_PASCAL;
+    const int nwarps = NWARPS_Q5_0_PASCAL;
+
+    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
+        load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q5_0_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q5_1_RDNA2  64
+#define  MMQ_Y_Q5_1_RDNA2  128
+#define NWARPS_Q5_1_RDNA2  8
+#define  MMQ_X_Q5_1_RDNA1  64
+#define  MMQ_Y_Q5_1_RDNA1  64
+#define NWARPS_Q5_1_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q5_1_AMPERE 4
+#define  MMQ_Y_Q5_1_AMPERE 32
+#define NWARPS_Q5_1_AMPERE 4
+#else
+#define  MMQ_X_Q5_1_AMPERE 128
+#define  MMQ_Y_Q5_1_AMPERE 64
+#define NWARPS_Q5_1_AMPERE 4
+#endif
+#define  MMQ_X_Q5_1_PASCAL 64
+#define  MMQ_Y_Q5_1_PASCAL 64
+#define NWARPS_Q5_1_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q5_1_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+mul_mat_q5_1(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q5_1_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q5_1_RDNA2;
+    const int nwarps = NWARPS_Q5_1_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q5_1_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q5_1_RDNA1;
+    const int nwarps = NWARPS_Q5_1_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
+        load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q5_1_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q5_1_AMPERE;
+    const int nwarps = NWARPS_Q5_1_AMPERE;
+
+    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
+        load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q5_1_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q5_1_PASCAL;
+    const int nwarps = NWARPS_Q5_1_PASCAL;
+
+    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
+        load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q5_1_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q8_0_RDNA2  64
+#define  MMQ_Y_Q8_0_RDNA2  128
+#define NWARPS_Q8_0_RDNA2  8
+#define  MMQ_X_Q8_0_RDNA1  64
+#define  MMQ_Y_Q8_0_RDNA1  64
+#define NWARPS_Q8_0_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q8_0_AMPERE 4
+#define  MMQ_Y_Q8_0_AMPERE 32
+#define NWARPS_Q8_0_AMPERE 4
+#else
+#define  MMQ_X_Q8_0_AMPERE 128
+#define  MMQ_Y_Q8_0_AMPERE 64
+#define NWARPS_Q8_0_AMPERE 4
+#endif
+#define  MMQ_X_Q8_0_PASCAL 64
+#define  MMQ_Y_Q8_0_PASCAL 64
+#define NWARPS_Q8_0_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q8_0_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+    mul_mat_q8_0(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q8_0_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q8_0_RDNA2;
+    const int nwarps = NWARPS_Q8_0_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q8_0_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q8_0_RDNA1;
+    const int nwarps = NWARPS_Q8_0_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
+        load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q8_0_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q8_0_AMPERE;
+    const int nwarps = NWARPS_Q8_0_AMPERE;
+
+    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
+        load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q8_0_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q8_0_PASCAL;
+    const int nwarps = NWARPS_Q8_0_PASCAL;
+
+    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
+        load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q8_0_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q2_K_RDNA2  64
+#define  MMQ_Y_Q2_K_RDNA2  128
+#define NWARPS_Q2_K_RDNA2  8
+#define  MMQ_X_Q2_K_RDNA1  128
+#define  MMQ_Y_Q2_K_RDNA1  32
+#define NWARPS_Q2_K_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q2_K_AMPERE 4
+#define  MMQ_Y_Q2_K_AMPERE 32
+#define NWARPS_Q2_K_AMPERE 4
+#else
+#define  MMQ_X_Q2_K_AMPERE 64
+#define  MMQ_Y_Q2_K_AMPERE 128
+#define NWARPS_Q2_K_AMPERE 4
+#endif
+#define  MMQ_X_Q2_K_PASCAL 64
+#define  MMQ_Y_Q2_K_PASCAL 64
+#define NWARPS_Q2_K_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q2_K_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+mul_mat_q2_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q2_K_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q2_K_RDNA2;
+    const int nwarps = NWARPS_Q2_K_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q2_K_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q2_K_RDNA1;
+    const int nwarps = NWARPS_Q2_K_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
+        load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q2_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q2_K_AMPERE;
+    const int nwarps = NWARPS_Q2_K_AMPERE;
+
+    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
+        load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q2_K_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q2_K_PASCAL;
+    const int nwarps = NWARPS_Q2_K_PASCAL;
+
+    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
+        load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q2_K_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q3_K_RDNA2  128
+#define  MMQ_Y_Q3_K_RDNA2  64
+#define NWARPS_Q3_K_RDNA2  8
+#define  MMQ_X_Q3_K_RDNA1  32
+#define  MMQ_Y_Q3_K_RDNA1  128
+#define NWARPS_Q3_K_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q3_K_AMPERE 4
+#define  MMQ_Y_Q3_K_AMPERE 32
+#define NWARPS_Q3_K_AMPERE 4
+#else
+#define  MMQ_X_Q3_K_AMPERE 128
+#define  MMQ_Y_Q3_K_AMPERE 128
+#define NWARPS_Q3_K_AMPERE 4
+#endif
+#define  MMQ_X_Q3_K_PASCAL 64
+#define  MMQ_Y_Q3_K_PASCAL 64
+#define NWARPS_Q3_K_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#elif __CUDA_ARCH__ < CC_VOLTA
+    __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_PASCAL, 2)
+#endif // __CUDA_ARCH__ < CC_VOLTA
+    mul_mat_q3_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q3_K_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q3_K_RDNA2;
+    const int nwarps = NWARPS_Q3_K_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q3_K_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q3_K_RDNA1;
+    const int nwarps = NWARPS_Q3_K_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
+        load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q3_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q3_K_AMPERE;
+    const int nwarps = NWARPS_Q3_K_AMPERE;
+
+    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
+        load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q3_K_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q3_K_PASCAL;
+    const int nwarps = NWARPS_Q3_K_PASCAL;
+
+    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
+        load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q3_K_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q4_K_RDNA2  64
+#define  MMQ_Y_Q4_K_RDNA2  128
+#define NWARPS_Q4_K_RDNA2  8
+#define  MMQ_X_Q4_K_RDNA1  32
+#define  MMQ_Y_Q4_K_RDNA1  64
+#define NWARPS_Q4_K_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q4_K_AMPERE 4
+#define  MMQ_Y_Q4_K_AMPERE 32
+#define NWARPS_Q4_K_AMPERE 4
+#else
+#define  MMQ_X_Q4_K_AMPERE 64
+#define  MMQ_Y_Q4_K_AMPERE 128
+#define NWARPS_Q4_K_AMPERE 4
+#endif
+#define  MMQ_X_Q4_K_PASCAL 64
+#define  MMQ_Y_Q4_K_PASCAL 64
+#define NWARPS_Q4_K_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#elif __CUDA_ARCH__ < CC_VOLTA
+    __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_PASCAL, 2)
+#endif // __CUDA_ARCH__ < CC_VOLTA
+    mul_mat_q4_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q4_K_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q4_K_RDNA2;
+    const int nwarps = NWARPS_Q4_K_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q4_K_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q4_K_RDNA1;
+    const int nwarps = NWARPS_Q4_K_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
+        load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q4_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q4_K_AMPERE;
+    const int nwarps = NWARPS_Q4_K_AMPERE;
+
+    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
+        load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q4_K_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q4_K_PASCAL;
+    const int nwarps = NWARPS_Q4_K_PASCAL;
+
+    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
+        load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q4_K_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q5_K_RDNA2  64
+#define  MMQ_Y_Q5_K_RDNA2  128
+#define NWARPS_Q5_K_RDNA2  8
+#define  MMQ_X_Q5_K_RDNA1  32
+#define  MMQ_Y_Q5_K_RDNA1  64
+#define NWARPS_Q5_K_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q5_K_AMPERE 4
+#define  MMQ_Y_Q5_K_AMPERE 32
+#define NWARPS_Q5_K_AMPERE 4
+#else
+#define  MMQ_X_Q5_K_AMPERE 64
+#define  MMQ_Y_Q5_K_AMPERE 128
+#define NWARPS_Q5_K_AMPERE 4
+#endif
+#define  MMQ_X_Q5_K_PASCAL 64
+#define  MMQ_Y_Q5_K_PASCAL 64
+#define NWARPS_Q5_K_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q5_K_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+mul_mat_q5_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q5_K_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q5_K_RDNA2;
+    const int nwarps = NWARPS_Q5_K_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q5_K_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q5_K_RDNA1;
+    const int nwarps = NWARPS_Q5_K_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
+        load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q5_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q5_K_AMPERE;
+    const int nwarps = NWARPS_Q5_K_AMPERE;
+
+    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
+        load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q5_K_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q5_K_PASCAL;
+    const int nwarps = NWARPS_Q5_K_PASCAL;
+
+    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
+        load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q5_K_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+#define  MMQ_X_Q6_K_RDNA2  64
+#define  MMQ_Y_Q6_K_RDNA2  128
+#define NWARPS_Q6_K_RDNA2  8
+#define  MMQ_X_Q6_K_RDNA1  32
+#define  MMQ_Y_Q6_K_RDNA1  64
+#define NWARPS_Q6_K_RDNA1  8
+#if defined(CUDA_USE_TENSOR_CORES)
+#define  MMQ_X_Q6_K_AMPERE 4
+#define  MMQ_Y_Q6_K_AMPERE 32
+#define NWARPS_Q6_K_AMPERE 4
+#else
+#define  MMQ_X_Q6_K_AMPERE 64
+#define  MMQ_Y_Q6_K_AMPERE 64
+#define NWARPS_Q6_K_AMPERE 4
+#endif
+#define  MMQ_X_Q6_K_PASCAL 64
+#define  MMQ_Y_Q6_K_PASCAL 64
+#define NWARPS_Q6_K_PASCAL 8
+
+template <bool need_check> static __global__ void
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_RDNA2, 2)
+#endif // defined(RDNA3) || defined(RDNA2)
+#elif __CUDA_ARCH__ < CC_VOLTA
+    __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_PASCAL, 2)
+#endif // __CUDA_ARCH__ < CC_VOLTA
+    mul_mat_q6_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA3) || defined(RDNA2)
+    const int mmq_x  =  MMQ_X_Q6_K_RDNA2;
+    const int mmq_y  =  MMQ_Y_Q6_K_RDNA2;
+    const int nwarps = NWARPS_Q6_K_RDNA2;
+#else
+    const int mmq_x  =  MMQ_X_Q6_K_RDNA1;
+    const int mmq_y  =  MMQ_Y_Q6_K_RDNA1;
+    const int nwarps = NWARPS_Q6_K_RDNA1;
+#endif // defined(RDNA3) || defined(RDNA2)
+
+    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
+        load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= CC_VOLTA
+    const int mmq_x  =  MMQ_X_Q6_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q6_K_AMPERE;
+    const int nwarps = NWARPS_Q6_K_AMPERE;
+
+    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
+        load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+
+#elif __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int mmq_x  =  MMQ_X_Q6_K_PASCAL;
+    const int mmq_y  =  MMQ_Y_Q6_K_PASCAL;
+    const int nwarps = NWARPS_Q6_K_PASCAL;
+
+    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
+        load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
+        (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+#else
+    GGML_UNUSED(vec_dot_q6_K_q8_1_mul_mat);
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= CC_VOLTA
+}
+
+static void ggml_mul_mat_q4_0_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q4_0_RDNA2;
+        mmq_y  =  MMQ_Y_Q4_0_RDNA2;
+        nwarps = NWARPS_Q4_0_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q4_0_RDNA1;
+        mmq_y  =  MMQ_Y_Q4_0_RDNA1;
+        nwarps = NWARPS_Q4_0_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q4_0_AMPERE;
+        mmq_y  =  MMQ_Y_Q4_0_AMPERE;
+        nwarps = NWARPS_Q4_0_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q4_0_PASCAL;
+        mmq_y  =  MMQ_Y_Q4_0_PASCAL;
+        nwarps = NWARPS_Q4_0_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q4_0<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q4_0<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q4_1_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q4_1_RDNA2;
+        mmq_y  =  MMQ_Y_Q4_1_RDNA2;
+        nwarps = NWARPS_Q4_1_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q4_1_RDNA1;
+        mmq_y  =  MMQ_Y_Q4_1_RDNA1;
+        nwarps = NWARPS_Q4_1_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q4_1_AMPERE;
+        mmq_y  =  MMQ_Y_Q4_1_AMPERE;
+        nwarps = NWARPS_Q4_1_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q4_1_PASCAL;
+        mmq_y  =  MMQ_Y_Q4_1_PASCAL;
+        nwarps = NWARPS_Q4_1_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q4_1<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q4_1<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q5_0_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q5_0_RDNA2;
+        mmq_y  =  MMQ_Y_Q5_0_RDNA2;
+        nwarps = NWARPS_Q5_0_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q5_0_RDNA1;
+        mmq_y  =  MMQ_Y_Q5_0_RDNA1;
+        nwarps = NWARPS_Q5_0_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q5_0_AMPERE;
+        mmq_y  =  MMQ_Y_Q5_0_AMPERE;
+        nwarps = NWARPS_Q5_0_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q5_0_PASCAL;
+        mmq_y  =  MMQ_Y_Q5_0_PASCAL;
+        nwarps = NWARPS_Q5_0_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q5_0<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q5_0<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q5_1_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q5_1_RDNA2;
+        mmq_y  =  MMQ_Y_Q5_1_RDNA2;
+        nwarps = NWARPS_Q5_1_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q5_1_RDNA1;
+        mmq_y  =  MMQ_Y_Q5_1_RDNA1;
+        nwarps = NWARPS_Q5_1_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q5_1_AMPERE;
+        mmq_y  =  MMQ_Y_Q5_1_AMPERE;
+        nwarps = NWARPS_Q5_1_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q5_1_PASCAL;
+        mmq_y  =  MMQ_Y_Q5_1_PASCAL;
+        nwarps = NWARPS_Q5_1_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q5_1<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q5_1<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q8_0_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q8_0_RDNA2;
+        mmq_y  =  MMQ_Y_Q8_0_RDNA2;
+        nwarps = NWARPS_Q8_0_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q8_0_RDNA1;
+        mmq_y  =  MMQ_Y_Q8_0_RDNA1;
+        nwarps = NWARPS_Q8_0_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q8_0_AMPERE;
+        mmq_y  =  MMQ_Y_Q8_0_AMPERE;
+        nwarps = NWARPS_Q8_0_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q8_0_PASCAL;
+        mmq_y  =  MMQ_Y_Q8_0_PASCAL;
+        nwarps = NWARPS_Q8_0_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q8_0<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q8_0<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q2_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q2_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q2_K_RDNA2;
+        nwarps = NWARPS_Q2_K_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q2_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q2_K_RDNA1;
+        nwarps = NWARPS_Q2_K_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q2_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q2_K_AMPERE;
+        nwarps = NWARPS_Q2_K_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q2_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q2_K_PASCAL;
+        nwarps = NWARPS_Q2_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q2_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q2_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q3_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+#if QK_K == 256
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q3_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q3_K_RDNA2;
+        nwarps = NWARPS_Q3_K_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q3_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q3_K_RDNA1;
+        nwarps = NWARPS_Q3_K_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q3_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q3_K_AMPERE;
+        nwarps = NWARPS_Q3_K_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q3_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q3_K_PASCAL;
+        nwarps = NWARPS_Q3_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+#endif
+}
+
+static void ggml_mul_mat_q4_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q4_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q4_K_RDNA2;
+        nwarps = NWARPS_Q4_K_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q4_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q4_K_RDNA1;
+        nwarps = NWARPS_Q4_K_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q4_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q4_K_AMPERE;
+        nwarps = NWARPS_Q4_K_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q4_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q4_K_PASCAL;
+        nwarps = NWARPS_Q4_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q4_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q4_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q5_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q5_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q5_K_RDNA2;
+        nwarps = NWARPS_Q5_K_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q5_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q5_K_RDNA1;
+        nwarps = NWARPS_Q5_K_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q5_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q5_K_AMPERE;
+        nwarps = NWARPS_Q5_K_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q5_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q5_K_PASCAL;
+        nwarps = NWARPS_Q5_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q5_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q5_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+static void ggml_mul_mat_q6_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+    const int compute_capability = ggml_cuda_info().devices[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= CC_RDNA2) {
+        mmq_x  =  MMQ_X_Q6_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q6_K_RDNA2;
+        nwarps = NWARPS_Q6_K_RDNA2;
+    } else if (compute_capability >= CC_OFFSET_AMD) {
+        mmq_x  =  MMQ_X_Q6_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q6_K_RDNA1;
+        nwarps = NWARPS_Q6_K_RDNA1;
+    } else if (compute_capability >= CC_VOLTA) {
+        mmq_x  =  MMQ_X_Q6_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q6_K_AMPERE;
+        nwarps = NWARPS_Q6_K_AMPERE;
+    } else if (compute_capability >= MIN_CC_DP4A) {
+        mmq_x  =  MMQ_X_Q6_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q6_K_PASCAL;
+        nwarps = NWARPS_Q6_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const dim3 block_nums(block_num_x, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        mul_mat_q6_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    } else {
+        const bool need_check = true;
+        mul_mat_q6_K<need_check><<<block_nums, block_dims, 0, stream>>>
+            (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
+    }
+}
+
+void ggml_cuda_op_mul_mat_q(
+    ggml_backend_cuda_context & ctx,
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, cudaStream_t stream) {
+
+    const int64_t ne00 = src0->ne[0];
+
+    const int64_t ne10 = src1->ne[0];
+    GGML_ASSERT(ne10 % QK8_1 == 0);
+
+    const int64_t ne0 = dst->ne[0];
+
+    const int64_t row_diff = row_high - row_low;
+
+    int id = ggml_cuda_get_device();
+
+    // the main device has a larger memory buffer to hold the results from all GPUs
+    // nrows_dst == nrows of the matrix that the kernel writes into
+    const int64_t nrows_dst = id == ctx.device ? ne0 : row_diff;
+
+    switch (src0->type) {
+        case GGML_TYPE_Q4_0:
+            ggml_mul_mat_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            ggml_mul_mat_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            ggml_mul_mat_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            ggml_mul_mat_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            ggml_mul_mat_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q2_K:
+            ggml_mul_mat_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q3_K:
+            ggml_mul_mat_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q4_K:
+            ggml_mul_mat_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_K:
+            ggml_mul_mat_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q6_K:
+            ggml_mul_mat_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_ddf_i);
+}
+
+bool ggml_cuda_supports_mmq(enum ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+            return true;
+        default:
+            return false;
+    }
+}
diff --git a/ggml-cuda/mmq.cuh b/ggml-cuda/mmq.cuh
new file mode 100644
index 000000000..807817c4a
--- /dev/null
+++ b/ggml-cuda/mmq.cuh
@@ -0,0 +1,9 @@
+#include "common.cuh"
+
+void ggml_cuda_op_mul_mat_q(
+    ggml_backend_cuda_context & ctx,
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, cudaStream_t stream);
+
+bool ggml_cuda_supports_mmq(enum ggml_type type);
diff --git a/ggml-cuda/mmvq.cu b/ggml-cuda/mmvq.cu
new file mode 100644
index 000000000..396559001
--- /dev/null
+++ b/ggml-cuda/mmvq.cu
@@ -0,0 +1,406 @@
+#include "mmvq.cuh"
+#include "vecdotq.cuh"
+
+typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs);
+
+template <int ncols_y, int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+// tell the compiler to use as many registers as it wants, see nwarps definition below
+__launch_bounds__((ncols_y <= 4 ? 4 : 2)*WARP_SIZE, 1)
+#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
+static __global__ void mul_mat_vec_q(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int nrows_dst) {
+
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && (defined(RDNA2) || defined(RDNA3))
+    constexpr int nwarps              = 1;
+    constexpr int rows_per_cuda_block = 1;
+#else
+    constexpr int nwarps              = ncols_y <= 4 ? 4 : 2;
+    constexpr int rows_per_cuda_block = ncols_y == 1 ? 1 : 2;
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && !defined(RDNA2) && !defined(RDNA3)
+
+    const     int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
+    const     int row0 = rows_per_cuda_block*blockIdx.x;
+    const     int blocks_per_row_x = ncols_x / qk;
+    const     int blocks_per_col_y = nrows_y / QK8_1;
+    constexpr int blocks_per_iter = vdr * nwarps*WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp[ncols_y][rows_per_cuda_block] = {0.0f};
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int kbx = tid / (qi/vdr); kbx < blocks_per_row_x; kbx += blocks_per_iter) {
+        const int kby = kbx * (qk/QK8_1); // y block index that aligns with kbx
+
+        // x block quant index when casting the quants to int
+        const int kqs = vdr * (tid % (qi/vdr));
+
+#pragma unroll
+        for (int j = 0; j < ncols_y; ++j) {
+#pragma unroll
+            for (int i = 0; i < rows_per_cuda_block; ++i) {
+                tmp[j][i] += vec_dot_q_cuda(
+                    &x[kbx + (row0 + i)*blocks_per_row_x], &y[j*blocks_per_col_y + kby], kqs);
+            }
+        }
+    }
+
+    __shared__ float tmp_shared[nwarps-1 > 0 ? nwarps-1 : 1][ncols_y][rows_per_cuda_block][WARP_SIZE];
+    if (threadIdx.y > 0) {
+#pragma unroll
+        for (int j = 0; j < ncols_y; ++j) {
+#pragma unroll
+            for (int i = 0; i < rows_per_cuda_block; ++i) {
+                tmp_shared[threadIdx.y-1][j][i][threadIdx.x] = tmp[j][i];
+            }
+        }
+    }
+    __syncthreads();
+    if (threadIdx.y > 0) {
+        return;
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int j = 0; j < ncols_y; ++j) {
+#pragma unroll
+        for (int i = 0; i < rows_per_cuda_block; ++i) {
+#pragma unroll
+            for (int l = 0; l < nwarps-1; ++l) {
+                tmp[j][i] += tmp_shared[l][j][i][threadIdx.x];
+            }
+            tmp[j][i] = warp_reduce_sum(tmp[j][i]);
+        }
+
+        if (threadIdx.x < rows_per_cuda_block) {
+            dst[j*nrows_dst + row0 + threadIdx.x] = tmp[j][threadIdx.x];
+        }
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot>
+static void mul_mat_vec_q_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    GGML_ASSERT(ncols_x % qk == 0);
+    GGML_ASSERT(ncols_y <= MMVQ_MAX_BATCH_SIZE);
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+
+    int64_t nwarps = 1;
+    int64_t rows_per_cuda_block = 1;
+
+    if (ggml_cuda_info().devices[id].cc < CC_RDNA2) { // NVIDIA and AMD older than RDNA2
+        switch(ncols_y) {
+            case 1:
+                nwarps = 4;
+                rows_per_cuda_block = 1;
+                break;
+            case 2:
+            case 3:
+            case 4:
+                nwarps = 4;
+                rows_per_cuda_block = 2;
+                break;
+            case 5:
+            case 6:
+            case 7:
+            case 8:
+                nwarps = 2;
+                rows_per_cuda_block = 2;
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
+    }
+    const int64_t nblocks = (nrows_x + rows_per_cuda_block - 1) / rows_per_cuda_block;
+    const dim3 block_nums(nblocks, 1, 1);
+    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+
+    switch (ncols_y) {
+        case 1:
+            mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            break;
+        case 2:
+            mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            break;
+        case 3:
+            mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            break;
+        case 4:
+            mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            break;
+        case 5:
+            mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            break;
+        case 6:
+            mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            break;
+        case 7:
+            mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            break;
+        case 8:
+            mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot>
+                <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+}
+
+static void mul_mat_vec_q4_0_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_q4_1_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK4_1, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_q5_0_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_q5_1_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_q8_0_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_q2_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_q3_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_q4_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_q5_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_q6_K_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_iq2_xxs_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI2_XXS, block_iq2_xxs, 1, vec_dot_iq2_xxs_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_iq2_xs_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI2_XS, block_iq2_xs, 1, vec_dot_iq2_xs_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_iq2_s_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI2_S, block_iq2_s, 1, vec_dot_iq2_s_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_iq3_xxs_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI3_XXS, block_iq3_xxs, 1, vec_dot_iq3_xxs_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_iq1_s_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI1_S, block_iq1_s, 1, vec_dot_iq1_s_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_iq1_m_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI1_S, block_iq1_m, 1, vec_dot_iq1_m_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_iq4_nl_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK4_NL, QI4_NL, block_iq4_nl, VDR_Q4_0_Q8_1_MMVQ, vec_dot_iq4_nl_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_iq4_xs_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI4_XS, block_iq4_xs, 1, vec_dot_iq4_xs_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+static void mul_mat_vec_iq3_s_q8_1_cuda(
+    const void * vx, const void * vy, float * dst,
+    const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
+
+    mul_mat_vec_q_cuda<QK_K, QI3_XS, block_iq3_s, 1, vec_dot_iq3_s_q8_1>
+        (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
+}
+
+void ggml_cuda_op_mul_mat_vec_q(
+    ggml_backend_cuda_context & ctx,
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, cudaStream_t stream) {
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t row_diff = row_high - row_low;
+
+    const int64_t ne10 = src1->ne[0];
+    GGML_ASSERT(ne10 % QK8_1 == 0);
+
+    const int64_t ne0 = dst->ne[0];
+
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+
+    // the main device has a larger memory buffer to hold the results from all GPUs
+    // nrows_dst == nrows of the matrix that the kernel writes into
+    const int64_t nrows_dst = id == ctx.device ? ne0 : row_diff;
+
+    switch (src0->type) {
+        case GGML_TYPE_Q4_0:
+            mul_mat_vec_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            mul_mat_vec_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            mul_mat_vec_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            mul_mat_vec_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            mul_mat_vec_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q2_K:
+            mul_mat_vec_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q3_K:
+            mul_mat_vec_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q4_K:
+            mul_mat_vec_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_K:
+            mul_mat_vec_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q6_K:
+            mul_mat_vec_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_IQ2_XXS:
+            mul_mat_vec_iq2_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_IQ2_XS:
+            mul_mat_vec_iq2_xs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_IQ2_S:
+            mul_mat_vec_iq2_s_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_IQ3_XXS:
+            mul_mat_vec_iq3_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_IQ1_S:
+            mul_mat_vec_iq1_s_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_IQ1_M:
+            mul_mat_vec_iq1_m_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_IQ4_NL:
+            mul_mat_vec_iq4_nl_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_IQ4_XS:
+            mul_mat_vec_iq4_xs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        case GGML_TYPE_IQ3_S:
+            mul_mat_vec_iq3_s_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+    GGML_UNUSED(src1_ddf_i);
+    GGML_UNUSED(src1_ncols);
+    GGML_UNUSED(src1_padded_row_size);
+}
diff --git a/ggml-cuda/mmvq.cuh b/ggml-cuda/mmvq.cuh
new file mode 100644
index 000000000..88c42c4b7
--- /dev/null
+++ b/ggml-cuda/mmvq.cuh
@@ -0,0 +1,7 @@
+#include "common.cuh"
+
+void ggml_cuda_op_mul_mat_vec_q(
+    ggml_backend_cuda_context & ctx,
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
+    const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
+    const int64_t src1_padded_row_size, cudaStream_t stream);
diff --git a/ggml-cuda/norm.cu b/ggml-cuda/norm.cu
new file mode 100644
index 000000000..86f774534
--- /dev/null
+++ b/ggml-cuda/norm.cu
@@ -0,0 +1,215 @@
+#include "norm.cuh"
+
+template <int block_size>
+static __global__ void norm_f32(const float * x, float * dst, const int ncols, const float eps) {
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
+    const int tid = threadIdx.x;
+
+    float2 mean_var = make_float2(0.f, 0.f);
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const float xi = x[row*ncols + col];
+        mean_var.x += xi;
+        mean_var.y += xi * xi;
+    }
+
+    // sum up partial sums
+    mean_var = warp_reduce_sum(mean_var);
+    if (block_size > WARP_SIZE) {
+        __shared__ float2 s_sum[32];
+        int warp_id = threadIdx.x / WARP_SIZE;
+        int lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = mean_var;
+        }
+        __syncthreads();
+        mean_var = s_sum[lane_id];
+        mean_var = warp_reduce_sum(mean_var);
+    }
+
+    const float mean = mean_var.x / ncols;
+    const float var = mean_var.y / ncols - mean * mean;
+    const float inv_std = rsqrtf(var + eps);
+
+    for (int col = tid; col < ncols; col += block_size) {
+        dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_std;
+    }
+}
+
+template <int block_size>
+static __global__ void group_norm_f32(const float * x, float * dst, const int group_size, const int ne_elements, const float eps) {
+    // blockIdx.x: num_groups idx
+    // threadIdx.x: block_size idx
+    int start = blockIdx.x * group_size;
+    int end = start + group_size;
+
+    start += threadIdx.x;
+
+    if (end >= ne_elements) {
+        end = ne_elements;
+    }
+
+    float tmp = 0.0f; // partial sum for thread in warp
+
+    for (int j = start; j < end; j += block_size) {
+        tmp += x[j];
+    }
+
+    tmp = warp_reduce_sum(tmp);
+    if (block_size > WARP_SIZE) {
+        __shared__ float s_sum[32];
+        int warp_id = threadIdx.x / WARP_SIZE;
+        int lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        __syncthreads();
+        tmp = s_sum[lane_id];
+        tmp = warp_reduce_sum(tmp);
+    }
+
+    float mean = tmp / group_size;
+    tmp = 0.0f;
+
+    for (int j = start; j < end; j += block_size) {
+        float xi = x[j] - mean;
+        dst[j] = xi;
+        tmp += xi * xi;
+    }
+
+    tmp = warp_reduce_sum(tmp);
+    if (block_size > WARP_SIZE) {
+        __shared__ float s_sum[32];
+        int warp_id = threadIdx.x / WARP_SIZE;
+        int lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        __syncthreads();
+        tmp = s_sum[lane_id];
+        tmp = warp_reduce_sum(tmp);
+    }
+
+    float variance = tmp / group_size;
+    float scale = rsqrtf(variance + eps);
+    for (int j = start; j < end; j += block_size) {
+        dst[j] *= scale;
+    }
+}
+
+template <int block_size>
+static __global__ void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps) {
+    const int row = blockIdx.x*blockDim.y + threadIdx.y;
+    const int tid = threadIdx.x;
+
+    float tmp = 0.0f; // partial sum for thread in warp
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const float xi = x[row*ncols + col];
+        tmp += xi * xi;
+    }
+
+    // sum up partial sums
+    tmp = warp_reduce_sum(tmp);
+    if (block_size > WARP_SIZE) {
+        __shared__ float s_sum[32];
+        int warp_id = threadIdx.x / WARP_SIZE;
+        int lane_id = threadIdx.x % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        __syncthreads();
+        tmp = s_sum[lane_id];
+        tmp = warp_reduce_sum(tmp);
+    }
+
+    const float mean = tmp / ncols;
+    const float scale = rsqrtf(mean + eps);
+
+    for (int col = tid; col < ncols; col += block_size) {
+        dst[row*ncols + col] = scale * x[row*ncols + col];
+    }
+}
+
+static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
+    GGML_ASSERT(ncols % WARP_SIZE == 0);
+    if (ncols < 1024) {
+        const dim3 block_dims(WARP_SIZE, 1, 1);
+        norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
+    } else {
+        const dim3 block_dims(1024, 1, 1);
+        norm_f32<1024><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
+    }
+}
+
+static void group_norm_f32_cuda(const float * x, float * dst, const int num_groups, const int group_size, const int ne_elements, cudaStream_t stream) {
+    static const float eps = 1e-6f;
+    if (group_size < 1024) {
+        const dim3 block_dims(WARP_SIZE, 1, 1);
+        group_norm_f32<WARP_SIZE><<<num_groups, block_dims, 0, stream>>>(x, dst, group_size, ne_elements, eps);
+    } else {
+        const dim3 block_dims(1024, 1, 1);
+        group_norm_f32<1024><<<num_groups, block_dims, 0, stream>>>(x, dst, group_size, ne_elements, eps);
+    }
+}
+
+static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
+    GGML_ASSERT(ncols % WARP_SIZE == 0);
+    if (ncols < 1024) {
+        const dim3 block_dims(WARP_SIZE, 1, 1);
+        rms_norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
+    } else {
+        const dim3 block_dims(1024, 1, 1);
+        rms_norm_f32<1024><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
+    }
+}
+
+void ggml_cuda_op_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    norm_f32_cuda(src0_d, dst_d, ne00, nrows, eps, stream);
+}
+
+void ggml_cuda_op_group_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    int num_groups = dst->op_params[0];
+    int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups);
+    group_norm_f32_cuda(src0_d, dst_d, num_groups * src0->ne[3], group_size, ggml_nelements(src0), stream);
+}
+
+void ggml_cuda_op_rms_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    rms_norm_f32_cuda(src0_d, dst_d, ne00, nrows, eps, stream);
+}
diff --git a/ggml-cuda/norm.cuh b/ggml-cuda/norm.cuh
new file mode 100644
index 000000000..431a8f74d
--- /dev/null
+++ b/ggml-cuda/norm.cuh
@@ -0,0 +1,7 @@
+#include "common.cuh"
+
+void ggml_cuda_op_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_group_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_rms_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/pad.cu b/ggml-cuda/pad.cu
new file mode 100644
index 000000000..aba539e8d
--- /dev/null
+++ b/ggml-cuda/pad.cu
@@ -0,0 +1,49 @@
+#include "pad.cuh"
+
+static __global__ void pad_f32(const float * x, float * dst, const int ne0, const int ne00, const int ne01, const int ne02, const int ne03) {
+    // blockIdx.z: idx of ne2*ne3, aka ne02*ne03
+    // blockIdx.y: idx of ne1
+    // blockIDx.x: idx of ne0 / BLOCK_SIZE
+    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
+    if (nidx >= ne0) {
+        return;
+    }
+
+    // operation
+    int offset_dst =
+        nidx +
+        blockIdx.y * ne0 +
+        blockIdx.z * ne0 * gridDim.y;
+    if (nidx < ne00 && blockIdx.y < ne01 && blockIdx.z < ne02*ne03) {
+        int offset_src =
+            nidx +
+            blockIdx.y * ne00 +
+            blockIdx.z * ne00 * ne01;
+        dst[offset_dst] = x[offset_src];
+    } else {
+        dst[offset_dst] = 0.0f;
+    }
+}
+
+static void pad_f32_cuda(const float * x, float * dst,
+    const int ne00, const int ne01, const int ne02, const int ne03,
+    const int ne0, const int ne1, const int ne2, const int ne3, cudaStream_t stream) {
+    int num_blocks = (ne0 + CUDA_PAD_BLOCK_SIZE - 1) / CUDA_PAD_BLOCK_SIZE;
+    dim3 gridDim(num_blocks, ne1, ne2*ne3);
+    pad_f32<<<gridDim, CUDA_PAD_BLOCK_SIZE, 0, stream>>>(x, dst, ne0, ne00, ne01, ne02, ne03);
+}
+
+void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
+
+    pad_f32_cuda(src0_d, dst_d,
+        src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
+        dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], stream);
+}
diff --git a/ggml-cuda/pad.cuh b/ggml-cuda/pad.cuh
new file mode 100644
index 000000000..8fd386b00
--- /dev/null
+++ b/ggml-cuda/pad.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_PAD_BLOCK_SIZE 256
+
+void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/pool2d.cu b/ggml-cuda/pool2d.cu
new file mode 100644
index 000000000..c6d51e4d6
--- /dev/null
+++ b/ggml-cuda/pool2d.cu
@@ -0,0 +1,94 @@
+#include "pool2d.cuh"
+
+template <typename Ti, typename To>
+static  __global__ void pool2d_nchw_kernel(
+        const int ih, const int iw, const int oh, const int ow,
+        const int kh, const int kw, const int sh, const int sw,
+        const int ph, const int pw, const int parallel_elements,
+        const Ti* src, To* dst, const enum ggml_op_pool op) {
+    int idx = threadIdx.x + blockIdx.x * blockDim.x;
+    if (idx >= parallel_elements) {
+        return;
+    }
+
+    const int I_HW = ih * iw;
+    const int O_HW = oh * ow;
+    const int nc = idx / O_HW;
+    const int cur_oh = idx % O_HW / ow;
+    const int cur_ow = idx % O_HW % ow;
+    const Ti* i_ptr = src + nc * I_HW;
+    To* o_ptr = dst + nc * O_HW;
+    const int start_h = cur_oh * sh - ph;
+    const int bh = max(0, start_h);
+    const int eh = min(ih, start_h + kh);
+    const int start_w = cur_ow * sw - pw;
+    const int bw = max(0, start_w);
+    const int ew = min(iw, start_w + kw);
+    const To scale = 1. / (kh * kw);
+    To res = 0;
+
+    switch (op) {
+        case GGML_OP_POOL_AVG: res = 0; break;
+        case GGML_OP_POOL_MAX: res = -FLT_MAX; break;
+        default: assert(false);
+    }
+
+    for (int i = bh; i < eh; i += 1) {
+        for (int j = bw; j < ew; j += 1) {
+#if __CUDA_ARCH__ >= 350
+            Ti cur = __ldg(i_ptr + i * iw + j);
+#else
+            Ti cur = i_ptr[i * iw + j];
+#endif
+            switch (op) {
+                case GGML_OP_POOL_AVG: res += cur * scale; break;
+                case GGML_OP_POOL_MAX: res = max(res, (To)cur); break;
+                default: assert(false);
+            }
+        }
+    }
+    o_ptr[cur_oh * ow + cur_ow] = res;
+}
+
+static void pool2d_nchw_kernel_f32_f32_cuda(
+        const int ih, const int iw, const int oh, const int ow,
+        const int kh, const int kw, const int sh, const int sw,
+        const int ph, const int pw, const int parallel_elements,
+        const float * src, float * dst, const enum ggml_op_pool op,
+        cudaStream_t stream) {
+
+    const int num_blocks = (parallel_elements + CUDA_POOL2D_BLOCK_SIZE - 1) / CUDA_POOL2D_BLOCK_SIZE;
+    dim3 block_nums(num_blocks);
+    pool2d_nchw_kernel<<<block_nums, CUDA_POOL2D_BLOCK_SIZE, 0, stream>>>(ih, iw, oh, ow, kh, kw, sh, sw, ph, pw, parallel_elements, src, dst, op);
+}
+
+void ggml_cuda_op_pool2d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int32_t * opts = (const int32_t *)dst->op_params;
+    enum ggml_op_pool op = static_cast<ggml_op_pool>(opts[0]);
+    const int k0 = opts[1];
+    const int k1 = opts[2];
+    const int s0 = opts[3];
+    const int s1 = opts[4];
+    const int p0 = opts[5];
+    const int p1 = opts[6];
+
+    const int64_t IH = src0->ne[1];
+    const int64_t IW = src0->ne[0];
+
+    const int64_t N = dst->ne[3];
+    const int64_t OC = dst->ne[2];
+    const int64_t OH = dst->ne[1];
+    const int64_t OW = dst->ne[0];
+
+    const int parallel_elements = N * OC * OH * OW;
+
+    pool2d_nchw_kernel_f32_f32_cuda(IH, IW, OH, OW, k1, k0, s1, s0, p1, p0, parallel_elements, src0_d, dst_d, op, stream);
+}
diff --git a/ggml-cuda/pool2d.cuh b/ggml-cuda/pool2d.cuh
new file mode 100644
index 000000000..7841292bc
--- /dev/null
+++ b/ggml-cuda/pool2d.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_POOL2D_BLOCK_SIZE 256
+
+void ggml_cuda_op_pool2d(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/quantize.cu b/ggml-cuda/quantize.cu
new file mode 100644
index 000000000..a1fbc9932
--- /dev/null
+++ b/ggml-cuda/quantize.cu
@@ -0,0 +1,45 @@
+#include "quantize.cuh"
+
+static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded) {
+    const int ix = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (ix >= kx_padded) {
+        return;
+    }
+
+    const int iy = blockDim.y*blockIdx.y + threadIdx.y;
+
+    const int i_padded = iy*kx_padded + ix;
+
+    block_q8_1 * y = (block_q8_1 *) vy;
+
+    const int ib = i_padded / QK8_1; // block index
+    const int iqs = i_padded % QK8_1; // quant index
+
+    const float xi = ix < kx ? x[iy*kx + ix] : 0.0f;
+    float amax = fabsf(xi);
+    float sum = xi;
+
+    amax = warp_reduce_max(amax);
+    sum = warp_reduce_sum(sum);
+
+    const float d = amax / 127;
+    const int8_t q = amax == 0.0f ? 0 : roundf(xi / d);
+
+    y[ib].qs[iqs] = q;
+
+    if (iqs > 0) {
+        return;
+    }
+
+    reinterpret_cast<half&>(y[ib].ds.x) = d;
+    reinterpret_cast<half&>(y[ib].ds.y) = sum;
+}
+
+void quantize_row_q8_1_cuda(const float * x, void * vy, const int kx, const int ky, const int kx_padded, cudaStream_t stream) {
+    const int block_num_x = (kx_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
+    const dim3 num_blocks(block_num_x, ky, 1);
+    const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE, 1, 1);
+    quantize_q8_1<<<num_blocks, block_size, 0, stream>>>(x, vy, kx, kx_padded);
+}
+
diff --git a/ggml-cuda/quantize.cuh b/ggml-cuda/quantize.cuh
new file mode 100644
index 000000000..adb89c83a
--- /dev/null
+++ b/ggml-cuda/quantize.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_QUANTIZE_BLOCK_SIZE 256
+
+void quantize_row_q8_1_cuda(const float * x, void * vy, const int kx, const int ky, const int kx_padded, cudaStream_t stream);
diff --git a/ggml-cuda/rope.cu b/ggml-cuda/rope.cu
new file mode 100644
index 000000000..4b0d2e5ad
--- /dev/null
+++ b/ggml-cuda/rope.cu
@@ -0,0 +1,308 @@
+#include "rope.cuh"
+
+struct rope_corr_dims {
+    float v[4];
+};
+
+static __device__ float rope_yarn_ramp(const float low, const float high, const int i0) {
+    const float y = (i0 / 2 - low) / max(0.001f, high - low);
+    return 1.0f - min(1.0f, max(0.0f, y));
+}
+
+// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
+// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
+static __device__ void rope_yarn(
+    float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale,
+    float * cos_theta, float * sin_theta
+) {
+    // Get n-d rotational scaling corrected for extrapolation
+    float theta_interp = freq_scale * theta_extrap;
+    float theta = theta_interp;
+    if (ext_factor != 0.0f) {
+        float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor;
+        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+
+        // Get n-d magnitude scaling corrected for interpolation
+        mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
+    }
+    *cos_theta = cosf(theta) * mscale;
+    *sin_theta = sinf(theta) * mscale;
+}
+
+// rope == RoPE == rotary positional embedding
+template<typename T, bool has_pos>
+static __global__ void rope(
+    const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
+    float ext_factor, float attn_factor, rope_corr_dims corr_dims
+) {
+    const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+    const int i = row*ncols + col;
+    const int i2 = row/p_delta_rows;
+
+    const int p = has_pos ? pos[i2] : 0;
+    const float theta_base = p*powf(freq_base, -float(col)/ncols);
+
+    float cos_theta, sin_theta;
+    rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + 1];
+
+    dst[i + 0] = x0*cos_theta - x1*sin_theta;
+    dst[i + 1] = x0*sin_theta + x1*cos_theta;
+}
+
+template<typename T, bool has_pos>
+static __global__ void rope_neox(
+    const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims
+) {
+    const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+    const int ib = col / n_dims;
+    const int ic = col % n_dims;
+
+    if (ib > 0) {
+        const int i = row*ncols + ib*n_dims + ic;
+
+        dst[i + 0] = x[i + 0];
+        dst[i + 1] = x[i + 1];
+
+        return;
+    }
+
+    const int i  = row*ncols + ib*n_dims + ic/2;
+    const int i2 = row/p_delta_rows;
+
+    float cur_rot = inv_ndims * ic - ib;
+
+    const int p = has_pos ? pos[i2] : 0;
+    const float theta_base = p*freq_scale*powf(theta_scale, col/2.0f);
+
+    float cos_theta, sin_theta;
+    rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + n_dims/2];
+
+    dst[i + 0]        = x0*cos_theta - x1*sin_theta;
+    dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
+}
+
+static __global__ void rope_glm_f32(
+    const float * x, float * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
+    int n_ctx
+) {
+    const int col = blockDim.x*blockIdx.x + threadIdx.x;
+    const int half_n_dims = ncols/4;
+
+    if (col >= half_n_dims) {
+        return;
+    }
+
+    const int row = blockDim.y*blockIdx.y + threadIdx.y;
+    const int i = row*ncols + col;
+    const int i2 = row/p_delta_rows;
+
+    const float col_theta_scale = powf(freq_base, -2.0f*col/ncols);
+     // FIXME: this is likely wrong
+    const int p = pos != nullptr ? pos[i2] : 0;
+
+    const float theta = min(p, n_ctx - 2)*freq_scale*col_theta_scale;
+    const float sin_theta = sinf(theta);
+    const float cos_theta = cosf(theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + half_n_dims];
+
+    dst[i + 0]           = x0*cos_theta - x1*sin_theta;
+    dst[i + half_n_dims] = x0*sin_theta + x1*cos_theta;
+
+    const float block_theta = ((float)max(p - n_ctx - 2, 0))*col_theta_scale;
+    const float sin_block_theta = sinf(block_theta);
+    const float cos_block_theta = cosf(block_theta);
+
+    const float x2 = x[i + half_n_dims * 2];
+    const float x3 = x[i + half_n_dims * 3];
+
+    dst[i + half_n_dims * 2] = x2*cos_block_theta - x3*sin_block_theta;
+    dst[i + half_n_dims * 3] = x2*sin_block_theta + x3*cos_block_theta;
+}
+
+
+template<typename T>
+static void rope_cuda(
+    const T * x, T * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream
+) {
+    GGML_ASSERT(ncols % 2 == 0);
+    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
+    const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const dim3 block_nums(nrows, num_blocks_x, 1);
+    if (pos == nullptr) {
+        rope<T, false><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
+        );
+    } else {
+        rope<T, true><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
+        );
+    }
+}
+
+template<typename T>
+static void rope_neox_cuda(
+    const T * x, T * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream
+) {
+    GGML_ASSERT(ncols % 2 == 0);
+    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
+    const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const dim3 block_nums(nrows, num_blocks_x, 1);
+
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+    const float inv_ndims = -1.0f / n_dims;
+
+    if (pos == nullptr) {
+        rope_neox<T, false><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
+            theta_scale, inv_ndims
+        );
+    } else {
+        rope_neox<T, true><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
+            theta_scale, inv_ndims
+        );
+    }
+}
+
+static void rope_glm_f32_cuda(
+    const float * x, float * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, int n_ctx, cudaStream_t stream
+) {
+    GGML_ASSERT(ncols % 4 == 0);
+    const dim3 block_dims(CUDA_ROPE_BLOCK_SIZE/4, 1, 1);
+    const int num_blocks_x = (ncols + CUDA_ROPE_BLOCK_SIZE - 1) / CUDA_ROPE_BLOCK_SIZE;
+    const dim3 block_nums(num_blocks_x, nrows, 1);
+    rope_glm_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, n_ctx);
+}
+
+static void rope_cuda_f16(
+    const half * x, half * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream) {
+
+    rope_cuda<half>(x, dst, ncols, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream);
+}
+
+static void rope_cuda_f32(
+    const float * x, float * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream) {
+
+    rope_cuda<float>(x, dst, ncols, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream);
+}
+
+static void rope_neox_cuda_f16(
+    const half * x, half * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream) {
+
+    rope_neox_cuda<half>(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream);
+}
+
+static void rope_neox_cuda_f32(
+    const float * x, float * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream
+) {
+
+    rope_neox_cuda<float>(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream);
+}
+
+void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const float * src0_d = (const float *)src0->data;
+    const float * src1_d = (const float *)src1->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32 ||  dst->type == GGML_TYPE_F16);
+    GGML_ASSERT(src0->type == dst->type);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne2 = dst->ne[2];
+    const int64_t nrows = ggml_nrows(src0);
+
+    //const int n_past      = ((int32_t *) dst->op_params)[0];
+    const int n_dims      = ((int32_t *) dst->op_params)[1];
+    const int mode        = ((int32_t *) dst->op_params)[2];
+    const int n_ctx       = ((int32_t *) dst->op_params)[3];
+    const int n_orig_ctx  = ((int32_t *) dst->op_params)[4];
+
+    // RoPE alteration for extended context
+    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
+    memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
+    memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
+    memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
+    memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
+    memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
+    memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
+
+    const int32_t * pos = nullptr;
+    if ((mode & 1) == 0) {
+        GGML_ASSERT(src1->type == GGML_TYPE_I32);
+        GGML_ASSERT(src1->ne[0] == ne2);
+        pos = (const int32_t *) src1_d;
+    }
+
+    const bool is_neox = mode & 2;
+    const bool is_glm  = mode & 4;
+
+    rope_corr_dims corr_dims;
+    ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v);
+
+    // compute
+    if (is_glm) {
+        GGML_ASSERT(false);
+        rope_glm_f32_cuda(src0_d, dst_d, ne00, nrows, pos, freq_scale, ne01, freq_base, n_ctx, stream);
+    } else if (is_neox) {
+        if (src0->type == GGML_TYPE_F32) {
+            rope_neox_cuda_f32(
+                (const float *)src0_d, (float *)dst_d, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, stream
+            );
+        } else if (src0->type == GGML_TYPE_F16) {
+            rope_neox_cuda_f16(
+                (const half *)src0_d, (half *)dst_d, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, stream
+            );
+        } else {
+            GGML_ASSERT(false);
+        }
+    } else {
+        if (src0->type == GGML_TYPE_F32) {
+            rope_cuda_f32(
+                (const float *)src0_d, (float *)dst_d, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, stream
+            );
+        } else if (src0->type == GGML_TYPE_F16) {
+            rope_cuda_f16(
+                (const half *)src0_d, (half *)dst_d, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, stream
+            );
+        } else {
+            GGML_ASSERT(false);
+        }
+    }
+}
diff --git a/ggml-cuda/rope.cuh b/ggml-cuda/rope.cuh
new file mode 100644
index 000000000..0f787a0b2
--- /dev/null
+++ b/ggml-cuda/rope.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_ROPE_BLOCK_SIZE 256
+
+void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/scale.cu b/ggml-cuda/scale.cu
new file mode 100644
index 000000000..6e3617d1c
--- /dev/null
+++ b/ggml-cuda/scale.cu
@@ -0,0 +1,32 @@
+#include "scale.cuh"
+
+static __global__ void scale_f32(const float * x, float * dst, const float scale, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    dst[i] = scale * x[i];
+}
+
+static void scale_f32_cuda(const float * x, float * dst, const float scale, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE;
+    scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, k);
+}
+
+void ggml_cuda_op_scale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    float scale;
+    memcpy(&scale, dst->op_params, sizeof(float));
+
+    scale_f32_cuda(src0_d, dst_d, scale, ggml_nelements(src0), stream);
+    CUDA_CHECK(cudaGetLastError());
+}
diff --git a/ggml-cuda/scale.cuh b/ggml-cuda/scale.cuh
new file mode 100644
index 000000000..8ff75c829
--- /dev/null
+++ b/ggml-cuda/scale.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_SCALE_BLOCK_SIZE 256
+
+void ggml_cuda_op_scale(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/softmax.cu b/ggml-cuda/softmax.cu
new file mode 100644
index 000000000..9bda18e58
--- /dev/null
+++ b/ggml-cuda/softmax.cu
@@ -0,0 +1,201 @@
+#include "softmax.cuh"
+
+template <bool vals_smem, int ncols_template, int block_size_template>
+static __global__ void soft_max_f32(const float * x, const float * mask, const float * pos, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) {
+    const int ncols = ncols_template == 0 ? ncols_par : ncols_template;
+
+    const int tid  = threadIdx.x;
+    const int rowx = blockIdx.x;
+    const int rowy = rowx % nrows_y; // broadcast the mask in the row dimension
+
+    const int block_size = block_size_template == 0 ? blockDim.x : block_size_template;
+
+    const int warp_id = threadIdx.x / WARP_SIZE;
+    const int lane_id = threadIdx.x % WARP_SIZE;
+
+    float slope = 0.0f;
+
+    // ALiBi
+    if (max_bias > 0.0f) {
+        const int h = rowx/nrows_y; // head index
+
+        const float base = h < n_head_log2 ? m0 : m1;
+        const int   exp  = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+        slope = powf(base, exp);
+    }
+
+    extern __shared__ float data_soft_max_f32[];
+    float * buf_iw = data_soft_max_f32; // shared memory buffer for inter-warp communication
+    // shared memory buffer to cache values between iterations:
+    float * vals = vals_smem ? buf_iw + WARP_SIZE : dst + rowx*ncols;
+
+    float max_val = -INFINITY;
+
+#pragma unroll
+    for (int col0 = 0; col0 < ncols; col0 += block_size) {
+        const int col = col0 + tid;
+
+        if (ncols_template == 0 && col >= ncols) {
+            break;
+        }
+
+        const int ix = rowx*ncols + col;
+        const int iy = rowy*ncols + col;
+
+        const float val = x[ix]*scale + (mask ? mask[iy] : 0.0f) + (pos ? slope*pos[col] : 0.0f);
+
+        vals[col] = val;
+        max_val = max(max_val, val);
+    }
+
+    // find the max value in the block
+    max_val = warp_reduce_max(max_val);
+    if (block_size > WARP_SIZE) {
+        if (warp_id == 0) {
+            buf_iw[lane_id] = -INFINITY;
+        }
+        __syncthreads();
+
+        if (lane_id == 0) {
+            buf_iw[warp_id] = max_val;
+        }
+        __syncthreads();
+
+        max_val = buf_iw[lane_id];
+        max_val = warp_reduce_max(max_val);
+    }
+
+    float tmp = 0.0f; // partial sum
+
+#pragma unroll
+    for (int col0 = 0; col0 < ncols; col0 += block_size) {
+        const int col = col0 + tid;
+
+        if (ncols_template == 0 && col >= ncols) {
+            break;
+        }
+
+        const float val = expf(vals[col] - max_val);
+        tmp += val;
+        vals[col] = val;
+    }
+
+    // find the sum of exps in the block
+    tmp = warp_reduce_sum(tmp);
+    if (block_size > WARP_SIZE) {
+        __syncthreads();
+        if (warp_id == 0) {
+            buf_iw[lane_id] = 0.0f;
+        }
+        __syncthreads();
+
+        if (lane_id == 0) {
+            buf_iw[warp_id] = tmp;
+        }
+        __syncthreads();
+
+        tmp = buf_iw[lane_id];
+        tmp = warp_reduce_sum(tmp);
+    }
+
+    const float inv_sum = 1.0f / tmp;
+
+#pragma unroll
+    for (int col0 = 0; col0 < ncols; col0 += block_size) {
+        const int col = col0 + tid;
+
+        if (ncols_template == 0 && col >= ncols) {
+            return;
+        }
+
+        const int idst = rowx*ncols + col;
+        dst[idst] = vals[col] * inv_sum;
+    }
+}
+
+static void soft_max_f32_cuda(const float * x, const float * mask, const float * pos, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) {
+    int nth = WARP_SIZE;
+    while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2;
+    const dim3 block_dims(nth,     1, 1);
+    const dim3 block_nums(nrows_x, 1, 1);
+    const size_t shmem = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE)*sizeof(float);
+    static_assert(CUDA_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted.");
+
+    const uint32_t n_head_kv   = nrows_x/nrows_y;
+    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
+
+    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+    if (shmem < ggml_cuda_info().devices[ggml_cuda_get_device()].smpb) {
+        switch (ncols_x) {
+            case 32:
+                soft_max_f32<true, 32, 32><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                break;
+            case 64:
+                soft_max_f32<true, 64, 64><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                break;
+            case 128:
+                soft_max_f32<true, 128, 128><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                break;
+            case 256:
+                soft_max_f32<true, 256, 256><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                break;
+            case 512:
+                soft_max_f32<true, 512, 512><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                break;
+            case 1024:
+                soft_max_f32<true, 1024, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                break;
+            case 2048:
+                soft_max_f32<true, 2048, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                break;
+            case 4096:
+                soft_max_f32<true, 4096, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                break;
+            default:
+                soft_max_f32<true, 0, 0><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+                break;
+        }
+    } else {
+        const size_t shmem_low = WARP_SIZE*sizeof(float);
+        soft_max_f32<false, 0, 0><<<block_nums, block_dims, shmem_low, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+    }
+}
+
+void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const float * src0_d = (const float *)src0->data;
+    const float * src1_d = src1 ? (const float *)src1->data : nullptr;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
+
+    const int64_t ne00    = src0->ne[0];
+    const int64_t nrows_x = ggml_nrows(src0);
+    const int64_t nrows_y = src0->ne[1];
+
+    float scale    = 1.0f;
+    float max_bias = 0.0f;
+
+    memcpy(&scale,    (float *) dst->op_params + 0, sizeof(float));
+    memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float));
+
+    // positions tensor
+    float * src2_dd = nullptr;
+
+    ggml_tensor * src2 = dst->src[2];
+    const bool use_src2 = src2 != nullptr;
+
+    if (use_src2) {
+        src2_dd = (float *)src2->data;
+    }
+
+    soft_max_f32_cuda(src0_d, src1_d, src2_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
+}
diff --git a/ggml-cuda/softmax.cuh b/ggml-cuda/softmax.cuh
new file mode 100644
index 000000000..4ef4ff86c
--- /dev/null
+++ b/ggml-cuda/softmax.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_SOFT_MAX_BLOCK_SIZE 1024
+
+void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/sumrows.cu b/ggml-cuda/sumrows.cu
new file mode 100644
index 000000000..82e8e875f
--- /dev/null
+++ b/ggml-cuda/sumrows.cu
@@ -0,0 +1,40 @@
+#include "sumrows.cuh"
+
+static __global__ void k_sum_rows_f32(const float * x, float * dst, const int ncols) {
+    const int row = blockIdx.x;
+    const int col = threadIdx.x;
+
+    float sum = 0.0f;
+    for (int i = col; i < ncols; i += blockDim.x) {
+        sum += x[row * ncols + i];
+    }
+
+    sum = warp_reduce_sum(sum);
+
+    if (col == 0) {
+        dst[row] = sum;
+    }
+}
+
+static void sum_rows_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    const dim3 block_dims(WARP_SIZE, 1, 1);
+    const dim3 block_nums(nrows, 1, 1);
+    k_sum_rows_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
+}
+
+void ggml_cuda_op_sum_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
+
+    const int64_t ncols = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    sum_rows_f32_cuda(src0_d, dst_d, ncols, nrows, stream);
+}
diff --git a/ggml-cuda/sumrows.cuh b/ggml-cuda/sumrows.cuh
new file mode 100644
index 000000000..e7545f83c
--- /dev/null
+++ b/ggml-cuda/sumrows.cuh
@@ -0,0 +1,3 @@
+#include "common.cuh"
+
+void ggml_cuda_op_sum_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/tsembd.cu b/ggml-cuda/tsembd.cu
new file mode 100644
index 000000000..153ddbcda
--- /dev/null
+++ b/ggml-cuda/tsembd.cu
@@ -0,0 +1,47 @@
+#include "tsembd.cuh"
+
+static __global__ void timestep_embedding_f32(const float * timesteps, float * dst, const int nb1, const int dim, const int max_period) {
+    // blockIDx.y: idx of timesteps->ne[0]
+    // blockIDx.x: idx of ((dim + 1) / 2) / BLOCK_SIZE
+    int i = blockIdx.y;
+    int j = threadIdx.x + blockIdx.x * blockDim.x;
+    float * embed_data = (float *)((char *)dst +  i*nb1);
+
+    if (dim % 2 != 0 && j == ((dim + 1) / 2)) {
+        embed_data[dim] = 0.f;
+    }
+
+    int half = dim / 2;
+    if (j >= half) {
+        return;
+    }
+
+    float timestep = timesteps[i];
+    float freq = (float)expf(-logf(max_period) * j / half);
+    float arg = timestep * freq;
+    embed_data[j] = cosf(arg);
+    embed_data[j + half] = sinf(arg);
+}
+
+static void timestep_embedding_f32_cuda(const float * x, float * dst, const int ne00, const int nb1,
+                                        const int dim, const int max_period, cudaStream_t stream) {
+    int half_ceil = (dim + 1) / 2;
+    int num_blocks = (half_ceil + CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE - 1) / CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE;
+    dim3 gridDim(num_blocks, ne00, 1);
+    timestep_embedding_f32<<<gridDim, CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE, 0, stream>>>(x, dst, nb1, dim, max_period);
+}
+
+void ggml_cuda_op_timestep_embedding(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    const int dim = dst->op_params[0];
+    const int max_period = dst->op_params[1];
+
+    timestep_embedding_f32_cuda(src0_d, dst_d, src0->ne[0], dst->nb[1], dim, max_period, stream);
+}
diff --git a/ggml-cuda/tsembd.cuh b/ggml-cuda/tsembd.cuh
new file mode 100644
index 000000000..84340e3d7
--- /dev/null
+++ b/ggml-cuda/tsembd.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE 256
+
+void ggml_cuda_op_timestep_embedding(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/unary.cu b/ggml-cuda/unary.cu
new file mode 100644
index 000000000..1a7f09469
--- /dev/null
+++ b/ggml-cuda/unary.cu
@@ -0,0 +1,240 @@
+#include "unary.cuh"
+
+static __global__ void gelu_f32(const float * x, float * dst, const int k) {
+    const float GELU_COEF_A    = 0.044715f;
+    const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    float xi = x[i];
+    dst[i] = 0.5f*xi*(1.0f + tanhf(SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi)));
+}
+
+static __global__ void gelu_quick_f32(const float * x, float * dst, int k) {
+    const float GELU_QUICK_COEF = -1.702f;
+    const int i  = blockDim.x*blockIdx.x + threadIdx.x;
+    if (i >= k) {
+        return;
+    }
+    dst[i] = x[i] * (1.0f / (1.0f + expf(GELU_QUICK_COEF * x[i])));
+}
+
+static __global__ void silu_f32(const float * x, float * dst, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = x[i] / (1.0f + expf(-x[i]));
+}
+
+static __global__ void tanh_f32(const float * x, float * dst, int k) {
+    const int i  = blockDim.x*blockIdx.x + threadIdx.x;
+    if (i >= k) {
+        return;
+    }
+    dst[i] = tanhf(x[i]);
+}
+
+static __global__ void relu_f32(const float * x, float * dst, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = fmaxf(x[i], 0);
+}
+
+static __global__ void hardsigmoid_f32(const float * x, float * dst, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f));
+}
+
+static __global__ void hardswish_f32(const float * x, float * dst, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f));
+}
+
+static __global__ void leaky_relu_f32(const float * x, float * dst, const int k, const float negative_slope) {
+    const int i  = blockDim.x*blockIdx.x + threadIdx.x;
+    if (i >= k) {
+        return;
+    }
+    dst[i] = fmaxf(x[i], 0) + fminf(x[i], 0.0f) * negative_slope;
+}
+
+static __global__ void sqr_f32(const float * x, float * dst, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = x[i] * x[i];
+}
+
+static void gelu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE;
+    gelu_f32<<<num_blocks, CUDA_GELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
+static void gelu_quick_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE;
+    gelu_quick_f32<<<num_blocks, CUDA_GELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
+static void silu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_SILU_BLOCK_SIZE - 1) / CUDA_SILU_BLOCK_SIZE;
+    silu_f32<<<num_blocks, CUDA_SILU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
+static void tanh_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_TANH_BLOCK_SIZE - 1) / CUDA_TANH_BLOCK_SIZE;
+    tanh_f32<<<num_blocks, CUDA_TANH_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
+static void relu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE;
+    relu_f32<<<num_blocks, CUDA_RELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
+static void hardsigmoid_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_HARDSIGMOID_BLOCK_SIZE - 1) / CUDA_HARDSIGMOID_BLOCK_SIZE;
+    hardsigmoid_f32<<<num_blocks, CUDA_HARDSIGMOID_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
+static void hardswish_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_HARDSWISH_BLOCK_SIZE - 1) / CUDA_HARDSWISH_BLOCK_SIZE;
+    hardswish_f32<<<num_blocks, CUDA_HARDSWISH_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
+static void leaky_relu_f32_cuda(const float * x, float * dst, const int k, const float negative_slope, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE;
+    leaky_relu_f32<<<num_blocks, CUDA_RELU_BLOCK_SIZE, 0, stream>>>(x, dst, k, negative_slope);
+}
+
+static void sqr_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_SQR_BLOCK_SIZE - 1) / CUDA_SQR_BLOCK_SIZE;
+    sqr_f32<<<num_blocks, CUDA_SQR_BLOCK_SIZE, 0, stream>>>(x, dst, k);
+}
+
+void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    gelu_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
+}
+
+void ggml_cuda_op_silu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    silu_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
+}
+
+void ggml_cuda_op_gelu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    gelu_quick_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
+}
+
+void ggml_cuda_op_tanh(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    tanh_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
+}
+
+void ggml_cuda_op_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    relu_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
+}
+
+void ggml_cuda_op_hardsigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    hardsigmoid_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
+}
+
+void ggml_cuda_op_hardswish(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    hardswish_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
+}
+
+void ggml_cuda_op_leaky_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    float negative_slope;
+    memcpy(&negative_slope, dst->op_params, sizeof(float));
+
+    leaky_relu_f32_cuda(src0_d, dst_d, ggml_nelements(src0), negative_slope, stream);
+}
+
+void ggml_cuda_op_sqr(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    sqr_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
+}
diff --git a/ggml-cuda/unary.cuh b/ggml-cuda/unary.cuh
new file mode 100644
index 000000000..2002ed989
--- /dev/null
+++ b/ggml-cuda/unary.cuh
@@ -0,0 +1,27 @@
+#include "common.cuh"
+
+#define CUDA_GELU_BLOCK_SIZE 256
+#define CUDA_SILU_BLOCK_SIZE 256
+#define CUDA_TANH_BLOCK_SIZE 256
+#define CUDA_RELU_BLOCK_SIZE 256
+#define CUDA_HARDSIGMOID_BLOCK_SIZE 256
+#define CUDA_HARDSWISH_BLOCK_SIZE 256
+#define CUDA_SQR_BLOCK_SIZE 256
+
+void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_silu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_gelu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_tanh(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_hardsigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_hardswish(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_leaky_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_sqr(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/upscale.cu b/ggml-cuda/upscale.cu
new file mode 100644
index 000000000..2f62fed48
--- /dev/null
+++ b/ggml-cuda/upscale.cu
@@ -0,0 +1,48 @@
+#include "upscale.cuh"
+
+static __global__ void upscale_f32(const float * x, float * dst, const int ne00, const int ne00xne01, const int scale_factor) {
+    // blockIdx.z: idx of ne02*ne03
+    // blockIdx.y: idx of ne01*scale_factor， aka ne1
+    // blockIDx.x: idx of ne00*scale_factor / BLOCK_SIZE
+    // ne00xne01: ne00 * ne01
+    int ne0 = ne00 * scale_factor;
+    int nidx = threadIdx.x + blockIdx.x * blockDim.x;
+    if (nidx >= ne0) {
+        return;
+    }
+    // operation
+    int i00 = nidx / scale_factor;
+    int i01 = blockIdx.y / scale_factor;
+    int offset_src =
+        i00 +
+        i01 * ne00 +
+        blockIdx.z * ne00xne01;
+    int offset_dst =
+        nidx +
+        blockIdx.y * ne0 +
+        blockIdx.z * ne0 * gridDim.y;
+    dst[offset_dst] = x[offset_src];
+}
+
+static void upscale_f32_cuda(const float * x, float * dst, const int ne00, const int ne01, const int ne02, const int ne03,
+                             const int scale_factor, cudaStream_t stream) {
+    int ne0 = (ne00 * scale_factor);
+    int num_blocks = (ne0 + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE;
+    dim3 gridDim(num_blocks, (ne01 * scale_factor), ne02*ne03);
+    upscale_f32<<<gridDim, CUDA_UPSCALE_BLOCK_SIZE, 0, stream>>>(x, dst, ne00, ne00 * ne01, scale_factor);
+}
+
+void ggml_cuda_op_upscale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
+
+    const int scale_factor = dst->op_params[0];
+
+    upscale_f32_cuda(src0_d, dst_d, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], scale_factor, stream);
+}
diff --git a/ggml-cuda/upscale.cuh b/ggml-cuda/upscale.cuh
new file mode 100644
index 000000000..d4d765230
--- /dev/null
+++ b/ggml-cuda/upscale.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_UPSCALE_BLOCK_SIZE 256
+
+void ggml_cuda_op_upscale(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml-cuda/vecdotq.cuh b/ggml-cuda/vecdotq.cuh
new file mode 100644
index 000000000..86b87fa93
--- /dev/null
+++ b/ggml-cuda/vecdotq.cuh
@@ -0,0 +1,1280 @@
+#include "common.cuh"
+
+static __device__ __forceinline__ int get_int_from_int8(const int8_t * x8, const int & i32) {
+    const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
+
+    int x32 = 0;
+    x32 |= x16[0] <<  0;
+    x32 |= x16[1] << 16;
+
+    return x32;
+}
+
+static __device__ __forceinline__ int get_int_from_uint8(const uint8_t * x8, const int & i32) {
+    const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
+
+    int x32 = 0;
+    x32 |= x16[0] <<  0;
+    x32 |= x16[1] << 16;
+
+    return x32;
+}
+
+static __device__ __forceinline__ int get_int_from_int8_aligned(const int8_t * x8, const int & i32) {
+    return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
+}
+
+static __device__ __forceinline__ int get_int_from_uint8_aligned(const uint8_t * x8, const int & i32) {
+    return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
+}
+
+
+// VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
+// MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
+
+#define VDR_Q4_0_Q8_1_MMVQ 2
+#define VDR_Q4_0_Q8_1_MMQ  4
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q4_0_q8_1_impl(
+    const int * v, const int * u, const float & d4, const half2 & ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
+        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
+
+        // SIMD dot product of quantized values
+        sumi = __dp4a(vi0, u[2*i+0], sumi);
+        sumi = __dp4a(vi1, u[2*i+1], sumi);
+    }
+
+    const float2 ds8f = __half22float2(ds8);
+
+    // second part effectively subtracts 8 from each quant value
+    return d4 * (sumi * ds8f.x - (8*vdr/QI4_0) * ds8f.y);
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q4_1_Q8_1_MMVQ 2
+#define VDR_Q4_1_Q8_1_MMQ  4
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q4_1_q8_1_impl(
+    const int * v, const int * u, const half2 & dm4, const half2 & ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
+        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
+
+        // SIMD dot product of quantized values
+        sumi = __dp4a(vi0, u[2*i+0], sumi);
+        sumi = __dp4a(vi1, u[2*i+1], sumi);
+    }
+
+#ifdef GGML_CUDA_F16
+    const float2 tmp = __half22float2(__hmul2(dm4, ds8));
+    const float d4d8 = tmp.x;
+    const float m4s8 = tmp.y;
+#else
+    const float2 dm4f = __half22float2(dm4);
+    const float2 ds8f = __half22float2(ds8);
+    const float d4d8 = dm4f.x * ds8f.x;
+    const float m4s8 = dm4f.y * ds8f.y;
+#endif // GGML_CUDA_F16
+
+    // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it
+    return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1));
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q5_0_Q8_1_MMVQ 2
+#define VDR_Q5_0_Q8_1_MMQ  4
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q5_0_q8_1_impl(
+    const int * vl, const int * vh, const int * u, const float & d5, const half2 & ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
+        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
+        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
+        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
+        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
+        sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
+
+        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
+        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
+        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
+        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
+        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
+        sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
+    }
+
+    const float2 ds8f = __half22float2(ds8);
+
+    // second part effectively subtracts 16 from each quant value
+    return d5 * (sumi * ds8f.x - (16*vdr/QI5_0) * ds8f.y);
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q5_1_Q8_1_MMVQ 2
+#define VDR_Q5_1_Q8_1_MMQ  4
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q5_1_q8_1_impl(
+    const int * vl, const int * vh, const int * u, const half2 & dm5, const half2 & ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
+        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
+        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
+        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
+        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
+        sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
+
+        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
+        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
+        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
+        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
+        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
+        sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
+    }
+
+#ifdef GGML_CUDA_F16
+    const float2 tmp = __half22float2(__hmul2(dm5, ds8));
+    const float d5d8 = tmp.x;
+    const float m5s8 = tmp.y;
+#else
+    const float2 dm5f = __half22float2(dm5);
+    const float2 ds8f = __half22float2(ds8);
+    const float d5d8 = dm5f.x * ds8f.x;
+    const float m5s8 = dm5f.y * ds8f.y;
+#endif // GGML_CUDA_F16
+
+    // scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it
+    return sumi*d5d8 + m5s8 / (QI5_1 / vdr);
+
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q8_0_Q8_1_MMVQ 2
+#define VDR_Q8_0_Q8_1_MMQ 8
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q8_0_q8_1_impl(
+    const int * v, const int * u, const float & d8_0, const float & d8_1) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        // SIMD dot product of quantized values
+        sumi = __dp4a(v[i], u[i], sumi);
+    }
+
+    return d8_0*d8_1 * sumi;
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+template <int vdr> static __device__ __forceinline__ float vec_dot_q8_1_q8_1_impl(
+    const int * v, const int * u, const half2 & dm8, const half2 & ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        // SIMD dot product of quantized values
+        sumi = __dp4a(v[i], u[i], sumi);
+    }
+
+#ifdef GGML_CUDA_F16
+    const float2 tmp = __half22float2(__hmul2(dm8, ds8));
+    const float d8d8 = tmp.x;
+    const float m8s8 = tmp.y;
+#else
+    const float2 dm8f = __half22float2(dm8);
+    const float2 ds8f = __half22float2(ds8);
+    const float d8d8 = dm8f.x * ds8f.x;
+    const float m8s8 = dm8f.y * ds8f.y;
+#endif // GGML_CUDA_F16
+
+    // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it
+    return sumi*d8d8 + m8s8 / (QI8_1 / vdr);
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q2_K_Q8_1_MMVQ 1
+#define VDR_Q2_K_Q8_1_MMQ  2
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq(
+    const int & v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
+    const half2 & dm2, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR2_K; ++i) {
+        const int sc = scales[2*i];
+
+        const int vi = (v >> (2*i)) & 0x03030303;
+
+        sumf_d += d8[i] * (__dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product
+
+        // fill int with 4x m
+        int m = sc >> 4;
+        m |= m <<  8;
+        m |= m << 16;
+        sumf_m += d8[i] * __dp4a(m, u[i], 0); // multiply constant q2_K part with sum of q8_1 values
+    }
+
+    const float2 dm2f = __half22float2(dm2);
+
+    return dm2f.x*sumf_d - dm2f.y*sumf_m;
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+// contiguous u/y values
+static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
+    const half2 & dm2, const float & d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi_d = 0;
+    int sumi_m = 0;
+
+#pragma unroll
+    for (int i0 = 0; i0 < QI8_1; i0 += QI8_1/2) {
+        int sumi_d_sc = 0;
+
+        const int sc = scales[i0 / (QI8_1/2)];
+
+        // fill int with 4x m
+        int m = sc >> 4;
+        m |= m <<  8;
+        m |= m << 16;
+
+#pragma unroll
+        for (int i = i0; i < i0 + QI8_1/2; ++i) {
+            sumi_d_sc = __dp4a(v[i], u[i], sumi_d_sc); // SIMD dot product
+            sumi_m    = __dp4a(m,    u[i], sumi_m); // multiply sum of q8_1 values with m
+        }
+
+        sumi_d += sumi_d_sc * (sc & 0xF);
+    }
+
+    const float2 dm2f = __half22float2(dm2);
+
+    return d8 * (dm2f.x*sumi_d - dm2f.y*sumi_m);
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q3_K_Q8_1_MMVQ 1
+#define VDR_Q3_K_Q8_1_MMQ  2
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq(
+    const int & vl, const int & vh, const int * __restrict__ u, const uint8_t * __restrict__ scales,
+    const int & scale_offset, const float & d3, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR3_K; ++i) {
+        const int isc = scale_offset + 2*i;
+
+        const int isc_low = isc % (QK_K/32);
+        const int sc_shift_low = 4 * (isc / (QK_K/32));
+        const int sc_low  = (scales[isc_low] >> sc_shift_low) & 0xF;
+
+        const int isc_high = isc % (QK_K/64);
+        const int sc_shift_high = 2 * (isc / (QK_K/64));
+        const int sc_high = ((scales[(QK_K/32) + isc_high] >> sc_shift_high) & 3) << 4;
+
+        const int sc = (sc_low | sc_high) - 32;
+
+        const int vil = (vl >> (2*i)) & 0x03030303;
+
+        const int vih = ((vh >> i) << 2) & 0x04040404;
+
+        const int vi = __vsubss4(vil, vih);
+
+        sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
+    }
+
+    return d3 * sumf;
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+// contiguous u/y values
+static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ scales,
+    const float & d3, const float & d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    int sumi = 0;
+
+#pragma unroll
+    for (int i0 = 0; i0 < QR3_K*VDR_Q3_K_Q8_1_MMQ; i0 += QI8_1/2) {
+        int sumi_sc = 0;
+
+        for (int i = i0; i < i0 + QI8_1/2; ++i) {
+            sumi_sc = __dp4a(v[i], u[i], sumi_sc); // SIMD dot product
+        }
+
+        sumi += sumi_sc * scales[i0 / (QI8_1/2)];
+    }
+
+    return d3*d8 * sumi;
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q4_K_Q8_1_MMVQ 2
+#define VDR_Q4_K_Q8_1_MMQ  8
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq(
+    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
+    const uint8_t * __restrict__ m, const half2 & dm4, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR4_K; ++i) {
+        const int v0i = (v[0] >> (4*i)) & 0x0F0F0F0F;
+        const int v1i = (v[1] >> (4*i)) & 0x0F0F0F0F;
+
+        const int dot1 = __dp4a(v1i, u[2*i+1], __dp4a(v0i, u[2*i+0], 0)); // SIMD dot product
+        const int dot2 = __dp4a(0x01010101, u[2*i+1], __dp4a(0x01010101, u[2*i+0], 0)); // sum of u
+
+        sumf_d += d8[i] * (dot1 * sc[i]);
+        sumf_m += d8[i] * (dot2 * m[i]);  // multiply constant part of q4_K with sum of q8_1 values
+    }
+
+    const float2 dm4f = __half22float2(dm4);
+
+    return dm4f.x*sumf_d - dm4f.y*sumf_m;
+
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+// contiguous u/y values
+static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
+    const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR4_K*VDR_Q4_K_Q8_1_MMQ/QI8_1; ++i) {
+        int sumi_d = 0;
+
+#pragma unroll
+        for (int j = 0; j < QI8_1; ++j) {
+            sumi_d = __dp4a((v[j] >> (4*i)) & 0x0F0F0F0F, u[i*QI8_1 + j], sumi_d); // SIMD dot product
+        }
+
+        const float2 ds8f = __half22float2(ds8[i]);
+
+        sumf_d += ds8f.x * (sc[i] * sumi_d);
+        sumf_m += ds8f.y *   m[i]; // sum of q8_1 block * q4_K min val
+    }
+
+    const float2 dm4f = __half22float2(dm4);
+
+    return dm4f.x*sumf_d - dm4f.y*sumf_m;
+
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q5_K_Q8_1_MMVQ 2
+#define VDR_Q5_K_Q8_1_MMQ  8
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq(
+    const int * __restrict__ vl, const int * __restrict__ vh, const int * __restrict__ u, const uint8_t * __restrict__ sc,
+    const uint8_t * __restrict__ m, const half2 & dm5, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K; ++i) {
+        const int vl0i = (vl[0] >> (4*i)) & 0x0F0F0F0F;
+        const int vl1i = (vl[1] >> (4*i)) & 0x0F0F0F0F;
+
+        const int vh0i = ((vh[0] >> i) << 4) & 0x10101010;
+        const int vh1i = ((vh[1] >> i) << 4) & 0x10101010;
+
+        const int v0i = vl0i | vh0i;
+        const int v1i = vl1i | vh1i;
+
+        const int dot1 = __dp4a(v0i, u[2*i+0], __dp4a(v1i, u[2*i+1], 0)); // SIMD dot product
+        const int dot2 = __dp4a(0x01010101, u[2*i+0], __dp4a(0x01010101, u[2*i+1], 0)); // sum of u
+
+        sumf_d += d8[i] * (dot1 * sc[i]);
+        sumf_m += d8[i] * (dot2 * m[i]);
+
+    }
+
+    const float2 dm5f = __half22float2(dm5);
+
+    return dm5f.x*sumf_d - dm5f.y*sumf_m;
+
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+// contiguous u/y values
+static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
+    const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K*VDR_Q5_K_Q8_1_MMQ/QI8_1; ++i) {
+        int sumi_d = 0;
+
+#pragma unroll
+        for (int j = 0; j < QI8_1; ++j) {
+            sumi_d = __dp4a(v[i*QI8_1 + j], u[i*QI8_1 + j], sumi_d); // SIMD dot product
+        }
+
+        const float2 ds8f = __half22float2(ds8[i]);
+
+        sumf_d += ds8f.x * (sc[i] * sumi_d);
+        sumf_m += ds8f.y *   m[i]; // sum of q8_1 block * q4_K min val
+    }
+
+    const float2 dm4f = __half22float2(dm4);
+
+    return dm4f.x*sumf_d - dm4f.y*sumf_m;
+
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+#define VDR_Q6_K_Q8_1_MMVQ 1
+#define VDR_Q6_K_Q8_1_MMQ  8
+
+// contiguous v/x values
+static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq(
+    const int & vl, const int & vh, const int * __restrict__ u, const int8_t * __restrict__ scales,
+    const float & d, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR6_K; ++i) {
+        const int sc = scales[4*i];
+
+        const int vil = (vl >> (4*i)) & 0x0F0F0F0F;
+
+        const int vih = ((vh >> (4*i)) << 4) & 0x30303030;
+
+        const int vi = __vsubss4((vil | vih), 0x20202020); // vi = (vil | vih) - 32
+
+        sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
+    }
+
+    return d*sumf;
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+// contiguous u/y values
+static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq(
+    const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ sc,
+    const float & d6, const float * __restrict__ d8) {
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    float sumf_d = 0.0f;
+
+#pragma unroll
+    for (int i0 = 0; i0 < VDR_Q6_K_Q8_1_MMQ; i0 += 4) {
+        int2 sumi_d = {0, 0}; // 2 q6_K scales per q8_1 scale
+
+#pragma unroll
+        for (int i = i0; i < i0 + 2; ++i) {
+            sumi_d.x = __dp4a(v[2*i+0], u[2*i+0], sumi_d.x); // SIMD dot product
+            sumi_d.x = __dp4a(v[2*i+1], u[2*i+1], sumi_d.x); // SIMD dot product
+
+            sumi_d.y = __dp4a(v[2*i+4], u[2*i+4], sumi_d.y); // SIMD dot product
+            sumi_d.y = __dp4a(v[2*i+5], u[2*i+5], sumi_d.y); // SIMD dot product
+        }
+
+        sumf_d += d8[i0/4] * (sc[i0/2+0]*sumi_d.x + sc[i0/2+1]*sumi_d.y);
+    }
+
+    return d6 * sumf_d;
+
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+}
+
+static __device__ __forceinline__ float vec_dot_q4_0_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq;
+
+    int v[VDR_Q4_0_Q8_1_MMVQ];
+    int u[2*VDR_Q4_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q4_0_Q8_1_MMVQ; ++i) {
+        v[i]     = get_int_from_uint8(bq4_0->qs, iqs + i);
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_0);
+    }
+
+    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMVQ>(v, u, bq4_0->d, bq8_1->ds);
+}
+
+
+static __device__ __forceinline__ float vec_dot_q4_1_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq;
+
+    int v[VDR_Q4_1_Q8_1_MMVQ];
+    int u[2*VDR_Q4_1_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) {
+        v[i]    = get_int_from_uint8_aligned(bq4_1->qs, iqs + i);
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_1);
+    }
+
+    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, bq4_1->dm, bq8_1->ds);
+}
+
+static __device__ __forceinline__ float vec_dot_q5_0_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq;
+
+    int vl[VDR_Q5_0_Q8_1_MMVQ];
+    int vh[VDR_Q5_0_Q8_1_MMVQ];
+    int  u[2*VDR_Q5_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q5_0_Q8_1_MMVQ; ++i) {
+        vl[i]    = get_int_from_uint8(bq5_0->qs, iqs + i);
+        vh[i]    = get_int_from_uint8(bq5_0->qh, 0) >> (4 * (iqs + i));
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_0);
+    }
+
+    return vec_dot_q5_0_q8_1_impl<VDR_Q5_0_Q8_1_MMVQ>(vl, vh, u, bq5_0->d, bq8_1->ds);
+}
+
+static __device__ __forceinline__ float vec_dot_q5_1_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq;
+
+    int vl[VDR_Q5_1_Q8_1_MMVQ];
+    int vh[VDR_Q5_1_Q8_1_MMVQ];
+    int  u[2*VDR_Q5_1_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) {
+        vl[i]   = get_int_from_uint8_aligned(bq5_1->qs, iqs + i);
+        vh[i]   = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * (iqs + i));
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_1);
+    }
+
+    return vec_dot_q5_1_q8_1_impl<VDR_Q5_1_Q8_1_MMVQ>(vl, vh, u, bq5_1->dm, bq8_1->ds);
+}
+
+static __device__ __forceinline__ float vec_dot_q8_0_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq;
+
+    int v[VDR_Q8_0_Q8_1_MMVQ];
+    int u[VDR_Q8_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q8_0_Q8_1_MMVQ; ++i) {
+        v[i] = get_int_from_int8(bq8_0->qs, iqs + i);
+        u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+    }
+
+    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d, __low2half(bq8_1->ds));
+}
+
+static __device__ __forceinline__ float vec_dot_q2_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q2_K * bq2_K = (const block_q2_K *) vbq;
+
+    const int bq8_offset = QR2_K * (iqs / QI8_1);
+    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
+
+    const uint8_t * scales = bq2_K->scales + scale_offset;
+
+    const int v = get_int_from_uint8_aligned(bq2_K->qs, iqs);
+    int    u[QR2_K];
+    float d8[QR2_K];
+
+#pragma unroll
+    for (int i = 0; i < QR2_K; ++ i) {
+        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
+        d8[i] = __low2float(bq8_1[bq8_offset + i].ds);
+    }
+
+    return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8);
+}
+
+static __device__ __forceinline__ float vec_dot_q3_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q3_K * bq3_K = (const block_q3_K *) vbq;
+
+    const int bq8_offset = QR3_K * (iqs / (QI3_K/2));
+    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
+
+    const float d = bq3_K->d;
+
+    const int vl = get_int_from_uint8(bq3_K->qs, iqs);
+
+    // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
+    const int vh = ~get_int_from_uint8(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset;
+
+    int    u[QR3_K];
+    float d8[QR3_K];
+
+#pragma unroll
+    for (int i = 0; i < QR3_K; ++i) {
+        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
+        d8[i] = __low2float(bq8_1[bq8_offset + i].ds);
+    }
+
+    return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8);
+}
+
+static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+#ifndef GGML_QKK_64
+    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
+
+    int    v[2];
+    int    u[2*QR4_K];
+    float d8[QR4_K];
+
+    // iqs is in 0,2..30. bq8_offset = iqs/4 -> bq8_offset = 0, 2, 4, 6
+    const int bq8_offset = QR4_K * ((iqs/2) / (QI8_1/2));
+
+    // iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12
+    // iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44
+    // iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76
+    // iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108
+
+    const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
+    v[0] = q4[0];
+    v[1] = q4[4];
+
+    const uint16_t * scales = (const uint16_t *)bq4_K->scales;
+    uint16_t aux[2];
+    const int j = bq8_offset/2;
+    if (j < 2) {
+        aux[0] = scales[j+0] & 0x3f3f;
+        aux[1] = scales[j+2] & 0x3f3f;
+    } else {
+        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
+        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
+    }
+    const uint8_t * sc = (const uint8_t *)aux;
+    const uint8_t * m  = sc + 2;
+
+    for (int i = 0; i < QR4_K; ++i) {
+        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
+        d8[i] = __low2float(bq8i->ds);
+
+        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
+        u[2*i+0] = q8[0];
+        u[2*i+1] = q8[4];
+    }
+
+    return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8);
+
+#else
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+    uint16_t aux16[2];
+    const uint8_t * s = (const uint8_t *)aux16;
+
+    const uint16_t * a = (const uint16_t *)bq4_K->scales;
+    aux16[0] = a[0] & 0x0f0f;
+    aux16[1] = (a[0] >> 4) & 0x0f0f;
+
+    const float dall = bq4_K->dm[0];
+    const float dmin = bq4_K->dm[1];
+
+    const float d8_1 = __low2float(bq8_1[0].ds);
+    const float d8_2 = __low2float(bq8_1[1].ds);
+
+    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
+    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
+    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
+    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
+
+    const int * q4 = (const int *)bq4_K->qs + (iqs/2);
+    const int v1 = q4[0];
+    const int v2 = q4[4];
+
+    const int dot1 = __dp4a(ui2, v2 & 0x0f0f0f0f, __dp4a(ui1, v1 & 0x0f0f0f0f, 0));
+    const int dot2 = __dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, __dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0));
+    const int dot3 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0));
+    const int dot4 = __dp4a(0x01010101, ui4, __dp4a(0x01010101, ui3, 0));
+
+    sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]);
+    sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]);
+
+    return dall * sumf_d - dmin * sumf_m;
+
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+
+#endif
+}
+
+static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+#ifndef GGML_QKK_64
+    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
+
+    int   vl[2];
+    int   vh[2];
+    int    u[2*QR5_K];
+    float d8[QR5_K];
+
+    const int bq8_offset = QR5_K * ((iqs/2) / (QI8_1/2));
+    const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
+    const int * qh = (const int *)(bq5_K->qh + 4 * ((iqs/2)%4));
+
+    vl[0] = ql[0];
+    vl[1] = ql[4];
+
+    vh[0] = qh[0] >> bq8_offset;
+    vh[1] = qh[4] >> bq8_offset;
+
+    const uint16_t * scales = (const uint16_t *)bq5_K->scales;
+    uint16_t aux[2];
+    const int j = bq8_offset/2;
+    if (j < 2) {
+        aux[0] = scales[j+0] & 0x3f3f;
+        aux[1] = scales[j+2] & 0x3f3f;
+    } else {
+        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
+        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
+    }
+    const uint8_t * sc = (const uint8_t *)aux;
+    const uint8_t * m  = sc + 2;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K; ++i) {
+        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
+        d8[i] = __low2float(bq8i->ds);
+
+        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
+        u[2*i+0] = q8[0];
+        u[2*i+1] = q8[4];
+    }
+
+    return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8);
+
+#else
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
+
+    const int8_t * s = bq5_K->scales;
+
+    const float d = bq5_K->d;
+
+    const float d8_1 = __low2half(bq8_1[0].ds);
+    const float d8_2 = __low2half(bq8_1[1].ds);
+
+    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
+    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
+    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
+    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
+
+    const int * ql = (const int *)bq5_K->qs + (iqs/2);
+    const int vl1 = ql[0];
+    const int vl2 = ql[4];
+
+    const int step = 4 * (iqs/2); // 0, 4, 8, 12
+    const int im = step/8; // = 0 for iqs = 0, 2, = 1 for iqs = 4, 6
+    const int in = step%8; // 0, 4, 0, 4
+    const int vh = (*((const int *)(bq5_K->qh + in))) >> im;
+
+    const int v1 = (((vh << 4) & 0x10101010) ^ 0x10101010) | ((vl1 >> 0) & 0x0f0f0f0f);
+    const int v2 = (((vh << 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 0) & 0x0f0f0f0f);
+    const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f);
+    const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f);
+
+    const float sumf_d = d8_1 * (__dp4a(ui1, v1, 0) * s[0] + __dp4a(ui2, v2, 0) * s[1])
+                       + d8_2 * (__dp4a(ui3, v3, 0) * s[2] + __dp4a(ui4, v4, 0) * s[3]);
+
+    return d * sumf_d;
+
+#else
+    NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+
+#endif
+}
+
+static __device__ __forceinline__ float vec_dot_q6_K_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_q6_K * bq6_K = (const block_q6_K *) vbq;
+
+    const int bq8_offset = 2 * QR6_K * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/4);
+    const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8);
+    const int vh_shift = 2 * ((iqs % (QI6_K/2)) / (QI6_K/4));
+
+    const int vl = get_int_from_uint8(bq6_K->ql, iqs);
+    const int vh = get_int_from_uint8(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift;
+
+    const int8_t * scales = bq6_K->scales + scale_offset;
+
+    int    u[QR6_K];
+    float d8[QR6_K];
+
+#pragma unroll
+    for (int i = 0; i < QR6_K; ++i) {
+        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1);
+        d8[i] = __low2float(bq8_1[bq8_offset + 2*i].ds);
+    }
+
+    return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8);
+}
+
+static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+#if QK_K == 256
+    const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq;
+
+#if QR2_XXS == 8
+    const int ib32 = iqs;
+    const uint16_t * q2 = bq2->qs + 4*ib32;
+    const uint8_t  * aux8 = (const uint8_t *)q2;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    uint32_t aux32 = q2[2] | (q2[3] << 16);
+    int sumi = 0;
+    for (int l = 0; l < 4; ++l) {
+        const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]);
+        const uint8_t  signs = ksigns_iq2xs[aux32 & 127];
+        for (int j = 0; j < 8; ++j) {
+            sumi += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
+        }
+        q8 += 8;
+        aux32 >>= 7;
+    }
+    const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.25f;
+    return d * sumi;
+#else
+    // iqs is 0...15
+    const int ib32 = iqs/2;
+    const int il = iqs%2;
+    const uint16_t * q2 = bq2->qs + 4*ib32;
+    const uint8_t  * aux8 = (const uint8_t *)q2;
+    const uint8_t  * grid1 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+0]);
+    const uint8_t  * grid2 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+1]);
+    const uint32_t aux32 = q2[2] | (q2[3] << 16);
+    const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * __low2float(bq8_1[ib32].ds) * 0.25f;
+    const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127];
+    const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127];
+    const int8_t * q8 = bq8_1[ib32].qs + 16*il;
+    int sumi1 = 0, sumi2 = 0;
+    for (int j = 0; j < 8; ++j) {
+        sumi1 += q8[j+0] * grid1[j] * (signs1 & kmask_iq2xs[j] ? -1 : 1);
+        sumi2 += q8[j+8] * grid2[j] * (signs2 & kmask_iq2xs[j] ? -1 : 1);
+    }
+    return d * (sumi1 + sumi2);
+#endif
+#else
+    NO_DEVICE_CODE;
+#endif
+}
+
+static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+#if QK_K == 256
+    const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq;
+
+    const int ib32 = iqs;
+    const uint16_t * q2 = bq2->qs + 4*ib32;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    const uint8_t ls1 = bq2->scales[ib32] & 0xf;
+    const uint8_t ls2 = bq2->scales[ib32] >>  4;
+    int sumi1 = 0;
+    for (int l = 0; l < 2; ++l) {
+        const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
+        const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
+        const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]);
+        const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]);
+        sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1);
+        sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1);
+        q8 += 8;
+    }
+    int sumi2 = 0;
+    for (int l = 2; l < 4; ++l) {
+        const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
+        const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
+        const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]);
+        const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]);
+        sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2);
+        sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2);
+        q8 += 8;
+    }
+    const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f;
+    return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
+#else
+    GGML_UNUSED(ksigns64);
+    NO_DEVICE_CODE;
+#endif
+#else
+    GGML_UNUSED(ksigns64);
+    NO_DEVICE_CODE;
+#endif
+}
+
+// TODO
+static __device__ __forceinline__ float vec_dot_iq2_s_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+#if QK_K == 256
+    const block_iq2_s * bq2 = (const block_iq2_s *) vbq;
+
+    const int ib32 = iqs;
+    const int8_t  * q8 = bq8_1[ib32].qs;
+    const uint8_t * signs = bq2->qs + QK_K/8 + 4*ib32;
+    const uint8_t ls1 = bq2->scales[ib32] & 0xf;
+    const uint8_t ls2 = bq2->scales[ib32] >>  4;
+    int sumi1 = 0;
+    for (int l = 0; l < 2; ++l) {
+        const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300)));
+        const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201);
+        const uint32_t signs1 = __vcmpeq4(((signs[l] >>  4) * 0x01010101) & 0x08040201, 0x08040201);
+        const int grid_l = __vsub4(grid[0] ^ signs0, signs0);
+        const int grid_h = __vsub4(grid[1] ^ signs1, signs1);
+        sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1);
+        sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1);
+        q8 += 8;
+    }
+    int sumi2 = 0;
+    for (int l = 2; l < 4; ++l) {
+        const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300)));
+        const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201);
+        const uint32_t signs1 = __vcmpeq4(((signs[l] >>  4) * 0x01010101) & 0x08040201, 0x08040201);
+        const int grid_l = __vsub4(grid[0] ^ signs0, signs0);
+        const int grid_h = __vsub4(grid[1] ^ signs1, signs1);
+        sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2);
+        sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2);
+        q8 += 8;
+    }
+    const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f;
+    return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
+#else
+    GGML_UNUSED(ksigns64);
+    NO_DEVICE_CODE;
+#endif
+#else
+    GGML_UNUSED(ksigns64);
+    NO_DEVICE_CODE;
+#endif
+}
+
+static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+#if QK_K == 256
+    const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq;
+
+    const int ib32 = iqs;
+    const uint8_t  * q3 = bq2->qs + 8*ib32;
+    const uint16_t * gas = (const uint16_t *)(bq2->qs + QK_K/4) + 2*ib32;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    uint32_t aux32 = gas[0] | (gas[1] << 16);
+    int sumi = 0;
+    for (int l = 0; l < 4; ++l) {
+        const uint32_t * grid1 = iq3xxs_grid + q3[2*l+0];
+        const uint32_t * grid2 = iq3xxs_grid + q3[2*l+1];
+        const uint32_t * signs = (const uint32_t *)(ksigns64 + (aux32 & 127));
+        const int grid_l = __vsub4(grid1[0] ^ signs[0], signs[0]);
+        const int grid_h = __vsub4(grid2[0] ^ signs[1], signs[1]);
+        sumi = __dp4a(grid_l, *((int *)q8+0), sumi);
+        sumi = __dp4a(grid_h, *((int *)q8+1), sumi);
+        q8 += 8;
+        aux32 >>= 7;
+    }
+    const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.5f;
+    return d * sumi;
+#else
+    NO_DEVICE_CODE;
+#endif
+#else
+    NO_DEVICE_CODE;
+#endif
+}
+
+// TODO: don't use lookup table for signs
+static __device__ __forceinline__ float vec_dot_iq3_s_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+#if QK_K == 256
+    const block_iq3_s * bq2 = (const block_iq3_s *) vbq;
+
+    const int ib32 = iqs;
+    const uint8_t  * qs = bq2->qs + 8*ib32;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    int sumi = 0;
+    for (int l = 0; l < 4; ++l) {
+        const uint32_t * grid1 = iq3s_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256));
+        const uint32_t * grid2 = iq3s_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256));
+        uint32_t signs0 = __vcmpeq4(((bq2->signs[4*ib32+l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201);
+        uint32_t signs1 = __vcmpeq4(((bq2->signs[4*ib32+l] >>  4) * 0x01010101) & 0x08040201, 0x08040201);
+        const int grid_l = __vsub4(grid1[0] ^ signs0, signs0);
+        const int grid_h = __vsub4(grid2[0] ^ signs1, signs1);
+        sumi = __dp4a(grid_l, *((int *)q8+0), sumi);
+        sumi = __dp4a(grid_h, *((int *)q8+1), sumi);
+        q8 += 8;
+    }
+    const float d = (float)bq2->d * (1 + 2*((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds);
+    return d * sumi;
+#else
+    NO_DEVICE_CODE;
+#endif
+#else
+    NO_DEVICE_CODE;
+#endif
+}
+
+static __device__ __forceinline__ float vec_dot_iq1_s_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+#if QK_K == 256
+    const block_iq1_s * bq1 = (const block_iq1_s *) vbq;
+
+    const int ib32 = iqs;
+    int sumi = 0;
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    const int * q8 = (const int *)bq8_1[ib32].qs;
+    for (int l = 0; l < 4; ++l) {
+        const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8)));
+        int grid0 = grid[0] & 0x0f0f0f0f;
+        int grid1 = (grid[0] >> 4) & 0x0f0f0f0f;
+        sumi = __dp4a(q8[2*l+1], grid1, __dp4a(q8[2*l+0], grid0, sumi));
+    }
+#else
+    const int8_t * q8 = bq8_1[ib32].qs;
+    for (int l = 0; l < 4; ++l) {
+        const uint8_t * grid = (const uint8_t *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8)));
+        for (int j = 0; j < 4; ++j) {
+            sumi += q8[j] * (grid[j] & 0xf) + q8[j+4] * (grid[j] >> 4);
+        }
+        q8 += 8;
+    }
+#endif
+    const float delta = bq1->qh[ib32] & 0x8000 ? -1-IQ1S_DELTA : -1+IQ1S_DELTA;
+    const float d1q = (float)bq1->d * (2*((bq1->qh[ib32] >> 12) & 7) + 1);
+    const float d = d1q * __low2float (bq8_1[ib32].ds);
+    const float m = d1q * __high2float(bq8_1[ib32].ds);
+    return d * sumi + m * delta;
+#else
+    NO_DEVICE_CODE;
+#endif
+}
+
+static __device__ __forceinline__ float vec_dot_iq1_m_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+#if QK_K == 256
+    const block_iq1_m * bq1 = (const block_iq1_m *) vbq;
+
+    const int ib32 = iqs;
+    int   sumi[2] = {0, 0};
+    float sumf[2] = {0.f, 0.f};
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    const int * q8 = (const int *)bq8_1[ib32].qs;
+    for (int l = 0; l < 4; ++l) {
+        const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 7) << 8)));
+        int grid0 = grid[0] & 0x0f0f0f0f;
+        int grid1 = (grid[0] >> 4) & 0x0f0f0f0f;
+        sumi[l/2] = __dp4a(q8[2*l+1], grid1, __dp4a(q8[2*l+0], grid0, sumi[l/2]));
+        const float delta = (bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 0x08 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA;
+        const int sumy = __dp4a(q8[2*l+1], 0x01010101, __dp4a(q8[2*l+0], 0x01010101, 0));
+        sumf[l/2] += delta*sumy;
+    }
+#else
+    const int8_t * q8 = bq8_1[ib32].qs;
+    for (int l = 0; l < 4; ++l) {
+        const uint8_t * grid = (const uint8_t *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8)));
+        int sumy = 0;
+        for (int j = 0; j < 4; ++j) {
+            sumi[l/2] += q8[j] * (grid[j] & 0xf) + q8[j+4] * (grid[j] >> 4);
+            sumy += q8[j] + q8[j+4];
+        }
+        const float delta = (bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 0x08 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA;
+        sumf[l/2] += delta*sumy;
+        q8 += 8;
+    }
+#endif
+    iq1m_scale_t scale;
+    const uint16_t * sc = (const uint16_t *)bq1->scales;
+    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+    const float d = (float)scale.f16 * __low2float (bq8_1[ib32].ds);
+    return d * ((sumi[0] + sumf[0]) * (2*((sc[ib32/2] >> 6*(ib32%2)) & 0x7) + 1) + (sumi[1] + sumf[1]) * (2*((sc[ib32/2] >> (6*(ib32%2)+3)) & 0x7) + 1));
+#else
+    NO_DEVICE_CODE;
+#endif
+}
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+static __device__ __forceinline__ void get_int_from_table_16(const uint32_t & q4, const uint8_t * values,
+        int & val1, int & val2) {
+
+    uint32_t aux32; const uint8_t * q8 = (const uint8_t *)&aux32;
+    aux32 = q4 & 0x0f0f0f0f;
+    uint16_t v1 = values[q8[0]] | (values[q8[1]] << 8);
+    uint16_t v2 = values[q8[2]] | (values[q8[3]] << 8);
+    val1 = v1 | (v2 << 16);
+    aux32 = (q4 >> 4) & 0x0f0f0f0f;
+    v1 = values[q8[0]] | (values[q8[1]] << 8);
+    v2 = values[q8[2]] | (values[q8[3]] << 8);
+    val2 = v1 | (v2 << 16);
+}
+#endif
+
+static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+    const block_iq4_nl * bq = (const block_iq4_nl *) vbq;
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+    const uint16_t * q4 = (const uint16_t *)bq->qs + 2*iqs;
+    const int32_t  * q8 = (const int32_t  *)bq8_1->qs + iqs;
+
+    const uint8_t * values = (const uint8_t *)kvalues_iq4nl;
+
+    int v1, v2;
+    int sumi1 = 0, sumi2 = 0;
+    for (int l = 0; l < VDR_Q4_0_Q8_1_MMVQ; ++l) {
+        const uint32_t aux = q4[2*l] | (q4[2*l+1] << 16);
+        get_int_from_table_16(aux, values, v1, v2);
+        sumi1 = __dp4a(v1, q8[l+0], sumi1);
+        sumi2 = __dp4a(v2, q8[l+4], sumi2);
+    }
+
+#else
+    const uint8_t * q4 = bq->qs + 4*iqs;
+    const int8_t  * q8 = bq8_1->qs + 4*iqs;
+
+    int sumi1 = 0, sumi2 = 0;
+    for (int l = 0; l < 4*VDR_Q4_0_Q8_1_MMVQ; ++l) {
+        sumi1 += q8[l+ 0] * kvalues_iq4nl[q4[l] & 0xf];
+        sumi2 += q8[l+16] * kvalues_iq4nl[q4[l] >>  4];
+    }
+#endif
+    const float d = (float)bq->d * __low2float(bq8_1->ds);
+    return d * (sumi1 + sumi2);
+}
+
+static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
+
+#if QK_K == 256
+#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+
+    const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq;
+    const uint8_t * values = (const uint8_t *)kvalues_iq4nl;
+
+    // iqs is 0...7
+    const int ib32 = iqs;
+    const int32_t  * q8 = (const int *)bq8_1[ib32].qs;
+    const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32;
+    const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4);
+    const float d = (float)bq4->d * (ls - 32) * __low2float(bq8_1[ib32].ds);
+    int v1, v2;
+    int sumi1 = 0, sumi2 = 0;
+    for (int j = 0; j < 4; ++j) {
+        get_int_from_table_16(q4[j], values, v1, v2);
+        sumi1 = __dp4a(v1, q8[j+0], sumi1);
+        sumi2 = __dp4a(v2, q8[j+4], sumi2);
+    }
+    return d * (sumi1 + sumi2);
+
+#else
+    NO_DEVICE_CODE;
+#endif
+#else
+    return vec_dot_iq4_xs_q8_1(vbq, bq8_1, iqs);
+#endif
+}
diff --git a/ggml-impl.h b/ggml-impl.h
index 2faced080..e68b72877 100644
--- a/ggml-impl.h
+++ b/ggml-impl.h
@@ -19,6 +19,7 @@ extern "C" {
 // fall back to the _Static_assert C11 keyword.
 // if C99 - static_assert is noop
 // ref: https://stackoverflow.com/a/53923785/4039976
+#ifndef __cplusplus
 #ifndef static_assert
 #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
 #define static_assert(cond, msg) _Static_assert(cond, msg)
@@ -26,6 +27,7 @@ extern "C" {
 #define static_assert(cond, msg) struct global_scope_noop_trick
 #endif
 #endif
+#endif
 
 // __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512
 #if defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__))
@@ -51,14 +53,30 @@ extern "C" {
 //
 #include <arm_neon.h>
 
-#define GGML_COMPUTE_FP16_TO_FP32(x) ((float) (x))
-#define GGML_COMPUTE_FP32_TO_FP16(x) (x)
+typedef __fp16 ggml_fp16_internal_t;
 
-#define GGML_FP16_TO_FP32(x) ((float) (x))
-#define GGML_FP32_TO_FP16(x) (x)
+#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
+#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
+
+#define GGML_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
+
+static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
+    ggml_fp16_internal_t tmp;
+    memcpy(&tmp, &h, sizeof(ggml_fp16_t));
+    return (float)tmp;
+}
+
+static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
+    ggml_fp16_t res;
+    ggml_fp16_internal_t tmp = f;
+    memcpy(&res, &tmp, sizeof(ggml_fp16_t));
+    return res;
+}
 
 #else
 
+typedef uint16_t ggml_fp16_internal_t;
+
 #ifdef __wasm_simd128__
 #include <wasm_simd128.h>
 #else
@@ -212,8 +230,7 @@ extern float ggml_table_f32_f16[1 << 16];
 // On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32,
 // so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON.
 // This is also true for POWER9.
-#if !defined(GGML_FP16_TO_FP32) || !defined(GGML_FP32_TO_FP16)
-
+#if !defined(GGML_FP16_TO_FP32)
 inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
     uint16_t s;
     memcpy(&s, &f, sizeof(uint16_t));
@@ -221,13 +238,17 @@ inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
 }
 
 #define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x)
-#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
+#endif
 
+#if !defined(GGML_FP32_TO_FP16)
+#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
 #endif
 
 #define GGML_HASHTABLE_FULL ((size_t)-1)
 #define GGML_HASHTABLE_ALREADY_EXISTS ((size_t)-2)
 
+struct ggml_hash_set ggml_hash_set_new(size_t size);
+
 bool   ggml_hash_contains      (const struct ggml_hash_set hash_set, struct ggml_tensor * key);
 
 // returns GGML_HASHTABLE_FULL if table is full, otherwise the current index of the key or where it should be inserted
diff --git a/ggml-kompute.cpp b/ggml-kompute.cpp
new file mode 100644
index 000000000..407062e6f
--- /dev/null
+++ b/ggml-kompute.cpp
@@ -0,0 +1,2006 @@
+#include "ggml.h"
+#include "ggml-backend.h"
+#include "ggml-backend-impl.h"
+#include "ggml-kompute.h"
+
+// These are generated at build time by cmake custom command
+#include "shaderop_scale.h"
+#include "shaderop_scale_8.h"
+#include "shaderop_add.h"
+#include "shaderop_addrow.h"
+#include "shaderop_mul.h"
+#include "shaderop_silu.h"
+#include "shaderop_relu.h"
+#include "shaderop_gelu.h"
+#include "shaderop_softmax.h"
+#include "shaderop_norm.h"
+#include "shaderop_rmsnorm.h"
+#include "shaderop_diagmask.h"
+#include "shaderop_mul_mat_f16.h"
+#include "shaderop_mul_mat_q8_0.h"
+#include "shaderop_mul_mat_q4_0.h"
+#include "shaderop_mul_mat_q4_1.h"
+#include "shaderop_mul_mat_q6_k.h"
+#include "shaderop_mul_mat_mat_f32.h"
+#include "shaderop_getrows_f16.h"
+#include "shaderop_getrows_q4_0.h"
+#include "shaderop_getrows_q4_1.h"
+#include "shaderop_getrows_q6_k.h"
+#include "shaderop_rope_f16.h"
+#include "shaderop_rope_f32.h"
+#include "shaderop_cpy_f16_f16.h"
+#include "shaderop_cpy_f16_f32.h"
+#include "shaderop_cpy_f32_f16.h"
+#include "shaderop_cpy_f32_f32.h"
+
+#include <algorithm>
+#include <array>
+#include <cassert>
+#include <cstdint>
+#include <cstdio>
+#include <cstring>
+#include <iostream>
+#include <memory>
+#include <stdexcept>
+#include <string>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+#include <kompute/Kompute.hpp>
+#include <vulkan/vulkan.hpp>
+
+#ifdef __linux__
+#include <cstdlib> // for setenv
+#endif
+
+#define QK4_0 32
+#define QR4_0 2
+#define QK4_1 32
+#define QK_NL 16
+
+typedef ggml_fp16_t half;
+
+static std::string ggml_kompute_format_name(int device) {
+    return "Kompute" + std::to_string(device);
+}
+
+struct ggml_kompute_context {
+    int device;
+    std::string name;
+    std::shared_ptr<vk::DescriptorPool> pool;
+
+    ggml_kompute_context(int device)
+        : device(device), name(ggml_kompute_format_name(device)) {}
+};
+
+// FIXME: It would be good to consolidate the kompute manager and the kompute context into one object
+// and consolidate the init functions and simplify object lifetime management. As it currently stands,
+// we *have* to have the kompute manager no matter what for device discovery, but the kompute context
+// is only created when a device is set and vulkan is explicitly turned on.
+static ggml_kompute_context *s_kompute_context = nullptr;
+
+class kompute_manager {
+    kp::Manager *s_mgr = nullptr;
+
+public:
+    kp::Manager *operator()() {
+        if (s_mgr && !s_mgr->hasInstance()) {
+            destroy();
+        }
+        if (!s_mgr) {
+            s_mgr = new kp::Manager;
+        }
+        return s_mgr;
+    }
+
+    void destroy() {
+        delete s_mgr;
+        s_mgr = nullptr;
+    }
+};
+
+static kompute_manager komputeManager;
+
+struct ggml_vk_memory {
+    void *data = nullptr;
+    size_t size = 0;
+    vk::DeviceMemory *primaryMemory = nullptr;
+    vk::Buffer *primaryBuffer = nullptr;
+    vk::DeviceMemory *stagingMemory = nullptr;
+    vk::Buffer *stagingBuffer = nullptr;
+};
+
+#ifdef __linux__
+__attribute__((constructor))
+static void enable_sam() {
+    setenv("RADV_PERFTEST", "sam", false);
+}
+#endif
+
+static bool ggml_vk_checkPhysicalDeviceFeatures(vk::PhysicalDevice physical_device) {
+    vk::PhysicalDeviceFeatures availableFeatures;
+    physical_device.getFeatures(&availableFeatures);
+
+    if (!availableFeatures.shaderInt16)
+        return false;
+
+    vk::PhysicalDeviceVulkan11Features availableFeatures11;
+    vk::PhysicalDeviceVulkan12Features availableFeatures12;
+
+    availableFeatures11.pNext = &availableFeatures12;
+    availableFeatures12.pNext = nullptr;
+
+    vk::PhysicalDeviceFeatures2 features2;
+    features2.pNext = &availableFeatures11;
+
+    physical_device.getFeatures2(&features2);
+
+    if (!availableFeatures11.uniformAndStorageBuffer16BitAccess ||
+        !availableFeatures11.storageBuffer16BitAccess) {
+        return false;
+    }
+
+    if (!availableFeatures12.storageBuffer8BitAccess ||
+        !availableFeatures12.uniformAndStorageBuffer8BitAccess ||
+        !availableFeatures12.shaderFloat16 ||
+        !availableFeatures12.shaderInt8) {
+        return false;
+    }
+
+    return true;
+}
+
+static const char * ggml_vk_getVendorName(uint32_t vendorID) {
+    switch (vendorID) {
+        case 0x10DE:
+            return "nvidia";
+        case 0x1002:
+            return "amd";
+        case 0x8086:
+            return "intel";
+        default:
+            return "unknown";
+    }
+}
+
+static std::vector<ggml_vk_device> ggml_vk_available_devices_internal(size_t memoryRequired) {
+    std::vector<ggml_vk_device> results;
+    if (!komputeManager()->hasVulkan() || !komputeManager()->hasInstance())
+        return results;
+
+    std::vector<vk::PhysicalDevice> physical_devices;
+    try {
+        physical_devices = komputeManager()->listDevices();
+    } catch (vk::SystemError & err) {
+        std::cerr << __func__ << ": ignoring Vulkan exception: " << err.what() << "\n";
+        return results;
+    }
+
+    uint32_t deviceCount = physical_devices.size();
+    if (deviceCount == 0)
+        return results;
+
+    std::unordered_map<std::string, size_t> count_by_name;
+
+    for (uint32_t i = 0; i < deviceCount; i++) {
+        const auto & physical_device = physical_devices[i];
+
+        VkPhysicalDeviceProperties dev_props = physical_device.getProperties();
+        VkPhysicalDeviceMemoryProperties memoryProperties = physical_device.getMemoryProperties();
+        const uint32_t major = VK_VERSION_MAJOR(dev_props.apiVersion);
+        const uint32_t minor = VK_VERSION_MINOR(dev_props.apiVersion);
+        if (major < 1 || minor < 2)
+            continue;
+
+        if (!ggml_vk_checkPhysicalDeviceFeatures(physical_device))
+            continue;
+
+        size_t heapSize = 0;
+        for (uint32_t j = 0; j < memoryProperties.memoryHeapCount; ++j) {
+            VkMemoryHeap heap = memoryProperties.memoryHeaps[j];
+            if (heap.flags & VK_MEMORY_HEAP_DEVICE_LOCAL_BIT) {
+                heapSize = heap.size;
+                break;
+            }
+        }
+
+        if (heapSize < memoryRequired)
+            continue;
+
+        auto ext_props = physical_device.enumerateDeviceExtensionProperties();
+        bool has_maintenance4 = false;
+
+        // Check if maintenance4 is supported
+        for (const auto & properties : ext_props) {
+            if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) {
+                has_maintenance4 = true;
+            }
+        }
+
+        vk::PhysicalDeviceSubgroupProperties subgroup_props;
+        vk::PhysicalDeviceProperties2 dev_props2;
+        vk::PhysicalDeviceMaintenance3Properties dev_props3;
+        vk::PhysicalDeviceMaintenance4Properties dev_props4;
+        dev_props2.pNext = &dev_props3;
+        dev_props3.pNext = &subgroup_props;
+        if (has_maintenance4) {
+            subgroup_props.pNext = &dev_props4;
+        }
+        physical_device.getProperties2(&dev_props2);
+
+        if (subgroup_props.subgroupSize < 32)
+            continue;
+
+        ggml_vk_device d;
+        d.index = i;
+        d.type = dev_props.deviceType;
+        d.heapSize = heapSize;
+        d.vendor = strdup(ggml_vk_getVendorName(dev_props.vendorID));
+        d.subgroupSize = subgroup_props.subgroupSize;
+        d.bufferAlignment = dev_props.limits.minStorageBufferOffsetAlignment;
+
+        if (has_maintenance4) {
+            d.maxAlloc = std::min(dev_props3.maxMemoryAllocationSize, dev_props4.maxBufferSize);
+        } else {
+            d.maxAlloc = dev_props3.maxMemoryAllocationSize;
+        }
+
+        std::string name(dev_props.deviceName);
+        size_t n_idx = ++count_by_name[name];
+        if (n_idx > 1) {
+            name += " (" + std::to_string(n_idx) + ")";
+        }
+        d.name = strdup(name.c_str());
+
+        results.push_back(d);
+    }
+
+    std::stable_sort(results.begin(), results.end(),
+        [](const ggml_vk_device& lhs, const ggml_vk_device& rhs) -> bool {
+            if (lhs.type != rhs.type) {
+                if (lhs.type == VK_PHYSICAL_DEVICE_TYPE_DISCRETE_GPU) return true;
+                if (rhs.type == VK_PHYSICAL_DEVICE_TYPE_DISCRETE_GPU) return false;
+
+                if (lhs.type == VK_PHYSICAL_DEVICE_TYPE_INTEGRATED_GPU) return true;
+                if (rhs.type == VK_PHYSICAL_DEVICE_TYPE_INTEGRATED_GPU) return false;
+            }
+            return lhs.heapSize < rhs.heapSize;
+        }
+    );
+
+    return results;
+}
+
+// public API returns a C-style array
+ggml_vk_device * ggml_vk_available_devices(size_t memoryRequired, size_t * count) {
+    auto devices = ggml_vk_available_devices_internal(memoryRequired);
+    *count = devices.size();
+    if (devices.empty()) {
+        return nullptr;
+    }
+
+    size_t nbytes = sizeof (ggml_vk_device) * (devices.size());
+    auto * arr = static_cast<ggml_vk_device *>(malloc(nbytes));
+    memcpy(arr, devices.data(), nbytes);
+    return arr;
+}
+
+static void ggml_vk_filterByVendor(std::vector<ggml_vk_device>& devices, const std::string& targetVendor) {
+    devices.erase(
+        std::remove_if(devices.begin(), devices.end(),
+            [&targetVendor](const ggml_vk_device& device) {
+                return device.vendor != targetVendor;
+            }),
+        devices.end()
+    );
+}
+
+static void ggml_vk_filterByName(std::vector<ggml_vk_device>& devices, const std::string& targetName) {
+    devices.erase(
+        std::remove_if(devices.begin(), devices.end(),
+            [&targetName](const ggml_vk_device& device) {
+                return device.name != targetName;
+            }),
+        devices.end()
+    );
+}
+
+static bool ggml_vk_get_device(ggml_vk_device * device, size_t memoryRequired, const std::string & name) {
+    if (name.empty())
+        return false;
+
+    auto devices = ggml_vk_available_devices_internal(memoryRequired);
+    if (name == "amd" || name == "nvidia" || name == "intel") {
+        ggml_vk_filterByVendor(devices, name);
+    } else if (name != "gpu") {
+        ggml_vk_filterByName(devices, name);
+    }
+
+    if (devices.empty())
+        return false;
+
+    *device = devices.front();
+    return true;
+}
+
+bool ggml_vk_get_device(ggml_vk_device * device, size_t memoryRequired, const char * name) {
+    return ggml_vk_get_device(device, memoryRequired, std::string(name));
+}
+
+bool ggml_vk_has_vulkan() {
+    return komputeManager()->hasVulkan();
+}
+
+bool ggml_vk_has_device() {
+    return komputeManager()->hasDevice();
+}
+
+ggml_vk_device ggml_vk_current_device() {
+    if (!komputeManager()->hasDevice())
+        return ggml_vk_device();
+
+    auto devices = ggml_vk_available_devices_internal(0);
+    ggml_vk_filterByName(devices, komputeManager()->physicalDevice()->getProperties().deviceName.data());
+    GGML_ASSERT(!devices.empty());
+    return devices.front();
+}
+
+static
+void ggml_vk_allocate_descriptor_pool(struct ggml_kompute_context * ctx, size_t size) {
+    std::vector<vk::DescriptorPoolSize> descriptorPoolSizes = {
+        vk::DescriptorPoolSize(
+          vk::DescriptorType::eStorageBuffer,
+          3 * size // Descriptor count is number of possible tensors to pass into an algorithm
+          )
+    };
+
+    vk::DescriptorPoolCreateInfo descriptorPoolInfo(
+      vk::DescriptorPoolCreateFlags(),
+      size, // Max sets
+      static_cast<uint32_t>(descriptorPoolSizes.size()),
+      descriptorPoolSizes.data());
+
+    ctx->pool = std::make_shared<vk::DescriptorPool>();
+    vk::Result r = komputeManager()->device()->createDescriptorPool(
+      &descriptorPoolInfo, nullptr, ctx->pool.get());
+    if (r != vk::Result::eSuccess)
+        std::cerr << "Error allocating descriptor pool" << vk::to_string(r);
+}
+
+static
+void ggml_vk_free_descriptor_pool(struct ggml_kompute_context * ctx) {
+    if (ctx->pool) {
+        komputeManager()->device()->destroy(
+          *ctx->pool,
+          (vk::Optional<const vk::AllocationCallbacks>)nullptr);
+        ctx->pool = nullptr;
+    }
+}
+
+static
+vk::Buffer *ggml_vk_allocate_buffer(size_t size) {
+    vk::BufferCreateInfo bufferCreateInfo;
+    bufferCreateInfo.size = size;
+    bufferCreateInfo.usage = vk::BufferUsageFlagBits::eStorageBuffer |
+                             vk::BufferUsageFlagBits::eTransferSrc |
+                             vk::BufferUsageFlagBits::eTransferDst;
+    bufferCreateInfo.sharingMode = vk::SharingMode::eExclusive;
+
+    vk::Buffer *vkBuffer = new vk::Buffer;
+    vk::Result r = komputeManager()->device()->createBuffer(&bufferCreateInfo, nullptr, vkBuffer);
+    if (r != vk::Result::eSuccess)
+        std::cerr << "Error allocating buffer " << vk::to_string(r) << std::endl;
+    return vkBuffer;
+}
+
+static
+vk::DeviceMemory *ggml_vk_allocate(size_t size, vk::MemoryPropertyFlags flags, vk::MemoryRequirements requirements, bool *isHostVisible) {
+
+    uint32_t memoryTypeIndex = -1;
+    bool memoryTypeIndexFound = false;
+    vk::PhysicalDeviceMemoryProperties memoryProperties = komputeManager()->physicalDevice()->getMemoryProperties();
+    for (uint32_t i = 0; i < memoryProperties.memoryTypeCount; i++) {
+        const vk::MemoryType &memoryType = memoryProperties.memoryTypes[i];
+        const vk::MemoryHeap &memoryHeap = memoryProperties.memoryHeaps[memoryType.heapIndex];
+        if (memoryHeap.size < size) {
+            continue;
+        }
+
+        if (requirements.memoryTypeBits & (1 << i)) {
+            if (((memoryProperties.memoryTypes[i]).propertyFlags &
+                 flags) == flags) {
+                memoryTypeIndex = i;
+                memoryTypeIndexFound = true;
+                if (isHostVisible && (memoryProperties.memoryTypes[i].propertyFlags & vk::MemoryPropertyFlagBits::eHostVisible)) {
+                    *isHostVisible = true;
+                }
+                break;
+            }
+        }
+    }
+    if (!memoryTypeIndexFound) {
+        throw std::runtime_error(
+          "Memory type index for buffer creation not found");
+    }
+
+    vk::MemoryAllocateInfo allocInfo;
+    allocInfo.allocationSize = size;
+    allocInfo.memoryTypeIndex = memoryTypeIndex;
+    vk::DeviceMemory *vkDeviceMemory =  new vk::DeviceMemory;
+    vk::Result r = komputeManager()->device()->allocateMemory(&allocInfo, nullptr, vkDeviceMemory);
+    if (r != vk::Result::eSuccess) {
+        std::cerr << "Error allocating memory " << vk::to_string(r) << std::endl;
+        throw std::runtime_error("Error allocating vulkan memory.");
+    }
+    return vkDeviceMemory;
+}
+
+static size_t ggml_vk_aligned_offset(ggml_backend_buffer_t buffer, size_t offset) {
+    size_t minStorageBufferOffsetAlignment = ggml_backend_buffer_get_alignment(buffer);
+
+    // If offset is already aligned, return it directly
+    if (offset % minStorageBufferOffsetAlignment == 0) {
+        return offset;
+    }
+
+    // Otherwise, return the largest multiple of minStorageBufferOffsetAlignment less than offset
+    return (offset / minStorageBufferOffsetAlignment) * minStorageBufferOffsetAlignment;
+}
+
+static ggml_vk_memory ggml_vk_allocate(size_t size) {
+    ggml_vk_memory memory;
+    bool isHostVisible = false;
+    {
+        memory.primaryBuffer = ggml_vk_allocate_buffer(size);
+        vk::MemoryRequirements memoryRequirements = komputeManager()->device()->getBufferMemoryRequirements(*memory.primaryBuffer);
+        vk::MemoryPropertyFlags memoryPropertyFlags = vk::MemoryPropertyFlagBits::eDeviceLocal;
+        memory.primaryMemory = ggml_vk_allocate(size, memoryPropertyFlags, memoryRequirements, &isHostVisible);
+        komputeManager()->device()->bindBufferMemory(*memory.primaryBuffer, *memory.primaryMemory, 0);
+        if (isHostVisible) {
+            vk::Result r = komputeManager()->device()->mapMemory(*memory.primaryMemory, 0, size, vk::MemoryMapFlags(), &memory.data);
+            if (r != vk::Result::eSuccess)
+                std::cerr << "Error mapping memory" << vk::to_string(r);
+        }
+    }
+
+    if (!isHostVisible) {
+        memory.stagingBuffer = ggml_vk_allocate_buffer(size);
+        vk::MemoryRequirements memoryRequirements = komputeManager()->device()->getBufferMemoryRequirements(*memory.stagingBuffer);
+        vk::MemoryPropertyFlags memoryPropertyFlags = vk::MemoryPropertyFlagBits::eHostVisible |
+                                                      vk::MemoryPropertyFlagBits::eHostCoherent |
+                                                      vk::MemoryPropertyFlagBits::eHostCached;
+        memory.stagingMemory = ggml_vk_allocate(size, memoryPropertyFlags, memoryRequirements, &isHostVisible);
+        komputeManager()->device()->bindBufferMemory(*memory.stagingBuffer, *memory.stagingMemory, 0);
+        vk::Result r = komputeManager()->device()->mapMemory(*memory.stagingMemory, 0, size, vk::MemoryMapFlags(), &memory.data);
+        if (r != vk::Result::eSuccess)
+            std::cerr << "Error mapping memory" << vk::to_string(r);
+    }
+
+    memory.size = size;
+    return memory;
+}
+
+static void ggml_vk_free_memory(ggml_vk_memory &memory)
+{
+    komputeManager()->device()->destroy(
+      *memory.primaryBuffer,
+      (vk::Optional<const vk::AllocationCallbacks>)nullptr);
+    if (memory.stagingBuffer) {
+        komputeManager()->device()->destroy(
+          *memory.stagingBuffer,
+          (vk::Optional<const vk::AllocationCallbacks>)nullptr);
+    }
+    komputeManager()->device()->freeMemory(
+      *memory.primaryMemory,
+      (vk::Optional<const vk::AllocationCallbacks>)nullptr);
+    if (memory.stagingMemory) {
+        komputeManager()->device()->freeMemory(
+          *memory.stagingMemory,
+          (vk::Optional<const vk::AllocationCallbacks>)nullptr);
+    }
+}
+
+static const char * ggml_backend_kompute_buffer_type_get_name(ggml_backend_buffer_type_t buft);
+
+static
+ggml_vk_memory * ggml_vk_find_tensor(const struct ggml_tensor * t, uint64_t & offset) {
+    ggml_backend_buffer_t buffer = t->view_src ? t->view_src->buffer : t->buffer;
+
+    // compatibility with ggml-backend
+    GGML_ASSERT(buffer && buffer->buft->iface.get_name == ggml_backend_kompute_buffer_type_get_name);
+
+    ggml_vk_memory * buf_ctx = static_cast<ggml_vk_memory *>(buffer->context);
+
+    const intptr_t ioffs = intptr_t(t->data) - intptr_t(buf_ctx->data);
+
+    GGML_ASSERT(ioffs >= 0 && ioffs + int64_t(ggml_nbytes(t)) <= int64_t(buffer->size));
+
+    offset = uint64_t(ioffs);
+    return buf_ctx;
+}
+
+static
+const std::shared_ptr<kp::Tensor> ggml_vk_get_tensor(const struct ggml_tensor * t, uint32_t * alignedOffset = nullptr) {
+    uint64_t originalOffset = 0;
+    auto * res = ggml_vk_find_tensor(t, originalOffset);
+    if (!res) {
+        static std::shared_ptr<kp::Tensor> nullTensor = nullptr;
+        return nullTensor;
+    }
+
+    // Create a tensor whose memory will be composed of our buffers at the correct offset
+    const size_t nelements = ggml_nelements(t);
+    size_t nbytes = ggml_nbytes(t);
+
+    size_t vulkanOffset = ggml_vk_aligned_offset(t->buffer, originalOffset);
+    if (alignedOffset) {
+        *alignedOffset = originalOffset - vulkanOffset;
+        nbytes += *alignedOffset;
+    }
+
+    return komputeManager()->tensor(
+        t->data,
+        nelements,
+        nbytes, kp::Tensor::TensorDataTypes::eFloat,
+        res->primaryMemory, res->primaryBuffer,
+        res->stagingMemory, res->stagingBuffer,
+        vulkanOffset);
+}
+
+static std::vector<uint32_t> getSpirvShader(const unsigned char* rawData, size_t size) {
+    if (size % sizeof(uint32_t) != 0) {
+        throw std::runtime_error("Invalid size: must be divisible by sizeof(uint32_t)");
+    }
+
+    const uint32_t* data_ptr = reinterpret_cast<const uint32_t*>(rawData);
+    size_t count = size / sizeof(uint32_t);
+    return std::vector<uint32_t>(data_ptr, data_ptr + count);
+}
+
+inline static
+uint32_t safe_divide(uint32_t a, uint32_t b) {
+    if (b <= 1) {
+        return a;
+    }
+    if ((a % b) != 0) {
+        fprintf(stderr, "((%u %% %u) == %u) != 0\n", a, b, a % b);
+        GGML_ASSERT(!"safe_divide result would've had remainder");
+    }
+    return a / b;
+}
+
+static void ggml_vk_add(
+    kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& inA,
+    const std::shared_ptr<kp::Tensor>& inB,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+    int32_t ne00, int32_t ne01, int32_t ne02, int32_t ne03,
+    int32_t nb00, int32_t nb01, int32_t nb02, int32_t nb03,
+    int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13,
+    int32_t nb10, int32_t nb11, int32_t nb12, int32_t nb13,
+    int32_t ne0,
+    int32_t nb0,  int32_t nb1,  int32_t nb2,  int32_t nb3
+) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_add_comp_spv,
+        kp::shader_data::op_add_comp_spv_len);
+
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        int32_t ne00;
+        int32_t nb00, nb01, nb02, nb03;
+        int32_t ne10, ne11, ne12, ne13;
+        int32_t nb10, nb11, nb12, nb13;
+        int32_t ne0;
+        int32_t nb0, nb1, nb2, nb3;
+    } const pushConsts {
+        safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4),
+        ne00,
+        nb00, nb01, nb02, nb03,
+        ne10, ne11, ne12, ne13,
+        nb10, nb11, nb12, nb13,
+        ne0,
+        nb0, nb1, nb2, nb3
+    };
+
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(__func__)) {
+        s_algo = komputeManager()->algorithm<float, PushConstants>(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(__func__);
+        s_algo->setTensors({inA, inB, out});
+        s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_addrow(kp::Sequence& seq,
+                 const std::shared_ptr<kp::Tensor>& inA,
+                 const std::shared_ptr<kp::Tensor>& inB,
+                 const std::shared_ptr<kp::Tensor>& out,
+                 uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+                 uint32_t size, uint32_t row = 0) {
+
+    const static auto spirv = getSpirvShader(kp::shader_data::op_addrow_comp_spv,
+        kp::shader_data::op_addrow_comp_spv_len);
+
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        uint32_t row;
+    } const pushConsts {
+        safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4),
+        row
+    };
+
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(__func__))
+        s_algo = komputeManager()->algorithm<float, PushConstants>(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {size}, {}, {pushConsts});
+    else {
+        s_algo = komputeManager()->getAlgorithm(__func__);
+        s_algo->setTensors({inA, inB, out});
+        s_algo->setWorkgroup({size});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_mul(
+    kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& inA,
+    const std::shared_ptr<kp::Tensor>& inB,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+    int32_t ne00, int32_t ne01, int32_t ne02, int32_t ne03,
+    int32_t nb00, int32_t nb01, int32_t nb02, int32_t nb03,
+    int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13,
+    int32_t nb10, int32_t nb11, int32_t nb12, int32_t nb13,
+    int32_t ne0,
+    int32_t nb0,  int32_t nb1,  int32_t nb2,  int32_t nb3
+) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_mul_comp_spv,
+        kp::shader_data::op_mul_comp_spv_len);
+
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        int32_t ne00;
+        int32_t nb00, nb01, nb02, nb03;
+        int32_t ne10, ne11, ne12, ne13;
+        int32_t nb10, nb11, nb12, nb13;
+        int32_t ne0;
+        int32_t nb0, nb1, nb2, nb3;
+    } const pushConsts {
+        safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4),
+        ne00,
+        nb00, nb01, nb02, nb03,
+        ne10, ne11, ne12, ne13,
+        nb10, nb11, nb12, nb13,
+        ne0,
+        nb0, nb1, nb2, nb3
+    };
+
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(__func__)) {
+        s_algo = komputeManager()->algorithm<float, PushConstants>(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(__func__);
+        s_algo->setTensors({inA, inB, out});
+        s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_scale(kp::Sequence& seq,
+                   const std::shared_ptr<kp::Tensor>& in,
+                   const std::shared_ptr<kp::Tensor>& out,
+                   uint32_t inOff, uint32_t outOff,
+                   uint32_t size, float scale) {
+    const static auto spirv_1 = getSpirvShader(
+        kp::shader_data::op_scale_comp_spv, kp::shader_data::op_scale_comp_spv_len
+    );
+    const static auto spirv_8 = getSpirvShader(
+        kp::shader_data::op_scale_8_comp_spv, kp::shader_data::op_scale_8_comp_spv_len
+    );
+
+    struct PushConstants {
+        uint32_t inOff, outOff;
+        float scale;
+    } const pushConsts {
+        safe_divide(inOff, 4), safe_divide(outOff, 4),
+        scale
+    };
+
+    const auto * spirv = &spirv_1;
+    std::string name(__func__);
+    if (size % 8 == 0) {
+        size /= 8;
+        name += "_8";
+        spirv = &spirv_8;
+    }
+
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(name)) {
+        s_algo = komputeManager()->algorithm<float, PushConstants>(name, s_kompute_context->pool.get(), {in, out}, *spirv, {size}, {}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(name);
+        s_algo->setTensors({in, out});
+        s_algo->setWorkgroup({size});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_xxlu(
+    const std::vector<uint32_t>& spirv, const char * suffix, kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& in,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inOff, uint32_t outOff,
+    uint32_t size
+) {
+    struct PushConstants {
+        uint32_t inOff, outOff;
+    } const pushConsts {
+        safe_divide(inOff, 4), safe_divide(outOff, 4),
+    };
+
+    auto name = std::string(__func__) + "_" + suffix;
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(name)) {
+        s_algo = komputeManager()->algorithm<float, PushConstants>(name, s_kompute_context->pool.get(), {in, out}, spirv, {size}, {}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(name);
+        s_algo->setTensors({in, out});
+        s_algo->setWorkgroup({size});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+template <typename... Args>
+static void ggml_vk_silu(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_silu_comp_spv,
+        kp::shader_data::op_silu_comp_spv_len);
+
+    ggml_vk_xxlu(spirv, "silu", std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_relu(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_relu_comp_spv,
+        kp::shader_data::op_relu_comp_spv_len);
+
+    ggml_vk_xxlu(spirv, "relu", std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_gelu(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_gelu_comp_spv,
+        kp::shader_data::op_gelu_comp_spv_len);
+
+    ggml_vk_xxlu(spirv, "gelu", std::forward<Args>(args)...);
+}
+
+static void ggml_vk_soft_max(
+    kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& inA,
+    const std::shared_ptr<kp::Tensor>& inB,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+    int32_t ne00, int32_t ne01, int32_t ne02, uint32_t ne03,
+    float scale
+) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_softmax_comp_spv,
+        kp::shader_data::op_softmax_comp_spv_len);
+
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        int32_t ne00, ne01, ne02;
+        float scale;
+        int32_t mask;
+    } pushConsts {
+        safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4),
+        ne00, ne01, ne02,
+        scale,
+        bool(inB)
+    };
+
+    auto & inB_ = inB ? inB : inA;
+
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(__func__)) {
+        // FIXME: The softmax kernel needs to be fixed to use the subgroupsize which can vary by device
+        const uint32_t local_x = 32;
+        s_algo = komputeManager()->algorithm<uint32_t, PushConstants>(__func__, s_kompute_context->pool.get(), {inA, inB_, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {local_x}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(__func__);
+        s_algo->setTensors({inA, inB_, out});
+        s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_norm_(
+    const std::vector<uint32_t>& spirv, const char * suffix, kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& in,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inOff, uint32_t outOff,
+    int32_t ne00, int32_t nb01,
+    int32_t nrows, float epsilon
+) {
+    GGML_ASSERT(nb01%sizeof(float) == 0);
+    GGML_ASSERT(ne00%sizeof(float) == 0);
+
+    struct PushConstants {
+        uint32_t inOff, outOff;
+        uint32_t ne00, nb01;
+        float eps;
+    } pushConsts {
+        safe_divide(inOff, 4), safe_divide(outOff, 4),
+        (uint32_t)ne00, (uint32_t)nb01, epsilon
+    };
+
+    auto name = std::string(__func__) + "_" + suffix;
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(name)) {
+        s_algo = komputeManager()->algorithm<float, PushConstants>(name, s_kompute_context->pool.get(), {in, out}, spirv, {(uint32_t)nrows}, {}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(name);
+        s_algo->setTensors({in, out});
+        s_algo->setWorkgroup({(uint32_t)nrows});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+template <typename... Args>
+static void ggml_vk_norm(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_norm_comp_spv,
+        kp::shader_data::op_norm_comp_spv_len);
+
+    ggml_vk_norm_(spirv, "norm", std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_rms_norm(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_rmsnorm_comp_spv,
+        kp::shader_data::op_rmsnorm_comp_spv_len);
+
+    ggml_vk_norm_(spirv, "rms", std::forward<Args>(args)...);
+}
+
+static void ggml_vk_diag_mask_inf(kp::Sequence& seq,
+                           const std::shared_ptr<kp::Tensor>& in,
+                           const std::shared_ptr<kp::Tensor>& out,
+                           uint32_t inOff, uint32_t outOff,
+                           uint32_t n_past,
+                           int32_t ne00, int32_t ne01, int32_t ne02) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_diagmask_comp_spv,
+        kp::shader_data::op_diagmask_comp_spv_len);
+
+    struct PushConstants {
+        uint32_t inOff, outOff;
+        uint32_t n_past;
+        int32_t ne00, ne01;
+    } pushConsts {
+        safe_divide(inOff, 4), safe_divide(outOff, 4),
+        n_past,
+        ne00, ne01
+    };
+
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(__func__))
+        s_algo = komputeManager()->algorithm<float, PushConstants>(__func__, s_kompute_context->pool.get(), {in, out}, spirv, {unsigned(ne00), unsigned(ne01), unsigned(ne02)}, {}, {pushConsts});
+    else {
+        s_algo = komputeManager()->getAlgorithm(__func__);
+        s_algo->setTensors({in, out});
+        s_algo->setWorkgroup({unsigned(ne00), unsigned(ne01), unsigned(ne02)});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_mul_mat_f16(
+    kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& inA,
+    const std::shared_ptr<kp::Tensor>& inB,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+    int32_t ne00, int32_t ne01, int32_t ne02,
+    uint32_t nb00, uint32_t nb01, uint32_t nb02,
+    int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13,
+    uint32_t nb10, uint32_t nb11, uint32_t nb12,
+    int32_t ne0, int32_t ne1,
+    uint32_t r2, uint32_t r3
+) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_f16_comp_spv,
+        kp::shader_data::op_mul_mat_f16_comp_spv_len);
+
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        int32_t ne00, ne01, ne02;
+        uint32_t nb00, nb01, nb02;
+        int32_t ne10, ne11, ne12;
+        uint32_t nb10, nb11, nb12;
+        int32_t ne0, ne1;
+        uint32_t r2, r3;
+    } pushConsts {
+        safe_divide(inAOff, 2), safe_divide(inBOff, 4), safe_divide(outOff, 4),
+        ne00, ne01, ne02,
+        nb00, nb01, nb02,
+        ne10, ne11, ne12,
+        nb10, nb11, nb12,
+        ne0, ne1,
+        r2, r3
+    };
+
+    const unsigned ny = unsigned((ne11 + 4 - 1)/4);
+
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(__func__)) {
+        const uint32_t local_x = ggml_vk_current_device().subgroupSize * 2;
+        s_algo = komputeManager()->algorithm<uint32_t, PushConstants>(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned(ne01), ny, unsigned(ne12*ne13)}, {local_x}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(__func__);
+        s_algo->setTensors({inA, inB, out});
+        s_algo->setWorkgroup({unsigned(ne01), ny, unsigned(ne12*ne13)});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_mul_mat_mat_f32(kp::Sequence& seq,
+                         const std::shared_ptr<kp::Tensor>& inA,
+                         const std::shared_ptr<kp::Tensor>& inB,
+                         const std::shared_ptr<kp::Tensor>& out,
+                         uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+                         int32_t ne00, int32_t ne01, int32_t ne02,
+                         uint32_t nb01, uint32_t nb02,
+                         int32_t ne11, int32_t ne12,
+                         uint32_t nb11, uint32_t nb12,
+                         uint32_t nb1, uint32_t nb2) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_mat_f32_comp_spv,
+        kp::shader_data::op_mul_mat_mat_f32_comp_spv_len);
+
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        int32_t ne00, ne01, ne02, ne11, ne12;
+        uint32_t nb01, nb02;
+        uint32_t nb11, nb12;
+        uint32_t nb1, nb2;
+    } pushConsts {
+        safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4),
+        ne00, ne01, ne02, ne11, ne12,
+        nb01, nb02, nb11, nb12,
+        nb1, nb2
+    };
+
+    const uint32_t local_x = ggml_vk_current_device().subgroupSize;
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(__func__)) {
+        s_algo = komputeManager()->algorithm<uint32_t, PushConstants>(__func__, s_kompute_context->pool.get(),
+        {inA, inB, out}, spirv,
+        {unsigned(ne01),
+         unsigned(ne11),
+         unsigned(std::max(ne12, ne02))
+         },
+        {local_x},
+        {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(__func__);
+        s_algo->setTensors({inA, inB, out});
+        s_algo->setWorkgroup({unsigned(ne01),
+                              unsigned(ne11),
+                              unsigned(std::max(ne12, ne02)),
+                              });
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_mul_mat_impl(
+    const std::vector<uint32_t>& spirv, const char * suffix, uint32_t block_size, kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& inA,
+    const std::shared_ptr<kp::Tensor>& inB,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+    int32_t ne00, int32_t ne01, int32_t ne02,
+    int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13,
+    int32_t ne0, int32_t ne1,
+    uint32_t r2, uint32_t r3
+) {
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        int32_t ne00, ne01, ne02;
+        int32_t ne10, ne12;
+        int32_t ne0, ne1;
+        uint32_t r2, r3;
+    } pushConsts {
+        safe_divide(inAOff, block_size), safe_divide(inBOff, 4), safe_divide(outOff, 4),
+        ne00, ne01, ne02,
+        ne10, ne12,
+        ne0, ne1,
+        r2, r3
+    };
+
+    auto name = std::string(__func__) + "_" + suffix;
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(name)) {
+        const uint32_t local_x = ggml_vk_current_device().subgroupSize * 2;
+        s_algo = komputeManager()->algorithm<uint32_t, PushConstants>(name, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned((ne01 + 7)/8), unsigned(ne11), unsigned(ne12*ne13)}, {local_x}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(name);
+        s_algo->setTensors({inA, inB, out});
+        s_algo->setWorkgroup({unsigned((ne01 + 7)/8), unsigned(ne11), unsigned(ne12*ne13)});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+template <typename... Args>
+static void ggml_vk_mul_mat_q4_0(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q4_0_comp_spv,
+        kp::shader_data::op_mul_mat_q4_0_comp_spv_len);
+
+    ggml_vk_mul_mat_impl(spirv, "q4_0", 1/*We access blocks unaligned*/, std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_mul_mat_q4_1(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q4_1_comp_spv,
+        kp::shader_data::op_mul_mat_q4_1_comp_spv_len);
+
+    ggml_vk_mul_mat_impl(spirv, "q4_1", 1/*We access blocks unaligned*/, std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_mul_mat_q8_0(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q8_0_comp_spv,
+        kp::shader_data::op_mul_mat_q8_0_comp_spv_len);
+
+    ggml_vk_mul_mat_impl(spirv, "q8_0", 1/*We access blocks unaligned*/, std::forward<Args>(args)...);
+}
+
+static void ggml_vk_mul_mat_q6_k(
+    kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& inA,
+    const std::shared_ptr<kp::Tensor>& inB,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+    int32_t ne00, int32_t ne10, int32_t ne0, int32_t ne1,
+    int32_t ne01, int32_t ne11, int32_t ne12, int32_t ne02
+) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q6_k_comp_spv,
+        kp::shader_data::op_mul_mat_q6_k_comp_spv_len);
+
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        int32_t ne00, ne10, ne0, ne1, ne01, gqa;
+    } pushConsts {
+        inAOff, safe_divide(inBOff, 4), safe_divide(outOff, 4),
+        ne00, ne10, ne0, ne1, ne01, ne12/ne02
+    };
+
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(__func__)) {
+        const uint32_t local_x = ggml_vk_current_device().subgroupSize * 2;
+        s_algo = komputeManager()->algorithm<uint32_t, PushConstants>(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned((ne01 + 1)/2), unsigned(ne11), unsigned(ne12)}, {local_x}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(__func__);
+        s_algo->setTensors({inA, inB, out});
+        s_algo->setWorkgroup({unsigned((ne01 + 1)/2), unsigned(ne11), unsigned(ne12)});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_get_rows(
+    const std::vector<uint32_t>& spirv,
+    const char * suffix,
+    unsigned element_size, unsigned qk,
+    kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& inA,
+    const std::shared_ptr<kp::Tensor>& inB,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+    int32_t ne00, int32_t nb01, int32_t nb1,
+    uint32_t size
+) {
+    GGML_ASSERT(nb01%element_size == 0);
+    GGML_ASSERT(nb1%sizeof(float) == 0);
+    if (qk) GGML_ASSERT(ne00%qk == 0);
+
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        int32_t ne00, nb01, nb1;
+    } pushConsts {
+        safe_divide(inAOff, element_size), safe_divide(inBOff, 4), safe_divide(outOff, 4),
+        ne00, nb01, nb1
+    };
+
+    auto name = std::string(__func__) + "_" + suffix;
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(name)) {
+        s_algo = komputeManager()->algorithm<float, PushConstants>(name, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {size}, {}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(name);
+        s_algo->setTensors({inA, inB, out});
+        s_algo->setWorkgroup({size});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+template <typename... Args>
+static void ggml_vk_get_rows_f16(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_f16_comp_spv,
+        kp::shader_data::op_getrows_f16_comp_spv_len);
+
+    ggml_vk_get_rows(spirv, "f16", sizeof(half), 0, std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_get_rows_q4_0(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_q4_0_comp_spv,
+        kp::shader_data::op_getrows_q4_0_comp_spv_len);
+
+    ggml_vk_get_rows(spirv, "q4_0", 1/*We access blocks unaligned*/, QK4_0, std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_get_rows_q4_1(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_q4_1_comp_spv,
+        kp::shader_data::op_getrows_q4_1_comp_spv_len);
+
+    ggml_vk_get_rows(spirv, "q4_1", 1/*We access blocks unaligned*/, QK4_1, std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_get_rows_q6_k(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_q6_k_comp_spv,
+        kp::shader_data::op_getrows_q6_k_comp_spv_len);
+    ggml_vk_get_rows(spirv, "q6_k", 1/*We access blocks unaligned*/, QK_NL, std::forward<Args>(args)...);
+}
+
+static void ggml_vk_rope(
+    kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& inA,
+    const std::shared_ptr<kp::Tensor>& inB,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+    ggml_type src0t, int32_t n_dims, int32_t mode, int32_t n_orig_ctx,
+    float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow,
+    int32_t ne01, int32_t ne02, int32_t ne03,
+    uint32_t nb00, uint32_t nb01, uint32_t nb02, uint32_t nb03,
+    int32_t ne0,
+    uint32_t nb0, uint32_t nb1, uint32_t nb2, uint32_t nb3
+) {
+    GGML_ASSERT(src0t == GGML_TYPE_F16 || src0t == GGML_TYPE_F32);
+
+    static const auto spirv_f16 = getSpirvShader(
+        kp::shader_data::op_rope_f16_comp_spv, kp::shader_data::op_rope_f16_comp_spv_len
+    );
+    static const auto spirv_f32 = getSpirvShader(
+        kp::shader_data::op_rope_f32_comp_spv, kp::shader_data::op_rope_f32_comp_spv_len
+    );
+
+    int type_size = src0t == GGML_TYPE_F16 ? 2 : 4;
+
+    GGML_ASSERT(nb03 % type_size == 0);
+    GGML_ASSERT(nb02 % type_size == 0);
+    GGML_ASSERT(nb01 % type_size == 0);
+    GGML_ASSERT(nb00 % type_size == 0);
+    GGML_ASSERT(nb3  % type_size == 0);
+    GGML_ASSERT(nb2  % type_size == 0);
+    GGML_ASSERT(nb1  % type_size == 0);
+    GGML_ASSERT(nb0  % type_size == 0);
+
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        int32_t n_dims, mode, n_orig_ctx;
+        float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
+        uint32_t nb00, nb01, nb02, nb03;
+        int32_t ne0;
+        uint32_t nb0, nb1, nb2, nb3;
+    } pushConsts {
+        safe_divide(inAOff, type_size), safe_divide(inBOff, 4), safe_divide(outOff, type_size),
+        n_dims, mode, n_orig_ctx,
+        freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow,
+        nb00, nb01, nb02, nb03,
+        ne0,
+        nb0, nb1, nb2, nb3
+    };
+
+    auto name = std::string(__func__) + (src0t == GGML_TYPE_F16 ? "_f16" : "_f32");
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(name)) {
+        s_algo = komputeManager()->algorithm<float, PushConstants>(
+            name, s_kompute_context->pool.get(), {inA, inB, out},
+            src0t == GGML_TYPE_F16 ? spirv_f16 : spirv_f32,
+            {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts}
+        );
+    } else {
+        s_algo = komputeManager()->getAlgorithm(name);
+        s_algo->setTensors({inA, inB, out});
+        s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_cpy(
+    const std::vector<uint32_t>& spirv,
+    uint32_t in_element_size, uint32_t out_element_size,
+    kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& in,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inOff, uint32_t outOff,
+    int32_t ne00, int32_t ne01, int32_t ne02, int32_t ne03,
+    uint32_t nb00, uint32_t nb01, uint32_t nb02, uint32_t nb03,
+    int32_t ne0, int32_t ne1, int32_t ne2,
+    uint32_t nb0, uint32_t nb1, uint32_t nb2, uint32_t nb3
+) {
+    struct PushConstants {
+        uint32_t inOff, outOff;
+        int32_t ne00, ne01, ne02;
+        uint32_t nb00, nb01, nb02, nb03;
+        int32_t ne0, ne1, ne2;
+        uint32_t nb0, nb1, nb2, nb3;
+    } pushConsts {
+        safe_divide(inOff, in_element_size), safe_divide(outOff, out_element_size),
+        ne00, ne01, ne02,
+        nb00, nb01, nb02, nb03,
+        ne0, ne1, ne2,
+        nb0, nb1, nb2, nb3
+    };
+
+    std::string name = std::string(__func__)
+                       + "_i_" + std::to_string(in_element_size)
+                       + "_o_" + std::to_string(out_element_size);
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(name))
+        s_algo = komputeManager()->algorithm<float, PushConstants>(name, s_kompute_context->pool.get(), {in, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts});
+    else {
+        s_algo = komputeManager()->getAlgorithm(name);
+        s_algo->setTensors({in, out});
+        s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+template <typename... Args>
+static void ggml_vk_cpy_f32_f16(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_cpy_f32_f16_comp_spv,
+        kp::shader_data::op_cpy_f32_f16_comp_spv_len);
+    ggml_vk_cpy(spirv, 4, 2, std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_cpy_f32_f32(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_cpy_f32_f32_comp_spv,
+        kp::shader_data::op_cpy_f32_f32_comp_spv_len);
+    ggml_vk_cpy(spirv, 4, 4, std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_cpy_f16_f16(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_cpy_f16_f16_comp_spv,
+        kp::shader_data::op_cpy_f16_f16_comp_spv_len);
+    ggml_vk_cpy(spirv, 2, 2, std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_cpy_f16_f32(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_cpy_f16_f32_comp_spv,
+        kp::shader_data::op_cpy_f16_f32_comp_spv_len);
+    ggml_vk_cpy(spirv, 2, 4, std::forward<Args>(args)...);
+}
+
+static bool ggml_vk_supports_op(const struct ggml_tensor * op) {
+    switch (op->type) {
+        case GGML_TYPE_F16:
+        case GGML_TYPE_F32:
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+            break;
+        default:
+            return false;
+    }
+
+    switch (op->op) {
+        case GGML_OP_UNARY:
+            switch (ggml_get_unary_op(op)) {
+                case GGML_UNARY_OP_RELU:
+                case GGML_UNARY_OP_GELU:
+                case GGML_UNARY_OP_SILU:
+                    return true;
+                default:
+                    ;
+            }
+            break;
+        case GGML_OP_NONE:
+        case GGML_OP_RESHAPE:
+        case GGML_OP_VIEW:
+        case GGML_OP_TRANSPOSE:
+        case GGML_OP_PERMUTE:
+        case GGML_OP_ADD:
+        case GGML_OP_MUL:
+        case GGML_OP_SCALE:
+        case GGML_OP_SOFT_MAX:
+        case GGML_OP_RMS_NORM:
+        case GGML_OP_NORM:
+        case GGML_OP_ROPE:
+            return true;
+        case GGML_OP_DUP:
+        case GGML_OP_CPY:
+        case GGML_OP_CONT:
+            switch (op->src[0]->type) {
+                case GGML_TYPE_F32:
+                case GGML_TYPE_F16:
+                    break;
+                default:
+                    return false;
+            }
+            switch (op->type) {
+                case GGML_TYPE_F32:
+                case GGML_TYPE_F16:
+                    break;
+                default:
+                    return false;
+            }
+            return true;
+        case GGML_OP_DIAG_MASK_INF:
+            return op->ne[3] == 1;
+        case GGML_OP_GET_ROWS:
+            switch (op->src[0]->type) {
+                case GGML_TYPE_F16:
+                case GGML_TYPE_Q4_0:
+                case GGML_TYPE_Q4_1:
+                case GGML_TYPE_Q6_K:
+                    return op->ne[2] == 1 && op->ne[3] == 1;
+                default:
+                    ;
+            }
+            return false;
+        case GGML_OP_MUL_MAT:
+            if (op->src[1]->type != GGML_TYPE_F32 || ggml_is_transposed(op->src[0]) || ggml_is_transposed(op->src[1]))
+                return false;
+
+            switch (op->src[0]->type) {
+                case GGML_TYPE_F32:
+                case GGML_TYPE_Q6_K:
+                    return op->ne[3] == 1;
+                case GGML_TYPE_F16:
+                case GGML_TYPE_Q8_0:
+                case GGML_TYPE_Q4_0:
+                case GGML_TYPE_Q4_1:
+                    return true;
+                default:
+                    ;
+            }
+        default:
+            ;
+    }
+    return false;
+}
+
+static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml_cgraph * gf) {
+    const int n_seq = 8;
+
+    // FIXME: Figure out if we can somehow optimize the size of the pool... right now we're setting
+    // it to the size of the graph, but I think it can be made smaller?
+    ggml_vk_allocate_descriptor_pool(ctx, gf->n_nodes);
+
+    std::vector<std::shared_ptr<kp::Sequence>> sequences(n_seq);
+
+    for (auto& sequence : sequences) {
+        sequence = komputeManager()->sequence();
+    }
+    for (int seq_idx = 0; seq_idx < n_seq; ++seq_idx) {
+        const int n_nodes_per_seq = (gf->n_nodes + n_seq - 1) / n_seq;
+
+        auto& seq = *sequences[seq_idx];
+
+        const int node_start = (seq_idx + 0) * n_nodes_per_seq;
+        const int node_end   = std::min((seq_idx == n_seq - 1) ? gf->n_nodes : (seq_idx + 1) * n_nodes_per_seq, gf->n_nodes);
+
+        bool any_commands_recorded = false;
+
+        for (int i = node_start; i < node_end; ++i) {
+            struct ggml_tensor * src0 = gf->nodes[i]->src[0];
+            struct ggml_tensor * src1 = gf->nodes[i]->src[1];
+            struct ggml_tensor * dst = gf->nodes[i];
+            GGML_ASSERT(dst->data != nullptr);
+
+            if (ggml_is_empty(dst)) {
+                continue;
+            }
+
+            switch (dst->op) {
+                case GGML_OP_NONE:
+                case GGML_OP_RESHAPE:
+                case GGML_OP_VIEW:
+                case GGML_OP_TRANSPOSE:
+                case GGML_OP_PERMUTE:
+                    continue; // noop -> next node
+                default:
+                    break;
+            }
+
+            any_commands_recorded = true;
+
+            if (!ggml_vk_supports_op(dst)) {
+                 fprintf(stderr, "%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst));
+                 GGML_ASSERT(!"unsupported op");
+            }
+
+            const int32_t ne00 = src0 ? src0->ne[0] : 0;
+            const int32_t ne01 = src0 ? src0->ne[1] : 0;
+            const int32_t ne02 = src0 ? src0->ne[2] : 0;
+            const int32_t ne03 = src0 ? src0->ne[3] : 0;
+
+            const uint32_t nb00 = src0 ? src0->nb[0] : 0;
+            const uint32_t nb01 = src0 ? src0->nb[1] : 0;
+            const uint32_t nb02 = src0 ? src0->nb[2] : 0;
+            const uint32_t nb03 = src0 ? src0->nb[3] : 0;
+
+            const int32_t ne10 = src1 ? src1->ne[0] : 0;
+            const int32_t ne11 = src1 ? src1->ne[1] : 0;
+            const int32_t ne12 = src1 ? src1->ne[2] : 0;
+            const int32_t ne13 = src1 ? src1->ne[3] : 0;
+
+            const uint32_t nb10 = src1 ? src1->nb[0] : 0;
+            const uint32_t nb11 = src1 ? src1->nb[1] : 0;
+            const uint32_t nb12 = src1 ? src1->nb[2] : 0;
+            const uint32_t nb13 = src1 ? src1->nb[3] : 0;
+
+            const int32_t ne0 = dst ? dst->ne[0] : 0;
+            const int32_t ne1 = dst ? dst->ne[1] : 0;
+            const int32_t ne2 = dst ? dst->ne[2] : 0;
+//            const int32_t ne3 = dst ? dst->ne[3] : 0;
+
+            const uint32_t nb0 = dst ? dst->nb[0] : 0;
+            const uint32_t nb1 = dst ? dst->nb[1] : 0;
+            const uint32_t nb2 = dst ? dst->nb[2] : 0;
+            const uint32_t nb3 = dst ? dst->nb[3] : 0;
+
+            const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT;
+            const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT;
+            const enum ggml_type dstt = dst ? dst->type : GGML_TYPE_COUNT;
+
+            const static std::shared_ptr<kp::Tensor> nullTensor = nullptr;
+            uint32_t off_src0 = 0;
+            uint32_t off_src1 = 0;
+            uint32_t off_dst  = 0;
+            const std::shared_ptr<kp::Tensor>& id_src0 = src0 ? ggml_vk_get_tensor(src0, &off_src0) : nullTensor;
+            const std::shared_ptr<kp::Tensor>& id_src1 = src1 ? ggml_vk_get_tensor(src1, &off_src1) : nullTensor;
+            const std::shared_ptr<kp::Tensor>& id_dst  = dst  ? ggml_vk_get_tensor(dst,  &off_dst)  : nullTensor;
+
+            switch (dst->op) {
+                case GGML_OP_ADD:
+                    {
+                        if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) {
+                            // src1 is a row
+                            ggml_vk_addrow(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ggml_nelements(dst)/4, ne00);
+                        } else {
+                            ggml_vk_add(
+                                seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst,
+                                ne00, ne01, ne02, ne03,
+                                nb00, nb01, nb02, nb03,
+                                ne10, ne11, ne12, ne13,
+                                nb10, nb11, nb12, nb13,
+                                ne0,
+                                nb0, nb1, nb2, nb3
+                            );
+                        }
+                    } break;
+                case GGML_OP_MUL:
+                    {
+                        ggml_vk_mul(
+                            seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst,
+                            ne00, ne01, ne02, ne03,
+                            nb00, nb01, nb02, nb03,
+                            ne10, ne11, ne12, ne13,
+                            nb10, nb11, nb12, nb13,
+                            ne0,
+                            nb0, nb1, nb2, nb3
+                        );
+                    } break;
+                case GGML_OP_SCALE:
+                    {
+                        float scale; memcpy(&scale, dst->op_params, sizeof(float));
+
+                        ggml_vk_scale(seq, id_src0, id_dst, off_src0, off_dst, ggml_nelements(dst), scale);
+                    } break;
+                case GGML_OP_UNARY:
+                    {
+                        int64_t n = ggml_nelements(dst);
+                        GGML_ASSERT(n % 4 == 0);
+                        switch (ggml_get_unary_op(gf->nodes[i])) {
+                            case GGML_UNARY_OP_SILU:
+                                {
+                                    ggml_vk_silu(seq, id_src0, id_dst, off_src0, off_dst, n/4);
+                                } break;
+                            case GGML_UNARY_OP_RELU:
+                                {
+                                    ggml_vk_relu(seq, id_src0, id_dst, off_src0, off_dst, n/4);
+                                } break;
+                            case GGML_UNARY_OP_GELU:
+                                {
+                                    GGML_ASSERT(n % 8 == 0);
+                                    ggml_vk_gelu(seq, id_src0, id_dst, off_src0, off_dst, n/8);
+                                } break;
+                            default:
+                                {
+                                    fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
+                                    GGML_ASSERT(false);
+                                }
+                        }
+                    } break;
+                case GGML_OP_SOFT_MAX:
+                    {
+                        float scale;
+                        memcpy(&scale, dst->op_params, sizeof(float));
+                        ggml_vk_soft_max(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, ne01, ne02, ne03, scale);
+                    } break;
+                case GGML_OP_DIAG_MASK_INF:
+                    {
+                        const int n_past = ((int32_t *)(dst->op_params))[0];
+                        ggml_vk_diag_mask_inf(seq, id_src0, id_dst, off_src0, off_dst, n_past, ne00, ne01, ne02);
+                    } break;
+                case GGML_OP_NORM:
+                    {
+                        float eps;
+                        memcpy(&eps, dst->op_params, sizeof(float));
+                        ggml_vk_norm(seq, id_src0, id_dst, off_src0, off_dst, ne00, nb01, ggml_nrows(src0), eps);
+                    } break;
+                case GGML_OP_RMS_NORM:
+                    {
+                        GGML_ASSERT(ne00 % 4 == 0);
+
+                        float eps;
+                        memcpy(&eps, dst->op_params, sizeof(float));
+                        ggml_vk_rms_norm(seq, id_src0, id_dst, off_src0, off_dst, ne00, nb01, ggml_nrows(src0), eps);
+                    } break;
+                case GGML_OP_MUL_MAT:
+                    {
+                        GGML_ASSERT(ne00 == ne10);
+
+                        // TODO: assert that dim2 and dim3 are contiguous
+                        GGML_ASSERT(ne12 % ne02 == 0);
+                        GGML_ASSERT(ne13 % ne03 == 0);
+
+                        const uint32_t r2 = ne12/ne02;
+                        const uint32_t r3 = ne13/ne03;
+
+                        if (src1t != GGML_TYPE_F32) {
+                            fprintf(stderr, "%s: %s: Unsupported src1 type: %u/%u\n", __func__, ggml_op_name(dst->op), src0t, src1t);
+                            goto not_implemented;
+                        }
+
+                        if (ggml_is_transposed(src0) ||
+                            ggml_is_transposed(src1)) {
+                            fprintf(stderr, "%s: %s: matmul on tranposed tensor not supported: %u/%u\n", __func__, ggml_op_name(dst->op), src0t, src1t);
+                            goto not_implemented;
+                        }
+
+                        switch (src0t) {
+                            case GGML_TYPE_F32:
+                                ggml_vk_mul_mat_mat_f32(
+                                    seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst,
+                                    ne00, ne01, ne02, nb01, nb02, ne11, ne12, nb11, nb12, nb1, nb2
+                                );
+                                break;
+                            case GGML_TYPE_F16:
+                                ggml_vk_mul_mat_f16(
+                                    seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst,
+                                    ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, ne13, nb10, nb11, nb12,
+                                    ne0, ne1, r2, r3
+                                );
+                                break;
+                            case GGML_TYPE_Q8_0:
+                                ggml_vk_mul_mat_q8_0(
+                                    seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst,
+                                    ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3
+                                );
+                                break;
+                            case GGML_TYPE_Q4_0:
+                                ggml_vk_mul_mat_q4_0(
+                                    seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst,
+                                    ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3
+                                );
+                                break;
+                            case GGML_TYPE_Q4_1:
+                                ggml_vk_mul_mat_q4_1(
+                                    seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst,
+                                    ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3
+                                );
+                                break;
+                            case GGML_TYPE_Q6_K:
+                                ggml_vk_mul_mat_q6_k(
+                                    seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst,
+                                    ne00, ne10, ne0, ne1, ne01, ne11, ne12, ne02
+                                );
+                                break;
+                            default: {
+                                fprintf(stderr, "%s: %s: Unsupported quantization: %u/%u\n", __func__, ggml_op_name(dst->op), src0t, src1t);
+                                goto not_implemented;
+                            }
+                        }
+
+                    } break;
+                case GGML_OP_GET_ROWS:
+                    {
+                        if (src0t == GGML_TYPE_F16) {
+                            ggml_vk_get_rows_f16(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1));
+                        } else if (src0t == GGML_TYPE_Q4_0) {
+                            ggml_vk_get_rows_q4_0(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1));
+                        } else if (src0t == GGML_TYPE_Q4_1) {
+                            ggml_vk_get_rows_q4_1(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1));
+                        } else if (src0t == GGML_TYPE_Q6_K) {
+                            ggml_vk_get_rows_q6_k(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1));
+                        } else {
+                            fprintf(stderr, "%s: %s: Unsupported quantization: %u\n", __func__, ggml_op_name(dst->op), src0t);
+                            goto not_implemented;
+                        }
+                    } break;
+                case GGML_OP_ROPE:
+                    {
+                        GGML_ASSERT(ne10 == ne02);
+                        GGML_ASSERT(src0t == dstt);
+                        // const int n_past = ((int32_t *) dst->op_params)[0];
+                        const int n_dims     = ((int32_t *) dst->op_params)[1];
+                        const int mode       = ((int32_t *) dst->op_params)[2];
+                        // skip 3, n_ctx used in GLM RoPE, unimplemented in Vulkan
+                        const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
+
+                        float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
+                        memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
+                        memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
+                        memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
+                        memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
+                        memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
+                        memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
+                        ggml_vk_rope(
+                            seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, src0t, n_dims, mode, n_orig_ctx,
+                            freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow,
+                            ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, nb0, nb1, nb2, nb3
+                        );
+                    } break;
+                case GGML_OP_DUP:
+                case GGML_OP_CPY:
+                case GGML_OP_CONT:
+                    {
+                        switch (src0t) {
+                            case GGML_TYPE_F32:
+                                {
+                                    switch (dstt) {
+                                        case GGML_TYPE_F16: ggml_vk_cpy_f32_f16(seq, id_src0, id_dst, off_src0, off_dst, ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, ne1, ne2, nb0, nb1, nb2, nb3); break;
+                                        case GGML_TYPE_F32: ggml_vk_cpy_f32_f32(seq, id_src0, id_dst, off_src0, off_dst, ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, ne1, ne2, nb0, nb1, nb2, nb3); break;
+                                        default: goto not_implemented;
+                                    }
+                                } break;
+                            case GGML_TYPE_F16:
+                                {
+                                    switch (dstt) {
+                                        case GGML_TYPE_F16: ggml_vk_cpy_f16_f16(seq, id_src0, id_dst, off_src0, off_dst, ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, ne1, ne2, nb0, nb1, nb2, nb3); break;
+                                        case GGML_TYPE_F32: ggml_vk_cpy_f16_f32(seq, id_src0, id_dst, off_src0, off_dst, ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, ne1, ne2, nb0, nb1, nb2, nb3); break;
+                                    default: goto not_implemented;
+                                } break;
+                            default: goto not_implemented;
+                            }
+                        }
+                    } break;
+                default: goto not_implemented;
+            }
+            continue;
+            not_implemented: {}
+            fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
+            //GGML_ASSERT(false);
+        }
+
+        // Evaluate sequence
+        if (any_commands_recorded) {
+            seq.evalAsync();
+        }
+    }
+
+    // Wait for all sequences to finish
+    for (auto& sequence : sequences) {
+        if (sequence->isRunning())
+            sequence->evalAwait();
+    }
+
+    ggml_vk_free_descriptor_pool(ctx);
+}
+
+template<>
+kp::Tensor::TensorDataTypes
+kp::TensorT<half>::dataType()
+{
+    return TensorDataTypes::eFloat;
+}
+
+template<>
+kp::Tensor::TensorDataTypes
+kp::TensorT<uint8_t>::dataType()
+{
+    return TensorDataTypes::eUnsignedInt;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
+// backend interface
+
+struct ggml_backend_kompute_buffer_type_context {
+    int         device;
+    int         device_ref = 0;
+    uint64_t    buffer_alignment;
+    uint64_t    max_alloc;
+    std::string name;
+
+    ggml_backend_kompute_buffer_type_context(int device, uint64_t buffer_alignment, uint64_t max_alloc)
+        : device(device), buffer_alignment(buffer_alignment), max_alloc(max_alloc), name(ggml_kompute_format_name(device)) {}
+};
+
+static void ggml_backend_kompute_device_ref(ggml_backend_buffer_type_t buft) {
+    auto * ctx = static_cast<ggml_backend_kompute_buffer_type_context *>(buft->context);
+
+    if (!ctx->device_ref) {
+        komputeManager()->initializeDevice(
+            ctx->device, {}, {
+                "VK_KHR_shader_float16_int8", "VK_KHR_8bit_storage",
+                "VK_KHR_16bit_storage", "VK_KHR_shader_non_semantic_info"
+            }
+        );
+    }
+
+    assert(ggml_vk_has_device());
+    ctx->device_ref++;
+}
+
+static void ggml_backend_kompute_device_unref(ggml_backend_buffer_type_t buft) {
+    auto * ctx = static_cast<ggml_backend_kompute_buffer_type_context *>(buft->context);
+
+    assert(ctx->device_ref > 0);
+
+    ctx->device_ref--;
+
+    if (!ctx->device_ref) {
+        komputeManager.destroy();
+    }
+}
+
+static const char * ggml_backend_kompute_buffer_get_name(ggml_backend_buffer_t buffer) {
+    auto * ctx = static_cast<ggml_backend_kompute_buffer_type_context *>(buffer->buft->context);
+    return ctx->name.c_str();
+}
+
+static void ggml_backend_kompute_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    auto * memory = (ggml_vk_memory *)buffer->context;
+    if (ggml_vk_has_device()) {
+        ggml_vk_free_memory(*memory);
+    }
+    delete memory;
+}
+
+static void * ggml_backend_kompute_buffer_get_base(ggml_backend_buffer_t buffer) {
+    return ((ggml_vk_memory *)buffer->context)->data;
+}
+
+static void ggml_backend_kompute_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    GGML_UNUSED(buffer);
+
+    const auto res = ggml_vk_get_tensor(tensor);
+    GGML_ASSERT(res);
+
+    memcpy((char *)tensor->data + offset, data, size);
+
+    komputeManager()->sequence()->eval<kp::OpTensorSyncDevice>({res});
+}
+
+static void ggml_backend_kompute_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    GGML_UNUSED(buffer);
+
+    const auto res = ggml_vk_get_tensor(tensor);
+    GGML_ASSERT(res);
+
+    komputeManager()->sequence()->eval<kp::OpTensorSyncLocal>({res});
+
+    memcpy(data, (const char *)tensor->data + offset, size);
+}
+
+static void ggml_backend_kompute_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    auto * memory = (ggml_vk_memory *)buffer->context;
+    memset(memory->data, value, buffer->size);
+
+    if (memory->stagingBuffer)
+        komputeManager()->sequence()->eval<kp::OpBufferSyncDevice>(memory->primaryBuffer, memory->stagingBuffer, memory->size);
+}
+
+static ggml_backend_buffer_i ggml_backend_kompute_buffer_i = {
+    /* .get_name        = */ ggml_backend_kompute_buffer_get_name,
+    /* .free_buffer     = */ ggml_backend_kompute_buffer_free_buffer,
+    /* .get_base        = */ ggml_backend_kompute_buffer_get_base,
+    /* .init_tensor     = */ NULL,
+    /* .set_tensor      = */ ggml_backend_kompute_buffer_set_tensor,
+    /* .get_tensor      = */ ggml_backend_kompute_buffer_get_tensor,
+    /* .cpy_tensor      = */ NULL,
+    /* .clear           = */ ggml_backend_kompute_buffer_clear,
+    /* .reset           = */ NULL,
+};
+
+// default buffer type
+
+static const char * ggml_backend_kompute_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
+    auto * ctx = static_cast<ggml_backend_kompute_buffer_type_context *>(buft->context);
+    return ctx->name.c_str();
+}
+
+static ggml_backend_buffer_t ggml_backend_kompute_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    ggml_backend_kompute_device_ref(buft);
+    auto * ctx = new ggml_vk_memory(ggml_vk_allocate(size));
+    return ggml_backend_buffer_init(buft, ggml_backend_kompute_buffer_i, ctx, size);
+}
+
+static size_t ggml_backend_kompute_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+    auto * ctx = static_cast<ggml_backend_kompute_buffer_type_context *>(buft->context);
+    return ctx->buffer_alignment;
+}
+
+static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
+    auto * ctx = static_cast<ggml_backend_kompute_buffer_type_context *>(buft->context);
+    return ctx->max_alloc;
+}
+
+static bool ggml_backend_kompute_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
+    GGML_UNUSED(buft);
+    return ggml_backend_is_kompute(backend);
+}
+
+static ggml_backend_buffer_type_i ggml_backend_kompute_buffer_type_interface = {
+    /* .get_name         = */ ggml_backend_kompute_buffer_type_get_name,
+    /* .alloc_buffer     = */ ggml_backend_kompute_buffer_type_alloc_buffer,
+    /* .get_alignment    = */ ggml_backend_kompute_buffer_type_get_alignment,
+    /* .get_max_size     = */ ggml_backend_vk_buffer_type_get_max_size,
+    /* .get_alloc_size   = */ NULL, // defaults to ggml_nbytes
+    /* .supports_backend = */ ggml_backend_kompute_buffer_type_supports_backend,
+    /* .is_host          = */ NULL,
+};
+
+ggml_backend_buffer_type_t ggml_backend_kompute_buffer_type(int device) {
+    static std::vector<ggml_backend_buffer_type> bufts = []() {
+        std::vector<ggml_backend_buffer_type> vec;
+        auto devices = ggml_vk_available_devices_internal(0);
+        vec.reserve(devices.size());
+
+        for (const auto & dev : devices) {
+            vec.push_back({
+                /* .iface   = */ ggml_backend_kompute_buffer_type_interface,
+                /* .context = */ new ggml_backend_kompute_buffer_type_context(dev.index, dev.bufferAlignment, dev.maxAlloc)
+            });
+        }
+        return vec;
+    }();
+
+    auto it = std::find_if(bufts.begin(), bufts.end(), [device](const ggml_backend_buffer_type & t) {
+        return device == static_cast<ggml_backend_kompute_buffer_type_context *>(t.context)->device;
+    });
+    return it < bufts.end() ? &*it : nullptr;
+}
+
+// backend
+
+static const char * ggml_backend_kompute_name(ggml_backend_t backend) {
+    auto * ctx = static_cast<ggml_kompute_context *>(backend->context);
+    return ctx->name.c_str();
+}
+
+static void ggml_backend_kompute_free(ggml_backend_t backend) {
+    auto * ctx = static_cast<ggml_kompute_context *>(backend->context);
+
+    assert(ctx == s_kompute_context);
+    s_kompute_context = nullptr;
+    if (ctx != nullptr) {
+        delete ctx;
+    }
+
+    delete backend;
+}
+
+static ggml_backend_buffer_type_t ggml_backend_kompute_get_default_buffer_type(ggml_backend_t backend) {
+    auto * ctx = static_cast<ggml_kompute_context *>(backend->context);
+    return ggml_backend_kompute_buffer_type(ctx->device);
+}
+
+static ggml_status ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    auto * ctx = static_cast<ggml_kompute_context *>(backend->context);
+    ggml_vk_graph_compute(ctx, cgraph);
+    return GGML_STATUS_SUCCESS;
+}
+
+static bool ggml_backend_kompute_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
+    GGML_UNUSED(backend);
+    return ggml_vk_supports_op(op);
+}
+
+static struct ggml_backend_i kompute_backend_i = {
+    /* .get_name                = */ ggml_backend_kompute_name,
+    /* .free                    = */ ggml_backend_kompute_free,
+    /* .get_default_buffer_type = */ ggml_backend_kompute_get_default_buffer_type,
+    /* .set_tensor_async        = */ NULL,
+    /* .get_tensor_async        = */ NULL,
+    /* .cpy_tensor_async        = */ NULL,
+    /* .synchronize             = */ NULL,
+    /* .graph_plan_create       = */ NULL,
+    /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_compute      = */ NULL,
+    /* .graph_compute           = */ ggml_backend_kompute_graph_compute,
+    /* .supports_op             = */ ggml_backend_kompute_supports_op,
+    /* .offload_op              = */ NULL,
+    /* .event_new               = */ NULL,
+    /* .event_free              = */ NULL,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .event_synchronize       = */ NULL,
+};
+
+static ggml_guid_t ggml_backend_kompute_guid() {
+    static ggml_guid guid = { 0x7b, 0x57, 0xdc, 0xaf, 0xde, 0x12, 0x1d, 0x49, 0xfb, 0x35, 0xfa, 0x9b, 0x18, 0x31, 0x1d, 0xca };
+    return &guid;
+}
+
+ggml_backend_t ggml_backend_kompute_init(int device) {
+    GGML_ASSERT(s_kompute_context == nullptr);
+    s_kompute_context = new ggml_kompute_context(device);
+
+    ggml_backend_t kompute_backend = new ggml_backend {
+        /* .guid      = */ ggml_backend_kompute_guid(),
+        /* .interface = */ kompute_backend_i,
+        /* .context   = */ s_kompute_context,
+    };
+
+    return kompute_backend;
+}
+
+bool ggml_backend_is_kompute(ggml_backend_t backend) {
+    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_kompute_guid());
+}
+
+static ggml_backend_t ggml_backend_reg_kompute_init(const char * params, void * user_data) {
+    GGML_UNUSED(params);
+    return ggml_backend_kompute_init(intptr_t(user_data));
+}
+
+extern "C" int ggml_backend_kompute_reg_devices();
+
+int ggml_backend_kompute_reg_devices() {
+    auto devices = ggml_vk_available_devices_internal(0);
+    for (const auto & device : devices) {
+        ggml_backend_register(
+            ggml_kompute_format_name(device.index).c_str(),
+            ggml_backend_reg_kompute_init,
+            ggml_backend_kompute_buffer_type(device.index),
+            reinterpret_cast<void *>(intptr_t(device.index))
+        );
+    }
+    return devices.size();
+}
diff --git a/ggml-kompute.h b/ggml-kompute.h
new file mode 100644
index 000000000..171465456
--- /dev/null
+++ b/ggml-kompute.h
@@ -0,0 +1,46 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#include <stdbool.h>
+#include <stddef.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+struct ggml_vk_device {
+    int index;
+    int type; // same as VkPhysicalDeviceType
+    size_t heapSize;
+    const char * name;
+    const char * vendor;
+    int subgroupSize;
+    uint64_t bufferAlignment;
+    uint64_t maxAlloc;
+};
+
+struct ggml_vk_device * ggml_vk_available_devices(size_t memoryRequired, size_t * count);
+bool ggml_vk_get_device(struct ggml_vk_device * device, size_t memoryRequired, const char * name);
+bool ggml_vk_has_vulkan(void);
+bool ggml_vk_has_device(void);
+struct ggml_vk_device ggml_vk_current_device(void);
+
+//
+// backend API
+//
+
+// forward declaration
+typedef struct ggml_backend * ggml_backend_t;
+
+GGML_API ggml_backend_t ggml_backend_kompute_init(int device);
+
+GGML_API bool ggml_backend_is_kompute(ggml_backend_t backend);
+
+GGML_API ggml_backend_buffer_type_t ggml_backend_kompute_buffer_type(int device);
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/ggml-metal.h b/ggml-metal.h
index c4b7325da..a5c542189 100644
--- a/ggml-metal.h
+++ b/ggml-metal.h
@@ -27,7 +27,6 @@
 
 // max memory buffers that can be mapped to the device
 #define GGML_METAL_MAX_BUFFERS 64
-#define GGML_METAL_MAX_COMMAND_BUFFERS 32
 
 struct ggml_tensor;
 struct ggml_cgraph;
@@ -36,79 +35,31 @@ struct ggml_cgraph;
 extern "C" {
 #endif
 
-//
-// internal API
-// temporary exposed to user-code
-//
-
-struct ggml_metal_context;
-
-void ggml_metal_log_set_callback(ggml_log_callback log_callback, void * user_data);
-
-// number of command buffers to use
-struct ggml_metal_context * ggml_metal_init(int n_cb);
-void ggml_metal_free(struct ggml_metal_context * ctx);
-
-void * ggml_metal_host_malloc(size_t n);
-void   ggml_metal_host_free  (void * data);
-
-// set the number of command buffers to use
-void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb);
-
-// creates a mapping between a host memory buffer and a device memory buffer
-// - make sure to map all buffers used in the graph before calling ggml_metal_graph_compute
-// - the mapping is used during computation to determine the arguments of the compute kernels
-// - you don't need to keep the host memory buffer allocated as it is never accessed by Metal
-// - max_size specifies the maximum size of a tensor and is used to create shared views such
-//   that it is guaranteed that the tensor will fit in at least one of the views
-//
-bool ggml_metal_add_buffer(
-        struct ggml_metal_context * ctx,
-                       const char * name,
-                             void * data,
-                           size_t   size,
-                           size_t   max_size);
-
-// set data from host memory into the device
-void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
-
-// get data from the device into host memory
-void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
-
-// try to find operations that can be run concurrently in the graph
-// you should run it again if the topology of your graph changes
-void ggml_metal_graph_find_concurrency(struct ggml_metal_context * ctx, struct ggml_cgraph * gf, bool check_mem);
-
-// if the graph has been optimized for concurrently dispatch, return length of the concur_list if optimized
-int ggml_metal_if_optimized(struct ggml_metal_context * ctx);
-
-// output the concur_list for ggml_alloc
-int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx);
-
-// same as ggml_graph_compute but uses Metal
-// creates gf->n_threads command buffers in parallel
-bool ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
-
 //
 // backend API
 // user-code should use only these functions
 //
 
+GGML_API void ggml_backend_metal_log_set_callback(ggml_log_callback log_callback, void * user_data);
+
 GGML_API ggml_backend_t ggml_backend_metal_init(void);
 
 GGML_API bool ggml_backend_is_metal(ggml_backend_t backend);
 
-GGML_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size);
+GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size);
 
 GGML_API void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb);
 
-GGML_API ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
+GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
 
 // helper to check if the device supports a specific family
 // ideally, the user code should be doing these checks
 // ref: https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf
 GGML_API bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family);
 
+// capture all command buffers committed the next time `ggml_backend_graph_compute` is called
+GGML_API void ggml_backend_metal_capture_next_compute(ggml_backend_t backend);
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/ggml-metal.m b/ggml-metal.m
index 6c2a8d04e..419d8b9e5 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -24,15 +24,170 @@
 
 #define UNUSED(x) (void)(x)
 
-#define GGML_MAX_CONCUR (2*GGML_DEFAULT_GRAPH_SIZE)
+struct ggml_metal_kernel {
+    id<MTLComputePipelineState> pipeline;
+};
 
-struct ggml_metal_buffer {
-    const char * name;
+enum ggml_metal_kernel_type {
+    GGML_METAL_KERNEL_TYPE_ADD,
+    GGML_METAL_KERNEL_TYPE_ADD_ROW,
+    GGML_METAL_KERNEL_TYPE_MUL,
+    GGML_METAL_KERNEL_TYPE_MUL_ROW,
+    GGML_METAL_KERNEL_TYPE_DIV,
+    GGML_METAL_KERNEL_TYPE_DIV_ROW,
+    GGML_METAL_KERNEL_TYPE_SCALE,
+    GGML_METAL_KERNEL_TYPE_SCALE_4,
+    GGML_METAL_KERNEL_TYPE_TANH,
+    GGML_METAL_KERNEL_TYPE_RELU,
+    GGML_METAL_KERNEL_TYPE_GELU,
+    GGML_METAL_KERNEL_TYPE_GELU_QUICK,
+    GGML_METAL_KERNEL_TYPE_SILU,
+    GGML_METAL_KERNEL_TYPE_SOFT_MAX,
+    GGML_METAL_KERNEL_TYPE_SOFT_MAX_4,
+    GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF,
+    GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_F32,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_F16,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_I32,
+    GGML_METAL_KERNEL_TYPE_RMS_NORM,
+    GGML_METAL_KERNEL_TYPE_GROUP_NORM,
+    GGML_METAL_KERNEL_TYPE_NORM,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32,
+  //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32,
+  //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW,
+  //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32,
+    GGML_METAL_KERNEL_TYPE_ROPE_F32,
+    GGML_METAL_KERNEL_TYPE_ROPE_F16,
+    GGML_METAL_KERNEL_TYPE_ALIBI_F32,
+    GGML_METAL_KERNEL_TYPE_IM2COL_F16,
+    GGML_METAL_KERNEL_TYPE_IM2COL_F32,
+    GGML_METAL_KERNEL_TYPE_UPSCALE_F32,
+    GGML_METAL_KERNEL_TYPE_PAD_F32,
+    GGML_METAL_KERNEL_TYPE_ARANGE_F32,
+    GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32,
+    GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,
+    GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC,
+    GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32,
+    GGML_METAL_KERNEL_TYPE_CPY_F32_F16,
+    GGML_METAL_KERNEL_TYPE_CPY_F32_F32,
+    GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0,
+    GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0,
+    GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1,
+    GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0,
+    GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1,
+    GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL,
+    GGML_METAL_KERNEL_TYPE_CPY_F16_F16,
+    GGML_METAL_KERNEL_TYPE_CPY_F16_F32,
+    GGML_METAL_KERNEL_TYPE_CONCAT,
+    GGML_METAL_KERNEL_TYPE_SQR,
+    GGML_METAL_KERNEL_TYPE_SUM_ROWS,
 
-    void   * data;
-    size_t   size;
-
-    id<MTLBuffer> metal;
+    GGML_METAL_KERNEL_TYPE_COUNT
 };
 
 struct ggml_metal_context {
@@ -40,145 +195,21 @@ struct ggml_metal_context {
 
     id<MTLDevice>       device;
     id<MTLCommandQueue> queue;
-    id<MTLLibrary>      library;
-
-    id<MTLCommandBuffer>         command_buffers [GGML_METAL_MAX_COMMAND_BUFFERS];
-    id<MTLComputeCommandEncoder> command_encoders[GGML_METAL_MAX_COMMAND_BUFFERS];
 
     dispatch_queue_t d_queue;
 
-    int n_buffers;
-    struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS];
+    struct ggml_metal_kernel kernels[GGML_METAL_KERNEL_TYPE_COUNT];
 
-    int concur_list[GGML_MAX_CONCUR];
-    int concur_list_len;
+    bool support_simdgroup_reduction;
+    bool support_simdgroup_mm;
 
-    // custom kernels
-#define GGML_METAL_DECL_KERNEL(name) \
-    id<MTLFunction>             function_##name; \
-    id<MTLComputePipelineState> pipeline_##name
-
-    GGML_METAL_DECL_KERNEL(add);
-    GGML_METAL_DECL_KERNEL(add_row); // TODO: avoid this extra kernel, instead extend the "add" kernel to support broadcast
-    GGML_METAL_DECL_KERNEL(mul);
-    GGML_METAL_DECL_KERNEL(mul_row); // TODO: avoid this extra kernel, instead extend the "mul" kernel to support broadcast
-    GGML_METAL_DECL_KERNEL(div);
-    GGML_METAL_DECL_KERNEL(div_row);
-    GGML_METAL_DECL_KERNEL(scale);
-    GGML_METAL_DECL_KERNEL(scale_4);
-    GGML_METAL_DECL_KERNEL(tanh);
-    GGML_METAL_DECL_KERNEL(relu);
-    GGML_METAL_DECL_KERNEL(gelu);
-    GGML_METAL_DECL_KERNEL(gelu_quick);
-    GGML_METAL_DECL_KERNEL(silu);
-    GGML_METAL_DECL_KERNEL(soft_max);
-    GGML_METAL_DECL_KERNEL(soft_max_4);
-    GGML_METAL_DECL_KERNEL(diag_mask_inf);
-    GGML_METAL_DECL_KERNEL(diag_mask_inf_8);
-    GGML_METAL_DECL_KERNEL(get_rows_f32);
-    GGML_METAL_DECL_KERNEL(get_rows_f16);
-    GGML_METAL_DECL_KERNEL(get_rows_q4_0);
-    GGML_METAL_DECL_KERNEL(get_rows_q4_1);
-    GGML_METAL_DECL_KERNEL(get_rows_q5_0);
-    GGML_METAL_DECL_KERNEL(get_rows_q5_1);
-    GGML_METAL_DECL_KERNEL(get_rows_q8_0);
-    GGML_METAL_DECL_KERNEL(get_rows_q2_K);
-    GGML_METAL_DECL_KERNEL(get_rows_q3_K);
-    GGML_METAL_DECL_KERNEL(get_rows_q4_K);
-    GGML_METAL_DECL_KERNEL(get_rows_q5_K);
-    GGML_METAL_DECL_KERNEL(get_rows_q6_K);
-    GGML_METAL_DECL_KERNEL(get_rows_i32);
-    GGML_METAL_DECL_KERNEL(get_rows_iq2_xxs);
-    GGML_METAL_DECL_KERNEL(rms_norm);
-    GGML_METAL_DECL_KERNEL(group_norm);
-    GGML_METAL_DECL_KERNEL(norm);
-    GGML_METAL_DECL_KERNEL(mul_mv_f32_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_f16_f16);
-    GGML_METAL_DECL_KERNEL(mul_mv_f16_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_f16_f32_1row);
-    GGML_METAL_DECL_KERNEL(mul_mv_f16_f32_l4);
-    GGML_METAL_DECL_KERNEL(mul_mv_q4_0_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_q4_1_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_q5_0_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_q5_1_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_q8_0_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_q2_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_q3_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_q4_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_q5_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_q6_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_iq2_xxs_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_id_f32_f32);
-    //GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f16);
-    GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f32);
-    //GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f32_1row);
-    //GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f32_l4);
-    GGML_METAL_DECL_KERNEL(mul_mv_id_q4_0_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_id_q4_1_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_id_q5_0_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_id_q5_1_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_id_q8_0_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_id_q2_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_id_q3_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_id_q4_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_id_q5_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_id_q6_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mv_id_iq2_xxs_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_f32_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_f16_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_q4_0_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_q4_1_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_q5_0_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_q5_1_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_q8_0_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_q2_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_q3_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_q4_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_q5_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_q6_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_iq2_xxs_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_id_f32_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_id_f16_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_id_q4_0_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_id_q4_1_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_id_q5_0_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_id_q5_1_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_id_q8_0_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_id_q2_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_id_q3_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_id_q4_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_id_q5_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_id_q6_K_f32);
-    GGML_METAL_DECL_KERNEL(mul_mm_id_iq2_xxs_f32);
-    GGML_METAL_DECL_KERNEL(rope_f32);
-    GGML_METAL_DECL_KERNEL(rope_f16);
-    GGML_METAL_DECL_KERNEL(alibi_f32);
-    GGML_METAL_DECL_KERNEL(im2col_f16);
-    GGML_METAL_DECL_KERNEL(upscale_f32);
-    GGML_METAL_DECL_KERNEL(pad_f32);
-    GGML_METAL_DECL_KERNEL(argsort_f32_i32_asc);
-    GGML_METAL_DECL_KERNEL(argsort_f32_i32_desc);
-    GGML_METAL_DECL_KERNEL(leaky_relu_f32);
-    GGML_METAL_DECL_KERNEL(cpy_f32_f16);
-    GGML_METAL_DECL_KERNEL(cpy_f32_f32);
-    GGML_METAL_DECL_KERNEL(cpy_f32_q8_0);
-    GGML_METAL_DECL_KERNEL(cpy_f32_q4_0);
-    GGML_METAL_DECL_KERNEL(cpy_f32_q4_1);
-    //GGML_METAL_DECL_KERNEL(cpy_f32_q5_0);
-    //GGML_METAL_DECL_KERNEL(cpy_f32_q5_1);
-    GGML_METAL_DECL_KERNEL(cpy_f16_f16);
-    GGML_METAL_DECL_KERNEL(cpy_f16_f32);
-    GGML_METAL_DECL_KERNEL(concat);
-    GGML_METAL_DECL_KERNEL(sqr);
-    GGML_METAL_DECL_KERNEL(sum_rows);
-
-#undef GGML_METAL_DECL_KERNEL
+    bool should_capture_next_compute;
 };
 
 // MSL code
 // TODO: move the contents here when ready
 //       for now it is easier to work in a separate file
-//static NSString * const msl_library_source = @"see metal.metal";
+// static NSString * const msl_library_source = @"see metal.metal";
 
 // Here to assist with NSBundle Path Hack
 @interface GGMLMetalClass : NSObject
@@ -186,7 +217,6 @@ struct ggml_metal_context {
 @implementation GGMLMetalClass
 @end
 
-
 static void ggml_metal_default_log_callback(enum ggml_log_level level, const char * msg, void * user_data) {
     fprintf(stderr, "%s", msg);
 
@@ -197,11 +227,6 @@ static void ggml_metal_default_log_callback(enum ggml_log_level level, const cha
 ggml_log_callback ggml_metal_log_callback = ggml_metal_default_log_callback;
 void * ggml_metal_log_user_data = NULL;
 
-void ggml_metal_log_set_callback(ggml_log_callback log_callback, void * user_data) {
-    ggml_metal_log_callback  = log_callback;
-    ggml_metal_log_user_data = user_data;
-}
-
 GGML_ATTRIBUTE_FORMAT(2, 3)
 static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
     if (ggml_metal_log_callback != NULL) {
@@ -224,37 +249,48 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
     }
 }
 
-struct ggml_metal_context * ggml_metal_init(int n_cb) {
+static void * ggml_metal_host_malloc(size_t n) {
+    void * data = NULL;
+    const int result = posix_memalign((void **) &data, sysconf(_SC_PAGESIZE), n);
+    if (result != 0) {
+        GGML_METAL_LOG_ERROR("%s: error: posix_memalign failed\n", __func__);
+        return NULL;
+    }
+
+    return data;
+}
+
+static struct ggml_metal_context * ggml_metal_init(int n_cb) {
     GGML_METAL_LOG_INFO("%s: allocating\n", __func__);
 
-    id<MTLDevice> device;
-    NSString * s;
-
-#if TARGET_OS_OSX
+#if TARGET_OS_OSX && !GGML_METAL_NDEBUG
     // Show all the Metal device instances in the system
     NSArray * devices = MTLCopyAllDevices();
-    for (device in devices) {
-        s = [device name];
-        GGML_METAL_LOG_INFO("%s: found device: %s\n", __func__, [s UTF8String]);
+    for (id<MTLDevice> device in devices) {
+        GGML_METAL_LOG_INFO("%s: found device: %s\n", __func__, [[device name] UTF8String]);
     }
+    [devices release]; // since it was created by a *Copy* C method
 #endif
 
     // Pick and show default Metal device
-    device = MTLCreateSystemDefaultDevice();
-    s = [device name];
-    GGML_METAL_LOG_INFO("%s: picking default device: %s\n", __func__, [s UTF8String]);
+    id<MTLDevice> device = MTLCreateSystemDefaultDevice();
+    GGML_METAL_LOG_INFO("%s: picking default device: %s\n", __func__, [[device name] UTF8String]);
 
     // Configure context
     struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context));
     ctx->device = device;
     ctx->n_cb   = MIN(n_cb, GGML_METAL_MAX_BUFFERS);
     ctx->queue  = [ctx->device newCommandQueue];
-    ctx->n_buffers = 0;
-    ctx->concur_list_len = 0;
-
     ctx->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT);
 
+    id<MTLLibrary> metal_library;
+
     // load library
+    //
+    // - first check if the library is embedded
+    // - then check if the library is in the bundle
+    // - if not found, load the source and compile it
+    // - if that fails, return NULL
     {
         NSBundle * bundle = nil;
 #ifdef SWIFT_PACKAGE
@@ -262,74 +298,131 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
 #else
         bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
 #endif
+
         NSError * error = nil;
-        NSString * libPath = [bundle pathForResource:@"default" ofType:@"metallib"];
-        if (libPath != nil) {
+
+#if GGML_METAL_EMBED_LIBRARY
+        const bool try_metallib = false;
+#else
+        const bool try_metallib = true;
+#endif
+
+        NSString * path_lib = [bundle pathForResource:@"default" ofType:@"metallib"];
+        if (try_metallib && path_lib != nil) {
             // pre-compiled library found
-            NSURL * libURL = [NSURL fileURLWithPath:libPath];
-            GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [libPath UTF8String]);
-            ctx->library = [ctx->device newLibraryWithURL:libURL error:&error];
-        } else {
-            GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
+            NSURL * libURL = [NSURL fileURLWithPath:path_lib];
+            GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [path_lib UTF8String]);
 
-            NSString * sourcePath;
-            NSString * ggmlMetalPathResources = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
-
-            GGML_METAL_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, ggmlMetalPathResources ? [ggmlMetalPathResources UTF8String] : "nil");
-
-            if (ggmlMetalPathResources) {
-                sourcePath = [ggmlMetalPathResources stringByAppendingPathComponent:@"ggml-metal.metal"];
-            } else {
-                sourcePath = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
-            }
-            if (sourcePath == nil) {
-                GGML_METAL_LOG_WARN("%s: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd\n", __func__);
-                sourcePath = @"ggml-metal.metal";
-            }
-            GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [sourcePath UTF8String]);
-            NSString * src = [NSString stringWithContentsOfFile:sourcePath encoding:NSUTF8StringEncoding error:&error];
+            metal_library = [ctx->device newLibraryWithURL:libURL error:&error];
             if (error) {
                 GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
                 return NULL;
             }
+        } else {
+#if GGML_METAL_EMBED_LIBRARY
+            GGML_METAL_LOG_INFO("%s: using embedded metal library\n", __func__);
+
+            extern const char ggml_metallib_start[];
+            extern const char ggml_metallib_end[];
+
+            NSString * src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding];
+#else
+            GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
+
+            NSString * path_source;
+            NSString * path_resource = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
+
+            GGML_METAL_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, path_resource ? [path_resource UTF8String] : "nil");
+
+            if (path_resource) {
+                path_source = [path_resource stringByAppendingPathComponent:@"ggml-metal.metal"];
+            } else {
+                path_source = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
+            }
+
+            if (path_source == nil) {
+                GGML_METAL_LOG_WARN("%s: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd\n", __func__);
+                path_source = @"ggml-metal.metal";
+            }
+
+            GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [path_source UTF8String]);
+
+            NSString * src = [NSString stringWithContentsOfFile:path_source encoding:NSUTF8StringEncoding error:&error];
+            if (error) {
+                GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
+                return NULL;
+            }
+#endif // GGML_METAL_EMBED_LIBRARY
+
+            @autoreleasepool {
+                // dictionary of preprocessor macros
+                NSMutableDictionary * prep = [NSMutableDictionary dictionary];
 
-            MTLCompileOptions* options = nil;
 #ifdef GGML_QKK_64
-            options = [MTLCompileOptions new];
-            options.preprocessorMacros = @{ @"QK_K" : @(64) };
+                prep[@"GGML_QKK_64"] = @(1);
 #endif
-            // try to disable fast-math
-            // NOTE: this seems to have no effect whatsoever
-            //       instead, in order to disable fast-math, we have to build default.metallib from the command line
-            //       using xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
-            //       and go through the "pre-compiled library found" path above
-            //[options setFastMathEnabled:false];
 
-            ctx->library = [ctx->device newLibraryWithSource:src options:options error:&error];
-        }
+                MTLCompileOptions* options = [MTLCompileOptions new];
+                options.preprocessorMacros = prep;
 
-        if (error) {
-            GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
-            return NULL;
+                //[options setFastMathEnabled:false];
+
+                metal_library = [ctx->device newLibraryWithSource:src options:options error:&error];
+                if (error) {
+                    GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
+                    return NULL;
+                }
+            }
         }
     }
 
-#if TARGET_OS_OSX
     // print MTL GPU family:
     GGML_METAL_LOG_INFO("%s: GPU name:   %s\n", __func__, [[ctx->device name] UTF8String]);
 
+    const NSInteger MTLGPUFamilyMetal3 = 5001;
+
     // determine max supported GPU family
     // https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf
     // https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf
-    for (int i = MTLGPUFamilyApple1 + 20; i >= MTLGPUFamilyApple1; --i) {
-        if ([ctx->device supportsFamily:i]) {
-            GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d (%d)\n", __func__, i - (int) MTLGPUFamilyApple1 + 1, i);
-            break;
+    {
+        for (int i = MTLGPUFamilyApple1 + 20; i >= MTLGPUFamilyApple1; --i) {
+            if ([ctx->device supportsFamily:i]) {
+                GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d  (%d)\n", __func__, i - (int) MTLGPUFamilyApple1 + 1, i);
+                break;
+            }
+        }
+
+        for (int i = MTLGPUFamilyCommon1 + 5; i >= MTLGPUFamilyCommon1; --i) {
+            if ([ctx->device supportsFamily:i]) {
+                GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyCommon%d (%d)\n", __func__, i - (int) MTLGPUFamilyCommon1 + 1, i);
+                break;
+            }
+        }
+
+        for (int i = MTLGPUFamilyMetal3 + 5; i >= MTLGPUFamilyMetal3; --i) {
+            if ([ctx->device supportsFamily:i]) {
+                GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyMetal%d  (%d)\n", __func__, i - (int) MTLGPUFamilyMetal3 + 3, i);
+                break;
+            }
         }
     }
 
+    ctx->support_simdgroup_reduction  = [ctx->device supportsFamily:MTLGPUFamilyApple7];
+    ctx->support_simdgroup_reduction |= [ctx->device supportsFamily:MTLGPUFamilyMetal3];
+
+    ctx->support_simdgroup_mm = [ctx->device supportsFamily:MTLGPUFamilyApple7];
+
+    GGML_METAL_LOG_INFO("%s: simdgroup reduction support   = %s\n",       __func__, ctx->support_simdgroup_reduction ? "true" : "false");
+    GGML_METAL_LOG_INFO("%s: simdgroup matrix mul. support = %s\n",       __func__, ctx->support_simdgroup_mm ? "true" : "false");
     GGML_METAL_LOG_INFO("%s: hasUnifiedMemory              = %s\n",       __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
-    GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize  = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1e6);
+
+    ctx->should_capture_next_compute = false;
+
+#if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15)
+    if (@available(macOS 10.12, iOS 16.0, *)) {
+        GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize  = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1e6);
+    }
+#elif TARGET_OS_OSX
     if (ctx->device.maxTransferRate != 0) {
         GGML_METAL_LOG_INFO("%s: maxTransferRate               = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1e6);
     } else {
@@ -341,270 +434,202 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
     {
         NSError * error = nil;
 
+        for (int i = 0; i < GGML_METAL_KERNEL_TYPE_COUNT; ++i) {
+            ctx->kernels[i].pipeline = nil;
+        }
+
         /*
-        GGML_METAL_LOG_INFO("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name, \
-                (int) ctx->pipeline_##name.maxTotalThreadsPerThreadgroup, \
-                (int) ctx->pipeline_##name.threadExecutionWidth); \
+            GGML_METAL_LOG_INFO("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \
+                    (int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \
+                    (int) kernel->pipeline.threadExecutionWidth); \
         */
-#define GGML_METAL_ADD_KERNEL(name) \
-        ctx->function_##name = [ctx->library newFunctionWithName:@"kernel_"#name]; \
-        ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:&error]; \
-        if (error) { \
-            GGML_METAL_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \
-            return NULL; \
+#define GGML_METAL_ADD_KERNEL(e, name, supported) \
+        if (supported) { \
+            struct ggml_metal_kernel * kernel = &ctx->kernels[e]; \
+            id<MTLFunction> metal_function = [metal_library newFunctionWithName:@"kernel_"#name]; \
+            kernel->pipeline = [ctx->device newComputePipelineStateWithFunction:metal_function error:&error]; \
+            [metal_function release]; \
+            if (error) { \
+                GGML_METAL_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \
+                [metal_library release]; \
+                return NULL; \
+            } \
+        } else { \
+            GGML_METAL_LOG_WARN("%s: skipping %-32s (not supported)\n", __func__, "kernel_"#name); \
         }
 
-        GGML_METAL_ADD_KERNEL(add);
-        GGML_METAL_ADD_KERNEL(add_row);
-        GGML_METAL_ADD_KERNEL(mul);
-        GGML_METAL_ADD_KERNEL(mul_row);
-        GGML_METAL_ADD_KERNEL(div);
-        GGML_METAL_ADD_KERNEL(div_row);
-        GGML_METAL_ADD_KERNEL(scale);
-        GGML_METAL_ADD_KERNEL(scale_4);
-        GGML_METAL_ADD_KERNEL(tanh);
-        GGML_METAL_ADD_KERNEL(relu);
-        GGML_METAL_ADD_KERNEL(gelu);
-        GGML_METAL_ADD_KERNEL(gelu_quick);
-        GGML_METAL_ADD_KERNEL(silu);
-        GGML_METAL_ADD_KERNEL(soft_max);
-        GGML_METAL_ADD_KERNEL(soft_max_4);
-        GGML_METAL_ADD_KERNEL(diag_mask_inf);
-        GGML_METAL_ADD_KERNEL(diag_mask_inf_8);
-        GGML_METAL_ADD_KERNEL(get_rows_f32);
-        GGML_METAL_ADD_KERNEL(get_rows_f16);
-        GGML_METAL_ADD_KERNEL(get_rows_q4_0);
-        GGML_METAL_ADD_KERNEL(get_rows_q4_1);
-        GGML_METAL_ADD_KERNEL(get_rows_q5_0);
-        GGML_METAL_ADD_KERNEL(get_rows_q5_1);
-        GGML_METAL_ADD_KERNEL(get_rows_q8_0);
-        GGML_METAL_ADD_KERNEL(get_rows_q2_K);
-        GGML_METAL_ADD_KERNEL(get_rows_q3_K);
-        GGML_METAL_ADD_KERNEL(get_rows_q4_K);
-        GGML_METAL_ADD_KERNEL(get_rows_q5_K);
-        GGML_METAL_ADD_KERNEL(get_rows_q6_K);
-        GGML_METAL_ADD_KERNEL(get_rows_i32);
-        GGML_METAL_ADD_KERNEL(get_rows_iq2_xxs);
-        GGML_METAL_ADD_KERNEL(rms_norm);
-        GGML_METAL_ADD_KERNEL(group_norm);
-        GGML_METAL_ADD_KERNEL(norm);
-        GGML_METAL_ADD_KERNEL(mul_mv_f32_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_f16_f16);
-        GGML_METAL_ADD_KERNEL(mul_mv_f16_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_f16_f32_1row);
-        GGML_METAL_ADD_KERNEL(mul_mv_f16_f32_l4);
-        GGML_METAL_ADD_KERNEL(mul_mv_q4_0_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_q4_1_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_q5_0_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_q5_1_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_q8_0_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_q2_K_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_q3_K_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_q4_K_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_q5_K_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_q6_K_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_iq2_xxs_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_id_f32_f32);
-        //GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f16);
-        GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f32);
-        //GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f32_1row);
-        //GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f32_l4);
-        GGML_METAL_ADD_KERNEL(mul_mv_id_q4_0_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_id_q4_1_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_id_q5_0_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_id_q5_1_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_id_q8_0_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_id_q2_K_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_id_q3_K_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_id_q4_K_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_id_q5_K_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_id_q6_K_f32);
-        GGML_METAL_ADD_KERNEL(mul_mv_id_iq2_xxs_f32);
-        if ([ctx->device supportsFamily:MTLGPUFamilyApple7]) {
-            GGML_METAL_ADD_KERNEL(mul_mm_f32_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_f16_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_q4_0_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_q4_1_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_q5_0_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_q5_1_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_q8_0_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_q2_K_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_q3_K_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_q4_K_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_q5_K_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_q6_K_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_iq2_xxs_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_id_f32_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_id_f16_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_id_q4_0_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_id_q4_1_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_id_q5_0_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_id_q5_1_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_id_q8_0_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_id_q2_K_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_id_q3_K_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_id_q4_K_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_id_q5_K_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_id_q6_K_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_id_iq2_xxs_f32);
-        }
-        GGML_METAL_ADD_KERNEL(rope_f32);
-        GGML_METAL_ADD_KERNEL(rope_f16);
-        GGML_METAL_ADD_KERNEL(alibi_f32);
-        GGML_METAL_ADD_KERNEL(im2col_f16);
-        GGML_METAL_ADD_KERNEL(upscale_f32);
-        GGML_METAL_ADD_KERNEL(pad_f32);
-        GGML_METAL_ADD_KERNEL(argsort_f32_i32_asc);
-        GGML_METAL_ADD_KERNEL(argsort_f32_i32_desc);
-        GGML_METAL_ADD_KERNEL(leaky_relu_f32);
-        GGML_METAL_ADD_KERNEL(cpy_f32_f16);
-        GGML_METAL_ADD_KERNEL(cpy_f32_f32);
-        GGML_METAL_ADD_KERNEL(cpy_f32_q8_0);
-        GGML_METAL_ADD_KERNEL(cpy_f32_q4_0);
-        GGML_METAL_ADD_KERNEL(cpy_f32_q4_1);
-        //GGML_METAL_ADD_KERNEL(cpy_f32_q5_0);
-        //GGML_METAL_ADD_KERNEL(cpy_f32_q5_1);
-        GGML_METAL_ADD_KERNEL(cpy_f16_f16);
-        GGML_METAL_ADD_KERNEL(cpy_f16_f32);
-        GGML_METAL_ADD_KERNEL(concat);
-        GGML_METAL_ADD_KERNEL(sqr);
-        GGML_METAL_ADD_KERNEL(sum_rows);
+        // simd_sum and simd_max requires MTLGPUFamilyApple7
 
-#undef GGML_METAL_ADD_KERNEL
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD,                       add,                    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW,                   add_row,                true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL,                       mul,                    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW,                   mul_row,                true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV,                       div,                    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW,                   div_row,                true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE,                     scale,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4,                   scale_4,                true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH,                      tanh,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU,                      relu,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU,                      gelu,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK,                gelu_quick,             true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU,                      silu,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX,                  soft_max,               ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_4,                soft_max_4,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF,             diag_mask_inf,          true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8,           diag_mask_inf_8,        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F32,              get_rows_f32,           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F16,              get_rows_f16,           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0,             get_rows_q4_0,          true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1,             get_rows_q4_1,          true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0,             get_rows_q5_0,          true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1,             get_rows_q5_1,          true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0,             get_rows_q8_0,          true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K,             get_rows_q2_K,          true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K,             get_rows_q3_K,          true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K,             get_rows_q4_K,          true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K,             get_rows_q5_K,          true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K,             get_rows_q6_K,          true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS,          get_rows_iq2_xxs,       true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS,           get_rows_iq2_xs,        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS,          get_rows_iq3_xxs,       true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S,            get_rows_iq3_s,         true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S,            get_rows_iq2_s,         true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S,            get_rows_iq1_s,         true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M,            get_rows_iq1_m,         true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL,           get_rows_iq4_nl,        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS,           get_rows_iq4_xs,        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32,              get_rows_i32,           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM,                  rms_norm,               ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM,                group_norm,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM,                      norm,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32,            mul_mv_f32_f32,         ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16,            mul_mv_f16_f16,         ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32,            mul_mv_f16_f32,         ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW,       mul_mv_f16_f32_1row,    ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4,         mul_mv_f16_f32_l4,      ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32,           mul_mv_q4_0_f32,        ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32,           mul_mv_q4_1_f32,        ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32,           mul_mv_q5_0_f32,        ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32,           mul_mv_q5_1_f32,        ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32,           mul_mv_q8_0_f32,        ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32,           mul_mv_q2_K_f32,        ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32,           mul_mv_q3_K_f32,        ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32,           mul_mv_q4_K_f32,        ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32,           mul_mv_q5_K_f32,        ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32,           mul_mv_q6_K_f32,        ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32,        mul_mv_iq2_xxs_f32,     ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32,         mul_mv_iq2_xs_f32,      ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32,        mul_mv_iq3_xxs_f32,     ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32,          mul_mv_iq3_s_f32,       ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32,          mul_mv_iq2_s_f32,       ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32,          mul_mv_iq1_s_f32,       ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32,          mul_mv_iq1_m_f32,       ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32,         mul_mv_iq4_nl_f32,      ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32,         mul_mv_iq4_xs_f32,      ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32,         mul_mv_id_f32_f32,      ctx->support_simdgroup_reduction);
+      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16,         mul_mv_id_f16_f16,      ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32,         mul_mv_id_f16_f32,      ctx->support_simdgroup_reduction);
+      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW,    mul_mv_id_f16_f32_1row, ctx->support_simdgroup_reduction);
+      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4,      mul_mv_id_f16_f32_l4,   ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32,        mul_mv_id_q4_0_f32,     ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32,        mul_mv_id_q4_1_f32,     ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32,        mul_mv_id_q5_0_f32,     ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32,        mul_mv_id_q5_1_f32,     ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32,        mul_mv_id_q8_0_f32,     ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32,        mul_mv_id_q2_K_f32,     ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32,        mul_mv_id_q3_K_f32,     ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32,        mul_mv_id_q4_K_f32,     ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32,        mul_mv_id_q5_K_f32,     ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32,        mul_mv_id_q6_K_f32,     ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32,     mul_mv_id_iq2_xxs_f32,  ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32,      mul_mv_id_iq2_xs_f32,   ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32,     mul_mv_id_iq3_xxs_f32,  ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32,       mul_mv_id_iq3_s_f32,    ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32,       mul_mv_id_iq2_s_f32,    ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32,       mul_mv_id_iq1_s_f32,    ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32,       mul_mv_id_iq1_m_f32,    ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32,      mul_mv_id_iq4_nl_f32,   ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32,      mul_mv_id_iq4_xs_f32,   ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32,            mul_mm_f32_f32,         ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32,            mul_mm_f16_f32,         ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32,           mul_mm_q4_0_f32,        ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32,           mul_mm_q4_1_f32,        ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32,           mul_mm_q5_0_f32,        ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32,           mul_mm_q5_1_f32,        ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32,           mul_mm_q8_0_f32,        ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32,           mul_mm_q2_K_f32,        ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32,           mul_mm_q3_K_f32,        ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32,           mul_mm_q4_K_f32,        ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32,           mul_mm_q5_K_f32,        ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32,           mul_mm_q6_K_f32,        ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32,        mul_mm_iq2_xxs_f32,     ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32,         mul_mm_iq2_xs_f32,      ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32,        mul_mm_iq3_xxs_f32,     ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32,          mul_mm_iq3_s_f32,       ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32,          mul_mm_iq2_s_f32,       ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32,          mul_mm_iq1_s_f32,       ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32,          mul_mm_iq1_m_f32,       ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32,         mul_mm_iq4_nl_f32,      ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32,         mul_mm_iq4_xs_f32,      ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32,         mul_mm_id_f32_f32,      ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32,         mul_mm_id_f16_f32,      ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32,        mul_mm_id_q4_0_f32,     ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32,        mul_mm_id_q4_1_f32,     ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32,        mul_mm_id_q5_0_f32,     ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32,        mul_mm_id_q5_1_f32,     ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32,        mul_mm_id_q8_0_f32,     ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32,        mul_mm_id_q2_K_f32,     ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32,        mul_mm_id_q3_K_f32,     ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32,        mul_mm_id_q4_K_f32,     ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32,        mul_mm_id_q5_K_f32,     ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32,        mul_mm_id_q6_K_f32,     ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32,     mul_mm_id_iq2_xxs_f32,  ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32,      mul_mm_id_iq2_xs_f32,   ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32,     mul_mm_id_iq3_xxs_f32,  ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32,       mul_mm_id_iq3_s_f32,    ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32,       mul_mm_id_iq2_s_f32,    ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32,       mul_mm_id_iq1_s_f32,    ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32,       mul_mm_id_iq1_m_f32,    ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32,      mul_mm_id_iq4_nl_f32,   ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32,      mul_mm_id_iq4_xs_f32,   ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32,                  rope_f32,               true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16,                  rope_f16,               true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32,                 alibi_f32,              true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16,                im2col_f16,             true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32,                im2col_f32,             true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32,               upscale_f32,            true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32,                   pad_f32,                true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32,    timestep_embedding_f32, true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32,                arange_f32,             true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,       argsort_f32_i32_asc,    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC,      argsort_f32_i32_desc,   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32,            leaky_relu_f32,         true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16,               cpy_f32_f16,            true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32,               cpy_f32_f32,            true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0,              cpy_f32_q8_0,           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0,              cpy_f32_q4_0,           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1,              cpy_f32_q4_1,           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0,              cpy_f32_q5_0,           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1,              cpy_f32_q5_1,           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL,            cpy_f32_iq4_nl,         true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16,               cpy_f16_f16,            true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32,               cpy_f16_f32,            true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT,                    concat,                 true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR,                       sqr,                    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS,                  sum_rows,               true);
     }
 
+    [metal_library release];
     return ctx;
 }
 
-void ggml_metal_free(struct ggml_metal_context * ctx) {
+static void ggml_metal_free(struct ggml_metal_context * ctx) {
     GGML_METAL_LOG_INFO("%s: deallocating\n", __func__);
-#define GGML_METAL_DEL_KERNEL(name) \
-    [ctx->function_##name release]; \
-    [ctx->pipeline_##name release];
 
-    GGML_METAL_DEL_KERNEL(add);
-    GGML_METAL_DEL_KERNEL(add_row);
-    GGML_METAL_DEL_KERNEL(mul);
-    GGML_METAL_DEL_KERNEL(mul_row);
-    GGML_METAL_DEL_KERNEL(div);
-    GGML_METAL_DEL_KERNEL(div_row);
-    GGML_METAL_DEL_KERNEL(scale);
-    GGML_METAL_DEL_KERNEL(scale_4);
-    GGML_METAL_DEL_KERNEL(tanh);
-    GGML_METAL_DEL_KERNEL(relu);
-    GGML_METAL_DEL_KERNEL(gelu);
-    GGML_METAL_DEL_KERNEL(gelu_quick);
-    GGML_METAL_DEL_KERNEL(silu);
-    GGML_METAL_DEL_KERNEL(soft_max);
-    GGML_METAL_DEL_KERNEL(soft_max_4);
-    GGML_METAL_DEL_KERNEL(diag_mask_inf);
-    GGML_METAL_DEL_KERNEL(diag_mask_inf_8);
-    GGML_METAL_DEL_KERNEL(get_rows_f32);
-    GGML_METAL_DEL_KERNEL(get_rows_f16);
-    GGML_METAL_DEL_KERNEL(get_rows_q4_0);
-    GGML_METAL_DEL_KERNEL(get_rows_q4_1);
-    GGML_METAL_DEL_KERNEL(get_rows_q5_0);
-    GGML_METAL_DEL_KERNEL(get_rows_q5_1);
-    GGML_METAL_DEL_KERNEL(get_rows_q8_0);
-    GGML_METAL_DEL_KERNEL(get_rows_q2_K);
-    GGML_METAL_DEL_KERNEL(get_rows_q3_K);
-    GGML_METAL_DEL_KERNEL(get_rows_q4_K);
-    GGML_METAL_DEL_KERNEL(get_rows_q5_K);
-    GGML_METAL_DEL_KERNEL(get_rows_q6_K);
-    GGML_METAL_DEL_KERNEL(get_rows_i32);
-    GGML_METAL_DEL_KERNEL(get_rows_iq2_xxs);
-    GGML_METAL_DEL_KERNEL(rms_norm);
-    GGML_METAL_DEL_KERNEL(group_norm);
-    GGML_METAL_DEL_KERNEL(norm);
-    GGML_METAL_DEL_KERNEL(mul_mv_f32_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_f16_f16);
-    GGML_METAL_DEL_KERNEL(mul_mv_f16_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_f16_f32_1row);
-    GGML_METAL_DEL_KERNEL(mul_mv_f16_f32_l4);
-    GGML_METAL_DEL_KERNEL(mul_mv_q4_0_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_q4_1_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_q5_0_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_q5_1_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_q8_0_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_q2_K_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_q3_K_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_q4_K_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_q5_K_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_q6_K_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_iq2_xxs_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_id_f32_f32);
-    //GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f16);
-    GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f32);
-    //GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f32_1row);
-    //GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f32_l4);
-    GGML_METAL_DEL_KERNEL(mul_mv_id_q4_0_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_id_q4_1_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_id_q5_0_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_id_q5_1_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_id_q8_0_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_id_q2_K_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_id_q3_K_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_id_q4_K_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_id_q5_K_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_id_q6_K_f32);
-    GGML_METAL_DEL_KERNEL(mul_mv_id_iq2_xxs_f32);
-    if ([ctx->device supportsFamily:MTLGPUFamilyApple7]) {
-        GGML_METAL_DEL_KERNEL(mul_mm_f32_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_f16_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_q4_0_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_q4_1_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_q5_0_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_q5_1_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_q8_0_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_q2_K_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_q3_K_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_q4_K_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_q5_K_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_q6_K_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_iq2_xxs_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_id_f32_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_id_f16_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_id_q4_0_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_id_q4_1_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_id_q5_0_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_id_q5_1_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_id_q8_0_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_id_q2_K_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_id_q3_K_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_id_q4_K_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_id_q5_K_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_id_q6_K_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_id_iq2_xxs_f32);
-    }
-    GGML_METAL_DEL_KERNEL(rope_f32);
-    GGML_METAL_DEL_KERNEL(rope_f16);
-    GGML_METAL_DEL_KERNEL(alibi_f32);
-    GGML_METAL_DEL_KERNEL(im2col_f16);
-    GGML_METAL_DEL_KERNEL(upscale_f32);
-    GGML_METAL_DEL_KERNEL(pad_f32);
-    GGML_METAL_DEL_KERNEL(argsort_f32_i32_asc);
-    GGML_METAL_DEL_KERNEL(argsort_f32_i32_desc);
-    GGML_METAL_DEL_KERNEL(leaky_relu_f32);
-    GGML_METAL_DEL_KERNEL(cpy_f32_f16);
-    GGML_METAL_DEL_KERNEL(cpy_f32_f32);
-    GGML_METAL_DEL_KERNEL(cpy_f32_q8_0);
-    GGML_METAL_DEL_KERNEL(cpy_f32_q4_0);
-    GGML_METAL_DEL_KERNEL(cpy_f32_q4_1);
-    //GGML_METAL_DEL_KERNEL(cpy_f32_q5_0);
-    //GGML_METAL_DEL_KERNEL(cpy_f32_q5_1);
-    GGML_METAL_DEL_KERNEL(cpy_f16_f16);
-    GGML_METAL_DEL_KERNEL(cpy_f16_f32);
-    GGML_METAL_DEL_KERNEL(concat);
-    GGML_METAL_DEL_KERNEL(sqr);
-    GGML_METAL_DEL_KERNEL(sum_rows);
-
-#undef GGML_METAL_DEL_KERNEL
-
-    for (int i = 0; i < ctx->n_buffers; ++i) {
-        [ctx->buffers[i].metal release];
+    for (int i = 0; i < GGML_METAL_KERNEL_TYPE_COUNT; ++i) {
+        [ctx->kernels[i].pipeline release];
     }
 
-    [ctx->library release];
     [ctx->queue release];
     [ctx->device release];
 
@@ -613,33 +638,6 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
     free(ctx);
 }
 
-void * ggml_metal_host_malloc(size_t n) {
-    void * data = NULL;
-    const int result = posix_memalign((void **) &data, sysconf(_SC_PAGESIZE), n);
-    if (result != 0) {
-        GGML_METAL_LOG_ERROR("%s: error: posix_memalign failed\n", __func__);
-        return NULL;
-    }
-
-    return data;
-}
-
-void ggml_metal_host_free(void * data) {
-    free(data);
-}
-
-void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb) {
-    ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS);
-}
-
-int ggml_metal_if_optimized(struct ggml_metal_context * ctx) {
-    return ctx->concur_list_len;
-}
-
-int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx) {
-    return ctx->concur_list;
-}
-
 // temporarily defined here for compatibility between ggml-backend and the old API
 
 struct ggml_backend_metal_buffer {
@@ -663,259 +661,35 @@ struct ggml_backend_metal_buffer_context {
 // the assumption is that there is 1-to-1 mapping between the host and device memory buffers, so we can find the
 // Metal buffer based on the host memory pointer
 //
-static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_metal_context * ctx, struct ggml_tensor * t, size_t * offs) {
+static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_tensor * t, size_t * offs) {
     //GGML_METAL_LOG_INFO("%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach);
 
     const int64_t tsize = ggml_nbytes(t);
 
     ggml_backend_buffer_t buffer = t->view_src ? t->view_src->buffer : t->buffer;
 
-    // compatibility with ggml-backend
-    if (buffer && buffer->buft == ggml_backend_metal_buffer_type()) {
-        struct ggml_backend_metal_buffer_context * buf_ctx = (struct ggml_backend_metal_buffer_context *) buffer->context;
-
-        // find the view that contains the tensor fully
-        for (int i = 0; i < buf_ctx->n_buffers; ++i) {
-            const int64_t ioffs = (int64_t) t->data - (int64_t) buf_ctx->buffers[i].data;
-
-            //GGML_METAL_LOG_INFO("ioffs = %10ld, tsize = %10ld, sum = %10ld, buf_ctx->buffers[%d].size = %10ld\n", ioffs, tsize, ioffs + tsize, i, buf_ctx->buffers[i].size);
-            if (ioffs >= 0 && ioffs + tsize <= (int64_t) buf_ctx->buffers[i].size) {
-                *offs = (size_t) ioffs;
-
-                //GGML_METAL_LOG_INFO("%s: tensor '%16s', offs = %8ld\n", __func__, t->name, *offs);
-
-                return buf_ctx->buffers[i].metal;
-            }
-        }
-
-        GGML_METAL_LOG_ERROR("%s: error: tensor '%s' buffer is nil\n", __func__, t->name);
-
-        return nil;
-    }
+    struct ggml_backend_metal_buffer_context * buf_ctx = (struct ggml_backend_metal_buffer_context *) buffer->context;
 
     // find the view that contains the tensor fully
-    for (int i = 0; i < ctx->n_buffers; ++i) {
-        const int64_t ioffs = (int64_t) t->data - (int64_t) ctx->buffers[i].data;
+    for (int i = 0; i < buf_ctx->n_buffers; ++i) {
+        const int64_t ioffs = (int64_t) t->data - (int64_t) buf_ctx->buffers[i].data;
 
-        //GGML_METAL_LOG_INFO("ioffs = %10ld, tsize = %10ld, sum = %10ld, ctx->buffers[%d].size = %10ld, name = %s\n", ioffs, tsize, ioffs + tsize, i, ctx->buffers[i].size, ctx->buffers[i].name);
-        if (ioffs >= 0 && ioffs + tsize <= (int64_t) ctx->buffers[i].size) {
+        //GGML_METAL_LOG_INFO("ioffs = %10ld, tsize = %10ld, sum = %10ld, buf_ctx->buffers[%d].size = %10ld\n", ioffs, tsize, ioffs + tsize, i, buf_ctx->buffers[i].size);
+        if (ioffs >= 0 && ioffs + tsize <= (int64_t) buf_ctx->buffers[i].size) {
             *offs = (size_t) ioffs;
 
-            //GGML_METAL_LOG_INFO("%s: '%s' tensor '%16s', offs = %8ld\n", __func__, ctx->buffers[i].name, t->name, *offs);
+            //GGML_METAL_LOG_INFO("%s: tensor '%16s', offs = %8ld\n", __func__, t->name, *offs);
 
-            return ctx->buffers[i].metal;
+            return buf_ctx->buffers[i].metal;
         }
     }
 
-    GGML_METAL_LOG_ERROR("%s: error: buffer is nil\n", __func__);
+    GGML_METAL_LOG_ERROR("%s: error: tensor '%s' buffer is nil\n", __func__, t->name);
 
     return nil;
 }
 
-bool ggml_metal_add_buffer(
-        struct ggml_metal_context * ctx,
-                     const char * name,
-                           void * data,
-                         size_t   size,
-                         size_t   max_size) {
-    if (ctx->n_buffers >= GGML_METAL_MAX_BUFFERS) {
-        GGML_METAL_LOG_ERROR("%s: error: too many buffers\n", __func__);
-        return false;
-    }
-
-    if (data) {
-        // verify that the buffer does not overlap with any of the existing buffers
-        for (int i = 0; i < ctx->n_buffers; ++i) {
-            const int64_t ioffs = (int64_t) data - (int64_t) ctx->buffers[i].data;
-
-            if (ioffs >= 0 && ioffs < (int64_t) ctx->buffers[i].size) {
-                GGML_METAL_LOG_ERROR("%s: error: buffer '%s' overlaps with '%s'\n", __func__, name, ctx->buffers[i].name);
-                return false;
-            }
-        }
-
-        const size_t size_page = sysconf(_SC_PAGESIZE);
-
-        size_t size_aligned = size;
-        if ((size_aligned % size_page) != 0) {
-            size_aligned += (size_page - (size_aligned % size_page));
-        }
-
-        // the buffer fits into the max buffer size allowed by the device
-        if (size_aligned <= ctx->device.maxBufferLength) {
-            ctx->buffers[ctx->n_buffers].name = name;
-            ctx->buffers[ctx->n_buffers].data = data;
-            ctx->buffers[ctx->n_buffers].size = size;
-
-            ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil];
-
-            if (ctx->buffers[ctx->n_buffers].metal == nil) {
-                GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MiB\n", __func__, name, size_aligned / 1024.0 / 1024.0);
-                return false;
-            }
-
-            GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MiB", __func__, name, size_aligned / 1024.0 / 1024.0);
-
-            ++ctx->n_buffers;
-        } else {
-            // this overlap between the views will guarantee that the tensor with the maximum size will fully fit into
-            // one of the views
-            const size_t size_ovlp = ((max_size + size_page - 1) / size_page + 1) * size_page; // round-up 2 pages just in case
-            const size_t size_step = ctx->device.maxBufferLength - size_ovlp;
-            const size_t size_view = ctx->device.maxBufferLength;
-
-            for (size_t i = 0; i < size; i += size_step) {
-                const size_t size_step_aligned = (i + size_view <= size) ? size_view : (size_aligned - i);
-
-                ctx->buffers[ctx->n_buffers].name = name;
-                ctx->buffers[ctx->n_buffers].data = (void *) ((uint8_t *) data + i);
-                ctx->buffers[ctx->n_buffers].size = size_step_aligned;
-
-                ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil];
-
-                if (ctx->buffers[ctx->n_buffers].metal == nil) {
-                    GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MiB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0);
-                    return false;
-                }
-
-                GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MiB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i);
-                if (i + size_step < size) {
-                    GGML_METAL_LOG_INFO("\n");
-                }
-
-                ++ctx->n_buffers;
-            }
-        }
-
-#if TARGET_OS_OSX
-        GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)",
-                ctx->device.currentAllocatedSize / 1024.0 / 1024.0,
-                ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
-
-        if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) {
-            GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__);
-        } else {
-            GGML_METAL_LOG_INFO("\n");
-        }
-#else
-        GGML_METAL_LOG_INFO(", (%8.2f)\n", ctx->device.currentAllocatedSize / 1024.0 / 1024.0);
-#endif
-    }
-
-    return true;
-}
-
-void ggml_metal_set_tensor(
-        struct ggml_metal_context * ctx,
-        struct ggml_tensor * t) {
-    size_t offs;
-    id<MTLBuffer> id_dst = ggml_metal_get_buffer(ctx, t, &offs);
-
-    memcpy((void *) ((uint8_t *) id_dst.contents + offs), t->data, ggml_nbytes(t));
-}
-
-void ggml_metal_get_tensor(
-        struct ggml_metal_context * ctx,
-        struct ggml_tensor * t) {
-    size_t offs;
-    id<MTLBuffer> id_src = ggml_metal_get_buffer(ctx, t, &offs);
-
-    memcpy(t->data, (void *) ((uint8_t *) id_src.contents + offs), ggml_nbytes(t));
-}
-
-void ggml_metal_graph_find_concurrency(
-        struct ggml_metal_context * ctx,
-        struct ggml_cgraph * gf, bool check_mem) {
-    int search_depth = gf->n_nodes; //we only find concurrency in this range to avoid wasting too much time
-    int nodes_unused[GGML_MAX_CONCUR];
-
-    for (int i = 0; i < GGML_MAX_CONCUR; i++) { ctx->concur_list[i] = 0; }
-    for (int i = 0; i < gf->n_nodes;     i++) { nodes_unused[i]     = 1; }
-    ctx->concur_list_len = 0;
-
-    int n_left    = gf->n_nodes;
-    int n_start   = 0; // all nodes before n_start at nodes_unused array have been sorted and store back to ctx->concur_list
-    int level_pos = 0; // at ctx->concur_list, the last layer (level) ends at level_pos
-
-    while (n_left > 0) {
-        // number of nodes at a layer (that can be issued concurrently)
-        int concurrency = 0;
-        for (int i = n_start; i < ((n_start + search_depth > gf->n_nodes) ? gf->n_nodes : n_start + search_depth); i++) {
-            if (nodes_unused[i]) {
-                // if the requirements for gf->nodes[i] are satisfied
-                int exe_flag = 1;
-
-                // scan all srcs
-                for (int src_ind = 0; src_ind < GGML_MAX_SRC; src_ind++) {
-                    struct ggml_tensor * src_cur = gf->nodes[i]->src[src_ind];
-                    if (src_cur) {
-                        // if is leaf nodes it's satisfied.
-                        // TODO: ggml_is_leaf()
-                        if (src_cur->op == GGML_OP_NONE && src_cur->grad == NULL) {
-                            continue;
-                        }
-
-                        // otherwise this src should be the output from previous nodes.
-                        int is_found = 0;
-
-                        // scan 2*search_depth back because we inserted barrier.
-                        //for (int j = ((level_pos - 2*search_depth) < 0 ? 0 : (level_pos - 2*search_depth)); j < level_pos; j++) {
-                        for (int j = MAX(0, level_pos - 2*search_depth); j < level_pos; j++) {
-                            if (ctx->concur_list[j] >= 0 && gf->nodes[ctx->concur_list[j]] == src_cur) {
-                                is_found = 1;
-                                break;
-                            }
-                        }
-                        if (is_found == 0) {
-                            exe_flag = 0;
-                            break;
-                        }
-                    }
-                }
-                if (exe_flag && check_mem) {
-                    // check if nodes[i]'s data will be overwritten by a node before nodes[i].
-                    // if node[5] and node[3] write to the same memory region, then we can't issue node[5] before node[3]
-                    int64_t data_start = (int64_t) gf->nodes[i]->data;
-                    int64_t length     = (int64_t) ggml_nbytes(gf->nodes[i]);
-                    for (int j = n_start; j < i; j++) {
-                        if (nodes_unused[j] && gf->nodes[j]->op != GGML_OP_RESHAPE \
-                                            && gf->nodes[j]->op != GGML_OP_VIEW \
-                                            && gf->nodes[j]->op != GGML_OP_TRANSPOSE \
-                                            && gf->nodes[j]->op != GGML_OP_PERMUTE) {
-                            if (((int64_t)gf->nodes[j]->data) >= data_start + length || \
-                                ((int64_t)gf->nodes[j]->data) + (int64_t) ggml_nbytes(gf->nodes[j]) <= data_start) {
-                                continue;
-                            }
-
-                            exe_flag = 0;
-                        }
-                    }
-                }
-                if (exe_flag) {
-                    ctx->concur_list[level_pos + concurrency] = i;
-                    nodes_unused[i] = 0;
-                    concurrency++;
-                    ctx->concur_list_len++;
-                }
-            }
-        }
-        n_left -= concurrency;
-        // adding a barrier different layer
-        ctx->concur_list[level_pos + concurrency] = -1;
-        ctx->concur_list_len++;
-        // jump all sorted nodes at nodes_bak
-        while (!nodes_unused[n_start]) {
-            n_start++;
-        }
-        level_pos += concurrency + 1;
-    }
-
-    if (ctx->concur_list_len > GGML_MAX_CONCUR) {
-        GGML_METAL_LOG_WARN("%s: too many elements for metal ctx->concur_list!\n", __func__);
-    }
-}
-
-static bool ggml_metal_supports_op(const struct ggml_tensor * op) {
+static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const struct ggml_tensor * op) {
     switch (op->op) {
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(op)) {
@@ -941,20 +715,30 @@ static bool ggml_metal_supports_op(const struct ggml_tensor * op) {
         case GGML_OP_SCALE:
         case GGML_OP_SQR:
         case GGML_OP_SUM_ROWS:
+            return true;
         case GGML_OP_SOFT_MAX:
         case GGML_OP_RMS_NORM:
         case GGML_OP_GROUP_NORM:
+            return ctx->support_simdgroup_reduction;
         case GGML_OP_NORM:
         case GGML_OP_ALIBI:
         case GGML_OP_ROPE:
         case GGML_OP_IM2COL:
+            return true;
+        case GGML_OP_POOL_1D:
+        case GGML_OP_POOL_2D:
+            return false;
         case GGML_OP_UPSCALE:
         case GGML_OP_PAD:
+        case GGML_OP_ARANGE:
+        case GGML_OP_TIMESTEP_EMBEDDING:
         case GGML_OP_ARGSORT:
         case GGML_OP_LEAKY_RELU:
+            return true;
         case GGML_OP_MUL_MAT:
         case GGML_OP_MUL_MAT_ID:
-            return true;
+            return ctx->support_simdgroup_reduction &&
+                (op->src[0]->type != GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F32);
         case GGML_OP_CPY:
         case GGML_OP_DUP:
         case GGML_OP_CONT:
@@ -967,6 +751,9 @@ static bool ggml_metal_supports_op(const struct ggml_tensor * op) {
                            case GGML_TYPE_Q8_0:
                            case GGML_TYPE_Q4_0:
                            case GGML_TYPE_Q4_1:
+                           case GGML_TYPE_Q5_0:
+                           case GGML_TYPE_Q5_1:
+                           case GGML_TYPE_IQ4_NL:
                                 return true;
                            default:
                                 return false;
@@ -992,1410 +779,296 @@ static bool ggml_metal_supports_op(const struct ggml_tensor * op) {
             return false;
     }
 }
-bool ggml_metal_graph_compute(
+
+static enum ggml_status ggml_metal_graph_compute(
         struct ggml_metal_context * ctx,
                struct ggml_cgraph * gf) {
+
     @autoreleasepool {
-
-    // if there is ctx->concur_list, dispatch concurrently
-    // else fallback to serial dispatch
     MTLComputePassDescriptor * edesc = MTLComputePassDescriptor.computePassDescriptor;
-
-    const bool has_concur = ctx->concur_list_len && ctx->concur_list_len <= GGML_MAX_CONCUR;
-
-    const int n_nodes  = has_concur ? ctx->concur_list_len      : gf->n_nodes;
-    edesc.dispatchType = has_concur ? MTLDispatchTypeConcurrent : MTLDispatchTypeSerial;
+    edesc.dispatchType = MTLDispatchTypeSerial;
 
     // create multiple command buffers and enqueue them
     // then, we encode the graph into the command buffers in parallel
 
+    const int n_nodes  = gf->n_nodes;
     const int n_cb = ctx->n_cb;
+    const int n_nodes_per_cb = (n_nodes + n_cb - 1) / n_cb;
 
-    for (int i = 0; i < n_cb; ++i) {
-        ctx->command_buffers[i] = [ctx->queue commandBuffer];
+    const bool should_capture = ctx->should_capture_next_compute;
+    if (should_capture) {
+        ctx->should_capture_next_compute = false;
 
-        // enqueue the command buffers in order to specify their execution order
-        [ctx->command_buffers[i] enqueue];
+        MTLCaptureDescriptor * descriptor = [MTLCaptureDescriptor new];
+        descriptor.captureObject = ctx->queue;
 
-        ctx->command_encoders[i] = [ctx->command_buffers[i] computeCommandEncoderWithDescriptor: edesc];
+        NSError * error = nil;
+        if (![[MTLCaptureManager sharedCaptureManager] startCaptureWithDescriptor:descriptor error:&error]) {
+            GGML_METAL_LOG_ERROR("%s: error: unable to start capture '%s'\n", __func__, [[error localizedDescription] UTF8String]);
+            GGML_ASSERT(!"capture failed");
+        }
     }
 
+    id<MTLCommandBuffer> command_buffer_builder[n_cb];
     for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) {
-        const int n_nodes_per_cb = (n_nodes + n_cb - 1) / n_cb;
+        id<MTLCommandBuffer> command_buffer  = [ctx->queue commandBufferWithUnretainedReferences];
+        command_buffer_builder[cb_idx] = command_buffer;
 
-        dispatch_async(ctx->d_queue, ^{
-            size_t offs_src0 = 0;
-            size_t offs_src1 = 0;
-            size_t offs_dst  = 0;
+        // enqueue the command buffers in order to specify their execution order
+        [command_buffer enqueue];
+    }
 
-            id<MTLCommandBuffer> command_buffer  = ctx->command_buffers[cb_idx];
-            id<MTLComputeCommandEncoder> encoder = ctx->command_encoders[cb_idx];
+    const id<MTLCommandBuffer> *command_buffers = command_buffer_builder;
 
-            const int node_start =                                      (cb_idx + 0) * n_nodes_per_cb;
-            const int node_end   = MIN((cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb, n_nodes);
+    dispatch_apply(n_cb, ctx->d_queue, ^(size_t iter) {
+        const int cb_idx = iter;
 
-            for (int ind = node_start; ind < node_end; ++ind) {
-                const int i = has_concur ? ctx->concur_list[ind] : ind;
+        size_t offs_src0 = 0;
+        size_t offs_src1 = 0;
+        size_t offs_src2 = 0;
+        size_t offs_dst  = 0;
 
-                if (i == -1) {
-                    [encoder memoryBarrierWithScope:MTLBarrierScopeBuffers];
-                    continue;
-                }
+        id<MTLCommandBuffer> command_buffer  = command_buffers[cb_idx];
+        id<MTLComputeCommandEncoder> encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
 
-                //GGML_METAL_LOG_INFO("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op));
+        const int node_start =                                      (cb_idx + 0) * n_nodes_per_cb;
+        const int node_end   = MIN((cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb, n_nodes);
 
-                struct ggml_tensor * src0 = gf->nodes[i]->src[0];
-                struct ggml_tensor * src1 = gf->nodes[i]->src[1];
-                struct ggml_tensor * dst  = gf->nodes[i];
+        for (int i = node_start; i < node_end; ++i) {
+            if (i == -1) {
+                [encoder memoryBarrierWithScope:MTLBarrierScopeBuffers];
+                continue;
+            }
 
-                switch (dst->op) {
-                    case GGML_OP_NONE:
-                    case GGML_OP_RESHAPE:
-                    case GGML_OP_VIEW:
-                    case GGML_OP_TRANSPOSE:
-                    case GGML_OP_PERMUTE:
-                        {
-                            // noop -> next node
-                        } continue;
-                    default:
-                        {
-                        } break;
-                }
+            //GGML_METAL_LOG_INFO("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op));
 
-                if (!ggml_metal_supports_op(dst)) {
-                    GGML_METAL_LOG_ERROR("%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst));
-                    GGML_ASSERT(!"unsupported op");
-                }
+            struct ggml_tensor * src0 = gf->nodes[i]->src[0];
+            struct ggml_tensor * src1 = gf->nodes[i]->src[1];
+            struct ggml_tensor * src2 = gf->nodes[i]->src[2];
+            struct ggml_tensor * dst  = gf->nodes[i];
 
-                const int64_t  ne00 = src0 ? src0->ne[0] : 0;
-                const int64_t  ne01 = src0 ? src0->ne[1] : 0;
-                const int64_t  ne02 = src0 ? src0->ne[2] : 0;
-                const int64_t  ne03 = src0 ? src0->ne[3] : 0;
+            if (ggml_is_empty(dst)) {
+                continue;
+            }
 
-                const uint64_t nb00 = src0 ? src0->nb[0] : 0;
-                const uint64_t nb01 = src0 ? src0->nb[1] : 0;
-                const uint64_t nb02 = src0 ? src0->nb[2] : 0;
-                const uint64_t nb03 = src0 ? src0->nb[3] : 0;
+            switch (dst->op) {
+                case GGML_OP_NONE:
+                case GGML_OP_RESHAPE:
+                case GGML_OP_VIEW:
+                case GGML_OP_TRANSPOSE:
+                case GGML_OP_PERMUTE:
+                    {
+                        // noop -> next node
+                    } continue;
+                default:
+                    {
+                    } break;
+            }
 
-                const int64_t  ne10 = src1 ? src1->ne[0] : 0;
-                const int64_t  ne11 = src1 ? src1->ne[1] : 0;
-                const int64_t  ne12 = src1 ? src1->ne[2] : 0;
-                const int64_t  ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13);
+            if (!ggml_metal_supports_op(ctx, dst)) {
+                GGML_METAL_LOG_ERROR("%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst));
+                GGML_ASSERT(!"unsupported op");
+            }
 
-                const uint64_t nb10 = src1 ? src1->nb[0] : 0;
-                const uint64_t nb11 = src1 ? src1->nb[1] : 0;
-                const uint64_t nb12 = src1 ? src1->nb[2] : 0;
-                const uint64_t nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13);
+            if (should_capture) {
+                [encoder pushDebugGroup:[NSString stringWithCString:ggml_op_desc(dst) encoding:NSUTF8StringEncoding]];
+            }
 
-                const int64_t  ne0  = dst ? dst->ne[0] : 0;
-                const int64_t  ne1  = dst ? dst->ne[1] : 0;
-                const int64_t  ne2  = dst ? dst->ne[2] : 0;
-                const int64_t  ne3  = dst ? dst->ne[3] : 0;
+            const int64_t  ne00 = src0 ? src0->ne[0] : 0;
+            const int64_t  ne01 = src0 ? src0->ne[1] : 0;
+            const int64_t  ne02 = src0 ? src0->ne[2] : 0;
+            const int64_t  ne03 = src0 ? src0->ne[3] : 0;
 
-                const uint64_t nb0  = dst ? dst->nb[0] : 0;
-                const uint64_t nb1  = dst ? dst->nb[1] : 0;
-                const uint64_t nb2  = dst ? dst->nb[2] : 0;
-                const uint64_t nb3  = dst ? dst->nb[3] : 0;
+            const uint64_t nb00 = src0 ? src0->nb[0] : 0;
+            const uint64_t nb01 = src0 ? src0->nb[1] : 0;
+            const uint64_t nb02 = src0 ? src0->nb[2] : 0;
+            const uint64_t nb03 = src0 ? src0->nb[3] : 0;
 
-                const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT;
-                const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT;
-                const enum ggml_type dstt  = dst  ? dst->type  : GGML_TYPE_COUNT;
+            const int64_t  ne10 = src1 ? src1->ne[0] : 0;
+            const int64_t  ne11 = src1 ? src1->ne[1] : 0;
+            const int64_t  ne12 = src1 ? src1->ne[2] : 0;
+            const int64_t  ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13);
 
-                id<MTLBuffer> id_src0 = src0 ? ggml_metal_get_buffer(ctx, src0, &offs_src0) : nil;
-                id<MTLBuffer> id_src1 = src1 ? ggml_metal_get_buffer(ctx, src1, &offs_src1) : nil;
-                id<MTLBuffer> id_dst  = dst  ? ggml_metal_get_buffer(ctx, dst,  &offs_dst)  : nil;
+            const uint64_t nb10 = src1 ? src1->nb[0] : 0;
+            const uint64_t nb11 = src1 ? src1->nb[1] : 0;
+            const uint64_t nb12 = src1 ? src1->nb[2] : 0;
+            const uint64_t nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13);
 
-                //GGML_METAL_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op));
-                //if (src0) {
-                //    GGML_METAL_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02,
-                //            ggml_is_contiguous(src0), src0->name);
-                //}
-                //if (src1) {
-                //    GGML_METAL_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12,
-                //            ggml_is_contiguous(src1), src1->name);
-                //}
-                //if (dst) {
-                //    GGML_METAL_LOG_INFO("%s: dst  - %4s [%5lld, %5lld, %5lld], 1, %s\n",  __func__, ggml_type_name(dstt),  ne0,  ne1,  ne2,
-                //            dst->name);
-                //}
+            const int64_t  ne0  = dst ? dst->ne[0] : 0;
+            const int64_t  ne1  = dst ? dst->ne[1] : 0;
+            const int64_t  ne2  = dst ? dst->ne[2] : 0;
+            const int64_t  ne3  = dst ? dst->ne[3] : 0;
 
-                switch (dst->op) {
-                    case GGML_OP_CONCAT:
-                        {
-                            const int64_t nb = ne00;
+            const uint64_t nb0  = dst ? dst->nb[0] : 0;
+            const uint64_t nb1  = dst ? dst->nb[1] : 0;
+            const uint64_t nb2  = dst ? dst->nb[2] : 0;
+            const uint64_t nb3  = dst ? dst->nb[3] : 0;
 
-                            [encoder setComputePipelineState:ctx->pipeline_concat];
-                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                            [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
-                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
-                            [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
-                            [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
-                            [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
-                            [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6];
-                            [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7];
-                            [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8];
-                            [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9];
-                            [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10];
-                            [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11];
-                            [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12];
-                            [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13];
-                            [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14];
-                            [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15];
-                            [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16];
-                            [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17];
-                            [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18];
-                            [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:19];
-                            [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:20];
-                            [encoder setBytes:&ne2  length:sizeof(ne2)  atIndex:21];
-                            [encoder setBytes:&ne3  length:sizeof(ne3)  atIndex:22];
-                            [encoder setBytes:&nb0  length:sizeof(nb0)  atIndex:23];
-                            [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:24];
-                            [encoder setBytes:&nb2  length:sizeof(nb2)  atIndex:25];
-                            [encoder setBytes:&nb3  length:sizeof(nb3)  atIndex:26];
-                            [encoder setBytes:&nb   length:sizeof(nb)   atIndex:27];
+            const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT;
+            const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT;
+            const enum ggml_type dstt  = dst  ? dst->type  : GGML_TYPE_COUNT;
 
-                            const int nth = MIN(1024, ne0);
+            id<MTLBuffer> id_src0 = src0 ? ggml_metal_get_buffer(src0, &offs_src0) : nil;
+            id<MTLBuffer> id_src1 = src1 ? ggml_metal_get_buffer(src1, &offs_src1) : nil;
+            id<MTLBuffer> id_src2 = src2 ? ggml_metal_get_buffer(src2, &offs_src2) : nil;
+            id<MTLBuffer> id_dst  = dst  ? ggml_metal_get_buffer(dst,  &offs_dst)  : nil;
 
-                            [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
-                        } break;
-                    case GGML_OP_ADD:
-                    case GGML_OP_MUL:
-                    case GGML_OP_DIV:
-                        {
-                            const size_t offs = 0;
+            //GGML_METAL_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op));
+            //if (src0) {
+            //    GGML_METAL_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02,
+            //            ggml_is_contiguous(src0), src0->name);
+            //}
+            //if (src1) {
+            //    GGML_METAL_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12,
+            //            ggml_is_contiguous(src1), src1->name);
+            //}
+            //if (dst) {
+            //    GGML_METAL_LOG_INFO("%s: dst  - %4s [%5lld, %5lld, %5lld], 1, %s\n",  __func__, ggml_type_name(dstt),  ne0,  ne1,  ne2,
+            //            dst->name);
+            //}
 
-                            bool bcast_row = false;
+            switch (dst->op) {
+                case GGML_OP_CONCAT:
+                    {
+                        const int64_t nb = ne00;
 
-                            int64_t nb = ne00;
+                        id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].pipeline;
 
-                            id<MTLComputePipelineState> pipeline = nil;
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                        [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
+                        [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
+                        [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
+                        [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
+                        [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
+                        [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6];
+                        [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7];
+                        [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8];
+                        [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9];
+                        [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10];
+                        [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11];
+                        [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12];
+                        [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13];
+                        [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14];
+                        [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15];
+                        [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16];
+                        [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17];
+                        [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18];
+                        [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:19];
+                        [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:20];
+                        [encoder setBytes:&ne2  length:sizeof(ne2)  atIndex:21];
+                        [encoder setBytes:&ne3  length:sizeof(ne3)  atIndex:22];
+                        [encoder setBytes:&nb0  length:sizeof(nb0)  atIndex:23];
+                        [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:24];
+                        [encoder setBytes:&nb2  length:sizeof(nb2)  atIndex:25];
+                        [encoder setBytes:&nb3  length:sizeof(nb3)  atIndex:26];
+                        [encoder setBytes:&nb   length:sizeof(nb)   atIndex:27];
 
-                            if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) {
-                                GGML_ASSERT(ggml_is_contiguous(src0));
+                        const int nth = MIN(1024, ne0);
 
-                                // src1 is a row
-                                GGML_ASSERT(ne11 == 1);
+                        [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                    } break;
+                case GGML_OP_ADD:
+                case GGML_OP_MUL:
+                case GGML_OP_DIV:
+                    {
+                        const size_t offs = 0;
 
-                                nb = ne00 / 4;
-                                switch (dst->op) {
-                                    case GGML_OP_ADD: pipeline = ctx->pipeline_add_row; break;
-                                    case GGML_OP_MUL: pipeline = ctx->pipeline_mul_row; break;
-                                    case GGML_OP_DIV: pipeline = ctx->pipeline_div_row; break;
-                                    default: GGML_ASSERT(false);
-                                }
+                        bool bcast_row = false;
 
-                                bcast_row = true;
-                            } else {
-                                switch (dst->op) {
-                                    case GGML_OP_ADD: pipeline = ctx->pipeline_add; break;
-                                    case GGML_OP_MUL: pipeline = ctx->pipeline_mul; break;
-                                    case GGML_OP_DIV: pipeline = ctx->pipeline_div; break;
-                                    default: GGML_ASSERT(false);
-                                }
+                        int64_t nb = ne00;
+
+                        id<MTLComputePipelineState> pipeline = nil;
+
+                        if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) {
+                            GGML_ASSERT(ggml_is_contiguous(src0));
+
+                            // src1 is a row
+                            GGML_ASSERT(ne11 == 1);
+
+                            nb = ne00 / 4;
+                            switch (dst->op) {
+                                case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW].pipeline; break;
+                                case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_ROW].pipeline; break;
+                                case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV_ROW].pipeline; break;
+                                default: GGML_ASSERT(false);
                             }
 
+                            bcast_row = true;
+                        } else {
+                            switch (dst->op) {
+                                case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; break;
+                                case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL].pipeline; break;
+                                case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV].pipeline; break;
+                                default: GGML_ASSERT(false);
+                            }
+                        }
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                        [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
+                        [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
+                        [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
+                        [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
+                        [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
+                        [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6];
+                        [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7];
+                        [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8];
+                        [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9];
+                        [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10];
+                        [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11];
+                        [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12];
+                        [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13];
+                        [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14];
+                        [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15];
+                        [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16];
+                        [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17];
+                        [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18];
+                        [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:19];
+                        [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:20];
+                        [encoder setBytes:&ne2  length:sizeof(ne2)  atIndex:21];
+                        [encoder setBytes:&ne3  length:sizeof(ne3)  atIndex:22];
+                        [encoder setBytes:&nb0  length:sizeof(nb0)  atIndex:23];
+                        [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:24];
+                        [encoder setBytes:&nb2  length:sizeof(nb2)  atIndex:25];
+                        [encoder setBytes:&nb3  length:sizeof(nb3)  atIndex:26];
+                        [encoder setBytes:&offs length:sizeof(offs) atIndex:27];
+                        [encoder setBytes:&nb   length:sizeof(nb)   atIndex:28];
+
+                        if (bcast_row) {
+                            const int64_t n = ggml_nelements(dst)/4;
+
+                            [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                        } else {
+                            const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0);
+
+                            [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                        }
+                    } break;
+                case GGML_OP_ACC:
+                    {
+                        GGML_ASSERT(src0t == GGML_TYPE_F32);
+                        GGML_ASSERT(src1t == GGML_TYPE_F32);
+                        GGML_ASSERT(dstt  == GGML_TYPE_F32);
+
+                        GGML_ASSERT(ggml_is_contiguous(src0));
+                        GGML_ASSERT(ggml_is_contiguous(src1));
+
+                        const size_t pnb1 = ((int32_t *) dst->op_params)[0];
+                        const size_t pnb2 = ((int32_t *) dst->op_params)[1];
+                        const size_t pnb3 = ((int32_t *) dst->op_params)[2];
+                        const size_t offs = ((int32_t *) dst->op_params)[3];
+
+                        const bool inplace = (bool) ((int32_t *) dst->op_params)[4];
+
+                        if (!inplace) {
+                            // run a separete kernel to cpy src->dst
+                            // not sure how to avoid this
+                            // TODO: make a simpler cpy_bytes kernel
+
+                            const id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline;
+
                             [encoder setComputePipelineState:pipeline];
-                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                            [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
-                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
-                            [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
-                            [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
-                            [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
-                            [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6];
-                            [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7];
-                            [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8];
-                            [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9];
-                            [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10];
-                            [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11];
-                            [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12];
-                            [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13];
-                            [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14];
-                            [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15];
-                            [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16];
-                            [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17];
-                            [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18];
-                            [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:19];
-                            [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:20];
-                            [encoder setBytes:&ne2  length:sizeof(ne2)  atIndex:21];
-                            [encoder setBytes:&ne3  length:sizeof(ne3)  atIndex:22];
-                            [encoder setBytes:&nb0  length:sizeof(nb0)  atIndex:23];
-                            [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:24];
-                            [encoder setBytes:&nb2  length:sizeof(nb2)  atIndex:25];
-                            [encoder setBytes:&nb3  length:sizeof(nb3)  atIndex:26];
-                            [encoder setBytes:&offs length:sizeof(offs) atIndex:27];
-                            [encoder setBytes:&nb   length:sizeof(nb)   atIndex:28];
-
-                            if (bcast_row) {
-                                const int64_t n = ggml_nelements(dst)/4;
-
-                                [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
-                            } else {
-                                const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0);
-
-                                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
-                            }
-                        } break;
-                    case GGML_OP_ACC:
-                        {
-                            GGML_ASSERT(src0t == GGML_TYPE_F32);
-                            GGML_ASSERT(src1t == GGML_TYPE_F32);
-                            GGML_ASSERT(dstt  == GGML_TYPE_F32);
-
-                            GGML_ASSERT(ggml_is_contiguous(src0));
-                            GGML_ASSERT(ggml_is_contiguous(src1));
-
-                            const size_t pnb1 = ((int32_t *) dst->op_params)[0];
-                            const size_t pnb2 = ((int32_t *) dst->op_params)[1];
-                            const size_t pnb3 = ((int32_t *) dst->op_params)[2];
-                            const size_t offs = ((int32_t *) dst->op_params)[3];
-
-                            const bool inplace = (bool) ((int32_t *) dst->op_params)[4];
-
-                            if (!inplace) {
-                                // run a separete kernel to cpy src->dst
-                                // not sure how to avoid this
-                                // TODO: make a simpler cpy_bytes kernel
-
-                                const int nth = MIN((int) ctx->pipeline_cpy_f32_f32.maxTotalThreadsPerThreadgroup, ne00);
-
-                                [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f32];
-                                [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
-                                [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
-                                [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
-                                [encoder setBytes:&ne01    length:sizeof( int64_t) atIndex:3];
-                                [encoder setBytes:&ne02    length:sizeof( int64_t) atIndex:4];
-                                [encoder setBytes:&ne03    length:sizeof( int64_t) atIndex:5];
-                                [encoder setBytes:&nb00    length:sizeof(uint64_t) atIndex:6];
-                                [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:7];
-                                [encoder setBytes:&nb02    length:sizeof(uint64_t) atIndex:8];
-                                [encoder setBytes:&nb03    length:sizeof(uint64_t) atIndex:9];
-                                [encoder setBytes:&ne0     length:sizeof( int64_t) atIndex:10];
-                                [encoder setBytes:&ne1     length:sizeof( int64_t) atIndex:11];
-                                [encoder setBytes:&ne2     length:sizeof( int64_t) atIndex:12];
-                                [encoder setBytes:&ne3     length:sizeof( int64_t) atIndex:13];
-                                [encoder setBytes:&nb0     length:sizeof(uint64_t) atIndex:14];
-                                [encoder setBytes:&nb1     length:sizeof(uint64_t) atIndex:15];
-                                [encoder setBytes:&nb2     length:sizeof(uint64_t) atIndex:16];
-                                [encoder setBytes:&nb3     length:sizeof(uint64_t) atIndex:17];
-
-                                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
-                            }
-
-                            [encoder setComputePipelineState:ctx->pipeline_add];
-                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                            [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
-                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
-                            [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
-                            [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
-                            [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
-                            [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6];
-                            [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7];
-                            [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:8];
-                            [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:9];
-                            [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:10];
-                            [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11];
-                            [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12];
-                            [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13];
-                            [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14];
-                            [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15];
-                            [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16];
-                            [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17];
-                            [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18];
-                            [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:19];
-                            [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:20];
-                            [encoder setBytes:&ne2  length:sizeof(ne2)  atIndex:21];
-                            [encoder setBytes:&ne3  length:sizeof(ne3)  atIndex:22];
-                            [encoder setBytes:&nb0  length:sizeof(nb0)  atIndex:23];
-                            [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:24];
-                            [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:25];
-                            [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:26];
-                            [encoder setBytes:&offs length:sizeof(offs) atIndex:27];
-
-                            const int nth = MIN((int) ctx->pipeline_add.maxTotalThreadsPerThreadgroup, ne00);
-
-                            [encoder dispatchThreadgroups:MTLSizeMake(ne11, ne12, ne13) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
-                        } break;
-                    case GGML_OP_SCALE:
-                        {
-                            GGML_ASSERT(ggml_is_contiguous(src0));
-
-                            const float scale = *(const float *) dst->op_params;
-
-                            int64_t n = ggml_nelements(dst);
-
-                            if (n % 4 == 0) {
-                                n /= 4;
-                                [encoder setComputePipelineState:ctx->pipeline_scale_4];
-                            } else {
-                                [encoder setComputePipelineState:ctx->pipeline_scale];
-                            }
-
-                            [encoder setBuffer:id_src0   offset:offs_src0 atIndex:0];
-                            [encoder setBuffer:id_dst    offset:offs_dst  atIndex:1];
-                            [encoder setBytes:&scale length:sizeof(scale) atIndex:2];
-
-                            [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
-                        } break;
-                    case GGML_OP_UNARY:
-                        switch (ggml_get_unary_op(gf->nodes[i])) {
-                            case GGML_UNARY_OP_TANH:
-                                {
-                                    [encoder setComputePipelineState:ctx->pipeline_tanh];
-                                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
-
-                                    const int64_t n = ggml_nelements(dst);
-
-                                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
-                                } break;
-                            case GGML_UNARY_OP_RELU:
-                                {
-                                    [encoder setComputePipelineState:ctx->pipeline_relu];
-                                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
-
-                                    const int64_t n = ggml_nelements(dst);
-
-                                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
-                                } break;
-                            case GGML_UNARY_OP_GELU:
-                                {
-                                    [encoder setComputePipelineState:ctx->pipeline_gelu];
-                                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
-
-                                    const int64_t n = ggml_nelements(dst);
-                                    GGML_ASSERT(n % 4 == 0);
-
-                                    [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
-                                } break;
-                            case GGML_UNARY_OP_GELU_QUICK:
-                                {
-                                    [encoder setComputePipelineState:ctx->pipeline_gelu_quick];
-                                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
-
-                                    const int64_t n = ggml_nelements(dst);
-                                    GGML_ASSERT(n % 4 == 0);
-
-                                    [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
-                                } break;
-                            case GGML_UNARY_OP_SILU:
-                                {
-                                    [encoder setComputePipelineState:ctx->pipeline_silu];
-                                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
-
-                                    const int64_t n = ggml_nelements(dst);
-                                    GGML_ASSERT(n % 4 == 0);
-
-                                    [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
-                                } break;
-                            default:
-                                {
-                                    GGML_METAL_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
-                                    GGML_ASSERT(false);
-                                }
-                        } break;
-                    case GGML_OP_SQR:
-                        {
-                            GGML_ASSERT(ggml_is_contiguous(src0));
-
-                            [encoder setComputePipelineState:ctx->pipeline_sqr];
-                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                            [encoder setBuffer:id_dst  offset:offs_dst atIndex:1];
-
-                            const int64_t n = ggml_nelements(dst);
-                            [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
-                        } break;
-                    case GGML_OP_SUM_ROWS:
-                        {
-                            GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
-
-                            [encoder setComputePipelineState:ctx->pipeline_sum_rows];
-                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
-                            [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
-                            [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
-                            [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
-                            [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5];
-                            [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
-                            [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
-                            [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
-                            [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9];
-                            [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10];
-                            [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11];
-                            [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12];
-                            [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13];
-                            [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14];
-                            [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15];
-                            [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16];
-                            [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:17];
-                            [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:18];
-                            [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:19];
-                            [encoder setBytes:&ne2  length:sizeof(ne2)  atIndex:20];
-                            [encoder setBytes:&ne3  length:sizeof(ne3)  atIndex:21];
-                            [encoder setBytes:&nb0  length:sizeof(nb0)  atIndex:22];
-                            [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:23];
-                            [encoder setBytes:&nb2  length:sizeof(nb2)  atIndex:24];
-                            [encoder setBytes:&nb3  length:sizeof(nb3)  atIndex:25];
-
-                            [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
-                        } break;
-                    case GGML_OP_SOFT_MAX:
-                        {
-                            int nth = 32; // SIMD width
-
-                            if (ne00%4 == 0) {
-                                while (nth < ne00/4 && nth < 256) {
-                                    nth *= 2;
-                                }
-                                [encoder setComputePipelineState:ctx->pipeline_soft_max_4];
-                            } else {
-                                while (nth < ne00 && nth < 1024) {
-                                    nth *= 2;
-                                }
-                                [encoder setComputePipelineState:ctx->pipeline_soft_max];
-                            }
-
-                            const float scale = ((float *) dst->op_params)[0];
-
-                            [encoder setBuffer:id_src0 offset:offs_src0   atIndex:0];
-                            if (id_src1) {
-                                [encoder setBuffer:id_src1 offset:offs_src1   atIndex:1];
-                            } else {
-                                [encoder setBuffer:id_src0 offset:offs_src0   atIndex:1];
-                            }
-                            [encoder setBuffer:id_dst  offset:offs_dst    atIndex:2];
-                            [encoder setBytes:&ne00  length:sizeof(ne00)  atIndex:3];
-                            [encoder setBytes:&ne01  length:sizeof(ne01)  atIndex:4];
-                            [encoder setBytes:&ne02  length:sizeof(ne02)  atIndex:5];
-                            [encoder setBytes:&scale length:sizeof(scale) atIndex:6];
-                            [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];
-
-                            [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
-                        } break;
-                    case GGML_OP_DIAG_MASK_INF:
-                        {
-                            const int n_past = ((int32_t *)(dst->op_params))[0];
-
-                            if (ne00%8 == 0) {
-                                [encoder setComputePipelineState:ctx->pipeline_diag_mask_inf_8];
-                            } else {
-                                [encoder setComputePipelineState:ctx->pipeline_diag_mask_inf];
-                            }
-                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
-                            [encoder setBytes:&ne00   length:sizeof(ne00) atIndex:2];
-                            [encoder setBytes:&ne01   length:sizeof(ne01) atIndex:3];
-                            [encoder setBytes:&n_past length:sizeof(int)  atIndex:4];
-
-                            if (ne00%8 == 0) {
-                                [encoder dispatchThreadgroups:MTLSizeMake(ne00*ne01*ne02/8, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
-                            }
-                            else {
-                                [encoder dispatchThreadgroups:MTLSizeMake(ne00, ne01, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
-                            }
-                        } break;
-                    case GGML_OP_MUL_MAT:
-                        {
-                            GGML_ASSERT(ne00 == ne10);
-
-                            // TODO: assert that dim2 and dim3 are contiguous
-                            GGML_ASSERT(ne12 % ne02 == 0);
-                            GGML_ASSERT(ne13 % ne03 == 0);
-
-                            const uint r2 = ne12/ne02;
-                            const uint r3 = ne13/ne03;
-
-                            // find the break-even point where the matrix-matrix kernel becomes more efficient compared
-                            // to the matrix-vector kernel
-                            int ne11_mm_min = 1;
-
-#if 0
-                            // the numbers below are measured on M2 Ultra for 7B and 13B models
-                            // these numbers do not translate to other devices or model sizes
-                            // TODO: need to find a better approach
-                            if ([ctx->device.name isEqualToString:@"Apple M2 Ultra"]) {
-                                switch (src0t) {
-                                    case GGML_TYPE_F16:  ne11_mm_min = 2;  break;
-                                    case GGML_TYPE_Q8_0: ne11_mm_min = 7;  break;
-                                    case GGML_TYPE_Q2_K: ne11_mm_min = 15; break;
-                                    case GGML_TYPE_Q3_K: ne11_mm_min = 7;  break;
-                                    case GGML_TYPE_Q4_0:
-                                    case GGML_TYPE_Q4_1: ne11_mm_min = 15; break;
-                                    case GGML_TYPE_Q4_K: ne11_mm_min = 11; break;
-                                    case GGML_TYPE_Q5_0:                          // not tested yet
-                                    case GGML_TYPE_Q5_1: ne11_mm_min = 13; break; // not tested yet
-                                    case GGML_TYPE_Q5_K: ne11_mm_min = 7;  break;
-                                    case GGML_TYPE_Q6_K: ne11_mm_min = 7;  break;
-                                    default:             ne11_mm_min = 1;  break;
-                                }
-                            }
-#endif
-
-                            // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
-                            // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
-                            if ([ctx->device supportsFamily:MTLGPUFamilyApple7] &&
-                                !ggml_is_transposed(src0) &&
-                                !ggml_is_transposed(src1) &&
-                                src1t == GGML_TYPE_F32 &&
-                                ne00 % 32 == 0 && ne00 >= 64 &&
-                                (ne11 > ne11_mm_min || (ggml_is_quantized(src0t) && ne12 > 1))) {
-                                //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);
-                                switch (src0->type) {
-                                    case GGML_TYPE_F32:  [encoder setComputePipelineState:ctx->pipeline_mul_mm_f32_f32];  break;
-                                    case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_mul_mm_f16_f32];  break;
-                                    case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_0_f32]; break;
-                                    case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_1_f32]; break;
-                                    case GGML_TYPE_Q5_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_0_f32]; break;
-                                    case GGML_TYPE_Q5_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_1_f32]; break;
-                                    case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q8_0_f32]; break;
-                                    case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q2_K_f32]; break;
-                                    case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q3_K_f32]; break;
-                                    case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_K_f32]; break;
-                                    case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_K_f32]; break;
-                                    case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q6_K_f32]; break;
-                                    case GGML_TYPE_IQ2_XXS: [encoder setComputePipelineState:ctx->pipeline_mul_mm_iq2_xxs_f32]; break;
-                                    default: GGML_ASSERT(false && "MUL MAT-MAT not implemented");
-                                }
-                                [encoder setBuffer:id_src0 offset:offs_src0    atIndex:0];
-                                [encoder setBuffer:id_src1 offset:offs_src1    atIndex:1];
-                                [encoder setBuffer:id_dst  offset:offs_dst     atIndex:2];
-                                [encoder setBytes:&ne00    length:sizeof(ne00) atIndex:3];
-                                [encoder setBytes:&ne02    length:sizeof(ne02) atIndex:4];
-                                [encoder setBytes:&nb01    length:sizeof(nb01) atIndex:5];
-                                [encoder setBytes:&nb02    length:sizeof(nb02) atIndex:6];
-                                [encoder setBytes:&ne12    length:sizeof(ne12) atIndex:7];
-                                [encoder setBytes:&nb10    length:sizeof(nb10) atIndex:8];
-                                [encoder setBytes:&nb11    length:sizeof(nb11) atIndex:9];
-                                [encoder setBytes:&nb12    length:sizeof(nb12) atIndex:10];
-                                [encoder setBytes:&ne0     length:sizeof(ne0)  atIndex:11];
-                                [encoder setBytes:&ne1     length:sizeof(ne1)  atIndex:12];
-                                [encoder setBytes:&r2      length:sizeof(r2)   atIndex:13];
-                                [encoder setBytes:&r3      length:sizeof(r3)   atIndex:14];
-                                [encoder setThreadgroupMemoryLength:8192 atIndex:0];
-                                [encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne01 + 63)/64, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
-                            } else {
-                                int nth0 = 32;
-                                int nth1 = 1;
-                                int nrows = 1;
-                                //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);
-
-                                // use custom matrix x vector kernel
-                                switch (src0t) {
-                                    case GGML_TYPE_F32:
-                                        {
-                                            GGML_ASSERT(src1t == GGML_TYPE_F32);
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_f32_f32];
-                                            nrows = 4;
-                                        } break;
-                                    case GGML_TYPE_F16:
-                                        {
-                                            nth0 = 32;
-                                            nth1 = 1;
-                                            if (src1t == GGML_TYPE_F32) {
-                                                if (ne11 * ne12 < 4) {
-                                                    [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32_1row];
-                                                } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) {
-                                                    [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32_l4];
-                                                    nrows = ne11;
-                                                } else {
-                                                    [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32];
-                                                    nrows = 4;
-                                                }
-                                            } else {
-                                                [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f16];
-                                                nrows = 4;
-                                            }
-                                        } break;
-                                    case GGML_TYPE_Q4_0:
-                                        {
-                                            nth0 = 8;
-                                            nth1 = 8;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q4_0_f32];
-                                        } break;
-                                    case GGML_TYPE_Q4_1:
-                                        {
-                                            nth0 = 8;
-                                            nth1 = 8;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q4_1_f32];
-                                        } break;
-                                    case GGML_TYPE_Q5_0:
-                                        {
-                                            nth0 = 8;
-                                            nth1 = 8;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q5_0_f32];
-                                        } break;
-                                    case GGML_TYPE_Q5_1:
-                                        {
-                                            nth0 = 8;
-                                            nth1 = 8;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q5_1_f32];
-                                        } break;
-                                    case GGML_TYPE_Q8_0:
-                                        {
-                                            nth0 = 8;
-                                            nth1 = 8;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q8_0_f32];
-                                        } break;
-                                    case GGML_TYPE_Q2_K:
-                                        {
-                                            nth0 = 2;
-                                            nth1 = 32;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q2_K_f32];
-                                        } break;
-                                    case GGML_TYPE_Q3_K:
-                                        {
-                                            nth0 = 2;
-                                            nth1 = 32;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q3_K_f32];
-                                        } break;
-                                    case GGML_TYPE_Q4_K:
-                                        {
-                                            nth0 = 4; //1;
-                                            nth1 = 8; //32;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q4_K_f32];
-                                        } break;
-                                    case GGML_TYPE_Q5_K:
-                                        {
-                                            nth0 = 2;
-                                            nth1 = 32;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q5_K_f32];
-                                        } break;
-                                    case GGML_TYPE_Q6_K:
-                                        {
-                                            nth0 = 2;
-                                            nth1 = 32;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q6_K_f32];
-                                        } break;
-                                    case GGML_TYPE_IQ2_XXS:
-                                        {
-                                            nth0 = 4;
-                                            nth1 = 16;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_iq2_xxs_f32];
-                                        } break;
-                                    default:
-                                        {
-                                            GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t);
-                                            GGML_ASSERT(false && "not implemented");
-                                        }
-                                };
-
-                                if (ggml_is_quantized(src0t)) {
-                                    GGML_ASSERT(ne00 >= nth0*nth1);
-                                }
-
-                                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                                [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
-                                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
-                                [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
-                                [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
-                                [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
-                                [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
-                                [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
-                                [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
-                                [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:9];
-                                [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:10];
-                                [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:11];
-                                [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:12];
-                                [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:13];
-                                [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:14];
-                                [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:15];
-                                [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:16];
-                                [encoder setBytes:&r2   length:sizeof(r2)   atIndex:17];
-                                [encoder setBytes:&r3   length:sizeof(r3)   atIndex:18];
-
-                                if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 ||
-                                    src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 ||
-                                    //src0t == GGML_TYPE_IQ2_XXS ||
-                                    src0t == GGML_TYPE_Q2_K) { // || src0t == GGML_TYPE_Q4_K) {
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                                }
-                                else if (src0t == GGML_TYPE_IQ2_XXS) {
-                                    [encoder setThreadgroupMemoryLength:(256*8+128) atIndex:0];
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                                }
-                                else if (src0t == GGML_TYPE_Q4_K) {
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                                }
-                                else if (src0t == GGML_TYPE_Q3_K) {
-#ifdef GGML_QKK_64
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-#else
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-#endif
-                                }
-                                else if (src0t == GGML_TYPE_Q5_K) {
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                                }
-                                else if (src0t == GGML_TYPE_Q6_K) {
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                                } else {
-                                    const int64_t ny = (ne11 + nrows - 1)/nrows;
-                                    [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                                }
-                            }
-                        } break;
-                    case GGML_OP_MUL_MAT_ID:
-                        {
-                            //GGML_ASSERT(ne00 == ne10);
-                            //GGML_ASSERT(ne03 == ne13);
-
-                            GGML_ASSERT(src0t == GGML_TYPE_I32);
-
-                            const int n_as = ((int32_t *) dst->op_params)[1];
-
-                            // TODO: make this more general
-                            GGML_ASSERT(n_as <= 8);
-
-                            // max size of the src1ids array in the kernel stack
-                            GGML_ASSERT(ne11 <= 512);
-
-                            struct ggml_tensor * src2 = gf->nodes[i]->src[2];
-
-                            const int64_t  ne20 = src2 ? src2->ne[0] : 0;
-                            const int64_t  ne21 = src2 ? src2->ne[1] : 0;
-                            const int64_t  ne22 = src2 ? src2->ne[2] : 0;
-                            const int64_t  ne23 = src2 ? src2->ne[3] : 0; GGML_UNUSED(ne23);
-
-                            const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20);
-                            const uint64_t nb21 = src2 ? src2->nb[1] : 0;
-                            const uint64_t nb22 = src2 ? src2->nb[2] : 0;
-                            const uint64_t nb23 = src2 ? src2->nb[3] : 0; GGML_UNUSED(nb23);
-
-                            const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t);
-
-                            GGML_ASSERT(!ggml_is_transposed(src2));
-                            GGML_ASSERT(!ggml_is_transposed(src1));
-
-                            GGML_ASSERT(src1t == GGML_TYPE_F32);
-
-                            const uint r2 = ne12/ne22;
-                            const uint r3 = ne13/ne23;
-
-                            // find the break-even point where the matrix-matrix kernel becomes more efficient compared
-                            // to the matrix-vector kernel
-                            int ne11_mm_min = n_as;
-
-                            const int idx = ((int32_t *) dst->op_params)[0];
-
-                            // batch size
-                            GGML_ASSERT(ne01 == ne11);
-
-                            // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
-                            // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
-                            // !!!
-                            // TODO: for now, always use mat-vec kernels until we figure out how to improve the
-                            //       indirect matrix multiplication
-                            // !!!
-                            if ([ctx->device supportsFamily:MTLGPUFamilyApple7] &&
-                                ne20 % 32 == 0 && ne20 >= 64 &&
-                                ne11 > ne11_mm_min) {
-                                switch (src2->type) {
-                                    case GGML_TYPE_F32:  [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_f32_f32];  break;
-                                    case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_f16_f32];  break;
-                                    case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q4_0_f32]; break;
-                                    case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q4_1_f32]; break;
-                                    case GGML_TYPE_Q5_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q5_0_f32]; break;
-                                    case GGML_TYPE_Q5_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q5_1_f32]; break;
-                                    case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q8_0_f32]; break;
-                                    case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q2_K_f32]; break;
-                                    case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q3_K_f32]; break;
-                                    case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q4_K_f32]; break;
-                                    case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q5_K_f32]; break;
-                                    case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q6_K_f32]; break;
-                                    case GGML_TYPE_IQ2_XXS: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_iq2_xxs_f32]; break;
-                                    default: GGML_ASSERT(false && "MUL_MAT_ID not implemented");
-                                }
-                                [encoder setBuffer:id_src0 offset:offs_src0    atIndex:0];
-                                [encoder setBuffer:id_src1 offset:offs_src1    atIndex:1];
-                                [encoder setBuffer:id_dst  offset:offs_dst     atIndex:2];
-                                [encoder setBytes:&nb01    length:sizeof(nb01) atIndex:3];
-                                [encoder setBytes:&ne20    length:sizeof(ne20) atIndex:4];
-                                [encoder setBytes:&ne22    length:sizeof(ne22) atIndex:5];
-                                [encoder setBytes:&nb21    length:sizeof(nb21) atIndex:6];
-                                [encoder setBytes:&nb22    length:sizeof(nb22) atIndex:7];
-                                [encoder setBytes:&ne12    length:sizeof(ne12) atIndex:8];
-                                [encoder setBytes:&ne13    length:sizeof(ne13) atIndex:9];
-                                [encoder setBytes:&nb10    length:sizeof(nb10) atIndex:10];
-                                [encoder setBytes:&nb11    length:sizeof(nb11) atIndex:11];
-                                [encoder setBytes:&nb12    length:sizeof(nb12) atIndex:12];
-                                [encoder setBytes:&ne0     length:sizeof(ne0)  atIndex:13];
-                                [encoder setBytes:&ne1     length:sizeof(ne1)  atIndex:14];
-                                [encoder setBytes:&nb1     length:sizeof(nb1)  atIndex:15];
-                                [encoder setBytes:&r2      length:sizeof(r2)   atIndex:16];
-                                [encoder setBytes:&r3      length:sizeof(r3)   atIndex:17];
-                                [encoder setBytes:&idx     length:sizeof(idx)  atIndex:18];
-                                // TODO: how to make this an array? read Metal docs
-                                for (int j = 0; j < 8; ++j) {
-                                    // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8
-                                    struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)];
-
-                                    size_t offs_src_cur = 0;
-                                    id<MTLBuffer> id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur);
-
-                                    [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:19 + j];
-                                }
-
-                                [encoder setThreadgroupMemoryLength:8192 atIndex:0];
-
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne11 + 31)/32, (ne21 + 63)/64, n_as*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
-                            } else {
-                                int nth0 = 32;
-                                int nth1 = 1;
-                                int nrows = 1;
-                                //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);
-
-                                // use custom matrix x vector kernel
-                                switch (src2t) {
-                                    case GGML_TYPE_F32:
-                                        {
-                                            GGML_ASSERT(src1t == GGML_TYPE_F32);
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_f32_f32];
-                                        } break;
-                                    case GGML_TYPE_F16:
-                                        {
-                                            GGML_ASSERT(src1t == GGML_TYPE_F32);
-                                            nth0 = 32;
-                                            nth1 = 1;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_f16_f32];
-                                        } break;
-                                    case GGML_TYPE_Q4_0:
-                                        {
-                                            nth0 = 8;
-                                            nth1 = 8;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q4_0_f32];
-                                        } break;
-                                    case GGML_TYPE_Q4_1:
-                                        {
-                                            nth0 = 8;
-                                            nth1 = 8;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q4_1_f32];
-                                        } break;
-                                    case GGML_TYPE_Q5_0:
-                                        {
-                                            nth0 = 8;
-                                            nth1 = 8;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q5_0_f32];
-                                        } break;
-                                    case GGML_TYPE_Q5_1:
-                                        {
-                                            nth0 = 8;
-                                            nth1 = 8;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q5_1_f32];
-                                        } break;
-                                    case GGML_TYPE_Q8_0:
-                                        {
-                                            nth0 = 8;
-                                            nth1 = 8;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q8_0_f32];
-                                        } break;
-                                    case GGML_TYPE_Q2_K:
-                                        {
-                                            nth0 = 2;
-                                            nth1 = 32;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q2_K_f32];
-                                        } break;
-                                    case GGML_TYPE_Q3_K:
-                                        {
-                                            nth0 = 2;
-                                            nth1 = 32;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q3_K_f32];
-                                        } break;
-                                    case GGML_TYPE_Q4_K:
-                                        {
-                                            nth0 = 4; //1;
-                                            nth1 = 8; //32;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q4_K_f32];
-                                        } break;
-                                    case GGML_TYPE_Q5_K:
-                                        {
-                                            nth0 = 2;
-                                            nth1 = 32;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q5_K_f32];
-                                        } break;
-                                    case GGML_TYPE_Q6_K:
-                                        {
-                                            nth0 = 2;
-                                            nth1 = 32;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q6_K_f32];
-                                        } break;
-                                    case GGML_TYPE_IQ2_XXS:
-                                        {
-                                            nth0 = 4;
-                                            nth1 = 16;
-                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_id_iq2_xxs_f32];
-                                        } break;
-                                    default:
-                                        {
-                                            GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t);
-                                            GGML_ASSERT(false && "not implemented");
-                                        }
-                                };
-
-                                if (ggml_is_quantized(src2t)) {
-                                    GGML_ASSERT(ne20 >= nth0*nth1);
-                                }
-
-                                const int64_t _ne1 = 1; // kernels needs a reference in constant memory
-
-                                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                                [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
-                                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
-                                [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:3];
-                                [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4];
-                                [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5];
-                                [encoder setBytes:&ne22 length:sizeof(ne22) atIndex:6];
-                                [encoder setBytes:&nb20 length:sizeof(nb20) atIndex:7];
-                                [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:8];
-                                [encoder setBytes:&nb22 length:sizeof(nb22) atIndex:9];
-                                [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10];
-                                [encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:11];
-                                [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12];
-                                [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13];
-                                [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14];
-                                [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15];
-                                [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16];
-                                [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:17];
-                                [encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:18];
-                                [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:19];
-                                [encoder setBytes:&r2   length:sizeof(r2)   atIndex:20];
-                                [encoder setBytes:&r3   length:sizeof(r3)   atIndex:21];
-                                [encoder setBytes:&idx  length:sizeof(idx)  atIndex:22];
-                                // TODO: how to make this an array? read Metal docs
-                                for (int j = 0; j < 8; ++j) {
-                                    // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8
-                                    struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)];
-
-                                    size_t offs_src_cur = 0;
-                                    id<MTLBuffer> id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur);
-
-                                    [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:23 + j];
-                                }
-
-                                if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 ||
-                                    src2t == GGML_TYPE_Q5_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 ||
-                                    //src2t == GGML_TYPE_IQ2_XXS ||
-                                    src2t == GGML_TYPE_Q2_K) { // || src2t == GGML_TYPE_Q4_K) {
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                                }
-                                else if (src2t == GGML_TYPE_IQ2_XXS) {
-                                    [encoder setThreadgroupMemoryLength:(256*8+128) atIndex:0];
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                                }
-                                else if (src2t == GGML_TYPE_Q4_K) {
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                                }
-                                else if (src2t == GGML_TYPE_Q3_K) {
-#ifdef GGML_QKK_64
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 1)/2, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-#else
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-#endif
-                                }
-                                else if (src2t == GGML_TYPE_Q5_K) {
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                                }
-                                else if (src2t == GGML_TYPE_Q6_K) {
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 1)/2, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                                } else {
-                                    const int64_t ny = (_ne1 + nrows - 1)/nrows;
-                                    [encoder dispatchThreadgroups:MTLSizeMake(ne21, ny, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                                }
-                            }
-                        } break;
-                    case GGML_OP_GET_ROWS:
-                        {
-                            switch (src0->type) {
-                                case GGML_TYPE_F32:  [encoder setComputePipelineState:ctx->pipeline_get_rows_f32];  break;
-                                case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_get_rows_f16];  break;
-                                case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break;
-                                case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_1]; break;
-                                case GGML_TYPE_Q5_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_0]; break;
-                                case GGML_TYPE_Q5_1: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_1]; break;
-                                case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q8_0]; break;
-                                case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q2_K]; break;
-                                case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q3_K]; break;
-                                case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_K]; break;
-                                case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_K]; break;
-                                case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_K]; break;
-                                case GGML_TYPE_I32:  [encoder setComputePipelineState:ctx->pipeline_get_rows_i32];  break;
-                                case GGML_TYPE_IQ2_XXS: [encoder setComputePipelineState:ctx->pipeline_get_rows_iq2_xxs]; break;
-                                default: GGML_ASSERT(false && "not implemented");
-                            }
-
-                            [encoder setBuffer:id_src0     offset:offs_src0 atIndex:0];
-                            [encoder setBuffer:id_src1     offset:offs_src1 atIndex:1];
-                            [encoder setBuffer:id_dst      offset:offs_dst  atIndex:2];
-                            [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3];
-                            [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:4];
-                            [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:5];
-                            [encoder setBytes:&ne10 length:sizeof( int64_t) atIndex:6];
-                            [encoder setBytes:&nb10 length:sizeof( int64_t) atIndex:7];
-                            [encoder setBytes:&nb11 length:sizeof( int64_t) atIndex:8];
-                            [encoder setBytes:&nb1  length:sizeof(uint64_t) atIndex:9];
-                            [encoder setBytes:&nb2  length:sizeof(uint64_t) atIndex:10];
-
-                            [encoder dispatchThreadgroups:MTLSizeMake(ne10, ne11, 1) threadsPerThreadgroup:MTLSizeMake(32, 1, 1)];
-                        } break;
-                    case GGML_OP_RMS_NORM:
-                        {
-                            GGML_ASSERT(ne00 % 4 == 0);
-
-                            float eps;
-                            memcpy(&eps, dst->op_params, sizeof(float));
-
-                            int nth = 32; // SIMD width
-
-                            while (nth < ne00/4 && nth < 1024) {
-                                nth *= 2;
-                            }
-
-                            [encoder setComputePipelineState:ctx->pipeline_rms_norm];
-                            [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
-                            [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
-                            [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
-                            [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:3];
-                            [encoder setBytes:&eps     length:sizeof(   float) atIndex:4];
-                            [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];
-
-                            const int64_t nrows = ggml_nrows(src0);
-
-                            [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
-                        } break;
-                    case GGML_OP_GROUP_NORM:
-                        {
-                            GGML_ASSERT(ne00 % 4 == 0);
-
-                            //float eps;
-                            //memcpy(&eps, dst->op_params, sizeof(float));
-
-                            const float eps = 1e-6f; // TODO: temporarily hardcoded
-
-                            const int32_t n_groups = ((int32_t *) dst->op_params)[0];
-
-                            int nth = 32; // SIMD width
-
-                            //while (nth < ne00/4 && nth < 1024) {
-                            //    nth *= 2;
-                            //}
-
-                            [encoder setComputePipelineState:ctx->pipeline_group_norm];
-                            [encoder setBuffer:id_src0  offset:offs_src0        atIndex:0];
-                            [encoder setBuffer:id_dst   offset:offs_dst         atIndex:1];
-                            [encoder setBytes:&ne00     length:sizeof( int64_t) atIndex:2];
-                            [encoder setBytes:&ne01     length:sizeof( int64_t) atIndex:3];
-                            [encoder setBytes:&ne02     length:sizeof( int64_t) atIndex:4];
-                            [encoder setBytes:&nb00     length:sizeof(uint64_t) atIndex:5];
-                            [encoder setBytes:&nb01     length:sizeof(uint64_t) atIndex:6];
-                            [encoder setBytes:&nb02     length:sizeof(uint64_t) atIndex:7];
-                            [encoder setBytes:&n_groups length:sizeof( int32_t) atIndex:8];
-                            [encoder setBytes:&eps      length:sizeof(   float) atIndex:9];
-                            [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];
-
-                            [encoder dispatchThreadgroups:MTLSizeMake(n_groups, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
-                        } break;
-                    case GGML_OP_NORM:
-                        {
-                            float eps;
-                            memcpy(&eps, dst->op_params, sizeof(float));
-
-                            const int nth = MIN(256, ne00);
-
-                            [encoder setComputePipelineState:ctx->pipeline_norm];
-                            [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
-                            [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
-                            [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
-                            [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:3];
-                            [encoder setBytes:&eps     length:sizeof(   float) atIndex:4];
-                            [encoder setThreadgroupMemoryLength:GGML_PAD(nth*sizeof(float), 16) atIndex:0];
-
-                            const int64_t nrows = ggml_nrows(src0);
-
-                            [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
-                        } break;
-                    case GGML_OP_ALIBI:
-                        {
-                            GGML_ASSERT((src0t == GGML_TYPE_F32));
-
-                            const int nth = MIN(1024, ne00);
-
-                            //const int n_past = ((int32_t *) dst->op_params)[0];
-                            const int n_head = ((int32_t *) dst->op_params)[1];
-                            float max_bias;
-                            memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
-
-                            const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
-                            const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
-                            const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
-
-                            [encoder setComputePipelineState:ctx->pipeline_alibi_f32];
-                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
-                            [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
-                            [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3];
-                            [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4];
-                            [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5];
-                            [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6];
-                            [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7];
-                            [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8];
-                            [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9];
-                            [encoder setBytes:&ne0  length:sizeof( int64_t) atIndex:10];
-                            [encoder setBytes:&ne1  length:sizeof( int64_t) atIndex:11];
-                            [encoder setBytes:&ne2  length:sizeof( int64_t) atIndex:12];
-                            [encoder setBytes:&ne3  length:sizeof( int64_t) atIndex:13];
-                            [encoder setBytes:&nb0  length:sizeof(uint64_t) atIndex:14];
-                            [encoder setBytes:&nb1  length:sizeof(uint64_t) atIndex:15];
-                            [encoder setBytes:&nb2  length:sizeof(uint64_t) atIndex:16];
-                            [encoder setBytes:&nb3  length:sizeof(uint64_t) atIndex:17];
-                            [encoder setBytes:&m0   length:sizeof(   float) atIndex:18];
-                            [encoder setBytes:&m1   length:sizeof(   float) atIndex:19];
-                            [encoder setBytes:&n_heads_log2_floor   length:sizeof(int) atIndex:20];
-
-                            [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
-                        } break;
-                    case GGML_OP_ROPE:
-                        {
-                            GGML_ASSERT(ne10 == ne02);
-
-                            const int nth = MIN(1024, ne00);
-
-                            const int n_past     = ((int32_t *) dst->op_params)[0];
-                            const int n_dims     = ((int32_t *) dst->op_params)[1];
-                            const int mode       = ((int32_t *) dst->op_params)[2];
-                            // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal
-                            const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
-
-                            float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
-                            memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
-                            memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
-                            memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
-                            memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
-                            memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
-                            memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
-
-                            switch (src0->type) {
-                                case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_rope_f32]; break;
-                                case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_rope_f16]; break;
-                                default: GGML_ASSERT(false);
-                            };
-
-                            [encoder setBuffer:id_src0     offset:offs_src0        atIndex:0];
-                            [encoder setBuffer:id_src1     offset:offs_src1        atIndex:1];
-                            [encoder setBuffer:id_dst      offset:offs_dst         atIndex:2];
-                            [encoder setBytes:&ne00        length:sizeof( int64_t) atIndex:3];
-                            [encoder setBytes:&ne01        length:sizeof( int64_t) atIndex:4];
-                            [encoder setBytes:&ne02        length:sizeof( int64_t) atIndex:5];
-                            [encoder setBytes:&ne03        length:sizeof( int64_t) atIndex:6];
-                            [encoder setBytes:&nb00        length:sizeof(uint64_t) atIndex:7];
-                            [encoder setBytes:&nb01        length:sizeof(uint64_t) atIndex:8];
-                            [encoder setBytes:&nb02        length:sizeof(uint64_t) atIndex:9];
-                            [encoder setBytes:&nb03        length:sizeof(uint64_t) atIndex:10];
-                            [encoder setBytes:&ne0         length:sizeof( int64_t) atIndex:11];
-                            [encoder setBytes:&ne1         length:sizeof( int64_t) atIndex:12];
-                            [encoder setBytes:&ne2         length:sizeof( int64_t) atIndex:13];
-                            [encoder setBytes:&ne3         length:sizeof( int64_t) atIndex:14];
-                            [encoder setBytes:&nb0         length:sizeof(uint64_t) atIndex:15];
-                            [encoder setBytes:&nb1         length:sizeof(uint64_t) atIndex:16];
-                            [encoder setBytes:&nb2         length:sizeof(uint64_t) atIndex:17];
-                            [encoder setBytes:&nb3         length:sizeof(uint64_t) atIndex:18];
-                            [encoder setBytes:&n_past      length:sizeof(     int) atIndex:19];
-                            [encoder setBytes:&n_dims      length:sizeof(     int) atIndex:20];
-                            [encoder setBytes:&mode        length:sizeof(     int) atIndex:21];
-                            [encoder setBytes:&n_orig_ctx  length:sizeof(     int) atIndex:22];
-                            [encoder setBytes:&freq_base   length:sizeof(   float) atIndex:23];
-                            [encoder setBytes:&freq_scale  length:sizeof(   float) atIndex:24];
-                            [encoder setBytes:&ext_factor  length:sizeof(   float) atIndex:25];
-                            [encoder setBytes:&attn_factor length:sizeof(   float) atIndex:26];
-                            [encoder setBytes:&beta_fast   length:sizeof(   float) atIndex:27];
-                            [encoder setBytes:&beta_slow   length:sizeof(   float) atIndex:28];
-
-                            [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
-                        } break;
-                    case GGML_OP_IM2COL:
-                        {
-                            GGML_ASSERT(src0->type == GGML_TYPE_F16);
-                            GGML_ASSERT(src1->type == GGML_TYPE_F32);
-                            GGML_ASSERT( dst->type == GGML_TYPE_F16);
-
-                            const int32_t s0 = ((const int32_t *)(dst->op_params))[0];
-                            const int32_t s1 = ((const int32_t *)(dst->op_params))[1];
-                            const int32_t p0 = ((const int32_t *)(dst->op_params))[2];
-                            const int32_t p1 = ((const int32_t *)(dst->op_params))[3];
-                            const int32_t d0 = ((const int32_t *)(dst->op_params))[4];
-                            const int32_t d1 = ((const int32_t *)(dst->op_params))[5];
-                            const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1;
-
-                            const int32_t N  = src1->ne[is_2D ? 3 : 2];
-                            const int32_t IC = src1->ne[is_2D ? 2 : 1];
-                            const int32_t IH = is_2D ? src1->ne[1] : 1;
-                            const int32_t IW =         src1->ne[0];
-
-                            const int32_t KH = is_2D ? src0->ne[1] : 1;
-                            const int32_t KW =         src0->ne[0];
-
-                            const int32_t OH = is_2D ? dst->ne[2] : 1;
-                            const int32_t OW =         dst->ne[1];
-
-                            const int32_t CHW = IC * KH * KW;
-
-                            const int32_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4;
-                            const int32_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4;
-
-                            switch (src0->type) {
-                                case GGML_TYPE_F32: GGML_ASSERT(false && "not implemented"); break;
-                                case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_im2col_f16]; break;
-                                default: GGML_ASSERT(false);
-                            };
-
-                            [encoder setBuffer:id_src1 offset:offs_src1        atIndex:0];
-                            [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
-                            [encoder setBytes:&ofs0    length:sizeof( int32_t) atIndex:2];
-                            [encoder setBytes:&ofs1    length:sizeof( int32_t) atIndex:3];
-                            [encoder setBytes:&IW      length:sizeof( int32_t) atIndex:4];
-                            [encoder setBytes:&IH      length:sizeof( int32_t) atIndex:5];
-                            [encoder setBytes:&CHW     length:sizeof( int32_t) atIndex:6];
-                            [encoder setBytes:&s0      length:sizeof( int32_t) atIndex:7];
-                            [encoder setBytes:&s1      length:sizeof( int32_t) atIndex:8];
-                            [encoder setBytes:&p0      length:sizeof( int32_t) atIndex:9];
-                            [encoder setBytes:&p1      length:sizeof( int32_t) atIndex:10];
-                            [encoder setBytes:&d0      length:sizeof( int32_t) atIndex:11];
-                            [encoder setBytes:&d1      length:sizeof( int32_t) atIndex:12];
-
-                            [encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)];
-                        } break;
-                    case GGML_OP_UPSCALE:
-                        {
-                            GGML_ASSERT(src0->type == GGML_TYPE_F32);
-
-                            const int sf = dst->op_params[0];
-
-                            [encoder setComputePipelineState:ctx->pipeline_upscale_f32];
-                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
-                            [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
-                            [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
-                            [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
-                            [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5];
-                            [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
-                            [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
-                            [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
-                            [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9];
-                            [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:10];
-                            [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:11];
-                            [encoder setBytes:&ne2  length:sizeof(ne2)  atIndex:12];
-                            [encoder setBytes:&ne3  length:sizeof(ne3)  atIndex:13];
-                            [encoder setBytes:&nb0  length:sizeof(nb0)  atIndex:14];
-                            [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:15];
-                            [encoder setBytes:&nb2  length:sizeof(nb2)  atIndex:16];
-                            [encoder setBytes:&nb3  length:sizeof(nb3)  atIndex:17];
-                            [encoder setBytes:&sf   length:sizeof(sf)   atIndex:18];
-
-                            const int nth = MIN((int) ctx->pipeline_upscale_f32.maxTotalThreadsPerThreadgroup, ne0);
-
-                            [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
-                        } break;
-                    case GGML_OP_PAD:
-                        {
-                            GGML_ASSERT(src0->type == GGML_TYPE_F32);
-
-                            [encoder setComputePipelineState:ctx->pipeline_pad_f32];
-                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
-                            [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
-                            [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
-                            [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
-                            [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5];
-                            [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
-                            [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
-                            [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
-                            [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9];
-                            [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:10];
-                            [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:11];
-                            [encoder setBytes:&ne2  length:sizeof(ne2)  atIndex:12];
-                            [encoder setBytes:&ne3  length:sizeof(ne3)  atIndex:13];
-                            [encoder setBytes:&nb0  length:sizeof(nb0)  atIndex:14];
-                            [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:15];
-                            [encoder setBytes:&nb2  length:sizeof(nb2)  atIndex:16];
-                            [encoder setBytes:&nb3  length:sizeof(nb3)  atIndex:17];
-
-                            const int nth = MIN(1024, ne0);
-
-                            [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
-                        } break;
-                    case GGML_OP_ARGSORT:
-                        {
-                            GGML_ASSERT(src0->type == GGML_TYPE_F32);
-                            GGML_ASSERT( dst->type == GGML_TYPE_I32);
-
-                            const int nrows = ggml_nrows(src0);
-
-                            enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0];
-
-                            switch (order) {
-                                case GGML_SORT_ASC:  [encoder setComputePipelineState:ctx->pipeline_argsort_f32_i32_asc];  break;
-                                case GGML_SORT_DESC: [encoder setComputePipelineState:ctx->pipeline_argsort_f32_i32_desc]; break;
-                                default: GGML_ASSERT(false);
-                            };
-
-                            [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
-                            [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
-                            [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
-
-                            [encoder dispatchThreadgroups:MTLSizeMake(1, nrows, 1) threadsPerThreadgroup:MTLSizeMake(ne00, 1, 1)];
-                        } break;
-                    case GGML_OP_LEAKY_RELU:
-                        {
-                            GGML_ASSERT(src0->type == GGML_TYPE_F32);
-
-                            float slope;
-                            memcpy(&slope, dst->op_params, sizeof(float));
-
-                            [encoder setComputePipelineState:ctx->pipeline_leaky_relu_f32];
-                            [encoder setBuffer:id_src0 offset:offs_src0   atIndex:0];
-                            [encoder setBuffer:id_dst  offset:offs_dst    atIndex:1];
-                            [encoder setBytes:&slope length:sizeof(slope) atIndex:2];
-
-                            const int64_t n = ggml_nelements(dst);
-
-                            [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
-                        } break;
-                    case GGML_OP_DUP:
-                    case GGML_OP_CPY:
-                    case GGML_OP_CONT:
-                        {
-                            GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0);
-
-                            int nth = MIN(1024, ne00/ggml_blck_size(src0->type));
-
-                            switch (src0t) {
-                                case GGML_TYPE_F32:
-                                    {
-                                        GGML_ASSERT(ne0 % ggml_blck_size(dst->type) == 0);
-
-                                        switch (dstt) {
-                                            case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f16];  break;
-                                            case GGML_TYPE_F32:  [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f32];  break;
-                                            case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_q8_0]; break;
-                                            case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_q4_0]; break;
-                                            case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_q4_1]; break;
-                                            //case GGML_TYPE_Q5_0: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_q5_0]; break;
-                                            //case GGML_TYPE_Q5_1: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_q5_1]; break;
-                                            default: GGML_ASSERT(false && "not implemented");
-                                        };
-                                    } break;
-                                case GGML_TYPE_F16:
-                                    {
-                                        switch (dstt) {
-                                            case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_cpy_f16_f16]; break;
-                                            case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_cpy_f16_f32]; break;
-                                            default: GGML_ASSERT(false && "not implemented");
-                                        };
-                                    } break;
-                                default: GGML_ASSERT(false && "not implemented");
-                            }
-
                             [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
                             [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
                             [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
@@ -2415,42 +1088,1471 @@ bool ggml_metal_graph_compute(
                             [encoder setBytes:&nb2     length:sizeof(uint64_t) atIndex:16];
                             [encoder setBytes:&nb3     length:sizeof(uint64_t) atIndex:17];
 
+                            const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00);
+
                             [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
-                        } break;
-                    default:
-                        {
-                            GGML_METAL_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
-                            GGML_ASSERT(false);
                         }
-                }
+
+                        const id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline;
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                        [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
+                        [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
+                        [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
+                        [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
+                        [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
+                        [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6];
+                        [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7];
+                        [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:8];
+                        [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:9];
+                        [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:10];
+                        [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11];
+                        [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12];
+                        [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13];
+                        [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14];
+                        [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15];
+                        [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16];
+                        [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17];
+                        [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18];
+                        [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:19];
+                        [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:20];
+                        [encoder setBytes:&ne2  length:sizeof(ne2)  atIndex:21];
+                        [encoder setBytes:&ne3  length:sizeof(ne3)  atIndex:22];
+                        [encoder setBytes:&nb0  length:sizeof(nb0)  atIndex:23];
+                        [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:24];
+                        [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:25];
+                        [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:26];
+                        [encoder setBytes:&offs length:sizeof(offs) atIndex:27];
+
+                        const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00);
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(ne11, ne12, ne13) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                    } break;
+                case GGML_OP_SCALE:
+                    {
+                        GGML_ASSERT(ggml_is_contiguous(src0));
+
+                        float scale;
+                        memcpy(&scale, dst->op_params, sizeof(scale));
+
+                        int64_t n = ggml_nelements(dst);
+
+                        id<MTLComputePipelineState> pipeline = nil;
+
+                        if (n % 4 == 0) {
+                            n /= 4;
+                            pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE_4].pipeline;
+                        } else {
+                            pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE].pipeline;
+                        }
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0   offset:offs_src0 atIndex:0];
+                        [encoder setBuffer:id_dst    offset:offs_dst  atIndex:1];
+                        [encoder setBytes:&scale length:sizeof(scale) atIndex:2];
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                    } break;
+                case GGML_OP_UNARY:
+                    switch (ggml_get_unary_op(gf->nodes[i])) {
+                        case GGML_UNARY_OP_TANH:
+                            {
+                                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TANH].pipeline;
+
+                                [encoder setComputePipelineState:pipeline];
+                                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+
+                                const int64_t n = ggml_nelements(dst);
+
+                                [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                            } break;
+                        case GGML_UNARY_OP_RELU:
+                            {
+                                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RELU].pipeline;
+
+                                [encoder setComputePipelineState:pipeline];
+                                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+
+                                const int64_t n = ggml_nelements(dst);
+
+                                [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                            } break;
+                        case GGML_UNARY_OP_GELU:
+                            {
+                                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline;
+
+                                [encoder setComputePipelineState:pipeline];
+                                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+
+                                const int64_t n = ggml_nelements(dst);
+                                GGML_ASSERT(n % 4 == 0);
+
+                                [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                            } break;
+                        case GGML_UNARY_OP_GELU_QUICK:
+                            {
+                                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline;
+
+                                [encoder setComputePipelineState:pipeline];
+                                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+
+                                const int64_t n = ggml_nelements(dst);
+                                GGML_ASSERT(n % 4 == 0);
+
+                                [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                            } break;
+                        case GGML_UNARY_OP_SILU:
+                            {
+                                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline;
+
+                                [encoder setComputePipelineState:pipeline];
+                                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+
+                                const int64_t n = ggml_nelements(dst);
+                                GGML_ASSERT(n % 4 == 0);
+
+                                [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                            } break;
+                        default:
+                            {
+                                GGML_METAL_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
+                                GGML_ASSERT(false);
+                            }
+                    } break;
+                case GGML_OP_SQR:
+                    {
+                        GGML_ASSERT(ggml_is_contiguous(src0));
+
+                        id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SQR].pipeline;
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                        [encoder setBuffer:id_dst  offset:offs_dst atIndex:1];
+
+                        const int64_t n = ggml_nelements(dst);
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                    } break;
+                case GGML_OP_SUM_ROWS:
+                    {
+                        GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
+
+                        id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline;
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                        [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+                        [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
+                        [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
+                        [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
+                        [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5];
+                        [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
+                        [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
+                        [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
+                        [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9];
+                        [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10];
+                        [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11];
+                        [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12];
+                        [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13];
+                        [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14];
+                        [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15];
+                        [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16];
+                        [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:17];
+                        [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:18];
+                        [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:19];
+                        [encoder setBytes:&ne2  length:sizeof(ne2)  atIndex:20];
+                        [encoder setBytes:&ne3  length:sizeof(ne3)  atIndex:21];
+                        [encoder setBytes:&nb0  length:sizeof(nb0)  atIndex:22];
+                        [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:23];
+                        [encoder setBytes:&nb2  length:sizeof(nb2)  atIndex:24];
+                        [encoder setBytes:&nb3  length:sizeof(nb3)  atIndex:25];
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                    } break;
+                case GGML_OP_SOFT_MAX:
+                    {
+                        int nth = 32; // SIMD width
+
+                        id<MTLComputePipelineState> pipeline = nil;
+
+                        if (ne00%4 == 0) {
+                            while (nth < ne00/4 && nth < 256) {
+                                nth *= 2;
+                            }
+                            pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_4].pipeline;
+                        } else {
+                            while (nth < ne00 && nth < 1024) {
+                                nth *= 2;
+                            }
+                            pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX].pipeline;
+                        }
+
+                        float scale;
+                        float max_bias;
+
+                        memcpy(&scale,    ((int32_t *) dst->op_params) + 0, sizeof(scale));
+                        memcpy(&max_bias, ((int32_t *) dst->op_params) + 1, sizeof(max_bias));
+
+                        const int64_t nrows_x = ggml_nrows(src0);
+                        const int64_t nrows_y = src0->ne[1];
+
+                        const uint32_t n_head_kv   = nrows_x/nrows_y;
+                        const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
+
+                        const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
+                        const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0 offset:offs_src0   atIndex:0];
+                        if (id_src1) {
+                            [encoder setBuffer:id_src1 offset:offs_src1   atIndex:1];
+                        } else {
+                            [encoder setBuffer:id_src0 offset:offs_src0   atIndex:1];
+                        }
+                        if (id_src2) {
+                            [encoder setBuffer:id_src2 offset:offs_src2   atIndex:2];
+                        } else {
+                            [encoder setBuffer:id_src0 offset:offs_src0   atIndex:2];
+                        }
+                        [encoder setBuffer:id_dst   offset:offs_dst          atIndex:3];
+                        [encoder setBytes:&ne00     length:sizeof(ne00)      atIndex:4];
+                        [encoder setBytes:&ne01     length:sizeof(ne01)      atIndex:5];
+                        [encoder setBytes:&ne02     length:sizeof(ne02)      atIndex:6];
+                        [encoder setBytes:&scale    length:sizeof(scale)     atIndex:7];
+                        [encoder setBytes:&max_bias length:sizeof(max_bias)  atIndex:8];
+                        [encoder setBytes:&m0       length:sizeof(m0)        atIndex:9];
+                        [encoder setBytes:&m1       length:sizeof(m1)        atIndex:10];
+                        [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:11];
+                        [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                    } break;
+                case GGML_OP_DIAG_MASK_INF:
+                    {
+                        const int n_past = ((int32_t *)(dst->op_params))[0];
+
+                        id<MTLComputePipelineState> pipeline = nil;
+
+                        if (ne00%8 == 0) {
+                            pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8].pipeline;
+                        } else {
+                            pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF].pipeline;
+                        }
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                        [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+                        [encoder setBytes:&ne00   length:sizeof(ne00) atIndex:2];
+                        [encoder setBytes:&ne01   length:sizeof(ne01) atIndex:3];
+                        [encoder setBytes:&n_past length:sizeof(int)  atIndex:4];
+
+                        if (ne00%8 == 0) {
+                            [encoder dispatchThreadgroups:MTLSizeMake(ne00*ne01*ne02/8, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                        }
+                        else {
+                            [encoder dispatchThreadgroups:MTLSizeMake(ne00, ne01, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                        }
+                    } break;
+                case GGML_OP_MUL_MAT:
+                    {
+                        GGML_ASSERT(ne00 == ne10);
+
+                        // TODO: assert that dim2 and dim3 are contiguous
+                        GGML_ASSERT(ne12 % ne02 == 0);
+                        GGML_ASSERT(ne13 % ne03 == 0);
+
+                        const uint r2 = ne12/ne02;
+                        const uint r3 = ne13/ne03;
+
+                        // find the break-even point where the matrix-matrix kernel becomes more efficient compared
+                        // to the matrix-vector kernel
+                        int ne11_mm_min = 1;
+
+#if 0
+                        // the numbers below are measured on M2 Ultra for 7B and 13B models
+                        // these numbers do not translate to other devices or model sizes
+                        // TODO: need to find a better approach
+                        if ([ctx->device.name isEqualToString:@"Apple M2 Ultra"]) {
+                            switch (src0t) {
+                                case GGML_TYPE_F16:  ne11_mm_min = 2;  break;
+                                case GGML_TYPE_Q8_0: ne11_mm_min = 7;  break;
+                                case GGML_TYPE_Q2_K: ne11_mm_min = 15; break;
+                                case GGML_TYPE_Q3_K: ne11_mm_min = 7;  break;
+                                case GGML_TYPE_Q4_0:
+                                case GGML_TYPE_Q4_1: ne11_mm_min = 15; break;
+                                case GGML_TYPE_Q4_K: ne11_mm_min = 11; break;
+                                case GGML_TYPE_Q5_0:                          // not tested yet
+                                case GGML_TYPE_Q5_1: ne11_mm_min = 13; break; // not tested yet
+                                case GGML_TYPE_Q5_K: ne11_mm_min = 7;  break;
+                                case GGML_TYPE_Q6_K: ne11_mm_min = 7;  break;
+                                default:             ne11_mm_min = 1;  break;
+                            }
+                        }
+#endif
+
+                        // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
+                        // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
+                        if ([ctx->device supportsFamily:MTLGPUFamilyApple7] &&
+                            !ggml_is_transposed(src0) &&
+                            !ggml_is_transposed(src1) &&
+                            src1t == GGML_TYPE_F32 &&
+                            ne00 % 32 == 0 && ne00 >= 64 &&
+                            (ne11 > ne11_mm_min || (ggml_is_quantized(src0t) && ne12 > 1))) {
+                            //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);
+
+                            // some Metal matrix data types require aligned pointers
+                            // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5)
+                            switch (src0->type) {
+                                case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break;
+                                case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8  == 0); break;
+                                default: break;
+                            }
+
+                            id<MTLComputePipelineState> pipeline = nil;
+
+                            switch (src0->type) {
+                                case GGML_TYPE_F32:     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32    ].pipeline; break;
+                                case GGML_TYPE_F16:     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32    ].pipeline; break;
+                                case GGML_TYPE_Q4_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32   ].pipeline; break;
+                                case GGML_TYPE_Q4_1:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32   ].pipeline; break;
+                                case GGML_TYPE_Q5_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32   ].pipeline; break;
+                                case GGML_TYPE_Q5_1:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32   ].pipeline; break;
+                                case GGML_TYPE_Q8_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32   ].pipeline; break;
+                                case GGML_TYPE_Q2_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32   ].pipeline; break;
+                                case GGML_TYPE_Q3_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32   ].pipeline; break;
+                                case GGML_TYPE_Q4_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32   ].pipeline; break;
+                                case GGML_TYPE_Q5_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32   ].pipeline; break;
+                                case GGML_TYPE_Q6_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32   ].pipeline; break;
+                                case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32].pipeline; break;
+                                case GGML_TYPE_IQ2_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break;
+                                case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32].pipeline; break;
+                                case GGML_TYPE_IQ3_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32  ].pipeline; break;
+                                case GGML_TYPE_IQ2_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32  ].pipeline; break;
+                                case GGML_TYPE_IQ1_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32  ].pipeline; break;
+                                case GGML_TYPE_IQ1_M:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32  ].pipeline; break;
+                                case GGML_TYPE_IQ4_NL:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32 ].pipeline; break;
+                                case GGML_TYPE_IQ4_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32 ].pipeline; break;
+                                default: GGML_ASSERT(false && "MUL MAT-MAT not implemented");
+                            }
+
+                            [encoder setComputePipelineState:pipeline];
+                            [encoder setBuffer:id_src0 offset:offs_src0    atIndex:0];
+                            [encoder setBuffer:id_src1 offset:offs_src1    atIndex:1];
+                            [encoder setBuffer:id_dst  offset:offs_dst     atIndex:2];
+                            [encoder setBytes:&ne00    length:sizeof(ne00) atIndex:3];
+                            [encoder setBytes:&ne02    length:sizeof(ne02) atIndex:4];
+                            [encoder setBytes:&nb01    length:sizeof(nb01) atIndex:5];
+                            [encoder setBytes:&nb02    length:sizeof(nb02) atIndex:6];
+                            [encoder setBytes:&ne12    length:sizeof(ne12) atIndex:7];
+                            [encoder setBytes:&nb10    length:sizeof(nb10) atIndex:8];
+                            [encoder setBytes:&nb11    length:sizeof(nb11) atIndex:9];
+                            [encoder setBytes:&nb12    length:sizeof(nb12) atIndex:10];
+                            [encoder setBytes:&ne0     length:sizeof(ne0)  atIndex:11];
+                            [encoder setBytes:&ne1     length:sizeof(ne1)  atIndex:12];
+                            [encoder setBytes:&r2      length:sizeof(r2)   atIndex:13];
+                            [encoder setBytes:&r3      length:sizeof(r3)   atIndex:14];
+                            [encoder setThreadgroupMemoryLength:8192 atIndex:0];
+                            [encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne01 + 63)/64, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
+                        } else {
+                            int nth0 = 32;
+                            int nth1 = 1;
+                            int nrows = 1;
+                            //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);
+
+                            id<MTLComputePipelineState> pipeline = nil;
+
+                            // use custom matrix x vector kernel
+                            switch (src0t) {
+                                case GGML_TYPE_F32:
+                                    {
+                                        GGML_ASSERT(src1t == GGML_TYPE_F32);
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32].pipeline;
+                                        nrows = 4;
+                                    } break;
+                                case GGML_TYPE_F16:
+                                    {
+                                        nth0 = 32;
+                                        nth1 = 1;
+                                        if (src1t == GGML_TYPE_F32) {
+                                            if (ne11 * ne12 < 4) {
+                                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW].pipeline;
+                                            } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) {
+                                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4].pipeline;
+                                                nrows = ne11;
+                                            } else {
+                                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32].pipeline;
+                                                nrows = 4;
+                                            }
+                                        } else {
+                                            pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16].pipeline;
+                                            nrows = 4;
+                                        }
+                                    } break;
+                                case GGML_TYPE_Q4_0:
+                                    {
+                                        nth0 = 8;
+                                        nth1 = 8;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_Q4_1:
+                                    {
+                                        nth0 = 8;
+                                        nth1 = 8;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_Q5_0:
+                                    {
+                                        nth0 = 8;
+                                        nth1 = 8;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_Q5_1:
+                                    {
+                                        nth0 = 8;
+                                        nth1 = 8;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_Q8_0:
+                                    {
+                                        nth0 = 8;
+                                        nth1 = 8;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_Q2_K:
+                                    {
+                                        nth0 = 2;
+                                        nth1 = 32;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_Q3_K:
+                                    {
+                                        nth0 = 2;
+                                        nth1 = 32;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_Q4_K:
+                                    {
+                                        nth0 = 4; //1;
+                                        nth1 = 8; //32;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_Q5_K:
+                                    {
+                                        nth0 = 2;
+                                        nth1 = 32;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_Q6_K:
+                                    {
+                                        nth0 = 2;
+                                        nth1 = 32;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ2_XXS:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ2_XS:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ3_XXS:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ3_S:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ2_S:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ1_S:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ1_M:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ4_NL:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ4_XS:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32].pipeline;
+                                    } break;
+                                default:
+                                    {
+                                        GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t);
+                                        GGML_ASSERT(false && "not implemented");
+                                    }
+                            };
+
+                            if (ggml_is_quantized(src0t)) {
+                                GGML_ASSERT(ne00 >= nth0*nth1);
+                            }
+
+                            [encoder setComputePipelineState:pipeline];
+                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                            [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
+                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
+                            [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
+                            [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
+                            [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
+                            [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
+                            [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
+                            [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
+                            [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:9];
+                            [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:10];
+                            [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:11];
+                            [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:12];
+                            [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:13];
+                            [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:14];
+                            [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:15];
+                            [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:16];
+                            [encoder setBytes:&r2   length:sizeof(r2)   atIndex:17];
+                            [encoder setBytes:&r3   length:sizeof(r3)   atIndex:18];
+
+                            if (src0t == GGML_TYPE_Q4_0  || src0t == GGML_TYPE_Q4_1  || src0t == GGML_TYPE_Q5_0 ||
+                                src0t == GGML_TYPE_Q5_1  || src0t == GGML_TYPE_Q8_0  || src0t == GGML_TYPE_Q2_K ||
+                                src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) {
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            }
+                            else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) {
+                                const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128;
+                                [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            }
+                            else if (src0t == GGML_TYPE_IQ3_XXS || src0t == GGML_TYPE_IQ3_S) {
+                                const int mem_size = src0t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4;
+                                [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            }
+                            else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) {
+                                const int mem_size = 32*sizeof(float);
+                                [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            }
+                            else if (src0t == GGML_TYPE_Q4_K) {
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            }
+                            else if (src0t == GGML_TYPE_Q3_K) {
+#ifdef GGML_QKK_64
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+#else
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+#endif
+                            }
+                            else if (src0t == GGML_TYPE_Q5_K) {
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            }
+                            else if (src0t == GGML_TYPE_Q6_K) {
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            } else {
+                                const int64_t ny = (ne11 + nrows - 1)/nrows;
+                                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            }
+                        }
+                    } break;
+                case GGML_OP_MUL_MAT_ID:
+                    {
+                        //GGML_ASSERT(ne00 == ne10);
+                        //GGML_ASSERT(ne03 == ne13);
+                        const int n_as = src0->ne[2];
+
+                        // max size of the src1ids array in the kernel shared buffer
+                        GGML_ASSERT(ne11 <= 4096);
+
+                        // src2 = ids
+                        const int64_t  ne20 = src2->ne[0]; GGML_UNUSED(ne20);
+                        const int64_t  ne21 = src2->ne[1];
+                        const int64_t  ne22 = src2->ne[2]; GGML_UNUSED(ne22);
+                        const int64_t  ne23 = src2->ne[3]; GGML_UNUSED(ne23);
+
+                        const uint64_t nb20 = src2->nb[0]; GGML_UNUSED(nb20);
+                        const uint64_t nb21 = src2->nb[1];
+                        const uint64_t nb22 = src2->nb[2]; GGML_UNUSED(nb22);
+                        const uint64_t nb23 = src2->nb[3]; GGML_UNUSED(nb23);
+
+                        const enum ggml_type src2t = src2->type; GGML_UNUSED(src2t);
+
+                        GGML_ASSERT(src2t == GGML_TYPE_I32);
+
+                        GGML_ASSERT(!ggml_is_transposed(src0));
+                        GGML_ASSERT(!ggml_is_transposed(src1));
+
+                        GGML_ASSERT(src1t == GGML_TYPE_F32);
+
+                        // find the break-even point where the matrix-matrix kernel becomes more efficient compared
+                        // to the matrix-vector kernel
+                        int ne11_mm_min = n_as;
+
+                        const int idx = ((int32_t *) dst->op_params)[0];
+
+                        // batch size
+                        GGML_ASSERT(ne21 == ne11); // ?
+                        GGML_ASSERT(ne12 == 1 && ne13 == 1); // no broadcasting
+                        const uint r2 = 1;
+                        const uint r3 = 1;
+
+                        // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
+                        // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
+                        // !!!
+                        // TODO: for now, always use mat-vec kernels until we figure out how to improve the
+                        //       indirect matrix multiplication
+                        // !!!
+                        if ([ctx->device supportsFamily:MTLGPUFamilyApple7] &&
+                            ne00 % 32 == 0 && ne00 >= 64 &&
+                            ne11 > ne11_mm_min) {
+
+                            // some Metal matrix data types require aligned pointers
+                            // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5)
+                            switch (src0->type) {
+                                case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break;
+                                case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8  == 0); break;
+                                default: break;
+                            }
+
+                            id<MTLComputePipelineState> pipeline = nil;
+
+                            switch (src0->type) {
+                                case GGML_TYPE_F32:     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32    ].pipeline; break;
+                                case GGML_TYPE_F16:     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32    ].pipeline; break;
+                                case GGML_TYPE_Q4_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32   ].pipeline; break;
+                                case GGML_TYPE_Q4_1:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32   ].pipeline; break;
+                                case GGML_TYPE_Q5_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32   ].pipeline; break;
+                                case GGML_TYPE_Q5_1:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32   ].pipeline; break;
+                                case GGML_TYPE_Q8_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32   ].pipeline; break;
+                                case GGML_TYPE_Q2_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32   ].pipeline; break;
+                                case GGML_TYPE_Q3_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32   ].pipeline; break;
+                                case GGML_TYPE_Q4_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32   ].pipeline; break;
+                                case GGML_TYPE_Q5_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32   ].pipeline; break;
+                                case GGML_TYPE_Q6_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32   ].pipeline; break;
+                                case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32].pipeline; break;
+                                case GGML_TYPE_IQ2_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break;
+                                case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32].pipeline; break;
+                                case GGML_TYPE_IQ3_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32  ].pipeline; break;
+                                case GGML_TYPE_IQ2_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32  ].pipeline; break;
+                                case GGML_TYPE_IQ1_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32  ].pipeline; break;
+                                case GGML_TYPE_IQ1_M:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32  ].pipeline; break;
+                                case GGML_TYPE_IQ4_NL:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32 ].pipeline; break;
+                                case GGML_TYPE_IQ4_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32 ].pipeline; break;
+                                default: GGML_ASSERT(false && "MUL_MAT_ID not implemented");
+                            }
+
+                            [encoder setComputePipelineState:pipeline];
+                            [encoder setBuffer:id_src0 offset:offs_src0    atIndex:0];
+                            [encoder setBuffer:id_src1 offset:offs_src1    atIndex:1];
+                            [encoder setBuffer:id_dst  offset:offs_dst     atIndex:2];
+                            [encoder setBuffer:id_src2 offset:offs_src2    atIndex:3];
+                            [encoder setBytes:&nb21    length:sizeof(nb21) atIndex:4];
+                            [encoder setBytes:&ne00    length:sizeof(ne00) atIndex:5];
+                            [encoder setBytes:&ne02    length:sizeof(ne02) atIndex:6];
+                            [encoder setBytes:&nb01    length:sizeof(nb01) atIndex:7];
+                            [encoder setBytes:&nb02    length:sizeof(nb02) atIndex:8];
+                            [encoder setBytes:&ne12    length:sizeof(ne12) atIndex:9];
+                            [encoder setBytes:&ne13    length:sizeof(ne13) atIndex:10];
+                            [encoder setBytes:&nb10    length:sizeof(nb10) atIndex:11];
+                            [encoder setBytes:&nb11    length:sizeof(nb11) atIndex:12];
+                            [encoder setBytes:&nb12    length:sizeof(nb12) atIndex:13];
+                            [encoder setBytes:&ne0     length:sizeof(ne0)  atIndex:14];
+                            [encoder setBytes:&ne1     length:sizeof(ne1)  atIndex:15];
+                            [encoder setBytes:&nb1     length:sizeof(nb1)  atIndex:16];
+                            [encoder setBytes:&r2      length:sizeof(r2)   atIndex:17];
+                            [encoder setBytes:&r3      length:sizeof(r3)   atIndex:18];
+                            [encoder setBytes:&idx     length:sizeof(idx)  atIndex:19];
+
+                            [encoder setThreadgroupMemoryLength:GGML_PAD(8192 + 2*ne11, 16) atIndex:0];
+
+                            [encoder dispatchThreadgroups:MTLSizeMake((ne11 + 31)/32, (ne01 + 63)/64, n_as*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
+                        } else {
+                            int nth0 = 32;
+                            int nth1 = 1;
+                            int nrows = 1;
+                            //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);
+
+                            id<MTLComputePipelineState> pipeline = nil;
+
+                            // use custom matrix x vector kernel
+                            switch (src0t) {
+                                case GGML_TYPE_F32:
+                                    {
+                                        GGML_ASSERT(src1t == GGML_TYPE_F32);
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_F16:
+                                    {
+                                        GGML_ASSERT(src1t == GGML_TYPE_F32);
+                                        nth0 = 32;
+                                        nth1 = 1;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_Q4_0:
+                                    {
+                                        nth0 = 8;
+                                        nth1 = 8;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_Q4_1:
+                                    {
+                                        nth0 = 8;
+                                        nth1 = 8;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_Q5_0:
+                                    {
+                                        nth0 = 8;
+                                        nth1 = 8;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_Q5_1:
+                                    {
+                                        nth0 = 8;
+                                        nth1 = 8;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_Q8_0:
+                                    {
+                                        nth0 = 8;
+                                        nth1 = 8;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_Q2_K:
+                                    {
+                                        nth0 = 2;
+                                        nth1 = 32;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_Q3_K:
+                                    {
+                                        nth0 = 2;
+                                        nth1 = 32;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_Q4_K:
+                                    {
+                                        nth0 = 4; //1;
+                                        nth1 = 8; //32;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_Q5_K:
+                                    {
+                                        nth0 = 2;
+                                        nth1 = 32;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_Q6_K:
+                                    {
+                                        nth0 = 2;
+                                        nth1 = 32;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ2_XXS:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ2_XS:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ3_XXS:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ3_S:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ2_S:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ1_S:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ1_M:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ4_NL:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline;
+                                    } break;
+                                case GGML_TYPE_IQ4_XS:
+                                    {
+                                        nth0 = 4;
+                                        nth1 = 16;
+                                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline;
+                                    } break;
+                                default:
+                                    {
+                                        GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t);
+                                        GGML_ASSERT(false && "not implemented");
+                                    }
+                            };
+
+                            if (ggml_is_quantized(src0t)) {
+                                GGML_ASSERT(ne00 >= nth0*nth1);
+                            }
+
+                            const int64_t _ne1 = 1; // kernels needs a reference in constant memory
+
+                            [encoder setComputePipelineState:pipeline];
+                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                            [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
+                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
+                            [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3];
+                            [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:4];
+                            [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:5];
+                            [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:6];
+                            [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:7];
+                            [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:8];
+                            [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:9];
+                            [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:10];
+                            [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11];
+                            [encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:12];
+                            [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13];
+                            [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14];
+                            [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15];
+                            [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16];
+                            [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17];
+                            [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:18];
+                            [encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:19];
+                            [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:20];
+                            [encoder setBytes:&r2   length:sizeof(r2)   atIndex:21];
+                            [encoder setBytes:&r3   length:sizeof(r3)   atIndex:22];
+                            [encoder setBytes:&idx  length:sizeof(idx)  atIndex:23];
+
+                            if (src0t == GGML_TYPE_Q4_0  || src0t == GGML_TYPE_Q4_1  || src0t == GGML_TYPE_Q5_0 ||
+                                src0t == GGML_TYPE_Q5_1  || src0t == GGML_TYPE_Q8_0  || src0t == GGML_TYPE_Q2_K ||
+                                src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) {
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            }
+                            else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) {
+                                const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128;
+                                [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            }
+                            else if (src0t == GGML_TYPE_IQ3_XXS || src0t == GGML_TYPE_IQ3_S) {
+                                const int mem_size = src0t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4;
+                                [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            }
+                            else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) {
+                                const int mem_size = 32*sizeof(float);
+                                [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            }
+                            else if (src0t == GGML_TYPE_Q4_K) {
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            }
+                            else if (src0t == GGML_TYPE_Q3_K) {
+#ifdef GGML_QKK_64
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+#else
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+#endif
+                            }
+                            else if (src0t == GGML_TYPE_Q5_K) {
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            }
+                            else if (src0t == GGML_TYPE_Q6_K) {
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            } else {
+                                const int64_t ny = (_ne1 + nrows - 1)/nrows;
+                                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                            }
+                        }
+                    } break;
+                case GGML_OP_GET_ROWS:
+                    {
+                        id<MTLComputePipelineState> pipeline = nil;
+
+                        switch (src0->type) {
+                            case GGML_TYPE_F32:     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F32    ].pipeline; break;
+                            case GGML_TYPE_F16:     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F16    ].pipeline; break;
+                            case GGML_TYPE_Q4_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0   ].pipeline; break;
+                            case GGML_TYPE_Q4_1:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1   ].pipeline; break;
+                            case GGML_TYPE_Q5_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0   ].pipeline; break;
+                            case GGML_TYPE_Q5_1:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1   ].pipeline; break;
+                            case GGML_TYPE_Q8_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0   ].pipeline; break;
+                            case GGML_TYPE_Q2_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K   ].pipeline; break;
+                            case GGML_TYPE_Q3_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K   ].pipeline; break;
+                            case GGML_TYPE_Q4_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K   ].pipeline; break;
+                            case GGML_TYPE_Q5_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K   ].pipeline; break;
+                            case GGML_TYPE_Q6_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K   ].pipeline; break;
+                            case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS].pipeline; break;
+                            case GGML_TYPE_IQ2_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break;
+                            case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS].pipeline; break;
+                            case GGML_TYPE_IQ3_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S  ].pipeline; break;
+                            case GGML_TYPE_IQ2_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S  ].pipeline; break;
+                            case GGML_TYPE_IQ1_S:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S  ].pipeline; break;
+                            case GGML_TYPE_IQ1_M:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M  ].pipeline; break;
+                            case GGML_TYPE_IQ4_NL:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL ].pipeline; break;
+                            case GGML_TYPE_IQ4_XS:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS ].pipeline; break;
+                            case GGML_TYPE_I32:     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32    ].pipeline; break;
+                            default: GGML_ASSERT(false && "not implemented");
+                        }
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0     offset:offs_src0 atIndex:0];
+                        [encoder setBuffer:id_src1     offset:offs_src1 atIndex:1];
+                        [encoder setBuffer:id_dst      offset:offs_dst  atIndex:2];
+                        [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3];
+                        [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:4];
+                        [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:5];
+                        [encoder setBytes:&ne10 length:sizeof( int64_t) atIndex:6];
+                        [encoder setBytes:&nb10 length:sizeof( int64_t) atIndex:7];
+                        [encoder setBytes:&nb11 length:sizeof( int64_t) atIndex:8];
+                        [encoder setBytes:&nb1  length:sizeof(uint64_t) atIndex:9];
+                        [encoder setBytes:&nb2  length:sizeof(uint64_t) atIndex:10];
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(ne10, ne11, 1) threadsPerThreadgroup:MTLSizeMake(32, 1, 1)];
+                    } break;
+                case GGML_OP_RMS_NORM:
+                    {
+                        GGML_ASSERT(ne00 % 4 == 0);
+
+                        float eps;
+                        memcpy(&eps, dst->op_params, sizeof(float));
+
+                        int nth = 32; // SIMD width
+
+                        while (nth < ne00/4 && nth < 1024) {
+                            nth *= 2;
+                        }
+
+                        id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RMS_NORM].pipeline;
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
+                        [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
+                        [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
+                        [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:3];
+                        [encoder setBytes:&eps     length:sizeof(   float) atIndex:4];
+                        [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];
+
+                        const int64_t nrows = ggml_nrows(src0);
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                    } break;
+                case GGML_OP_GROUP_NORM:
+                    {
+                        GGML_ASSERT(ne00 % 4 == 0);
+
+                        //float eps;
+                        //memcpy(&eps, dst->op_params, sizeof(float));
+
+                        const float eps = 1e-6f; // TODO: temporarily hardcoded
+
+                        const int32_t n_groups = ((int32_t *) dst->op_params)[0];
+
+                        int nth = 32; // SIMD width
+
+                        //while (nth < ne00/4 && nth < 1024) {
+                        //    nth *= 2;
+                        //}
+
+                        id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GROUP_NORM].pipeline;
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0  offset:offs_src0        atIndex:0];
+                        [encoder setBuffer:id_dst   offset:offs_dst         atIndex:1];
+                        [encoder setBytes:&ne00     length:sizeof( int64_t) atIndex:2];
+                        [encoder setBytes:&ne01     length:sizeof( int64_t) atIndex:3];
+                        [encoder setBytes:&ne02     length:sizeof( int64_t) atIndex:4];
+                        [encoder setBytes:&nb00     length:sizeof(uint64_t) atIndex:5];
+                        [encoder setBytes:&nb01     length:sizeof(uint64_t) atIndex:6];
+                        [encoder setBytes:&nb02     length:sizeof(uint64_t) atIndex:7];
+                        [encoder setBytes:&n_groups length:sizeof( int32_t) atIndex:8];
+                        [encoder setBytes:&eps      length:sizeof(   float) atIndex:9];
+                        [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(n_groups, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                    } break;
+                case GGML_OP_NORM:
+                    {
+                        float eps;
+                        memcpy(&eps, dst->op_params, sizeof(float));
+
+                        const int nth = MIN(256, ne00);
+
+                        id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_NORM].pipeline;
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
+                        [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
+                        [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
+                        [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:3];
+                        [encoder setBytes:&eps     length:sizeof(   float) atIndex:4];
+                        [encoder setThreadgroupMemoryLength:GGML_PAD(nth*sizeof(float), 16) atIndex:0];
+
+                        const int64_t nrows = ggml_nrows(src0);
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                    } break;
+                case GGML_OP_ALIBI:
+                    {
+                        GGML_ASSERT((src0t == GGML_TYPE_F32));
+
+                        const int nth = MIN(1024, ne00);
+
+                        //const int n_past = ((int32_t *) dst->op_params)[0];
+                        const int n_head = ((int32_t *) dst->op_params)[1];
+
+                        float max_bias;
+                        memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
+
+                        const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
+                        const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
+                        const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
+
+                        id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ALIBI_F32].pipeline;
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                        [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+                        [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
+                        [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3];
+                        [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4];
+                        [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5];
+                        [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6];
+                        [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7];
+                        [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8];
+                        [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9];
+                        [encoder setBytes:&ne0  length:sizeof( int64_t) atIndex:10];
+                        [encoder setBytes:&ne1  length:sizeof( int64_t) atIndex:11];
+                        [encoder setBytes:&ne2  length:sizeof( int64_t) atIndex:12];
+                        [encoder setBytes:&ne3  length:sizeof( int64_t) atIndex:13];
+                        [encoder setBytes:&nb0  length:sizeof(uint64_t) atIndex:14];
+                        [encoder setBytes:&nb1  length:sizeof(uint64_t) atIndex:15];
+                        [encoder setBytes:&nb2  length:sizeof(uint64_t) atIndex:16];
+                        [encoder setBytes:&nb3  length:sizeof(uint64_t) atIndex:17];
+                        [encoder setBytes:&m0   length:sizeof(   float) atIndex:18];
+                        [encoder setBytes:&m1   length:sizeof(   float) atIndex:19];
+                        [encoder setBytes:&n_heads_log2_floor   length:sizeof(int) atIndex:20];
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                    } break;
+                case GGML_OP_ROPE:
+                    {
+                        GGML_ASSERT(ne10 == ne02);
+
+                        const int nth = MIN(1024, ne00);
+
+                        const int n_past     = ((int32_t *) dst->op_params)[0];
+                        const int n_dims     = ((int32_t *) dst->op_params)[1];
+                        const int mode       = ((int32_t *) dst->op_params)[2];
+                        // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal
+                        const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
+
+                        float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
+                        memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
+                        memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
+                        memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
+                        memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
+                        memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
+                        memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
+
+                        id<MTLComputePipelineState> pipeline = nil;
+
+                        switch (src0->type) {
+                            case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F32].pipeline; break;
+                            case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F16].pipeline; break;
+                            default: GGML_ASSERT(false);
+                        };
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0     offset:offs_src0        atIndex:0];
+                        [encoder setBuffer:id_src1     offset:offs_src1        atIndex:1];
+                        [encoder setBuffer:id_dst      offset:offs_dst         atIndex:2];
+                        [encoder setBytes:&ne00        length:sizeof( int64_t) atIndex:3];
+                        [encoder setBytes:&ne01        length:sizeof( int64_t) atIndex:4];
+                        [encoder setBytes:&ne02        length:sizeof( int64_t) atIndex:5];
+                        [encoder setBytes:&ne03        length:sizeof( int64_t) atIndex:6];
+                        [encoder setBytes:&nb00        length:sizeof(uint64_t) atIndex:7];
+                        [encoder setBytes:&nb01        length:sizeof(uint64_t) atIndex:8];
+                        [encoder setBytes:&nb02        length:sizeof(uint64_t) atIndex:9];
+                        [encoder setBytes:&nb03        length:sizeof(uint64_t) atIndex:10];
+                        [encoder setBytes:&ne0         length:sizeof( int64_t) atIndex:11];
+                        [encoder setBytes:&ne1         length:sizeof( int64_t) atIndex:12];
+                        [encoder setBytes:&ne2         length:sizeof( int64_t) atIndex:13];
+                        [encoder setBytes:&ne3         length:sizeof( int64_t) atIndex:14];
+                        [encoder setBytes:&nb0         length:sizeof(uint64_t) atIndex:15];
+                        [encoder setBytes:&nb1         length:sizeof(uint64_t) atIndex:16];
+                        [encoder setBytes:&nb2         length:sizeof(uint64_t) atIndex:17];
+                        [encoder setBytes:&nb3         length:sizeof(uint64_t) atIndex:18];
+                        [encoder setBytes:&n_past      length:sizeof(     int) atIndex:19];
+                        [encoder setBytes:&n_dims      length:sizeof(     int) atIndex:20];
+                        [encoder setBytes:&mode        length:sizeof(     int) atIndex:21];
+                        [encoder setBytes:&n_orig_ctx  length:sizeof(     int) atIndex:22];
+                        [encoder setBytes:&freq_base   length:sizeof(   float) atIndex:23];
+                        [encoder setBytes:&freq_scale  length:sizeof(   float) atIndex:24];
+                        [encoder setBytes:&ext_factor  length:sizeof(   float) atIndex:25];
+                        [encoder setBytes:&attn_factor length:sizeof(   float) atIndex:26];
+                        [encoder setBytes:&beta_fast   length:sizeof(   float) atIndex:27];
+                        [encoder setBytes:&beta_slow   length:sizeof(   float) atIndex:28];
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                    } break;
+                case GGML_OP_IM2COL:
+                    {
+                        GGML_ASSERT(src0->type == GGML_TYPE_F16);
+                        GGML_ASSERT(src1->type == GGML_TYPE_F32);
+                        GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
+
+                        const int32_t s0 = ((const int32_t *)(dst->op_params))[0];
+                        const int32_t s1 = ((const int32_t *)(dst->op_params))[1];
+                        const int32_t p0 = ((const int32_t *)(dst->op_params))[2];
+                        const int32_t p1 = ((const int32_t *)(dst->op_params))[3];
+                        const int32_t d0 = ((const int32_t *)(dst->op_params))[4];
+                        const int32_t d1 = ((const int32_t *)(dst->op_params))[5];
+
+                        const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1;
+
+                        const int32_t N  = src1->ne[is_2D ? 3 : 2];
+                        const int32_t IC = src1->ne[is_2D ? 2 : 1];
+                        const int32_t IH = is_2D ? src1->ne[1] : 1;
+                        const int32_t IW =         src1->ne[0];
+
+                        const int32_t KH = is_2D ? src0->ne[1] : 1;
+                        const int32_t KW =         src0->ne[0];
+
+                        const int32_t OH = is_2D ? dst->ne[2] : 1;
+                        const int32_t OW =         dst->ne[1];
+
+                        const int32_t CHW = IC * KH * KW;
+
+                        const int32_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4;
+                        const int32_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4;
+
+                        id<MTLComputePipelineState> pipeline = nil;
+
+                        switch (dst->type) {
+                            case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline; break;
+                            case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline; break;
+                            default: GGML_ASSERT(false);
+                        };
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src1 offset:offs_src1        atIndex:0];
+                        [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
+                        [encoder setBytes:&ofs0    length:sizeof( int32_t) atIndex:2];
+                        [encoder setBytes:&ofs1    length:sizeof( int32_t) atIndex:3];
+                        [encoder setBytes:&IW      length:sizeof( int32_t) atIndex:4];
+                        [encoder setBytes:&IH      length:sizeof( int32_t) atIndex:5];
+                        [encoder setBytes:&CHW     length:sizeof( int32_t) atIndex:6];
+                        [encoder setBytes:&s0      length:sizeof( int32_t) atIndex:7];
+                        [encoder setBytes:&s1      length:sizeof( int32_t) atIndex:8];
+                        [encoder setBytes:&p0      length:sizeof( int32_t) atIndex:9];
+                        [encoder setBytes:&p1      length:sizeof( int32_t) atIndex:10];
+                        [encoder setBytes:&d0      length:sizeof( int32_t) atIndex:11];
+                        [encoder setBytes:&d1      length:sizeof( int32_t) atIndex:12];
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)];
+                    } break;
+                case GGML_OP_UPSCALE:
+                    {
+                        GGML_ASSERT(src0->type == GGML_TYPE_F32);
+
+                        const int sf = dst->op_params[0];
+
+                        const id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UPSCALE_F32].pipeline;
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                        [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+                        [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
+                        [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
+                        [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
+                        [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5];
+                        [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
+                        [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
+                        [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
+                        [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9];
+                        [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:10];
+                        [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:11];
+                        [encoder setBytes:&ne2  length:sizeof(ne2)  atIndex:12];
+                        [encoder setBytes:&ne3  length:sizeof(ne3)  atIndex:13];
+                        [encoder setBytes:&nb0  length:sizeof(nb0)  atIndex:14];
+                        [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:15];
+                        [encoder setBytes:&nb2  length:sizeof(nb2)  atIndex:16];
+                        [encoder setBytes:&nb3  length:sizeof(nb3)  atIndex:17];
+                        [encoder setBytes:&sf   length:sizeof(sf)   atIndex:18];
+
+                        const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0);
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                    } break;
+                case GGML_OP_PAD:
+                    {
+                        GGML_ASSERT(src0->type == GGML_TYPE_F32);
+
+                        id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_PAD_F32].pipeline;
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                        [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+                        [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
+                        [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
+                        [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
+                        [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5];
+                        [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
+                        [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
+                        [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
+                        [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9];
+                        [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:10];
+                        [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:11];
+                        [encoder setBytes:&ne2  length:sizeof(ne2)  atIndex:12];
+                        [encoder setBytes:&ne3  length:sizeof(ne3)  atIndex:13];
+                        [encoder setBytes:&nb0  length:sizeof(nb0)  atIndex:14];
+                        [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:15];
+                        [encoder setBytes:&nb2  length:sizeof(nb2)  atIndex:16];
+                        [encoder setBytes:&nb3  length:sizeof(nb3)  atIndex:17];
+
+                        const int nth = MIN(1024, ne0);
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                    } break;
+                case GGML_OP_ARANGE:
+                    {
+                        GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+                        float start;
+                        float step;
+
+                        memcpy(&start, ((int32_t *) dst->op_params) + 0, sizeof(float));
+                        memcpy(&step,  ((int32_t *) dst->op_params) + 2, sizeof(float));
+
+                        id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARANGE_F32].pipeline;
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_dst  offset:offs_dst    atIndex:0];
+                        [encoder setBytes:&ne0   length:sizeof(ne0)   atIndex:1];
+                        [encoder setBytes:&start length:sizeof(start) atIndex:2];
+                        [encoder setBytes:&step  length:sizeof(step)  atIndex:3];
+
+                        const int nth = MIN(1024, ne0);
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(1, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                    } break;
+                case GGML_OP_TIMESTEP_EMBEDDING:
+                    {
+                        GGML_ASSERT(src0->type == GGML_TYPE_F32);
+
+                        const int dim        = dst->op_params[0];
+                        const int max_period = dst->op_params[1];
+
+                        const int half = dim / 2;
+
+                        id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32].pipeline;
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                        [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+                        [encoder setBytes:&nb1   length:sizeof(nb1) atIndex:2];
+                        [encoder setBytes:&dim   length:sizeof(dim) atIndex:3];
+                        [encoder setBytes:&max_period length:sizeof(max_period) atIndex:4];
+
+                        const int nth = MIN(1024, half);
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(ne00, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                    } break;
+                case GGML_OP_ARGSORT:
+                    {
+                        GGML_ASSERT(src0->type == GGML_TYPE_F32);
+                        GGML_ASSERT( dst->type == GGML_TYPE_I32);
+
+                        const int nrows = ggml_nrows(src0);
+
+                        enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0];
+
+                        // bitonic sort requires the number of elements to be power of 2
+                        int64_t ne00_padded = 1;
+                        while (ne00_padded < ne00) {
+                            ne00_padded *= 2;
+                        }
+
+                        // Metal kernels require the buffer size to be multiple of 16 bytes
+                        // https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/1443142-setthreadgroupmemorylength
+                        const int mem_size = GGML_PAD(ne00_padded*sizeof(int32_t), 16);
+
+                        id<MTLComputePipelineState> pipeline = nil;
+
+                        switch (order) {
+                            case GGML_SORT_ORDER_ASC:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline;  break;
+                            case GGML_SORT_ORDER_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break;
+                            default: GGML_ASSERT(false);
+                        };
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0     offset:offs_src0        atIndex:0];
+                        [encoder setBuffer:id_dst      offset:offs_dst         atIndex:1];
+                        [encoder setBytes:&ne00        length:sizeof( int64_t) atIndex:2];
+                        [encoder setBytes:&ne00_padded length:sizeof( int64_t) atIndex:3];
+                        [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(1, nrows, 1) threadsPerThreadgroup:MTLSizeMake(ne00_padded, 1, 1)];
+                    } break;
+                case GGML_OP_LEAKY_RELU:
+                    {
+                        GGML_ASSERT(src0->type == GGML_TYPE_F32);
+
+                        float slope;
+                        memcpy(&slope, dst->op_params, sizeof(float));
+
+                        id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32].pipeline;
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0 offset:offs_src0   atIndex:0];
+                        [encoder setBuffer:id_dst  offset:offs_dst    atIndex:1];
+                        [encoder setBytes:&slope length:sizeof(slope) atIndex:2];
+
+                        const int64_t n = ggml_nelements(dst);
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                    } break;
+                case GGML_OP_DUP:
+                case GGML_OP_CPY:
+                case GGML_OP_CONT:
+                    {
+                        GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0);
+
+                        int nth = MIN(1024, ne00/ggml_blck_size(src0->type));
+
+                        id<MTLComputePipelineState> pipeline = nil;
+
+                        switch (src0t) {
+                            case GGML_TYPE_F32:
+                                {
+                                    GGML_ASSERT(ne0 % ggml_blck_size(dst->type) == 0);
+
+                                    switch (dstt) {
+                                        case GGML_TYPE_F16:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline;  break;
+                                        case GGML_TYPE_F32:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline;  break;
+                                        case GGML_TYPE_Q8_0:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break;
+                                        case GGML_TYPE_Q4_0:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break;
+                                        case GGML_TYPE_Q4_1:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break;
+                                        case GGML_TYPE_Q5_0:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0].pipeline; break;
+                                        case GGML_TYPE_Q5_1:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1].pipeline; break;
+                                        case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL].pipeline; break;
+                                        default: GGML_ASSERT(false && "not implemented");
+                                    };
+                                } break;
+                            case GGML_TYPE_F16:
+                                {
+                                    switch (dstt) {
+                                        case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline; break;
+                                        case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F32].pipeline; break;
+                                        default: GGML_ASSERT(false && "not implemented");
+                                    };
+                                } break;
+                            default: GGML_ASSERT(false && "not implemented");
+                        }
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
+                        [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
+                        [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
+                        [encoder setBytes:&ne01    length:sizeof( int64_t) atIndex:3];
+                        [encoder setBytes:&ne02    length:sizeof( int64_t) atIndex:4];
+                        [encoder setBytes:&ne03    length:sizeof( int64_t) atIndex:5];
+                        [encoder setBytes:&nb00    length:sizeof(uint64_t) atIndex:6];
+                        [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:7];
+                        [encoder setBytes:&nb02    length:sizeof(uint64_t) atIndex:8];
+                        [encoder setBytes:&nb03    length:sizeof(uint64_t) atIndex:9];
+                        [encoder setBytes:&ne0     length:sizeof( int64_t) atIndex:10];
+                        [encoder setBytes:&ne1     length:sizeof( int64_t) atIndex:11];
+                        [encoder setBytes:&ne2     length:sizeof( int64_t) atIndex:12];
+                        [encoder setBytes:&ne3     length:sizeof( int64_t) atIndex:13];
+                        [encoder setBytes:&nb0     length:sizeof(uint64_t) atIndex:14];
+                        [encoder setBytes:&nb1     length:sizeof(uint64_t) atIndex:15];
+                        [encoder setBytes:&nb2     length:sizeof(uint64_t) atIndex:16];
+                        [encoder setBytes:&nb3     length:sizeof(uint64_t) atIndex:17];
+
+                        [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                    } break;
+                default:
+                    {
+                        GGML_METAL_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
+                        GGML_ASSERT(false);
+                    }
             }
 
-            if (encoder != nil) {
-                [encoder endEncoding];
-                encoder = nil;
+            if (should_capture) {
+                [encoder popDebugGroup];
             }
+        }
 
-            [command_buffer commit];
-        });
-    }
+        [encoder endEncoding];
 
-    // wait for all threads to finish
-    dispatch_barrier_sync(ctx->d_queue, ^{});
+        [command_buffer commit];
+    });
 
-    // check status of command buffers
+    // Wait for completion and check status of each command buffer
     // needed to detect if the device ran out-of-memory for example (#1881)
-    for (int i = 0; i < n_cb; i++) {
-        [ctx->command_buffers[i] waitUntilCompleted];
 
-        MTLCommandBufferStatus status = (MTLCommandBufferStatus) [ctx->command_buffers[i] status];
+    for (int i = 0; i < n_cb; ++i) {
+        id<MTLCommandBuffer> command_buffer = command_buffers[i];
+        [command_buffer waitUntilCompleted];
+
+        MTLCommandBufferStatus status = [command_buffer status];
         if (status != MTLCommandBufferStatusCompleted) {
             GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status);
-            return false;
+            return GGML_STATUS_FAILED;
         }
     }
 
-    return true;
+    if (should_capture) {
+        [[MTLCaptureManager sharedCaptureManager] stopCapture];
     }
+
+    }
+    return GGML_STATUS_SUCCESS;
 }
 
 ////////////////////////////////////////////////////////////////////////////////
@@ -2482,13 +2584,13 @@ static void ggml_backend_metal_free_device(void) {
     }
 }
 
-static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) {
-    struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context;
+GGML_CALL static const char * ggml_backend_metal_buffer_get_name(ggml_backend_buffer_t buffer) {
+    return "Metal";
 
-    return ctx->all_data;
+    UNUSED(buffer);
 }
 
-static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+GGML_CALL static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) {
     struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context;
 
     for (int i = 0; i < ctx->n_buffers; i++) {
@@ -2503,50 +2605,80 @@ static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer)
     free(ctx);
 }
 
-static void ggml_backend_metal_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+GGML_CALL static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) {
+    struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context;
+
+    return ctx->all_data;
+}
+
+GGML_CALL static void ggml_backend_metal_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
     memcpy((char *)tensor->data + offset, data, size);
 
     UNUSED(buffer);
 }
 
-static void ggml_backend_metal_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+GGML_CALL static void ggml_backend_metal_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
     memcpy(data, (const char *)tensor->data + offset, size);
 
     UNUSED(buffer);
 }
 
-static void ggml_backend_metal_buffer_cpy_tensor_from(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst) {
-    ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src));
+GGML_CALL static bool ggml_backend_metal_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
+    if (ggml_backend_buffer_is_host(src->buffer)) {
+        memcpy(dst->data, src->data, ggml_nbytes(src));
+        return true;
+    }
+    return false;
 
     UNUSED(buffer);
 }
 
-static void ggml_backend_metal_buffer_cpy_tensor_to(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst) {
-    ggml_backend_tensor_set(dst, src->data, 0, ggml_nbytes(src));
-
-    UNUSED(buffer);
-}
-
-static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+GGML_CALL static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
     struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context;
 
     memset(ctx->all_data, value, ctx->all_size);
 }
 
 static struct ggml_backend_buffer_i ggml_backend_metal_buffer_i = {
+    /* .get_name        = */ ggml_backend_metal_buffer_get_name,
     /* .free_buffer     = */ ggml_backend_metal_buffer_free_buffer,
     /* .get_base        = */ ggml_backend_metal_buffer_get_base,
     /* .init_tensor     = */ NULL,
     /* .set_tensor      = */ ggml_backend_metal_buffer_set_tensor,
     /* .get_tensor      = */ ggml_backend_metal_buffer_get_tensor,
-    /* .cpy_tensor_from = */ ggml_backend_metal_buffer_cpy_tensor_from,
-    /* .cpy_tensor_to   = */ ggml_backend_metal_buffer_cpy_tensor_to,
+    /* .cpy_tensor      = */ ggml_backend_metal_buffer_cpy_tensor,
     /* .clear           = */ ggml_backend_metal_buffer_clear,
+    /* .reset           = */ NULL,
 };
 
 // default buffer type
 
-static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+GGML_CALL static const char * ggml_backend_metal_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
+    return "Metal";
+
+    UNUSED(buft);
+}
+
+static void ggml_backend_metal_log_allocated_size(id<MTLDevice> device) {
+#if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15)
+    if (@available(macOS 10.12, iOS 16.0, *)) {
+        GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)",
+                device.currentAllocatedSize / 1024.0 / 1024.0,
+                device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
+
+        if (device.currentAllocatedSize > device.recommendedMaxWorkingSetSize) {
+            GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__);
+        } else {
+            GGML_METAL_LOG_INFO("\n");
+        }
+    } else {
+        GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0);
+    }
+#endif
+    UNUSED(device);
+}
+
+GGML_CALL static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
     struct ggml_backend_metal_buffer_context * ctx = malloc(sizeof(struct ggml_backend_metal_buffer_context));
 
     const size_t size_page = sysconf(_SC_PAGESIZE);
@@ -2578,48 +2710,45 @@ static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_ba
     }
 
     GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB", __func__, size_aligned / 1024.0 / 1024.0);
-
-
-#if TARGET_OS_OSX
-    GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)",
-            device.currentAllocatedSize / 1024.0 / 1024.0,
-            device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
-
-    if (device.currentAllocatedSize > device.recommendedMaxWorkingSetSize) {
-        GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__);
-    } else {
-        GGML_METAL_LOG_INFO("\n");
-    }
-#else
-    GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0);
-#endif
-
+    ggml_backend_metal_log_allocated_size(device);
 
     return ggml_backend_buffer_init(buft, ggml_backend_metal_buffer_i, ctx, size);
 }
 
-static size_t ggml_backend_metal_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+GGML_CALL static size_t ggml_backend_metal_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
     return 32;
     UNUSED(buft);
 }
 
-static bool ggml_backend_metal_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
+GGML_CALL static size_t ggml_backend_metal_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
+    id<MTLDevice> device = ggml_backend_metal_get_device();
+    size_t max_size = device.maxBufferLength;
+    ggml_backend_metal_free_device();
+
+    return max_size;
+
+    UNUSED(buft);
+}
+
+GGML_CALL static bool ggml_backend_metal_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
     return ggml_backend_is_metal(backend) || ggml_backend_is_cpu(backend);
 
     UNUSED(buft);
 }
 
-static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
+GGML_CALL static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
     return true;
 
     UNUSED(buft);
 }
 
-ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) {
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) {
     static struct ggml_backend_buffer_type ggml_backend_buffer_type_metal = {
         /* .iface = */ {
+            /* .get_name         = */ ggml_backend_metal_buffer_type_get_name,
             /* .alloc_buffer     = */ ggml_backend_metal_buffer_type_alloc_buffer,
             /* .get_alignment    = */ ggml_backend_metal_buffer_type_get_alignment,
+            /* .get_max_size     = */ ggml_backend_metal_buffer_type_get_max_size,
             /* .get_alloc_size   = */ NULL, // defaults to ggml_nbytes
             /* .supports_backend = */ ggml_backend_metal_buffer_type_supports_backend,
             /* .is_host          = */ ggml_backend_metal_buffer_type_is_host,
@@ -2632,7 +2761,7 @@ ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) {
 
 // buffer from ptr
 
-ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size) {
+GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size) {
     struct ggml_backend_metal_buffer_context * ctx = malloc(sizeof(struct ggml_backend_metal_buffer_context));
 
     ctx->all_data = data;
@@ -2641,6 +2770,14 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz
     ctx->n_buffers = 0;
 
     const size_t size_page = sysconf(_SC_PAGESIZE);
+
+    // page-align the data ptr
+    {
+        const uintptr_t offs = (uintptr_t) data % size_page;
+        data  = (void *) ((char *) data - offs);
+        size += offs;
+    }
+
     size_t size_aligned = size;
     if ((size_aligned % size_page) != 0) {
         size_aligned += (size_page - (size_aligned % size_page));
@@ -2692,71 +2829,74 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz
         }
     }
 
-#if TARGET_OS_OSX
-    GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)",
-            device.currentAllocatedSize / 1024.0 / 1024.0,
-            device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
-
-    if (device.currentAllocatedSize > device.recommendedMaxWorkingSetSize) {
-        GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__);
-    } else {
-        GGML_METAL_LOG_INFO("\n");
-    }
-#else
-    GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0);
-#endif
+    ggml_backend_metal_log_allocated_size(device);
 
     return ggml_backend_buffer_init(ggml_backend_metal_buffer_type(), ggml_backend_metal_buffer_i, ctx, size);
 }
 
 // backend
 
-static const char * ggml_backend_metal_name(ggml_backend_t backend) {
+GGML_CALL static const char * ggml_backend_metal_name(ggml_backend_t backend) {
     return "Metal";
 
     UNUSED(backend);
 }
 
-static void ggml_backend_metal_free(ggml_backend_t backend) {
+GGML_CALL static void ggml_backend_metal_free(ggml_backend_t backend) {
     struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context;
     ggml_metal_free(ctx);
     free(backend);
 }
 
-static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffer_type(ggml_backend_t backend) {
+GGML_CALL static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffer_type(ggml_backend_t backend) {
     return ggml_backend_metal_buffer_type();
 
     UNUSED(backend);
 }
 
-static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+GGML_CALL static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
     struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context;
 
     return ggml_metal_graph_compute(metal_ctx, cgraph);
 }
 
-static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
-    return ggml_metal_supports_op(op);
+GGML_CALL static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
+    struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context;
 
-    UNUSED(backend);
+    return ggml_metal_supports_op(metal_ctx, op);
 }
 
-static struct ggml_backend_i metal_backend_i = {
+static struct ggml_backend_i ggml_backend_metal_i = {
     /* .get_name                = */ ggml_backend_metal_name,
     /* .free                    = */ ggml_backend_metal_free,
     /* .get_default_buffer_type = */ ggml_backend_metal_get_default_buffer_type,
     /* .set_tensor_async        = */ NULL,
     /* .get_tensor_async        = */ NULL,
-    /* .cpy_tensor_from_async   = */ NULL,
-    /* .cpy_tensor_to_async     = */ NULL,
+    /* .cpy_tensor_async        = */ NULL,
     /* .synchronize             = */ NULL,
     /* .graph_plan_create       = */ NULL,
     /* .graph_plan_free         = */ NULL,
     /* .graph_plan_compute      = */ NULL,
     /* .graph_compute           = */ ggml_backend_metal_graph_compute,
     /* .supports_op             = */ ggml_backend_metal_supports_op,
+    /* .offload_op              = */ NULL,
+    /* .event_new               = */ NULL,
+    /* .event_free              = */ NULL,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .event_synchronize       = */ NULL,
 };
 
+void ggml_backend_metal_log_set_callback(ggml_log_callback log_callback, void * user_data) {
+    ggml_metal_log_callback  = log_callback;
+    ggml_metal_log_user_data = user_data;
+}
+
+static ggml_guid_t ggml_backend_metal_guid(void) {
+    static ggml_guid guid = { 0x81, 0xa1, 0x8b, 0x1e, 0x71, 0xec, 0x79, 0xed, 0x2b, 0x85, 0xdc, 0x8a, 0x61, 0x98, 0x30, 0xe6 };
+    return &guid;
+}
+
 ggml_backend_t ggml_backend_metal_init(void) {
     struct ggml_metal_context * ctx = ggml_metal_init(GGML_DEFAULT_N_THREADS);
 
@@ -2767,7 +2907,8 @@ ggml_backend_t ggml_backend_metal_init(void) {
     ggml_backend_t metal_backend = malloc(sizeof(struct ggml_backend));
 
     *metal_backend = (struct ggml_backend) {
-        /* .interface = */ metal_backend_i,
+        /* .guid      = */ ggml_backend_metal_guid(),
+        /* .interface = */ ggml_backend_metal_i,
         /* .context   = */ ctx,
     };
 
@@ -2775,7 +2916,7 @@ ggml_backend_t ggml_backend_metal_init(void) {
 }
 
 bool ggml_backend_is_metal(ggml_backend_t backend) {
-    return backend->iface.get_name == ggml_backend_metal_name;
+    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_metal_guid());
 }
 
 void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) {
@@ -2783,7 +2924,7 @@ void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) {
 
     struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context;
 
-    ggml_metal_set_n_cb(ctx, n_cb);
+    ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS);
 }
 
 bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family) {
@@ -2794,9 +2935,16 @@ bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family) {
     return [ctx->device supportsFamily:(MTLGPUFamilyApple1 + family - 1)];
 }
 
-ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data); // silence warning
+void ggml_backend_metal_capture_next_compute(ggml_backend_t backend) {
+    GGML_ASSERT(ggml_backend_is_metal(backend));
 
-ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data) {
+    struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context;
+    ctx->should_capture_next_compute = true;
+}
+
+GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data); // silence warning
+
+GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data) {
     return ggml_backend_metal_init();
 
     GGML_UNUSED(params);
diff --git a/ggml-metal.metal b/ggml-metal.metal
index 229efb8b6..9a29f57a3 100644
--- a/ggml-metal.metal
+++ b/ggml-metal.metal
@@ -1,3 +1,7 @@
+#define GGML_COMMON_DECL_METAL
+#define GGML_COMMON_IMPL_METAL
+#include "ggml-common.h"
+
 #include <metal_stdlib>
 
 using namespace metal;
@@ -6,46 +10,11 @@ using namespace metal;
 #define MIN(x, y) ((x) < (y) ? (x) : (y))
 #define SWAP(x, y) { auto tmp = (x); (x) = (y); (y) = tmp; }
 
-#define QK4_0 32
-#define QR4_0 2
-typedef struct {
-    half    d;             // delta
-    uint8_t qs[QK4_0 / 2]; // nibbles / quants
-} block_q4_0;
-
-#define QK4_1 32
-typedef struct {
-    half d;                 // delta
-    half m;                 // min
-    uint8_t qs[QK4_1 / 2];  // nibbles / quants
-} block_q4_1;
-
-#define QK5_0 32
-typedef struct {
-    half d;                // delta
-    uint8_t qh[4];         // 5-th bit of quants
-    uint8_t qs[QK5_0 / 2]; // nibbles / quants
-} block_q5_0;
-
-#define QK5_1 32
-typedef struct {
-    half d;                 // delta
-    half m;                 // min
-    uint8_t qh[4];          // 5-th bit of quants
-    uint8_t qs[QK5_1 / 2];  // nibbles / quants
-} block_q5_1;
-
-#define QK8_0 32
-typedef struct {
-    half    d;         // delta
-    int8_t  qs[QK8_0]; // quants
-} block_q8_0;
-
 #define N_SIMDWIDTH 32 // assuming SIMD group size is 32
 
 enum ggml_sort_order {
-    GGML_SORT_ASC,
-    GGML_SORT_DESC,
+    GGML_SORT_ORDER_ASC,
+    GGML_SORT_ORDER_DESC,
 };
 
 // general-purpose kernel for addition, multiplication and division of two tensors
@@ -351,12 +320,17 @@ kernel void kernel_sum_rows(
 kernel void kernel_soft_max(
         device const float * src0,
         device const float * src1,
+        device const float * src2,
         device       float * dst,
         constant   int64_t & ne00,
         constant   int64_t & ne01,
         constant   int64_t & ne02,
         constant     float & scale,
-        threadgroup float  * buf [[threadgroup(0)]],
+        constant     float & max_bias,
+        constant     float & m0,
+        constant     float & m1,
+        constant  uint32_t & n_head_log2,
+        threadgroup  float * buf [[threadgroup(0)]],
         uint  tgpig[[threadgroup_position_in_grid]],
         uint  tpitg[[thread_position_in_threadgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]],
@@ -368,13 +342,26 @@ kernel void kernel_soft_max(
 
     device const float * psrc0 =         src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
     device const float * pmask = src1 != src0 ? src1                               + i01*ne00 : nullptr;
+    device const float * ppos  = src2 != src0 ? src2                                          : nullptr;
     device       float * pdst  =         dst  + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
 
+    float slope = 0.0f;
+
+    // ALiBi
+    if (max_bias > 0.0f) {
+        const int64_t h = i02;
+
+        const float base = h < n_head_log2 ? m0 : m1;
+        const int   exp  = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+        slope = pow(base, exp);
+    }
+
     // parallel max
     float lmax = -INFINITY;
 
     for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
-        lmax = MAX(lmax, psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f));
+        lmax = MAX(lmax, psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f));
     }
 
     // find the max value in the block
@@ -399,7 +386,7 @@ kernel void kernel_soft_max(
     // parallel sum
     float lsum = 0.0f;
     for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
-        const float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f)) - max_val);
+        const float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f)) - max_val);
         lsum += exp_psrc0;
         pdst[i00] = exp_psrc0;
     }
@@ -437,12 +424,17 @@ kernel void kernel_soft_max(
 kernel void kernel_soft_max_4(
         device const float * src0,
         device const float * src1,
+        device const float * src2,
         device       float * dst,
         constant   int64_t & ne00,
         constant   int64_t & ne01,
         constant   int64_t & ne02,
         constant     float & scale,
-        threadgroup float  * buf [[threadgroup(0)]],
+        constant     float & max_bias,
+        constant     float & m0,
+        constant     float & m1,
+        constant  uint32_t & n_head_log2,
+        threadgroup  float * buf [[threadgroup(0)]],
         uint  tgpig[[threadgroup_position_in_grid]],
         uint  tpitg[[thread_position_in_threadgroup]],
         uint  sgitg[[simdgroup_index_in_threadgroup]],
@@ -454,13 +446,25 @@ kernel void kernel_soft_max_4(
 
     device const float4 * psrc4 =                (device const float4 *)(src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
     device const float4 * pmask = src1 != src0 ? (device const float4 *)(src1 +                                      i01*ne00) : nullptr;
+    device const float4 * ppos  = src2 != src0 ? (device const float4 *)(src2)                                                 : nullptr;
     device       float4 * pdst4 =                (device       float4 *)(dst  + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
 
+    float slope = 0.0f;
+
+    if (max_bias > 0.0f) {
+        const int64_t h = i02;
+
+        const float base = h < n_head_log2 ? m0 : m1;
+        const int   exp  = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+        slope = pow(base, exp);
+    }
+
     // parallel max
     float4 lmax4 = -INFINITY;
 
     for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) {
-        lmax4 = fmax(lmax4, psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f));
+        lmax4 = fmax(lmax4, psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f));
     }
 
     const float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3]));
@@ -486,7 +490,7 @@ kernel void kernel_soft_max_4(
     // parallel sum
     float4 lsum4 = 0.0f;
     for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) {
-        const float4 exp_psrc4 = exp((psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f)) - max_val);
+        const float4 exp_psrc4 = exp((psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f)) - max_val);
         lsum4 += exp_psrc4;
         pdst4[i00] = exp_psrc4;
     }
@@ -1775,9 +1779,29 @@ kernel void kernel_rope(
 template [[host_name("kernel_rope_f32")]] kernel rope_t kernel_rope<float>;
 template [[host_name("kernel_rope_f16")]] kernel rope_t kernel_rope<half>;
 
-kernel void kernel_im2col_f16(
+typedef void (im2col_t)(
         device const float * x,
-        device       half * dst,
+        device        char * dst,
+        constant   int32_t & ofs0,
+        constant   int32_t & ofs1,
+        constant   int32_t & IW,
+        constant   int32_t & IH,
+        constant   int32_t & CHW,
+        constant   int32_t & s0,
+        constant   int32_t & s1,
+        constant   int32_t & p0,
+        constant   int32_t & p1,
+        constant   int32_t & d0,
+        constant   int32_t & d1,
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint3  tgpg[[threadgroups_per_grid]],
+        uint3 tpitg[[thread_position_in_threadgroup]],
+        uint3   ntg[[threads_per_threadgroup]]);
+
+template <typename T>
+kernel void kernel_im2col(
+        device const float * x,
+        device        char * dst,
         constant   int32_t & ofs0,
         constant   int32_t & ofs1,
         constant   int32_t & IW,
@@ -1800,14 +1824,19 @@ kernel void kernel_im2col_f16(
         (tpitg[0] * tgpg[1] * tgpg[2] + tgpig[1] * tgpg[2] + tgpig[2]) * CHW +
         (tgpig[0] * (ntg[1] * ntg[2]) + tpitg[1] * ntg[2] + tpitg[2]);
 
+    device T * pdst = (device T *) (dst);
+
     if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
-        dst[offset_dst] = 0.0f;
+        pdst[offset_dst] = 0.0f;
     } else {
         const int32_t offset_src = tpitg[0] * ofs0 + tgpig[0] * ofs1;
-        dst[offset_dst] = x[offset_src + iih * IW + iiw];
+        pdst[offset_dst] = x[offset_src + iih * IW + iiw];
     }
 }
 
+template [[host_name("kernel_im2col_f32")]] kernel im2col_t kernel_im2col<float>;
+template [[host_name("kernel_im2col_f16")]] kernel im2col_t kernel_im2col<half>;
+
 kernel void kernel_upscale_f32(
     device  const char * src0,
     device        char * dst,
@@ -1899,11 +1928,56 @@ kernel void kernel_pad_f32(
     }
 }
 
+kernel void kernel_arange_f32(
+    device        char * dst,
+    constant   int64_t & ne0,
+    constant   float   & start,
+    constant   float   & step,
+    uint3 tgpig[[threadgroup_position_in_grid]],
+    uint3 tpitg[[thread_position_in_threadgroup]],
+    uint3   ntg[[threads_per_threadgroup]]) {
+
+    device float * dst_ptr = (device float *) dst;
+
+    for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) {
+        dst_ptr[i0] = start + step * i0;
+    }
+}
+
+kernel void kernel_timestep_embedding_f32(
+    device  const char * src0,
+    device        char * dst,
+    constant  uint64_t & nb1,
+    constant  int      & dim,
+    constant  int      & max_period,
+    uint3 tgpig[[threadgroup_position_in_grid]],
+    uint3 tpitg[[thread_position_in_threadgroup]],
+    uint3   ntg[[threads_per_threadgroup]]) {
+
+    int i = tgpig.x;
+    device float * embed_data = (device float *)(dst +  i*nb1);
+
+    int half_ = dim / 2;
+    for (int j = tpitg.x; j < half_; j += ntg.x) {
+        float timestep = ((device float *)src0)[i];
+        float freq = (float)exp(-log((float)max_period) * j / half_);
+        float arg = timestep * freq;
+        embed_data[j        ] = cos(arg);
+        embed_data[j + half_] = sin(arg);
+    }
+
+    if (dim % 2 != 0 && tpitg.x == 0) {
+        embed_data[dim] = 0.f;
+    }
+}
+
 // bitonic sort implementation following the CUDA kernels as reference
 typedef void (argsort_t)(
-        device const float * x,
-        device     int32_t * dst,
-        constant   int64_t & ncols,
+        device const float  * x,
+        device     int32_t  * dst,
+        constant   int64_t  & ncols,
+        constant   int64_t  & ncols_pad,
+        threadgroup int32_t * shared_values [[threadgroup(0)]],
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint3 tpitg[[thread_position_in_threadgroup]]);
 
@@ -1912,33 +1986,42 @@ kernel void kernel_argsort_f32_i32(
         device const float   * x,
         device       int32_t * dst,
         constant     int64_t & ncols,
+        constant     int64_t & ncols_pad,
+        threadgroup int32_t  * shared_values [[threadgroup(0)]],
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint3 tpitg[[thread_position_in_threadgroup]]) {
     // bitonic sort
     int col = tpitg[0];
     int row = tgpig[1];
 
-    if (col >= ncols) return;
+    if (col >= ncols_pad) return;
 
-    device const float   * x_row   = x   + row * ncols;
-    device       int32_t * dst_row = dst + row * ncols;
+    device const float   * x_row   = x + row * ncols;
+    threadgroup int32_t  * dst_row = shared_values;
 
     // initialize indices
-    if (col < ncols) {
-        dst_row[col] = col;
-    }
+    dst_row[col] = col;
+
     threadgroup_barrier(mem_flags::mem_threadgroup);
 
-    for (int k = 2; k <= ncols; k *= 2) {
+    for (int k = 2; k <= ncols_pad; k *= 2) {
         for (int j = k / 2; j > 0; j /= 2) {
             int ixj = col ^ j;
             if (ixj > col) {
                 if ((col & k) == 0) {
-                    if (order == GGML_SORT_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) {
+                    if (dst_row[col] >= ncols ||
+                        (dst_row[ixj] < ncols && (order == GGML_SORT_ORDER_ASC ?
+                            x_row[dst_row[col]] > x_row[dst_row[ixj]] :
+                            x_row[dst_row[col]] < x_row[dst_row[ixj]]))
+                    ) {
                         SWAP(dst_row[col], dst_row[ixj]);
                     }
                 } else {
-                    if (order == GGML_SORT_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) {
+                    if (dst_row[ixj] >= ncols ||
+                        (dst_row[col] < ncols && (order == GGML_SORT_ORDER_ASC ?
+                            x_row[dst_row[col]] < x_row[dst_row[ixj]] :
+                            x_row[dst_row[col]] > x_row[dst_row[ixj]]))
+                    ) {
                         SWAP(dst_row[col], dst_row[ixj]);
                     }
                 }
@@ -1946,10 +2029,15 @@ kernel void kernel_argsort_f32_i32(
             threadgroup_barrier(mem_flags::mem_threadgroup);
         }
     }
+
+    // copy the result to dst without the padding
+    if (col < ncols) {
+        dst[row * ncols + col] = dst_row[col];
+    }
 }
 
-template [[host_name("kernel_argsort_f32_i32_asc")]]  kernel argsort_t kernel_argsort_f32_i32<GGML_SORT_ASC>;
-template [[host_name("kernel_argsort_f32_i32_desc")]] kernel argsort_t kernel_argsort_f32_i32<GGML_SORT_DESC>;
+template [[host_name("kernel_argsort_f32_i32_asc")]]  kernel argsort_t kernel_argsort_f32_i32<GGML_SORT_ORDER_ASC>;
+template [[host_name("kernel_argsort_f32_i32_desc")]] kernel argsort_t kernel_argsort_f32_i32<GGML_SORT_ORDER_DESC>;
 
 kernel void kernel_leaky_relu_f32(
         device const float * src0,
@@ -2316,6 +2404,242 @@ kernel void kernel_cpy_f32_q4_1(
     }
 }
 
+kernel void kernel_cpy_f32_q5_0(
+        device const float * src0,
+        device        void * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant   int64_t & ne03,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant  uint64_t & nb03,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   int64_t & ne2,
+        constant   int64_t & ne3,
+        constant  uint64_t & nb0,
+        constant  uint64_t & nb1,
+        constant  uint64_t & nb2,
+        constant  uint64_t & nb3,
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint3 tpitg[[thread_position_in_threadgroup]],
+        uint3   ntg[[threads_per_threadgroup]]) {
+    const int64_t i03 = tgpig[2];
+    const int64_t i02 = tgpig[1];
+    const int64_t i01 = tgpig[0];
+
+    const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+
+    const int64_t i3 = n / (ne2*ne1*ne0);
+    const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
+    const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
+    const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK5_0;
+
+    device block_q5_0 * dst_data = (device block_q5_0 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+
+    for (int64_t i00 = tpitg.x*QK5_0; i00 < ne00; i00 += ntg.x*QK5_0) {
+        device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
+
+        float amax = 0.0f; // absolute max
+        float max  = 0.0f;
+
+        for (int j = 0; j < QK5_0; j++) {
+            const float v = src[j];
+            if (amax < fabs(v)) {
+                amax = fabs(v);
+                max  = v;
+            }
+        }
+
+        const float d = max / -16;
+        const float id = d ? 1.0f/d : 0.0f;
+
+        dst_data[i00/QK5_0].d = d;
+
+        uint32_t qh = 0;
+        for (int j = 0; j < QK5_0/2; ++j) {
+            const float x0 = src[0       + j]*id;
+            const float x1 = src[QK5_0/2 + j]*id;
+
+            const uint8_t xi0 = MIN(31, (int8_t)(x0 + 16.5f));
+            const uint8_t xi1 = MIN(31, (int8_t)(x1 + 16.5f));
+
+            dst_data[i00/QK5_0].qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
+            qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
+            qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2);
+        }
+        thread const uint8_t * qh8 = (thread const uint8_t *)&qh;
+        for (int j = 0; j < 4; ++j) {
+            dst_data[i00/QK5_0].qh[j] = qh8[j];
+        }
+    }
+}
+
+kernel void kernel_cpy_f32_q5_1(
+        device const float * src0,
+        device        void * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant   int64_t & ne03,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant  uint64_t & nb03,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   int64_t & ne2,
+        constant   int64_t & ne3,
+        constant  uint64_t & nb0,
+        constant  uint64_t & nb1,
+        constant  uint64_t & nb2,
+        constant  uint64_t & nb3,
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint3 tpitg[[thread_position_in_threadgroup]],
+        uint3   ntg[[threads_per_threadgroup]]) {
+    const int64_t i03 = tgpig[2];
+    const int64_t i02 = tgpig[1];
+    const int64_t i01 = tgpig[0];
+
+    const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+
+    const int64_t i3 = n / (ne2*ne1*ne0);
+    const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
+    const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
+    const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK5_1;
+
+    device block_q5_1 * dst_data = (device block_q5_1 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+
+    for (int64_t i00 = tpitg.x*QK5_1; i00 < ne00; i00 += ntg.x*QK5_1) {
+        device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
+
+        float max = src[0];
+        float min = src[0];
+
+        for (int j = 1; j < QK5_1; j++) {
+            const float v = src[j];
+            min = v < min ? v : min;
+            max = v > max ? v : max;
+        }
+
+        const float d = (max - min) / 31;
+        const float id = d ? 1.0f/d : 0.0f;
+
+        dst_data[i00/QK5_1].d = d;
+        dst_data[i00/QK5_1].m = min;
+
+        uint32_t qh = 0;
+        for (int j = 0; j < QK5_1/2; ++j) {
+            const float x0 = (src[0       + j] - min)*id;
+            const float x1 = (src[QK5_1/2 + j] - min)*id;
+
+            const uint8_t xi0 = (uint8_t)(x0 + 0.5f);
+            const uint8_t xi1 = (uint8_t)(x1 + 0.5f);
+
+            dst_data[i00/QK5_1].qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
+            qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
+            qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_1/2);
+        }
+        thread const uint8_t * qh8 = (thread const uint8_t *)&qh;
+        for (int j = 0; j < 4; ++j) {
+            dst_data[i00/QK5_1].qh[j] = qh8[j];
+        }
+    }
+}
+
+static inline int best_index_int8(int n, constant float * val, float x) {
+    if (x <= val[0]) return 0;
+    if (x >= val[n-1]) return n-1;
+    int ml = 0, mu = n-1;
+    while (mu-ml > 1) {
+        int mav = (ml+mu)/2;
+        if (x < val[mav]) mu = mav; else ml = mav;
+    }
+    return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
+}
+
+constexpr constant static float kvalues_iq4nl_f[16] = {
+    -127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f, 1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f
+};
+
+kernel void kernel_cpy_f32_iq4_nl(
+        device const float * src0,
+        device        void * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant   int64_t & ne03,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant  uint64_t & nb03,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   int64_t & ne2,
+        constant   int64_t & ne3,
+        constant  uint64_t & nb0,
+        constant  uint64_t & nb1,
+        constant  uint64_t & nb2,
+        constant  uint64_t & nb3,
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint3 tpitg[[thread_position_in_threadgroup]],
+        uint3   ntg[[threads_per_threadgroup]]) {
+    const int64_t i03 = tgpig[2];
+    const int64_t i02 = tgpig[1];
+    const int64_t i01 = tgpig[0];
+
+    const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+
+    const int64_t i3 = n / (ne2*ne1*ne0);
+    const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
+    const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
+    const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK4_NL;
+
+    device block_iq4_nl * dst_data = (device block_iq4_nl *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+
+    for (int64_t i00 = tpitg.x*QK4_NL; i00 < ne00; i00 += ntg.x*QK4_NL) {
+        device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
+
+        float amax = 0.0f; // absolute max
+        float max  = 0.0f;
+
+        for (int j = 0; j < QK4_0; j++) {
+            const float v = src[j];
+            if (amax < fabs(v)) {
+                amax = fabs(v);
+                max  = v;
+            }
+        }
+
+        const float d = max / kvalues_iq4nl_f[0];
+        const float id = d ? 1.0f/d : 0.0f;
+
+        float sumqx = 0, sumq2 = 0;
+        for (int j = 0; j < QK4_NL/2; ++j) {
+            const float x0 = src[0        + j]*id;
+            const float x1 = src[QK4_NL/2 + j]*id;
+
+            const uint8_t xi0 = best_index_int8(16, kvalues_iq4nl_f, x0);
+            const uint8_t xi1 = best_index_int8(16, kvalues_iq4nl_f, x1);
+
+            dst_data[i00/QK4_NL].qs[j] = xi0 | (xi1 << 4);
+
+            const float v0 = kvalues_iq4nl_f[xi0];
+            const float v1 = kvalues_iq4nl_f[xi1];
+            const float w0 = src[0        + j]*src[0        + j];
+            const float w1 = src[QK4_NL/2 + j]*src[QK4_NL/2 + j];
+            sumqx += w0*v0*src[j] + w1*v1*src[QK4_NL/2 + j];
+            sumq2 += w0*v0*v0 + w1*v1*v1;
+
+        }
+
+        dst_data[i00/QK4_NL].d = sumq2 > 0 ? sumqx/sumq2 : d;
+
+    }
+}
+
 kernel void kernel_concat(
     device  const char * src0,
     device  const char * src1,
@@ -2372,88 +2696,6 @@ kernel void kernel_concat(
     }
 }
 
-//============================================ k-quants ======================================================
-
-#ifndef QK_K
-#define QK_K 256
-#else
-static_assert(QK_K == 256 || QK_K == 64, "QK_K must be 256 or 64");
-#endif
-
-#if QK_K == 256
-#define K_SCALE_SIZE 12
-#else
-#define K_SCALE_SIZE 4
-#endif
-
-typedef struct {
-    uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
-    uint8_t qs[QK_K/4];      // quants
-    half d;           // super-block scale for quantized scales
-    half dmin;        // super-block scale for quantized mins
-} block_q2_K;
-// 84 bytes / block
-
-typedef struct {
-    uint8_t hmask[QK_K/8];     // quants - high bit
-    uint8_t qs[QK_K/4];        // quants - low 2 bits
-#if QK_K == 64
-    uint8_t scales[2];
-#else
-    uint8_t scales[K_SCALE_SIZE]; // scales, quantized with 6 bits
-#endif
-    half d;             // super-block scale
-} block_q3_K;
-
-#if QK_K == 64
-typedef struct {
-    half    d[2];          // super-block scales/mins
-    uint8_t scales[2];
-    uint8_t qs[QK_K/2];    // 4-bit quants
-} block_q4_K;
-#else
-typedef struct {
-    half d;             // super-block scale for quantized scales
-    half dmin;          // super-block scale for quantized mins
-    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
-    uint8_t qs[QK_K/2];        // 4--bit quants
-} block_q4_K;
-#endif
-
-#if QK_K == 64
-typedef struct {
-    half  d;                     // super-block scales/mins
-    int8_t  scales[QK_K/16];     // 8-bit block scales
-    uint8_t qh[QK_K/8];          // quants, high bit
-    uint8_t qs[QK_K/2];          // quants, low 4 bits
-} block_q5_K;
-#else
-typedef struct {
-    half d;                      // super-block scale for quantized scales
-    half dmin;                   // super-block scale for quantized mins
-    uint8_t scales[3*QK_K/64];   // scales and mins, quantized with 6 bits
-    uint8_t qh[QK_K/8];          // quants, high bit
-    uint8_t qs[QK_K/2];          // quants, low 4 bits
-} block_q5_K;
-// 176 bytes / block
-#endif
-
-typedef struct {
-    uint8_t ql[QK_K/2];      // quants, lower 4 bits
-    uint8_t qh[QK_K/4];      // quants, upper 2 bits
-    int8_t  scales[QK_K/16]; // scales, quantized with 8 bits
-    half d;                  // super-block scale
-} block_q6_K;
-// 210 bytes / block
-
-typedef struct {
-    half d;
-    uint16_t qs[QK_K/8];
-} block_iq2_xxs;
-// 66 bytes / block for QK_K = 256, so 2.0625 bpw
-
-//====================================== dot products =========================
-
 void kernel_mul_mv_q2_K_f32_impl(
         device const  void * src0,
         device const float * src1,
@@ -3476,86 +3718,6 @@ kernel void kernel_mul_mv_q6_K_f32(
 
 // ======================= "True" 2-bit
 
-constexpr constant static uint64_t kgrid_iq2xxs[256] = {
-    0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
-    0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808,
-    0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819,
-    0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819,
-    0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b,
-    0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808,
-    0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08,
-    0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b,
-    0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819,
-    0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08,
-    0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808,
-    0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08,
-    0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808,
-    0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808,
-    0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919,
-    0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819,
-    0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08,
-    0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908,
-    0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819,
-    0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808,
-    0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808,
-    0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908,
-    0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808,
-    0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08,
-    0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819,
-    0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819,
-    0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819,
-    0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908,
-    0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19,
-    0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819,
-    0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b,
-    0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808,
-    0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908,
-    0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08,
-    0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08,
-    0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908,
-    0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819,
-    0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808,
-    0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808,
-    0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19,
-    0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819,
-    0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919,
-    0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b,
-    0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08,
-    0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808,
-    0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908,
-    0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b,
-    0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819,
-    0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08,
-    0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08,
-    0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808,
-    0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b,
-    0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b,
-    0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908,
-    0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819,
-    0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808,
-    0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908,
-    0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b,
-    0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808,
-    0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b,
-    0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b,
-    0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808,
-    0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19,
-    0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908,
-};
-
-constexpr constant static uint8_t ksigns_iq2xs[128] = {
-      0, 129, 130,   3, 132,   5,   6, 135, 136,   9,  10, 139,  12, 141, 142,  15,
-    144,  17,  18, 147,  20, 149, 150,  23,  24, 153, 154,  27, 156,  29,  30, 159,
-    160,  33,  34, 163,  36, 165, 166,  39,  40, 169, 170,  43, 172,  45,  46, 175,
-     48, 177, 178,  51, 180,  53,  54, 183, 184,  57,  58, 187,  60, 189, 190,  63,
-    192,  65,  66, 195,  68, 197, 198,  71,  72, 201, 202,  75, 204,  77,  78, 207,
-     80, 209, 210,  83, 212,  85,  86, 215, 216,  89,  90, 219,  92, 221, 222,  95,
-     96, 225, 226,  99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111,
-    240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255,
-};
-
-constexpr constant static uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128};
-
 void kernel_mul_mv_iq2_xxs_f32_impl(
         device const  void * src0,
         device const float * src1,
@@ -3600,14 +3762,13 @@ void kernel_mul_mv_iq2_xxs_f32_impl(
     {
         int nval = 4;
         int pos  = (32*sgitg + tiisg)*nval;
-        for (int i = 0; i < nval; ++i) values[pos + i] = kgrid_iq2xxs[pos + i];
+        for (int i = 0; i < nval; ++i) values[pos + i] = iq2xxs_grid[pos + i];
         nval = 2;
         pos  = (32*sgitg + tiisg)*nval;
         for (int i = 0; i < nval; ++i) shared_signs[pos+i] = ksigns_iq2xs[pos+i];
         threadgroup_barrier(mem_flags::mem_threadgroup);
     }
 
-#if QK_K == 256
     const int ix = tiisg;
 
     device const float * y4 = y + 32 * ix;
@@ -3648,9 +3809,6 @@ void kernel_mul_mv_iq2_xxs_f32_impl(
 
         y4 += 32 * 32;
     }
-#else
-    // TODO
-#endif
 
     for (int row = 0; row < N_DST; ++row) {
         all_sum = simd_sum(sumf[row]);
@@ -3689,6 +3847,1046 @@ kernel void kernel_mul_mv_iq2_xxs_f32(
     kernel_mul_mv_iq2_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
 }
 
+void kernel_mul_mv_iq2_xs_f32_impl(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        threadgroup int8_t * shared_values [[threadgroup(0)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    const int nb = ne00/QK_K;
+    const int r0 = tgpig.x;
+    const int r1 = tgpig.y;
+    const int im = tgpig.z;
+
+    const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST;
+    const int ib_row = first_row * nb;
+
+    const uint i12 = im%ne12;
+    const uint i13 = im/ne12;
+
+    const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+
+    device const block_iq2_xs * x = (device const block_iq2_xs *) src0 + ib_row + offset0;
+    device const float        * y = (device const float        *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float yl[32];
+    float sumf[N_DST]={0.f}, all_sum;
+
+    const int nb32 = nb * (QK_K / 32);
+
+    threadgroup uint64_t * values = (threadgroup uint64_t *)shared_values;
+    threadgroup uint8_t  * shared_signs = (threadgroup uint8_t *)(values + 512);
+    {
+        int nval = 8;
+        int pos  = (32*sgitg + tiisg)*nval;
+        for (int i = 0; i < nval; ++i) values[pos + i] = iq2xs_grid[pos + i];
+        nval = 2;
+        pos  = (32*sgitg + tiisg)*nval;
+        for (int i = 0; i < nval; ++i) shared_signs[pos+i] = ksigns_iq2xs[pos+i];
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+    }
+
+    const int ix = tiisg;
+
+    device const float * y4 = y + 32 * ix;
+
+    for (int ib32 = ix; ib32 < nb32; ib32 += 32) {
+
+        for (int i = 0; i < 32; ++i) {
+            yl[i] = y4[i];
+        }
+
+        const int ibl = ib32 / (QK_K / 32);
+        const int ib  = ib32 % (QK_K / 32);
+
+        device const block_iq2_xs * xr = x + ibl;
+        device const uint16_t * q2 = xr->qs + 4 * ib;
+        device const uint8_t  * sc = xr->scales + ib;
+        device const half * dh = &xr->d;
+
+        for (int row = 0; row < N_DST; row++) {
+
+            const float db = dh[0];
+            const uint8_t ls1 = sc[0] & 0xf;
+            const uint8_t ls2 = sc[0] >>  4;
+            const float d1 = db * (0.5f + ls1);
+            const float d2 = db * (0.5f + ls2);
+
+            float sum1 = 0, sum2 = 0;
+            for (int l = 0; l < 2; ++l) {
+                const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(values + (q2[l] & 511));
+                const uint8_t signs = shared_signs[(q2[l] >> 9)];
+                for (int j = 0; j < 8; ++j) {
+                    sum1 += yl[8*l + j] * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
+                }
+            }
+            for (int l = 2; l < 4; ++l) {
+                const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(values + (q2[l] & 511));
+                const uint8_t signs = shared_signs[(q2[l] >> 9)];
+                for (int j = 0; j < 8; ++j) {
+                    sum2 += yl[8*l + j] * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
+                }
+            }
+            sumf[row] += d1 * sum1 + d2 * sum2;
+
+            dh += nb*sizeof(block_iq2_xs)/2;
+            q2 += nb*sizeof(block_iq2_xs)/2;
+            sc += nb*sizeof(block_iq2_xs);
+        }
+
+        y4 += 32 * 32;
+    }
+
+    for (int row = 0; row < N_DST; ++row) {
+        all_sum = simd_sum(sumf[row]);
+        if (tiisg == 0) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.25f;
+        }
+    }
+}
+
+[[host_name("kernel_mul_mv_iq2_xs_f32")]]
+kernel void kernel_mul_mv_iq2_xs_f32(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        threadgroup int8_t * shared_values [[threadgroup(0)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    kernel_mul_mv_iq2_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
+}
+
+void kernel_mul_mv_iq3_xxs_f32_impl(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        threadgroup int8_t * shared_values [[threadgroup(0)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    const int nb = ne00/QK_K;
+    const int r0 = tgpig.x;
+    const int r1 = tgpig.y;
+    const int im = tgpig.z;
+
+    const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST;
+    const int ib_row = first_row * nb;
+
+    const uint i12 = im%ne12;
+    const uint i13 = im/ne12;
+
+    const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+
+    device const block_iq3_xxs * x = (device const block_iq3_xxs *) src0 + ib_row + offset0;
+    device const float         * y = (device const float         *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float yl[32];
+    float sumf[N_DST]={0.f}, all_sum;
+
+    const int nb32 = nb * (QK_K / 32);
+
+    threadgroup uint32_t * values = (threadgroup uint32_t *)shared_values;
+    threadgroup uint8_t  * shared_signs = (threadgroup uint8_t *)(values + 256);
+    {
+        int nval = 4;
+        int pos  = (32*sgitg + tiisg)*nval;
+        for (int i = 0; i < nval; ++i) values[pos + i] = iq3xxs_grid[pos + i];
+        nval = 2;
+        pos  = (32*sgitg + tiisg)*nval;
+        for (int i = 0; i < nval; ++i) shared_signs[pos+i] = ksigns_iq2xs[pos+i];
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+    }
+
+    const int ix = tiisg;
+
+    device const float * y4 = y + 32 * ix;
+
+    for (int ib32 = ix; ib32 < nb32; ib32 += 32) {
+
+        for (int i = 0; i < 32; ++i) {
+            yl[i] = y4[i];
+        }
+
+        const int ibl = ib32 / (QK_K / 32);
+        const int ib  = ib32 % (QK_K / 32);
+
+        device const block_iq3_xxs * xr = x + ibl;
+        device const uint8_t  * q3 = xr->qs + 8 * ib;
+        device const uint16_t * gas = (device const uint16_t *)(xr->qs + QK_K/4) + 2 * ib;
+        device const half * dh = &xr->d;
+
+        for (int row = 0; row < N_DST; row++) {
+
+            const float db = dh[0];
+            const uint32_t aux32 = gas[0] | (gas[1] << 16);
+            const float d = db * (0.5f + (aux32 >> 28));
+
+            float2 sum = {0};
+            for (int l = 0; l < 4; ++l) {
+                const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + q3[2*l+0]);
+                const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + q3[2*l+1]);
+                const uint8_t signs = shared_signs[(aux32 >> 7*l) & 127];
+                for (int j = 0; j < 4; ++j) {
+                    sum[0] += yl[8*l + j + 0] * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
+                    sum[1] += yl[8*l + j + 4] * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
+                }
+            }
+            sumf[row] += d * (sum[0] + sum[1]);
+
+            dh  += nb*sizeof(block_iq3_xxs)/2;
+            q3  += nb*sizeof(block_iq3_xxs);
+            gas += nb*sizeof(block_iq3_xxs)/2;
+        }
+
+        y4 += 32 * 32;
+    }
+
+    for (int row = 0; row < N_DST; ++row) {
+        all_sum = simd_sum(sumf[row]);
+        if (tiisg == 0) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.5f;
+        }
+    }
+}
+
+[[host_name("kernel_mul_mv_iq3_xxs_f32")]]
+kernel void kernel_mul_mv_iq3_xxs_f32(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        threadgroup int8_t * shared_values [[threadgroup(0)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    kernel_mul_mv_iq3_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
+}
+
+void kernel_mul_mv_iq3_s_f32_impl(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        threadgroup int8_t * shared_values [[threadgroup(0)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    const int nb = ne00/QK_K;
+    const int r0 = tgpig.x;
+    const int r1 = tgpig.y;
+    const int im = tgpig.z;
+
+    const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST;
+    const int ib_row = first_row * nb;
+
+    const uint i12 = im%ne12;
+    const uint i13 = im/ne12;
+
+    const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+
+    device const block_iq3_s * x = (device const block_iq3_s *) src0 + ib_row + offset0;
+    device const float       * y = (device const float       *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float yl[32];
+    float sumf[N_DST]={0.f}, all_sum;
+
+    const int nb32 = nb * (QK_K / 32);
+
+    threadgroup uint32_t * values = (threadgroup uint32_t *)shared_values;
+    {
+        int nval = 8;
+        int pos  = (32*sgitg + tiisg)*nval;
+        for (int i = 0; i < nval; ++i) values[pos + i] = iq3s_grid[pos + i];
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+    }
+
+    const int ix = tiisg;
+
+    device const float * y4 = y + 32 * ix;
+
+    for (int ib32 = ix; ib32 < nb32; ib32 += 32) {
+
+        for (int i = 0; i < 32; ++i) {
+            yl[i] = y4[i];
+        }
+
+        const int ibl = ib32 / (QK_K / 32);
+        const int ib  = ib32 % (QK_K / 32);
+
+        device const block_iq3_s * xr = x + ibl;
+        device const uint8_t * qs = xr->qs + 8 * ib;
+        device const uint8_t * qh = xr->qh + ib;
+        device const uint8_t * sc = xr->scales + (ib/2);
+        device const uint8_t * signs = xr->signs + 4 * ib;
+        device const half * dh = &xr->d;
+
+        for (int row = 0; row < N_DST; row++) {
+
+            const float db = dh[0];
+            const float d = db * (1 + 2*((sc[0] >> 4*(ib%2)) & 0xf));
+
+            float2 sum = {0};
+            for (int l = 0; l < 4; ++l) {
+                const threadgroup uint32_t * table1 = qh[0] & kmask_iq2xs[2*l+0] ? values + 256 : values;
+                const threadgroup uint32_t * table2 = qh[0] & kmask_iq2xs[2*l+1] ? values + 256 : values;
+                const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(table1 + qs[2*l+0]);
+                const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(table2 + qs[2*l+1]);
+                for (int j = 0; j < 4; ++j) {
+                    sum[0] += yl[8*l + j + 0] * grid1[j] * select(1, -1, signs[l] & kmask_iq2xs[j+0]);
+                    sum[1] += yl[8*l + j + 4] * grid2[j] * select(1, -1, signs[l] & kmask_iq2xs[j+4]);
+                }
+            }
+            sumf[row] += d * (sum[0] + sum[1]);
+
+            dh  += nb*sizeof(block_iq3_s)/2;
+            qs  += nb*sizeof(block_iq3_s);
+            qh  += nb*sizeof(block_iq3_s);
+            sc  += nb*sizeof(block_iq3_s);
+            signs += nb*sizeof(block_iq3_s);
+        }
+
+        y4 += 32 * 32;
+    }
+
+    for (int row = 0; row < N_DST; ++row) {
+        all_sum = simd_sum(sumf[row]);
+        if (tiisg == 0) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
+        }
+    }
+}
+
+[[host_name("kernel_mul_mv_iq3_s_f32")]]
+kernel void kernel_mul_mv_iq3_s_f32(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        threadgroup int8_t * shared_values [[threadgroup(0)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    kernel_mul_mv_iq3_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
+}
+
+void kernel_mul_mv_iq2_s_f32_impl(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        threadgroup int8_t * shared_values [[threadgroup(0)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    const int nb = ne00/QK_K;
+    const int r0 = tgpig.x;
+    const int r1 = tgpig.y;
+    const int im = tgpig.z;
+
+    const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST;
+    const int ib_row = first_row * nb;
+
+    const uint i12 = im%ne12;
+    const uint i13 = im/ne12;
+
+    const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+
+    device const block_iq2_s * x = (device const block_iq2_s *) src0 + ib_row + offset0;
+    device const float       * y = (device const float       *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float yl[32];
+    float sumf[N_DST]={0.f}, all_sum;
+
+    const int nb32 = nb * (QK_K / 32);
+
+    //threadgroup uint64_t * values = (threadgroup uint64_t *)shared_values;
+    //{
+    //    int nval = 32;
+    //    int pos  = (32*sgitg + tiisg)*nval;
+    //    for (int i = 0; i < nval; ++i) values[pos + i] = iq2s_grid[pos + i];
+    //    threadgroup_barrier(mem_flags::mem_threadgroup);
+    //}
+
+    const int ix = tiisg;
+
+    device const float * y4 = y + 32 * ix;
+
+    for (int ib32 = ix; ib32 < nb32; ib32 += 32) {
+
+        for (int i = 0; i < 32; ++i) {
+            yl[i] = y4[i];
+        }
+
+        const int ibl = ib32 / (QK_K / 32);
+        const int ib  = ib32 % (QK_K / 32);
+
+        device const block_iq2_s * xr = x + ibl;
+        device const uint8_t * qs = xr->qs + 4 * ib;
+        device const uint8_t * qh = xr->qh + ib;
+        device const uint8_t * sc = xr->scales + ib;
+        device const uint8_t * signs = qs + QK_K/8;
+        device const half * dh = &xr->d;
+
+        for (int row = 0; row < N_DST; row++) {
+
+            const float db = dh[0];
+            const float d1 = db * (0.5f + (sc[0] & 0xf));
+            const float d2 = db * (0.5f + (sc[0] >>  4));
+
+            float2 sum = {0};
+            for (int l = 0; l < 2; ++l) {
+                //const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + (qs[l+0] | ((qh[0] << (8-2*l)) & 0x300)));
+                //const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + (qs[l+2] | ((qh[0] << (4-2*l)) & 0x300)));
+                constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[l+0] | ((qh[0] << (8-2*l)) & 0x300)));
+                constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[l+2] | ((qh[0] << (4-2*l)) & 0x300)));
+                for (int j = 0; j < 8; ++j) {
+                    sum[0] += yl[8*l + j +  0] * grid1[j] * select(1, -1, signs[l+0] & kmask_iq2xs[j]);
+                    sum[1] += yl[8*l + j + 16] * grid2[j] * select(1, -1, signs[l+2] & kmask_iq2xs[j]);
+                }
+            }
+            sumf[row] += d1 * sum[0] + d2 * sum[1];
+
+            dh  += nb*sizeof(block_iq2_s)/2;
+            qs  += nb*sizeof(block_iq2_s);
+            qh  += nb*sizeof(block_iq2_s);
+            sc  += nb*sizeof(block_iq2_s);
+            signs += nb*sizeof(block_iq2_s);
+        }
+
+        y4 += 32 * 32;
+    }
+
+    for (int row = 0; row < N_DST; ++row) {
+        all_sum = simd_sum(sumf[row]);
+        if (tiisg == 0) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.25f;
+        }
+    }
+}
+
+[[host_name("kernel_mul_mv_iq2_s_f32")]]
+kernel void kernel_mul_mv_iq2_s_f32(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        threadgroup int8_t * shared_values [[threadgroup(0)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    kernel_mul_mv_iq2_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
+}
+
+void kernel_mul_mv_iq1_s_f32_impl(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    const int nb = ne00/QK_K;
+    const int r0 = tgpig.x;
+    const int r1 = tgpig.y;
+    const int im = tgpig.z;
+
+    const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST;
+    const int ib_row = first_row * nb;
+
+    const uint i12 = im%ne12;
+    const uint i13 = im/ne12;
+
+    const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+    device const block_iq1_s * x = (device const block_iq1_s *) src0 + ib_row + offset0;
+    device const float       * y = (device const float       *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float yl[32];
+    float sumf[N_DST]={0.f}, all_sum;
+
+    const int nb32 = nb * (QK_K / 32);
+
+    const int ix = tiisg;
+
+    device const float * y4 = y + 32 * ix;
+
+    for (int ib32 = ix; ib32 < nb32; ib32 += 32) {
+
+        float sumy = 0;
+        for (int i = 0; i < 32; ++i) {
+            yl[i] = y4[i];
+            sumy += yl[i];
+        }
+
+        const int ibl = ib32 / (QK_K / 32);
+        const int ib  = ib32 % (QK_K / 32);
+
+        device const block_iq1_s * xr = x + ibl;
+        device const uint8_t  * qs = xr->qs + 4 * ib;
+        device const uint16_t * qh = xr->qh + ib;
+        device const half     * dh = &xr->d;
+
+        for (int row = 0; row < N_DST; row++) {
+
+            constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
+            constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 5) & 0x700)));
+            constant uint8_t * grid3 = (constant uint8_t *)(iq1s_grid_gpu + (qs[2] | ((qh[0] << 2) & 0x700)));
+            constant uint8_t * grid4 = (constant uint8_t *)(iq1s_grid_gpu + (qs[3] | ((qh[0] >> 1) & 0x700)));
+
+            float sum = 0;
+            for (int j = 0; j < 4; ++j) {
+                sum += yl[j+ 0] * (grid1[j] & 0xf) + yl[j+ 4] * (grid1[j] >> 4)
+                     + yl[j+ 8] * (grid2[j] & 0xf) + yl[j+12] * (grid2[j] >> 4)
+                     + yl[j+16] * (grid3[j] & 0xf) + yl[j+20] * (grid3[j] >> 4)
+                     + yl[j+24] * (grid4[j] & 0xf) + yl[j+28] * (grid4[j] >> 4);
+            }
+            sumf[row] += (float)dh[0] * (sum + sumy * (qh[0] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA)) * (2*((qh[0] >> 12) & 7) + 1);
+
+            dh += nb*sizeof(block_iq1_s)/2;
+            qs += nb*sizeof(block_iq1_s);
+            qh += nb*sizeof(block_iq1_s)/2;
+        }
+
+        y4 += 32 * 32;
+    }
+
+    for (int row = 0; row < N_DST; ++row) {
+        all_sum = simd_sum(sumf[row]);
+        if (tiisg == 0) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
+        }
+    }
+}
+
+void kernel_mul_mv_iq1_m_f32_impl(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    const int nb = ne00/QK_K;
+    const int r0 = tgpig.x;
+    const int r1 = tgpig.y;
+    const int im = tgpig.z;
+
+    const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST;
+    const int ib_row = first_row * nb;
+
+    const uint i12 = im%ne12;
+    const uint i13 = im/ne12;
+
+    const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+    device const block_iq1_m * x = (device const block_iq1_m *) src0 + ib_row + offset0;
+    device const float       * y = (device const float       *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float yl[32];
+    float sumf[N_DST]={0.f}, all_sum;
+
+    const int nb32 = nb * (QK_K / 32);
+
+    const int ix = tiisg;
+
+    device const float * y4 = y + 32 * ix;
+
+#if QK_K != 64
+    iq1m_scale_t scale;
+#endif
+
+    for (int ib32 = ix; ib32 < nb32; ib32 += 32) {
+
+        float4 sumy = {0.f};
+        for (int i = 0; i < 8; ++i) {
+            yl[i+ 0] = y4[i+ 0]; sumy[0] += yl[i+ 0];
+            yl[i+ 8] = y4[i+ 8]; sumy[1] += yl[i+ 8];
+            yl[i+16] = y4[i+16]; sumy[2] += yl[i+16];
+            yl[i+24] = y4[i+24]; sumy[3] += yl[i+24];
+        }
+
+        const int ibl = ib32 / (QK_K / 32);
+        const int ib  = ib32 % (QK_K / 32);
+
+        device const block_iq1_m * xr = x + ibl;
+        device const uint8_t  * qs = xr->qs + 4 * ib;
+        device const uint8_t  * qh = xr->qh + 2 * ib;
+        device const uint16_t * sc = (device const uint16_t *)xr->scales;
+
+        for (int row = 0; row < N_DST; row++) {
+
+#if QK_K != 64
+            scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+#endif
+
+            constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
+            constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700)));
+            constant uint8_t * grid3 = (constant uint8_t *)(iq1s_grid_gpu + (qs[2] | ((qh[1] << 8) & 0x700)));
+            constant uint8_t * grid4 = (constant uint8_t *)(iq1s_grid_gpu + (qs[3] | ((qh[1] << 4) & 0x700)));
+
+            float2 sum = {0.f};
+            for (int j = 0; j < 4; ++j) {
+                sum[0] += yl[j+ 0] * (grid1[j] & 0xf) + yl[j+ 4] * (grid1[j] >> 4)
+                        + yl[j+ 8] * (grid2[j] & 0xf) + yl[j+12] * (grid2[j] >> 4);
+                sum[1] += yl[j+16] * (grid3[j] & 0xf) + yl[j+20] * (grid3[j] >> 4)
+                        + yl[j+24] * (grid4[j] & 0xf) + yl[j+28] * (grid4[j] >> 4);
+            }
+            const float delta1 = sumy[0] * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[1] * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
+            const float delta2 = sumy[2] * (qh[1] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[3] * (qh[1] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
+#if QK_K == 64
+            const float d = (float) *((device const half *)(sc - 1));
+            sumf[row] += d * ((sum[0] + delta1) * (2*((sc[0] >> (8*(ib%2)+0)) & 0xf) + 1) +
+                              (sum[1] + delta2) * (2*((sc[0] >> (8*(ib%2)+4)) & 0xf) + 1));
+#else
+            sumf[row] += (float)scale.f16 * ((sum[0] + delta1) * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 7) + 1) +
+                                             (sum[1] + delta2) * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 7) + 1));
+#endif
+
+            sc += nb*sizeof(block_iq1_m)/2;
+            qs += nb*sizeof(block_iq1_m);
+            qh += nb*sizeof(block_iq1_m);
+        }
+
+        y4 += 32 * 32;
+    }
+
+    for (int row = 0; row < N_DST; ++row) {
+        all_sum = simd_sum(sumf[row]);
+        if (tiisg == 0) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
+        }
+    }
+}
+
+void kernel_mul_mv_iq4_nl_f32_impl(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        threadgroup float  * shared_values [[threadgroup(0)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    const int nb = ne00/QK4_NL;
+    const int r0 = tgpig.x;
+    const int r1 = tgpig.y;
+    const int im = tgpig.z;
+    const int first_row = (r0 * 2 + sgitg) * 2;
+    const int ib_row = first_row * nb;
+
+    const uint i12 = im%ne12;
+    const uint i13 = im/ne12;
+
+    const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+    device const block_iq4_nl * x = (device const block_iq4_nl *) src0 + ib_row + offset0;
+    device const float        * y = (device const float        *) src1 + r1*ne10 + im*ne00*ne1;
+
+    const int ix = tiisg/2;  // 0...15
+    const int it = tiisg%2;  // 0 or 1
+
+    shared_values[tiisg] = kvalues_iq4nl_f[tiisg%16];
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    float4 yl[4];
+    float sumf[2]={0.f}, all_sum;
+
+    device const float * yb = y + ix * QK4_NL + it * 8;
+
+    uint32_t aux32[2];
+    thread const uint8_t * q8 = (thread const uint8_t *)aux32;
+
+    float4 qf1, qf2;
+
+    for (int ib = ix; ib < nb; ib += 16) {
+
+        device const float4 * y4 = (device const float4 *)yb;
+        yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5];
+
+        for (int row = 0; row < 2; ++row) {
+
+            device const block_iq4_nl & xb = x[row*nb + ib];
+            device const uint16_t * q4 = (device const uint16_t *)(xb.qs + 8*it);
+
+            float4 acc1 = {0.f}, acc2 = {0.f};
+
+            aux32[0] = q4[0] | (q4[1] << 16);
+            aux32[1] = (aux32[0] >> 4) & 0x0f0f0f0f;
+            aux32[0] &= 0x0f0f0f0f;
+            qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]};
+            qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]};
+            acc1 += yl[0] * qf1;
+            acc2 += yl[1] * qf2;
+
+            aux32[0] = q4[2] | (q4[3] << 16);
+            aux32[1] = (aux32[0] >> 4) & 0x0f0f0f0f;
+            aux32[0] &= 0x0f0f0f0f;
+            qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]};
+            qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]};
+            acc1 += yl[2] * qf1;
+            acc2 += yl[3] * qf2;
+
+            acc1 += acc2;
+
+            sumf[row] += (float)xb.d * (acc1[0] + acc1[1] + acc1[2] + acc1[3]);
+
+        }
+
+        yb += 16 * QK4_NL;
+    }
+
+    for (int row = 0; row < 2; ++row) {
+        all_sum = simd_sum(sumf[row]);
+        if (tiisg == 0) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
+        }
+    }
+}
+
+#if QK_K != 64
+void kernel_mul_mv_iq4_xs_f32_impl(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        threadgroup float  * shared_values [[threadgroup(0)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    const int nb = ne00/QK_K;
+    const int r0 = tgpig.x;
+    const int r1 = tgpig.y;
+    const int im = tgpig.z;
+    const int first_row = (r0 * 2 + sgitg) * 2;
+    const int ib_row = first_row * nb;
+
+    const uint i12 = im%ne12;
+    const uint i13 = im/ne12;
+
+    const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+    device const block_iq4_xs * x = (device const block_iq4_xs *) src0 + ib_row + offset0;
+    device const float        * y = (device const float        *) src1 + r1*ne10 + im*ne00*ne1;
+
+    const int ix = tiisg/16;  // 0 or 1
+    const int it = tiisg%16;  // 0...15
+    const int ib = it/2;
+    const int il = it%2;
+
+    shared_values[tiisg] = kvalues_iq4nl_f[tiisg%16];
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    float4 yl[4];
+    float sumf[2]={0.f}, all_sum;
+
+    device const float * yb = y + ix * QK_K + ib * 32 + il * 8;
+
+    uint32_t aux32[2];
+    thread const uint8_t * q8 = (thread const uint8_t *)aux32;
+
+    float4 qf1, qf2;
+
+    for (int ibl = ix; ibl < nb; ibl += 2) {
+
+        device const float4 * y4 = (device const float4 *)yb;
+        yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5];
+
+        for (int row = 0; row < 2; ++row) {
+
+            device const block_iq4_xs & xb = x[row*nb + ibl];
+            device const uint32_t * q4 = (device const uint32_t *)(xb.qs + 16*ib + 8*il);
+
+            float4 acc1 = {0.f}, acc2 = {0.f};
+
+            aux32[0] = q4[0] & 0x0f0f0f0f;
+            aux32[1] = (q4[0] >> 4) & 0x0f0f0f0f;
+            qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]};
+            qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]};
+            acc1 += yl[0] * qf1;
+            acc2 += yl[1] * qf2;
+
+            aux32[0] = q4[1] & 0x0f0f0f0f;
+            aux32[1] = (q4[1] >> 4) & 0x0f0f0f0f;
+            qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]};
+            qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]};
+            acc1 += yl[2] * qf1;
+            acc2 += yl[3] * qf2;
+
+            acc1 += acc2;
+
+            const int ls = (((xb.scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((xb.scales_h >> 2*ib) & 3) << 4)) - 32;
+            sumf[row] += (float)xb.d * ls * (acc1[0] + acc1[1] + acc1[2] + acc1[3]);
+
+        }
+
+        yb += 2 * QK_K;
+    }
+
+    for (int row = 0; row < 2; ++row) {
+        all_sum = simd_sum(sumf[row]);
+        if (tiisg == 0) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
+        }
+    }
+}
+#endif
+
+[[host_name("kernel_mul_mv_iq1_s_f32")]]
+kernel void kernel_mul_mv_iq1_s_f32(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    kernel_mul_mv_iq1_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg);
+}
+
+[[host_name("kernel_mul_mv_iq1_m_f32")]]
+kernel void kernel_mul_mv_iq1_m_f32(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    kernel_mul_mv_iq1_m_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg);
+}
+
+[[host_name("kernel_mul_mv_iq4_nl_f32")]]
+kernel void kernel_mul_mv_iq4_nl_f32(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        threadgroup float * shared_values [[threadgroup(0)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint tiisg[[thread_index_in_simdgroup]],
+        uint sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
+}
+
+[[host_name("kernel_mul_mv_iq4_xs_f32")]]
+kernel void kernel_mul_mv_iq4_xs_f32(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01,
+        constant   int64_t & ne02,
+        constant  uint64_t & nb00,
+        constant  uint64_t & nb01,
+        constant  uint64_t & nb02,
+        constant   int64_t & ne10,
+        constant   int64_t & ne11,
+        constant   int64_t & ne12,
+        constant  uint64_t & nb10,
+        constant  uint64_t & nb11,
+        constant  uint64_t & nb12,
+        constant   int64_t & ne0,
+        constant   int64_t & ne1,
+        constant   uint    & r2,
+        constant   uint    & r3,
+        threadgroup float * shared_values [[threadgroup(0)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint tiisg[[thread_index_in_simdgroup]],
+        uint sgitg[[simdgroup_index_in_threadgroup]]) {
+
+#if QK_K == 64
+    kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
+#else
+    kernel_mul_mv_iq4_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
+#endif
+}
+
 //============================= templates and their specializations =============================
 
 // NOTE: this is not dequantizing - we are simply fitting the template
@@ -3884,6 +5082,8 @@ void dequantize_q4_K(device const block_q4_K *xb, short il, thread type4x4 & reg
     const float dl = d * sc[0];
     const float ml = min * sc[1];
 #else
+    (void) get_scale_min_k4_just2;
+
     q = q + 16 * (il&1);
     device const uint8_t * s = xb->scales;
     device const half2 * dh = (device const half2 *)xb->d;
@@ -3973,18 +5173,202 @@ void dequantize_iq2_xxs(device const block_iq2_xxs * xb, short il, thread type4x
     const uint32_t aux32_s = q2[2] | (q2[3] << 16);
     thread const uint8_t * aux8 = (thread const uint8_t *)&aux32_g;
     const float dl = d * (0.5f + (aux32_s >> 28)) * 0.25f;
-    constant uint8_t * grid = (constant uint8_t *)(kgrid_iq2xxs + aux8[2*il+0]);
+    constant uint8_t * grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+0]);
     uint8_t signs = ksigns_iq2xs[(aux32_s >> 14*il) & 127];
     for (int i = 0; i < 8; ++i) {
         reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
     }
-    grid = (constant uint8_t *)(kgrid_iq2xxs + aux8[2*il+1]);
+    grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+1]);
     signs = ksigns_iq2xs[(aux32_s >> (14*il+7)) & 127];
     for (int i = 0; i < 8; ++i) {
         reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
     }
 }
 
+template <typename type4x4>
+void dequantize_iq2_xs(device const block_iq2_xs * xb, short il, thread type4x4 & reg) {
+    // il is 0...15 for QK_K = 256 => index of block of 32 is il/2
+    const float d = xb->d;
+    const int ib32 = il/2;
+    il = il%2;
+    // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
+    device const uint16_t * q2 = xb->qs + 4*ib32;
+    const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f;
+    constant uint8_t * grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+0] & 511));
+    uint8_t signs = ksigns_iq2xs[q2[2*il+0] >> 9];
+    for (int i = 0; i < 8; ++i) {
+        reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
+    }
+    grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+1] & 511));
+    signs = ksigns_iq2xs[q2[2*il+1] >> 9];
+    for (int i = 0; i < 8; ++i) {
+        reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
+    }
+}
+
+template <typename type4x4>
+void dequantize_iq3_xxs(device const block_iq3_xxs * xb, short il, thread type4x4 & reg) {
+    // il is 0...15 for QK_K = 256 => index of block of 32 is il/2
+    const float d = xb->d;
+    const int ib32 = il/2;
+    il = il%2;
+    // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
+    device const uint8_t * q3 = xb->qs + 8*ib32;
+    device const uint16_t * gas = (device const uint16_t *)(xb->qs + QK_K/4) + 2*ib32;
+    const uint32_t aux32 = gas[0] | (gas[1] << 16);
+    const float dl = d * (0.5f + (aux32 >> 28)) * 0.5f;
+    constant uint8_t * grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+0]);
+    constant uint8_t * grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+1]);
+    uint8_t signs = ksigns_iq2xs[(aux32 >> 14*il) & 127];
+    for (int i = 0; i < 4; ++i) {
+        reg[0][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f);
+        reg[1][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f);
+    }
+    grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+2]);
+    grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+3]);
+    signs = ksigns_iq2xs[(aux32 >> (14*il+7)) & 127];
+    for (int i = 0; i < 4; ++i) {
+        reg[2][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f);
+        reg[3][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f);
+    }
+}
+
+template <typename type4x4>
+void dequantize_iq3_s(device const block_iq3_s * xb, short il, thread type4x4 & reg) {
+    // il is 0...15 for QK_K = 256 => index of block of 32 is il/2
+    const float d = xb->d;
+    const int ib32 = il/2;
+    il = il%2;
+    // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
+    device const uint8_t * qs = xb->qs + 8*ib32;
+    device const uint8_t * signs = xb->signs + 4*ib32 + 2*il;
+    const uint8_t qh = xb->qh[ib32] >> 4*il;
+    const float dl = d * (1 + 2*((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf));
+    constant uint8_t * grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+0] | ((qh << 8) & 256)));
+    constant uint8_t * grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+1] | ((qh << 7) & 256)));
+    for (int i = 0; i < 4; ++i) {
+        reg[0][i] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i+0]);
+        reg[1][i] = dl * grid2[i] * select(1, -1, signs[0] & kmask_iq2xs[i+4]);
+    }
+    grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+2] | ((qh << 6) & 256)));
+    grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+3] | ((qh << 5) & 256)));
+    for (int i = 0; i < 4; ++i) {
+        reg[2][i] = dl * grid1[i] * select(1, -1, signs[1] & kmask_iq2xs[i+0]);
+        reg[3][i] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i+4]);
+    }
+}
+
+template <typename type4x4>
+void dequantize_iq2_s(device const block_iq2_s * xb, short il, thread type4x4 & reg) {
+    // il is 0...15 for QK_K = 256 => index of block of 32 is il/2
+    const float d = xb->d;
+    const int ib32 = il/2;
+    il = il%2;
+    // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
+    device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
+    device const uint8_t * signs = qs + QK_K/8;
+    const uint8_t qh = xb->qh[ib32] >> 4*il;
+    const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f;
+    constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[0] | ((qh << 8) & 0x300)));
+    constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[1] | ((qh << 6) & 0x300)));
+    for (int i = 0; i < 8; ++i) {
+        reg[i/4+0][i%4] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i]);
+        reg[i/4+2][i%4] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i]);
+    }
+}
+
+template <typename type4x4>
+void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & reg) {
+    // il is 0...15 for QK_K = 256 => index of block of 32 is il/2
+    const int ib32 = il/2;
+    il = il%2;
+    const float d = xb->d;
+    device const uint8_t  * qs = xb->qs + 4*ib32 + 2*il;
+    device const uint16_t * qh = xb->qh;
+    const float dl = d * (2*((qh[ib32] >> 12) & 7) + 1);
+    const float ml = dl * (qh[ib32] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA);
+    const uint16_t h = qh[ib32] >> 6*il;
+    constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((h << 8) & 0x700)));
+    constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((h << 5) & 0x700)));
+    for (int i = 0; i < 4; ++i) {
+        reg[0][i] = dl * (grid1[i] & 0xf) + ml;
+        reg[1][i] = dl * (grid1[i] >>  4) + ml;
+        reg[2][i] = dl * (grid2[i] & 0xf) + ml;
+        reg[3][i] = dl * (grid2[i] >>  4) + ml;
+    }
+}
+
+template <typename type4x4>
+void dequantize_iq1_m(device const block_iq1_m * xb, short il, thread type4x4 & reg) {
+    // il is 0...15 for QK_K = 256 => index of block of 32 is il/2
+    const int ib32 = il/2;
+    il = il%2;
+    device const uint16_t * sc = (device const uint16_t *)xb->scales;
+#if QK_K == 64
+    const float d = xb->d;
+#else
+    iq1m_scale_t scale;
+    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+    const float d = scale.f16;
+#endif
+    device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
+    device const uint8_t * qh = xb->qh + 2*ib32 + il;
+#if QK_K == 64
+    const float dl  = d * (2*((sc[ib32/2] >> (8*(ib32%2)+4*il)) & 0xf) + 1);
+#else
+    const float dl  = d * (2*((sc[ib32/2] >> (6*(ib32%2)+3*il)) & 7) + 1);
+#endif
+    const float ml1 = dl * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
+    const float ml2 = dl * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
+    constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
+    constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700)));
+    for (int i = 0; i < 4; ++i) {
+        reg[0][i] = dl * (grid1[i] & 0xf) + ml1;
+        reg[1][i] = dl * (grid1[i] >>  4) + ml1;
+        reg[2][i] = dl * (grid2[i] & 0xf) + ml2;
+        reg[3][i] = dl * (grid2[i] >>  4) + ml2;
+    }
+}
+
+template <typename type4x4>
+void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 & reg) {
+    device const uint16_t * q4 = (device const uint16_t *)xb->qs;
+    const float d = xb->d;
+    uint32_t aux32;
+    thread const uint8_t * q8 = (thread const uint8_t *)&aux32;
+    for (int i = 0; i < 4; ++i) {
+        aux32 = ((q4[2*i] | (q4[2*i+1] << 16)) >> 4*il) & 0x0f0f0f0f;
+        reg[i][0] = d * kvalues_iq4nl_f[q8[0]];
+        reg[i][1] = d * kvalues_iq4nl_f[q8[1]];
+        reg[i][2] = d * kvalues_iq4nl_f[q8[2]];
+        reg[i][3] = d * kvalues_iq4nl_f[q8[3]];
+    }
+}
+
+template <typename type4x4>
+void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) {
+#if QK_K == 64
+    dequantize_iq4_nl(xb, il, reg);
+#else
+    // il is 0...15 for QK_K = 256 => index of block of 32 is il/2
+    const int ib32 = il/2;
+    il = il%2;
+    // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
+    device const uint32_t * q4 = (device const uint32_t *)xb->qs + 4*ib32;
+    const int ls = ((xb->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((xb->scales_h >> 2*ib32) & 3) << 4);
+    const float d = (float)xb->d * (ls - 32);
+    uint32_t aux32;
+    thread const uint8_t * q8 = (thread const uint8_t *)&aux32;
+    for (int i = 0; i < 4; ++i) {
+        aux32 = (q4[i] >> 4*il) & 0x0f0f0f0f;
+        reg[i][0] = d * kvalues_iq4nl_f[q8[0]];
+        reg[i][1] = d * kvalues_iq4nl_f[q8[1]];
+        reg[i][2] = d * kvalues_iq4nl_f[q8[2]];
+        reg[i][3] = d * kvalues_iq4nl_f[q8[3]];
+    }
+#endif
+}
+
 template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread float4x4 &)>
 kernel void kernel_get_rows(
         device const  void * src0,
@@ -4253,7 +5637,7 @@ template<typename block_q, short nl, void (*dequantize_func)(device const block_
 void kernel_mul_mm_id_impl(
         device const  uchar * src0,
         device const  uchar * src1,
-        thread        short * src1ids,
+        threadgroup   short * src1ids,
         device        float * dst,
         constant    int64_t & ne00,
         constant    int64_t & ne02,
@@ -4417,9 +5801,10 @@ kernel void kernel_mul_mm(device const  uchar * src0,
 
 template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread half4x4 &)>
 kernel void kernel_mul_mm_id(
-        device const   uchar * ids,
+        device const   uchar * src0s,
         device const   uchar * src1,
         device         float * dst,
+        device const   uchar * ids,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne02,
@@ -4436,29 +5821,21 @@ kernel void kernel_mul_mm_id(
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
-        device const   uchar * src00,
-        device const   uchar * src01,
-        device const   uchar * src02,
-        device const   uchar * src03,
-        device const   uchar * src04,
-        device const   uchar * src05,
-        device const   uchar * src06,
-        device const   uchar * src07,
         threadgroup    uchar * shared_memory [[threadgroup(0)]],
         uint3                  tgpig[[threadgroup_position_in_grid]],
         uint                   tiitg[[thread_index_in_threadgroup]],
         uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const uchar * src0s[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
 
     // expert id
     const int32_t id = tgpig.z/(ne12*ne13);
+    device const uchar * src0 = src0s + id*nb02;
 
     tgpig.z = tgpig.z%(ne12*ne13);
 
     // row indices of src1 for expert id
-    int64_t _ne1 = 0;
-    short src1ids[512];
+    threadgroup short * src1ids = (threadgroup short *)(shared_memory + 8192);
 
+    int64_t _ne1 = 0;
     for (int64_t i1 = 0; i1 < ne1; i1++) {
         if (((device int32_t *) (ids + i1*nbi1))[idx] == id) {
             src1ids[_ne1++] = i1;
@@ -4466,7 +5843,7 @@ kernel void kernel_mul_mm_id(
     }
 
     kernel_mul_mm_id_impl<block_q, nl, dequantize_func>(
-        src0s[id],
+        src0,
         src1,
         src1ids,
         dst,
@@ -4525,6 +5902,18 @@ template [[host_name("kernel_get_rows_q4_K")]] kernel get_rows_t kernel_get_rows
 template [[host_name("kernel_get_rows_q5_K")]] kernel get_rows_t kernel_get_rows<block_q5_K, QK_NL, dequantize_q5_K>;
 template [[host_name("kernel_get_rows_q6_K")]] kernel get_rows_t kernel_get_rows<block_q6_K, QK_NL, dequantize_q6_K>;
 template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_t kernel_get_rows<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
+template [[host_name("kernel_get_rows_iq2_xs")]]  kernel get_rows_t kernel_get_rows<block_iq2_xs,  QK_NL, dequantize_iq2_xs>;
+template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_t kernel_get_rows<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
+template [[host_name("kernel_get_rows_iq3_s")]]   kernel get_rows_t kernel_get_rows<block_iq3_s,   QK_NL, dequantize_iq3_s>;
+template [[host_name("kernel_get_rows_iq2_s")]]   kernel get_rows_t kernel_get_rows<block_iq2_s,   QK_NL, dequantize_iq2_s>;
+template [[host_name("kernel_get_rows_iq1_s")]]   kernel get_rows_t kernel_get_rows<block_iq1_s,   QK_NL, dequantize_iq1_s>;
+template [[host_name("kernel_get_rows_iq1_m")]]   kernel get_rows_t kernel_get_rows<block_iq1_m,   QK_NL, dequantize_iq1_m>;
+template [[host_name("kernel_get_rows_iq4_nl")]]  kernel get_rows_t kernel_get_rows<block_iq4_nl,  2,     dequantize_iq4_nl>;
+#if QK_K == 64
+template [[host_name("kernel_get_rows_iq4_xs")]]  kernel get_rows_t kernel_get_rows<block_iq4_xs,  2,     dequantize_iq4_xs>;
+#else
+template [[host_name("kernel_get_rows_iq4_xs")]]  kernel get_rows_t kernel_get_rows<block_iq4_xs,  QK_NL, dequantize_iq4_xs>;
+#endif
 
 //
 // matrix-matrix multiplication
@@ -4549,28 +5938,41 @@ typedef void (mat_mm_t)(
         threadgroup   uchar *,
         uint3, uint, uint);
 
-template [[host_name("kernel_mul_mm_f32_f32")]]  kernel mat_mm_t kernel_mul_mm<float4x4,   1,     dequantize_f32>;
-template [[host_name("kernel_mul_mm_f16_f32")]]  kernel mat_mm_t kernel_mul_mm<half4x4,    1,     dequantize_f16>;
-template [[host_name("kernel_mul_mm_q4_0_f32")]] kernel mat_mm_t kernel_mul_mm<block_q4_0, 2,     dequantize_q4_0>;
-template [[host_name("kernel_mul_mm_q4_1_f32")]] kernel mat_mm_t kernel_mul_mm<block_q4_1, 2,     dequantize_q4_1>;
-template [[host_name("kernel_mul_mm_q5_0_f32")]] kernel mat_mm_t kernel_mul_mm<block_q5_0, 2,     dequantize_q5_0>;
-template [[host_name("kernel_mul_mm_q5_1_f32")]] kernel mat_mm_t kernel_mul_mm<block_q5_1, 2,     dequantize_q5_1>;
-template [[host_name("kernel_mul_mm_q8_0_f32")]] kernel mat_mm_t kernel_mul_mm<block_q8_0, 2,     dequantize_q8_0>;
-template [[host_name("kernel_mul_mm_q2_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q2_K, QK_NL, dequantize_q2_K>;
-template [[host_name("kernel_mul_mm_q3_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q3_K, QK_NL, dequantize_q3_K>;
-template [[host_name("kernel_mul_mm_q4_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q4_K, QK_NL, dequantize_q4_K>;
-template [[host_name("kernel_mul_mm_q5_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q5_K, QK_NL, dequantize_q5_K>;
-template [[host_name("kernel_mul_mm_q6_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q6_K, QK_NL, dequantize_q6_K>;
+template [[host_name("kernel_mul_mm_f32_f32")]]     kernel mat_mm_t kernel_mul_mm<float4x4,      1,     dequantize_f32>;
+template [[host_name("kernel_mul_mm_f16_f32")]]     kernel mat_mm_t kernel_mul_mm<half4x4,       1,     dequantize_f16>;
+template [[host_name("kernel_mul_mm_q4_0_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q4_0,    2,     dequantize_q4_0>;
+template [[host_name("kernel_mul_mm_q4_1_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q4_1,    2,     dequantize_q4_1>;
+template [[host_name("kernel_mul_mm_q5_0_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q5_0,    2,     dequantize_q5_0>;
+template [[host_name("kernel_mul_mm_q5_1_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q5_1,    2,     dequantize_q5_1>;
+template [[host_name("kernel_mul_mm_q8_0_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q8_0,    2,     dequantize_q8_0>;
+template [[host_name("kernel_mul_mm_q2_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q2_K,    QK_NL, dequantize_q2_K>;
+template [[host_name("kernel_mul_mm_q3_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q3_K,    QK_NL, dequantize_q3_K>;
+template [[host_name("kernel_mul_mm_q4_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q4_K,    QK_NL, dequantize_q4_K>;
+template [[host_name("kernel_mul_mm_q5_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q5_K,    QK_NL, dequantize_q5_K>;
+template [[host_name("kernel_mul_mm_q6_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q6_K,    QK_NL, dequantize_q6_K>;
 template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
+template [[host_name("kernel_mul_mm_iq2_xs_f32")]]  kernel mat_mm_t kernel_mul_mm<block_iq2_xs,  QK_NL, dequantize_iq2_xs>;
+template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
+template [[host_name("kernel_mul_mm_iq3_s_f32")]]   kernel mat_mm_t kernel_mul_mm<block_iq3_s,   QK_NL, dequantize_iq3_s>;
+template [[host_name("kernel_mul_mm_iq2_s_f32")]]   kernel mat_mm_t kernel_mul_mm<block_iq2_s,   QK_NL, dequantize_iq2_s>;
+template [[host_name("kernel_mul_mm_iq1_s_f32")]]   kernel mat_mm_t kernel_mul_mm<block_iq1_s,   QK_NL, dequantize_iq1_s>;
+template [[host_name("kernel_mul_mm_iq1_m_f32")]]   kernel mat_mm_t kernel_mul_mm<block_iq1_m,   QK_NL, dequantize_iq1_m>;
+template [[host_name("kernel_mul_mm_iq4_nl_f32")]]  kernel mat_mm_t kernel_mul_mm<block_iq4_nl,  2,     dequantize_iq4_nl>;
+#if QK_K == 64
+template [[host_name("kernel_mul_mm_iq4_xs_f32")]]  kernel mat_mm_t kernel_mul_mm<block_iq4_nl,  2,     dequantize_iq4_xs>;
+#else
+template [[host_name("kernel_mul_mm_iq4_xs_f32")]]  kernel mat_mm_t kernel_mul_mm<block_iq4_xs,  QK_NL, dequantize_iq4_xs>;
+#endif
 
 //
 // indirect matrix-matrix multiplication
 //
 
 typedef void (mat_mm_id_t)(
-        device const   uchar * ids,
+        device const   uchar * src0s,
         device const   uchar * src1,
         device         float * dst,
+        device const   uchar * ids,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne02,
@@ -4587,30 +5989,34 @@ typedef void (mat_mm_id_t)(
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
-        device const   uchar * src00,
-        device const   uchar * src01,
-        device const   uchar * src02,
-        device const   uchar * src03,
-        device const   uchar * src04,
-        device const   uchar * src05,
-        device const   uchar * src06,
-        device const   uchar * src07,
         threadgroup    uchar *,
         uint3, uint, uint);
 
-template [[host_name("kernel_mul_mm_id_f32_f32")]]  kernel mat_mm_id_t kernel_mul_mm_id<float4x4,   1,     dequantize_f32>;
-template [[host_name("kernel_mul_mm_id_f16_f32")]]  kernel mat_mm_id_t kernel_mul_mm_id<half4x4,    1,     dequantize_f16>;
-template [[host_name("kernel_mul_mm_id_q4_0_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q4_0, 2,     dequantize_q4_0>;
-template [[host_name("kernel_mul_mm_id_q4_1_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q4_1, 2,     dequantize_q4_1>;
-template [[host_name("kernel_mul_mm_id_q5_0_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q5_0, 2,     dequantize_q5_0>;
-template [[host_name("kernel_mul_mm_id_q5_1_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q5_1, 2,     dequantize_q5_1>;
-template [[host_name("kernel_mul_mm_id_q8_0_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q8_0, 2,     dequantize_q8_0>;
-template [[host_name("kernel_mul_mm_id_q2_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q2_K, QK_NL, dequantize_q2_K>;
-template [[host_name("kernel_mul_mm_id_q3_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q3_K, QK_NL, dequantize_q3_K>;
-template [[host_name("kernel_mul_mm_id_q4_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q4_K, QK_NL, dequantize_q4_K>;
-template [[host_name("kernel_mul_mm_id_q5_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q5_K, QK_NL, dequantize_q5_K>;
-template [[host_name("kernel_mul_mm_id_q6_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_q6_K, QK_NL, dequantize_q6_K>;
+template [[host_name("kernel_mul_mm_id_f32_f32")]]     kernel mat_mm_id_t kernel_mul_mm_id<float4x4,      1,     dequantize_f32>;
+template [[host_name("kernel_mul_mm_id_f16_f32")]]     kernel mat_mm_id_t kernel_mul_mm_id<half4x4,       1,     dequantize_f16>;
+template [[host_name("kernel_mul_mm_id_q4_0_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q4_0,    2,     dequantize_q4_0>;
+template [[host_name("kernel_mul_mm_id_q4_1_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q4_1,    2,     dequantize_q4_1>;
+template [[host_name("kernel_mul_mm_id_q5_0_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q5_0,    2,     dequantize_q5_0>;
+template [[host_name("kernel_mul_mm_id_q5_1_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q5_1,    2,     dequantize_q5_1>;
+template [[host_name("kernel_mul_mm_id_q8_0_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q8_0,    2,     dequantize_q8_0>;
+template [[host_name("kernel_mul_mm_id_q2_K_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q2_K,    QK_NL, dequantize_q2_K>;
+template [[host_name("kernel_mul_mm_id_q3_K_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q3_K,    QK_NL, dequantize_q3_K>;
+template [[host_name("kernel_mul_mm_id_q4_K_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q4_K,    QK_NL, dequantize_q4_K>;
+template [[host_name("kernel_mul_mm_id_q5_K_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q5_K,    QK_NL, dequantize_q5_K>;
+template [[host_name("kernel_mul_mm_id_q6_K_f32")]]    kernel mat_mm_id_t kernel_mul_mm_id<block_q6_K,    QK_NL, dequantize_q6_K>;
 template [[host_name("kernel_mul_mm_id_iq2_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
+template [[host_name("kernel_mul_mm_id_iq2_xs_f32")]]  kernel mat_mm_id_t kernel_mul_mm_id<block_iq2_xs,  QK_NL, dequantize_iq2_xs>;
+template [[host_name("kernel_mul_mm_id_iq3_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
+template [[host_name("kernel_mul_mm_id_iq3_s_f32")]]   kernel mat_mm_id_t kernel_mul_mm_id<block_iq3_s,   QK_NL, dequantize_iq3_s>;
+template [[host_name("kernel_mul_mm_id_iq2_s_f32")]]   kernel mat_mm_id_t kernel_mul_mm_id<block_iq2_s,   QK_NL, dequantize_iq2_s>;
+template [[host_name("kernel_mul_mm_id_iq1_s_f32")]]   kernel mat_mm_id_t kernel_mul_mm_id<block_iq1_s,   QK_NL, dequantize_iq1_s>;
+template [[host_name("kernel_mul_mm_id_iq1_m_f32")]]   kernel mat_mm_id_t kernel_mul_mm_id<block_iq1_m,   QK_NL, dequantize_iq1_m>;
+template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]]  kernel mat_mm_id_t kernel_mul_mm_id<block_iq4_nl,  2,     dequantize_iq4_nl>;
+#if QK_K == 64
+template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]]  kernel mat_mm_id_t kernel_mul_mm_id<block_iq4_xs,  2,     dequantize_iq4_xs>;
+#else
+template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]]  kernel mat_mm_id_t kernel_mul_mm_id<block_iq4_xs,  QK_NL, dequantize_iq4_xs>;
+#endif
 
 //
 // matrix-vector multiplication
@@ -4618,9 +6024,10 @@ template [[host_name("kernel_mul_mm_id_iq2_xxs_f32")]] kernel mat_mm_id_t kernel
 
 [[host_name("kernel_mul_mv_id_f32_f32")]]
 kernel void kernel_mul_mv_id_f32_f32(
-        device const    char * ids,
+        device const    char * src0s,
         device const    char * src1,
         device         float * dst,
+        device const    char * ids,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -4641,28 +6048,19 @@ kernel void kernel_mul_mv_id_f32_f32(
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
         uint3                  tgpig[[threadgroup_position_in_grid]],
         uint                   tiitg[[thread_index_in_threadgroup]],
         uint                   tiisg[[thread_index_in_simdgroup]],
         uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
     const int64_t bid = tgpig.z/(ne12*ne13);
 
     tgpig.z = tgpig.z%(ne12*ne13);
 
     const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
 
     kernel_mul_mv_f32_f32_impl(
-        src0[id],
+        src0,
         src1 + bid*nb11,
         dst  + bid*ne0,
         ne00,
@@ -4687,9 +6085,10 @@ kernel void kernel_mul_mv_id_f32_f32(
 
 [[host_name("kernel_mul_mv_id_f16_f32")]]
 kernel void kernel_mul_mv_id_f16_f32(
-        device const    char * ids,
+        device const    char * src0s,
         device const    char * src1,
         device         float * dst,
+        device const    char * ids,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -4710,28 +6109,19 @@ kernel void kernel_mul_mv_id_f16_f32(
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
         uint3                  tgpig[[threadgroup_position_in_grid]],
         uint                   tiitg[[thread_index_in_threadgroup]],
         uint                   tiisg[[thread_index_in_simdgroup]],
         uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
     const int64_t bid = tgpig.z/(ne12*ne13);
 
     tgpig.z = tgpig.z%(ne12*ne13);
 
     const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
 
     kernel_mul_mv_f16_f32_impl(
-        src0[id],
+        src0,
         src1 + bid*nb11,
         dst  + bid*ne0,
         ne00,
@@ -4756,9 +6146,10 @@ kernel void kernel_mul_mv_id_f16_f32(
 
 [[host_name("kernel_mul_mv_id_q8_0_f32")]]
 kernel void kernel_mul_mv_id_q8_0_f32(
-        device const    char * ids,
+        device const    char * src0s,
         device const    char * src1,
         device         float * dst,
+        device const    char * ids,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -4779,28 +6170,19 @@ kernel void kernel_mul_mv_id_q8_0_f32(
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
         uint3                  tgpig[[threadgroup_position_in_grid]],
         uint                   tiitg[[thread_index_in_threadgroup]],
         uint                   tiisg[[thread_index_in_simdgroup]],
         uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
     const int64_t bid = tgpig.z/(ne12*ne13);
 
     tgpig.z = tgpig.z%(ne12*ne13);
 
     const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
 
     kernel_mul_mv_q8_0_f32_impl(
-        src0[id],
+        src0,
         (device const float *) (src1 + bid*nb11),
         dst + bid*ne0,
         ne00,
@@ -4819,9 +6201,10 @@ kernel void kernel_mul_mv_id_q8_0_f32(
 
 [[host_name("kernel_mul_mv_id_q4_0_f32")]]
 kernel void kernel_mul_mv_id_q4_0_f32(
-        device const    char * ids,
+        device const    char * src0s,
         device const    char * src1,
         device         float * dst,
+        device const    char * ids,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -4842,28 +6225,19 @@ kernel void kernel_mul_mv_id_q4_0_f32(
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
         uint3                  tgpig[[threadgroup_position_in_grid]],
         uint                   tiitg[[thread_index_in_threadgroup]],
         uint                   tiisg[[thread_index_in_simdgroup]],
         uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
     const int64_t bid = tgpig.z/(ne12*ne13);
 
     tgpig.z = tgpig.z%(ne12*ne13);
 
     const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
 
     mul_vec_q_n_f32_impl<block_q4_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
-        src0[id],
+        src0,
         (device const float *) (src1 + bid*nb11),
         dst + bid*ne0,
         ne00,
@@ -4882,9 +6256,10 @@ kernel void kernel_mul_mv_id_q4_0_f32(
 
 [[host_name("kernel_mul_mv_id_q4_1_f32")]]
 kernel void kernel_mul_mv_id_q4_1_f32(
-        device const    char * ids,
+        device const    char * src0s,
         device const    char * src1,
         device         float * dst,
+        device const    char * ids,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -4905,28 +6280,19 @@ kernel void kernel_mul_mv_id_q4_1_f32(
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
         uint3                  tgpig[[threadgroup_position_in_grid]],
         uint                   tiitg[[thread_index_in_threadgroup]],
         uint                   tiisg[[thread_index_in_simdgroup]],
         uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
     const int64_t bid = tgpig.z/(ne12*ne13);
 
     tgpig.z = tgpig.z%(ne12*ne13);
 
     const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
 
     mul_vec_q_n_f32_impl<block_q4_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
-        src0[id],
+        src0,
         (device const float *) (src1 + bid*nb11),
         dst + bid*ne0,
         ne00,
@@ -4945,9 +6311,10 @@ kernel void kernel_mul_mv_id_q4_1_f32(
 
 [[host_name("kernel_mul_mv_id_q5_0_f32")]]
 kernel void kernel_mul_mv_id_q5_0_f32(
-        device const    char * ids,
+        device const    char * src0s,
         device const    char * src1,
         device         float * dst,
+        device const    char * ids,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -4968,28 +6335,19 @@ kernel void kernel_mul_mv_id_q5_0_f32(
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
         uint3                  tgpig[[threadgroup_position_in_grid]],
         uint                   tiitg[[thread_index_in_threadgroup]],
         uint                   tiisg[[thread_index_in_simdgroup]],
         uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
     const int64_t bid = tgpig.z/(ne12*ne13);
 
     tgpig.z = tgpig.z%(ne12*ne13);
 
     const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
 
     mul_vec_q_n_f32_impl<block_q5_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
-        src0[id],
+        src0,
         (device const float *) (src1 + bid*nb11),
         dst + bid*ne0,
         ne00,
@@ -5008,9 +6366,10 @@ kernel void kernel_mul_mv_id_q5_0_f32(
 
 [[host_name("kernel_mul_mv_id_q5_1_f32")]]
 kernel void kernel_mul_mv_id_q5_1_f32(
-        device const    char * ids,
+        device const    char * src0s,
         device const    char * src1,
         device         float * dst,
+        device const    char * ids,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -5031,28 +6390,19 @@ kernel void kernel_mul_mv_id_q5_1_f32(
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
         uint3                  tgpig[[threadgroup_position_in_grid]],
         uint                   tiitg[[thread_index_in_threadgroup]],
         uint                   tiisg[[thread_index_in_simdgroup]],
         uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
     const int64_t bid = tgpig.z/(ne12*ne13);
 
     tgpig.z = tgpig.z%(ne12*ne13);
 
     const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
 
     mul_vec_q_n_f32_impl<block_q5_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
-        src0[id],
+        src0,
         (device const float *) (src1 + bid*nb11),
         dst + bid*ne0,
         ne00,
@@ -5071,9 +6421,10 @@ kernel void kernel_mul_mv_id_q5_1_f32(
 
 [[host_name("kernel_mul_mv_id_q2_K_f32")]]
 kernel void kernel_mul_mv_id_q2_K_f32(
-        device const    char * ids,
+        device const    char * src0s,
         device const    char * src1,
         device         float * dst,
+        device const    char * ids,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -5094,28 +6445,19 @@ kernel void kernel_mul_mv_id_q2_K_f32(
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
         uint3                  tgpig[[threadgroup_position_in_grid]],
         uint                   tiitg[[thread_index_in_threadgroup]],
         uint                   tiisg[[thread_index_in_simdgroup]],
         uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
     const int64_t bid = tgpig.z/(ne12*ne13);
 
     tgpig.z = tgpig.z%(ne12*ne13);
 
     const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
 
     kernel_mul_mv_q2_K_f32_impl(
-        src0[id],
+        src0,
         (device const float *) (src1 + bid*nb11),
         dst + bid*ne0,
         ne00,
@@ -5134,9 +6476,10 @@ kernel void kernel_mul_mv_id_q2_K_f32(
 
 [[host_name("kernel_mul_mv_id_q3_K_f32")]]
 kernel void kernel_mul_mv_id_q3_K_f32(
-        device const    char * ids,
+        device const    char * src0s,
         device const    char * src1,
         device         float * dst,
+        device const    char * ids,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -5157,28 +6500,19 @@ kernel void kernel_mul_mv_id_q3_K_f32(
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
         uint3                  tgpig[[threadgroup_position_in_grid]],
         uint                   tiitg[[thread_index_in_threadgroup]],
         uint                   tiisg[[thread_index_in_simdgroup]],
         uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
     const int64_t bid = tgpig.z/(ne12*ne13);
 
     tgpig.z = tgpig.z%(ne12*ne13);
 
     const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
 
     kernel_mul_mv_q3_K_f32_impl(
-        src0[id],
+        src0,
         (device const float *) (src1 + bid*nb11),
         dst + bid*ne0,
         ne00,
@@ -5197,9 +6531,10 @@ kernel void kernel_mul_mv_id_q3_K_f32(
 
 [[host_name("kernel_mul_mv_id_q4_K_f32")]]
 kernel void kernel_mul_mv_id_q4_K_f32(
-        device const    char * ids,
+        device const    char * src0s,
         device const    char * src1,
         device         float * dst,
+        device const    char * ids,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -5220,28 +6555,19 @@ kernel void kernel_mul_mv_id_q4_K_f32(
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
         uint3                  tgpig[[threadgroup_position_in_grid]],
         uint                   tiitg[[thread_index_in_threadgroup]],
         uint                   tiisg[[thread_index_in_simdgroup]],
         uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
     const int64_t bid = tgpig.z/(ne12*ne13);
 
     tgpig.z = tgpig.z%(ne12*ne13);
 
     const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
 
     kernel_mul_mv_q4_K_f32_impl(
-        src0[id],
+        src0,
         (device const float *) (src1 + bid*nb11),
         dst + bid*ne0,
         ne00,
@@ -5260,9 +6586,10 @@ kernel void kernel_mul_mv_id_q4_K_f32(
 
 [[host_name("kernel_mul_mv_id_q5_K_f32")]]
 kernel void kernel_mul_mv_id_q5_K_f32(
-        device const    char * ids,
+        device const    char * src0s,
         device const    char * src1,
         device         float * dst,
+        device const    char * ids,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -5283,28 +6610,19 @@ kernel void kernel_mul_mv_id_q5_K_f32(
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
         uint3                  tgpig[[threadgroup_position_in_grid]],
         uint                   tiitg[[thread_index_in_threadgroup]],
         uint                   tiisg[[thread_index_in_simdgroup]],
         uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
     const int64_t bid = tgpig.z/(ne12*ne13);
 
     tgpig.z = tgpig.z%(ne12*ne13);
 
     const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
 
     kernel_mul_mv_q5_K_f32_impl(
-        src0[id],
+        src0,
         (device const float *) (src1 + bid*nb11),
         dst + bid*ne0,
         ne00,
@@ -5323,9 +6641,10 @@ kernel void kernel_mul_mv_id_q5_K_f32(
 
 [[host_name("kernel_mul_mv_id_q6_K_f32")]]
 kernel void kernel_mul_mv_id_q6_K_f32(
-        device const    char * ids,
+        device const    char * src0s,
         device const    char * src1,
         device         float * dst,
+        device const    char * ids,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -5346,28 +6665,19 @@ kernel void kernel_mul_mv_id_q6_K_f32(
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
         uint3                  tgpig[[threadgroup_position_in_grid]],
         uint                   tiitg[[thread_index_in_threadgroup]],
         uint                   tiisg[[thread_index_in_simdgroup]],
         uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
     const int64_t bid = tgpig.z/(ne12*ne13);
 
     tgpig.z = tgpig.z%(ne12*ne13);
 
     const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
 
     kernel_mul_mv_q6_K_f32_impl(
-        src0[id],
+        src0,
         (device const float *) (src1 + bid*nb11),
         dst + bid*ne0,
         ne00,
@@ -5386,9 +6696,10 @@ kernel void kernel_mul_mv_id_q6_K_f32(
 
 [[host_name("kernel_mul_mv_id_iq2_xxs_f32")]]
 kernel void kernel_mul_mv_id_iq2_xxs_f32(
-        device const    char * ids,
+        device const    char * src0s,
         device const    char * src1,
         device         float * dst,
+        device const    char * ids,
         constant    uint64_t & nbi1,
         constant     int64_t & ne00,
         constant     int64_t & ne01,
@@ -5409,29 +6720,476 @@ kernel void kernel_mul_mv_id_iq2_xxs_f32(
         constant        uint & r2,
         constant        uint & r3,
         constant         int & idx,
-        device const    char * src00,
-        device const    char * src01,
-        device const    char * src02,
-        device const    char * src03,
-        device const    char * src04,
-        device const    char * src05,
-        device const    char * src06,
-        device const    char * src07,
         threadgroup int8_t   * shared_values [[threadgroup(0)]],
         uint3                  tgpig[[threadgroup_position_in_grid]],
         uint                   tiitg[[thread_index_in_threadgroup]],
         uint                   tiisg[[thread_index_in_simdgroup]],
         uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
-    device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
-
     const int64_t bid = tgpig.z/(ne12*ne13);
 
     tgpig.z = tgpig.z%(ne12*ne13);
 
     const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
 
     kernel_mul_mv_iq2_xxs_f32_impl(
-        src0[id],
+        src0,
+        (device const float *) (src1 + bid*nb11),
+        dst + bid*ne0,
+        ne00,
+        ne01,
+        ne02,
+        ne10,
+        ne12,
+        ne0,
+        ne1,
+        r2,
+        r3,
+        shared_values,
+        tgpig,
+        tiisg,
+        sgitg);
+}
+
+[[host_name("kernel_mul_mv_id_iq2_xs_f32")]]
+kernel void kernel_mul_mv_id_iq2_xs_f32(
+        device const    char * src0s,
+        device const    char * src1,
+        device         float * dst,
+        device const    char * ids,
+        constant    uint64_t & nbi1,
+        constant     int64_t & ne00,
+        constant     int64_t & ne01,
+        constant     int64_t & ne02,
+        constant    uint64_t & nb00,
+        constant    uint64_t & nb01,
+        constant    uint64_t & nb02,
+        constant     int64_t & ne10,
+        constant     int64_t & ne11,
+        constant     int64_t & ne12,
+        constant     int64_t & ne13,
+        constant    uint64_t & nb10,
+        constant    uint64_t & nb11,
+        constant    uint64_t & nb12,
+        constant     int64_t & ne0,
+        constant     int64_t & ne1,
+        constant    uint64_t & nb1,
+        constant        uint & r2,
+        constant        uint & r3,
+        constant         int & idx,
+        threadgroup int8_t   * shared_values [[threadgroup(0)]],
+        uint3                  tgpig[[threadgroup_position_in_grid]],
+        uint                   tiitg[[thread_index_in_threadgroup]],
+        uint                   tiisg[[thread_index_in_simdgroup]],
+        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
+    const int64_t bid = tgpig.z/(ne12*ne13);
+
+    tgpig.z = tgpig.z%(ne12*ne13);
+
+    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
+
+    kernel_mul_mv_iq2_xs_f32_impl(
+        src0,
+        (device const float *) (src1 + bid*nb11),
+        dst + bid*ne0,
+        ne00,
+        ne01,
+        ne02,
+        ne10,
+        ne12,
+        ne0,
+        ne1,
+        r2,
+        r3,
+        shared_values,
+        tgpig,
+        tiisg,
+        sgitg);
+}
+
+[[host_name("kernel_mul_mv_id_iq3_xxs_f32")]]
+kernel void kernel_mul_mv_id_iq3_xxs_f32(
+        device const    char * src0s,
+        device const    char * src1,
+        device         float * dst,
+        device const    char * ids,
+        constant    uint64_t & nbi1,
+        constant     int64_t & ne00,
+        constant     int64_t & ne01,
+        constant     int64_t & ne02,
+        constant    uint64_t & nb00,
+        constant    uint64_t & nb01,
+        constant    uint64_t & nb02,
+        constant     int64_t & ne10,
+        constant     int64_t & ne11,
+        constant     int64_t & ne12,
+        constant     int64_t & ne13,
+        constant    uint64_t & nb10,
+        constant    uint64_t & nb11,
+        constant    uint64_t & nb12,
+        constant     int64_t & ne0,
+        constant     int64_t & ne1,
+        constant    uint64_t & nb1,
+        constant        uint & r2,
+        constant        uint & r3,
+        constant         int & idx,
+        threadgroup int8_t   * shared_values [[threadgroup(0)]],
+        uint3                  tgpig[[threadgroup_position_in_grid]],
+        uint                   tiitg[[thread_index_in_threadgroup]],
+        uint                   tiisg[[thread_index_in_simdgroup]],
+        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
+    const int64_t bid = tgpig.z/(ne12*ne13);
+
+    tgpig.z = tgpig.z%(ne12*ne13);
+
+    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
+
+    kernel_mul_mv_iq3_xxs_f32_impl(
+        src0,
+        (device const float *) (src1 + bid*nb11),
+        dst + bid*ne0,
+        ne00,
+        ne01,
+        ne02,
+        ne10,
+        ne12,
+        ne0,
+        ne1,
+        r2,
+        r3,
+        shared_values,
+        tgpig,
+        tiisg,
+        sgitg);
+}
+
+[[host_name("kernel_mul_mv_id_iq3_s_f32")]]
+kernel void kernel_mul_mv_id_iq3_s_f32(
+        device const    char * src0s,
+        device const    char * src1,
+        device         float * dst,
+        device const    char * ids,
+        constant    uint64_t & nbi1,
+        constant     int64_t & ne00,
+        constant     int64_t & ne01,
+        constant     int64_t & ne02,
+        constant    uint64_t & nb00,
+        constant    uint64_t & nb01,
+        constant    uint64_t & nb02,
+        constant     int64_t & ne10,
+        constant     int64_t & ne11,
+        constant     int64_t & ne12,
+        constant     int64_t & ne13,
+        constant    uint64_t & nb10,
+        constant    uint64_t & nb11,
+        constant    uint64_t & nb12,
+        constant     int64_t & ne0,
+        constant     int64_t & ne1,
+        constant    uint64_t & nb1,
+        constant        uint & r2,
+        constant        uint & r3,
+        constant         int & idx,
+        threadgroup int8_t   * shared_values [[threadgroup(0)]],
+        uint3                  tgpig[[threadgroup_position_in_grid]],
+        uint                   tiitg[[thread_index_in_threadgroup]],
+        uint                   tiisg[[thread_index_in_simdgroup]],
+        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
+    const int64_t bid = tgpig.z/(ne12*ne13);
+
+    tgpig.z = tgpig.z%(ne12*ne13);
+
+    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
+
+    kernel_mul_mv_iq3_s_f32_impl(
+        src0,
+        (device const float *) (src1 + bid*nb11),
+        dst + bid*ne0,
+        ne00,
+        ne01,
+        ne02,
+        ne10,
+        ne12,
+        ne0,
+        ne1,
+        r2,
+        r3,
+        shared_values,
+        tgpig,
+        tiisg,
+        sgitg);
+}
+
+[[host_name("kernel_mul_mv_id_iq2_s_f32")]]
+kernel void kernel_mul_mv_id_iq2_s_f32(
+        device const    char * src0s,
+        device const    char * src1,
+        device         float * dst,
+        device const    char * ids,
+        constant    uint64_t & nbi1,
+        constant     int64_t & ne00,
+        constant     int64_t & ne01,
+        constant     int64_t & ne02,
+        constant    uint64_t & nb00,
+        constant    uint64_t & nb01,
+        constant    uint64_t & nb02,
+        constant     int64_t & ne10,
+        constant     int64_t & ne11,
+        constant     int64_t & ne12,
+        constant     int64_t & ne13,
+        constant    uint64_t & nb10,
+        constant    uint64_t & nb11,
+        constant    uint64_t & nb12,
+        constant     int64_t & ne0,
+        constant     int64_t & ne1,
+        constant    uint64_t & nb1,
+        constant        uint & r2,
+        constant        uint & r3,
+        constant         int & idx,
+        threadgroup int8_t   * shared_values [[threadgroup(0)]],
+        uint3                  tgpig[[threadgroup_position_in_grid]],
+        uint                   tiitg[[thread_index_in_threadgroup]],
+        uint                   tiisg[[thread_index_in_simdgroup]],
+        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
+    const int64_t bid = tgpig.z/(ne12*ne13);
+
+    tgpig.z = tgpig.z%(ne12*ne13);
+
+    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
+
+    kernel_mul_mv_iq2_s_f32_impl(
+        src0,
+        (device const float *) (src1 + bid*nb11),
+        dst + bid*ne0,
+        ne00,
+        ne01,
+        ne02,
+        ne10,
+        ne12,
+        ne0,
+        ne1,
+        r2,
+        r3,
+        shared_values,
+        tgpig,
+        tiisg,
+        sgitg);
+}
+
+[[host_name("kernel_mul_mv_id_iq1_s_f32")]]
+kernel void kernel_mul_mv_id_iq1_s_f32(
+        device const    char * src0s,
+        device const    char * src1,
+        device         float * dst,
+        device const    char * ids,
+        constant    uint64_t & nbi1,
+        constant     int64_t & ne00,
+        constant     int64_t & ne01,
+        constant     int64_t & ne02,
+        constant    uint64_t & nb00,
+        constant    uint64_t & nb01,
+        constant    uint64_t & nb02,
+        constant     int64_t & ne10,
+        constant     int64_t & ne11,
+        constant     int64_t & ne12,
+        constant     int64_t & ne13,
+        constant    uint64_t & nb10,
+        constant    uint64_t & nb11,
+        constant    uint64_t & nb12,
+        constant     int64_t & ne0,
+        constant     int64_t & ne1,
+        constant    uint64_t & nb1,
+        constant        uint & r2,
+        constant        uint & r3,
+        constant         int & idx,
+        uint3                  tgpig[[threadgroup_position_in_grid]],
+        uint                   tiitg[[thread_index_in_threadgroup]],
+        uint                   tiisg[[thread_index_in_simdgroup]],
+        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
+    const int64_t bid = tgpig.z/(ne12*ne13);
+
+    tgpig.z = tgpig.z%(ne12*ne13);
+
+    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
+
+    kernel_mul_mv_iq1_s_f32_impl(
+        src0,
+        (device const float *) (src1 + bid*nb11),
+        dst + bid*ne0,
+        ne00,
+        ne01,
+        ne02,
+        ne10,
+        ne12,
+        ne0,
+        ne1,
+        r2,
+        r3,
+        tgpig,
+        tiisg,
+        sgitg);
+}
+
+[[host_name("kernel_mul_mv_id_iq1_m_f32")]]
+kernel void kernel_mul_mv_id_iq1_m_f32(
+        device const    char * src0s,
+        device const    char * src1,
+        device         float * dst,
+        device const    char * ids,
+        constant    uint64_t & nbi1,
+        constant     int64_t & ne00,
+        constant     int64_t & ne01,
+        constant     int64_t & ne02,
+        constant    uint64_t & nb00,
+        constant    uint64_t & nb01,
+        constant    uint64_t & nb02,
+        constant     int64_t & ne10,
+        constant     int64_t & ne11,
+        constant     int64_t & ne12,
+        constant     int64_t & ne13,
+        constant    uint64_t & nb10,
+        constant    uint64_t & nb11,
+        constant    uint64_t & nb12,
+        constant     int64_t & ne0,
+        constant     int64_t & ne1,
+        constant    uint64_t & nb1,
+        constant        uint & r2,
+        constant        uint & r3,
+        constant         int & idx,
+        uint3                  tgpig[[threadgroup_position_in_grid]],
+        uint                   tiitg[[thread_index_in_threadgroup]],
+        uint                   tiisg[[thread_index_in_simdgroup]],
+        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
+    const int64_t bid = tgpig.z/(ne12*ne13);
+
+    tgpig.z = tgpig.z%(ne12*ne13);
+
+    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
+
+    kernel_mul_mv_iq1_m_f32_impl(
+        src0,
+        (device const float *) (src1 + bid*nb11),
+        dst + bid*ne0,
+        ne00,
+        ne01,
+        ne02,
+        ne10,
+        ne12,
+        ne0,
+        ne1,
+        r2,
+        r3,
+        tgpig,
+        tiisg,
+        sgitg);
+}
+
+[[host_name("kernel_mul_mv_id_iq4_nl_f32")]]
+kernel void kernel_mul_mv_id_iq4_nl_f32(
+        device const    char * src0s,
+        device const    char * src1,
+        device         float * dst,
+        device const    char * ids,
+        constant    uint64_t & nbi1,
+        constant     int64_t & ne00,
+        constant     int64_t & ne01,
+        constant     int64_t & ne02,
+        constant    uint64_t & nb00,
+        constant    uint64_t & nb01,
+        constant    uint64_t & nb02,
+        constant     int64_t & ne10,
+        constant     int64_t & ne11,
+        constant     int64_t & ne12,
+        constant     int64_t & ne13,
+        constant    uint64_t & nb10,
+        constant    uint64_t & nb11,
+        constant    uint64_t & nb12,
+        constant     int64_t & ne0,
+        constant     int64_t & ne1,
+        constant    uint64_t & nb1,
+        constant        uint & r2,
+        constant        uint & r3,
+        constant         int & idx,
+        threadgroup float    * shared_values [[threadgroup(0)]],
+        uint3                  tgpig[[threadgroup_position_in_grid]],
+        uint                   tiitg[[thread_index_in_threadgroup]],
+        uint                   tiisg[[thread_index_in_simdgroup]],
+        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
+    const int64_t bid = tgpig.z/(ne12*ne13);
+
+    tgpig.z = tgpig.z%(ne12*ne13);
+
+    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
+
+    kernel_mul_mv_iq4_nl_f32_impl(
+        src0,
+        (device const float *) (src1 + bid*nb11),
+        dst + bid*ne0,
+        ne00,
+        ne01,
+        ne02,
+        ne10,
+        ne12,
+        ne0,
+        ne1,
+        r2,
+        r3,
+        shared_values,
+        tgpig,
+        tiisg,
+        sgitg);
+}
+
+[[host_name("kernel_mul_mv_id_iq4_xs_f32")]]
+kernel void kernel_mul_mv_id_iq4_xs_f32(
+        device const    char * src0s,
+        device const    char * src1,
+        device         float * dst,
+        device const    char * ids,
+        constant    uint64_t & nbi1,
+        constant     int64_t & ne00,
+        constant     int64_t & ne01,
+        constant     int64_t & ne02,
+        constant    uint64_t & nb00,
+        constant    uint64_t & nb01,
+        constant    uint64_t & nb02,
+        constant     int64_t & ne10,
+        constant     int64_t & ne11,
+        constant     int64_t & ne12,
+        constant     int64_t & ne13,
+        constant    uint64_t & nb10,
+        constant    uint64_t & nb11,
+        constant    uint64_t & nb12,
+        constant     int64_t & ne0,
+        constant     int64_t & ne1,
+        constant    uint64_t & nb1,
+        constant        uint & r2,
+        constant        uint & r3,
+        constant         int & idx,
+        threadgroup float    * shared_values [[threadgroup(0)]],
+        uint3                  tgpig[[threadgroup_position_in_grid]],
+        uint                   tiitg[[thread_index_in_threadgroup]],
+        uint                   tiisg[[thread_index_in_simdgroup]],
+        uint                   sgitg[[simdgroup_index_in_threadgroup]]) {
+    const int64_t bid = tgpig.z/(ne12*ne13);
+
+    tgpig.z = tgpig.z%(ne12*ne13);
+
+    const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
+    device const char * src0 = src0s + id*nb02;
+
+#if QK_K == 64
+    kernel_mul_mv_iq4_nl_f32_impl(
+#else
+    kernel_mul_mv_iq4_xs_f32_impl(
+#endif
+        src0,
         (device const float *) (src1 + bid*nb11),
         dst + bid*ne0,
         ne00,
diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp
index 496f9cdca..b3f8b7eaf 100644
--- a/ggml-opencl.cpp
+++ b/ggml-opencl.cpp
@@ -1,5 +1,6 @@
 #include "ggml.h"
 #include "ggml-opencl.h"
+#include "ggml-backend-impl.h"
 
 #include <array>
 #include <atomic>
@@ -10,7 +11,7 @@
 #include <sstream>
 #include <vector>
 
-#define CL_TARGET_OPENCL_VERSION 110
+#define CL_TARGET_OPENCL_VERSION 120
 #include <clblast.h>
 
 #if defined(_MSC_VER)
@@ -713,7 +714,6 @@ __kernel void dequantize_mul_mat_vec_q6_K(__global const struct block_q6_K * xx,
         dst[row] = tmp[0];
     }
 }
-
 );
 
 
@@ -783,6 +783,7 @@ __kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float
         dst[row] = tmp[0];
     }
 }
+
 );
 
 
@@ -798,6 +799,18 @@ __kernel void KERNEL_NAME(__global TYPE* x, const int x_offset, __global TYPE* y
 }
 );
 
+std::string add_template = MULTILINE_QUOTE(
+__kernel void add_f32(__global float * x, const int x_offset, __global float * y, const int y_offset, __global float * dst, const int dst_offset, const int ky) {
+    const int i = get_group_id(0)*get_local_size(0) + get_local_id(0);
+
+    if (i >= get_global_size(0)) {
+        return;
+    }
+
+    dst[dst_offset + i] = x[x_offset + i] + y[y_offset + i%ky];
+}
+);
+
 #define CL_CHECK(err)                                               \
     do {                                                            \
         cl_int err_ = (err);                                        \
@@ -877,6 +890,7 @@ static std::string generate_kernels() {
         }
         src << mul_kernel << '\n';
     }
+    src << add_template << '\n';
 
     return src.str();
 }
@@ -892,6 +906,7 @@ static cl_kernel dequantize_mul_mat_vec_q4_0_cl, dequantize_mul_mat_vec_q4_1_cl,
 static cl_kernel dequantize_block_q2_k_cl, dequantize_block_q3_k_cl, dequantize_block_q4_k_cl, dequantize_block_q5_k_cl, dequantize_block_q6_k_cl;
 static cl_kernel dequantize_mul_mat_vec_q2_K_cl, dequantize_mul_mat_vec_q3_K_cl, dequantize_mul_mat_vec_q4_K_cl, dequantize_mul_mat_vec_q5_K_cl, dequantize_mul_mat_vec_q6_K_cl;
 static cl_kernel mul_f32_cl;
+static cl_kernel add_f32_cl;
 static bool fp16_support;
 
 static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer) {
@@ -929,6 +944,12 @@ static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, co
 }
 
 void ggml_cl_init(void) {
+    static bool initialized = false;
+    if (initialized) {
+        return;
+    }
+    initialized = true;
+
     cl_int err;
 
     struct cl_device;
@@ -1093,9 +1114,10 @@ void ggml_cl_init(void) {
     char *ext_buffer = (char *)alloca(ext_str_size + 1);
     clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, ext_str_size, ext_buffer, NULL);
     ext_buffer[ext_str_size] = '\0'; // ensure it is null terminated
+    // Disabled due to faulty outputs
     // Check if ext_buffer contains cl_khr_fp16
-    fp16_support = strstr(ext_buffer, "cl_khr_fp16") != NULL;
-    fprintf(stderr, "ggml_opencl: device FP16 support: %s\n", fp16_support ? "true" : "false");
+    fp16_support = false;  // strstr(ext_buffer, "cl_khr_fp16") != NULL;
+    // fprintf(stderr, "ggml_opencl: device FP16 support: %s\n", fp16_support ? "true" : "false");
 
     cl_context_properties properties[] = {
         (intptr_t)CL_CONTEXT_PLATFORM, (intptr_t)platform, 0
@@ -1143,6 +1165,8 @@ void ggml_cl_init(void) {
 
     // mul kernel
     CL_CHECK((mul_f32_cl = clCreateKernel(program, "mul_f32", &err), err));
+
+    CL_CHECK((add_f32_cl = clCreateKernel(program, "add_f32", &err), err));
 }
 
 static cl_kernel* ggml_get_to_fp32_cl(ggml_type type) {
@@ -1330,7 +1354,7 @@ static void ggml_cl_pool_free(cl_mem mem, size_t size) {
 }
 
 void ggml_cl_free_data(const struct ggml_tensor* tensor) {
-    if (tensor->backend != GGML_BACKEND_GPU) {
+    if (tensor->backend != GGML_BACKEND_TYPE_GPU) {
         return;
     }
 
@@ -1388,7 +1412,7 @@ static cl_int ggml_cl_h2d_tensor_2d(cl_command_queue queue, cl_mem dst, size_t o
 }
 
 static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
     const int64_t ne00 = src0->ne[0];
     const int64_t ne01 = src0->ne[1];
     const int64_t ne02 = src0->ne[2];
@@ -1451,6 +1475,70 @@ void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src
     ggml_cl_mul_f32(src0, src1, dst);
 }
 
+static void ggml_cl_add_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+    const int64_t ne03 = src0->ne[3];
+    const int64_t ne10 = src1->ne[0];
+    const int64_t ne11 = src1->ne[1];
+    const int64_t ne12 = src1->ne[2];
+    const int64_t ne13 = src1->ne[3];
+    const int nb2  = dst->nb[2];
+    const int nb3  = dst->nb[3];
+    size_t x_size;
+    size_t d_size;
+
+    cl_mem d_X = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &x_size); // src0
+    cl_mem d_Y = (cl_mem) src1->extra; // src1 is already on device, broadcasted.
+    cl_mem d_D = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &d_size); // dst
+
+
+    for (int64_t i03 = 0; i03 < ne03; i03++) {
+        for (int64_t i02 = 0; i02 < ne02; i02++) {
+            cl_event ev;
+
+            // copy src0 to device
+            CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, &ev));
+
+            const int64_t i13 = i03%ne13;
+            const int64_t i12 = i02%ne12;
+            const int i1 = i13*ne12*ne11 + i12*ne11;
+
+            cl_int x_offset = 0;
+            cl_int y_offset = i1*ne10;
+            cl_int d_offset = 0;
+
+            size_t global = ne00 * ne01;
+            cl_int ky = ne10 * ne11;
+
+            CL_CHECK(clSetKernelArg(add_f32_cl, 0, sizeof(cl_mem), &d_X));
+            CL_CHECK(clSetKernelArg(add_f32_cl, 1, sizeof(cl_int), &x_offset));
+            CL_CHECK(clSetKernelArg(add_f32_cl, 2, sizeof(cl_mem), &d_Y));
+            CL_CHECK(clSetKernelArg(add_f32_cl, 3, sizeof(cl_int), &y_offset));
+            CL_CHECK(clSetKernelArg(add_f32_cl, 4, sizeof(cl_mem), &d_D));
+            CL_CHECK(clSetKernelArg(add_f32_cl, 5, sizeof(cl_int), &d_offset));
+            CL_CHECK(clSetKernelArg(add_f32_cl, 6, sizeof(cl_int), &ky));
+            CL_CHECK(clEnqueueNDRangeKernel(queue, add_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL));
+
+            CL_CHECK(clReleaseEvent(ev));
+            CL_CHECK(clFinish(queue));
+
+            // copy dst to host
+            float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
+            CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * ne00*ne01, d, 0, NULL, NULL));
+        }
+    }
+    ggml_cl_pool_free(d_X, x_size);
+    ggml_cl_pool_free(d_D, d_size);
+}
+
+void ggml_cl_add(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
+    ggml_cl_add_f32(src0, src1, dst);
+}
+
 static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     const int64_t ne00 = src0->ne[0];
     const int64_t ne01 = src0->ne[1];
@@ -1478,13 +1566,13 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
     size_t y_size;
     size_t d_size;
     cl_mem d_X;
-    if (src0->backend == GGML_BACKEND_GPU) { // NOLINT
+    if (src0->backend == GGML_BACKEND_TYPE_GPU) { // NOLINT
         d_X = (cl_mem) src0->extra;
     } else {
         d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size);
     }
-    cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
-    cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
+    cl_mem d_Y = src1->backend == GGML_BACKEND_TYPE_GPU ? (cl_mem) src1->extra : ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
+    cl_mem d_D =  dst->backend == GGML_BACKEND_TYPE_GPU ? (cl_mem)  dst->extra : ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
 
     size_t x_offset = 0;
 
@@ -1492,7 +1580,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
         // TODO: copy src0 here when r3>1
         for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
             for (int64_t i02 = 0; i02 < ne02; i02++) {
-                if (src0->backend == GGML_BACKEND_GPU) {
+                if (src0->backend == GGML_BACKEND_TYPE_GPU) {
                     x_offset = (i03 * ne02 + i02) * x_ne;
                 } else {
                     // copy src0 to device
@@ -1501,7 +1589,9 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
 
                 for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) {
                     // copy src1 to device
-                    CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
+                    if (src1->backend == GGML_BACKEND_TYPE_CPU) {
+                        CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
+                    }
 
                     CL_CHECK(clFinish(queue));
 
@@ -1522,18 +1612,24 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
                     }
 
                     // copy dst to host
-                    float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
-                    CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL));
+                    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
+                        float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
+                        CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL));
+                    }
                 }
             }
         }
     }
 
-    if (src0->backend != GGML_BACKEND_GPU) {
+    if (src0->backend != GGML_BACKEND_TYPE_GPU) {
         ggml_cl_pool_free(d_X, x_size);
     }
-    ggml_cl_pool_free(d_Y, y_size);
-    ggml_cl_pool_free(d_D, d_size);
+    if (src1->backend != GGML_BACKEND_TYPE_GPU) {
+        ggml_cl_pool_free(d_Y, y_size);
+    }
+    if (dst->backend != GGML_BACKEND_TYPE_GPU) {
+        ggml_cl_pool_free(d_D, d_size);
+    }
 }
 
 static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, void * wdata, size_t wsize) {
@@ -1574,7 +1670,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
     size_t y_size;
     size_t d_size;
     cl_mem d_X;
-    if (src0->backend == GGML_BACKEND_GPU) { // NOLINT
+    if (src0->backend == GGML_BACKEND_TYPE_GPU) { // NOLINT
         d_X = (cl_mem) src0->extra;
     } else {
         d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size);
@@ -1591,13 +1687,15 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
         // TODO: copy src0 here when r3>1
         for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
             for (int64_t i02 = 0; i02 < ne02; i02++) {
-                if (src0->backend == GGML_BACKEND_GPU) {
+                if (src0->backend == GGML_BACKEND_TYPE_GPU) {
                     x_offset = (i03 * ne02 + i02) * x_ne;
                 } else {
                     // copy src0 to device
                     CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
                 }
 
+                // FIXME: convert on device
+
                 for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) {
                     // convert src1 to fp16
                     // TODO: use multiple threads
@@ -1643,17 +1741,19 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
                     }
 
                     // copy dst to host, then convert to float
-                    CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(ggml_fp16_t) * d_ne, tmp, 1, &ev_sgemm, NULL));
-
-                    float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
-
-                    ggml_fp16_to_fp32_row(tmp, d, d_ne);
+                    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
+                        CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(ggml_fp16_t) * d_ne, tmp, 1, &ev_sgemm, NULL));
+                        float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
+                        ggml_fp16_to_fp32_row(tmp, d, d_ne);
+                    } else {
+                        // FIXME: convert dst to fp32 on device
+                    }
                 }
             }
         }
     }
 
-    if (src0->backend != GGML_BACKEND_GPU) {
+    if (src0->backend != GGML_BACKEND_TYPE_GPU) {
         ggml_cl_pool_free(d_X, x_size);
     }
     ggml_cl_pool_free(d_Y, y_size);
@@ -1698,7 +1798,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
     cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
     cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
     cl_mem d_Q;
-    if (src0->backend == GGML_BACKEND_CPU) {
+    if (src0->backend == GGML_BACKEND_TYPE_CPU) {
         d_Q = ggml_cl_pool_malloc(q_sz, &q_size);
     }
 
@@ -1717,10 +1817,10 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
         for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
             for (int64_t i02 = 0; i02 < ne02; i02++) {
                 // copy src0 to device if necessary
-                if (src0->backend == GGML_BACKEND_CPU) {
+                if (src0->backend == GGML_BACKEND_TYPE_CPU) {
                     events.emplace_back();
                     CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, events.data() + ev_idx++));
-                } else if (src0->backend == GGML_BACKEND_GPU) {
+                } else if (src0->backend == GGML_BACKEND_TYPE_GPU) {
                     d_Q = (cl_mem) src0->extra;
                 } else {
                     GGML_ASSERT(false);
@@ -1729,7 +1829,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
                 if (!mul_mat_vec) {
                     // convert src0 to fp32 on device
                     const size_t global = x_ne / global_denom;
-                    const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0;
+                    const size_t offset = src0->backend == GGML_BACKEND_TYPE_GPU ? (i03 * ne02 + i02) * x_bps : 0;
                     CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q));
                     CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X));
                     CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, &offset, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL));
@@ -1743,7 +1843,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
 
                         // compute
                         const size_t global = ne01 * local;
-                        const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0;
+                        const size_t offset = src0->backend == GGML_BACKEND_TYPE_GPU ? (i03 * ne02 + i02) * x_bps : 0;
                         const cl_int ncols = ne00;
                         events.emplace_back();
                         CL_CHECK(clSetKernelArg(*dmmv, 0, sizeof(cl_mem), &d_Q));
@@ -1795,13 +1895,13 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
     }
     ggml_cl_pool_free(d_Y, y_size);
     ggml_cl_pool_free(d_D, d_size);
-    if (src0->backend == GGML_BACKEND_CPU) {
+    if (src0->backend == GGML_BACKEND_TYPE_CPU) {
         ggml_cl_pool_free(d_Q, q_size);
     }
 }
 
 
-bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
+bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst) {
     const int64_t ne10 = src1->ne[0];
 
     const int64_t ne0 = dst->ne[0];
@@ -1811,7 +1911,7 @@ bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tens
     if ((src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
         src1->type == GGML_TYPE_F32 &&
         dst->type == GGML_TYPE_F32 &&
-        ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_GPU)) {
+        ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_TYPE_GPU)) {
         return true;
     }
 
@@ -1893,5 +1993,309 @@ void ggml_cl_transform_tensor(void * data, ggml_tensor * tensor) {
     CL_CHECK(clFinish(queue));
 
     tensor->extra = dst;
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 }
+
+// ggml-backend
+
+// buffer
+
+struct ggml_backend_opencl_buffer_context {
+    ~ggml_backend_opencl_buffer_context() {
+        if (buffer) {
+            clReleaseMemObject(buffer);
+        }
+        for (auto * sub_buffer : sub_buffers) {
+            clReleaseMemObject(sub_buffer);
+        }
+    }
+
+    cl_mem buffer;
+    std::vector<cl_mem> sub_buffers;
+};
+
+static void * const cl_ptr_base = (void *)(uintptr_t) 0x1000;
+
+static const char * ggml_backend_opencl_buffer_get_name(ggml_backend_buffer_t buffer) {
+    return "OpenCL";
+
+    GGML_UNUSED(buffer);
+}
+
+static void ggml_backend_opencl_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
+    delete ctx;
+}
+
+static void * ggml_backend_opencl_buffer_get_base(ggml_backend_buffer_t buffer) {
+    return cl_ptr_base;
+
+    GGML_UNUSED(buffer);
+}
+
+static void ggml_backend_opencl_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
+    if (tensor->view_src != NULL && tensor->view_offs == 0) {
+        tensor->extra = tensor->view_src->extra;
+    } else {
+        ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
+        cl_buffer_region region = {(size_t)((char *)tensor->data - (char *)cl_ptr_base), ggml_nbytes(tensor)};
+        cl_int err;
+        cl_mem sub_buffer = clCreateSubBuffer(ctx->buffer, CL_MEM_READ_WRITE, CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+        CL_CHECK(err);
+        ctx->sub_buffers.push_back(sub_buffer);
+        tensor->extra = sub_buffer;
+    }
+    tensor->backend = GGML_BACKEND_TYPE_GPU;
+}
+
+static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    cl_mem tensor_buffer = (cl_mem) tensor->extra;
+    CL_CHECK(clEnqueueWriteBuffer(queue, tensor_buffer, true, offset, size, data, 0, NULL, NULL));
+    CL_CHECK(clFinish(queue));
+
+    GGML_UNUSED(buffer);
+}
+
+static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    cl_mem tensor_buffer = (cl_mem) tensor->extra;
+    CL_CHECK(clEnqueueReadBuffer(queue, tensor_buffer, true, offset, size, data, 0, NULL, NULL));
+    CL_CHECK(clFinish(queue));
+
+    GGML_UNUSED(buffer);
+}
+
+static void ggml_backend_opencl_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
+    CL_CHECK(clEnqueueFillBuffer(queue, ctx->buffer, &value, sizeof(value), 0, buffer->size, 0, NULL, NULL));
+    CL_CHECK(clFinish(queue));
+}
+
+static void ggml_backend_opencl_buffer_reset(ggml_backend_buffer_t buffer) {
+    ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
+    for (auto * sub_buffer : ctx->sub_buffers) {
+        clReleaseMemObject(sub_buffer);
+    }
+    ctx->sub_buffers.clear();
+}
+
+static ggml_backend_buffer_i ggml_backend_opencl_buffer_interface = {
+    /* .get_name        = */ ggml_backend_opencl_buffer_get_name,
+    /* .free_buffer     = */ ggml_backend_opencl_buffer_free_buffer,
+    /* .get_base        = */ ggml_backend_opencl_buffer_get_base,
+    /* .init_tensor     = */ ggml_backend_opencl_buffer_init_tensor,
+    /* .set_tensor      = */ ggml_backend_opencl_buffer_set_tensor,
+    /* .get_tensor      = */ ggml_backend_opencl_buffer_get_tensor,
+    /* .cpy_tensor      = */ NULL,
+    /* .clear           = */ ggml_backend_opencl_buffer_clear,
+    /* .reset           = */ ggml_backend_opencl_buffer_reset,
+};
+
+// buffer type
+
+static const char * ggml_backend_opencl_buffer_type_name(ggml_backend_buffer_type_t buffer_type) {
+    return "OpenCL";
+
+    GGML_UNUSED(buffer_type);
+}
+
+static ggml_backend_buffer_t ggml_backend_opencl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buffer_type, size_t size) {
+    ggml_cl_init();
+
+    cl_int err;
+    cl_mem mem = clCreateBuffer(context, CL_MEM_READ_WRITE, size, NULL, &err);
+    if (err != CL_SUCCESS) {
+        fprintf(stderr, "%s: failed to allocate %.2f MiB\n", __func__, size / 1024.0 / 1024.0);
+        return nullptr;
+    }
+
+    ggml_backend_opencl_buffer_context * ctx = new ggml_backend_opencl_buffer_context{mem, {}};
+
+    return ggml_backend_buffer_init(buffer_type, ggml_backend_opencl_buffer_interface, ctx, size);
+}
+
+static size_t ggml_backend_opencl_buffer_type_get_alignment(ggml_backend_buffer_type_t buffer_type) {
+    // FIXME: not thread safe, device may not be initialized yet
+    static cl_uint alignment = -1;
+    if (alignment == (cl_uint)-1) {
+        ggml_cl_init();
+        clGetDeviceInfo(device, CL_DEVICE_MEM_BASE_ADDR_ALIGN, sizeof(cl_uint), &alignment, NULL);
+    }
+    return alignment;
+
+    GGML_UNUSED(buffer_type);
+}
+
+static size_t ggml_backend_opencl_buffer_type_get_max_size(ggml_backend_buffer_type_t buffer_type) {
+    static size_t max_size = -1;
+    if (max_size == (size_t)-1) {
+        ggml_cl_init();
+        clGetDeviceInfo(device, CL_DEVICE_MAX_MEM_ALLOC_SIZE, sizeof(size_t), &max_size, NULL);
+    }
+    return max_size;
+}
+
+static bool ggml_backend_opencl_buffer_type_supports_backend(ggml_backend_buffer_type_t buffer_type, ggml_backend_t backend) {
+    //return ggml_backend_is_opencl(backend); // opencl must be used through the cpu backend
+    return ggml_backend_is_cpu(backend);
+
+    GGML_UNUSED(buffer_type);
+}
+
+static ggml_backend_buffer_type_i ggml_backend_opencl_buffer_type_interface = {
+    /* .get_name         = */ ggml_backend_opencl_buffer_type_name,
+    /* .alloc_buffer     = */ ggml_backend_opencl_buffer_type_alloc_buffer,
+    /* .get_alignment    = */ ggml_backend_opencl_buffer_type_get_alignment,
+    /* .get_max_size     = */ ggml_backend_opencl_buffer_type_get_max_size,
+    /* .get_alloc_size   = */ NULL,
+    /* .supports_backend = */ ggml_backend_opencl_buffer_type_supports_backend,
+    /* .is_host          = */ NULL,
+};
+
+
+ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type() {
+    static ggml_backend_buffer_type buffer_type = {
+        /* .iface   = */ ggml_backend_opencl_buffer_type_interface,
+        /* .context = */ nullptr,
+    };
+
+    return &buffer_type;
+}
+
+#if 0
+// host buffer type
+
+static const char * ggml_backend_opencl_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    return "CL_Host";
+
+    GGML_UNUSED(buft);
+}
+
+static const char * ggml_backend_opencl_host_buffer_name(ggml_backend_buffer_t buffer) {
+    return "CL_Host";
+
+    GGML_UNUSED(buffer);
+}
+
+static void ggml_backend_opencl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    ggml_cl_host_free(buffer->context);
+}
+
+static ggml_backend_buffer_t ggml_backend_opencl_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    void * ptr = ggml_cl_host_malloc(size);
+
+    if (ptr == nullptr) {
+        // fallback to cpu buffer
+        return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size);
+    }
+
+    ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
+    buffer->buft = buft;
+    buffer->iface.get_name = ggml_backend_opencl_host_buffer_name;
+    buffer->iface.free_buffer = ggml_backend_opencl_host_buffer_free_buffer;
+
+    return buffer;
+}
+
+ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type() {
+    static struct ggml_backend_buffer_type ggml_backend_opencl_buffer_type_host = {
+        /* .iface    = */ {
+            /* .get_name         = */ ggml_backend_opencl_host_buffer_type_name,
+            /* .alloc_buffer     = */ ggml_backend_opencl_host_buffer_type_alloc_buffer,
+            /* .get_alignment    = */ ggml_backend_cpu_buffer_type()->iface.get_alignment,
+            /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
+            /* .get_alloc_size   = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
+            /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend,
+            /* .is_host          = */ ggml_backend_cpu_buffer_type()->iface.is_host,
+        },
+        /* .context  = */ nullptr,
+    };
+
+    return &ggml_backend_opencl_buffer_type_host;
+}
+
+// backend
+
+static const char * ggml_backend_opencl_name(ggml_backend_t backend) {
+    return "OpenCL";
+
+    GGML_UNUSED(backend);
+}
+
+static void ggml_backend_opencl_free(ggml_backend_t backend) {
+    GGML_UNUSED(backend);
+}
+
+static ggml_backend_buffer_type_t ggml_backend_opencl_get_default_buffer_type(ggml_backend_t backend) {
+    return ggml_backend_opencl_buffer_type();
+
+    GGML_UNUSED(backend);
+}
+
+static ggml_status ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) {
+    for (int i = 0; i < graph->n_nodes; ++i) {
+        ggml_tensor * node = graph->nodes[i];
+
+        if (ggml_is_empty(node)) {
+            continue;
+        }
+
+        switch (node->op) {
+            case GGML_OP_MUL_MAT:
+                ggml_cl_mul_mat(node->src[0], node->src[1], node, nullptr, 0);
+                break;
+            case GGML_OP_MUL:
+                ggml_cl_mul(node->src[0], node->src[1], node);
+                break;
+            default:
+                GGML_ASSERT(false);
+        }
+    }
+
+    return GGML_STATUS_SUCCESS;
+
+    GGML_UNUSED(backend);
+}
+
+static bool ggml_backend_opencl_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
+    switch (op->op) {
+        case GGML_OP_MUL_MAT:
+            return ggml_cl_can_mul_mat(op->src[0], op->src[1], op);
+        case GGML_OP_MUL:
+            // return ggml_can_repeat_rows(op->src[1], op->src[0]);
+            return true;
+        default:
+            return false;
+    }
+
+    GGML_UNUSED(backend);
+}
+
+static ggml_backend_i opencl_backend_i = {
+    /* .get_name                = */ ggml_backend_opencl_name,
+    /* .free                    = */ ggml_backend_opencl_free,
+    /* .get_default_buffer_type = */ ggml_backend_opencl_get_default_buffer_type,
+    /* .set_tensor_async        = */ NULL,
+    /* .get_tensor_async        = */ NULL,
+    /* .cpy_tensor_from_async   = */ NULL,
+    /* .cpy_tensor_to_async     = */ NULL,
+    /* .synchronize             = */ NULL,
+    /* .graph_plan_create       = */ NULL,
+    /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_compute      = */ NULL,
+    /* .graph_compute           = */ ggml_backend_opencl_graph_compute,
+    /* .supports_op             = */ ggml_backend_opencl_supports_op,
+};
+
+ggml_backend_t ggml_backend_opencl_init() {
+    ggml_backend_t backend = new ggml_backend {
+        /* .interface = */ opencl_backend_i,
+        /* .context   = */ nullptr
+    };
+
+    return backend;
+}
+
+bool ggml_backend_is_opencl(ggml_backend_t backend) {
+    return backend && backend->iface.get_name == ggml_backend_opencl_name;
+}
+#endif
diff --git a/ggml-opencl.h b/ggml-opencl.h
index 44d05bd64..257a6be6a 100644
--- a/ggml-opencl.h
+++ b/ggml-opencl.h
@@ -1,6 +1,7 @@
 #pragma once
 
 #include "ggml.h"
+#include "ggml-backend.h"
 
 #ifdef  __cplusplus
 extern "C" {
@@ -9,17 +10,27 @@ extern "C" {
 GGML_API void ggml_cl_init(void);
 
 GGML_API void   ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
-GGML_API bool   ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+GGML_API void   ggml_cl_add(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+GGML_API bool   ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst);
 GGML_API size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
 GGML_API void   ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
 
-GGML_API void * ggml_cl_host_malloc(size_t size);
-GGML_API void   ggml_cl_host_free(void * ptr);
+// GGML_API void * ggml_cl_host_malloc(size_t size);
+// GGML_API void   ggml_cl_host_free(void * ptr);
 
 GGML_API void ggml_cl_free_data(const struct ggml_tensor* tensor);
 
 GGML_API void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor);
 
+// backend API
+
+// GGML_API ggml_backend_t ggml_backend_opencl_init(void);
+
+// GGML_API bool ggml_backend_is_opencl(ggml_backend_t backend);
+
+GGML_API ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type(void);
+// GGML_API ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type(void);
+
 #ifdef  __cplusplus
 }
 #endif
diff --git a/ggml-quants.c b/ggml-quants.c
index d497e6de9..f2e6c4bd1 100644
--- a/ggml-quants.c
+++ b/ggml-quants.c
@@ -1,10 +1,18 @@
+#define GGML_COMMON_IMPL_C
+#include "ggml-common.h"
+
 #include "ggml-quants.h"
 #include "ggml-impl.h"
 
+#define GGML_COMMON_IMPL_C
+#include "ggml-common.h"
+
 #include <math.h>
 #include <string.h>
 #include <assert.h>
 #include <float.h>
+#include <stdlib.h> // for qsort
+#include <stdio.h>  // for GGML_ASSERT
 
 #ifdef __ARM_NEON
 
@@ -47,6 +55,9 @@
 #define MIN(a, b) ((a) < (b) ? (a) : (b))
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
 
+#define UNUSED GGML_UNUSED
+
+// some compilers don't provide _mm256_set_m128i, e.g. gcc 7
 #define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1)
 
 #if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__)
@@ -121,7 +132,7 @@ static inline __m256 sum_i16_pairs_float(const __m256i x) {
 }
 
 static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) {
-#if __AVXVNNI__
+#if defined(__AVXVNNI__) || defined(__AVX512VNNI__)
     const __m256i zero = _mm256_setzero_si256();
     const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy);
     return _mm256_cvtepi32_ps(summed_pairs);
@@ -266,16 +277,30 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128
 #endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__)
 
 #if defined(__ARM_NEON)
+
+#ifdef _MSC_VER
+
+#define ggml_vld1q_u32(w,x,y,z) { ((w) + ((uint64_t)(x) << 32)), ((y) + ((uint64_t)(z) << 32)) }
+
+#else
+
+#define ggml_vld1q_u32(w,x,y,z) { (w), (x), (y), (z) }
+
+#endif
+
 #if !defined(__aarch64__)
 
 // 64-bit compatibility
 
 // vaddvq_s16
 // vpaddq_s16
+// vpaddq_s32
 // vaddvq_s32
 // vaddvq_f32
 // vmaxvq_f32
 // vcvtnq_s32_f32
+// vzip1_u8
+// vzip2_u8
 
 inline static int32_t vaddvq_s16(int16x8_t v) {
     return
@@ -291,6 +316,12 @@ inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) {
     return vcombine_s16(a0, b0);
 }
 
+inline static int32x4_t vpaddq_s32(int32x4_t a, int32x4_t b) {
+    int32x2_t a0 = vpadd_s32(vget_low_s32(a), vget_high_s32(a));
+    int32x2_t b0 = vpadd_s32(vget_low_s32(b), vget_high_s32(b));
+    return vcombine_s32(a0, b0);
+}
+
 inline static int32_t vaddvq_s32(int32x4_t v) {
     return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3);
 }
@@ -316,6 +347,28 @@ inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) {
     return res;
 }
 
+inline static uint8x8_t vzip1_u8(uint8x8_t a, uint8x8_t b) {
+    uint8x8_t res;
+
+    res[0] = a[0]; res[1] = b[0];
+    res[2] = a[1]; res[3] = b[1];
+    res[4] = a[2]; res[5] = b[2];
+    res[6] = a[3]; res[7] = b[3];
+
+    return res;
+}
+
+inline static uint8x8_t vzip2_u8(uint8x8_t a, uint8x8_t b) {
+    uint8x8_t res;
+
+    res[0] = a[4]; res[1] = b[4];
+    res[2] = a[5]; res[3] = b[5];
+    res[4] = a[6]; res[5] = b[6];
+    res[6] = a[7]; res[7] = b[7];
+
+    return res;
+}
+
 // vld1q_s16_x2
 // vld1q_u8_x2
 // vld1q_u8_x4
@@ -392,6 +445,54 @@ inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) {
     return res;
 }
 
+// NOTE: not tested
+inline static int8x16_t ggml_vqtbl1q_s8(int8x16_t a, uint8x16_t b) {
+    int8x16_t res;
+
+    res[ 0] = a[b[ 0]];
+    res[ 1] = a[b[ 1]];
+    res[ 2] = a[b[ 2]];
+    res[ 3] = a[b[ 3]];
+    res[ 4] = a[b[ 4]];
+    res[ 5] = a[b[ 5]];
+    res[ 6] = a[b[ 6]];
+    res[ 7] = a[b[ 7]];
+    res[ 8] = a[b[ 8]];
+    res[ 9] = a[b[ 9]];
+    res[10] = a[b[10]];
+    res[11] = a[b[11]];
+    res[12] = a[b[12]];
+    res[13] = a[b[13]];
+    res[14] = a[b[14]];
+    res[15] = a[b[15]];
+
+    return res;
+}
+
+// NOTE: not tested
+inline static uint8x16_t ggml_vqtbl1q_u8(uint8x16_t a, uint8x16_t b) {
+    uint8x16_t res;
+
+    res[ 0] = a[b[ 0]];
+    res[ 1] = a[b[ 1]];
+    res[ 2] = a[b[ 2]];
+    res[ 3] = a[b[ 3]];
+    res[ 4] = a[b[ 4]];
+    res[ 5] = a[b[ 5]];
+    res[ 6] = a[b[ 6]];
+    res[ 7] = a[b[ 7]];
+    res[ 8] = a[b[ 8]];
+    res[ 9] = a[b[ 9]];
+    res[10] = a[b[10]];
+    res[11] = a[b[11]];
+    res[12] = a[b[12]];
+    res[13] = a[b[13]];
+    res[14] = a[b[14]];
+    res[15] = a[b[15]];
+
+    return res;
+}
+
 #else
 
 #define ggml_int16x8x2_t  int16x8x2_t
@@ -405,6 +506,8 @@ inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) {
 #define ggml_vld1q_u8_x4  vld1q_u8_x4
 #define ggml_vld1q_s8_x2  vld1q_s8_x2
 #define ggml_vld1q_s8_x4  vld1q_s8_x4
+#define ggml_vqtbl1q_s8   vqtbl1q_s8
+#define ggml_vqtbl1q_u8   vqtbl1q_u8
 
 #endif
 
@@ -482,6 +585,7 @@ void quantize_row_q4_0(const float * restrict x, void * restrict y, int k) {
     quantize_row_q4_0_reference(x, y, k);
 }
 
+
 void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k) {
     const int qk = QK4_1;
 
@@ -850,7 +954,7 @@ void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict
         const float d = amax / ((1 << 7) - 1);
         const float id = d ? 1.0f/d : 0.0f;
 
-        y[i].d = d;
+        y[i].d = GGML_FP32_TO_FP16(d);
 
         int sum = 0;
 
@@ -865,7 +969,7 @@ void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict
             sum += y[i].qs[QK8_1/2 + j];
         }
 
-        y[i].s = sum*d;
+        y[i].s = GGML_FP32_TO_FP16(sum*d);
     }
 }
 
@@ -893,7 +997,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
         const float d = amax / ((1 << 7) - 1);
         const float id = d ? 1.0f/d : 0.0f;
 
-        y[i].d = d;
+        y[i].d = GGML_FP32_TO_FP16(d);
 
         int32x4_t accv = vdupq_n_s32(0);
 
@@ -909,7 +1013,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
             accv = vaddq_s32(accv, vi);
         }
 
-        y[i].s = d * vaddvq_s32(accv);
+        y[i].s = GGML_FP32_TO_FP16(d * vaddvq_s32(accv));
     }
 #elif defined(__wasm_simd128__)
     for (int i = 0; i < nb; i++) {
@@ -932,7 +1036,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
         const float d = amax / ((1 << 7) - 1);
         const float id = d ? 1.0f/d : 0.0f;
 
-        y[i].d = d;
+        y[i].d = GGML_FP32_TO_FP16(d);
 
         v128_t accv = wasm_i32x4_splat(0);
 
@@ -948,10 +1052,11 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
             accv = wasm_i32x4_add(accv, vi);
         }
 
-        y[i].s = d * (wasm_i32x4_extract_lane(accv, 0) +
-                      wasm_i32x4_extract_lane(accv, 1) +
-                      wasm_i32x4_extract_lane(accv, 2) +
-                      wasm_i32x4_extract_lane(accv, 3));
+        y[i].s = GGML_FP32_TO_FP16(
+                d * (wasm_i32x4_extract_lane(accv, 0) +
+                     wasm_i32x4_extract_lane(accv, 1) +
+                     wasm_i32x4_extract_lane(accv, 2) +
+                     wasm_i32x4_extract_lane(accv, 3)));
     }
 #elif defined(__AVX2__) || defined(__AVX__)
     for (int i = 0; i < nb; i++) {
@@ -976,7 +1081,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
 
         // Quantize these floats
         const float d = maxScalar / 127.f;
-        y[i].d = d;
+        y[i].d = GGML_FP32_TO_FP16(d);
         const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f;
         const __m256 mul = _mm256_set1_ps( id );
 
@@ -1000,7 +1105,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
 
 #if defined(__AVX2__)
         // Compute the sum of the quants and set y[i].s
-        y[i].s = d * hsum_i32_8(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3)));
+        y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_8(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3))));
 
         // Convert int32 to int16
         i0 = _mm256_packs_epi32( i0, i1 );	// 0, 1, 2, 3,  8, 9, 10, 11,  4, 5, 6, 7, 12, 13, 14, 15
@@ -1030,7 +1135,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
         // Compute the sum of the quants and set y[i].s
         const __m128i s0 = _mm_add_epi32(_mm_add_epi32(ni0, ni1), _mm_add_epi32(ni2, ni3));
         const __m128i s1 = _mm_add_epi32(_mm_add_epi32(ni4, ni5), _mm_add_epi32(ni6, ni7));
-        y[i].s = d * hsum_i32_4(_mm_add_epi32(s0, s1));
+        y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_4(_mm_add_epi32(s0, s1)));
 
         // Convert int32 to int16
         ni0 = _mm_packs_epi32( ni0, ni1 );
@@ -1061,7 +1166,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
         const float d  = amax / ((1 << 7) - 1);
         const float id = d ? 1.0f/d : 0.0f;
 
-        y[i].d = d;
+        y[i].d = GGML_FP32_TO_FP16(d);
 
         vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl);
 
@@ -1078,7 +1183,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int k) {
 
         // set y[i].s
         int sum = __riscv_vmv_x_s_i16m1_i16(vwrs);
-        y[i].s = sum*d;
+        y[i].s = GGML_FP32_TO_FP16(sum*d);
     }
 #else
     GGML_UNUSED(nb);
@@ -1211,7 +1316,8 @@ static inline int nearest_int(float fval) {
     return (i & 0x007fffff) - 0x00400000;
 }
 
-static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * restrict L, int rmse_type) {
+static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * restrict L, int rmse_type,
+        const float * restrict qw) {
     float max = 0;
     float amax = 0;
     for (int i = 0; i < n; ++i) {
@@ -1237,14 +1343,18 @@ static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t *
         rmse_type = -rmse_type;
         return_early = true;
     }
-    int weight_type = rmse_type%2;
     float sumlx = 0;
     float suml2 = 0;
+#ifdef HAVE_BUGGY_APPLE_LINKER
+    // use 'volatile' to prevent unroll and work around a bug in Apple ld64 1015.7
+    for (volatile int i = 0; i < n; ++i) {
+#else
     for (int i = 0; i < n; ++i) {
+#endif
         int l = nearest_int(iscale * x[i]);
         l = MAX(-nmax, MIN(nmax-1, l));
         L[i] = l + nmax;
-        float w = weight_type == 1 ? x[i] * x[i] : 1;
+        float w = qw ? qw[i] : rmse_type == 1 ? x[i] * x[i] : rmse_type == 2 ? 1 : rmse_type == 3 ? fabsf(x[i]) : sqrtf(fabsf(x[i]));
         sumlx += w*x[i]*l;
         suml2 += w*l*l;
     }
@@ -1260,7 +1370,7 @@ static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t *
         for (int i = 0; i < n; ++i) {
             int l = nearest_int(iscale * x[i]);
             l = MAX(-nmax, MIN(nmax-1, l));
-            float w = weight_type == 1 ? x[i] * x[i] : 1;
+            float w = qw ? qw[i] : rmse_type == 1 ? x[i] * x[i] : rmse_type == 2 ? 1 : rmse_type == 3 ? fabsf(x[i]) : sqrtf(fabsf(x[i]));
             sumlx += w*x[i]*l;
             suml2 += w*l*l;
         }
@@ -1598,14 +1708,272 @@ void quantize_row_q2_K(const float * restrict x, void * restrict vy, int k) {
     quantize_row_q2_K_reference(x, vy, k);
 }
 
-size_t ggml_quantize_q2_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
-    (void)hist; // TODO: collect histograms
-
-    for (int j = 0; j < n; j += k) {
-        block_q2_K * restrict y = (block_q2_K *)dst + j/QK_K;
-        quantize_row_q2_K_reference(src + j, y, k);
+static float make_qkx3_quants(int n, int nmax, const float * restrict x, const float * restrict weights,
+        uint8_t * restrict L, float * restrict the_min, uint8_t * restrict Laux,
+        float rmin, float rdelta, int nstep, bool use_mad) {
+    float min = x[0];
+    float max = x[0];
+    float sum_w = weights ? weights[0] : x[0]*x[0];
+    float sum_x = sum_w * x[0];
+#ifdef HAVE_BUGGY_APPLE_LINKER
+    // use 'volatile' to prevent unroll and work around a bug in Apple ld64 1015.7
+    for (volatile int i = 1; i < n; ++i) {
+#else
+    for (int i = 1; i < n; ++i) {
+#endif
+        if (x[i] < min) min = x[i];
+        if (x[i] > max) max = x[i];
+        float w = weights ? weights[i] : x[i]*x[i];
+        sum_w += w;
+        sum_x += w * x[i];
     }
-    return (n/QK_K*sizeof(block_q2_K));
+    if (min > 0) {
+        min = 0;
+    }
+    if (max <= min) {
+        memset(L, 0, n);
+        *the_min = -min;
+        return 0.f;
+    }
+    float iscale = nmax/(max - min);
+    float scale = 1/iscale;
+    float best_mad = 0;
+    for (int i = 0; i < n; ++i) {
+        int l = nearest_int(iscale*(x[i] - min));
+        L[i] = MAX(0, MIN(nmax, l));
+        float diff = scale * L[i] + min - x[i];
+        diff = use_mad ? fabsf(diff) : diff*diff;
+        float w = weights ? weights[i] : x[i]*x[i];
+        best_mad += w * diff;
+    }
+    if (nstep < 1) {
+        *the_min = -min;
+        return scale;
+    }
+    for (int is = 0; is <= nstep; ++is) {
+        iscale = (rmin + rdelta*is + nmax)/(max - min);
+        float sum_l = 0, sum_l2 = 0, sum_xl = 0;
+        for (int i = 0; i < n; ++i) {
+            int l = nearest_int(iscale*(x[i] - min));
+            l = MAX(0, MIN(nmax, l));
+            Laux[i] = l;
+            float w = weights ? weights[i] : x[i]*x[i];
+            sum_l  += w*l;
+            sum_l2 += w*l*l;
+            sum_xl += w*l*x[i];
+        }
+        float D = sum_w * sum_l2 - sum_l * sum_l;
+        if (D > 0) {
+            float this_scale = (sum_w * sum_xl - sum_x * sum_l)/D;
+            float this_min   = (sum_l2 * sum_x - sum_l * sum_xl)/D;
+            if (this_min > 0) {
+                this_min = 0;
+                this_scale = sum_xl / sum_l2;
+            }
+            float mad = 0;
+            for (int i = 0; i < n; ++i) {
+                float diff = this_scale * Laux[i] + this_min - x[i];
+                diff = use_mad ? fabsf(diff) : diff*diff;
+                float w = weights ? weights[i] : x[i]*x[i];
+                mad += w * diff;
+            }
+            if (mad < best_mad) {
+                for (int i = 0; i < n; ++i) {
+                    L[i] = Laux[i];
+                }
+                best_mad = mad;
+                scale = this_scale;
+                min = this_min;
+            }
+        }
+    }
+    *the_min = -min;
+    return scale;
+}
+
+static float make_qp_quants(int n, int nmax, const float * restrict x, uint8_t * restrict L, const float * quant_weights) {
+    float max = 0;
+    for (int i = 0; i < n; ++i) {
+        max = MAX(max, x[i]);
+    }
+    if (!max) { // all zero
+        for (int i = 0; i < n; ++i) { L[i] = 0; }
+        return 0.f;
+    }
+    float iscale = nmax / max;
+    for (int i = 0; i < n; ++i) {
+        L[i] = nearest_int(iscale * x[i]);
+    }
+    float scale = 1/iscale;
+    float best_mse = 0;
+    for (int i = 0; i < n; ++i) {
+        float diff = x[i] - scale*L[i];
+        float w = quant_weights[i];
+        best_mse += w*diff*diff;
+    }
+    for (int is = -4; is <= 4; ++is) {
+        if (is == 0) continue;
+        float iscale_is = (0.1f*is + nmax)/max;
+        float scale_is = 1/iscale_is;
+        float mse = 0;
+        for (int i = 0; i < n; ++i) {
+            int l = nearest_int(iscale_is*x[i]);
+            l = MIN(nmax, l);
+            float diff = x[i] - scale_is*l;
+            float w = quant_weights[i];
+            mse += w*diff*diff;
+        }
+        if (mse < best_mse) {
+            best_mse = mse;
+            iscale = iscale_is;
+        }
+    }
+    float sumlx = 0;
+    float suml2 = 0;
+    for (int i = 0; i < n; ++i) {
+        int l = nearest_int(iscale * x[i]);
+        l = MIN(nmax, l);
+        L[i] = l;
+        float w = quant_weights[i];
+        sumlx += w*x[i]*l;
+        suml2 += w*l*l;
+    }
+    for (int itry = 0; itry < 5; ++itry) {
+        int n_changed = 0;
+        for (int i = 0; i < n; ++i) {
+            float w = quant_weights[i];
+            float slx = sumlx - w*x[i]*L[i];
+            float sl2 = suml2 - w*L[i]*L[i];
+            if (slx > 0 && sl2 > 0) {
+                int new_l = nearest_int(x[i] * sl2 / slx);
+                new_l = MIN(nmax, new_l);
+                if (new_l != L[i]) {
+                    slx += w*x[i]*new_l;
+                    sl2 += w*new_l*new_l;
+                    if (slx*slx*suml2 > sumlx*sumlx*sl2) {
+                        L[i] = new_l; sumlx = slx; suml2 = sl2;
+                        ++n_changed;
+                    }
+                }
+            }
+        }
+        if (!n_changed) {
+            break;
+        }
+    }
+    return sumlx / suml2;
+}
+
+static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restrict y, int k, const float * restrict quant_weights) {
+    GGML_ASSERT(quant_weights);
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+    const bool requantize = true;
+
+    uint8_t L[QK_K];
+    uint8_t Laux[16];
+    float mins[QK_K/16];
+    float scales[QK_K/16];
+    float sw[QK_K/16];
+    float weight[16];
+    uint8_t Ls[QK_K/16], Lm[QK_K/16];
+
+    for (int i = 0; i < nb; i++) {
+        memset(sw, 0, QK_K/16*sizeof(float));
+        float sumx2 = 0;
+        for (int j = 0; j < QK_K; ++j) sumx2 += x[j]*x[j];
+        float sigma2 = sumx2/QK_K;
+        for (int j = 0; j < QK_K/16; ++j) {
+            const float * restrict qw = quant_weights + QK_K * i + 16*j;
+            for (int l = 0; l < 16; ++l) weight[l] = qw[l] * sqrtf(sigma2 + x[16*j + l]*x[16*j + l]);
+            for (int l = 0; l < QK_K/16; ++l) sw[j] += weight[l];
+            scales[j] = make_qkx3_quants(16, 3, x + 16*j, weight, L + 16*j, &mins[j], Laux, -0.9f, 0.05f, 36, false);
+        }
+
+        float dm, mm;
+#if QK_K == 64
+        float max_scale = 0, max_min = 0;
+        for (int j = 0; j < QK_K/16; ++j) {
+            max_scale = MAX(max_scale, scales[j]);
+            max_min   = MAX(max_min,   mins[j]);
+        }
+        dm = max_scale/15;
+        mm = max_min/15;
+        if (max_scale) {
+            float id = 1/dm;
+            for (int j = 0; j < QK_K/16; ++j) {
+                int l = nearest_int(id*scales[j]);
+                Ls[j] = MAX(0, MIN(15, l));
+            }
+        } else {
+            memset(Ls, 0, QK_K/16);
+        }
+        if (max_min) {
+            float id = 1/mm;
+            for (int j = 0; j < QK_K/16; ++j) {
+                int l = nearest_int(id*mins[j]);
+                Lm[j] = MAX(0, MIN(15, l));
+            }
+        } else {
+            memset(Lm, 0, QK_K/16);
+        }
+#else
+        dm  = make_qp_quants(QK_K/16, 15, scales, Ls, sw);
+        mm  = make_qp_quants(QK_K/16, 15, mins,   Lm, sw);
+#endif
+        y[i].d    = GGML_FP32_TO_FP16(dm);
+        y[i].dmin = GGML_FP32_TO_FP16(mm);
+        dm        = GGML_FP16_TO_FP32(y[i].d);
+        mm        = GGML_FP16_TO_FP32(y[i].dmin);
+
+        for (int j = 0; j < QK_K/16; ++j) {
+            y[i].scales[j] = Ls[j] | (Lm[j] << 4);
+        }
+
+        if (requantize) {
+            for (int j = 0; j < QK_K/16; ++j) {
+                const float d = dm * (y[i].scales[j] & 0xF);
+                if (!d) continue;
+                const float m = mm * (y[i].scales[j] >> 4);
+                for (int ii = 0; ii < 16; ++ii) {
+                    int l = nearest_int((x[16*j + ii] + m)/d);
+                    l = MAX(0, MIN(3, l));
+                    L[16*j + ii] = l;
+                }
+            }
+        }
+
+#if QK_K == 256
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) {
+                y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6);
+            }
+        }
+#else
+        for (int l = 0; l < 16; ++l) {
+            y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6);
+        }
+#endif
+
+        x += QK_K;
+
+    }
+}
+
+size_t quantize_q2_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
+    size_t row_size = ggml_row_size(GGML_TYPE_Q2_K, n_per_row);
+    if (!quant_weights) {
+        quantize_row_q2_K_reference(src, dst, nrow*n_per_row);
+    }
+    else {
+        char * qrow = (char *)dst;
+        for (int row = 0; row < nrow; ++row) {
+            quantize_row_q2_K_impl(src, (block_q2_K*)qrow, n_per_row, quant_weights);
+            src += n_per_row;
+            qrow += row_size;
+        }
+    }
+    return nrow * row_size;
 }
 
 //========================= 3-bit (de)-quantization
@@ -1811,14 +2179,109 @@ void quantize_row_q3_K(const float * restrict x, void * restrict vy, int k) {
     quantize_row_q3_K_reference(x, vy, k);
 }
 
-size_t ggml_quantize_q3_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
-    (void)hist; // TODO: collect histograms
+static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restrict y, int n_per_row, const float * restrict quant_weights) {
+#if QK_K != 256
+    (void)quant_weights;
+    quantize_row_q3_K_reference(x, y, n_per_row);
+#else
+    assert(n_per_row % QK_K == 0);
+    const int nb = n_per_row / QK_K;
 
-    for (int j = 0; j < n; j += k) {
-        block_q3_K * restrict y = (block_q3_K *)dst + j/QK_K;
-        quantize_row_q3_K_reference(src + j, y, k);
+    int8_t L[QK_K];
+    float scales[QK_K / 16];
+    float weight[16];
+    float sw[QK_K / 16];
+    int8_t Ls[QK_K / 16];
+
+    for (int i = 0; i < nb; i++) {
+
+        float sumx2 = 0;
+        for (int j = 0; j < QK_K; ++j) sumx2 += x[j]*x[j];
+        float sigma2 = 2*sumx2/QK_K;
+
+        for (int j = 0; j < QK_K/16; ++j) {
+            if (quant_weights) {
+                const float * qw = quant_weights ? quant_weights + QK_K * i + 16*j : NULL;
+                for (int l = 0; l < 16; ++l) weight[l] = qw[l] * sqrtf(sigma2 + x[16*j+l]*x[16*j+l]);
+            } else {
+                for (int l = 0; l < 16; ++l) weight[l] = x[16*j+l]*x[16*j+l];
+            }
+            float sumw = 0;
+            for (int l = 0; l < 16; ++l) sumw += weight[l];
+            sw[j] = sumw;
+
+            scales[j] = make_qx_quants(16, 4, x + 16*j, L + 16*j, 1, weight);
+
+        }
+
+        memset(y[i].scales, 0, 12);
+
+        float d_block = make_qx_quants(QK_K/16, 32, scales, Ls, 1, sw);
+        for (int j = 0; j < QK_K/16; ++j) {
+            int l = Ls[j];
+            if (j < 8) {
+                y[i].scales[j] = l & 0xF;
+            } else {
+                y[i].scales[j-8] |= ((l & 0xF) << 4);
+            }
+            l >>= 4;
+            y[i].scales[j%4 + 8] |= (l << (2*(j/4)));
+        }
+        y[i].d = GGML_FP32_TO_FP16(d_block);
+
+        int8_t sc;
+        for (int j = 0; j < QK_K/16; ++j) {
+            sc = j < 8 ? y[i].scales[j] & 0xF : y[i].scales[j-8] >> 4;
+            sc = (sc | (((y[i].scales[8 + j%4] >> (2*(j/4))) & 3) << 4)) - 32;
+            float d = GGML_FP16_TO_FP32(y[i].d) * sc;
+            if (!d) {
+                continue;
+            }
+            for (int ii = 0; ii < 16; ++ii) {
+                int l = nearest_int(x[16*j + ii]/d);
+                l = MAX(-4, MIN(3, l));
+                L[16*j + ii] = l + 4;
+            }
+        }
+
+        memset(y[i].hmask, 0, QK_K/8);
+        // We put the high-bit for the 1st 8 quants into bit 0, the next 8 into bit 1, etc.
+        int m = 0;
+        uint8_t hm = 1;
+        for (int j = 0; j < QK_K; ++j) {
+            if (L[j] > 3) {
+                y[i].hmask[m] |= hm;
+                L[j] -= 4;
+            }
+            if (++m == QK_K/8) {
+                m = 0; hm <<= 1;
+            }
+        }
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) {
+                y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6);
+            }
+        }
+
+        x += QK_K;
     }
-    return (n/QK_K*sizeof(block_q3_K));
+#endif
+}
+
+size_t quantize_q3_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
+    size_t row_size = ggml_row_size(GGML_TYPE_Q3_K, n_per_row);
+    if (!quant_weights) {
+        quantize_row_q3_K_reference(src, dst, nrow*n_per_row);
+    }
+    else {
+        char * qrow = (char *)dst;
+        for (int row = 0; row < nrow; ++row) {
+            quantize_row_q3_K_impl(src, (block_q3_K*)qrow, n_per_row, quant_weights);
+            src += n_per_row;
+            qrow += row_size;
+        }
+    }
+    return nrow * row_size;
 }
 
 // ====================== 4-bit (de)-quantization
@@ -1975,15 +2438,98 @@ void quantize_row_q4_K(const float * restrict x, void * restrict vy, int k) {
     quantize_row_q4_K_reference(x, y, k);
 }
 
-size_t ggml_quantize_q4_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
-    assert(k % QK_K == 0);
-    (void)hist; // TODO: collect histograms
+static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restrict y, int n_per_row, const float * quant_weights) {
+#if QK_K != 256
+    (void)quant_weights;
+    quantize_row_q4_K_reference(x, y, n_per_row);
+#else
+    assert(n_per_row % QK_K == 0);
+    const int nb = n_per_row / QK_K;
+
+    uint8_t L[QK_K];
+    uint8_t Laux[32];
+    uint8_t Ls[QK_K/32];
+    uint8_t Lm[QK_K/32];
+    float   weights[32];
+    float   sw[QK_K/32];
+    float   mins[QK_K/32];
+    float   scales[QK_K/32];
+
+    for (int i = 0; i < nb; i++) {
+
+        float sum_x2 = 0;
+        for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l];
+        float sigma2 = 2*sum_x2/QK_K;
+        float av_x = sqrtf(sigma2);
+
+        for (int j = 0; j < QK_K/32; ++j) {
+            if (quant_weights) {
+                const float * qw = quant_weights + QK_K*i + 32*j;
+                for (int l = 0; l < 32; ++l) weights[l] = qw[l] * sqrtf(sigma2 + x[32*j + l]*x[32*j + l]);
+            } else {
+                for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
+            }
+            float sumw = 0;
+            for (int l = 0; l < 32; ++l) sumw += weights[l];
+            sw[j] = sumw;
+            scales[j] = make_qkx3_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false);
+        }
+
+        float d_block = make_qp_quants(QK_K/32, 63, scales, Ls, sw);
+        float m_block = make_qp_quants(QK_K/32, 63, mins,   Lm, sw);
+        for (int j = 0; j < QK_K/32; ++j) {
+            uint8_t ls = Ls[j];
+            uint8_t lm = Lm[j];
+            if (j < 4) {
+                y[i].scales[j] = ls;
+                y[i].scales[j+4] = lm;
+            } else {
+                y[i].scales[j+4] = (ls & 0xF) | ((lm & 0xF) << 4);
+                y[i].scales[j-4] |= ((ls >> 4) << 6);
+                y[i].scales[j-0] |= ((lm >> 4) << 6);
+            }
+        }
+        y[i].d = GGML_FP32_TO_FP16(d_block);
+        y[i].dmin = GGML_FP32_TO_FP16(m_block);
+
+        uint8_t sc, m;
+        for (int j = 0; j < QK_K/32; ++j) {
+            get_scale_min_k4(j, y[i].scales, &sc, &m);
+            const float d = GGML_FP16_TO_FP32(y[i].d) * sc;
+            if (!d) continue;
+            const float dm = GGML_FP16_TO_FP32(y[i].dmin) * m;
+            for (int ii = 0; ii < 32; ++ii) {
+                int l = nearest_int((x[32*j + ii] + dm)/d);
+                l = MAX(0, MIN(15, l));
+                L[32*j + ii] = l;
+            }
+        }
+        uint8_t * q = y[i].qs;
+        for (int j = 0; j < QK_K; j += 64) {
+            for (int l = 0; l < 32; ++l) q[l] = L[j + l] | (L[j + l + 32] << 4);
+            q += 32;
+        }
+
+        x += QK_K;
 
-    for (int j = 0; j < n; j += k) {
-        block_q4_K * restrict y = (block_q4_K *)dst + j/QK_K;
-        quantize_row_q4_K_reference(src + j, y, k);
     }
-    return (n/QK_K*sizeof(block_q4_K));
+#endif
+}
+
+size_t quantize_q4_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
+    size_t row_size = ggml_row_size(GGML_TYPE_Q4_K, n_per_row);
+    if (!quant_weights) {
+        quantize_row_q4_K_reference(src, dst, nrow*n_per_row);
+    }
+    else {
+        char * qrow = (char *)dst;
+        for (int row = 0; row < nrow; ++row) {
+            quantize_row_q4_K_impl(src, (block_q4_K*)qrow, n_per_row, quant_weights);
+            src += n_per_row;
+            qrow += row_size;
+        }
+    }
+    return nrow * row_size;
 }
 
 // ====================== 5-bit (de)-quantization
@@ -2081,7 +2627,7 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict
 #else
         float max_scale = 0, amax = 0;
         for (int j = 0; j < QK_K/16; ++j) {
-            scales[j] = make_qx_quants(16, 16, x + 16*j, L + 16*j, 1);
+            scales[j] = make_qx_quants(16, 16, x + 16*j, L + 16*j, 1, NULL);
             float abs_scale = fabsf(scales[j]);
             if (abs_scale > amax) {
                 amax = abs_scale;
@@ -2181,15 +2727,118 @@ void quantize_row_q5_K(const float * restrict x, void * restrict vy, int k) {
     quantize_row_q5_K_reference(x, y, k);
 }
 
-size_t ggml_quantize_q5_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
-    assert(k % QK_K == 0);
-    (void)hist; // TODO: collect histograms
+static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restrict y, int n_per_row, const float * quant_weights) {
+#if QK_K != 256
+    (void)quant_weights;
+    quantize_row_q5_K_reference(x, y, n_per_row);
+#else
+    assert(n_per_row % QK_K == 0);
+    const int nb = n_per_row / QK_K;
+
+    uint8_t L[QK_K];
+    uint8_t Laux[32];
+    uint8_t Ls[QK_K/32];
+    uint8_t Lm[QK_K/32];
+    float   mins[QK_K/32];
+    float   scales[QK_K/32];
+    float   sw[QK_K/32];
+    float   weights[32];
+
+    for (int i = 0; i < nb; i++) {
+
+        float sum_x2 = 0;
+        for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l];
+        float sigma2 = 2*sum_x2/QK_K;
+        float av_x = sqrtf(sigma2);
+
+        for (int j = 0; j < QK_K/32; ++j) {
+            if (quant_weights) {
+                const float * qw = quant_weights + QK_K*i + 32*j;
+                for (int l = 0; l < 32; ++l) weights[l] = qw[l] * sqrtf(sigma2 + x[32*j + l]*x[32*j + l]);
+            } else {
+                for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
+            }
+            float sumw = 0;
+            for (int l = 0; l < 32; ++l) sumw += weights[l];
+            sw[j] = sumw;
+
+            scales[j] = make_qkx3_quants(32, 31, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false);
+        }
+
+        float d_block = make_qp_quants(QK_K/32, 63, scales, Ls, sw);
+        float m_block = make_qp_quants(QK_K/32, 63, mins,   Lm, sw);
+
+        for (int j = 0; j < QK_K/32; ++j) {
+            uint8_t ls = Ls[j];
+            uint8_t lm = Lm[j];
+            ls = MIN(63, ls);
+            lm = MIN(63, lm);
+            if (j < 4) {
+                y[i].scales[j] = ls;
+                y[i].scales[j+4] = lm;
+            } else {
+                y[i].scales[j+4] = (ls & 0xF) | ((lm & 0xF) << 4);
+                y[i].scales[j-4] |= ((ls >> 4) << 6);
+                y[i].scales[j-0] |= ((lm >> 4) << 6);
+            }
+        }
+        y[i].d = GGML_FP32_TO_FP16(d_block);
+        y[i].dmin = GGML_FP32_TO_FP16(m_block);
+
+        uint8_t sc, m;
+        for (int j = 0; j < QK_K/32; ++j) {
+            get_scale_min_k4(j, y[i].scales, &sc, &m);
+            const float d = GGML_FP16_TO_FP32(y[i].d) * sc;
+            if (!d) continue;
+            const float dm = GGML_FP16_TO_FP32(y[i].dmin) * m;
+            for (int ii = 0; ii < 32; ++ii) {
+                int l = nearest_int((x[32*j + ii] + dm)/d);
+                l = MAX(0, MIN(31, l));
+                L[32*j + ii] = l;
+            }
+        }
+
+        uint8_t * restrict qh = y[i].qh;
+        uint8_t * restrict ql = y[i].qs;
+        memset(qh, 0, QK_K/8);
+
+        uint8_t m1 = 1, m2 = 2;
+        for (int n = 0; n < QK_K; n += 64) {
+            for (int j = 0; j < 32; ++j) {
+                int l1 = L[n + j];
+                if (l1 > 15) {
+                    l1 -= 16; qh[j] |= m1;
+                }
+                int l2 = L[n + j + 32];
+                if (l2 > 15) {
+                    l2 -= 16; qh[j] |= m2;
+                }
+                ql[j] = l1 | (l2 << 4);
+            }
+            m1 <<= 2; m2 <<= 2;
+            ql += 32;
+        }
+
+        x += QK_K;
 
-    for (int j = 0; j < n; j += k) {
-        block_q5_K * restrict y = (block_q5_K *)dst + j/QK_K;
-        quantize_row_q5_K_reference(src + j, y, k);
     }
-    return (n/QK_K*sizeof(block_q5_K));
+#endif
+}
+
+size_t quantize_q5_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
+    size_t row_size = ggml_row_size(GGML_TYPE_Q5_K, n_per_row);
+    if (!quant_weights) {
+        quantize_row_q5_K_reference(src, dst, nrow*n_per_row);
+    }
+    else {
+        char * qrow = (char *)dst;
+        for (int row = 0; row < nrow; ++row) {
+            quantize_row_q5_K_impl(src, (block_q5_K*)qrow, n_per_row, quant_weights);
+            src += n_per_row;
+            qrow += row_size;
+        }
+    }
+    return nrow * row_size;
 }
 
 // ====================== 6-bit (de)-quantization
@@ -2208,7 +2857,7 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict
 
         for (int ib = 0; ib < QK_K/16; ++ib) {
 
-            const float scale = make_qx_quants(16, 32, x + 16*ib, L + 16*ib, 1);
+            const float scale = make_qx_quants(16, 32, x + 16*ib, L + 16*ib, 1, NULL);
             scales[ib] = scale;
 
             const float abs_scale = fabsf(scale);
@@ -2329,106 +2978,315 @@ void quantize_row_q6_K(const float * restrict x, void * restrict vy, int k) {
     quantize_row_q6_K_reference(x, y, k);
 }
 
-size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist) {
-    assert(k % QK_K == 0);
-    (void)hist; // TODO: collect histograms
+static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restrict y, int n_per_row, const float * quant_weights) {
+#if QK_K != 256
+    (void)quant_weights;
+    quantize_row_q6_K_reference(x, y, n_per_row);
+#else
+    assert(n_per_row % QK_K == 0);
+    const int nb = n_per_row / QK_K;
+
+    int8_t L[QK_K];
+    float   scales[QK_K/16];
+    //float   weights[16];
+
+    for (int i = 0; i < nb; i++) {
+
+        //float sum_x2 = 0;
+        //for (int j = 0; j < QK_K; ++j) sum_x2 += x[j]*x[j];
+        //float sigma2 = sum_x2/QK_K;
+
+        float max_scale = 0;
+        float max_abs_scale = 0;
+
+        for (int ib = 0; ib < QK_K/16; ++ib) {
+
+            float scale;
+            if (quant_weights) {
+                const float * qw = quant_weights + QK_K*i + 16*ib;
+                //for (int j = 0; j < 16; ++j) weights[j] = qw[j] * sqrtf(sigma2 + x[16*ib + j]*x[16*ib + j]);
+                //scale = make_qx_quants(16, 32, x + 16*ib, L + 16*ib, 1, weights);
+                scale = make_qx_quants(16, 32, x + 16*ib, L + 16*ib, 1, qw);
+            } else {
+                scale = make_qx_quants(16, 32, x + 16*ib, L + 16*ib, 1, NULL);
+            }
+            scales[ib] = scale;
+
+            const float abs_scale = fabsf(scale);
+            if (abs_scale > max_abs_scale) {
+                max_abs_scale = abs_scale;
+                max_scale = scale;
+            }
+
+        }
+
+        if (!max_abs_scale) {
+            memset(&y[i], 0, sizeof(block_q6_K));
+            y[i].d = GGML_FP32_TO_FP16(0.f);
+            x += QK_K;
+            continue;
+        }
+
+        float iscale = -128.f/max_scale;
+        y[i].d = GGML_FP32_TO_FP16(1/iscale);
+        for (int ib = 0; ib < QK_K/16; ++ib) {
+            y[i].scales[ib] = MIN(127, nearest_int(iscale*scales[ib]));
+        }
+
+        for (int j = 0; j < QK_K/16; ++j) {
+            float d = GGML_FP16_TO_FP32(y[i].d) * y[i].scales[j];
+            if (!d) {
+                continue;
+            }
+            for (int ii = 0; ii < 16; ++ii) {
+                int l = nearest_int(x[16*j + ii]/d);
+                l = MAX(-32, MIN(31, l));
+                L[16*j + ii] = l + 32;
+            }
+        }
+
+        uint8_t * restrict ql = y[i].ql;
+        uint8_t * restrict qh = y[i].qh;
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) {
+                const uint8_t q1 = L[j + l +  0] & 0xF;
+                const uint8_t q2 = L[j + l + 32] & 0xF;
+                const uint8_t q3 = L[j + l + 64] & 0xF;
+                const uint8_t q4 = L[j + l + 96] & 0xF;
+                ql[l+ 0] = q1 | (q3 << 4);
+                ql[l+32] = q2 | (q4 << 4);
+                qh[l] = (L[j + l] >> 4) | ((L[j + l + 32] >> 4) << 2) | ((L[j + l + 64] >> 4) << 4) | ((L[j + l + 96] >> 4) << 6);
+            }
+            ql += 64;
+            qh += 32;
+        }
+
+        x += QK_K;
 
-    for (int j = 0; j < n; j += k) {
-        block_q6_K * restrict y = (block_q6_K *)dst + j/QK_K;
-        quantize_row_q6_K_reference(src + j, y, k);
     }
-    return (n/QK_K*sizeof(block_q6_K));
+#endif
+}
+
+size_t quantize_q6_K(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
+    size_t row_size = ggml_row_size(GGML_TYPE_Q6_K, n_per_row);
+    if (!quant_weights) {
+        quantize_row_q6_K_reference(src, dst, nrow*n_per_row);
+    }
+    else {
+        char * qrow = (char *)dst;
+        for (int row = 0; row < nrow; ++row) {
+            quantize_row_q6_K_impl(src, (block_q6_K*)qrow, n_per_row, quant_weights);
+            src += n_per_row;
+            qrow += row_size;
+        }
+    }
+    return nrow * row_size;
+}
+
+static void quantize_row_q4_0_impl(const float * restrict x, block_q4_0 * restrict y, int n_per_row, const float * quant_weights) {
+    static_assert(QK4_0 == 32, "QK4_0 must be 32");
+
+    if (!quant_weights) {
+        quantize_row_q4_0_reference(x, y, n_per_row);
+        return;
+    }
+
+    float weight[QK4_0];
+    int8_t L[QK4_0];
+
+    float sum_x2 = 0;
+    for (int j = 0; j < n_per_row; ++j) sum_x2 += x[j]*x[j];
+    float sigma2 = sum_x2/n_per_row;
+
+    const int nb = n_per_row/QK4_0;
+    for (int ib = 0; ib < nb; ++ib) {
+        const float * xb = x + QK4_0 * ib;
+        const float * qw = quant_weights + QK4_0 * ib;
+        for (int j = 0; j < QK4_0; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]);
+        float d = make_qx_quants(QK4_0, 8, xb, L, 1, weight);
+        y[ib].d = GGML_FP32_TO_FP16(d);
+        for (int j = 0; j < 16; ++j) {
+            y[ib].qs[j] = L[j] | (L[j+16] << 4);
+        }
+    }
+}
+
+size_t quantize_q4_0(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
+    if (!quant_weights) {
+        quantize_row_q4_0_reference(src, dst, nrow*n_per_row);
+        return nrow * ggml_row_size(GGML_TYPE_Q4_0, n_per_row);
+    }
+    size_t row_size = ggml_row_size(GGML_TYPE_Q4_0, n_per_row);
+    char * qrow = (char *)dst;
+    for (int row = 0; row < nrow; ++row) {
+        quantize_row_q4_0_impl(src, (block_q4_0*)qrow, n_per_row, quant_weights);
+        src += n_per_row;
+        qrow += row_size;
+    }
+    return nrow * row_size;
+}
+
+static void quantize_row_q4_1_impl(const float * restrict x, block_q4_1 * restrict y, int n_per_row, const float * quant_weights) {
+    static_assert(QK4_1 == 32, "QK4_1 must be 32");
+
+    if (!quant_weights) {
+        quantize_row_q4_1_reference(x, y, n_per_row);
+        return;
+    }
+
+    float weight[QK4_1];
+    uint8_t L[QK4_1], Laux[QK4_1];
+
+    float sum_x2 = 0;
+    for (int j = 0; j < n_per_row; ++j) sum_x2 += x[j]*x[j];
+    float sigma2 = sum_x2/n_per_row;
+
+    const int nb = n_per_row/QK4_1;
+    for (int ib = 0; ib < nb; ++ib) {
+        const float * xb = x + QK4_1 * ib;
+        const float * qw = quant_weights + QK4_1 * ib;
+        for (int j = 0; j < QK4_1; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]);
+        float min;
+        float d = make_qkx3_quants(QK4_1, 15, xb, weight, L, &min, Laux, -0.9f, 0.05f, 36, false);
+        y[ib].d = GGML_FP32_TO_FP16(d);
+        y[ib].m = GGML_FP32_TO_FP16(-min);
+        for (int j = 0; j < 16; ++j) {
+            y[ib].qs[j] = L[j] | (L[j+16] << 4);
+        }
+    }
+}
+
+size_t quantize_q4_1(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
+    if (!quant_weights) {
+        quantize_row_q4_1_reference(src, dst, nrow*n_per_row);
+        return nrow * ggml_row_size(GGML_TYPE_Q4_1, n_per_row);
+    }
+    size_t row_size = ggml_row_size(GGML_TYPE_Q4_1, n_per_row);
+    char * qrow = (char *)dst;
+    for (int row = 0; row < nrow; ++row) {
+        quantize_row_q4_1_impl(src, (block_q4_1*)qrow, n_per_row, quant_weights);
+        src += n_per_row;
+        qrow += row_size;
+    }
+    return nrow * row_size;
+}
+
+static void quantize_row_q5_0_impl(const float * restrict x, block_q5_0 * restrict y, int n_per_row, const float * quant_weights) {
+    static_assert(QK5_0 == 32, "QK5_0 must be 32");
+
+    if (!quant_weights) {
+        quantize_row_q5_0_reference(x, y, n_per_row);
+        return;
+    }
+
+    float weight[QK5_0];
+    int8_t L[QK5_0];
+
+    float sum_x2 = 0;
+    for (int j = 0; j < n_per_row; ++j) sum_x2 += x[j]*x[j];
+    float sigma2 = sum_x2/n_per_row;
+
+    const int nb = n_per_row/QK5_0;
+    for (int ib = 0; ib < nb; ++ib) {
+        const float * xb = x + QK5_0 * ib;
+        const float * qw = quant_weights + QK5_0 * ib;
+        for (int j = 0; j < QK5_0; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]);
+        float d = make_qx_quants(QK5_0, 16, xb, L, 1, weight);
+        y[ib].d = GGML_FP32_TO_FP16(d);
+
+        uint32_t qh = 0;
+
+        for (int j = 0; j < 16; ++j) {
+            const uint8_t xi0 = L[j];
+            const uint8_t xi1 = L[j+16];
+            y[ib].qs[j] = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4);
+
+            // get the 5-th bit and store it in qh at the right position
+            qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
+            qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2);
+        }
+
+        memcpy(&y[ib].qh, &qh, sizeof(qh));
+    }
+}
+
+size_t quantize_q5_0(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
+    if (!quant_weights) {
+        quantize_row_q5_0_reference(src, dst, nrow*n_per_row);
+        return nrow * ggml_row_size(GGML_TYPE_Q5_0, n_per_row);
+    }
+    size_t row_size = ggml_row_size(GGML_TYPE_Q5_0, n_per_row);
+    char * qrow = (char *)dst;
+    for (int row = 0; row < nrow; ++row) {
+        quantize_row_q5_0_impl(src, (block_q5_0*)qrow, n_per_row, quant_weights);
+        src += n_per_row;
+        qrow += row_size;
+    }
+    return nrow * row_size;
+}
+
+static void quantize_row_q5_1_impl(const float * restrict x, block_q5_1 * restrict y, int n_per_row, const float * quant_weights) {
+    static_assert(QK5_1 == 32, "QK5_1 must be 32");
+
+    if (!quant_weights) {
+        quantize_row_q5_1_reference(x, y, n_per_row);
+        return;
+    }
+
+    float weight[QK5_1];
+    uint8_t L[QK5_1], Laux[QK5_1];
+
+    float sum_x2 = 0;
+    for (int j = 0; j < n_per_row; ++j) sum_x2 += x[j]*x[j];
+    float sigma2 = sum_x2/n_per_row;
+
+    const int nb = n_per_row/QK5_1;
+    for (int ib = 0; ib < nb; ++ib) {
+        const float * xb = x + QK5_1 * ib;
+        const float * qw = quant_weights + QK5_1 * ib;
+        for (int j = 0; j < QK5_1; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]);
+        float min;
+        float d = make_qkx3_quants(QK5_1, 31, xb, weight, L, &min, Laux, -0.9f, 0.05f, 36, false);
+        y[ib].d = GGML_FP32_TO_FP16(d);
+        y[ib].m = GGML_FP32_TO_FP16(-min);
+
+        uint32_t qh = 0;
+        for (int j = 0; j < 16; ++j) {
+            const uint8_t xi0 = L[j];
+            const uint8_t xi1 = L[j+16];
+            y[ib].qs[j] = (xi0 & 0x0F) | ((xi1 & 0x0F) << 4);
+            // get the 5-th bit and store it in qh at the right position
+            qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
+            qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2);
+        }
+        memcpy(&y[ib].qh, &qh, sizeof(qh));
+    }
+}
+
+size_t quantize_q5_1(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
+    if (!quant_weights) {
+        quantize_row_q5_1_reference(src, dst, nrow*n_per_row);
+        return nrow * ggml_row_size(GGML_TYPE_Q5_1, n_per_row);
+    }
+    size_t row_size = ggml_row_size(GGML_TYPE_Q5_1, n_per_row);
+    char * qrow = (char *)dst;
+    for (int row = 0; row < nrow; ++row) {
+        quantize_row_q5_1_impl(src, (block_q5_1*)qrow, n_per_row, quant_weights);
+        src += n_per_row;
+        qrow += row_size;
+    }
+    return nrow * row_size;
+}
+
+size_t quantize_q8_0(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
+    (void)quant_weights; // not used
+    const size_t row_size = ggml_row_size(GGML_TYPE_Q8_0, n_per_row);
+    quantize_row_q8_0_reference(src, dst, nrow*n_per_row);
+    return nrow * row_size;
 }
 
 // ====================== "True" 2-bit (de)-quantization
 
-void quantize_row_iq2_xxs_reference(const float * restrict x, block_iq2_xxs * restrict y, int k) {
-    (void)x;
-    (void)y;
-    (void)k;
-    assert(k % QK_K == 0);
-    //fprintf(stderr, "=========================== %s: not implemented\n", __func__);
-}
-
-static const uint64_t iq2xxs_grid[256] = {
-    0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
-    0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x08080808082b0808,
-    0x08080808082b082b, 0x08080808082b2b08, 0x08080808082b2b2b, 0x0808080819080819,
-    0x0808080819081908, 0x0808080819190808, 0x0808080819192b08, 0x08080808192b0819,
-    0x08080808192b1908, 0x080808082b080808, 0x080808082b08082b, 0x080808082b082b2b,
-    0x080808082b2b082b, 0x0808081908080819, 0x0808081908081908, 0x0808081908190808,
-    0x0808081908191919, 0x0808081919080808, 0x080808192b081908, 0x080808192b192b08,
-    0x0808082b08080808, 0x0808082b0808082b, 0x0808082b082b082b, 0x0808082b2b08082b,
-    0x0808190808080819, 0x0808190808081908, 0x0808190808190808, 0x08081908082b0819,
-    0x08081908082b1908, 0x0808190819080808, 0x080819081908082b, 0x0808190819082b08,
-    0x08081908192b0808, 0x080819082b080819, 0x080819082b081908, 0x080819082b190808,
-    0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, 0x0808191908082b08,
-    0x08081919082b0808, 0x080819191908192b, 0x08081919192b2b19, 0x080819192b080808,
-    0x080819192b190819, 0x0808192b08082b19, 0x0808192b08190808, 0x0808192b19080808,
-    0x0808192b2b081908, 0x0808192b2b2b1908, 0x08082b0808080808, 0x08082b0808081919,
-    0x08082b0808082b08, 0x08082b0808191908, 0x08082b08082b2b08, 0x08082b0819080819,
-    0x08082b0819081908, 0x08082b0819190808, 0x08082b081919082b, 0x08082b082b082b08,
-    0x08082b1908081908, 0x08082b1919080808, 0x08082b2b0808082b, 0x08082b2b08191908,
-    0x0819080808080819, 0x0819080808081908, 0x0819080808190808, 0x08190808082b0819,
-    0x0819080819080808, 0x08190808192b0808, 0x081908082b081908, 0x081908082b190808,
-    0x081908082b191919, 0x0819081908080808, 0x0819081908082b08, 0x08190819082b0808,
-    0x0819081919190808, 0x0819081919192b2b, 0x081908192b080808, 0x0819082b082b1908,
-    0x0819082b19081919, 0x0819190808080808, 0x0819190808082b08, 0x08191908082b0808,
-    0x08191908082b1919, 0x0819190819082b19, 0x081919082b080808, 0x0819191908192b08,
-    0x08191919192b082b, 0x0819192b08080808, 0x0819192b0819192b, 0x08192b0808080819,
-    0x08192b0808081908, 0x08192b0808190808, 0x08192b0819080808, 0x08192b082b080819,
-    0x08192b1908080808, 0x08192b1908081919, 0x08192b192b2b0808, 0x08192b2b19190819,
-    0x082b080808080808, 0x082b08080808082b, 0x082b080808082b2b, 0x082b080819081908,
-    0x082b0808192b0819, 0x082b08082b080808, 0x082b08082b08082b, 0x082b0819082b2b19,
-    0x082b081919082b08, 0x082b082b08080808, 0x082b082b0808082b, 0x082b190808080819,
-    0x082b190808081908, 0x082b190808190808, 0x082b190819080808, 0x082b19081919192b,
-    0x082b191908080808, 0x082b191919080819, 0x082b1919192b1908, 0x082b192b2b190808,
-    0x082b2b0808082b08, 0x082b2b08082b0808, 0x082b2b082b191908, 0x082b2b2b19081908,
-    0x1908080808080819, 0x1908080808081908, 0x1908080808190808, 0x1908080808192b08,
-    0x19080808082b0819, 0x19080808082b1908, 0x1908080819080808, 0x1908080819082b08,
-    0x190808081919192b, 0x19080808192b0808, 0x190808082b080819, 0x190808082b081908,
-    0x190808082b190808, 0x1908081908080808, 0x19080819082b0808, 0x19080819192b0819,
-    0x190808192b080808, 0x190808192b081919, 0x1908082b08080819, 0x1908082b08190808,
-    0x1908082b19082b08, 0x1908082b1919192b, 0x1908082b192b2b08, 0x1908190808080808,
-    0x1908190808082b08, 0x19081908082b0808, 0x190819082b080808, 0x190819082b192b19,
-    0x190819190819082b, 0x19081919082b1908, 0x1908192b08080808, 0x19082b0808080819,
-    0x19082b0808081908, 0x19082b0808190808, 0x19082b0819080808, 0x19082b0819081919,
-    0x19082b1908080808, 0x19082b1919192b08, 0x19082b19192b0819, 0x19082b192b08082b,
-    0x19082b2b19081919, 0x19082b2b2b190808, 0x1919080808080808, 0x1919080808082b08,
-    0x1919080808190819, 0x1919080808192b19, 0x19190808082b0808, 0x191908082b080808,
-    0x191908082b082b08, 0x1919081908081908, 0x191908191908082b, 0x191908192b2b1908,
-    0x1919082b2b190819, 0x191919082b190808, 0x191919082b19082b, 0x1919191908082b2b,
-    0x1919192b08080819, 0x1919192b19191908, 0x19192b0808080808, 0x19192b0808190819,
-    0x19192b0808192b19, 0x19192b08192b1908, 0x19192b1919080808, 0x19192b2b08082b08,
-    0x192b080808081908, 0x192b080808190808, 0x192b080819080808, 0x192b0808192b2b08,
-    0x192b081908080808, 0x192b081919191919, 0x192b082b08192b08, 0x192b082b192b0808,
-    0x192b190808080808, 0x192b190808081919, 0x192b191908190808, 0x192b19190819082b,
-    0x192b19192b081908, 0x192b2b081908082b, 0x2b08080808080808, 0x2b0808080808082b,
-    0x2b08080808082b2b, 0x2b08080819080819, 0x2b0808082b08082b, 0x2b08081908081908,
-    0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819,
-    0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808,
-    0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908,
-    0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b,
-    0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808,
-    0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b,
-    0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b,
-    0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808,
-    0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19,
-    0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908,
-};
-
-static const uint8_t ksigns_iq2xs[128] = {
-      0, 129, 130,   3, 132,   5,   6, 135, 136,   9,  10, 139,  12, 141, 142,  15,
-    144,  17,  18, 147,  20, 149, 150,  23,  24, 153, 154,  27, 156,  29,  30, 159,
-    160,  33,  34, 163,  36, 165, 166,  39,  40, 169, 170,  43, 172,  45,  46, 175,
-     48, 177, 178,  51, 180,  53,  54, 183, 184,  57,  58, 187,  60, 189, 190,  63,
-    192,  65,  66, 195,  68, 197, 198,  71,  72, 201, 202,  75, 204,  77,  78, 207,
-     80, 209, 210,  83, 212,  85,  86, 215, 216,  89,  90, 219,  92, 221, 222,  95,
-     96, 225, 226,  99, 228, 101, 102, 231, 232, 105, 106, 235, 108, 237, 238, 111,
-    240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255,
-};
-static const uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128};
-
 void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
@@ -2455,21 +3313,271 @@ void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y
     }
 }
 
-void quantize_row_iq2_xxs(const float * restrict x, void * restrict vy, int k) {
+// ====================== 2.3125 bpw (de)-quantization
+
+void dequantize_row_iq2_xs(const block_iq2_xs * restrict x, float * restrict y, int k) {
     assert(k % QK_K == 0);
-    block_iq2_xxs * restrict y = vy;
-    quantize_row_iq2_xxs_reference(x, y, k);
+    const int nb = k / QK_K;
+
+    float db[2];
+
+    for (int i = 0; i < nb; i++) {
+
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            db[0] = d * (0.5f + (x[i].scales[ib32] & 0xf)) * 0.25f;
+            db[1] = d * (0.5f + (x[i].scales[ib32] >>  4)) * 0.25f;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (x[i].qs[4*ib32 + l] & 511));
+                const uint8_t  signs = ksigns_iq2xs[x[i].qs[4*ib32 + l] >> 9];
+                for (int j = 0; j < 8; ++j) {
+                    y[j] = db[l/2] * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
+                }
+                y += 8;
+            }
+        }
+    }
 }
 
-size_t ggml_quantize_iq2_xxs(const float * src, void * dst, int n, int k, int64_t * hist) {
-    assert(k % QK_K == 0);
-    (void)hist; // TODO: collect histograms
+// ====================== 2.5625 bpw (de)-quantization
 
-    for (int j = 0; j < n; j += k) {
-        block_iq2_xxs * restrict y = (block_iq2_xxs *)dst + j/QK_K;
-        quantize_row_iq2_xxs_reference(src + j, y, k);
+void dequantize_row_iq2_s(const block_iq2_s * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    float db[2];
+
+    for (int i = 0; i < nb; i++) {
+
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+        const uint8_t * qs = x[i].qs;
+        const uint8_t * qh = x[i].qh;
+        const uint8_t * signs = qs + QK_K/8;
+
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            db[0] = d * (0.5f + (x[i].scales[ib32] & 0xf)) * 0.25f;
+            db[1] = d * (0.5f + (x[i].scales[ib32] >>  4)) * 0.25f;
+            for (int l = 0; l < 4; ++l) {
+                const float dl = db[l/2];
+                const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300)));
+                for (int j = 0; j < 8; ++j) {
+                    y[j] = dl * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1.f : 1.f);
+                }
+                y += 8;
+            }
+            qs += 4;
+            signs += 4;
+        }
     }
-    return (n/QK_K*sizeof(block_iq2_xxs));
+}
+
+// ====================== 3.0625 bpw (de)-quantization
+
+void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    uint32_t aux32;
+
+    for (int i = 0; i < nb; i++) {
+
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+        const uint8_t * qs = x[i].qs;
+        const uint8_t * scales_and_signs = qs + QK_K/4;
+
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            memcpy(&aux32, scales_and_signs + 4*ib32, sizeof(uint32_t));
+            const float db = d * (0.5f + (aux32 >> 28)) * 0.5f;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t  signs = ksigns_iq2xs[(aux32 >> 7*l) & 127];
+                const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + qs[2*l+0]);
+                const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + qs[2*l+1]);
+                for (int j = 0; j < 4; ++j) {
+                    y[j+0] = db * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
+                    y[j+4] = db * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
+                }
+                y += 8;
+            }
+            qs += 8;
+        }
+    }
+}
+
+// ====================== 3.3125 bpw (de)-quantization
+
+void dequantize_row_iq3_s(const block_iq3_s * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    for (int i = 0; i < nb; i++) {
+
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+        const uint8_t * qs = x[i].qs;
+        const uint8_t * qh = x[i].qh;
+        const uint8_t * signs = x[i].signs;
+
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const float db1 = d * (1 + 2*(x[i].scales[ib32/2] & 0xf));
+            const float db2 = d * (1 + 2*(x[i].scales[ib32/2] >>  4));
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[0] << (8-2*l)) & 256)));
+                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[0] << (7-2*l)) & 256)));
+                for (int j = 0; j < 4; ++j) {
+                    y[j+0] = db1 * grid1[j] * (signs[l] & kmask_iq2xs[j+0] ? -1.f : 1.f);
+                    y[j+4] = db1 * grid2[j] * (signs[l] & kmask_iq2xs[j+4] ? -1.f : 1.f);
+                }
+                y += 8;
+            }
+            qs += 8;
+            signs += 4;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[1] << (8-2*l)) & 256)));
+                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[1] << (7-2*l)) & 256)));
+                for (int j = 0; j < 4; ++j) {
+                    y[j+0] = db2 * grid1[j] * (signs[l] & kmask_iq2xs[j+0] ? -1.f : 1.f);
+                    y[j+4] = db2 * grid2[j] * (signs[l] & kmask_iq2xs[j+4] ? -1.f : 1.f);
+                }
+                y += 8;
+            }
+            qh += 2;
+            qs += 8;
+            signs += 4;
+        }
+    }
+}
+
+// ====================== 1.5625 bpw (de)-quantization
+
+void dequantize_row_iq1_s(const block_iq1_s * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    for (int i = 0; i < nb; i++) {
+
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+        const uint8_t  * qs = x[i].qs;
+        const uint16_t * qh = x[i].qh;
+
+        for (int ib = 0; ib < QK_K/32; ++ib) {
+            const float dl = d * (2*((qh[ib] >> 12) & 7) + 1);
+            const float delta = qh[ib] & 0x8000 ? -IQ1S_DELTA : IQ1S_DELTA;
+            for (int l = 0; l < 4; ++l) {
+                const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((qh[ib] >> 3*l) & 7) << 8)));
+                for (int j = 0; j < 8; ++j) {
+                    y[j] = dl * (grid[j] + delta);
+                }
+                y += 8;
+            }
+            qs += 4;
+        }
+    }
+}
+
+void dequantize_row_iq1_m(const block_iq1_m * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    float delta[4];
+    uint16_t idx[4];
+
+#if QK_K != 64
+    iq1m_scale_t scale;
+#endif
+
+    for (int i = 0; i < nb; i++) {
+
+        const uint16_t * sc = (const uint16_t *)x[i].scales;
+#if QK_K == 64
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+#else
+        scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+        const float d = GGML_FP16_TO_FP32(scale.f16);
+#endif
+        const uint8_t * qs = x[i].qs;
+        const uint8_t * qh = x[i].qh;
+
+        for (int ib = 0; ib < QK_K/32; ++ib) {
+#if QK_K == 64
+            const float dl1 = d * (2*((sc[ib/2] >> (8*(ib%2)+0)) & 0xf) + 1);
+            const float dl2 = d * (2*((sc[ib/2] >> (8*(ib%2)+4)) & 0xf) + 1);
+#else
+            const float dl1 = d * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1);
+            const float dl2 = d * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1);
+#endif
+            idx[0] = qs[0] | ((qh[0] << 8) & 0x700);
+            idx[1] = qs[1] | ((qh[0] << 4) & 0x700);
+            idx[2] = qs[2] | ((qh[1] << 8) & 0x700);
+            idx[3] = qs[3] | ((qh[1] << 4) & 0x700);
+            delta[0] = qh[0] & 0x08 ? -IQ1S_DELTA : IQ1S_DELTA;
+            delta[1] = qh[0] & 0x80 ? -IQ1S_DELTA : IQ1S_DELTA;
+            delta[2] = qh[1] & 0x08 ? -IQ1S_DELTA : IQ1S_DELTA;
+            delta[3] = qh[1] & 0x80 ? -IQ1S_DELTA : IQ1S_DELTA;
+            for (int l = 0; l < 2; ++l) {
+                const int8_t * grid = (const int8_t *)(iq1s_grid + idx[l]);
+                for (int j = 0; j < 8; ++j) {
+                    y[j] = dl1 * (grid[j] + delta[l]);
+                }
+                y += 8;
+            }
+            for (int l = 2; l < 4; ++l) {
+                const int8_t * grid = (const int8_t *)(iq1s_grid + idx[l]);
+                for (int j = 0; j < 8; ++j) {
+                    y[j] = dl2 * (grid[j] + delta[l]);
+                }
+                y += 8;
+            }
+            qs += 4;
+            qh += 2;
+        }
+    }
+}
+
+static const int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
+
+void dequantize_row_iq4_nl(const block_iq4_nl * restrict x, float * restrict y, int k) {
+    assert(k % QK4_NL == 0);
+    const int nb = k / QK4_NL;
+
+    for (int i = 0; i < nb; i++) {
+
+        const uint8_t * qs = x[i].qs;
+
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+        for (int j = 0; j < QK4_NL/2; ++j) {
+            y[j+       0] = d * kvalues_iq4nl[qs[j] & 0xf];
+            y[j+QK4_NL/2] = d * kvalues_iq4nl[qs[j] >>  4];
+        }
+        y  += QK4_NL;
+        qs += QK4_NL/2;
+    }
+}
+
+void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y, int k) {
+    assert(k % QK_K == 0);
+#if QK_K == 64
+    dequantize_row_iq4_nl((const block_iq4_nl *)x, y, k);
+#else
+    const int nb = k / QK_K;
+
+    for (int i = 0; i < nb; i++) {
+
+        const uint8_t * qs = x[i].qs;
+
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+
+        for (int ib = 0; ib < QK_K/32; ++ib) {
+            const int ls = ((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4);
+            const float dl = d * (ls - 32);
+            for (int j = 0; j < 16; ++j) {
+                y[j+ 0] = dl * kvalues_iq4nl[qs[j] & 0xf];
+                y[j+16] = dl * kvalues_iq4nl[qs[j] >>  4];
+            }
+            y  += 32;
+            qs += 16;
+        }
+    }
+#endif
 }
 
 //===================================== Q8_K ==============================================
@@ -2573,15 +3681,92 @@ static inline __m128i get_scale_shuffle(int i) {
 }
 #endif
 
-void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     const int qk = QK8_0;
     const int nb = n / qk;
 
     assert(n % qk == 0);
+#if defined(__ARM_FEATURE_MATMUL_INT8)
+    assert((nrc == 2) || (nrc == 1));
+#else
+    assert(nrc == 1);
+#endif
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
 
     const block_q4_0 * restrict x = vx;
     const block_q8_0 * restrict y = vy;
 
+#if defined(__ARM_FEATURE_MATMUL_INT8)
+    if (nrc == 2) {
+        const block_q4_0 * restrict vx0 = vx;
+        const block_q4_0 * restrict vx1 = vx + bx;
+
+        const block_q8_0 * restrict vy0 = vy;
+        const block_q8_0 * restrict vy1 = vy + by;
+
+        float32x4_t sumv0 = vdupq_n_f32(0.0f);
+
+        for (int i = 0; i < nb; i++) {
+            const block_q4_0 * restrict b_x0 = &vx0[i];
+            const block_q4_0 * restrict b_x1 = &vx1[i];
+            const block_q8_0 * restrict b_y0 = &vy0[i];
+            const block_q8_0 * restrict b_y1 = &vy1[i];
+
+            const uint8x16_t m4b = vdupq_n_u8(0x0F);
+            const int8x16_t  s8b = vdupq_n_s8(0x8);
+
+            const uint8x16_t v0_0 = vld1q_u8(b_x0->qs);
+            const uint8x16_t v0_1 = vld1q_u8(b_x1->qs);
+
+            // 4-bit -> 8-bit
+            const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8  (v0_0, m4b));
+            const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
+            const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8  (v0_1, m4b));
+            const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
+
+            // sub 8
+            const int8x16_t x0_l = vsubq_s8(v0_0l, s8b);
+            const int8x16_t x0_h = vsubq_s8(v0_0h, s8b);
+            const int8x16_t x1_l = vsubq_s8(v0_1l, s8b);
+            const int8x16_t x1_h = vsubq_s8(v0_1h, s8b);
+
+            // load y
+            const int8x16_t y0_l = vld1q_s8(b_y0->qs);
+            const int8x16_t y0_h = vld1q_s8(b_y0->qs + 16);
+            const int8x16_t y1_l = vld1q_s8(b_y1->qs);
+            const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16);
+
+            float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d),
+                                 GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d),
+                                 GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d),
+                                 GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)};
+
+            int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l)));
+            int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l)));
+
+            int8x16_t l2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h)));
+            int8x16_t l3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h)));
+
+            int8x16_t r0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l)));
+            int8x16_t r1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l)));
+
+            int8x16_t r2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h)));
+            int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h)));
+
+            sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)),
+                                                                                l1, r1)), l2, r2)), l3, r3))), scale);
+        }
+        float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2);
+        float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1);
+
+        vst1_f32(s, vget_low_f32(sumv2));
+        vst1_f32(s + bs, vget_high_f32(sumv2));
+        return;
+    }
+#endif
 #if defined(__ARM_NEON)
     float32x4_t sumv0 = vdupq_n_f32(0.0f);
     float32x4_t sumv1 = vdupq_n_f32(0.0f);
@@ -2636,15 +3821,15 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx,
         /* Compute combined scale for the block */
         const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
 
-        __m256i bx = bytes_from_nibbles_32(x[i].qs);
+        __m256i qx = bytes_from_nibbles_32(x[i].qs);
 
         // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval.
         const __m256i off = _mm256_set1_epi8( 8 );
-        bx = _mm256_sub_epi8( bx, off );
+        qx = _mm256_sub_epi8( qx, off );
 
-        __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        __m256i qy = _mm256_loadu_si256((const __m256i *)y[i].qs);
 
-        const __m256 q = mul_sum_i8_pairs_float(bx, by);
+        const __m256 q = mul_sum_i8_pairs_float(qx, qy);
 
         /* Multiply q with scale and accumulate */
         acc = _mm256_fmadd_ps( d, q, acc );
@@ -2665,15 +3850,15 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx,
 
         const __m128i tmp = _mm_loadu_si128((const __m128i *)x[i].qs);
 
-        __m128i bx = _mm_and_si128(lowMask, tmp);
-        __m128i by = _mm_loadu_si128((const __m128i *)y[i].qs);
-        bx = _mm_sub_epi8(bx, off);
-        const __m128i i32_0 = mul_sum_i8_pairs(bx, by);
+        __m128i bx_0 = _mm_and_si128(lowMask, tmp);
+        __m128i by_0 = _mm_loadu_si128((const __m128i *)y[i].qs);
+        bx_0 = _mm_sub_epi8(bx_0, off);
+        const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);
 
-        bx = _mm_and_si128(lowMask, _mm_srli_epi64(tmp, 4));
-        by = _mm_loadu_si128((const __m128i *)(y[i].qs + 16));
-        bx = _mm_sub_epi8(bx, off);
-        const __m128i i32_1 = mul_sum_i8_pairs(bx, by);
+        bx_0 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp, 4));
+        by_0 = _mm_loadu_si128((const __m128i *)(y[i].qs + 16));
+        bx_0 = _mm_sub_epi8(bx_0, off);
+        const __m128i i32_1 = mul_sum_i8_pairs(bx_0, by_0);
 
         // Convert int32_t to float
         __m256 p = _mm256_cvtepi32_ps(MM256_SET_M128I(i32_0, i32_1));
@@ -2863,15 +4048,93 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx,
 #endif
 }
 
-void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     const int qk = QK8_1;
     const int nb = n / qk;
 
     assert(n % qk == 0);
+#if defined(__ARM_FEATURE_MATMUL_INT8)
+    assert((nrc == 2) || (nrc == 1));
+#else
+    assert(nrc == 1);
+#endif
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
 
     const block_q4_1 * restrict x = vx;
     const block_q8_1 * restrict y = vy;
 
+#if defined(__ARM_FEATURE_MATMUL_INT8)
+    if (nrc == 2) {
+        const block_q4_1 * restrict vx0 = vx;
+        const block_q4_1 * restrict vx1 = vx + bx;
+        const block_q8_1 * restrict vy0 = vy;
+        const block_q8_1 * restrict vy1 = vy + by;
+
+        float32x4_t sumv0 = vdupq_n_f32(0.0f);
+        float32x4_t summs0 = vdupq_n_f32(0.0f);
+
+        for (int i = 0; i < nb; i++) {
+            const block_q4_1 * restrict b_x0 = &vx0[i];
+            const block_q4_1 * restrict b_x1 = &vx1[i];
+            const block_q8_1 * restrict b_y0 = &vy0[i];
+            const block_q8_1 * restrict b_y1 = &vy1[i];
+
+            float32x4_t summs_t = {GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y0->s),
+                                   GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y0->s),
+                                   GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y1->s),
+                                   GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y1->s)};
+            summs0 += summs_t;
+
+            const uint8x16_t m4b = vdupq_n_u8(0x0F);
+
+            const uint8x16_t v0_0 = vld1q_u8(b_x0->qs);
+            const uint8x16_t v0_1 = vld1q_u8(b_x1->qs);
+
+            // 4-bit -> 8-bit
+            const int8x16_t x0_l = vreinterpretq_s8_u8(vandq_u8  (v0_0, m4b));
+            const int8x16_t x0_h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
+            const int8x16_t x1_l = vreinterpretq_s8_u8(vandq_u8  (v0_1, m4b));
+            const int8x16_t x1_h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
+
+            // load y
+            const int8x16_t y0_l = vld1q_s8(b_y0->qs);
+            const int8x16_t y0_h = vld1q_s8(b_y0->qs + 16);
+            const int8x16_t y1_l = vld1q_s8(b_y1->qs);
+            const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16);
+
+            // mmla into int32x4_t
+            float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*b_y0->d,
+                                 GGML_FP16_TO_FP32(b_x0->d)*b_y1->d,
+                                 GGML_FP16_TO_FP32(b_x1->d)*b_y0->d,
+                                 GGML_FP16_TO_FP32(b_x1->d)*b_y1->d};
+
+            int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l)));
+            int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l)));
+
+            int8x16_t l2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h)));
+            int8x16_t l3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h)));
+
+            int8x16_t r0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l)));
+            int8x16_t r1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l)));
+
+            int8x16_t r2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h)));
+            int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h)));
+            sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)),
+                                                                                l1, r1)), l2, r2)), l3, r3))), scale);
+        }
+
+        float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2);
+        float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1);
+        sumv2 = sumv2 + summs0;
+
+        vst1_f32(s, vget_low_f32(sumv2));
+        vst1_f32(s + bs, vget_high_f32(sumv2));
+        return;
+    }
+#endif
     // TODO: add WASM SIMD
 #if defined(__ARM_NEON)
     float32x4_t sumv0 = vdupq_n_f32(0.0f);
@@ -2887,7 +4150,7 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri
         const block_q8_1 * restrict y0 = &y[i + 0];
         const block_q8_1 * restrict y1 = &y[i + 1];
 
-        summs += GGML_FP16_TO_FP32(x0->m) * y0->s + GGML_FP16_TO_FP32(x1->m) * y1->s;
+        summs += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s) + GGML_FP16_TO_FP32(x1->m) * GGML_FP16_TO_FP32(y1->s);
 
         const uint8x16_t m4b = vdupq_n_u8(0x0F);
 
@@ -2910,8 +4173,8 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri
         const int32x4_t p_0 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h);
         const int32x4_t p_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h);
 
-        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*y0->d);
-        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*y1->d);
+        sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
+        sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
     }
 
     *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs;
@@ -2924,9 +4187,9 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri
     // Main loop
     for (int i = 0; i < nb; ++i) {
         const float d0 = GGML_FP16_TO_FP32(x[i].d);
-        const float d1 = y[i].d;
+        const float d1 = GGML_FP16_TO_FP32(y[i].d);
 
-        summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;
+        summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s);
 
         const __m256 d0v = _mm256_set1_ps( d0 );
         const __m256 d1v = _mm256_set1_ps( d1 );
@@ -2935,10 +4198,10 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri
         const __m256 d0d1 = _mm256_mul_ps( d0v, d1v );
 
         // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes
-        const __m256i bx = bytes_from_nibbles_32(x[i].qs);
-        const __m256i by = _mm256_loadu_si256( (const __m256i *)y[i].qs );
+        const __m256i qx = bytes_from_nibbles_32(x[i].qs);
+        const __m256i qy = _mm256_loadu_si256( (const __m256i *)y[i].qs );
 
-        const __m256 xy = mul_sum_us8_pairs_float(bx, by);
+        const __m256 xy = mul_sum_us8_pairs_float(qx, qy);
 
         // Accumulate d0*d1*x*y
 #if defined(__AVX2__)
@@ -2978,7 +4241,7 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri
 
         int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s);
     }
 
     *s = sumf;
@@ -2996,19 +4259,24 @@ void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restri
             sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
         }
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s);
     }
 
     *s = sumf;
 #endif
 }
 
-void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     const int qk = QK8_0;
     const int nb = n / qk;
 
     assert(n % qk == 0);
     assert(qk == QK5_0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
 
     const block_q5_0 * restrict x = vx;
     const block_q8_0 * restrict y = vy;
@@ -3152,14 +4420,14 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri
         /* Compute combined scale for the block */
         const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
 
-        __m256i bx = bytes_from_nibbles_32(x[i].qs);
+        __m256i qx = bytes_from_nibbles_32(x[i].qs);
         __m256i bxhi = bytes_from_bits_32(x[i].qh);
         bxhi = _mm256_andnot_si256(bxhi, _mm256_set1_epi8((char)0xF0));
-        bx = _mm256_or_si256(bx, bxhi);
+        qx = _mm256_or_si256(qx, bxhi);
 
-        __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        __m256i qy = _mm256_loadu_si256((const __m256i *)y[i].qs);
 
-        const __m256 q = mul_sum_i8_pairs_float(bx, by);
+        const __m256 q = mul_sum_i8_pairs_float(qx, qy);
 
         /* Multiply q with scale and accumulate */
         acc = _mm256_fmadd_ps(d, q, acc);
@@ -3176,21 +4444,21 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri
         /* Compute combined scale for the block */
         const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
 
-        __m256i bx = bytes_from_nibbles_32(x[i].qs);
+        __m256i bx_0 = bytes_from_nibbles_32(x[i].qs);
         const __m256i bxhi = bytes_from_bits_32(x[i].qh);
         __m128i bxhil = _mm256_castsi256_si128(bxhi);
         __m128i bxhih = _mm256_extractf128_si256(bxhi, 1);
         bxhil = _mm_andnot_si128(bxhil, mask);
         bxhih = _mm_andnot_si128(bxhih, mask);
-        __m128i bxl = _mm256_castsi256_si128(bx);
-        __m128i bxh = _mm256_extractf128_si256(bx, 1);
+        __m128i bxl = _mm256_castsi256_si128(bx_0);
+        __m128i bxh = _mm256_extractf128_si256(bx_0, 1);
         bxl = _mm_or_si128(bxl, bxhil);
         bxh = _mm_or_si128(bxh, bxhih);
-        bx = MM256_SET_M128I(bxh, bxl);
+        bx_0 = MM256_SET_M128I(bxh, bxl);
 
-        const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[i].qs);
 
-        const __m256 q = mul_sum_i8_pairs_float(bx, by);
+        const __m256 q = mul_sum_i8_pairs_float(bx_0, by_0);
 
         /* Multiply q with scale and accumulate */
         acc = _mm256_add_ps(_mm256_mul_ps(d, q), acc);
@@ -3289,12 +4557,17 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri
 #endif
 }
 
-void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     const int qk = QK8_1;
     const int nb = n / qk;
 
     assert(n % qk == 0);
     assert(qk == QK5_1);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
 
     const block_q5_1 * restrict x = vx;
     const block_q8_1 * restrict y = vy;
@@ -3322,8 +4595,8 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri
 
         const uint8x16_t m4b = vdupq_n_u8(0x0F);
 
-        summs0 += GGML_FP16_TO_FP32(x0->m) * y0->s;
-        summs1 += GGML_FP16_TO_FP32(x1->m) * y1->s;
+        summs0 += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s);
+        summs1 += GGML_FP16_TO_FP32(x1->m) * GGML_FP16_TO_FP32(y1->s);
 
         // extract the 5th bit via lookup table ((b) << 4)
         memcpy(&qh0, x0->qh, sizeof(qh0));
@@ -3367,10 +4640,10 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri
 
         sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32(
                         ggml_vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l),
-                        ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*y0->d);
+                        ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
         sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32(
                         ggml_vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l),
-                        ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*y1->d);
+                        ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
     }
 
     *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1;
@@ -3387,7 +4660,7 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri
         const block_q5_1 * restrict x0 = &x[i];
         const block_q8_1 * restrict y0 = &y[i];
 
-        summs += GGML_FP16_TO_FP32(x0->m) * y0->s;
+        summs += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s);
 
         const v128_t m4b = wasm_i8x16_splat(0x0F);
 
@@ -3434,7 +4707,7 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri
                                            wasm_i32x4_dot_i16x8(v0lfh, v1lh)),
                             wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl),
                                            wasm_i32x4_dot_i16x8(v0hfh, v1hh)))),
-                    wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * y0->d)));
+                    wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d))));
     }
 
     *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
@@ -3449,17 +4722,17 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri
     for (int i = 0; i < nb; i++) {
         const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d));
 
-        summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;
+        summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s);
 
-        __m256i bx = bytes_from_nibbles_32(x[i].qs);
+        __m256i qx = bytes_from_nibbles_32(x[i].qs);
         __m256i bxhi = bytes_from_bits_32(x[i].qh);
         bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10));
-        bx = _mm256_or_si256(bx, bxhi);
+        qx = _mm256_or_si256(qx, bxhi);
 
-        const __m256 dy = _mm256_set1_ps(y[i].d);
-        const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[i].d));
+        const __m256i qy = _mm256_loadu_si256((const __m256i *)y[i].qs);
 
-        const __m256 q = mul_sum_us8_pairs_float(bx, by);
+        const __m256 q = mul_sum_us8_pairs_float(qx, qy);
 
         acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc);
     }
@@ -3476,24 +4749,24 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri
     for (int i = 0; i < nb; i++) {
         const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d));
 
-        summs += GGML_FP16_TO_FP32(x[i].m) * y[i].s;
+        summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s);
 
-        __m256i bx = bytes_from_nibbles_32(x[i].qs);
+        __m256i bx_0 = bytes_from_nibbles_32(x[i].qs);
         const __m256i bxhi = bytes_from_bits_32(x[i].qh);
         __m128i bxhil = _mm256_castsi256_si128(bxhi);
         __m128i bxhih = _mm256_extractf128_si256(bxhi, 1);
         bxhil = _mm_and_si128(bxhil, mask);
         bxhih = _mm_and_si128(bxhih, mask);
-        __m128i bxl = _mm256_castsi256_si128(bx);
-        __m128i bxh = _mm256_extractf128_si256(bx, 1);
+        __m128i bxl = _mm256_castsi256_si128(bx_0);
+        __m128i bxh = _mm256_extractf128_si256(bx_0, 1);
         bxl = _mm_or_si128(bxl, bxhil);
         bxh = _mm_or_si128(bxh, bxhih);
-        bx = MM256_SET_M128I(bxh, bxl);
+        bx_0 = MM256_SET_M128I(bxh, bxl);
 
-        const __m256 dy = _mm256_set1_ps(y[i].d);
-        const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[i].d));
+        const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[i].qs);
 
-        const __m256 q = mul_sum_us8_pairs_float(bx, by);
+        const __m256 q = mul_sum_us8_pairs_float(bx_0, by_0);
 
         acc = _mm256_add_ps(_mm256_mul_ps(q, _mm256_mul_ps(dx, dy)), acc);
     }
@@ -3557,7 +4830,7 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri
 
         int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s);
     }
 
     *s = sumf;
@@ -3581,22 +4854,86 @@ void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restri
             sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]);
         }
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s;
+        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s);
     }
 
     *s = sumf;
 #endif
 }
 
-void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     const int qk = QK8_0;
     const int nb = n / qk;
 
     assert(n % qk == 0);
+#if defined(__ARM_FEATURE_MATMUL_INT8)
+    assert((nrc == 2) || (nrc == 1));
+#else
+    assert(nrc == 1);
+#endif
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
 
     const block_q8_0 * restrict x = vx;
     const block_q8_0 * restrict y = vy;
 
+#if defined(__ARM_FEATURE_MATMUL_INT8)
+    if (nrc == 2) {
+        const block_q8_0 * restrict vx0 = vx;
+        const block_q8_0 * restrict vx1 = vx + bx;
+        const block_q8_0 * restrict vy0 = vy;
+        const block_q8_0 * restrict vy1 = vy + by;
+
+        float32x4_t sumv0 = vdupq_n_f32(0.0f);
+
+        for (int i = 0; i < nb; i++) {
+            const block_q8_0 * restrict b_x0 = &vx0[i];
+            const block_q8_0 * restrict b_y0 = &vy0[i];
+
+            const block_q8_0 * restrict b_x1 = &vx1[i];
+            const block_q8_0 * restrict b_y1 = &vy1[i];
+
+            const int8x16_t x0_l = vld1q_s8(b_x0->qs);
+            const int8x16_t x0_h = vld1q_s8(b_x0->qs + 16);
+            const int8x16_t x1_l = vld1q_s8(b_x1->qs);
+            const int8x16_t x1_h = vld1q_s8(b_x1->qs + 16);
+
+            // load y
+            const int8x16_t y0_l = vld1q_s8(b_y0->qs);
+            const int8x16_t y0_h = vld1q_s8(b_y0->qs + 16);
+            const int8x16_t y1_l = vld1q_s8(b_y1->qs);
+            const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16);
+
+            float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d),
+                             GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d),
+                             GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d),
+                             GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)};
+
+            int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l)));
+            int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l)));
+
+            int8x16_t l2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h)));
+            int8x16_t l3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h)));
+
+            int8x16_t r0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l)));
+            int8x16_t r1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l)));
+
+            int8x16_t r2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h)));
+            int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h)));
+
+            sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)),
+                                                                                       l1, r1)), l2, r2)), l3, r3))), scale);
+        }
+        float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2);
+        float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1);
+
+        vst1_f32(s, vget_low_f32(sumv2));
+        vst1_f32(s + bs, vget_high_f32(sumv2));
+        return;
+    }
+#endif
 #if defined(__ARM_NEON)
     float32x4_t sumv0 = vdupq_n_f32(0.0f);
     float32x4_t sumv1 = vdupq_n_f32(0.0f);
@@ -3638,10 +4975,10 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri
     for (int i = 0; i < nb; ++i) {
         // Compute combined scale for the block
         const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
-        __m256i bx = _mm256_loadu_si256((const __m256i *)x[i].qs);
-        __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        __m256i qx = _mm256_loadu_si256((const __m256i *)x[i].qs);
+        __m256i qy = _mm256_loadu_si256((const __m256i *)y[i].qs);
 
-        const __m256 q = mul_sum_i8_pairs_float(bx, by);
+        const __m256 q = mul_sum_i8_pairs_float(qx, qy);
 
         // Multiply q with scale and accumulate
 #if defined(__AVX2__)
@@ -3658,10 +4995,10 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; i++) {
         // load elements
-        vint8m1_t bx = __riscv_vle8_v_i8m1(x[i].qs, vl);
-        vint8m1_t by = __riscv_vle8_v_i8m1(y[i].qs, vl);
+        vint8m1_t bx_0 = __riscv_vle8_v_i8m1(x[i].qs, vl);
+        vint8m1_t by_0 = __riscv_vle8_v_i8m1(y[i].qs, vl);
 
-        vint16m2_t vw_mul = __riscv_vwmul_vv_i16m2(bx, by, vl);
+        vint16m2_t vw_mul = __riscv_vwmul_vv_i16m2(bx_0, by_0, vl);
 
         vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, vl);
         vint32m1_t v_sum = __riscv_vwredsum_vs_i16m2_i32m1(vw_mul, v_zero, vl);
@@ -3691,7 +5028,12 @@ void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restri
 }
 
 #if QK_K == 256
-void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
 
     const block_q2_K * restrict x = vx;
     const block_q8_K * restrict y = vy;
@@ -4067,7 +5409,12 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
 
 #else
 
-void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
 
     const block_q2_K * restrict x = vx;
     const block_q8_K * restrict y = vy;
@@ -4088,8 +5435,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * (float)x[i].d;
-        const float dmin = -y[i].d * (float)x[i].dmin;
+        const float d    =  y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         const uint8_t * restrict q2 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -4238,8 +5585,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * (float)x[i].d;
-        const float dmin = -y[i].d * (float)x[i].dmin;
+        const float d    =  y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
         const uint8_t * restrict q2 = x[i].qs;
         const int8_t  * restrict q8 = y[i].qs;
@@ -4291,7 +5638,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
 
     float sumf = 0;
 
-    int isum[4];
+    int isum[QK_K/16];
 
     for (int i = 0; i < nb; ++i) {
 
@@ -4307,14 +5654,14 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
         const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d);
         const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
 
-        isum[0] = isum[1] = isum[2] = isum[3] = 0;
+        memset(isum, 0, (QK_K/16)*sizeof(int));
         for (int l =  0; l < 16; ++l) {
             isum[0] += q8[l+ 0] * ((q2[l] >> 0) & 3);
             isum[1] += q8[l+16] * ((q2[l] >> 2) & 3);
             isum[2] += q8[l+32] * ((q2[l] >> 4) & 3);
             isum[3] += q8[l+48] * ((q2[l] >> 6) & 3);
         }
-        for (int l = 0; l < 4; ++l) {
+        for (int l = 0; l < QK_K/16; ++l) {
             isum[l] *= (sc[l] & 0xF);
         }
         sumf += dall * (isum[0] + isum[1] + isum[2] + isum[3]) - dmin * summs;
@@ -4325,8 +5672,13 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
 #endif
 
 #if QK_K == 256
-void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     assert(n % QK_K == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
 
     const uint32_t kmask1 = 0x03030303;
     const uint32_t kmask2 = 0x0f0f0f0f;
@@ -4845,8 +6197,13 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
 
 #else
 
-void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     assert(n % QK_K == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
 
     const block_q3_K * restrict x = vx;
     const block_q8_K * restrict y = vy;
@@ -4882,7 +6239,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
 
         int32_t isum = -4*(scales[0] * y[i].bsums[0] + scales[2] * y[i].bsums[1] + scales[1] * y[i].bsums[2] + scales[3] * y[i].bsums[3]);
 
-        const float d = y[i].d * (float)x[i].d;
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
 
         const uint8x16_t htmp = vcombine_u8(hbits, vshr_n_u8(hbits, 1));
         q3h.val[0] = vandq_u8(mh, vshlq_n_u8(htmp, 2));
@@ -5084,7 +6441,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
 
         int32_t isum = -4*(scales[0] * y[i].bsums[0] + scales[2] * y[i].bsums[1] + scales[1] * y[i].bsums[2] + scales[3] * y[i].bsums[3]);
 
-        const float d = y[i].d * (float)x[i].d;
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
 
         vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1);
 
@@ -5188,8 +6545,13 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
 #endif
 
 #if QK_K == 256
-void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     assert(n % QK_K == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
 
     const block_q4_K * restrict x = vx;
     const block_q8_K * restrict y = vy;
@@ -5544,8 +6906,13 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
 #endif
 }
 #else
-void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     assert(n % QK_K == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
 
     const block_q4_K * restrict x = vx;
     const block_q8_K * restrict y = vy;
@@ -5577,9 +6944,9 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
         aux16[1] = (a[0] >> 4) & 0x0f0f;
 
         const int32_t summi = scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]);
-        sum_mins += y[i].d * (float)x[i].d[1] * summi;
+        sum_mins += y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * summi;
 
-        const float d = y[i].d * (float)x[i].d[0];
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]);
 
         const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4);
 
@@ -5787,8 +7154,13 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
 #endif
 
 #if QK_K == 256
-void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy,  size_t by, int nrc) {
     assert(n % QK_K == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
 
     const block_q5_K * restrict x = vx;
     const block_q8_K * restrict y = vy;
@@ -6207,8 +7579,13 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
 
 #else
 
-void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     assert(n % QK_K == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
 
     const block_q5_K * restrict x = vx;
     const block_q8_K * restrict y = vy;
@@ -6227,7 +7604,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * (float)x[i].d;
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
         const int8_t * sc = x[i].scales;
 
         const uint8_t * restrict q5 = x[i].qs;
@@ -6369,7 +7746,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d = y[i].d * (float)x[i].d;
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
         const int8_t * sc = x[i].scales;
 
         const uint8_t * restrict q5 = x[i].qs;
@@ -6473,8 +7850,13 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
 
 
 #if QK_K == 256
-void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     assert(n % QK_K == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
 
     const block_q6_K * restrict x = vx;
     const block_q8_K * restrict y = vy;
@@ -6905,8 +8287,13 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
 
 #else
 
-void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     assert(n % QK_K == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
 
     const block_q6_K * restrict x = vx;
     const block_q8_K * restrict y = vy;
@@ -6927,7 +8314,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d_all = (float)x[i].d;
+        const float d_all = GGML_FP16_TO_FP32(x[i].d);
 
         const uint8_t * restrict q6 = x[i].ql;
         const uint8_t * restrict qh = x[i].qh;
@@ -7098,7 +8485,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
 
     for (int i = 0; i < nb; ++i) {
 
-        const float d_all = (float)x[i].d;
+        const float d_all = GGML_FP16_TO_FP32(x[i].d);
 
         const uint8_t * restrict q6 = x[i].ql;
         const uint8_t * restrict qh = x[i].qh;
@@ -7200,6 +8587,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
 
 #endif
 
+#if defined (__AVX2__) || defined (__ARM_NEON)
 static const int8_t keven_signs_q2xs[1024] = {
      1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1, -1,  1, -1,  1,  1,  1,  1,  1, -1, -1, -1,  1,  1,  1,  1,  1,  1,
      1,  1, -1,  1,  1,  1,  1, -1, -1,  1, -1,  1,  1,  1,  1,  1,  1, -1, -1,  1,  1,  1,  1,  1, -1, -1, -1,  1,  1,  1,  1, -1,
@@ -7234,9 +8622,15 @@ static const int8_t keven_signs_q2xs[1024] = {
      1,  1,  1, -1, -1, -1, -1,  1, -1,  1,  1, -1, -1, -1, -1, -1,  1, -1,  1, -1, -1, -1, -1, -1, -1, -1,  1, -1, -1, -1, -1,  1,
      1,  1, -1, -1, -1, -1, -1, -1, -1,  1, -1, -1, -1, -1, -1,  1,  1, -1, -1, -1, -1, -1, -1,  1, -1, -1, -1, -1, -1, -1, -1, -1,
 };
+#endif
 
-void ggml_vec_dot_iq2_xxs_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     assert(n % QK_K == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
 
     const block_iq2_xxs * restrict x = vx;
     const block_q8_K    * restrict y = vy;
@@ -7357,3 +8751,3928 @@ void ggml_vec_dot_iq2_xxs_q8_K(const int n, float * restrict s, const void * res
     *s = 0.125f * sumf;
 #endif
 }
+
+void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
+    assert(n % QK_K == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+
+    const block_iq2_xs * restrict x = vx;
+    const block_q8_K   * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+#if defined(__ARM_NEON)
+
+    const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs;
+
+    ggml_int8x16x4_t q2u;
+    ggml_int8x16x4_t q2s;
+    ggml_int8x16x4_t q8b;
+
+    int32x4x4_t scales32;
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint16_t * restrict q2 = x[i].qs;
+        const int8_t   * restrict q8 = y[i].qs;
+        const uint8x8_t scales8 = vld1_u8(x[i].scales);
+        const uint8x8_t scales_l = vand_u8(scales8, vdup_n_u8(0xf));
+        const uint8x8_t scales_h = vshr_n_u8(scales8, 4);
+        uint8x16_t scales = vcombine_u8(vzip1_u8(scales_l, scales_h), vzip2_u8(scales_l, scales_h));
+        scales = vaddq_u8(vshlq_n_u8(scales, 1), vdupq_n_u8(1));
+        const uint16x8_t scales1 = vmovl_u8(vget_low_u8(scales));
+        const uint16x8_t scales2 = vmovl_u8(vget_high_u8(scales));
+        scales32.val[0] = vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(scales1)));
+        scales32.val[1] = vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(scales1)));
+        scales32.val[2] = vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(scales2)));
+        scales32.val[3] = vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(scales2)));
+        int32x4_t sumi = vdupq_n_s32(0);
+        for (int ib64 = 0; ib64 < QK_K/64; ++ib64) {
+            q8b = ggml_vld1q_s8_x4(q8); q8 += 64;
+            q2u.val[0] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[0] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[1] & 511))));
+            q2u.val[1] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[2] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[3] & 511))));
+            q2u.val[2] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[4] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[5] & 511))));
+            q2u.val[3] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[6] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[7] & 511))));
+            q2s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[0] >> 9))), vld1_s8((const void *)(signs64 + (q2[1] >> 9))));
+            q2s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[2] >> 9))), vld1_s8((const void *)(signs64 + (q2[3] >> 9))));
+            q2s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[4] >> 9))), vld1_s8((const void *)(signs64 + (q2[5] >> 9))));
+            q2s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[6] >> 9))), vld1_s8((const void *)(signs64 + (q2[7] >> 9))));
+            q2u.val[0] = vmulq_s8(q2u.val[0], q2s.val[0]);
+            q2u.val[1] = vmulq_s8(q2u.val[1], q2s.val[1]);
+            q2u.val[2] = vmulq_s8(q2u.val[2], q2s.val[2]);
+            q2u.val[3] = vmulq_s8(q2u.val[3], q2s.val[3]);
+            const int32x4_t p1 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[0], q8b.val[0]);
+            const int32x4_t p2 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[1], q8b.val[1]);
+            const int32x4_t p3 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[2], q8b.val[2]);
+            const int32x4_t p4 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[3], q8b.val[3]);
+            const int32x4_t p = vpaddq_s32(vpaddq_s32(p1, p2), vpaddq_s32(p3, p4));
+            sumi = vmlaq_s32(sumi, p, scales32.val[ib64]);
+            q2 += 8;
+        }
+        sumf += d*vaddvq_s32(sumi);
+    }
+    *s = 0.125f * sumf;
+
+#elif defined(__AVX2__)
+
+    const __m256i mone = _mm256_set1_epi8(1);
+    static const char block_sign_shuffle_mask_1[32] = {
+        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02,
+        0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06,
+    };
+    static const char block_sign_shuffle_mask_2[32] = {
+        0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a,
+        0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e,
+    };
+    static const uint8_t bit_selector_mask_bytes[32] = {
+        0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+        0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+    };
+
+    const __m256i bit_selector_mask = _mm256_loadu_si256((const __m256i*)bit_selector_mask_bytes);
+    const __m256i block_sign_shuffle_1 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_1);
+    const __m256i block_sign_shuffle_2 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_2);
+
+#if QK_K == 64
+    static const uint8_t k_bit_helper[16] = {
+        0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00,
+    };
+    const __m128i bit_helper = _mm_loadu_si128((const __m128i*)k_bit_helper);
+    const __m128i m511 = _mm_set1_epi16(511);
+    typedef union {
+        __m128i vec_index;
+        uint16_t index[8];
+    } index_t;
+
+    index_t idx;
+    __m256 accumf = _mm256_setzero_ps();
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const __m128i q2_data = _mm_loadu_si128((const __m128i*)x[i].qs);
+        idx.vec_index = _mm_and_si128(q2_data, m511);
+
+        const __m128i partial_sign_bits = _mm_srli_epi16(q2_data, 9);
+        const __m128i partial_sign_bits_upper = _mm_srli_epi16(q2_data, 13);
+        const __m128i partial_sign_bits_for_counting = _mm_xor_si128(partial_sign_bits, partial_sign_bits_upper);
+
+        const __m128i odd_bits = _mm_shuffle_epi8(bit_helper, partial_sign_bits_for_counting);
+        const __m128i full_sign_bits = _mm_or_si128(partial_sign_bits, odd_bits);
+        const __m256i full_signs = MM256_SET_M128I(full_sign_bits, full_sign_bits);
+
+        const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)(y[i].qs+32));
+
+        const __m256i q2_1 = _mm256_set_epi64x(iq2xs_grid[idx.index[3]], iq2xs_grid[idx.index[2]],
+                                               iq2xs_grid[idx.index[1]], iq2xs_grid[idx.index[0]]);
+        const __m256i q2_2 = _mm256_set_epi64x(iq2xs_grid[idx.index[7]], iq2xs_grid[idx.index[6]],
+                                               iq2xs_grid[idx.index[5]], iq2xs_grid[idx.index[4]]);
+
+        __m256i signs;
+        signs = _mm256_shuffle_epi8(full_signs, block_sign_shuffle_1);
+        signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask);
+        const __m256i q8s_1 = _mm256_sign_epi8(q8_1, _mm256_or_si256(signs, mone));
+
+        signs = _mm256_shuffle_epi8(full_signs, block_sign_shuffle_2);
+        signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask);
+        const __m256i q8s_2 = _mm256_sign_epi8(q8_2, _mm256_or_si256(signs, mone));
+
+        const __m256i dot1  = _mm256_maddubs_epi16(q2_1, q8s_1);
+        const __m256i dot2  = _mm256_maddubs_epi16(q2_2, q8s_2);
+
+        const __m256i sc1 = MM256_SET_M128I(_mm_set1_epi16(2*(x[i].scales[0] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[0] & 0xf)+1));
+        const __m256i sc2 = MM256_SET_M128I(_mm_set1_epi16(2*(x[i].scales[1] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[1] & 0xf)+1));
+
+        const __m256i sum = _mm256_add_epi32(_mm256_madd_epi16(sc1, dot1), _mm256_madd_epi16(sc2, dot2));
+
+        accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(sum), accumf);
+
+    }
+
+    *s = 0.125f * hsum_float_8(accumf);
+#else
+
+    static const uint8_t k_bit_helper[32] = {
+        0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00,
+        0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00,
+    };
+    const __m256i bit_helper = _mm256_loadu_si256((const __m256i*)k_bit_helper);
+    const __m256i m511 = _mm256_set1_epi16(511);
+    const __m128i m4 = _mm_set1_epi8(0xf);
+    const __m128i m1 = _mm_set1_epi8(1);
+
+    uint64_t aux64;
+
+    // somewhat hacky, but gives a significant boost in performance
+    __m256i aux_gindex;
+    const uint16_t * gindex = (const uint16_t *)&aux_gindex;
+
+    __m256 accumf = _mm256_setzero_ps();
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint16_t * restrict q2 = x[i].qs;
+        const int8_t   * restrict q8 = y[i].qs;
+
+        memcpy(&aux64, x[i].scales, 8);
+        __m128i stmp = _mm_set1_epi64x(aux64);
+        stmp = _mm_unpacklo_epi8(_mm_and_si128(stmp, m4), _mm_and_si128(_mm_srli_epi16(stmp, 4), m4));
+        const __m128i scales = _mm_add_epi8(_mm_slli_epi16(stmp, 1), m1);
+
+        __m256i sumi1 = _mm256_setzero_si256();
+        __m256i sumi2 = _mm256_setzero_si256();
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 4) {
+
+            const __m256i q2_data = _mm256_loadu_si256((const __m256i*)q2);  q2 += 16;
+            aux_gindex = _mm256_and_si256(q2_data, m511);
+
+            const __m256i partial_sign_bits = _mm256_srli_epi16(q2_data, 9);
+            const __m256i partial_sign_bits_upper = _mm256_srli_epi16(q2_data, 13);
+            const __m256i partial_sign_bits_for_counting = _mm256_xor_si256(partial_sign_bits, partial_sign_bits_upper);
+
+            const __m256i odd_bits = _mm256_shuffle_epi8(bit_helper, partial_sign_bits_for_counting);
+            const __m256i full_sign_bits = _mm256_or_si256(partial_sign_bits, odd_bits);
+
+            const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+            const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+            const __m256i q8_3 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+            const __m256i q8_4 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+
+            const __m256i q2_1 = _mm256_set_epi64x(iq2xs_grid[gindex[ 3]], iq2xs_grid[gindex[ 2]],
+                                                   iq2xs_grid[gindex[ 1]], iq2xs_grid[gindex[ 0]]);
+            const __m256i q2_2 = _mm256_set_epi64x(iq2xs_grid[gindex[ 7]], iq2xs_grid[gindex[ 6]],
+                                                   iq2xs_grid[gindex[ 5]], iq2xs_grid[gindex[ 4]]);
+            const __m256i q2_3 = _mm256_set_epi64x(iq2xs_grid[gindex[11]], iq2xs_grid[gindex[10]],
+                                                   iq2xs_grid[gindex[ 9]], iq2xs_grid[gindex[ 8]]);
+            const __m256i q2_4 = _mm256_set_epi64x(iq2xs_grid[gindex[15]], iq2xs_grid[gindex[14]],
+                                                   iq2xs_grid[gindex[13]], iq2xs_grid[gindex[12]]);
+
+            const __m128i full_signs_l = _mm256_castsi256_si128(full_sign_bits);
+            const __m128i full_signs_h = _mm256_extractf128_si256(full_sign_bits, 1);
+            const __m256i full_signs_1 = MM256_SET_M128I(full_signs_l, full_signs_l);
+            const __m256i full_signs_2 = MM256_SET_M128I(full_signs_h, full_signs_h);
+
+            __m256i signs;
+            signs = _mm256_shuffle_epi8(full_signs_1, block_sign_shuffle_1);
+            signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask);
+            const __m256i q8s_1 = _mm256_sign_epi8(q8_1, _mm256_or_si256(signs, mone));
+
+            signs = _mm256_shuffle_epi8(full_signs_1, block_sign_shuffle_2);
+            signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask);
+            const __m256i q8s_2 = _mm256_sign_epi8(q8_2, _mm256_or_si256(signs, mone));
+
+            signs = _mm256_shuffle_epi8(full_signs_2, block_sign_shuffle_1);
+            signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask);
+            const __m256i q8s_3 = _mm256_sign_epi8(q8_3, _mm256_or_si256(signs, mone));
+
+            signs = _mm256_shuffle_epi8(full_signs_2, block_sign_shuffle_2);
+            signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask);
+            const __m256i q8s_4 = _mm256_sign_epi8(q8_4, _mm256_or_si256(signs, mone));
+
+            const __m256i dot1  = _mm256_maddubs_epi16(q2_1, q8s_1);
+            const __m256i dot2  = _mm256_maddubs_epi16(q2_2, q8s_2);
+            const __m256i dot3  = _mm256_maddubs_epi16(q2_3, q8s_3);
+            const __m256i dot4  = _mm256_maddubs_epi16(q2_4, q8s_4);
+
+            const __m256i sc1 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+0)));
+            const __m256i sc2 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+1)));
+            const __m256i sc3 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+2)));
+            const __m256i sc4 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+3)));
+
+            sumi1 = _mm256_add_epi32(sumi1, _mm256_madd_epi16(dot1, sc1));
+            sumi2 = _mm256_add_epi32(sumi2, _mm256_madd_epi16(dot2, sc2));
+            sumi1 = _mm256_add_epi32(sumi1, _mm256_madd_epi16(dot3, sc3));
+            sumi2 = _mm256_add_epi32(sumi2, _mm256_madd_epi16(dot4, sc4));
+        }
+
+        accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf);
+
+    }
+
+    *s = 0.125f * hsum_float_8(accumf);
+#endif
+
+#else
+
+    float sumf = 0.f;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint16_t * restrict q2 = x[i].qs;
+        const uint8_t  * restrict sc = x[i].scales;
+        const int8_t   * restrict q8 = y[i].qs;
+        int32_t bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            const uint16_t ls1 = 2*(sc[ib32] & 0xf) + 1;
+            const uint16_t ls2 = 2*(sc[ib32] >>  4) + 1;
+            int32_t sumi = 0;
+            for (int l = 0; l < 2; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511));
+                const uint8_t  signs = ksigns_iq2xs[q2[l] >> 9];
+                for (int j = 0; j < 8; ++j) {
+                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            bsum += sumi * ls1;
+            sumi = 0;
+            for (int l = 2; l < 4; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511));
+                const uint8_t  signs = ksigns_iq2xs[q2[l] >> 9];
+                for (int j = 0; j < 8; ++j) {
+                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            bsum += sumi * ls2;
+            q2 += 4;
+        }
+        sumf += d * bsum;
+    }
+    *s = 0.125f * sumf;
+#endif
+}
+
+void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
+    assert(n % QK_K == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+
+    const block_iq2_s * restrict x = vx;
+    const block_q8_K  * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+#if defined(__ARM_NEON)
+
+   static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
+                                       0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03
+   };
+
+    static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,};
+
+    const ggml_uint8x16x2_t mask1 = ggml_vld1q_u8_x2(k_mask1);
+    const uint8x16_t        mask2 = vld1q_u8(k_mask2);
+    const uint8x16_t m1 = vdupq_n_u8(1);
+    const int32x4_t vzero = vdupq_n_s32(0);
+
+    uint8x16x2_t vs;
+    ggml_int8x16x4_t q2s;
+    ggml_int8x16x4_t q8b;
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+
+        const uint8_t * restrict qs = x[i].qs;
+        const uint8_t * restrict qh = x[i].qh;
+        const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8);
+        const int8_t  * restrict q8 = y[i].qs;
+
+        int sumi1 = 0, sumi2 = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            q8b = ggml_vld1q_s8_x4(q8); q8 += 64;
+            q2s.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[0] | ((qh[ib32+0] << 8) & 0x300)))),
+                                     vld1_s8((const int8_t *)(iq2s_grid + (qs[1] | ((qh[ib32+0] << 6) & 0x300)))));
+            q2s.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[2] | ((qh[ib32+0] << 4) & 0x300)))),
+                                     vld1_s8((const int8_t *)(iq2s_grid + (qs[3] | ((qh[ib32+0] << 2) & 0x300)))));
+            q2s.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[4] | ((qh[ib32+1] << 8) & 0x300)))),
+                                     vld1_s8((const int8_t *)(iq2s_grid + (qs[5] | ((qh[ib32+1] << 6) & 0x300)))));
+            q2s.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[6] | ((qh[ib32+1] << 4) & 0x300)))),
+                                     vld1_s8((const int8_t *)(iq2s_grid + (qs[7] | ((qh[ib32+1] << 2) & 0x300)))));
+            qs += 8;
+
+            vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | ((uint32_t) signs[1] << 16)));
+            vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2);
+            vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2);
+            vs.val[0] = vceqq_u8(vs.val[0], mask2);
+            vs.val[1] = vceqq_u8(vs.val[1], mask2);
+
+            q2s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[0], m1)), q2s.val[0]);
+            q2s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[1], m1)), q2s.val[1]);
+
+            vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | ((uint32_t) signs[3] << 16)));
+            vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2);
+            vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2);
+            vs.val[0] = vceqq_u8(vs.val[0], mask2);
+            vs.val[1] = vceqq_u8(vs.val[1], mask2);
+
+            signs += 4;
+
+            q2s.val[2] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[0], m1)), q2s.val[2]);
+            q2s.val[3] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[1], m1)), q2s.val[3]);
+
+            const int32x4_t p1 = ggml_vdotq_s32(vzero, q2s.val[0], q8b.val[0]);
+            const int32x4_t p2 = ggml_vdotq_s32(vzero, q2s.val[1], q8b.val[1]);
+            const int32x4_t p3 = ggml_vdotq_s32(vzero, q2s.val[2], q8b.val[2]);
+            const int32x4_t p4 = ggml_vdotq_s32(vzero, q2s.val[3], q8b.val[3]);
+
+            sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32+0] & 0xf));
+            sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32+0] >>  4));
+            sumi1 += vaddvq_s32(p3) * (1 + 2*(x[i].scales[ib32+1] & 0xf));
+            sumi2 += vaddvq_s32(p4) * (1 + 2*(x[i].scales[ib32+1] >>  4));
+        }
+        sumf += d*(sumi1 + sumi2);
+    }
+
+    *s = 0.125f * sumf;
+
+#elif defined(__AVX2__)
+
+   static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
+                                       0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03
+   };
+
+    static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+                                        0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+    };
+
+    const __m128i m4 = _mm_set1_epi8(0xf);
+    const __m128i m1 = _mm_set1_epi8(1);
+
+    const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1);
+    const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2);
+
+    uint64_t aux64;
+
+    __m256 accumf = _mm256_setzero_ps();
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * restrict qs = x[i].qs;
+        const uint8_t * restrict qh = x[i].qh;
+        const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8);
+        const int8_t  * restrict q8 = y[i].qs;
+
+        memcpy(&aux64, x[i].scales, 8);
+        const __m128i scales8 = _mm_add_epi8(_mm_slli_epi16(_mm_and_si128(_mm_set_epi64x(aux64 >> 4, aux64), m4), 1), m1);
+        const __m256i scales16 = _mm256_cvtepi8_epi16(scales8); // 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 15
+
+        __m256i sumi1 = _mm256_setzero_si256();
+        __m256i sumi2 = _mm256_setzero_si256();
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+            const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+            const __m256i q2_1 = _mm256_set_epi64x(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)],
+                                                   iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)],
+                                                   iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)],
+                                                   iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]);
+            const __m256i q2_2 = _mm256_set_epi64x(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)],
+                                                   iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)],
+                                                   iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)],
+                                                   iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]);
+            qs += 8;
+
+            __m256i aux256 = _mm256_set1_epi32(signs[0] | ((uint32_t) signs[1] << 16));
+            aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2);
+            const __m256i s2_1 = _mm256_cmpeq_epi8(aux256, mask2);
+            const __m256i q8s_1 = _mm256_sub_epi8(_mm256_xor_si256(s2_1, q8_1), s2_1);
+
+            aux256 = _mm256_set1_epi32(signs[2] | ((uint32_t) signs[3] << 16));
+            aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2);
+            const __m256i s2_2 = _mm256_cmpeq_epi8(aux256, mask2);
+            const __m256i q8s_2 = _mm256_sub_epi8(_mm256_xor_si256(s2_2, q8_2), s2_2);
+
+            signs += 4;
+
+            const __m256i dot1  = _mm256_maddubs_epi16(q2_1, q8s_1); // blocks 2*ib32+0, 2*ib32+1
+            const __m256i dot2  = _mm256_maddubs_epi16(q2_2, q8s_2); // blocks 2*ib32+2, 2*ib32+3
+
+            const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+0)));
+            const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+1)));
+            sumi1 = _mm256_add_epi32(sumi1, p1);
+            sumi2 = _mm256_add_epi32(sumi2, p2);
+        }
+
+        accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf);
+
+    }
+
+    *s = 0.125f * hsum_float_8(accumf);
+
+#else
+
+    float sumf = 0;
+    for (int i = 0; i < nb; i++) {
+
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const int8_t  * q8 = y[i].qs;
+        const uint8_t * qs = x[i].qs;
+        const uint8_t * qh = x[i].qh;
+        const uint8_t * signs = qs + QK_K/8;
+
+        int bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            int ls1 = 1 + 2*(x[i].scales[ib32] & 0xf);
+            int ls2 = 1 + 2*(x[i].scales[ib32] >>  4);
+            int sumi1 = 0, sumi2 = 0;
+            for (int l = 0; l < 2; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300)));
+                for (int j = 0; j < 8; ++j) {
+                    sumi1 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            for (int l = 2; l < 4; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300)));
+                for (int j = 0; j < 8; ++j) {
+                    sumi2 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            bsum += ls1 * sumi1 + ls2 * sumi2;
+            qs += 4;
+            signs += 4;
+        }
+
+        sumf += d * bsum;
+    }
+
+    *s = 0.125f * sumf;
+
+#endif
+
+}
+
+void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
+    assert(n % QK_K == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+
+    const block_iq3_xxs * restrict x = vx;
+    const block_q8_K    * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+#if defined(__ARM_NEON)
+
+    const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs;
+
+    uint32_t aux32[2];
+
+    ggml_int8x16x4_t q3s;
+    ggml_int8x16x4_t q8b;
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * restrict q3 = x[i].qs;
+        const uint8_t * restrict gas = x[i].qs + QK_K/4;
+        const int8_t   * restrict q8 = y[i].qs;
+        float sumf1 = 0, sumf2 = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            q8b = ggml_vld1q_s8_x4(q8); q8 += 64;
+            memcpy(aux32, gas, 2*sizeof(uint32_t)); gas += 2*sizeof(uint32_t);
+            const uint32x4_t aux32x4_0 = ggml_vld1q_u32(iq3xxs_grid[q3[ 0]], iq3xxs_grid[q3[ 1]], iq3xxs_grid[q3[ 2]], iq3xxs_grid[q3[ 3]]);
+            const uint32x4_t aux32x4_1 = ggml_vld1q_u32(iq3xxs_grid[q3[ 4]], iq3xxs_grid[q3[ 5]], iq3xxs_grid[q3[ 6]], iq3xxs_grid[q3[ 7]]);
+            const uint32x4_t aux32x4_2 = ggml_vld1q_u32(iq3xxs_grid[q3[ 8]], iq3xxs_grid[q3[ 9]], iq3xxs_grid[q3[10]], iq3xxs_grid[q3[11]]);
+            const uint32x4_t aux32x4_3 = ggml_vld1q_u32(iq3xxs_grid[q3[12]], iq3xxs_grid[q3[13]], iq3xxs_grid[q3[14]], iq3xxs_grid[q3[15]]);
+            q3 += 16;
+            q3s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >>  0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >>  7) & 127))));
+            q3s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 21) & 127))));
+            q3s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >>  0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >>  7) & 127))));
+            q3s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 21) & 127))));
+            q3s.val[0] = vmulq_s8(q3s.val[0], vreinterpretq_s8_u32(aux32x4_0));
+            q3s.val[1] = vmulq_s8(q3s.val[1], vreinterpretq_s8_u32(aux32x4_1));
+            q3s.val[2] = vmulq_s8(q3s.val[2], vreinterpretq_s8_u32(aux32x4_2));
+            q3s.val[3] = vmulq_s8(q3s.val[3], vreinterpretq_s8_u32(aux32x4_3));
+            const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]);
+            const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]);
+            sumf1 += vaddvq_s32(p1) * (0.5f + (aux32[0] >> 28));
+            sumf2 += vaddvq_s32(p2) * (0.5f + (aux32[1] >> 28));
+        }
+        sumf += d*(sumf1 + sumf2);
+    }
+    *s = 0.5f * sumf;
+
+#elif defined(__AVX2__)
+
+    const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs;
+
+    uint32_t aux32[2];
+
+    __m256 accumf = _mm256_setzero_ps();
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * restrict q3 = x[i].qs;
+        const uint8_t * restrict gas = x[i].qs + QK_K/4;
+        const int8_t  * restrict q8 = y[i].qs;
+        __m256i sumi1 = _mm256_setzero_si256();
+        __m256i sumi2 = _mm256_setzero_si256();
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+            const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+            const __m256i q2_1 = _mm256_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]],
+                                                  iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]);
+            q3 += 8;
+            const __m256i q2_2 = _mm256_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]],
+                                                  iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]);
+            q3 += 8;
+            memcpy(aux32, gas, 8); gas += 8;
+            const __m256i s2_1 = _mm256_set_epi64x(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127],
+                                                   signs64[(aux32[0] >>  7) & 127], signs64[(aux32[0] >>  0) & 127]);
+            const __m256i s2_2 = _mm256_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127],
+                                                   signs64[(aux32[1] >>  7) & 127], signs64[(aux32[1] >>  0) & 127]);
+            const __m256i q8s_1 = _mm256_sign_epi8(q8_1, s2_1);
+            const __m256i q8s_2 = _mm256_sign_epi8(q8_2, s2_2);
+            const __m256i dot1  = _mm256_maddubs_epi16(q2_1, q8s_1);
+            const __m256i dot2  = _mm256_maddubs_epi16(q2_2, q8s_2);
+            const uint16_t ls1 = aux32[0] >> 28;
+            const uint16_t ls2 = aux32[1] >> 28;
+            const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1));
+            const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1));
+            sumi1 = _mm256_add_epi32(sumi1, p1);
+            sumi2 = _mm256_add_epi32(sumi2, p2);
+        }
+
+        accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf);
+
+    }
+
+    *s = 0.25f * hsum_float_8(accumf);
+
+#else
+
+    uint32_t aux32;
+
+    float sumf = 0.f;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * restrict q3 = x[i].qs;
+        const uint8_t * restrict gas = x[i].qs + QK_K/4;
+        const int8_t  * restrict q8 = y[i].qs;
+        int32_t bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            memcpy(&aux32, gas, sizeof(uint32_t)); gas += sizeof(uint32_t);
+            const uint32_t ls = 2*(aux32 >> 28) + 1;
+            int32_t sumi = 0;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*l+0]);
+                const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*l+1]);
+                const uint8_t  signs = ksigns_iq2xs[(aux32 >> 7*l) & 127];
+                for (int j = 0; j < 4; ++j) {
+                    sumi += grid1[j] * q8[j+0] * (signs & kmask_iq2xs[j+0] ? -1 : 1);
+                    sumi += grid2[j] * q8[j+4] * (signs & kmask_iq2xs[j+4] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            q3 += 8;
+            bsum += sumi * ls;
+        }
+        sumf += d * bsum;
+    }
+    *s = 0.25f * sumf;
+#endif
+}
+
+void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
+    assert(n % QK_K == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+
+    const block_iq3_s * restrict x = vx;
+    const block_q8_K  * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+#if defined(__ARM_NEON)
+
+    typedef union {
+        uint16x8_t vec_index;
+        uint16_t   index[8];
+    } vec_index_t;
+
+   static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
+                                       0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03
+   };
+
+    static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,};
+
+    static const int16_t k_shift[8] = {8, 7, 6, 5, 4, 3, 2, 1};
+
+    const ggml_uint8x16x2_t mask1 = ggml_vld1q_u8_x2(k_mask1);
+    const uint8x16_t        mask2 = vld1q_u8(k_mask2);
+
+    const int16x8_t  hshift = vld1q_s16(k_shift);
+    const uint16x8_t m256   = vdupq_n_u16(256);
+    const uint8x16_t m1     = vdupq_n_u8(1);
+
+    uint8x16x2_t vs;
+    ggml_int8x16x4_t q3s;
+    ggml_int8x16x4_t q8b;
+    vec_index_t idx;
+
+#if QK_K == 256
+    uint32_t scales32[2];
+    const uint8_t * scales8 = (const uint8_t *)scales32;
+#endif
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * restrict qs = x[i].qs;
+        const uint8_t * restrict qh = x[i].qh;
+        const uint16_t * restrict signs = (const uint16_t *)x[i].signs;
+        const int8_t   * restrict q8 = y[i].qs;
+
+#if QK_K == 256
+        memcpy(scales32, x[i].scales, 4);
+        scales32[1] = (((scales32[0] >> 4) & 0x0f0f0f0f) << 1) | 0x01010101;
+        scales32[0] = ((scales32[0] & 0x0f0f0f0f) << 1) | 0x01010101;
+#endif
+
+        int sumi1 = 0, sumi2 = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            q8b = ggml_vld1q_s8_x4(q8); q8 += 64;
+
+            const uint8x16_t idx_l = vld1q_u8(qs); qs += 16;
+            idx.vec_index = vorrq_u16(vmovl_u8(vget_low_u8 (idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+0]), hshift), m256));
+            const uint32x4_t aux32x4_0 = ggml_vld1q_u32(iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]],
+                                                        iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]);
+            const uint32x4_t aux32x4_1 = ggml_vld1q_u32(iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]],
+                                                        iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]);
+            idx.vec_index = vorrq_u16(vmovl_u8(vget_high_u8(idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+1]), hshift), m256));
+            const uint32x4_t aux32x4_2 = ggml_vld1q_u32(iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]],
+                                                        iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]);
+            const uint32x4_t aux32x4_3 = ggml_vld1q_u32(iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]],
+                                                        iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]);
+
+
+            vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | ((uint32_t) signs[1] << 16)));
+            vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2);
+            vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2);
+            vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1);
+            vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1);
+
+            q3s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_0));
+            q3s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_1));
+
+            vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | ((uint32_t) signs[3] << 16)));
+            vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2);
+            vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2);
+            vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1);
+            vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1);
+
+            signs += 4;
+
+            q3s.val[2] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_2));
+            q3s.val[3] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_3));
+
+            const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]);
+            const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]);
+#if QK_K == 256
+            sumi1 += vaddvq_s32(p1) * scales8[ib32/2+0];
+            sumi2 += vaddvq_s32(p2) * scales8[ib32/2+4];
+#else
+            sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32/2] & 0xf));
+            sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32/2] >>  4));
+#endif
+        }
+        sumf += d*(sumi1 + sumi2);
+    }
+    *s = sumf;
+
+#elif defined(__AVX2__)
+
+   static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
+                                       0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03
+   };
+
+    static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+                                        0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
+    };
+
+    const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1);
+    const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2);
+
+    const __m256i idx_shift = _mm256_set_epi32(1, 2, 3, 4, 5, 6, 7, 8);
+    const __m256i idx_mask  = _mm256_set1_epi32(256);
+
+    typedef union {
+        __m256i  vec[2];
+        uint32_t index[16];
+    } index_t;
+
+    index_t idx;
+
+    __m256 accumf = _mm256_setzero_ps();
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * restrict qs = x[i].qs;
+        const uint8_t * restrict qh = x[i].qh;
+        const uint16_t * restrict signs = (const uint16_t *)x[i].signs;
+        const int8_t  * restrict q8 = y[i].qs;
+        __m256i sumi1 = _mm256_setzero_si256();
+        __m256i sumi2 = _mm256_setzero_si256();
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+            const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+            const __m256i idx_l = _mm256_cvtepu8_epi16(_mm_loadu_si128((const __m128i *)qs)); qs += 16;
+            idx.vec[0] = _mm256_set1_epi32(qh[ib32+0]);
+            idx.vec[1] = _mm256_set1_epi32(qh[ib32+1]);
+            idx.vec[0] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[0], idx_shift), idx_mask);
+            idx.vec[1] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[1], idx_shift), idx_mask);
+            idx.vec[0] = _mm256_or_si256(idx.vec[0], _mm256_cvtepi16_epi32(_mm256_castsi256_si128(idx_l)));
+            idx.vec[1] = _mm256_or_si256(idx.vec[1], _mm256_cvtepi16_epi32(_mm256_extractf128_si256(idx_l, 1)));
+
+            // At leat on my CPU (Ryzen 7950X), using _mm256_i32gather_epi32 is slower than _mm256_set_epi32. Strange.
+            //const __m256i q2_1 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[0], 4);
+            //const __m256i q2_2 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[1], 4);
+            const __m256i q2_1 = _mm256_set_epi32(
+                    iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]],
+                    iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]]
+            );
+            const __m256i q2_2 = _mm256_set_epi32(
+                    iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]],
+                    iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[ 9]], iq3s_grid[idx.index[ 8]]
+            );
+
+            __m256i aux256 = _mm256_set1_epi32(signs[0] | (signs[1] << 16));
+            aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2);
+            const __m256i s2_1 = _mm256_cmpeq_epi8(aux256, mask2);
+            const __m256i q8s_1 = _mm256_sub_epi8(_mm256_xor_si256(s2_1, q8_1), s2_1);
+
+            aux256 = _mm256_set1_epi32(signs[2] | (signs[3] << 16));
+            aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2);
+            const __m256i s2_2 = _mm256_cmpeq_epi8(aux256, mask2);
+            const __m256i q8s_2 = _mm256_sub_epi8(_mm256_xor_si256(s2_2, q8_2), s2_2);
+
+            signs += 4;
+
+            const __m256i dot1  = _mm256_maddubs_epi16(q2_1, q8s_1);
+            const __m256i dot2  = _mm256_maddubs_epi16(q2_2, q8s_2);
+            const uint16_t ls1 = x[i].scales[ib32/2] & 0xf;
+            const uint16_t ls2 = x[i].scales[ib32/2] >>  4;
+            const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1));
+            const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1));
+            sumi1 = _mm256_add_epi32(sumi1, p1);
+            sumi2 = _mm256_add_epi32(sumi2, p2);
+        }
+
+        accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf);
+
+    }
+
+    *s = hsum_float_8(accumf);
+
+#else
+
+    float sumf = 0.f;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * restrict qs = x[i].qs;
+        const uint8_t * restrict qh = x[i].qh;
+        const uint8_t * restrict signs = x[i].signs;
+        const int8_t  * restrict q8 = y[i].qs;
+        int32_t bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const uint32_t ls1 = 2*(x[i].scales[ib32/2] & 0xf) + 1;
+            const uint32_t ls2 = 2*(x[i].scales[ib32/2] >>  4) + 1;
+            int32_t sumi = 0;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256)));
+                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256)));
+                for (int j = 0; j < 4; ++j) {
+                    sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
+                    sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            qs += 8;
+            signs += 4;
+            bsum += sumi * ls1;
+            sumi = 0;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256)));
+                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256)));
+                for (int j = 0; j < 4; ++j) {
+                    sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
+                    sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            qs += 8;
+            signs += 4;
+            bsum += sumi * ls2;
+        }
+        sumf += d * bsum;
+    }
+    *s = sumf;
+#endif
+}
+
+
+#ifdef __AVX2__
+static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) {
+    const __m256i ax = _mm256_sign_epi8(x, x);
+    const __m256i sy = _mm256_sign_epi8(y, x);
+    return _mm256_maddubs_epi16(ax, sy);
+}
+#endif
+
+void ggml_vec_dot_iq1_s_q8_K  (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
+    assert(n % QK_K == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+
+    const block_iq1_s * restrict x = vx;
+    const block_q8_K  * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+#if defined __ARM_NEON
+
+    ggml_int8x16x4_t q1b;
+    ggml_int8x16x4_t q8b;
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+
+        const int8_t   * q8 = y[i].qs;
+        const uint8_t  * qs = x[i].qs;
+        const uint16_t * qh = x[i].qh;
+
+        int sumi1 = 0, sumi2 = 0, sumi3 = 0;
+
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+
+            q1b.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[0] | ((qh[ib+0] << 8) & 0x700)))),
+                                     vld1_s8((const int8_t *)(iq1s_grid + (qs[1] | ((qh[ib+0] << 5) & 0x700)))));
+            q1b.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[2] | ((qh[ib+0] << 2) & 0x700)))),
+                                     vld1_s8((const int8_t *)(iq1s_grid + (qs[3] | ((qh[ib+0] >> 1) & 0x700)))));
+            q1b.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[4] | ((qh[ib+1] << 8) & 0x700)))),
+                                     vld1_s8((const int8_t *)(iq1s_grid + (qs[5] | ((qh[ib+1] << 5) & 0x700)))));
+            q1b.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[6] | ((qh[ib+1] << 2) & 0x700)))),
+                                     vld1_s8((const int8_t *)(iq1s_grid + (qs[7] | ((qh[ib+1] >> 1) & 0x700)))));
+            qs += 8;
+
+            q8b = ggml_vld1q_s8_x4(q8); q8 += 64;
+
+            const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q1b.val[0], q8b.val[0]), q1b.val[1], q8b.val[1]);
+            const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q1b.val[2], q8b.val[2]), q1b.val[3], q8b.val[3]);
+
+            const int ls1 = 2*((qh[ib+0] >> 12) & 7) + 1;
+            const int ls2 = 2*((qh[ib+1] >> 12) & 7) + 1;
+            sumi1 += vaddvq_s32(p1) * ls1;
+            sumi2 += vaddvq_s32(p2) * ls2;
+            sumi3 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * ls1 * (qh[ib+0] & 0x8000 ? -1 : 1)
+                   + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * ls2 * (qh[ib+1] & 0x8000 ? -1 : 1);
+
+        }
+
+        sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * (sumi1 + sumi2 + IQ1S_DELTA * sumi3);
+    }
+
+    *s = sumf;
+
+#elif defined __AVX2__
+
+    __m256 accum = _mm256_setzero_ps();
+    float accum1 = 0;
+    for (int i = 0; i < nb; ++i) {
+
+        const int8_t   * q8 = y[i].qs;
+        const uint8_t  * qs = x[i].qs;
+        const uint16_t * qh = x[i].qh;
+
+        __m256i sumi = _mm256_setzero_si256();
+        int sumi1 = 0;
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+            const __m256i q1b_1 = _mm256_set_epi64x(iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)],
+                                                    iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)]);
+            const __m256i q1b_2 = _mm256_set_epi64x(iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)],
+                                                    iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)]);
+            qs += 8;
+            const __m256i q8b_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+            const __m256i q8b_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+
+            const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1);
+            const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2);
+            const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1;
+            const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1;
+            const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(ls1));
+            const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(ls2));
+
+            sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p1, p2));
+            sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1
+                   + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2;
+        }
+
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        accum = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(sumi), accum);
+        accum1 += d * sumi1;
+
+    }
+
+    *s = hsum_float_8(accum) + IQ1S_DELTA * accum1;
+
+#else
+
+    float sumf = 0;
+    for (int i = 0; i < nb; i++) {
+
+        const int8_t   * q8 = y[i].qs;
+        const uint8_t  * qs = x[i].qs;
+        const uint16_t * qh = x[i].qh;
+
+        int sumi = 0, sumi1 = 0;
+        for (int ib = 0; ib < QK_K/32; ++ib) {
+            const int ls = 2*((qh[ib] >> 12) & 7) + 1;
+            const int delta = qh[ib] & 0x8000 ? -1 : 1;
+            int lsum = 0;
+            for (int l = 0; l < 4; ++l) {
+                const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((qh[ib] >> 3*l) & 7) << 8)));
+                for (int j = 0; j < 8; ++j) {
+                    lsum += q8[j] * grid[j];
+                }
+                q8 += 8;
+            }
+            sumi  += ls * lsum;
+            sumi1 += ls * delta * (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]);
+            qs += 4;
+        }
+
+        sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * (sumi + IQ1S_DELTA * sumi1);
+    }
+
+    *s = sumf;
+
+#endif
+}
+
+void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
+    assert(n % QK_K == 0);
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+
+    const block_iq1_m * restrict x = vx;
+    const block_q8_K  * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+#if QK_K != 64
+    iq1m_scale_t scale;
+#endif
+
+#if defined __ARM_NEON
+
+#if QK_K == 64
+    const int32x4_t mask  = vdupq_n_s32(0xf);
+#else
+    const int32x4_t mask  = vdupq_n_s32(0x7);
+#endif
+    const int32x4_t mone  = vdupq_n_s32(1);
+    const int32x4_t mzero = vdupq_n_s32(0);
+
+    ggml_int8x16x4_t deltas;
+    deltas.val[0] = vcombine_s8(vdup_n_s8(+1), vdup_n_s8(+1));
+    deltas.val[1] = vcombine_s8(vdup_n_s8(-1), vdup_n_s8(+1));
+    deltas.val[2] = vcombine_s8(vdup_n_s8(+1), vdup_n_s8(-1));
+    deltas.val[3] = vcombine_s8(vdup_n_s8(-1), vdup_n_s8(-1));
+
+    ggml_int8x16x4_t q1b;
+    ggml_int8x16x4_t q8b;
+
+    uint32_t aux32;
+    const uint8_t * aux8 = (const uint8_t *)&aux32;
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+
+        const int8_t   * q8 = y[i].qs;
+        const uint8_t  * qs = x[i].qs;
+        const uint8_t  * qh = x[i].qh;
+        const uint16_t * sc = (const uint16_t *)x[i].scales;
+
+#if QK_K != 64
+        scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+#endif
+
+        int32x4_t sumi1 = mzero;
+        int32x4_t sumi2 = mzero;
+
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+
+            q1b.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[0] | ((qh[0] << 8) & 0x700)))),
+                                     vld1_s8((const int8_t *)(iq1s_grid + (qs[1] | ((qh[0] << 4) & 0x700)))));
+            q1b.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[2] | ((qh[1] << 8) & 0x700)))),
+                                     vld1_s8((const int8_t *)(iq1s_grid + (qs[3] | ((qh[1] << 4) & 0x700)))));
+            q1b.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[4] | ((qh[2] << 8) & 0x700)))),
+                                     vld1_s8((const int8_t *)(iq1s_grid + (qs[5] | ((qh[2] << 4) & 0x700)))));
+            q1b.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[6] | ((qh[3] << 8) & 0x700)))),
+                                     vld1_s8((const int8_t *)(iq1s_grid + (qs[7] | ((qh[3] << 4) & 0x700)))));
+
+            q8b = ggml_vld1q_s8_x4(q8); q8 += 64;
+
+            const int32x4_t p1 = vpaddq_s32(ggml_vdotq_s32(mzero, q1b.val[0], q8b.val[0]), ggml_vdotq_s32(mzero, q1b.val[1], q8b.val[1]));
+            const int32x4_t p2 = vpaddq_s32(ggml_vdotq_s32(mzero, q1b.val[2], q8b.val[2]), ggml_vdotq_s32(mzero, q1b.val[3], q8b.val[3]));
+            const int32x4_t p12 = vpaddq_s32(p1, p2);
+
+            const uint32_t * qh32 = (const uint32_t *)qh; // we are 4-byte aligned, so we can do that
+            aux32 = ((qh32[0] >> 3) & 0x01010101) | ((qh32[0] >> 6) & 0x02020202);
+
+            const int32x4_t p3 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[0]], q8b.val[0]), ggml_vdotq_s32(mzero, deltas.val[aux8[1]], q8b.val[1]));
+            const int32x4_t p4 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[2]], q8b.val[2]), ggml_vdotq_s32(mzero, deltas.val[aux8[3]], q8b.val[3]));
+            const int32x4_t p34 = vpaddq_s32(p3, p4);
+
+#if QK_K == 64
+            int32x4_t scales_4 = ggml_vld1q_u32(sc[0] >> 0, sc[0] >> 4, sc[0] >> 8, sc[0] >> 12);
+#else
+            int32x4_t scales_4 = ggml_vld1q_u32(sc[ib/2] >> 0, sc[ib/2] >> 3, sc[ib/2] >> 6, sc[ib/2] >> 9);
+#endif
+            scales_4 = vaddq_s32(vshlq_n_s32(vandq_s32(scales_4, mask), 1), mone);
+
+            sumi1 = vmlaq_s32(sumi1, scales_4, p12);
+            sumi2 = vmlaq_s32(sumi2, scales_4, p34);
+
+            qs += 8; qh += 4;
+
+        }
+
+#if QK_K == 64
+        sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2));
+#else
+        sumf += y[i].d * GGML_FP16_TO_FP32(scale.f16) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2));
+#endif
+    }
+
+    *s = sumf;
+
+#elif defined __AVX2__
+
+#if QK_K == 64
+    const __m256i mask = _mm256_set1_epi16(0xf);
+#else
+    const __m256i mask = _mm256_set1_epi16(0x7);
+#endif
+    const __m256i mone = _mm256_set1_epi16(1);
+
+    __m256 accum1 = _mm256_setzero_ps();
+    __m256 accum2 = _mm256_setzero_ps();
+    for (int i = 0; i < nb; ++i) {
+
+        const int8_t   * q8 = y[i].qs;
+        const uint8_t  * qs = x[i].qs;
+        const uint8_t  * qh = x[i].qh;
+        const uint16_t * sc = (const uint16_t *)x[i].scales;
+
+#if QK_K != 64
+        scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+#endif
+
+        __m256i sumi1 = _mm256_setzero_si256();
+        __m256i sumi2 = _mm256_setzero_si256();
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+            const __m256i q1b_1 = _mm256_set_epi64x(
+                    iq1s_grid[qs[3] | (((uint16_t)qh[1] << 4) & 0x700)], iq1s_grid[qs[2] | (((uint16_t)qh[1] << 8) & 0x700)],
+                    iq1s_grid[qs[1] | (((uint16_t)qh[0] << 4) & 0x700)], iq1s_grid[qs[0] | (((uint16_t)qh[0] << 8) & 0x700)]
+            );
+            const __m256i q1b_2 = _mm256_set_epi64x(
+                    iq1s_grid[qs[7] | (((uint16_t)qh[3] << 4) & 0x700)], iq1s_grid[qs[6] | (((uint16_t)qh[3] << 8) & 0x700)],
+                    iq1s_grid[qs[5] | (((uint16_t)qh[2] << 4) & 0x700)], iq1s_grid[qs[4] | (((uint16_t)qh[2] << 8) & 0x700)]
+            );
+            const __m256i q8b_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+            const __m256i q8b_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
+
+            const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1);
+            const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2);
+
+            const __m256i delta1 = _mm256_set_epi64x(qh[1] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
+                                                     qh[1] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101,
+                                                     qh[0] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
+                                                     qh[0] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101);
+            const __m256i delta2 = _mm256_set_epi64x(qh[3] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
+                                                     qh[3] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101,
+                                                     qh[2] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
+                                                     qh[2] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101);
+
+            const __m256i dot3 = mul_add_epi8(delta1, q8b_1);
+            const __m256i dot4 = mul_add_epi8(delta2, q8b_2);
+#if QK_K == 64
+            __m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[0] >>  4), _mm_set1_epi16(sc[0] >> 0));
+            __m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[0] >> 12), _mm_set1_epi16(sc[0] >> 8));
+#else
+            __m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 3), _mm_set1_epi16(sc[ib/2] >> 0));
+            __m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 9), _mm_set1_epi16(sc[ib/2] >> 6));
+#endif
+            scale1 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale1, mask), 1), mone);
+            scale2 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale2, mask), 1), mone);
+            const __m256i p1 = _mm256_madd_epi16(dot1, scale1);
+            const __m256i p2 = _mm256_madd_epi16(dot2, scale2);
+            const __m256i p3 = _mm256_madd_epi16(dot3, scale1);
+            const __m256i p4 = _mm256_madd_epi16(dot4, scale2);
+
+            sumi1 = _mm256_add_epi32(sumi1, _mm256_add_epi32(p1, p2));
+            sumi2 = _mm256_add_epi32(sumi2, _mm256_add_epi32(p3, p4));
+
+            qs += 8; qh += 4;
+        }
+
+#if QK_K == 64
+        const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d));
+#else
+        const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(scale.f16));
+#endif
+        accum1 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi1), accum1);
+        accum2 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi2), accum2);
+
+    }
+
+    *s = hsum_float_8(accum1) + IQ1M_DELTA * hsum_float_8(accum2);
+
+#else
+
+    int sum1[2], sum2[2], delta[4];
+
+    float sumf = 0;
+    for (int i = 0; i < nb; i++) {
+
+        const int8_t   * q8 = y[i].qs;
+        const uint8_t  * qs = x[i].qs;
+        const uint8_t  * qh = x[i].qh;
+        const uint16_t * sc = (const uint16_t *)x[i].scales;
+
+#if QK_K != 64
+        scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+#endif
+
+        int sumi1 = 0, sumi2 = 0;
+        for (int ib = 0; ib < QK_K/32; ++ib) {
+            delta[0] = qh[0] & 0x08 ? -1 : 1;
+            delta[1] = qh[0] & 0x80 ? -1 : 1;
+            delta[2] = qh[1] & 0x08 ? -1 : 1;
+            delta[3] = qh[1] & 0x80 ? -1 : 1;
+            sum1[0] = sum1[1] = sum2[0] = sum2[1] = 0;
+            for (int l = 0; l < 4; ++l) {
+                const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((uint16_t)qh[l/2] << (8 - 4*(l%2))) & 0x700)));
+                int lsum1 = 0, lsum2 = 0;
+                for (int j = 0; j < 8; ++j) {
+                    lsum1 += q8[j] * grid[j];
+                    lsum2 += q8[j];
+                }
+                q8 += 8;
+                sum1[l/2] += lsum1;
+                sum2[l/2] += lsum2*delta[l];
+            }
+#if QK_K == 64
+            const int ls1 = 2*((sc[0] >> (8*(ib%2)+0)) & 0xf) + 1;
+            const int ls2 = 2*((sc[0] >> (8*(ib%2)+4)) & 0xf) + 1;
+#else
+            const int ls1 = 2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1;
+            const int ls2 = 2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1;
+#endif
+            sumi1 += sum1[0] * ls1 + sum1[1] * ls2;
+            sumi2 += sum2[0] * ls1 + sum2[1] * ls2;
+            qs += 4;
+            qh += 2;
+        }
+
+#if QK_K == 64
+        sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2);
+#else
+        sumf += GGML_FP16_TO_FP32(scale.f16) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2);
+#endif
+    }
+
+    *s = sumf;
+
+#endif
+}
+
+void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+    assert(n % QK4_NL == 0);
+    static_assert(QK4_NL == QK8_0, "QK4_NL and QK8_0 must be the same");
+
+    const block_iq4_nl * restrict x = vx;
+    const block_q8_0   * restrict y = vy;
+
+    const int nb = n / QK4_NL;
+
+#if defined __ARM_NEON
+    const int8x16_t values = vld1q_s8(kvalues_iq4nl);
+    const uint8x16_t m4b = vdupq_n_u8(0x0f);
+    uint8x16x2_t q4bits;
+    int8x16x4_t q4b;
+    int8x16x4_t q8b;
+    int32x4_t prod_1, prod_2;
+
+    float sumf = 0;
+
+    for (int ib = 0; ib < nb; ib += 2) {
+
+        q4bits.val[0] = vld1q_u8(x[ib+0].qs);
+        q4bits.val[1] = vld1q_u8(x[ib+1].qs);
+        q8b.val[0]    = vld1q_s8(y[ib+0].qs);
+        q8b.val[1]    = vld1q_s8(y[ib+0].qs + 16);
+        q8b.val[2]    = vld1q_s8(y[ib+1].qs);
+        q8b.val[3]    = vld1q_s8(y[ib+1].qs + 16);
+
+        q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits.val[0], m4b));
+        q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4));
+        q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits.val[1], m4b));
+        q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4));
+
+        prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]);
+        prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]);
+
+        sumf +=
+            GGML_FP16_TO_FP32(x[ib+0].d) * GGML_FP16_TO_FP32(y[ib+0].d) * vaddvq_s32(prod_1) +
+            GGML_FP16_TO_FP32(x[ib+1].d) * GGML_FP16_TO_FP32(y[ib+1].d) * vaddvq_s32(prod_2);
+    }
+
+    *s = sumf;
+
+#elif defined __AVX2__
+
+    const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl);
+    const __m128i m4b  = _mm_set1_epi8(0x0f);
+    const __m256i mone = _mm256_set1_epi16(1);
+
+    __m256 accum1 = _mm256_setzero_ps();
+    __m256 accum2 = _mm256_setzero_ps();
+    for (int ib = 0; ib < nb; ib += 2) {
+        const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)x[0].qs);
+        const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)x[1].qs);
+        const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)y[0].qs);
+        const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)y[1].qs);
+        const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)),
+                                              _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)));
+        const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)),
+                                              _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b)));
+        const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1);
+        const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2);
+        const __m256i p_1 = _mm256_madd_epi16(p16_1, mone);
+        const __m256i p_2 = _mm256_madd_epi16(p16_2, mone);
+        accum1 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[0].d)*GGML_FP16_TO_FP32(x[0].d)),
+                _mm256_cvtepi32_ps(p_1), accum1);
+        accum2 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[1].d)*GGML_FP16_TO_FP32(x[1].d)),
+                _mm256_cvtepi32_ps(p_2), accum2);
+
+        y += 2;
+        x += 2;
+    }
+
+    *s = hsum_float_8(_mm256_add_ps(accum1, accum2));
+
+#else
+    float sumf = 0;
+    for (int ib = 0; ib < nb; ++ib) {
+        const float d = GGML_FP16_TO_FP32(y[ib].d)*GGML_FP16_TO_FP32(x[ib].d);
+        int sumi1 = 0, sumi2 = 0;
+        for (int j = 0; j < QK4_NL/2; ++j) {
+            sumi1 += y[ib].qs[j+       0] * kvalues_iq4nl[x[ib].qs[j] & 0xf];
+            sumi2 += y[ib].qs[j+QK4_NL/2] * kvalues_iq4nl[x[ib].qs[j] >>  4];
+        }
+        sumf += d * (sumi1 + sumi2);
+    }
+    *s = sumf;
+#endif
+}
+
+void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+    assert(n % QK_K == 0);
+#if QK_K == 64
+    ggml_vec_dot_iq4_nl_q8_0(n, s, bs, vx, bx, vy, by, nrc);
+#else
+
+    const block_iq4_xs * restrict x = vx;
+    const block_q8_K   * restrict y = vy;
+
+    const int nb = n / QK_K;
+
+#if defined __ARM_NEON
+    const int8x16_t values = vld1q_s8(kvalues_iq4nl);
+    const uint8x16_t m4b = vdupq_n_u8(0x0f);
+    ggml_uint8x16x2_t q4bits;
+    ggml_int8x16x4_t q4b;
+    ggml_int8x16x4_t q8b;
+    int32x4_t prod_1, prod_2;
+
+    float sumf = 0;
+
+    for (int ibl = 0; ibl < nb; ++ibl) {
+
+        const int8_t  * q8 = y[ibl].qs;
+        const uint8_t * q4 = x[ibl].qs;
+        uint16_t h = x[ibl].scales_h;
+
+        int sumi1 = 0, sumi2 = 0;
+        for (int ib = 0; ib < QK_K/64; ++ib) {
+
+            q4bits = ggml_vld1q_u8_x2(q4); q4 += 32;
+            q8b    = ggml_vld1q_s8_x4(q8); q8 += 64;
+
+            q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits.val[0], m4b));
+            q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4));
+            q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits.val[1], m4b));
+            q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4));
+
+            prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]);
+            prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]);
+
+            int ls1 = ((x[ibl].scales_l[ib] & 0xf) | ((h << 4) & 0x30)) - 32;
+            int ls2 = ((x[ibl].scales_l[ib] >>  4) | ((h << 2) & 0x30)) - 32;
+            h >>= 4;
+            sumi1 += vaddvq_s32(prod_1) * ls1;
+            sumi2 += vaddvq_s32(prod_2) * ls2;
+
+        }
+
+        sumf += GGML_FP16_TO_FP32(x[ibl].d) * y[ibl].d * (sumi1 + sumi2);
+    }
+
+    *s = sumf;
+
+#elif defined __AVX2__
+
+    const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl);
+    const __m128i m4b  = _mm_set1_epi8(0x0f);
+
+    __m256 accum = _mm256_setzero_ps();
+    for (int ibl = 0; ibl < nb; ++ibl) {
+        const uint8_t * qs = x[ibl].qs;
+        const int8_t  * q8 = y[ibl].qs;
+        uint16_t sh = x[ibl].scales_h;
+        __m256i sumi1 = _mm256_setzero_si256();
+        __m256i sumi2 = _mm256_setzero_si256();
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+            const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)qs);  qs += 16;
+            const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)qs);  qs += 16;
+            const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+            const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
+            const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)),
+                                                  _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)));
+            const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)),
+                                                  _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b)));
+            const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1);
+            const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2);
+            const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32;
+            const int16_t ls2 = ((x[ibl].scales_l[ib/2] >>  4) | ((sh << 2) & 0x30)) - 32;
+            sh >>= 4;
+            const __m256i p_1 = _mm256_madd_epi16(p16_1, _mm256_set1_epi16(ls1));
+            const __m256i p_2 = _mm256_madd_epi16(p16_2, _mm256_set1_epi16(ls2));
+            sumi1 = _mm256_add_epi32(p_1, sumi1);
+            sumi2 = _mm256_add_epi32(p_2, sumi2);
+        }
+        accum = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d),
+                _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accum);
+    }
+
+    *s = hsum_float_8(accum);
+
+#else
+    float sumf = 0;
+    for (int ibl = 0; ibl < nb; ++ibl) {
+        const float d4d8 = GGML_FP16_TO_FP32(x[ibl].d) * y[ibl].d;
+        uint16_t h = x[ibl].scales_h;
+        const uint8_t * qs = x[ibl].qs;
+        const int8_t  * q8 = y[ibl].qs;
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+            const uint8_t ls1 = (x[ibl].scales_l[ib/2] & 0xf) | ((h << 4) & 0x30);
+            const uint8_t ls2 = (x[ibl].scales_l[ib/2] >>  4) | ((h << 2) & 0x30);
+            h >>= 4;
+            const float d1 = d4d8*(ls1 - 32);
+            const float d2 = d4d8*(ls2 - 32);
+            int sumi1 = 0, sumi2 = 0;
+            for (int j = 0; j < 16; ++j) {
+                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
+                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
+            }
+            sumf += d1 * (sumi1 + sumi2);
+            qs += 16;
+            q8 += 32;
+            sumi1 = sumi2 = 0;
+            for (int j = 0; j < 16; ++j) {
+                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
+                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
+            }
+            sumf += d2 * (sumi1 + sumi2);
+            qs += 16;
+            q8 += 32;
+        }
+    }
+    *s = sumf;
+#endif
+#endif
+}
+
+// ================================ IQ2 quantization =============================================
+
+typedef struct {
+    uint64_t * grid;
+    int      * map;
+    uint16_t * neighbours;
+} iq2_entry_t;
+
+static iq2_entry_t iq2_data[4] = {
+    {NULL, NULL, NULL},
+    {NULL, NULL, NULL},
+    {NULL, NULL, NULL},
+    {NULL, NULL, NULL},
+};
+
+static inline int iq2_data_index(enum ggml_type type) {
+    GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M || type == GGML_TYPE_IQ2_S);
+    return type == GGML_TYPE_IQ2_XXS ? 0 :
+           type == GGML_TYPE_IQ2_XS  ? 1 :
+           type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? 2 : 3;
+}
+
+static inline int iq2_grid_size(enum ggml_type type) {
+    GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M || type == GGML_TYPE_IQ2_S);
+    return type == GGML_TYPE_IQ2_XXS ? 256 :
+           type == GGML_TYPE_IQ2_XS  ? 512 :
+           type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? NGRID_IQ1S : 1024;
+}
+
+static int iq2_compare_func(const void * left, const void * right) {
+    const int * l = (const int *)left;
+    const int * r = (const int *)right;
+    return l[0] < r[0] ? -1 : l[0] > r[0] ? 1 : l[1] < r[1] ? -1 : l[1] > r[1] ? 1 : 0;
+}
+
+void iq2xs_init_impl(enum ggml_type type) {
+    const int gindex = iq2_data_index(type);
+    const int grid_size = iq2_grid_size(type);
+    if (iq2_data[gindex].grid) {
+        return;
+    }
+    static const uint16_t kgrid_2bit_256[256] = {
+            0,     2,     5,     8,    10,    17,    20,    32,    34,    40,    42,    65,    68,    80,    88,    97,
+          100,   128,   130,   138,   162,   257,   260,   272,   277,   320,   388,   408,   512,   514,   546,   642,
+         1025,  1028,  1040,  1057,  1060,  1088,  1090,  1096,  1120,  1153,  1156,  1168,  1188,  1280,  1282,  1288,
+         1312,  1350,  1385,  1408,  1425,  1545,  1552,  1600,  1668,  1700,  2048,  2053,  2056,  2068,  2088,  2113,
+         2116,  2128,  2130,  2184,  2308,  2368,  2562,  2580,  4097,  4100,  4112,  4129,  4160,  4192,  4228,  4240,
+         4245,  4352,  4360,  4384,  4432,  4442,  4480,  4644,  4677,  5120,  5128,  5152,  5157,  5193,  5248,  5400,
+         5474,  5632,  5654,  6145,  6148,  6160,  6208,  6273,  6400,  6405,  6560,  6737,  8192,  8194,  8202,  8260,
+         8289,  8320,  8322,  8489,  8520,  8704,  8706,  9217,  9220,  9232,  9280,  9302,  9472,  9537,  9572,  9872,
+        10248, 10272, 10388, 10820, 16385, 16388, 16400, 16408, 16417, 16420, 16448, 16456, 16470, 16480, 16513, 16516,
+        16528, 16640, 16672, 16737, 16768, 16773, 16897, 16912, 16968, 16982, 17000, 17408, 17416, 17440, 17536, 17561,
+        17682, 17700, 17920, 18433, 18436, 18448, 18496, 18501, 18688, 18776, 18785, 18818, 19013, 19088, 20480, 20488,
+        20497, 20505, 20512, 20608, 20616, 20740, 20802, 20900, 21137, 21648, 21650, 21770, 22017, 22100, 22528, 22545,
+        22553, 22628, 22848, 23048, 24580, 24592, 24640, 24680, 24832, 24917, 25112, 25184, 25600, 25605, 25872, 25874,
+        25988, 26690, 32768, 32770, 32778, 32833, 32898, 33028, 33048, 33088, 33297, 33793, 33796, 33808, 33813, 33856,
+        33888, 34048, 34118, 34196, 34313, 34368, 34400, 34818, 35076, 35345, 36868, 36880, 36900, 36928, 37025, 37142,
+        37248, 37445, 37888, 37922, 37956, 38225, 39041, 39200, 40962, 41040, 41093, 41225, 41472, 42008, 43088, 43268,
+    };
+    static const uint16_t kgrid_2bit_512[512] = {
+            0,     2,     5,     8,    10,    17,    20,    22,    25,    32,    34,    37,    40,    65,    68,    70,
+           73,    80,    82,    85,    88,    97,   100,   128,   130,   133,   136,   145,   148,   153,   160,   257,
+          260,   262,   265,   272,   274,   277,   280,   282,   289,   292,   320,   322,   325,   328,   337,   340,
+          352,   360,   385,   388,   400,   512,   514,   517,   520,   529,   532,   544,   577,   580,   592,   597,
+          640,   650,  1025,  1028,  1030,  1033,  1040,  1042,  1045,  1048,  1057,  1060,  1088,  1090,  1093,  1096,
+         1105,  1108,  1110,  1120,  1153,  1156,  1168,  1280,  1282,  1285,  1288,  1297,  1300,  1312,  1345,  1348,
+         1360,  1377,  1408,  1537,  1540,  1552,  1574,  1600,  1602,  1668,  2048,  2050,  2053,  2056,  2058,  2065,
+         2068,  2080,  2085,  2113,  2116,  2128,  2136,  2176,  2208,  2218,  2305,  2308,  2320,  2368,  2433,  2441,
+         2560,  2592,  2600,  2710,  2720,  4097,  4100,  4102,  4105,  4112,  4114,  4117,  4120,  4129,  4132,  4160,
+         4162,  4165,  4168,  4177,  4180,  4192,  4202,  4225,  4228,  4240,  4352,  4354,  4357,  4360,  4369,  4372,
+         4384,  4417,  4420,  4432,  4480,  4500,  4502,  4609,  4612,  4614,  4624,  4672,  4704,  5120,  5122,  5125,
+         5128,  5137,  5140,  5152,  5185,  5188,  5193,  5200,  5220,  5248,  5377,  5380,  5392,  5440,  5632,  5652,
+         5705,  6145,  6148,  6160,  6162,  6208,  6228,  6278,  6400,  6405,  6502,  6737,  6825,  8192,  8194,  8197,
+         8200,  8202,  8209,  8212,  8224,  8257,  8260,  8272,  8320,  8352,  8449,  8452,  8464,  8512,  8520,  8549,
+         8704,  8738,  8832,  8872,  9217,  9220,  9232,  9257,  9280,  9472,  9537,  9554,  9625,  9729,  9754,  9894,
+        10240, 10248, 10250, 10272, 10325, 10376, 10402, 10600, 10640, 10760, 10784, 10882, 10888, 10890, 16385, 16388,
+        16390, 16393, 16400, 16402, 16405, 16408, 16417, 16420, 16448, 16450, 16453, 16456, 16458, 16465, 16468, 16480,
+        16485, 16513, 16516, 16528, 16640, 16642, 16645, 16648, 16657, 16660, 16672, 16705, 16708, 16720, 16768, 16773,
+        16802, 16897, 16900, 16912, 16914, 16937, 16960, 17408, 17410, 17413, 17416, 17425, 17428, 17433, 17440, 17473,
+        17476, 17488, 17536, 17556, 17665, 17668, 17680, 17700, 17728, 17818, 17920, 17930, 17988, 18000, 18433, 18436,
+        18448, 18496, 18501, 18516, 18530, 18688, 18705, 18756, 18768, 18793, 18948, 20480, 20482, 20485, 20488, 20497,
+        20500, 20512, 20520, 20545, 20548, 20560, 20608, 20737, 20740, 20752, 20757, 20800, 20802, 20992, 21060, 21162,
+        21505, 21508, 21520, 21537, 21568, 21600, 21633, 21665, 21760, 21768, 21888, 21896, 22049, 22120, 22177, 22528,
+        22548, 22593, 22608, 22681, 22810, 22848, 22850, 23173, 24577, 24580, 24592, 24640, 24660, 24674, 24710, 24745,
+        24832, 25124, 25162, 25234, 25600, 25622, 25872, 25920, 25925, 26020, 26625, 26730, 26917, 27142, 27220, 27234,
+        32768, 32770, 32773, 32776, 32785, 32788, 32800, 32810, 32833, 32836, 32848, 32896, 32898, 32936, 32938, 33025,
+        33028, 33030, 33040, 33088, 33105, 33113, 33280, 33312, 33408, 33410, 33440, 33448, 33793, 33796, 33808, 33810,
+        33813, 33856, 33888, 33929, 34048, 34116, 34213, 34328, 34410, 34816, 34824, 34853, 34906, 34944, 34946, 34984,
+        35078, 35362, 35456, 35464, 35478, 35496, 36865, 36868, 36880, 36928, 36950, 36996, 37120, 37154, 37220, 37462,
+        37513, 37888, 37893, 37956, 37968, 37976, 38185, 38288, 38290, 38465, 38993, 39078, 39241, 39445, 39520, 40960,
+        40962, 40968, 40970, 40992, 41002, 41120, 41297, 41305, 41382, 41472, 41474, 41480, 41514, 41600, 41632, 42048,
+        42133, 42597, 42648, 43018, 43040, 43042, 43048, 43168, 43176, 43268, 43396, 43398, 43560, 43562, 43665, 43690,
+    };
+    static const uint16_t kgrid_1bit_2048[NGRID_IQ1S] = {
+            0,     2,     5,     8,    10,    17,    21,    32,    34,    40,    42,    69,    81,    84,    86,   101,
+          128,   130,   136,   138,   149,   160,   162,   168,   170,   260,   261,   273,   276,   278,   281,   282,
+          293,   321,   326,   329,   338,   341,   346,   353,   356,   358,   360,   389,   401,   404,   406,   421,
+          512,   514,   520,   522,   533,   544,   546,   552,   554,   581,   593,   601,   612,   617,   640,   642,
+          648,   650,   657,   661,   665,   672,   674,   680,   682,  1041,  1044,  1046,  1061,  1089,  1097,  1109,
+         1114,  1124,  1125,  1169,  1177,  1189,  1281,  1284,  1285,  1286,  1301,  1304,  1306,  1321,  1344,  1349,
+         1354,  1360,  1361,  1364,  1365,  1366,  1369,  1376,  1378,  1381,  1384,  1386,  1409,  1425,  1429,  1432,
+         1434,  1441,  1444,  1445,  1446,  1449,  1556,  1561,  1601,  1604,  1616,  1618,  1621,  1624,  1632,  1633,
+         1638,  1641,  1669,  1681,  1684,  1689,  2048,  2050,  2056,  2058,  2069,  2080,  2082,  2088,  2090,  2117,
+         2129,  2134,  2149,  2176,  2178,  2184,  2186,  2197,  2208,  2210,  2216,  2218,  2309,  2321,  2324,  2329,
+         2340,  2341,  2369,  2384,  2385,  2389,  2401,  2404,  2409,  2449,  2452,  2454,  2457,  2469,  2560,  2562,
+         2568,  2570,  2581,  2592,  2594,  2600,  2602,  2629,  2641,  2649,  2657,  2661,  2688,  2690,  2693,  2696,
+         2698,  2709,  2720,  2722,  2728,  2730,  4112,  4113,  4116,  4121,  4132,  4133,  4161,  4164,  4176,  4181,
+         4184,  4193,  4196,  4197,  4201,  4241,  4244,  4246,  4257,  4261,  4353,  4356,  4358,  4361,  4368,  4370,
+         4373,  4376,  4385,  4388,  4393,  4421,  4426,  4432,  4433,  4434,  4436,  4437,  4438,  4441,  4448,  4453,
+         4484,  4498,  4501,  4513,  4516,  4625,  4628,  4630,  4645,  4672,  4678,  4681,  4690,  4693,  4696,  4698,
+         4708,  4710,  4741,  4753,  4756,  4758,  4773,  5121,  5126,  5129,  5140,  5141,  5144,  5145,  5153,  5158,
+         5185,  5189,  5190,  5192,  5194,  5201,  5204,  5205,  5206,  5209,  5218,  5221,  5224,  5252,  5257,  5264,
+         5268,  5269,  5272,  5273,  5274,  5281,  5284,  5285,  5289,  5378,  5381,  5386,  5393,  5396,  5397,  5398,
+         5401,  5408,  5410,  5413,  5416,  5418,  5441,  5444,  5445,  5446,  5457,  5458,  5460,  5461,  5462,  5465,
+         5466,  5473,  5476,  5477,  5478,  5481,  5504,  5506,  5508,  5509,  5512,  5514,  5520,  5521,  5524,  5525,
+         5526,  5529,  5530,  5536,  5538,  5541,  5633,  5636,  5637,  5638,  5653,  5654,  5656,  5658,  5665,  5670,
+         5696,  5698,  5700,  5701,  5704,  5706,  5713,  5717,  5718,  5720,  5721,  5729,  5732,  5733,  5736,  5737,
+         5738,  5766,  5770,  5778,  5781,  5796,  5801,  6161,  6166,  6181,  6209,  6212,  6214,  6217,  6224,  6229,
+         6232,  6234,  6240,  6241,  6244,  6246,  6249,  6277,  6289,  6292,  6309,  6416,  6418,  6421,  6426,  6433,
+         6437,  6466,  6468,  6469,  6472,  6481,  6484,  6485,  6486,  6489,  6490,  6496,  6501,  6506,  6537,  6545,
+         6546,  6549,  6552,  6561,  6566,  6569,  6665,  6678,  6692,  6694,  6724,  6726,  6729,  6736,  6738,  6741,
+         6744,  6753,  6758,  6761,  6789,  6801,  6806,  6810,  8192,  8194,  8200,  8202,  8213,  8224,  8226,  8229,
+         8232,  8234,  8261,  8273,  8281,  8289,  8293,  8320,  8322,  8328,  8330,  8341,  8352,  8354,  8357,  8360,
+         8362,  8453,  8465,  8468,  8473,  8485,  8514,  8516,  8521,  8533,  8536,  8538,  8545,  8548,  8549,  8550,
+         8581,  8592,  8598,  8601,  8613,  8705,  8712,  8714,  8721,  8725,  8736,  8738,  8744,  8746,  8773,  8785,
+         8790,  8793,  8805,  8833,  8840,  8842,  8849,  8853,  8864,  8866,  8872,  8874,  9221,  9236,  9238,  9241,
+         9253,  9284,  9285,  9286,  9289,  9298,  9301,  9304,  9306,  9318,  9349,  9361,  9364,  9369,  9377,  9381,
+         9481,  9493,  9505,  9513,  9536,  9541,  9544,  9553,  9556,  9557,  9561,  9570,  9573,  9576,  9609,  9616,
+         9620,  9621,  9624,  9626,  9633,  9636,  9638,  9641,  9733,  9744,  9746,  9753,  9765,  9793,  9801,  9813,
+         9824,  9825,  9833,  9860,  9862,  9872,  9882, 10240, 10242, 10248, 10250, 10261, 10272, 10274, 10280, 10282,
+        10309, 10321, 10324, 10341, 10368, 10370, 10376, 10378, 10400, 10402, 10408, 10410, 10505, 10513, 10516, 10521,
+        10533, 10566, 10569, 10578, 10581, 10593, 10596, 10598, 10601, 10629, 10640, 10646, 10649, 10660, 10661, 10752,
+        10754, 10760, 10762, 10784, 10786, 10792, 10794, 10821, 10833, 10838, 10841, 10853, 10880, 10882, 10888, 10890,
+        10901, 10912, 10914, 10920, 10922, 16389, 16401, 16406, 16421, 16457, 16466, 16469, 16472, 16474, 16481, 16484,
+        16486, 16532, 16537, 16545, 16550, 16640, 16641, 16644, 16646, 16649, 16658, 16661, 16662, 16664, 16666, 16673,
+        16678, 16681, 16709, 16712, 16714, 16721, 16724, 16725, 16726, 16729, 16730, 16741, 16744, 16746, 16769, 16772,
+        16774, 16784, 16786, 16789, 16800, 16801, 16802, 16901, 16913, 16916, 16918, 16933, 16961, 16978, 16981, 16986,
+        16996, 17001, 17033, 17044, 17061, 17409, 17429, 17433, 17449, 17477, 17480, 17482, 17489, 17492, 17493, 17494,
+        17505, 17506, 17509, 17512, 17514, 17537, 17542, 17545, 17552, 17554, 17557, 17568, 17569, 17577, 17665, 17666,
+        17669, 17674, 17681, 17684, 17685, 17686, 17689, 17696, 17701, 17706, 17729, 17732, 17733, 17734, 17737, 17744,
+        17745, 17748, 17749, 17750, 17752, 17753, 17761, 17764, 17765, 17766, 17769, 17794, 17796, 17797, 17800, 17809,
+        17812, 17813, 17814, 17817, 17818, 17829, 17832, 17834, 17921, 17925, 17929, 17940, 17941, 17944, 17946, 17953,
+        17956, 17961, 17984, 17986, 17989, 17992, 18000, 18001, 18002, 18005, 18006, 18009, 18018, 18021, 18024, 18049,
+        18053, 18058, 18068, 18069, 18081, 18084, 18086, 18437, 18449, 18453, 18458, 18469, 18498, 18505, 18512, 18517,
+        18520, 18529, 18532, 18534, 18537, 18565, 18577, 18580, 18582, 18585, 18597, 18689, 18693, 18694, 18698, 18704,
+        18708, 18709, 18712, 18721, 18724, 18726, 18752, 18757, 18762, 18769, 18770, 18772, 18773, 18774, 18777, 18784,
+        18786, 18789, 18790, 18794, 18822, 18825, 18834, 18837, 18838, 18840, 18849, 18852, 18854, 18857, 18966, 19012,
+        19014, 19017, 19029, 19032, 19034, 19044, 19049, 19092, 19109, 20481, 20484, 20485, 20486, 20489, 20498, 20501,
+        20506, 20513, 20516, 20521, 20544, 20549, 20552, 20561, 20564, 20565, 20566, 20569, 20581, 20584, 20614, 20617,
+        20629, 20632, 20640, 20641, 20646, 20649, 20741, 20744, 20745, 20746, 20753, 20756, 20757, 20758, 20760, 20761,
+        20768, 20773, 20774, 20776, 20778, 20801, 20804, 20805, 20806, 20809, 20816, 20817, 20818, 20820, 20821, 20822,
+        20824, 20825, 20826, 20833, 20836, 20837, 20838, 20841, 20866, 20869, 20881, 20884, 20885, 20886, 20889, 20896,
+        20901, 20906, 20993, 20998, 21010, 21013, 21018, 21025, 21028, 21058, 21061, 21066, 21073, 21076, 21077, 21078,
+        21081, 21090, 21093, 21125, 21136, 21138, 21141, 21145, 21146, 21156, 21508, 21509, 21521, 21524, 21525, 21526,
+        21528, 21529, 21537, 21541, 21544, 21546, 21569, 21572, 21573, 21574, 21577, 21578, 21584, 21585, 21588, 21589,
+        21590, 21592, 21593, 21594, 21601, 21602, 21604, 21605, 21606, 21609, 21632, 21640, 21642, 21649, 21652, 21653,
+        21654, 21657, 21665, 21668, 21669, 21674, 21761, 21762, 21764, 21765, 21766, 21769, 21776, 21777, 21778, 21780,
+        21781, 21782, 21785, 21786, 21793, 21796, 21797, 21798, 21801, 21824, 21825, 21826, 21828, 21829, 21830, 21832,
+        21833, 21840, 21841, 21842, 21844, 21845, 21846, 21848, 21849, 21850, 21856, 21857, 21860, 21861, 21862, 21864,
+        21865, 21866, 21889, 21892, 21893, 21897, 21898, 21904, 21905, 21908, 21909, 21910, 21912, 21913, 21921, 21924,
+        21925, 21926, 21929, 22016, 22017, 22018, 22020, 22022, 22024, 22025, 22033, 22036, 22037, 22040, 22041, 22048,
+        22049, 22050, 22052, 22053, 22054, 22056, 22057, 22081, 22085, 22086, 22088, 22089, 22090, 22096, 22097, 22098,
+        22100, 22101, 22102, 22104, 22105, 22106, 22113, 22116, 22117, 22121, 22146, 22149, 22150, 22152, 22153, 22154,
+        22161, 22165, 22170, 22178, 22181, 22182, 22184, 22185, 22532, 22533, 22534, 22537, 22544, 22549, 22552, 22561,
+        22570, 22597, 22600, 22602, 22609, 22612, 22613, 22614, 22616, 22617, 22624, 22626, 22628, 22629, 22658, 22665,
+        22672, 22674, 22677, 22680, 22689, 22697, 22785, 22786, 22789, 22794, 22801, 22804, 22805, 22806, 22809, 22821,
+        22849, 22852, 22853, 22854, 22857, 22864, 22865, 22866, 22868, 22869, 22870, 22872, 22873, 22874, 22881, 22884,
+        22885, 22886, 22889, 22913, 22917, 22921, 22929, 22932, 22933, 22934, 22936, 22937, 22949, 23044, 23048, 23061,
+        23066, 23072, 23077, 23078, 23081, 23109, 23112, 23113, 23121, 23125, 23126, 23128, 23129, 23138, 23141, 23144,
+        23146, 23169, 23178, 23186, 23189, 23190, 23192, 23194, 23201, 24581, 24596, 24598, 24601, 24613, 24644, 24656,
+        24661, 24662, 24664, 24666, 24673, 24676, 24678, 24681, 24705, 24726, 24741, 24833, 24836, 24838, 24841, 24850,
+        24853, 24865, 24866, 24870, 24873, 24901, 24905, 24913, 24917, 24918, 24921, 24933, 24934, 24938, 24964, 24970,
+        24978, 24981, 24993, 24998, 25001, 25105, 25110, 25113, 25152, 25153, 25158, 25173, 25174, 25176, 25184, 25221,
+        25233, 25238, 25253, 25617, 25618, 25621, 25622, 25626, 25633, 25638, 25641, 25664, 25666, 25669, 25672, 25674,
+        25681, 25684, 25685, 25686, 25689, 25690, 25696, 25698, 25701, 25732, 25733, 25737, 25744, 25746, 25748, 25749,
+        25750, 25752, 25754, 25761, 25764, 25769, 25861, 25864, 25866, 25873, 25877, 25878, 25881, 25924, 25925, 25926,
+        25929, 25936, 25937, 25940, 25941, 25942, 25945, 25953, 25956, 25957, 25958, 25961, 25990, 25993, 25994, 26001,
+        26005, 26006, 26009, 26010, 26018, 26021, 26022, 26024, 26114, 26121, 26133, 26144, 26150, 26152, 26153, 26176,
+        26181, 26184, 26186, 26193, 26196, 26197, 26198, 26200, 26202, 26208, 26213, 26216, 26240, 26242, 26245, 26250,
+        26260, 26262, 26264, 26265, 26272, 26276, 26278, 26282, 26646, 26649, 26661, 26689, 26706, 26709, 26714, 26721,
+        26729, 26757, 26769, 26776, 26790, 26881, 26884, 26896, 26901, 26913, 26916, 26918, 26921, 26944, 26945, 26949,
+        26950, 26952, 26961, 26964, 26965, 26966, 26969, 26976, 26981, 26986, 27010, 27012, 27018, 27029, 27041, 27044,
+        27045, 27049, 27153, 27158, 27160, 27201, 27204, 27209, 27216, 27221, 27224, 27226, 27236, 27237, 27241, 27270,
+        27284, 27288, 27290, 27302, 32768, 32770, 32776, 32778, 32800, 32802, 32808, 32810, 32837, 32848, 32849, 32852,
+        32854, 32857, 32869, 32896, 32898, 32904, 32906, 32917, 32928, 32930, 32936, 32938, 33029, 33041, 33044, 33046,
+        33049, 33061, 33089, 33092, 33097, 33104, 33106, 33109, 33110, 33112, 33113, 33124, 33126, 33129, 33157, 33161,
+        33172, 33174, 33177, 33189, 33280, 33282, 33288, 33290, 33301, 33312, 33314, 33320, 33322, 33361, 33364, 33369,
+        33381, 33408, 33410, 33416, 33418, 33429, 33440, 33442, 33448, 33450, 33812, 33817, 33857, 33860, 33873, 33877,
+        33882, 33889, 33892, 33897, 33940, 33945, 34049, 34057, 34066, 34069, 34074, 34086, 34089, 34112, 34113, 34117,
+        34120, 34129, 34132, 34133, 34134, 34137, 34138, 34149, 34150, 34152, 34154, 34177, 34180, 34182, 34185, 34192,
+        34194, 34197, 34200, 34214, 34321, 34326, 34329, 34341, 34369, 34372, 34377, 34378, 34384, 34389, 34393, 34394,
+        34401, 34406, 34410, 34437, 34449, 34458, 34468, 34816, 34818, 34824, 34826, 34837, 34848, 34850, 34856, 34858,
+        34881, 34885, 34897, 34900, 34905, 34917, 34921, 34944, 34946, 34952, 34954, 34965, 34976, 34978, 34984, 34986,
+        35077, 35078, 35089, 35092, 35094, 35109, 35137, 35140, 35142, 35145, 35152, 35154, 35157, 35162, 35169, 35172,
+        35205, 35222, 35225, 35237, 35328, 35330, 35336, 35338, 35349, 35360, 35362, 35368, 35370, 35397, 35409, 35412,
+        35414, 35456, 35458, 35464, 35466, 35477, 35488, 35490, 35496, 35498, 36869, 36881, 36886, 36888, 36889, 36901,
+        36929, 36934, 36937, 36949, 36952, 36954, 36969, 36970, 36997, 37009, 37012, 37014, 37017, 37029, 37121, 37124,
+        37126, 37129, 37136, 37141, 37144, 37146, 37153, 37156, 37158, 37161, 37184, 37189, 37200, 37201, 37204, 37205,
+        37206, 37209, 37218, 37221, 37252, 37254, 37266, 37269, 37272, 37281, 37284, 37286, 37289, 37381, 37393, 37396,
+        37401, 37413, 37444, 37446, 37449, 37456, 37458, 37461, 37464, 37478, 37481, 37509, 37524, 37526, 37545, 37889,
+        37892, 37894, 37904, 37909, 37912, 37926, 37952, 37962, 37969, 37972, 37973, 37974, 37976, 37977, 37984, 37985,
+        37986, 37989, 38020, 38022, 38034, 38036, 38037, 38040, 38049, 38057, 38144, 38149, 38152, 38154, 38160, 38161,
+        38164, 38165, 38166, 38169, 38177, 38181, 38185, 38186, 38209, 38212, 38213, 38214, 38217, 38224, 38225, 38226,
+        38228, 38229, 38230, 38232, 38233, 38234, 38241, 38244, 38245, 38246, 38249, 38273, 38277, 38280, 38289, 38290,
+        38292, 38293, 38294, 38297, 38298, 38304, 38306, 38309, 38312, 38314, 38401, 38404, 38416, 38421, 38425, 38432,
+        38438, 38441, 38469, 38472, 38473, 38481, 38482, 38485, 38486, 38489, 38501, 38504, 38530, 38532, 38537, 38538,
+        38546, 38548, 38549, 38564, 38566, 38569, 38917, 38934, 38937, 38949, 38977, 38982, 38992, 38994, 38997, 38998,
+        39002, 39012, 39013, 39045, 39057, 39062, 39065, 39077, 39172, 39174, 39177, 39184, 39186, 39189, 39192, 39194,
+        39200, 39201, 39204, 39206, 39232, 39234, 39237, 39240, 39242, 39249, 39252, 39253, 39254, 39257, 39266, 39269,
+        39270, 39274, 39297, 39300, 39312, 39314, 39317, 39322, 39329, 39334, 39429, 39445, 39461, 39492, 39494, 39497,
+        39504, 39509, 39512, 39521, 39557, 39569, 39572, 39573, 39574, 40960, 40962, 40968, 40970, 40981, 40992, 40994,
+        41000, 41002, 41029, 41041, 41044, 41046, 41049, 41088, 41090, 41096, 41098, 41109, 41120, 41122, 41128, 41130,
+        41221, 41225, 41233, 41236, 41238, 41241, 41242, 41286, 41289, 41297, 41301, 41304, 41306, 41313, 41316, 41349,
+        41360, 41362, 41366, 41369, 41474, 41480, 41482, 41488, 41497, 41506, 41512, 41514, 41541, 41553, 41558, 41561,
+        41573, 41600, 41602, 41608, 41610, 41621, 41632, 41634, 41640, 41642, 42009, 42021, 42049, 42052, 42064, 42068,
+        42069, 42072, 42074, 42081, 42085, 42086, 42088, 42089, 42117, 42246, 42249, 42256, 42258, 42261, 42264, 42278,
+        42281, 42306, 42309, 42321, 42324, 42325, 42326, 42329, 42341, 42346, 42369, 42372, 42373, 42374, 42377, 42386,
+        42389, 42392, 42501, 42513, 42518, 42522, 42529, 42533, 42564, 42566, 42570, 42578, 42581, 42582, 42584, 42592,
+        42594, 42630, 42640, 42645, 42646, 42649, 42657, 42660, 42662, 43008, 43010, 43016, 43018, 43040, 43042, 43048,
+        43050, 43089, 43092, 43094, 43097, 43136, 43138, 43144, 43146, 43157, 43168, 43170, 43176, 43178, 43269, 43284,
+        43289, 43297, 43301, 43329, 43344, 43349, 43354, 43361, 43366, 43369, 43408, 43414, 43520, 43522, 43528, 43530,
+        43552, 43554, 43560, 43562, 43601, 43604, 43606, 43648, 43650, 43656, 43658, 43669, 43680, 43682, 43688, 43690,
+    };
+    static const uint16_t kgrid_2bit_1024[1024] = {
+            0,     2,     5,     8,    10,    17,    20,    22,    25,    32,    34,    37,    40,    65,    68,    70,
+           73,    80,    82,    85,    88,    97,   100,   102,   105,   128,   130,   133,   136,   145,   148,   160,
+          165,   170,   257,   260,   262,   265,   272,   274,   277,   280,   289,   292,   320,   322,   325,   328,
+          337,   340,   342,   345,   352,   357,   360,   385,   388,   400,   402,   405,   417,   420,   512,   514,
+          517,   520,   529,   532,   544,   554,   577,   580,   582,   585,   592,   597,   640,   645,   650,   660,
+          674,  1025,  1028,  1030,  1033,  1040,  1042,  1045,  1048,  1057,  1060,  1062,  1065,  1088,  1090,  1093,
+         1096,  1098,  1105,  1108,  1110,  1113,  1120,  1122,  1125,  1153,  1156,  1158,  1161,  1168,  1173,  1176,
+         1185,  1188,  1280,  1282,  1285,  1288,  1290,  1297,  1300,  1302,  1305,  1312,  1317,  1320,  1345,  1348,
+         1350,  1353,  1360,  1362,  1365,  1368,  1377,  1380,  1408,  1410,  1413,  1416,  1425,  1428,  1440,  1537,
+         1540,  1542,  1545,  1552,  1557,  1600,  1605,  1608,  1617,  1620,  1632,  1665,  1668,  1680,  2048,  2050,
+         2053,  2056,  2065,  2068,  2070,  2073,  2080,  2085,  2090,  2113,  2116,  2118,  2121,  2128,  2130,  2133,
+         2136,  2145,  2148,  2176,  2181,  2196,  2218,  2305,  2308,  2320,  2322,  2325,  2328,  2337,  2368,  2373,
+         2376,  2385,  2388,  2400,  2433,  2448,  2560,  2577,  2580,  2594,  2600,  2602,  2640,  2713,  4097,  4100,
+         4102,  4105,  4112,  4114,  4117,  4120,  4129,  4132,  4134,  4160,  4162,  4165,  4168,  4177,  4180,  4182,
+         4185,  4192,  4194,  4197,  4200,  4225,  4228,  4230,  4240,  4245,  4248,  4257,  4260,  4352,  4354,  4357,
+         4360,  4362,  4369,  4372,  4374,  4377,  4384,  4386,  4389,  4392,  4417,  4420,  4422,  4425,  4432,  4434,
+         4437,  4440,  4449,  4452,  4480,  4482,  4485,  4488,  4497,  4500,  4609,  4612,  4617,  4624,  4629,  4641,
+         4644,  4672,  4677,  4689,  4692,  4737,  4740,  4752,  5120,  5122,  5125,  5128,  5137,  5140,  5142,  5145,
+         5152,  5157,  5160,  5185,  5188,  5190,  5193,  5200,  5202,  5205,  5208,  5217,  5220,  5248,  5250,  5253,
+         5256,  5265,  5268,  5280,  5377,  5380,  5382,  5385,  5392,  5394,  5397,  5400,  5409,  5412,  5440,  5442,
+         5445,  5448,  5457,  5460,  5472,  5505,  5508,  5520,  5632,  5637,  5640,  5649,  5652,  5664,  5697,  5700,
+         5712,  5760,  5802,  6145,  6148,  6150,  6153,  6160,  6165,  6168,  6177,  6208,  6210,  6213,  6216,  6225,
+         6228,  6240,  6273,  6276,  6400,  6402,  6405,  6408,  6417,  6420,  6432,  6465,  6468,  6480,  6505,  6562,
+         6660,  6672,  6720,  6742,  8192,  8194,  8197,  8200,  8209,  8212,  8214,  8217,  8224,  8229,  8234,  8257,
+         8260,  8272,  8274,  8277,  8292,  8320,  8330,  8340,  8362,  8449,  8452,  8464,  8466,  8469,  8481,  8512,
+         8514,  8517,  8529,  8532,  8544,  8577,  8580,  8592,  8704,  8714,  8738,  8744,  8746,  8772,  8784,  8840,
+         8842,  8872,  9217,  9220,  9222,  9225,  9232,  9237,  9240,  9249,  9252,  9280,  9282,  9285,  9288,  9297,
+         9300,  9312,  9345,  9348,  9360,  9472,  9477,  9480,  9489,  9492,  9504,  9537,  9540,  9552,  9574,  9600,
+         9729,  9732,  9744,  9792,  9817, 10240, 10245, 10257, 10260, 10305, 10308, 10320, 10378, 10410, 10497, 10500,
+        10512, 10645, 10762, 10786, 10852, 10888, 10890, 16385, 16388, 16390, 16393, 16400, 16402, 16405, 16408, 16410,
+        16417, 16420, 16422, 16448, 16450, 16453, 16456, 16458, 16465, 16468, 16470, 16473, 16480, 16482, 16485, 16513,
+        16516, 16528, 16533, 16536, 16545, 16548, 16640, 16642, 16645, 16648, 16657, 16660, 16662, 16665, 16672, 16674,
+        16677, 16705, 16708, 16710, 16713, 16720, 16722, 16725, 16728, 16737, 16740, 16768, 16770, 16773, 16776, 16785,
+        16788, 16800, 16897, 16900, 16912, 16914, 16917, 16920, 16932, 16960, 16965, 16968, 16977, 16980, 16992, 17025,
+        17028, 17408, 17410, 17413, 17416, 17418, 17425, 17428, 17430, 17433, 17440, 17442, 17445, 17448, 17473, 17476,
+        17478, 17481, 17488, 17490, 17493, 17496, 17505, 17508, 17536, 17538, 17541, 17544, 17553, 17556, 17568, 17665,
+        17668, 17670, 17673, 17680, 17682, 17685, 17688, 17697, 17700, 17728, 17730, 17733, 17736, 17745, 17748, 17760,
+        17770, 17793, 17796, 17808, 17920, 17922, 17925, 17928, 17937, 17940, 17952, 17985, 17988, 18000, 18048, 18085,
+        18433, 18436, 18441, 18448, 18450, 18453, 18456, 18465, 18468, 18496, 18498, 18501, 18504, 18513, 18516, 18528,
+        18564, 18576, 18688, 18690, 18693, 18696, 18705, 18708, 18720, 18753, 18756, 18768, 18816, 18838, 18945, 18948,
+        18960, 19008, 20480, 20482, 20485, 20488, 20497, 20500, 20502, 20505, 20512, 20514, 20517, 20520, 20545, 20548,
+        20550, 20553, 20560, 20562, 20565, 20568, 20577, 20580, 20608, 20610, 20613, 20616, 20625, 20628, 20737, 20740,
+        20742, 20745, 20752, 20754, 20757, 20760, 20769, 20772, 20800, 20802, 20805, 20808, 20817, 20820, 20832, 20865,
+        20868, 20880, 20992, 20997, 21000, 21009, 21012, 21024, 21057, 21060, 21072, 21097, 21120, 21505, 21508, 21510,
+        21513, 21520, 21522, 21525, 21528, 21537, 21540, 21568, 21570, 21573, 21576, 21585, 21588, 21600, 21633, 21636,
+        21648, 21760, 21762, 21765, 21768, 21777, 21780, 21792, 21825, 21828, 21840, 21888, 22017, 22020, 22032, 22054,
+        22080, 22528, 22530, 22533, 22536, 22545, 22548, 22560, 22593, 22596, 22608, 22618, 22656, 22785, 22788, 22800,
+        22848, 23040, 23065, 23173, 23208, 24577, 24580, 24582, 24592, 24594, 24597, 24600, 24609, 24612, 24640, 24645,
+        24648, 24657, 24660, 24672, 24708, 24720, 24832, 24834, 24837, 24840, 24849, 24852, 24864, 24897, 24900, 24912,
+        24960, 24985, 25092, 25104, 25152, 25174, 25249, 25600, 25605, 25608, 25617, 25620, 25632, 25665, 25668, 25680,
+        25728, 25857, 25860, 25872, 25920, 25930, 25960, 26002, 26112, 26260, 26625, 26628, 26640, 26725, 26776, 26880,
+        26922, 27202, 27297, 32768, 32770, 32773, 32776, 32785, 32788, 32793, 32800, 32805, 32833, 32836, 32848, 32850,
+        32853, 32856, 32865, 32896, 32901, 32913, 32916, 33025, 33028, 33033, 33040, 33042, 33045, 33048, 33057, 33060,
+        33088, 33090, 33093, 33096, 33105, 33108, 33153, 33156, 33168, 33193, 33280, 33285, 33290, 33297, 33300, 33345,
+        33348, 33360, 33793, 33796, 33798, 33801, 33808, 33810, 33813, 33816, 33825, 33856, 33858, 33861, 33864, 33873,
+        33876, 33888, 33921, 33924, 33936, 34048, 34050, 34053, 34056, 34065, 34068, 34080, 34113, 34116, 34128, 34176,
+        34186, 34305, 34308, 34320, 34345, 34368, 34816, 34821, 34833, 34836, 34881, 34884, 34896, 34978, 35073, 35076,
+        35136, 35173, 35362, 35416, 35418, 35458, 35490, 36865, 36868, 36873, 36880, 36882, 36885, 36888, 36900, 36928,
+        36930, 36933, 36936, 36945, 36948, 36960, 36993, 36996, 37008, 37120, 37125, 37137, 37140, 37185, 37188, 37200,
+        37210, 37377, 37380, 37392, 37440, 37542, 37888, 37890, 37893, 37896, 37905, 37908, 37920, 37953, 37956, 37968,
+        38016, 38038, 38145, 38148, 38160, 38208, 38296, 38305, 38400, 38470, 38500, 38913, 38916, 38928, 38950, 38976,
+        39081, 39168, 39241, 39250, 39568, 40960, 40965, 40970, 40980, 40994, 41002, 41025, 41028, 41040, 41122, 41130,
+        41280, 41317, 41474, 41482, 41506, 41512, 41514, 41602, 41608, 41610, 41640, 41985, 41988, 42000, 42048, 42121,
+        42148, 42240, 42265, 42577, 43018, 43048, 43170, 43348, 43398, 43528, 43530, 43552, 43554, 43560, 43656, 43690,
+    };
+
+    const int kmap_size = 43692;
+    //const int nwant = type == GGML_TYPE_IQ1_S ? 3 : 2;
+    const int nwant = type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? 3 : type == GGML_TYPE_IQ2_S ? 1 : 2;
+    const uint16_t * kgrid = type == GGML_TYPE_IQ2_XXS ? kgrid_2bit_256 :
+                             type == GGML_TYPE_IQ2_XS  ? kgrid_2bit_512 :
+                             type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? kgrid_1bit_2048 : kgrid_2bit_1024;
+    uint64_t * kgrid_q2xs;
+    int      * kmap_q2xs;
+    uint16_t * kneighbors_q2xs;
+
+    //printf("================================================================= %s(grid_size = %d)\n", __func__, grid_size);
+    uint64_t * the_grid = (uint64_t *)malloc(grid_size*sizeof(uint64_t));
+    for (int k = 0; k < grid_size; ++k) {
+        int8_t * pos = (int8_t *)(the_grid + k);
+        for (int i = 0; i < 8; ++i) {
+            int l = (kgrid[k] >> 2*i) & 0x3;
+            pos[i] = 2*l + 1;
+        }
+    }
+    kgrid_q2xs = the_grid;
+    iq2_data[gindex].grid = the_grid;
+    kmap_q2xs = (int *)malloc(kmap_size*sizeof(int));
+    iq2_data[gindex].map = kmap_q2xs;
+    for (int i = 0; i < kmap_size; ++i) kmap_q2xs[i] = -1;
+    uint64_t aux64;
+    uint8_t * aux8 = (uint8_t *)&aux64;
+    for (int i = 0; i < grid_size; ++i) {
+        aux64 = kgrid_q2xs[i];
+        uint16_t index = 0;
+        for (int k=0; k<8; ++k) {
+            uint16_t q = (aux8[k] - 1)/2;
+            index |= (q << 2*k);
+        }
+        kmap_q2xs[index] = i;
+    }
+    int8_t pos[8];
+    int * dist2 = (int *)malloc(2*grid_size*sizeof(int));
+    int num_neighbors = 0, num_not_in_map = 0;
+    for (int i = 0; i < kmap_size; ++i) {
+        if (kmap_q2xs[i] >= 0) continue;
+        ++num_not_in_map;
+        for (int k = 0; k < 8; ++k) {
+            int l = (i >> 2*k) & 0x3;
+            pos[k] = 2*l + 1;
+        }
+        for (int j = 0; j < grid_size; ++j) {
+            const int8_t * pg = (const int8_t *)(kgrid_q2xs + j);
+            int d2 = 0;
+            for (int k = 0; k < 8; ++k) d2 += (pg[k] - pos[k])*(pg[k] - pos[k]);
+            dist2[2*j+0] = d2;
+            dist2[2*j+1] = j;
+        }
+        qsort(dist2, grid_size, 2*sizeof(int), iq2_compare_func);
+        int n = 0; int d2 = dist2[0];
+        int nhave = 1;
+        for (int j = 0; j < grid_size; ++j) {
+            if (dist2[2*j] > d2) {
+                if (nhave == nwant) break;
+                d2 = dist2[2*j];
+                ++nhave;
+            }
+            ++n;
+        }
+        num_neighbors += n;
+    }
+    //printf("%s: %d neighbours in total\n", __func__, num_neighbors);
+    kneighbors_q2xs = (uint16_t *)malloc((num_neighbors + num_not_in_map)*sizeof(uint16_t));
+    iq2_data[gindex].neighbours = kneighbors_q2xs;
+    int counter = 0;
+    for (int i = 0; i < kmap_size; ++i) {
+        if (kmap_q2xs[i] >= 0) continue;
+        for (int k = 0; k < 8; ++k) {
+            int l = (i >> 2*k) & 0x3;
+            pos[k] = 2*l + 1;
+        }
+        for (int j = 0; j < grid_size; ++j) {
+            const int8_t * pg = (const int8_t *)(kgrid_q2xs + j);
+            int d2 = 0;
+            for (int k = 0; k < 8; ++k) d2 += (pg[k] - pos[k])*(pg[k] - pos[k]);
+            dist2[2*j+0] = d2;
+            dist2[2*j+1] = j;
+        }
+        qsort(dist2, grid_size, 2*sizeof(int), iq2_compare_func);
+        kmap_q2xs[i] = -(counter + 1);
+        int d2 = dist2[0];
+        uint16_t * start = &kneighbors_q2xs[counter++];
+        int n = 0, nhave = 1;
+        for (int j = 0; j < grid_size; ++j) {
+            if (dist2[2*j] > d2) {
+                if (nhave == nwant) break;
+                d2 = dist2[2*j];
+                ++nhave;
+            }
+            kneighbors_q2xs[counter++] = dist2[2*j+1];
+            ++n;
+        }
+        *start = n;
+    }
+    free(dist2);
+}
+
+void iq2xs_free_impl(enum ggml_type type) {
+    GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M || type == GGML_TYPE_IQ2_S);
+    const int gindex = iq2_data_index(type);
+    if (iq2_data[gindex].grid) {
+        free(iq2_data[gindex].grid);       iq2_data[gindex].grid = NULL;
+        free(iq2_data[gindex].map);        iq2_data[gindex].map  = NULL;
+        free(iq2_data[gindex].neighbours); iq2_data[gindex].neighbours = NULL;
+    }
+}
+
+static int iq2_find_best_neighbour(const uint16_t * restrict neighbours, const uint64_t * restrict grid,
+        const float * restrict xval, const float * restrict weight, float scale, int8_t * restrict L) {
+    int num_neighbors = neighbours[0];
+    GGML_ASSERT(num_neighbors > 0);
+    float best_d2 = FLT_MAX;
+    int grid_index = -1;
+    for (int j = 1; j <= num_neighbors; ++j) {
+        const int8_t * pg = (const int8_t *)(grid + neighbours[j]);
+        float d2 = 0;
+        for (int i = 0; i < 8; ++i) {
+            float q = pg[i];
+            float diff = scale*q - xval[i];
+            d2 += weight[i]*diff*diff;
+        }
+        if (d2 < best_d2) {
+            best_d2 = d2; grid_index = neighbours[j];
+        }
+    }
+    GGML_ASSERT(grid_index >= 0);
+    const int8_t * pg = (const int8_t *)(grid + grid_index);
+    for (int i = 0; i < 8; ++i) L[i] = (pg[i] - 1)/2;
+    return grid_index;
+}
+
+static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) {
+
+    const int gindex = iq2_data_index(GGML_TYPE_IQ2_XXS);
+
+    const uint64_t * kgrid_q2xs      = iq2_data[gindex].grid;
+    const int      * kmap_q2xs       = iq2_data[gindex].map;
+    const uint16_t * kneighbors_q2xs = iq2_data[gindex].neighbours;
+
+    GGML_ASSERT(quant_weights   && "missing quantization weights");
+    GGML_ASSERT(kgrid_q2xs      && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kmap_q2xs       && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(n%QK_K == 0);
+
+    const int kMaxQ = 3;
+
+    const int nbl = n/QK_K;
+
+    block_iq2_xxs * y = vy;
+
+    float scales[QK_K/32];
+    float weight[32];
+    float xval[32];
+    int8_t L[32];
+    int8_t Laux[32];
+    float  waux[32];
+    uint8_t block_signs[4];
+    uint32_t q2[2*(QK_K/32)];
+
+    for (int ibl = 0; ibl < nbl; ++ibl) {
+
+        y[ibl].d = GGML_FP32_TO_FP16(0.f);
+        memset(q2, 0, QK_K/4);
+
+        float max_scale = 0;
+
+        const float * xbl = x + QK_K*ibl;
+        float sumx2 = 0;
+        for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i];
+        float sigma2 = sumx2/QK_K;
+
+        for (int ib = 0; ib < QK_K/32; ++ib) {
+            const float * xb = xbl + 32*ib;
+            const float * qw = quant_weights + QK_K*ibl + 32*ib;
+            for (int i = 0; i < 32; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
+            for (int i = 0; i < 32; ++i) waux[i] = sqrtf(weight[i]);
+            for (int k = 0; k < 4; ++k) {
+                int nflip = 0;
+                uint8_t s = 0;
+                for (int i = 0; i < 8; ++i) {
+                    if (xb[8*k + i] >= 0) xval[8*k + i] = xb[8*k + i];
+                    else {
+                        xval[8*k + i] = -xb[8*k + i]; ++nflip; s |= (1 << i);
+                    }
+                }
+                if (nflip%2) {
+                    int imin = 0; float min = weight[8*k+imin]*xb[8*k+imin]*xb[8*k+imin];
+                    for (int i = 1; i < 8; ++i) {
+                        float ax = weight[8*k+i]*xb[8*k+i]*xb[8*k+i];
+                        if (ax < min) {
+                            min = ax; imin = i;
+                        }
+                    }
+                    xval[8*k+imin] = -xval[8*k+imin];
+                    s ^= (1 << imin);
+                }
+                block_signs[k] = s & 127;
+            }
+            float max = xval[0];
+            for (int i = 1; i < 32; ++i) max = MAX(max, xval[i]);
+            if (!max) {
+                scales[ib] = 0;
+                memset(L, 0, 32);
+                continue;
+            }
+            float scale = make_qp_quants(32, kMaxQ+1, xval, (uint8_t*)L, weight);
+            float eff_max = scale*kMaxQ;
+            float best = 0;
+            for (int is = -6; is <= 6; ++is) {
+                float id = (2*kMaxQ-1+is*0.1f)/eff_max;
+                float this_scale = 1/id;
+                for (int k = 0; k < 4; ++k) {
+                    for (int i = 0; i < 8; ++i) {
+                        int l = nearest_int(0.5f*(id*xval[8*k+i]-1));
+                        Laux[8*k+i] = MAX(0, MIN(kMaxQ-1, l));
+                    }
+                    uint16_t u = 0;
+                    for (int i = 0; i < 8; ++i) u |= (Laux[8*k+i] << 2*i);
+                    int grid_index = kmap_q2xs[u];
+                    if (grid_index < 0) {
+                        const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1;
+                        grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, this_scale, Laux + 8*k);
+                    }
+                }
+                float sumqx = 0, sumq2 = 0;
+                for (int i = 0; i < 32; ++i) {
+                    float w = weight[i];
+                    float q = 2*Laux[i] + 1;
+                    sumqx += w*xval[i]*q;
+                    sumq2 += w*q*q;
+                }
+                if (sumq2 > 0 && sumqx*sumqx > best*sumq2) {
+                    scale = sumqx/sumq2; best = scale*sumqx;
+                    memcpy(L, Laux, 32);
+                }
+            }
+            if (scale > 0) {
+                float id = 1/scale;
+                for (int k = 0; k < 4; ++k) {
+                    uint16_t u = 0;
+                    for (int i = 0; i < 8; ++i) {
+                        int l = nearest_int(0.5f*(id*xval[8*k+i]-1));
+                        l = MAX(0, MIN(kMaxQ-1, l));
+                        u |= (l << 2*i);
+                    }
+                    int grid_index = kmap_q2xs[u];
+                    if (grid_index < 0) {
+                        const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1;
+                        grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, scale, L + 8*k);
+                    }
+                    const int8_t * pg = (const int8_t *)(kgrid_q2xs + grid_index);
+                    for (int i = 0; i < 8; ++i) L[8*k+i] = (pg[i] - 1)/2;
+                }
+                float sumqx = 0, sumq2 = 0;
+                for (int i = 0; i < 32; ++i) {
+                    float w = weight[i];
+                    float q = 2*L[i] + 1;
+                    sumqx += w*xval[i]*q;
+                    sumq2 += w*q*q;
+                }
+                if (sumq2 > 0) scale = sumqx/sumq2;
+            }
+            if (scale < 0) {
+                // This should never happen, but just in case, flip scale so that it is positive (we use uint's to encode the scale)
+                // and correspondingly flip quant signs.
+                scale = -scale;
+                for (int k = 0; k < 4; ++k) block_signs[k] = (~block_signs[k]) & 127;
+            }
+            for (int k = 0; k < 4; ++k) {
+                uint16_t u = 0;
+                for (int i = 0; i < 8; ++i) u |= (L[8*k+i] << 2*i);
+                int grid_index = kmap_q2xs[u];
+                if (grid_index < 0) {
+                    printf("Oops: found point %u not on grid:", u);
+                    for (int i = 0; i < 8; ++i) printf(" %d", L[8*k+i]);
+                    printf("\n");
+                    GGML_ASSERT(false);
+                }
+                q2[2*ib+0] |= (grid_index << 8*k);
+                q2[2*ib+1] |= (block_signs[k] << 7*k);
+            }
+            GGML_ASSERT(scale >= 0);
+            scales[ib] = scale;
+            max_scale = MAX(max_scale, scale);
+        }
+
+        if (!max_scale) {
+            memset(y[ibl].qs, 0, QK_K/4);
+            continue;
+        }
+
+        float d = max_scale/31;
+        y[ibl].d = GGML_FP32_TO_FP16(d);
+        float id = 1/d;
+        for (int ib = 0; ib < QK_K/32; ++ib) {
+            int l = nearest_int(0.5f*(id*scales[ib]-1));
+            l = MAX(0, MIN(15, l));
+            q2[2*ib+1] |= ((uint32_t)l << 28);
+        }
+        memcpy(y[ibl].qs, q2, QK_K/4);
+    }
+}
+
+static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) {
+
+    const int gindex = iq2_data_index(GGML_TYPE_IQ2_XS);
+
+    const uint64_t * kgrid_q2xs      = iq2_data[gindex].grid;
+    const int      * kmap_q2xs       = iq2_data[gindex].map;
+    const uint16_t * kneighbors_q2xs = iq2_data[gindex].neighbours;
+
+    GGML_ASSERT(quant_weights   && "missing quantization weights");
+    GGML_ASSERT(kmap_q2xs       && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kgrid_q2xs      && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(n%QK_K == 0);
+
+    const int kMaxQ = 3;
+
+    const int nbl = n/QK_K;
+
+    block_iq2_xs * y = vy;
+
+    float scales[QK_K/16];
+    float weight[16];
+    float xval[16];
+    int8_t L[16];
+    int8_t Laux[16];
+    float  waux[16];
+    bool   is_on_grid[2];
+    bool   is_on_grid_aux[2];
+    uint8_t block_signs[2];
+    uint16_t q2[2*(QK_K/16)];
+
+    for (int ibl = 0; ibl < nbl; ++ibl) {
+
+        y[ibl].d = GGML_FP32_TO_FP16(0.f);
+        memset(q2, 0, QK_K/4);
+        memset(y[ibl].scales, 0, QK_K/32);
+
+        float max_scale = 0;
+
+        const float * xbl = x + QK_K*ibl;
+        float sumx2 = 0;
+        for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i];
+        float sigma2 = sumx2/QK_K;
+
+        for (int ib = 0; ib < QK_K/16; ++ib) {
+            const float * xb = xbl + 16*ib;
+            const float * qw = quant_weights + QK_K*ibl + 16*ib;
+            for (int i = 0; i < 16; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
+            for (int i = 0; i < 16; ++i) waux[i] = sqrtf(weight[i]);
+            for (int k = 0; k < 2; ++k) {
+                int nflip = 0;
+                uint8_t s = 0;
+                for (int i = 0; i < 8; ++i) {
+                    if (xb[8*k + i] >= 0) xval[8*k + i] = xb[8*k + i];
+                    else {
+                        xval[8*k + i] = -xb[8*k + i]; ++nflip; s |= (1 << i);
+                    }
+                }
+                if (nflip%2) {
+                    int imin = 0; float min = weight[8*k+imin]*xb[8*k+imin]*xb[8*k+imin];
+                    for (int i = 1; i < 8; ++i) {
+                        float ax = weight[8*k+i]*xb[8*k+i]*xb[8*k+i];
+                        if (ax < min) {
+                            min = ax; imin = i;
+                        }
+                    }
+                    xval[8*k+imin] = -xval[8*k+imin];
+                    s ^= (1 << imin);
+                }
+                block_signs[k] = s & 127;
+            }
+            float max = xval[0];
+            for (int i = 1; i < 16; ++i) max = MAX(max, xval[i]);
+            if (!max) {
+                scales[ib] = 0;
+                memset(L, 0, 16);
+                continue;
+            }
+            float best = 0;
+            float scale = max/(2*kMaxQ-1);
+            is_on_grid[0] = is_on_grid[1] = true;
+            for (int is = -9; is <= 9; ++is) {
+                float id = (2*kMaxQ-1+is*0.1f)/max;
+                float this_scale = 1/id;
+                for (int k = 0; k < 2; ++k) {
+                    for (int i = 0; i < 8; ++i) {
+                        int l = nearest_int(0.5f*(id*xval[8*k+i]-1));
+                        Laux[8*k+i] = MAX(0, MIN(kMaxQ-1, l));
+                    }
+                    uint16_t u = 0;
+                    for (int i = 0; i < 8; ++i) u |= (Laux[8*k+i] << 2*i);
+                    int grid_index = kmap_q2xs[u];
+                    is_on_grid_aux[k] = true;
+                    if (grid_index < 0) {
+                        is_on_grid_aux[k] = false;
+                        const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1;
+                        grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, this_scale, Laux + 8*k);
+                    }
+                }
+                float sumqx = 0, sumq2 = 0;
+                for (int i = 0; i < 16; ++i) {
+                    float w = weight[i];
+                    float q = 2*Laux[i] + 1;
+                    sumqx += w*xval[i]*q;
+                    sumq2 += w*q*q;
+                }
+                if (sumq2 > 0 && sumqx*sumqx > best*sumq2) {
+                    scale = sumqx/sumq2; best = scale*sumqx;
+                    for (int i = 0; i < 16; ++i) L[i] = Laux[i];
+                    for (int k = 0; k <  2; ++k) is_on_grid[k] = is_on_grid_aux[k];
+                }
+            }
+            int n_not_ongrid = 0;
+            for (int k = 0; k < 2; ++k) if (!is_on_grid[k]) ++n_not_ongrid;
+            if (n_not_ongrid > 0 && scale > 0) {
+                float id = 1/scale;
+                for (int k = 0; k < 2; ++k) {
+                    if (is_on_grid[k]) continue;
+                    uint16_t u = 0;
+                    for (int i = 0; i < 8; ++i) {
+                        int l = nearest_int(0.5f*(id*xval[8*k+i]-1));
+                        l = MAX(0, MIN(kMaxQ-1, l));
+                        u |= (l << 2*i);
+                        L[8*k + i] = l;
+                    }
+                    int grid_index = kmap_q2xs[u];
+                    if (grid_index < 0) {
+                        const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1;
+                        grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, scale, L + 8*k);
+                    }
+                }
+                float sumqx = 0, sumq2 = 0;
+                for (int i = 0; i < 16; ++i) {
+                    float w = weight[i];
+                    float q = 2*L[i] + 1;
+                    sumqx += w*xval[i]*q;
+                    sumq2 += w*q*q;
+                }
+                if (sumq2 > 0) scale = sumqx/sumq2;
+            }
+            if (scale < 0) {
+                scale = -scale;
+                for (int k = 0; k < 2; ++k) block_signs[k] = (~block_signs[k]) & 127;
+            }
+            for (int k = 0; k < 2; ++k) {
+                uint16_t u = 0;
+                for (int i = 0; i < 8; ++i) u |= (L[8*k+i] << 2*i);
+                int grid_index = kmap_q2xs[u];
+                if (grid_index < 0) {
+                    printf("Oops: found point %u not on grid:", u);
+                    for (int i = 0; i < 8; ++i) printf(" %d", L[8*k+i]);
+                    printf("\n");
+                    GGML_ASSERT(false);
+                }
+                q2[2*ib+k] = grid_index | (block_signs[k] << 9);
+            }
+            GGML_ASSERT(scale >= 0);
+            scales[ib] = scale;
+            max_scale = MAX(max_scale, scale);
+        }
+
+        if (!max_scale) {
+            memset(y[ibl].qs, 0, QK_K/4);
+            continue;
+        }
+
+        float d = max_scale/31;
+        y[ibl].d = GGML_FP32_TO_FP16(d);
+        float id = 1/d;
+        for (int ib = 0; ib < QK_K/16; ++ib) {
+            int l = nearest_int(0.5f*(id*scales[ib]-1));
+            l = MAX(0, MIN(15, l));
+            if (ib%2 == 0) y[ibl].scales[ib/2] = l;
+            else y[ibl].scales[ib/2] |= (l << 4);
+        }
+        memcpy(y[ibl].qs, q2, QK_K/4);
+
+    }
+}
+
+size_t quantize_iq2_xxs(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
+    GGML_ASSERT(n_per_row%QK_K == 0);
+    int nblock = n_per_row/QK_K;
+    char * qrow = (char *)dst;
+    for (int row = 0; row < nrow; ++row) {
+        quantize_row_iq2_xxs_impl(src, qrow, n_per_row, quant_weights);
+        src += n_per_row;
+        qrow += nblock*sizeof(block_iq2_xxs);
+    }
+    return nrow * nblock * sizeof(block_iq2_xxs);
+}
+
+size_t quantize_iq2_xs(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
+    GGML_ASSERT(n_per_row%QK_K == 0);
+    int nblock = n_per_row/QK_K;
+    char * qrow = (char *)dst;
+    for (int row = 0; row < nrow; ++row) {
+        quantize_row_iq2_xs_impl(src, qrow, n_per_row, quant_weights);
+        src += n_per_row;
+        qrow += nblock*sizeof(block_iq2_xs);
+    }
+    return nrow * nblock * sizeof(block_iq2_xs);
+}
+
+//
+// ============================================= 3-bit using D4 lattice
+//
+
+typedef struct {
+    uint32_t * grid;
+    int      * map;
+    uint16_t * neighbours;
+} iq3_entry_t;
+
+static iq3_entry_t iq3_data[2] = {
+    {NULL, NULL, NULL},
+    {NULL, NULL, NULL},
+};
+
+static inline int iq3_data_index(int grid_size) {
+    (void)grid_size;
+    GGML_ASSERT(grid_size == 256 || grid_size == 512);
+    return grid_size == 256 ? 0 : 1;
+}
+
+static int iq3_compare_func(const void * left, const void * right) {
+    const int * l = (const int *)left;
+    const int * r = (const int *)right;
+    return l[0] < r[0] ? -1 : l[0] > r[0] ? 1 : l[1] < r[1] ? -1 : l[1] > r[1] ? 1 : 0;
+}
+
+void iq3xs_init_impl(int grid_size) {
+    const int gindex = iq3_data_index(grid_size);
+    if (iq3_data[gindex].grid) {
+        return;
+    }
+    static const uint16_t kgrid_256[256] = {
+            0,     2,     4,     9,    11,    15,    16,    18,    25,    34,    59,    61,    65,    67,    72,    74,
+           81,    85,    88,    90,    97,   108,   120,   128,   130,   132,   137,   144,   146,   153,   155,   159,
+          169,   175,   189,   193,   199,   200,   202,   213,   248,   267,   287,   292,   303,   315,   317,   321,
+          327,   346,   362,   413,   436,   456,   460,   462,   483,   497,   513,   515,   520,   522,   529,   531,
+          536,   538,   540,   551,   552,   576,   578,   585,   592,   594,   641,   643,   648,   650,   657,   664,
+          698,   704,   706,   720,   729,   742,   758,   769,   773,   808,   848,   852,   870,   889,   901,   978,
+          992,  1024,  1026,  1033,  1035,  1040,  1042,  1046,  1049,  1058,  1089,  1091,  1093,  1096,  1098,  1105,
+         1112,  1139,  1143,  1144,  1152,  1154,  1161,  1167,  1168,  1170,  1183,  1184,  1197,  1217,  1224,  1228,
+         1272,  1276,  1309,  1323,  1347,  1367,  1377,  1404,  1473,  1475,  1486,  1509,  1537,  1544,  1546,  1553,
+         1555,  1576,  1589,  1594,  1600,  1602,  1616,  1625,  1636,  1638,  1665,  1667,  1672,  1685,  1706,  1722,
+         1737,  1755,  1816,  1831,  1850,  1856,  1862,  1874,  1901,  1932,  1950,  1971,  2011,  2032,  2052,  2063,
+         2077,  2079,  2091,  2095,  2172,  2192,  2207,  2208,  2224,  2230,  2247,  2277,  2308,  2345,  2356,  2389,
+         2403,  2424,  2501,  2504,  2506,  2520,  2570,  2593,  2616,  2624,  2630,  2646,  2669,  2700,  2714,  2746,
+         2754,  2795,  2824,  2835,  2839,  2874,  2882,  2905,  2984,  3028,  3042,  3092,  3108,  3110,  3124,  3153,
+         3185,  3215,  3252,  3288,  3294,  3364,  3397,  3434,  3483,  3523,  3537,  3587,  3589,  3591,  3592,  3610,
+         3626,  3670,  3680,  3722,  3749,  3754,  3776,  3789,  3803,  3824,  3857,  3873,  3904,  3906,  3924,  3992,
+    };
+    static const uint16_t kgrid_512[512] = {
+            0,     1,     2,     5,     7,     8,     9,    10,    12,    14,    16,    17,    21,    27,    32,    34,
+           37,    39,    41,    43,    48,    50,    57,    60,    63,    64,    65,    66,    68,    72,    73,    77,
+           80,    83,    87,    89,    93,   100,   113,   117,   122,   128,   129,   133,   135,   136,   139,   142,
+          145,   149,   152,   156,   162,   165,   167,   169,   171,   184,   187,   195,   201,   205,   208,   210,
+          217,   219,   222,   228,   232,   234,   247,   249,   253,   256,   267,   271,   273,   276,   282,   288,
+          291,   297,   312,   322,   324,   336,   338,   342,   347,   353,   357,   359,   374,   379,   390,   393,
+          395,   409,   426,   441,   448,   450,   452,   464,   466,   470,   475,   488,   492,   512,   513,   514,
+          516,   520,   521,   523,   525,   527,   528,   530,   537,   540,   542,   556,   558,   561,   570,   576,
+          577,   579,   582,   584,   588,   593,   600,   603,   609,   616,   618,   632,   638,   640,   650,   653,
+          655,   656,   660,   666,   672,   675,   685,   688,   698,   705,   708,   711,   712,   715,   721,   727,
+          728,   732,   737,   754,   760,   771,   773,   778,   780,   793,   795,   802,   806,   808,   812,   833,
+          840,   843,   849,   856,   858,   873,   912,   916,   919,   932,   934,   961,   963,   968,   970,   977,
+          989,   993,  1010,  1016,  1024,  1025,  1027,  1029,  1031,  1032,  1034,  1036,  1038,  1041,  1043,  1047,
+         1048,  1050,  1057,  1059,  1061,  1064,  1066,  1079,  1080,  1083,  1085,  1088,  1090,  1096,  1099,  1103,
+         1106,  1109,  1113,  1116,  1122,  1129,  1153,  1156,  1159,  1169,  1171,  1176,  1183,  1185,  1195,  1199,
+         1209,  1212,  1216,  1218,  1221,  1225,  1234,  1236,  1241,  1243,  1250,  1256,  1270,  1281,  1287,  1296,
+         1299,  1306,  1309,  1313,  1338,  1341,  1348,  1353,  1362,  1375,  1376,  1387,  1400,  1408,  1410,  1415,
+         1425,  1453,  1457,  1477,  1481,  1494,  1496,  1507,  1512,  1538,  1545,  1547,  1549,  1551,  1554,  1561,
+         1563,  1565,  1570,  1572,  1575,  1577,  1587,  1593,  1601,  1603,  1605,  1612,  1617,  1619,  1632,  1648,
+         1658,  1662,  1664,  1674,  1680,  1690,  1692,  1704,  1729,  1736,  1740,  1745,  1747,  1751,  1752,  1761,
+         1763,  1767,  1773,  1787,  1795,  1801,  1806,  1810,  1817,  1834,  1840,  1844,  1857,  1864,  1866,  1877,
+         1882,  1892,  1902,  1915,  1934,  1953,  1985,  1987,  2000,  2002,  2013,  2048,  2052,  2058,  2064,  2068,
+         2071,  2074,  2081,  2088,  2104,  2114,  2119,  2121,  2123,  2130,  2136,  2141,  2147,  2153,  2157,  2177,
+         2179,  2184,  2189,  2193,  2203,  2208,  2223,  2226,  2232,  2244,  2249,  2251,  2256,  2258,  2265,  2269,
+         2304,  2306,  2324,  2335,  2336,  2361,  2373,  2375,  2385,  2418,  2443,  2460,  2480,  2504,  2509,  2520,
+         2531,  2537,  2562,  2568,  2572,  2578,  2592,  2596,  2599,  2602,  2614,  2620,  2625,  2627,  2629,  2634,
+         2641,  2650,  2682,  2688,  2697,  2707,  2712,  2718,  2731,  2754,  2759,  2760,  2775,  2788,  2793,  2805,
+         2811,  2817,  2820,  2832,  2842,  2854,  2890,  2902,  2921,  2923,  2978,  3010,  3012,  3026,  3081,  3083,
+         3085,  3097,  3099,  3120,  3136,  3152,  3159,  3188,  3210,  3228,  3234,  3245,  3250,  3256,  3264,  3276,
+         3281,  3296,  3349,  3363,  3378,  3392,  3395,  3420,  3440,  3461,  3488,  3529,  3531,  3584,  3588,  3591,
+         3600,  3602,  3614,  3616,  3628,  3634,  3650,  3657,  3668,  3683,  3685,  3713,  3716,  3720,  3726,  3729,
+         3736,  3753,  3778,  3802,  3805,  3819,  3841,  3845,  3851,  3856,  3880,  3922,  3938,  3970,  3993,  4032,
+    };
+
+    const int kmap_size = 4096;
+    const int nwant = grid_size == 256 ? 2 : 3;
+    const uint16_t * kgrid = grid_size == 256 ? kgrid_256 : kgrid_512;
+    uint32_t * kgrid_q3xs;
+    int      * kmap_q3xs;
+    uint16_t * kneighbors_q3xs;
+
+    //printf("================================================================= %s(grid_size = %d)\n", __func__, grid_size);
+    uint32_t * the_grid = (uint32_t *)malloc(grid_size*sizeof(uint32_t));
+    for (int k = 0; k < grid_size; ++k) {
+        int8_t * pos = (int8_t *)(the_grid + k);
+        for (int i = 0; i < 4; ++i) {
+            int l = (kgrid[k] >> 3*i) & 0x7;
+            pos[i] = 2*l + 1;
+        }
+    }
+    kgrid_q3xs = the_grid;
+    iq3_data[gindex].grid = the_grid;
+    kmap_q3xs = (int *)malloc(kmap_size*sizeof(int));
+    iq3_data[gindex].map = kmap_q3xs;
+    for (int i = 0; i < kmap_size; ++i) kmap_q3xs[i] = -1;
+    uint32_t aux32;
+    uint8_t * aux8 = (uint8_t *)&aux32;
+    for (int i = 0; i < grid_size; ++i) {
+        aux32 = kgrid_q3xs[i];
+        uint16_t index = 0;
+        for (int k=0; k<4; ++k) {
+            uint16_t q = (aux8[k] - 1)/2;
+            index |= (q << 3*k);
+        }
+        kmap_q3xs[index] = i;
+    }
+    int8_t pos[4];
+    int * dist2 = (int *)malloc(2*grid_size*sizeof(int));
+    int num_neighbors = 0, num_not_in_map = 0;
+    for (int i = 0; i < kmap_size; ++i) {
+        if (kmap_q3xs[i] >= 0) continue;
+        ++num_not_in_map;
+        for (int k = 0; k < 4; ++k) {
+            int l = (i >> 3*k) & 0x7;
+            pos[k] = 2*l + 1;
+        }
+        for (int j = 0; j < grid_size; ++j) {
+            const int8_t * pg = (const int8_t *)(kgrid_q3xs + j);
+            int d2 = 0;
+            for (int k = 0; k < 4; ++k) d2 += (pg[k] - pos[k])*(pg[k] - pos[k]);
+            dist2[2*j+0] = d2;
+            dist2[2*j+1] = j;
+        }
+        qsort(dist2, grid_size, 2*sizeof(int), iq3_compare_func);
+        int n = 0; int d2 = dist2[0];
+        int nhave = 1;
+        for (int j = 0; j < grid_size; ++j) {
+            if (dist2[2*j] > d2) {
+                if (nhave == nwant) break;
+                d2 = dist2[2*j];
+                ++nhave;
+            }
+            ++n;
+        }
+        num_neighbors += n;
+    }
+    //printf("%s: %d neighbours in total\n", __func__, num_neighbors);
+    kneighbors_q3xs = (uint16_t *)malloc((num_neighbors + num_not_in_map)*sizeof(uint16_t));
+    iq3_data[gindex].neighbours = kneighbors_q3xs;
+    int counter = 0;
+    for (int i = 0; i < kmap_size; ++i) {
+        if (kmap_q3xs[i] >= 0) continue;
+        for (int k = 0; k < 4; ++k) {
+            int l = (i >> 3*k) & 0x7;
+            pos[k] = 2*l + 1;
+        }
+        for (int j = 0; j < grid_size; ++j) {
+            const int8_t * pg = (const int8_t *)(kgrid_q3xs + j);
+            int d2 = 0;
+            for (int k = 0; k < 4; ++k) d2 += (pg[k] - pos[k])*(pg[k] - pos[k]);
+            dist2[2*j+0] = d2;
+            dist2[2*j+1] = j;
+        }
+        qsort(dist2, grid_size, 2*sizeof(int), iq3_compare_func);
+        kmap_q3xs[i] = -(counter + 1);
+        int d2 = dist2[0];
+        uint16_t * start = &kneighbors_q3xs[counter++];
+        int n = 0, nhave = 1;
+        for (int j = 0; j < grid_size; ++j) {
+            if (dist2[2*j] > d2) {
+                if (nhave == nwant) break;
+                d2 = dist2[2*j];
+                ++nhave;
+            }
+            kneighbors_q3xs[counter++] = dist2[2*j+1];
+            ++n;
+        }
+        *start = n;
+    }
+    free(dist2);
+}
+
+void iq3xs_free_impl(int grid_size) {
+    GGML_ASSERT(grid_size == 256 || grid_size == 512);
+    const int gindex = iq3_data_index(grid_size);
+    if (iq3_data[gindex].grid) {
+        free(iq3_data[gindex].grid);       iq3_data[gindex].grid = NULL;
+        free(iq3_data[gindex].map);        iq3_data[gindex].map  = NULL;
+        free(iq3_data[gindex].neighbours); iq3_data[gindex].neighbours = NULL;
+    }
+}
+
+static int iq3_find_best_neighbour(const uint16_t * restrict neighbours, const uint32_t * restrict grid,
+        const float * restrict xval, const float * restrict weight, float scale, int8_t * restrict L) {
+    int num_neighbors = neighbours[0];
+    GGML_ASSERT(num_neighbors > 0);
+    float best_d2 = FLT_MAX;
+    int grid_index = -1;
+    for (int j = 1; j <= num_neighbors; ++j) {
+        const int8_t * pg = (const int8_t *)(grid + neighbours[j]);
+        float d2 = 0;
+        for (int i = 0; i < 4; ++i) {
+            float q = pg[i];
+            float diff = scale*q - xval[i];
+            d2 += weight[i]*diff*diff;
+        }
+        if (d2 < best_d2) {
+            best_d2 = d2; grid_index = neighbours[j];
+        }
+    }
+    GGML_ASSERT(grid_index >= 0);
+    const int8_t * pg = (const int8_t *)(grid + grid_index);
+    for (int i = 0; i < 4; ++i) L[i] = (pg[i] - 1)/2;
+    return grid_index;
+}
+
+static void quantize_row_iq3_xxs_impl(int grid_size, const float * restrict x, void * restrict vy, int n,
+        const float * restrict quant_weights) {
+
+    const int gindex = iq3_data_index(grid_size);
+
+    const uint32_t * kgrid_q3xs      = iq3_data[gindex].grid;
+    const int      * kmap_q3xs       = iq3_data[gindex].map;
+    const uint16_t * kneighbors_q3xs = iq3_data[gindex].neighbours;
+
+    //GGML_ASSERT(quant_weights   && "missing quantization weights");
+    GGML_ASSERT(kgrid_q3xs      && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kmap_q3xs       && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kneighbors_q3xs && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(n%QK_K == 0);
+
+    const int kMaxQ = 8;
+
+    const int nbl = n/QK_K;
+
+    ggml_fp16_t * dh;
+    uint8_t * qs;
+    int block_size;
+    if (grid_size == 256) {
+        block_iq3_xxs * y = vy;
+        dh = &y->d;
+        qs = y->qs;
+        block_size = sizeof(block_iq3_xxs);
+    } else {
+        block_iq3_s * y = vy;
+        dh = &y->d;
+        qs = y->qs;
+        block_size = sizeof(block_iq3_s);
+    }
+    int quant_size = block_size - sizeof(ggml_fp16_t);
+
+    float scales[QK_K/32];
+    float weight[32];
+    float xval[32];
+    int8_t L[32];
+    int8_t Laux[32];
+    float  waux[32];
+    bool   is_on_grid[8];
+    bool   is_on_grid_aux[8];
+    uint8_t block_signs[8];
+    uint8_t q3[3*(QK_K/8)+QK_K/32];
+    uint32_t * scales_and_signs = (uint32_t *)(q3 + QK_K/4);
+    uint8_t  * qh = q3 + 3*(QK_K/8);
+
+    for (int ibl = 0; ibl < nbl; ++ibl) {
+
+        dh[0] = GGML_FP32_TO_FP16(0.f);
+        memset(q3, 0, 3*QK_K/8+QK_K/32);
+
+        float max_scale = 0;
+
+        const float * xbl = x + QK_K*ibl;
+        float sumx2 = 0;
+        for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i];
+        float sigma2 = 2*sumx2/QK_K;
+
+        for (int ib = 0; ib < QK_K/32; ++ib) {
+            const float * xb = xbl + 32*ib;
+            if (quant_weights) {
+                const float * qw = quant_weights + QK_K*ibl + 32*ib;
+                for (int i = 0; i < 32; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
+            } else {
+                for (int i = 0; i < 32; ++i) weight[i] = xb[i]*xb[i];
+            }
+            for (int i = 0; i < 32; ++i) waux[i] = sqrtf(weight[i]);
+            for (int k = 0; k < 4; ++k) {
+                int nflip = 0;
+                uint8_t s = 0;
+                for (int i = 0; i < 8; ++i) {
+                    if (xb[8*k + i] >= 0) xval[8*k + i] = xb[8*k + i];
+                    else {
+                        xval[8*k + i] = -xb[8*k + i]; ++nflip; s |= (1 << i);
+                    }
+                }
+                if (nflip%2) {
+                    int imin = 0; float min = weight[8*k+imin]*xb[8*k+imin]*xb[8*k+imin];
+                    for (int i = 1; i < 8; ++i) {
+                        float ax = weight[8*k+i]*xb[8*k+i]*xb[8*k+i];
+                        if (ax < min) {
+                            min = ax; imin = i;
+                        }
+                    }
+                    xval[8*k+imin] = -xval[8*k+imin];
+                    s ^= (1 << imin);
+                }
+                block_signs[k] = s & 127;
+            }
+            float max = xval[0];
+            for (int i = 1; i < 32; ++i) max = MAX(max, xval[i]);
+            if (!max) {
+                scales[ib] = 0;
+                memset(L, 0, 32);
+                continue;
+            }
+            float best = 0;
+            float scale = max/(2*kMaxQ-1);
+            for (int is = -15; is <= 15; ++is) {
+                float id = (2*kMaxQ-1+is*0.2f)/max;
+                float this_scale = 1/id;
+                for (int k = 0; k < 8; ++k) {
+                    for (int i = 0; i < 4; ++i) {
+                        int l = nearest_int(0.5f*(id*xval[4*k+i]-1));
+                        Laux[4*k+i] = MAX(0, MIN(kMaxQ-1, l));
+                    }
+                    uint16_t u = 0;
+                    for (int i = 0; i < 4; ++i) u |= (Laux[4*k+i] << 3*i);
+                    int grid_index = kmap_q3xs[u];
+                    is_on_grid_aux[k] = true;
+                    if (grid_index < 0) {
+                        is_on_grid_aux[k] = false;
+                        const uint16_t * neighbours = kneighbors_q3xs - kmap_q3xs[u] - 1;
+                        grid_index = iq3_find_best_neighbour(neighbours, kgrid_q3xs, xval + 4*k, waux + 4*k, this_scale, Laux + 4*k);
+                    }
+                }
+                float sumqx = 0, sumq2 = 0;
+                for (int i = 0; i < 32; ++i) {
+                    float w = weight[i];
+                    float q = 2*Laux[i] + 1;
+                    sumqx += w*xval[i]*q;
+                    sumq2 += w*q*q;
+                }
+                if (sumq2 > 0 && sumqx*sumqx > best*sumq2) {
+                    scale = sumqx/sumq2; best = scale*sumqx;
+                    for (int i = 0; i < 32; ++i) L[i] = Laux[i];
+                    for (int k = 0; k <  8; ++k) is_on_grid[k] = is_on_grid_aux[k];
+                }
+            }
+            int n_not_ongrid = 0;
+            for (int k = 0; k < 8; ++k) if (!is_on_grid[k]) ++n_not_ongrid;
+            if (n_not_ongrid > 0 && scale > 0) {
+                float id = 1/scale;
+                for (int k = 0; k < 8; ++k) {
+                    if (is_on_grid[k]) continue;
+                    uint16_t u = 0;
+                    for (int i = 0; i < 4; ++i) {
+                        int l = nearest_int(0.5f*(id*xval[4*k+i]-1));
+                        l = MAX(0, MIN(kMaxQ-1, l));
+                        u |= (l << 3*i);
+                    }
+                    int grid_index = kmap_q3xs[u];
+                    if (grid_index < 0) {
+                        const uint16_t * neighbours = kneighbors_q3xs - kmap_q3xs[u] - 1;
+                        grid_index = iq3_find_best_neighbour(neighbours, kgrid_q3xs, xval + 4*k, waux + 4*k, scale, L + 4*k);
+                    }
+                    const int8_t * pg = (const int8_t *)(kgrid_q3xs + grid_index);
+                    for (int i = 0; i < 4; ++i) L[4*k+i] = (pg[i] - 1)/2;
+                }
+                float sumqx = 0, sumq2 = 0;
+                for (int i = 0; i < 32; ++i) {
+                    float w = weight[i];
+                    float q = 2*L[i] + 1;
+                    sumqx += w*xval[i]*q;
+                    sumq2 += w*q*q;
+                }
+                if (sumq2 > 0) scale = sumqx/sumq2;
+            }
+            if (scale < 0) {
+                // This should never happen, but just in case, flip scale so that it is positive (we use uint's to encode the scale)
+                // and correspondingly flip quant signs.
+                scale = -scale;
+                for (int k = 0; k < 4; ++k) block_signs[k] = (~block_signs[k]) & 127;
+            }
+            for (int k = 0; k < 8; ++k) {
+                uint16_t u = 0;
+                for (int i = 0; i < 4; ++i) u |= (L[4*k+i] << 3*i);
+                int grid_index = kmap_q3xs[u];
+                if (grid_index < 0) {
+                    printf("Oops: found point %u not on grid:", u);
+                    for (int i = 0; i < 4; ++i) printf(" %d", L[4*k+i]);
+                    printf("\n");
+                    GGML_ASSERT(false);
+                }
+                if (grid_size == 256) {
+                    q3[8*ib+k] = grid_index;
+                } else {
+                    q3[8*ib+k] = grid_index & 255;
+                    qh[ib] |= ((grid_index >> 8) << k);
+                }
+
+            }
+            scales_and_signs[ib] = block_signs[0] | (block_signs[1] << 7) | (block_signs[2] << 14) | (block_signs[3] << 21);
+            GGML_ASSERT(scale >= 0);
+            scales[ib] = scale;
+            max_scale = MAX(max_scale, scale);
+        }
+
+        if (!max_scale) {
+            memset(qs, 0, quant_size);
+            dh += block_size/sizeof(ggml_fp16_t);
+            qs += block_size;
+            continue;
+        }
+
+        float d = max_scale/31;
+        dh[0] = GGML_FP32_TO_FP16(d * 1.0125f);  // small improvement via this fudge factor
+        float id = 1/d;
+        for (int ib = 0; ib < QK_K/32; ++ib) {
+            int l = nearest_int(0.5f*(id*scales[ib]-1));
+            l = MAX(0, MIN(15, l));
+            scales_and_signs[ib] |= ((uint32_t)l << 28);
+        }
+        memcpy(qs, q3, quant_size);
+
+        dh += block_size/sizeof(ggml_fp16_t);
+        qs += block_size;
+
+    }
+}
+
+size_t quantize_iq3_xxs(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
+    GGML_ASSERT(n_per_row%QK_K == 0);
+    int nblock = n_per_row/QK_K;
+    char * qrow = (char *)dst;
+    for (int row = 0; row < nrow; ++row) {
+        quantize_row_iq3_xxs_impl(256, src, qrow, n_per_row, quant_weights);
+        src += n_per_row;
+        qrow += nblock*sizeof(block_iq3_xxs);
+    }
+    return nrow * nblock * sizeof(block_iq3_xxs);
+}
+
+void quantize_row_iq3_xxs(const float * restrict x, void * restrict vy, int k) {
+    assert(k % QK_K == 0);
+    block_iq3_xxs * restrict y = vy;
+    quantize_row_iq3_xxs_reference(x, y, k);
+}
+
+void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * restrict y, int k) {
+    assert(k % QK_K == 0);
+    quantize_row_iq3_xxs_impl(256, x, y, k, NULL);
+}
+
+static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, void * restrict vy, int n,
+        const float * restrict quant_weights,
+        float   * scales,
+        float   * weight,
+        float   * xval,
+        int8_t  * L,
+        int8_t  * Laux,
+        float   * waux,
+        bool    * is_on_grid,
+        bool    * is_on_grid_aux,
+        uint8_t * block_signs) {
+
+    const int gindex = iq3_data_index(512);
+
+    const uint32_t * kgrid_q3xs      = iq3_data[gindex].grid;
+    const int      * kmap_q3xs       = iq3_data[gindex].map;
+    const uint16_t * kneighbors_q3xs = iq3_data[gindex].neighbours;
+
+    //GGML_ASSERT(quant_weights   && "missing quantization weights");
+    GGML_ASSERT(kgrid_q3xs      && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kmap_q3xs       && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kneighbors_q3xs && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(n%QK_K == 0);
+
+    const int kMaxQ = 8;
+
+    const int nbl = n/QK_K;
+
+    block_iq3_s * y = vy;
+
+    const int bs4 = block_size/4;
+    const int bs8 = block_size/8;
+
+    for (int ibl = 0; ibl < nbl; ++ibl) {
+
+        memset(&y[ibl], 0, sizeof(block_iq3_s));
+        y[ibl].d = GGML_FP32_TO_FP16(0.f);
+
+        uint8_t * qs = y[ibl].qs;
+        uint8_t * qh = y[ibl].qh;
+        uint8_t * signs = y[ibl].signs;
+
+        float max_scale = 0;
+
+        const float * xbl = x + QK_K*ibl;
+        float sumx2 = 0;
+        for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i];
+        float sigma2 = 2*sumx2/QK_K;
+
+        for (int ib = 0; ib < QK_K/block_size; ++ib) {
+            const float * xb = xbl + block_size*ib;
+            if (quant_weights) {
+                const float * qw = quant_weights + QK_K*ibl + block_size*ib;
+                for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
+            } else {
+                for (int i = 0; i < block_size; ++i) weight[i] = xb[i]*xb[i];
+            }
+            for (int i = 0; i < block_size; ++i) waux[i] = sqrtf(weight[i]);
+            for (int k = 0; k < bs8; ++k) {
+                uint8_t s = 0;
+                for (int i = 0; i < 8; ++i) {
+                    if (xb[8*k + i] >= 0) xval[8*k + i] = xb[8*k + i];
+                    else {
+                        xval[8*k + i] = -xb[8*k + i]; s |= (1 << i);
+                    }
+                }
+                block_signs[k] = s;
+            }
+            float max = xval[0];
+            for (int i = 1; i < block_size; ++i) max = MAX(max, xval[i]);
+            if (!max) {
+                scales[ib] = 0;
+                continue;
+            }
+            float best = 0;
+            float scale = max/(2*kMaxQ-1);
+            for (int k = 0; k < bs4; ++k) is_on_grid[k] = false;
+            for (int is = -9; is <= 9; ++is) {
+                float id = (2*kMaxQ-1+is*0.2f)/max;
+                float this_scale = 1/id;
+                for (int k = 0; k < bs4; ++k) {
+                    for (int i = 0; i < 4; ++i) {
+                        int l = nearest_int(0.5f*(id*xval[4*k+i]-1));
+                        Laux[4*k+i] = MAX(0, MIN(kMaxQ-1, l));
+                    }
+                    uint16_t u = 0;
+                    for (int i = 0; i < 4; ++i) u |= (Laux[4*k+i] << 3*i);
+                    int grid_index = kmap_q3xs[u];
+                    is_on_grid_aux[k] = true;
+                    if (grid_index < 0) {
+                        is_on_grid_aux[k] = false;
+                        const uint16_t * neighbours = kneighbors_q3xs - kmap_q3xs[u] - 1;
+                        grid_index = iq3_find_best_neighbour(neighbours, kgrid_q3xs, xval + 4*k, waux + 4*k, this_scale, Laux + 4*k);
+                    }
+                }
+                float sumqx = 0, sumq2 = 0;
+                for (int i = 0; i < block_size; ++i) {
+                    float w = weight[i];
+                    float q = 2*Laux[i] + 1;
+                    sumqx += w*xval[i]*q;
+                    sumq2 += w*q*q;
+                }
+                if (sumq2 > 0 && sumqx*sumqx > best*sumq2) {
+                    scale = sumqx/sumq2; best = scale*sumqx;
+                    for (int i = 0; i < block_size; ++i) L[i] = Laux[i];
+                    for (int k = 0; k < bs4; ++k) is_on_grid[k] = is_on_grid_aux[k];
+                }
+            }
+            int n_not_ongrid = 0;
+            for (int k = 0; k < bs4; ++k) if (!is_on_grid[k]) ++n_not_ongrid;
+            if (n_not_ongrid > 0 && scale > 0) {
+                float id = 1/scale;
+                for (int k = 0; k < bs4; ++k) {
+                    //if (is_on_grid[k]) continue;
+                    uint16_t u = 0;
+                    for (int i = 0; i < 4; ++i) {
+                        int l = nearest_int(0.5f*(id*xval[4*k+i]-1));
+                        l = MAX(0, MIN(kMaxQ-1, l));
+                        u |= (l << 3*i);
+                    }
+                    int grid_index = kmap_q3xs[u];
+                    if (grid_index < 0) {
+                        const uint16_t * neighbours = kneighbors_q3xs - kmap_q3xs[u] - 1;
+                        grid_index = iq3_find_best_neighbour(neighbours, kgrid_q3xs, xval + 4*k, waux + 4*k, scale, L + 4*k);
+                    }
+                    const int8_t * pg = (const int8_t *)(kgrid_q3xs + grid_index);
+                    for (int i = 0; i < 4; ++i) L[4*k+i] = (pg[i] - 1)/2;
+                }
+                float sumqx = 0, sumq2 = 0;
+                for (int i = 0; i < block_size; ++i) {
+                    float w = weight[i];
+                    float q = 2*L[i] + 1;
+                    sumqx += w*xval[i]*q;
+                    sumq2 += w*q*q;
+                }
+                if (sumq2 > 0) scale = sumqx/sumq2;
+            }
+            if (scale < 0) {
+                // This should never happen, but just in case, flip scale so that it is positive (we use uint's to encode the scale)
+                // and correspondingly flip quant signs.
+                scale = -scale;
+                for (int k = 0; k < bs8; ++k) block_signs[k] = ~block_signs[k];
+            }
+            for (int k = 0; k < bs4; ++k) {
+                uint16_t u = 0;
+                for (int i = 0; i < 4; ++i) u |= (L[4*k+i] << 3*i);
+                int grid_index = kmap_q3xs[u];
+                if (grid_index < 0) {
+                    printf("Oops: found point %u not on grid:", u);
+                    for (int i = 0; i < 4; ++i) printf(" %d", L[4*k+i]);
+                    printf("\n");
+                    GGML_ASSERT(false);
+                }
+                qs[k] = grid_index & 255;
+                qh[(ib*bs4+k)/8] |= ((grid_index >> 8) << ((ib*bs4+k)%8));
+            }
+            qs += bs4;
+            for (int k = 0; k < bs8; ++k) signs[k] = block_signs[k];
+            signs += bs8;
+            GGML_ASSERT(scale >= 0);
+            scales[ib] = scale;
+            max_scale = MAX(max_scale, scale);
+        }
+
+        if (!max_scale) {
+            continue;
+        }
+
+        float d = max_scale/31;
+        y[ibl].d = GGML_FP32_TO_FP16(d * 1.033f);
+        float id = 1/d;
+        for (int ib = 0; ib < QK_K/block_size; ib += 2) {
+            int l1 = nearest_int(0.5f*(id*scales[ib+0]-1));
+            l1 = MAX(0, MIN(15, l1));
+            int l2 = nearest_int(0.5f*(id*scales[ib+1]-1));
+            l2 = MAX(0, MIN(15, l2));
+            y[ibl].scales[ib/2] = l1 | (l2 << 4);
+        }
+
+    }
+}
+
+#define IQ3S_BLOCK_SIZE 32
+size_t quantize_iq3_s(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
+    GGML_ASSERT(n_per_row%QK_K == 0);
+    int nblock = n_per_row/QK_K;
+    float scales[QK_K/IQ3S_BLOCK_SIZE];
+    float weight[IQ3S_BLOCK_SIZE];
+    float xval[IQ3S_BLOCK_SIZE];
+    int8_t L[IQ3S_BLOCK_SIZE];
+    int8_t Laux[IQ3S_BLOCK_SIZE];
+    float  waux[IQ3S_BLOCK_SIZE];
+    bool   is_on_grid[IQ3S_BLOCK_SIZE/4];
+    bool   is_on_grid_aux[IQ3S_BLOCK_SIZE/4];
+    uint8_t block_signs[IQ3S_BLOCK_SIZE/8];
+    char * qrow = (char *)dst;
+    for (int row = 0; row < nrow; ++row) {
+        quantize_row_iq3_s_impl(IQ3S_BLOCK_SIZE, src, qrow, n_per_row, quant_weights,
+                scales, weight, xval, L, Laux, waux, is_on_grid, is_on_grid_aux, block_signs);
+        src += n_per_row;
+        qrow += nblock*sizeof(block_iq3_s);
+    }
+    return nrow * nblock * sizeof(block_iq3_s);
+}
+
+void quantize_row_iq3_s(const float * restrict x, void * restrict vy, int k) {
+    assert(k % QK_K == 0);
+    block_iq3_s * restrict y = vy;
+    quantize_row_iq3_s_reference(x, y, k);
+}
+
+void quantize_row_iq3_s_reference(const float * restrict x, block_iq3_s * restrict y, int k) {
+    assert(k % QK_K == 0);
+    quantize_iq3_s(x, y, 1, k, NULL);
+}
+
+
+// =================================== 1.5 bpw ===================================================
+
+static int iq1_find_best_neighbour(const uint16_t * restrict neighbours, const uint64_t * restrict grid,
+        const float * restrict xval, const float * restrict weight, float * scale, int8_t * restrict L, int ngrid) {
+    int num_neighbors = neighbours[0];
+    GGML_ASSERT(num_neighbors > 0);
+    float best_score = 0;
+    int grid_index = -1;
+    for (int j = 1; j <= num_neighbors; ++j) {
+        const int8_t * pg = (const int8_t *)(grid + neighbours[j]);
+        float sumqx = 0, sumq2 = 0;
+        for (int i = 0; i < 8; ++i) {
+            float q = (pg[i] - 3)/2;
+            float w = weight[i];
+            sumqx += w*q*xval[i];
+            sumq2 += w*q*q;
+        }
+        if (sumqx > 0 && sumq2 > 0 && sumqx*sumqx > best_score*sumq2) {
+            *scale = sumqx/sumq2; best_score = *scale * sumqx;
+            grid_index = neighbours[j];
+        }
+    }
+    if (grid_index < 0) {
+        for (int i = 0; i < ngrid; ++i) {
+            const int8_t * grid_i = (const int8_t *)(grid + i);
+            float sumqx = 0, sumq2 = 0;
+            for (int j = 0; j < 8; ++j) {
+                float w = weight[j];
+                float q = (grid_i[j] - 3)/2;
+                sumqx += w*q*xval[j];
+                sumq2 += w*q*q;
+            }
+            if (sumqx > 0 && sumq2 > 0 && sumqx*sumqx > best_score*sumq2) {
+                *scale = sumqx/sumq2; best_score = *scale*sumqx;
+                grid_index = i;
+            }
+        }
+    }
+    if (grid_index < 0) {
+        printf("Oops, did not find grid point\n");
+        printf("Have %d neighbours\n", num_neighbors);
+        for (int j = 1; j <= num_neighbors; ++j) {
+            const int8_t * pg = (const int8_t *)(grid + neighbours[j]);
+            float sumqx = 0, sumq2 = 0;
+            for (int i = 0; i < 8; ++i) {
+                float q = (pg[i] - 3)/2;
+                float w = weight[i];
+                sumqx += w*q*xval[i];
+                sumq2 += w*q*q;
+            }
+            printf("    neighbour %d: sumqx = %g sumq2 = %g\n", j, (double)sumqx, (double)sumq2);
+        }
+    }
+    GGML_ASSERT(grid_index >= 0);
+    //!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+    *scale *= 1.05f;  // This is a fudge factor. Don't ask me why it improves the result.
+    //!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+    const int8_t * pg = (const int8_t *)(grid + grid_index);
+    for (int i = 0; i < 8; ++i) L[i] = (pg[i] - 1)/2;
+    return grid_index;
+}
+
+static int iq1_find_best_neighbour2(const uint16_t * restrict neighbours, const uint64_t * restrict grid,
+        const float * restrict xval, const float * restrict weight, float scale, const float * restrict xg, int8_t * restrict L, int ngrid) {
+    int num_neighbors = neighbours[0];
+    GGML_ASSERT(num_neighbors > 0);
+    float best_score = FLT_MAX;
+    int grid_index = -1;
+    for (int j = 1; j <= num_neighbors; ++j) {
+        const int8_t * pg = (const int8_t *)(grid + neighbours[j]);
+        float d2 = 0;
+        for (int i = 0; i < 8; ++i) {
+            float q = xg[(pg[i] - 1)/2];
+            float w = weight[i];
+            float diff = scale*q - xval[i];
+            d2 += w*diff*diff;
+        }
+        if (d2 < best_score) {
+            best_score = d2;
+            grid_index = neighbours[j];
+        }
+    }
+    if (grid_index < 0) {
+        for (int i = 0; i < ngrid; ++i) {
+            const int8_t * grid_i = (const int8_t *)(grid + i);
+            float d2 = 0;
+            for (int j = 0; j < 8; ++j) {
+                float w = weight[j];
+                float q = xg[(grid_i[j] - 1)/2];
+                float diff = scale*q - xval[i];
+                d2 += w*diff*diff;
+            }
+            if (d2 < best_score) {
+                best_score = d2;
+                grid_index = i;
+            }
+        }
+    }
+    if (grid_index < 0) {
+        printf("Oops, did not find grid point\n");
+        printf("Have %d neighbours\n", num_neighbors);
+        for (int j = 1; j <= num_neighbors; ++j) {
+            const int8_t * pg = (const int8_t *)(grid + neighbours[j]);
+            float sumqx = 0, sumq2 = 0;
+            for (int i = 0; i < 8; ++i) {
+                float q = xg[(pg[i] - 1)/2];
+                float w = weight[i];
+                sumqx += w*q*xval[i];
+                sumq2 += w*q*q;
+            }
+            printf("    neighbour %d: sumqx = %g sumq2 = %g\n", j, (double)sumqx, (double)sumq2);
+        }
+    }
+    GGML_ASSERT(grid_index >= 0);
+    const int8_t * pg = (const int8_t *)(grid + grid_index);
+    for (int i = 0; i < 8; ++i) L[i] = (pg[i] - 1)/2;
+    return grid_index;
+}
+
+static int iq1_sort_helper(const void * left, const void * right) {
+    const float * l = left;
+    const float * r = right;
+    return *l < *r ? -1 : *l > *r ? 1 : 0;
+}
+
+#define IQ1S_BLOCK_SIZE 32
+#define IQ1M_BLOCK_SIZE 16
+static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights,
+        float    * scales,
+        float    * weight,
+        float    * sumx,
+        float    * sumw,
+        float    * pairs,
+        int8_t   * L,
+        uint16_t * index,
+        int8_t   * shifts) {
+
+    const int gindex = iq2_data_index(GGML_TYPE_IQ1_S);
+
+    const uint64_t * kgrid_q2xs      = iq2_data[gindex].grid;
+    const int      * kmap_q2xs       = iq2_data[gindex].map;
+    const uint16_t * kneighbors_q2xs = iq2_data[gindex].neighbours;
+
+    GGML_ASSERT(quant_weights   && "missing quantization weights");
+    GGML_ASSERT(kgrid_q2xs      && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kmap_q2xs       && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(n%QK_K == 0);
+
+    block_iq1_s * y = vy;
+
+    const int nbl = n/QK_K;
+
+    const int block_size = IQ1S_BLOCK_SIZE;
+
+    const float x_p[3] = {-1 + IQ1S_DELTA,  IQ1S_DELTA, 1 + IQ1S_DELTA};
+    const float x_m[3] = {-1 - IQ1S_DELTA, -IQ1S_DELTA, 1 - IQ1S_DELTA};
+
+
+    int * idx = (int *)(pairs + 1);
+
+    for (int ibl = 0; ibl < nbl; ++ibl) {
+
+        y[ibl].d = GGML_FP32_TO_FP16(0.f);
+        memset(y[ibl].qs, 0, QK_K/8);
+        memset(y[ibl].qh, 0, QK_K/16);
+
+        float max_scale = 0;
+
+        const float * xbl = x + QK_K*ibl;
+        float sumx2 = 0;
+        for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i];
+        float sigma2 = 2*sumx2/QK_K;
+
+        for (int ib = 0; ib < QK_K/block_size; ++ib) {
+            const float * xb = xbl + block_size*ib;
+            const float * qw = quant_weights + QK_K*ibl + block_size*ib;
+            for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
+            float max = fabsf(xb[0]);
+            for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i]));
+            if (!max) {
+                scales[ib] = 0;
+                memset(L, 1, block_size);
+                continue;
+            }
+            // Here we solve exactly the sum of squared difference (SSD) weighted minimization problem.
+            // With just 3 allowed quant values (-1, 0, 1), we can search exhaustively for the two
+            // boundaries that split the weights xb[i] into 3 groups. To do so, we sort the weights
+            // in ascending order, compute Si = sum[weight[j] xb[j], j = 0...i] and
+            // Wi = sum[weight[j], j = 0...i], and use these to quckly get get the optimum scale
+            // for each possible and score for each split.
+            for (int j = 0; j < block_size; ++j) {
+                pairs[2*j] = xb[j];
+                idx[2*j] = j;
+            }
+            qsort(pairs, block_size, 2*sizeof(float), iq1_sort_helper);
+            {
+                sumx[0] = sumw[0] = 0;
+                for (int j = 0; j < block_size; ++j) {
+                    int i = idx[2*j];
+                    sumx[j+1] = sumx[j] + weight[i]*xb[i];
+                    sumw[j+1] = sumw[j] + weight[i];
+                }
+            }
+            float best_score = 0, scale = max;
+            int besti1 = -1, besti2 = -1, best_shift = 0;
+            for (int i1 = 0; i1 <= block_size; ++i1) {
+                for (int i2 = i1; i2 <= block_size; ++i2) {
+                    float sumqx = (sumx[i1] - sumx[0])*x_p[0] + (sumx[i2] - sumx[i1])*x_p[1] + (sumx[block_size] - sumx[i2])*x_p[2];
+                    float sumq2 = (sumw[i1] - sumw[0])*x_p[0]*x_p[0] + (sumw[i2] - sumw[i1])*x_p[1]*x_p[1] + (sumw[block_size] - sumw[i2])*x_p[2]*x_p[2];
+                    if (sumq2 > 0 && sumqx*sumqx > best_score*sumq2) {
+                        scale = sumqx/sumq2; best_score = scale*sumqx;
+                        besti1 = i1; besti2 = i2; best_shift = 1;
+                    }
+                    sumqx = (sumx[i1] - sumx[0])*x_m[0] + (sumx[i2] - sumx[i1])*x_m[1] + (sumx[block_size] - sumx[i2])*x_m[2];
+                    sumq2 = (sumw[i1] - sumw[0])*x_m[0]*x_m[0] + (sumw[i2] - sumw[i1])*x_m[1]*x_m[1] + (sumw[block_size] - sumw[i2])*x_m[2]*x_m[2];
+                    if (sumq2 > 0 && sumqx*sumqx > best_score*sumq2) {
+                        scale = sumqx/sumq2; best_score = scale*sumqx;
+                        besti1 = i1; besti2 = i2; best_shift = -1;
+                    }
+                }
+            }
+            GGML_ASSERT(besti1 >= 0 && besti2 >= 0 && best_shift != 0);
+            for (int j =      0; j < besti1; ++j) L[idx[2*j]] = 0;
+            for (int j = besti1; j < besti2; ++j) L[idx[2*j]] = 1;
+            for (int j = besti2; j < block_size; ++j) L[idx[2*j]] = 2;
+            if (scale < 0) {
+                for (int j = 0; j < block_size; ++j) L[j] = 2 - L[j];
+                scale = -scale; best_shift = -best_shift;
+            }
+            bool all_on_grid = true;
+            const float * xx = best_shift == 1 ? x_p : x_m;
+            for (int k = 0; k < block_size/8; ++k) {
+                uint16_t u = 0;
+                for (int j = 0; j < 8; ++j) u |= (L[8*k+j] << 2*j);
+                int grid_index = kmap_q2xs[u];
+                if (grid_index < 0) {
+                    all_on_grid = false;
+                    const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1;
+                    grid_index = iq1_find_best_neighbour2(neighbours, kgrid_q2xs, xb + 8*k, weight + 8*k, scale, xx, L + 8*k, NGRID_IQ1S);
+                    GGML_ASSERT(grid_index >= 0);
+                }
+                index[k] = grid_index;
+            }
+            if (!all_on_grid) {
+                float sumqx = 0, sumq2 = 0;
+                for (int k = 0; k < block_size/8; ++k) {
+                    const int8_t * pg = (const int8_t *)(kgrid_q2xs + index[k]);
+                    for (int j = 0; j < 8; ++j) {
+                        float w = weight[8*k + j];
+                        float q = xx[(pg[j] - 1)/2];
+                        sumqx += w*q*xb[8*k+j];
+                        sumq2 += w*q*q;
+                    }
+                }
+                if (sumqx > 0 && sumq2 > 0) scale = sumqx/sumq2;
+            }
+            uint16_t h = 0;
+            for (int k = 0; k < block_size/8; ++k) {
+                y[ibl].qs[(block_size/8)*ib + k] = index[k] & 255;
+                h |= (index[k] >> 8) << 3*k;
+            }
+            y[ibl].qh[ib] = h;
+            GGML_ASSERT(scale >= 0);
+            scales[ib] = scale;
+            shifts[ib] = best_shift;
+            max_scale = MAX(max_scale, scale);
+        }
+
+        if (!max_scale) {
+            continue;
+        }
+
+        float d = max_scale/15;
+        y[ibl].d = GGML_FP32_TO_FP16(d*1.125f); // 1.125f is another fudge factor. Don't ask me why it is needed.
+        float id = 1/d;
+        for (int ib = 0; ib < QK_K/block_size; ++ib) {
+            int l = nearest_int(0.5f*(id*scales[ib]-1));
+            l = MAX(0, MIN(7, l));
+            if (shifts[ib] == -1) l |= 8;
+            y[ibl].qh[ib] |= (l << 12);
+        }
+    }
+}
+
+size_t quantize_iq1_s(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
+    GGML_ASSERT(n_per_row%QK_K == 0);
+    float  scales[QK_K/IQ1S_BLOCK_SIZE];
+    float  weight[IQ1S_BLOCK_SIZE];
+    int8_t L[IQ1S_BLOCK_SIZE];
+    float  sumx[IQ1S_BLOCK_SIZE+1];
+    float  sumw[IQ1S_BLOCK_SIZE+1];
+    float  pairs[2*IQ1S_BLOCK_SIZE];
+    uint16_t index[IQ1S_BLOCK_SIZE/8];
+    int8_t shifts[QK_K/IQ1S_BLOCK_SIZE];
+    int nblock = n_per_row/QK_K;
+    char * qrow = (char *)dst;
+    for (int row = 0; row < nrow; ++row) {
+        quantize_row_iq1_s_impl(src, qrow, n_per_row, quant_weights, scales, weight, sumx, sumw, pairs, L, index, shifts);
+        src += n_per_row;
+        qrow += nblock*sizeof(block_iq1_s);
+    }
+    return nrow * nblock * sizeof(block_iq1_s);
+}
+
+static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights,
+        float    * scales,
+        float    * weight,
+        float    * pairs,
+        int8_t   * L,
+        uint16_t * index,
+        int8_t   * shifts) {
+
+    const int gindex = iq2_data_index(GGML_TYPE_IQ1_M);
+
+    const uint64_t * kgrid_q2xs      = iq2_data[gindex].grid;
+    const int      * kmap_q2xs       = iq2_data[gindex].map;
+    const uint16_t * kneighbors_q2xs = iq2_data[gindex].neighbours;
+
+    //GGML_ASSERT(quant_weights   && "missing quantization weights");
+    GGML_ASSERT(kgrid_q2xs      && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kmap_q2xs       && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(n%QK_K == 0);
+
+    block_iq1_m * y = vy;
+
+    const int nbl = n/QK_K;
+
+    const int block_size = IQ1M_BLOCK_SIZE;
+
+    const float x_p[3] = {-1 + IQ1M_DELTA,  IQ1M_DELTA, 1 + IQ1M_DELTA};
+    const float x_m[3] = {-1 - IQ1M_DELTA, -IQ1M_DELTA, 1 - IQ1M_DELTA};
+    const uint8_t masks[4] = {0x00, 0x80, 0x08, 0x88};
+
+    int * idx = (int *)(pairs + 1);
+
+    float sumqx[4], sumq2[4];
+
+    iq1m_scale_t s;
+    const float * xx;
+
+    for (int ibl = 0; ibl < nbl; ++ibl) {
+
+#if QK_K == 64
+        y[ibl].d = GGML_FP32_TO_FP16(0.f);
+#endif
+        memset(y[ibl].qs, 0, QK_K/8);
+        memset(y[ibl].qh, 0, QK_K/16);
+        memset(y[ibl].scales, 0, QK_K/32);
+
+        float max_scale = 0;
+
+        const float * xbl = x + QK_K*ibl;
+        float sumx2 = 0;
+        for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i];
+        float sigma2 = 2*sumx2/QK_K;
+
+        for (int ib = 0; ib < QK_K/block_size; ++ib) {
+            const float * xb = xbl + block_size*ib;
+            if (quant_weights) {
+                const float * qw = quant_weights + QK_K*ibl + block_size*ib;
+                for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
+            } else {
+                for (int i = 0; i < block_size; ++i) weight[i] = xb[i]*xb[i];
+            }
+            float max = fabsf(xb[0]);
+            for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i]));
+            if (!max) {
+                scales[ib] = 0;
+                memset(L, 1, block_size);
+                continue;
+            }
+            // Here we solve exactly the sum of squared difference (SSD) weighted minimization problem.
+            // With just 3 allowed quant values (-1, 0, 1), we can search exhaustively for the two
+            // boundaries that split the weights xb[i] into 3 groups. To do so, we sort the weights
+            // in ascending order, compute Si = sum[weight[j] xb[j], j = 0...i] and
+            // Wi = sum[weight[j], j = 0...i], and use these to quckly get get the optimum scale
+            // for each possible and score for each split.
+            for (int j = 0; j < block_size; ++j) {
+                pairs[2*j] = xb[j];
+                idx[2*j] = j;
+            }
+            qsort(pairs, block_size, 2*sizeof(float), iq1_sort_helper);
+            float best_score = 0, scale = max;
+            int besti1 = -1, besti2 = -1, best_k = -1;
+            // 0: +, +
+            // 1: +, -
+            // 2: -, +
+            // 3: -, -
+            for (int i1 = 0; i1 <= block_size; ++i1) {
+                for (int i2 = i1; i2 <= block_size; ++i2) {
+                    memset(sumqx, 0, 4*sizeof(float));
+                    memset(sumq2, 0, 4*sizeof(float));
+                    for (int j = 0; j < i1; ++j) {
+                        int i = idx[2*j];
+                        if (i < block_size/2) {
+                            sumqx[0] += weight[i]*x_p[0]*xb[i];
+                            sumqx[1] += weight[i]*x_p[0]*xb[i];
+                            sumqx[2] += weight[i]*x_m[0]*xb[i];
+                            sumqx[3] += weight[i]*x_m[0]*xb[i];
+                            sumq2[0] += weight[i]*x_p[0]*x_p[0];
+                            sumq2[1] += weight[i]*x_p[0]*x_p[0];
+                            sumq2[2] += weight[i]*x_m[0]*x_m[0];
+                            sumq2[3] += weight[i]*x_m[0]*x_m[0];
+                        } else {
+                            sumqx[0] += weight[i]*x_p[0]*xb[i];
+                            sumqx[2] += weight[i]*x_p[0]*xb[i];
+                            sumqx[1] += weight[i]*x_m[0]*xb[i];
+                            sumqx[3] += weight[i]*x_m[0]*xb[i];
+                            sumq2[0] += weight[i]*x_p[0]*x_p[0];
+                            sumq2[2] += weight[i]*x_p[0]*x_p[0];
+                            sumq2[1] += weight[i]*x_m[0]*x_m[0];
+                            sumq2[3] += weight[i]*x_m[0]*x_m[0];
+                        }
+                    }
+                    for (int j = i1; j < i2; ++j) {
+                        int i = idx[2*j];
+                        if (i < block_size/2) {
+                            sumqx[0] += weight[i]*x_p[1]*xb[i];
+                            sumqx[1] += weight[i]*x_p[1]*xb[i];
+                            sumqx[2] += weight[i]*x_m[1]*xb[i];
+                            sumqx[3] += weight[i]*x_m[1]*xb[i];
+                            sumq2[0] += weight[i]*x_p[1]*x_p[1];
+                            sumq2[1] += weight[i]*x_p[1]*x_p[1];
+                            sumq2[2] += weight[i]*x_m[1]*x_m[1];
+                            sumq2[3] += weight[i]*x_m[1]*x_m[1];
+                        } else {
+                            sumqx[0] += weight[i]*x_p[1]*xb[i];
+                            sumqx[2] += weight[i]*x_p[1]*xb[i];
+                            sumqx[1] += weight[i]*x_m[1]*xb[i];
+                            sumqx[3] += weight[i]*x_m[1]*xb[i];
+                            sumq2[0] += weight[i]*x_p[1]*x_p[1];
+                            sumq2[2] += weight[i]*x_p[1]*x_p[1];
+                            sumq2[1] += weight[i]*x_m[1]*x_m[1];
+                            sumq2[3] += weight[i]*x_m[1]*x_m[1];
+                        }
+                    }
+                    for (int j = i2; j < block_size; ++j) {
+                        int i = idx[2*j];
+                        if (i < block_size/2) {
+                            sumqx[0] += weight[i]*x_p[2]*xb[i];
+                            sumqx[1] += weight[i]*x_p[2]*xb[i];
+                            sumqx[2] += weight[i]*x_m[2]*xb[i];
+                            sumqx[3] += weight[i]*x_m[2]*xb[i];
+                            sumq2[0] += weight[i]*x_p[2]*x_p[2];
+                            sumq2[1] += weight[i]*x_p[2]*x_p[2];
+                            sumq2[2] += weight[i]*x_m[2]*x_m[2];
+                            sumq2[3] += weight[i]*x_m[2]*x_m[2];
+                        } else {
+                            sumqx[0] += weight[i]*x_p[2]*xb[i];
+                            sumqx[2] += weight[i]*x_p[2]*xb[i];
+                            sumqx[1] += weight[i]*x_m[2]*xb[i];
+                            sumqx[3] += weight[i]*x_m[2]*xb[i];
+                            sumq2[0] += weight[i]*x_p[2]*x_p[2];
+                            sumq2[2] += weight[i]*x_p[2]*x_p[2];
+                            sumq2[1] += weight[i]*x_m[2]*x_m[2];
+                            sumq2[3] += weight[i]*x_m[2]*x_m[2];
+                        }
+                    }
+                    for (int k = 0; k < 4; ++k) {
+                        if (sumq2[k] > 0 && sumqx[k]*sumqx[k] > best_score*sumq2[k]) {
+                            scale = sumqx[k]/sumq2[k]; best_score = scale*sumqx[k];
+                            besti1 = i1; besti2 = i2; best_k = k;
+                        }
+                    }
+                }
+            }
+            GGML_ASSERT(besti1 >= 0 && besti2 >= 0 && best_k >= 0);
+            for (int j =      0; j < besti1; ++j) L[idx[2*j]] = 0;
+            for (int j = besti1; j < besti2; ++j) L[idx[2*j]] = 1;
+            for (int j = besti2; j < block_size; ++j) L[idx[2*j]] = 2;
+            if (scale < 0) {
+                for (int j = 0; j < block_size; ++j) L[j] = 2 - L[j];
+                scale = -scale;
+                best_k = best_k == 0 ? 3 : best_k == 1 ? 2 : best_k == 2 ? 1 : 0;
+            }
+            bool all_on_grid = true;
+            for (int k = 0; k < block_size/8; ++k) {
+                if (k == 0) xx = best_k < 2 ? x_p : x_m;
+                else xx = best_k%2 == 0 ? x_p : x_m;
+                uint16_t u = 0;
+                for (int j = 0; j < 8; ++j) u |= (L[8*k+j] << 2*j);
+                int grid_index = kmap_q2xs[u];
+                if (grid_index < 0) {
+                    all_on_grid = false;
+                    const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1;
+                    grid_index = iq1_find_best_neighbour2(neighbours, kgrid_q2xs, xb + 8*k, weight + 8*k, scale, xx, L + 8*k, NGRID_IQ1S);
+                    GGML_ASSERT(grid_index >= 0);
+                }
+                index[k] = grid_index;
+            }
+            if (!all_on_grid) {
+                float sumqx_f = 0, sumq2_f = 0;
+                for (int k = 0; k < block_size/8; ++k) {
+                    if (k == 0) xx = best_k < 2 ? x_p : x_m;
+                    else xx = best_k%2 == 0 ? x_p : x_m;
+                    const int8_t * pg = (const int8_t *)(kgrid_q2xs + index[k]);
+                    for (int j = 0; j < 8; ++j) {
+                        float w = weight[8*k + j];
+                        float q = xx[(pg[j] - 1)/2];
+                        sumqx_f += w*q*xb[8*k+j];
+                        sumq2_f += w*q*q;
+                    }
+                }
+                if (sumqx_f > 0 && sumq2_f > 0) scale = sumqx_f/sumq2_f;
+            }
+            y[ibl].qs[2*ib + 0] = index[0] & 255;
+            y[ibl].qs[2*ib + 1] = index[1] & 255;
+            y[ibl].qh[ib] = (index[0] >> 8) | ((index[1] >> 8) << 4);
+            GGML_ASSERT(scale >= 0);
+            scales[ib] = scale;
+            shifts[ib] = best_k;
+            max_scale = MAX(max_scale, scale);
+        }
+
+        if (!max_scale) {
+            continue;
+        }
+
+        uint16_t * sc = (uint16_t *)y[ibl].scales;
+#if QK_K == 64
+        float d = max_scale/31;
+#else
+        float d = max_scale/15;
+#endif
+        float id = 1/d;
+        float sumqx_f = 0, sumq2_f = 0;
+        for (int ib = 0; ib < QK_K/block_size; ++ib) {
+            int l = nearest_int(0.5f*(id*scales[ib+0]-1));
+#if QK_K == 64
+            l = MAX(0, MIN(15, l));
+            sc[ib/4] |= (l << 4*(ib%4));
+#else
+            l = MAX(0, MIN(7, l));
+            sc[ib/4] |= (l << 3*(ib%4));
+#endif
+            y[ibl].qh[ib] |= masks[shifts[ib]];
+            const float * xb = xbl + block_size*ib;
+            if (quant_weights) {
+                const float * qw = quant_weights + QK_K*ibl + block_size*ib;
+                for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
+            } else {
+                for (int i = 0; i < block_size; ++i) weight[i] = xb[i]*xb[i];
+            }
+            for (int k = 0; k < block_size/8; ++k) {
+                if (k == 0) xx = shifts[ib] < 2 ? x_p : x_m;
+                else xx = shifts[ib]%2 == 0 ? x_p : x_m;
+                const int8_t * pg = (const int8_t *)(kgrid_q2xs + y[ibl].qs[2*ib+k] + ((y[ibl].qh[ib] << (8 - 4*k)) & 0x700));
+                for (int j = 0; j < 8; ++j) {
+                    float w = weight[8*k + j];
+                    float q = xx[(pg[j] - 1)/2]*(2*l+1);
+                    sumqx_f += w*q*xb[8*k+j];
+                    sumq2_f += w*q*q;
+                }
+            }
+        }
+        if (sumq2_f > 0) d = sumqx_f/sumq2_f;
+        s.f16 = GGML_FP32_TO_FP16(d*1.1125f); // 1.1125f is another fudge factor. Don't ask me why it is needed.
+#if QK_K == 64
+        y[ibl].d = s.f16;
+#else
+        sc[0] |= ((s.u16 & 0x000f) << 12);
+        sc[1] |= ((s.u16 & 0x00f0) <<  8);
+        sc[2] |= ((s.u16 & 0x0f00) <<  4);
+        sc[3] |= ((s.u16 & 0xf000) <<  0);
+#endif
+    }
+}
+
+size_t quantize_iq1_m(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
+    GGML_ASSERT(n_per_row%QK_K == 0);
+    float  scales[QK_K/IQ1M_BLOCK_SIZE];
+    float  weight[IQ1M_BLOCK_SIZE];
+    int8_t L[IQ1M_BLOCK_SIZE];
+    float  pairs[2*IQ1M_BLOCK_SIZE];
+    uint16_t index[IQ1M_BLOCK_SIZE/8];
+    int8_t shifts[QK_K/IQ1M_BLOCK_SIZE];
+    int nblock = n_per_row/QK_K;
+    char * qrow = (char *)dst;
+    for (int row = 0; row < nrow; ++row) {
+        quantize_row_iq1_m_impl(src, qrow, n_per_row, quant_weights, scales, weight, pairs, L, index, shifts);
+        src += n_per_row;
+        qrow += nblock*sizeof(block_iq1_m);
+    }
+    return nrow * nblock * sizeof(block_iq1_m);
+}
+
+// ============================ 4-bit non-linear quants
+
+static inline int best_index_int8(int n, const int8_t * val, float x) {
+    if (x <= val[0]) return 0;
+    if (x >= val[n-1]) return n-1;
+    int ml = 0, mu = n-1;
+    while (mu-ml > 1) {
+        int mav = (ml+mu)/2;
+        if (x < val[mav]) mu = mav; else ml = mav;
+    }
+    return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
+}
+
+static void quantize_row_iq4_nl_impl(const int super_block_size, const int block_size, const float * restrict x,
+        ggml_fp16_t * dh, uint8_t * q4, uint16_t * scales_h, uint8_t * scales_l,
+        float * scales, float * weight, uint8_t * L,
+        const int8_t * values,
+        const float * quant_weights,
+        const int ntry) {
+
+    float sigma2 = 0;
+    for (int j = 0; j < super_block_size; ++j) sigma2 += x[j]*x[j];
+    sigma2 *= 2.f/super_block_size;
+
+    memset(q4, 0, super_block_size/2);
+    dh[0] = GGML_FP32_TO_FP16(0.f);
+
+    float max_scale = 0, amax_scale = 0;
+    for (int ib = 0; ib < super_block_size/block_size; ++ib) {
+        const float * xb = x + ib*block_size;
+        uint8_t * Lb = L + ib*block_size;
+        if (quant_weights) {
+            const float * qw = quant_weights + ib*block_size;
+            for (int j = 0; j < block_size; ++j) weight[j] = qw[j] * sqrtf(sigma2 + xb[j]*xb[j]);
+        } else {
+            for (int j = 0; j < block_size; ++j) weight[j] = xb[j]*xb[j];
+        }
+        float amax = 0, max = 0;
+        for (int j = 0; j < block_size; ++j) {
+            float ax = fabsf(xb[j]);
+            if (ax > amax) {
+                amax = ax; max = xb[j];
+            }
+        }
+        if (!amax) {
+            scales[ib] = 0;
+            continue;
+        }
+        float d = ntry > 0 ? -max/values[0] : max/values[0];
+        float id = 1/d;
+        float sumqx = 0, sumq2 = 0;
+        for (int j = 0; j < block_size; ++j) {
+            float al = id*xb[j];
+            int l = best_index_int8(16, values, al);
+            Lb[j] = l;
+            float q = values[l];
+            float w = weight[j];
+            sumqx += w*q*xb[j];
+            sumq2 += w*q*q;
+        }
+        d = sumqx/sumq2;
+        float best = d*sumqx;
+        for (int itry = -ntry; itry <= ntry; ++itry) {
+            id = (itry + values[0])/max;
+            sumqx = sumq2 = 0;
+            for (int j = 0; j < block_size; ++j) {
+                float al = id*xb[j];
+                int l = best_index_int8(16, values, al);
+                float q = values[l];
+                float w = weight[j];
+                sumqx += w*q*xb[j];
+                sumq2 += w*q*q;
+            }
+            if (sumq2 > 0 && sumqx*sumqx > best*sumq2) {
+                d = sumqx/sumq2; best = d * sumqx;
+            }
+        }
+        scales[ib] = d;
+        float abs_d = fabsf(d);
+        if (abs_d > amax_scale) {
+            amax_scale = abs_d; max_scale = d;
+        }
+    }
+
+    if (super_block_size/block_size > 1) {
+        int nb = super_block_size/block_size;
+        memset(scales_h, 0, ((nb+7)/8)*sizeof(uint16_t));
+        float d = -max_scale/32;
+        dh[0] = GGML_FP32_TO_FP16(d);
+        float id = d ? 1/d : 0.f;
+        for (int ib = 0; ib < super_block_size/block_size; ++ib) {
+            int l = nearest_int(id*scales[ib]);
+            l = MAX(-32, MIN(31, l));
+            float dl = d * l;
+            float idl = dl ? 1/dl : 0.f;
+            uint8_t * Lb = L + ib*block_size;
+            const float * xb = x + ib*block_size;
+            for (int j = 0; j < block_size; ++j) {
+                Lb[j] = best_index_int8(16, values, idl*xb[j]);
+            }
+            l += 32;
+            uint8_t l_l = l & 0xf;
+            uint8_t l_h = l >>  4;
+            if (ib%2 == 0) scales_l[ib/2] = l_l;
+            else scales_l[ib/2] |= (l_l << 4);
+            scales_h[ib/8] |= (l_h << 2*(ib%8));
+        }
+    } else {
+        dh[0] = GGML_FP32_TO_FP16(scales[0]);
+        if (ntry > 0) {
+            float id = scales[0] ? 1/scales[0] : 0;
+            for (int j = 0; j < super_block_size; ++j) {
+                L[j] = best_index_int8(16, values, id*x[j]);
+            }
+        }
+    }
+
+    for (int i = 0; i < super_block_size/32; ++i) {
+        for (int j = 0; j < 16; ++j) {
+            q4[16*i + j] = L[32*i + j] | (L[32*i + 16 + j] << 4);
+        }
+    }
+}
+
+size_t quantize_iq4_nl(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
+    GGML_ASSERT(n_per_row%QK4_NL == 0);
+    int nblock = n_per_row/QK4_NL;
+    char * qrow = (char *)dst;
+    uint8_t L[QK4_NL];
+    float weight[QK4_NL];
+    uint16_t unused_h;
+    uint8_t * unused_l = NULL;
+    float scale;
+    for (int row = 0; row < nrow; ++row) {
+        block_iq4_nl * iq4 = (block_iq4_nl *)qrow;
+        for (int ibl = 0; ibl < nblock; ++ibl) {
+            const float * qw = quant_weights ? quant_weights + QK4_NL*ibl : NULL;
+            quantize_row_iq4_nl_impl(QK4_NL, 32, src + QK4_NL*ibl, &iq4[ibl].d, iq4[ibl].qs, &unused_h, unused_l,
+                    &scale, weight, L, kvalues_iq4nl, qw, 7);
+        }
+        src += n_per_row;
+        qrow += nblock*sizeof(block_iq4_nl);
+    }
+    return nrow * nblock * sizeof(block_iq4_nl);
+}
+
+void quantize_row_iq4_nl(const float * restrict x, void * restrict vy, int k) {
+    GGML_ASSERT(k%QK4_NL == 0);
+    int nblock = k/QK4_NL;
+    uint8_t L[QK4_NL];
+    float weight[QK4_NL];
+    uint16_t unused_h;
+    uint8_t * unused_l = NULL;
+    float scale;
+    block_iq4_nl * iq4 = (block_iq4_nl *)vy;
+    for (int ibl = 0; ibl < nblock; ++ibl) {
+        quantize_row_iq4_nl_impl(QK4_NL, 32, x + QK4_NL*ibl, &iq4[ibl].d, iq4[ibl].qs, &unused_h, unused_l,
+                &scale, weight, L, kvalues_iq4nl, NULL, -1);
+    }
+}
+
+void quantize_row_iq4_nl_reference(const float * restrict x, block_iq4_nl * restrict y, int k) {
+    assert(k % QK4_NL == 0);
+    quantize_row_iq4_nl(x, y, k);
+}
+
+size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
+#if QK_K == 64
+    return quantize_iq4_nl(src, dst, nrow, n_per_row, quant_weights);
+#else
+    GGML_ASSERT(n_per_row%QK_K == 0);
+    int nblock = n_per_row/QK_K;
+    char * qrow = (char *)dst;
+    uint8_t L[QK_K];
+    float weight[32];
+    float scales[QK_K/32];
+    for (int row = 0; row < nrow; ++row) {
+        block_iq4_xs * iq4 = (block_iq4_xs *)qrow;
+        for (int ibl = 0; ibl < nblock; ++ibl) {
+            const float * qw = quant_weights ? quant_weights + QK_K*ibl : NULL;
+            quantize_row_iq4_nl_impl(QK_K, 32, src + QK_K*ibl, &iq4[ibl].d, iq4[ibl].qs, &iq4[ibl].scales_h, iq4[ibl].scales_l,
+                    scales, weight, L, kvalues_iq4nl, qw, 7);
+        }
+        src += n_per_row;
+        qrow += nblock*sizeof(block_iq4_xs);
+    }
+    return nrow * nblock * sizeof(block_iq4_xs);
+#endif
+}
+
+void quantize_row_iq4_xs(const float * restrict x, void * restrict vy, int k) {
+    assert(k % QK_K == 0);
+    block_iq4_xs * restrict y = vy;
+    quantize_row_iq4_xs_reference(x, y, k);
+}
+
+void quantize_row_iq4_xs_reference(const float * restrict x, block_iq4_xs * restrict y, int k) {
+    assert(k % QK_K == 0);
+    quantize_iq4_xs(x, y, 1, k, NULL);
+}
+
+// =============================== 2.5625 bpw
+
+static void quantize_row_iq2_s_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) {
+
+    const int gindex = iq2_data_index(GGML_TYPE_IQ2_S);
+
+    const uint64_t * kgrid_q2xs      = iq2_data[gindex].grid;
+    const int      * kmap_q2xs       = iq2_data[gindex].map;
+    const uint16_t * kneighbors_q2xs = iq2_data[gindex].neighbours;
+
+    GGML_ASSERT(kmap_q2xs       && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kgrid_q2xs      && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?");
+    GGML_ASSERT(n%QK_K == 0);
+
+    const int kMaxQ = 3;
+
+    const int nbl = n/QK_K;
+
+    block_iq2_s * y = vy;
+
+    float scales[QK_K/16];
+    float weight[16];
+    float xval[16];
+    int8_t L[16];
+    int8_t Laux[16];
+    float  waux[16];
+    bool   is_on_grid[2];
+    bool   is_on_grid_aux[2];
+    uint8_t block_signs[2];
+
+    for (int ibl = 0; ibl < nbl; ++ibl) {
+
+        memset(&y[ibl], 0, sizeof(block_iq2_s));
+        y[ibl].d = GGML_FP32_TO_FP16(0.f);
+
+        float max_scale = 0;
+
+        const float * xbl = x + QK_K*ibl;
+        float sumx2 = 0;
+        for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i];
+        float sigma2 = 2*sumx2/QK_K;
+
+        for (int ib = 0; ib < QK_K/16; ++ib) {
+            const float * xb = xbl + 16*ib;
+            if (quant_weights) {
+                const float * qw = quant_weights + QK_K*ibl + 16*ib;
+                for (int i = 0; i < 16; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
+            } else {
+                for (int i = 0; i < 16; ++i) weight[i] = 0.25f*sigma2 + xb[i]*xb[i];
+            }
+            for (int i = 0; i < 16; ++i) waux[i] = sqrtf(weight[i]);
+            for (int k = 0; k < 2; ++k) {
+                uint8_t s = 0;
+                for (int i = 0; i < 8; ++i) {
+                    if (xb[8*k + i] >= 0) xval[8*k + i] = xb[8*k + i];
+                    else {
+                        xval[8*k + i] = -xb[8*k + i]; s |= (1 << i);
+                    }
+                }
+                block_signs[k] = s;
+            }
+            float max = xval[0];
+            for (int i = 1; i < 16; ++i) max = MAX(max, xval[i]);
+            if (!max) {
+                scales[ib] = 0;
+                continue;
+            }
+            float best = 0;
+            float scale = max/(2*kMaxQ-1);
+            is_on_grid[0] = is_on_grid[1] = true;
+            for (int is = -9; is <= 9; ++is) {
+                float id = (2*kMaxQ-1+is*0.1f)/max;
+                float this_scale = 1/id;
+                for (int k = 0; k < 2; ++k) {
+                    for (int i = 0; i < 8; ++i) {
+                        int l = nearest_int(0.5f*(id*xval[8*k+i]-1));
+                        Laux[8*k+i] = MAX(0, MIN(kMaxQ-1, l));
+                    }
+                    uint16_t u = 0;
+                    for (int i = 0; i < 8; ++i) u |= (Laux[8*k+i] << 2*i);
+                    int grid_index = kmap_q2xs[u];
+                    is_on_grid_aux[k] = true;
+                    if (grid_index < 0) {
+                        is_on_grid_aux[k] = false;
+                        const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1;
+                        grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, this_scale, Laux + 8*k);
+                    }
+                }
+                float sumqx = 0, sumq2 = 0;
+                for (int i = 0; i < 16; ++i) {
+                    float w = weight[i];
+                    float q = 2*Laux[i] + 1;
+                    sumqx += w*xval[i]*q;
+                    sumq2 += w*q*q;
+                }
+                if (sumq2 > 0 && sumqx*sumqx > best*sumq2) {
+                    scale = sumqx/sumq2; best = scale*sumqx;
+                    for (int i = 0; i < 16; ++i) L[i] = Laux[i];
+                    for (int k = 0; k <  2; ++k) is_on_grid[k] = is_on_grid_aux[k];
+                }
+            }
+            int n_not_ongrid = 0;
+            for (int k = 0; k < 2; ++k) if (!is_on_grid[k]) ++n_not_ongrid;
+            if (n_not_ongrid > 0 && scale > 0) {
+                float id = 1/scale;
+                for (int k = 0; k < 2; ++k) {
+                    if (is_on_grid[k]) continue;
+                    uint16_t u = 0;
+                    for (int i = 0; i < 8; ++i) {
+                        int l = nearest_int(0.5f*(id*xval[8*k+i]-1));
+                        l = MAX(0, MIN(kMaxQ-1, l));
+                        u |= (l << 2*i);
+                        L[8*k + i] = l;
+                    }
+                    int grid_index = kmap_q2xs[u];
+                    if (grid_index < 0) {
+                        const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1;
+                        grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, scale, L + 8*k);
+                    }
+                }
+                float sumqx = 0, sumq2 = 0;
+                for (int i = 0; i < 16; ++i) {
+                    float w = weight[i];
+                    float q = 2*L[i] + 1;
+                    sumqx += w*xval[i]*q;
+                    sumq2 += w*q*q;
+                }
+                if (sumq2 > 0) scale = sumqx/sumq2;
+            }
+            if (scale < 0) {
+                scale = -scale;
+                for (int k = 0; k < 2; ++k) block_signs[k] = ~block_signs[k];
+            }
+            for (int k = 0; k < 2; ++k) {
+                uint16_t u = 0;
+                for (int i = 0; i < 8; ++i) u |= (L[8*k+i] << 2*i);
+                int grid_index = kmap_q2xs[u];
+                if (grid_index < 0) {
+                    printf("Oops: found point %u not on grid:", u);
+                    for (int i = 0; i < 8; ++i) printf(" %d", L[8*k+i]);
+                    printf("\n");
+                    GGML_ASSERT(false);
+                }
+                const int i8 = 2*ib + k;
+                y[ibl].qs[i8] = grid_index & 255;
+                y[ibl].qh[i8/4] |= ((grid_index >> 8) << 2*(i8%4));
+                y[ibl].qs[QK_K/8 + i8] = block_signs[k];
+            }
+            GGML_ASSERT(scale >= 0);
+            scales[ib] = scale;
+            max_scale = MAX(max_scale, scale);
+        }
+
+        if (!max_scale) {
+            continue;
+        }
+
+        float d = max_scale/31;
+        y[ibl].d = GGML_FP32_TO_FP16(d * 0.9875f);
+        float id = 1/d;
+        for (int ib = 0; ib < QK_K/16; ++ib) {
+            int l = nearest_int(0.5f*(id*scales[ib]-1));
+            l = MAX(0, MIN(15, l));
+            if (ib%2 == 0) y[ibl].scales[ib/2] = l;
+            else y[ibl].scales[ib/2] |= (l << 4);
+        }
+    }
+}
+
+size_t quantize_iq2_s(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
+    GGML_ASSERT(n_per_row%QK_K == 0);
+    int nblock = n_per_row/QK_K;
+    char * qrow = (char *)dst;
+    for (int row = 0; row < nrow; ++row) {
+        quantize_row_iq2_s_impl(src, qrow, n_per_row, quant_weights);
+        src += n_per_row;
+        qrow += nblock*sizeof(block_iq2_s);
+    }
+    return nrow * nblock * sizeof(block_iq2_s);
+}
+
+void quantize_row_iq2_s_reference(const float * restrict x, block_iq2_s * restrict y, int k) {
+    assert(k % QK_K == 0);
+    quantize_iq2_s(x, y, 1, k, NULL);
+}
+
+void quantize_row_iq2_s(const float * restrict x, void * restrict vy, int k) {
+    assert(k % QK_K == 0);
+    block_iq2_s * restrict y = vy;
+    quantize_row_iq2_s_reference(x, y, k);
+}
diff --git a/ggml-quants.h b/ggml-quants.h
index 8dd911d41..ac1091c3d 100644
--- a/ggml-quants.h
+++ b/ggml-quants.h
@@ -1,236 +1,133 @@
 #pragma once
 
-#include "ggml-impl.h"
+#define GGML_COMMON_DECL_C
+#include "ggml-common.h"
+
+#include "ggml.h"
 
 // GGML internal header
 
-#include <stdint.h>
-#include <stddef.h>
-
-#define QK4_0 32
-typedef struct {
-    ggml_fp16_t d;          // delta
-    uint8_t qs[QK4_0 / 2];  // nibbles / quants
-} block_q4_0;
-static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
-
-#define QK4_1 32
-typedef struct {
-    ggml_fp16_t d;          // delta
-    ggml_fp16_t m;          // min
-    uint8_t qs[QK4_1 / 2];  // nibbles / quants
-} block_q4_1;
-static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_fp16_t) + QK4_1 / 2, "wrong q4_1 block size/padding");
-
-#define QK5_0 32
-typedef struct {
-    ggml_fp16_t d;         // delta
-    uint8_t qh[4];         // 5-th bit of quants
-    uint8_t qs[QK5_0 / 2]; // nibbles / quants
-} block_q5_0;
-static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
-
-#define QK5_1 32
-typedef struct {
-    ggml_fp16_t d;         // delta
-    ggml_fp16_t m;         // min
-    uint8_t qh[4];         // 5-th bit of quants
-    uint8_t qs[QK5_1 / 2]; // nibbles / quants
-} block_q5_1;
-static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
-
-#define QK8_0 32
-typedef struct {
-    ggml_fp16_t d;         // delta
-    int8_t  qs[QK8_0];     // quants
-} block_q8_0;
-static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
-
-#define QK8_1 32
-typedef struct {
-    float d;               // delta
-    float s;               // d * sum(qs[i])
-    int8_t  qs[QK8_1];     // quants
-} block_q8_1;
-static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block size/padding");
-
-//
-// Super-block quantization structures
-//
-
-// Super-block size
-#ifdef GGML_QKK_64
-#define QK_K 64
-#define K_SCALE_SIZE 4
-#else
-#define QK_K 256
-#define K_SCALE_SIZE 12
+#ifdef __cplusplus
+extern "C" {
 #endif
 
-// 2-bit quantization
-// weight is represented as x = a * q + b
-// 16 blocks of 16 elements each
-// Effectively 2.625 bits per weight
-typedef struct {
-    uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
-    uint8_t qs[QK_K/4];      // quants
-    ggml_fp16_t d;           // super-block scale for quantized scales
-    ggml_fp16_t dmin;        // super-block scale for quantized mins
-} block_q2_K;
-static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
-
-// 3-bit quantization
-// weight is represented as x = a * q
-// 16 blocks of 16 elements each
-// Effectively 3.4375 bits per weight
-#ifdef GGML_QKK_64
-typedef struct {
-    uint8_t hmask[QK_K/8];     // quants - high bit
-    uint8_t qs[QK_K/4];        // quants - low 2 bits
-    uint8_t scales[2];
-    ggml_fp16_t d;             // super-block scale
-} block_q3_K;
-static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding");
-#else
-typedef struct {
-    uint8_t hmask[QK_K/8];     // quants - high bit
-    uint8_t qs[QK_K/4];        // quants - low 2 bits
-    uint8_t scales[12];        // scales, quantized with 6 bits
-    ggml_fp16_t d;             // super-block scale
-} block_q3_K;
-static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding");
-#endif
-
-// 4-bit quantization
-// 8 blocks of 32 elements each
-// weight is represented as x = a * q + b
-// Effectively 4.5 bits per weight
-#ifdef GGML_QKK_64
-typedef struct {
-    ggml_fp16_t d[2];          // super-block scales/mins
-    uint8_t scales[2];         // 4-bit block scales/mins
-    uint8_t qs[QK_K/2];        // 4--bit quants
-} block_q4_K;
-static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
-#else
-typedef struct {
-    ggml_fp16_t d;             // super-block scale for quantized scales
-    ggml_fp16_t dmin;          // super-block scale for quantized mins
-    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
-    uint8_t qs[QK_K/2];        // 4--bit quants
-} block_q4_K;
-static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding");
-#endif
-
-// 5-bit quantization
-// 8 blocks of 32 elements each
-// weight is represented as x = a * q + b
-// Effectively 5.5 bits per weight
-#ifdef GGML_QKK_64
-typedef struct {
-    ggml_fp16_t d;               // super-block scale
-    int8_t  scales[QK_K/16];     // 8-bit block scales
-    uint8_t qh[QK_K/8];          // quants, high bit
-    uint8_t qs[QK_K/2];          // quants, low 4 bits
-} block_q5_K;
-static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
-#else
-typedef struct {
-    ggml_fp16_t d;               // super-block scale for quantized scales
-    ggml_fp16_t dmin;            // super-block scale for quantized mins
-    uint8_t scales[K_SCALE_SIZE];   // scales and mins, quantized with 6 bits
-    uint8_t qh[QK_K/8];          // quants, high bit
-    uint8_t qs[QK_K/2];          // quants, low 4 bits
-} block_q5_K;
-static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
-#endif
-
-// 6-bit quantization
-// weight is represented as x = a * q
-// 16 blocks of 16 elements each
-// Effectively 6.5625 bits per weight
-typedef struct {
-    uint8_t ql[QK_K/2];      // quants, lower 4 bits
-    uint8_t qh[QK_K/4];      // quants, upper 2 bits
-    int8_t  scales[QK_K/16]; // scales, quantized with 8 bits
-    ggml_fp16_t d;           // super-block scale
-} block_q6_K;
-static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + QK_K / 16 + 3*QK_K/4, "wrong q6_K block size/padding");
-
-// This is only used for intermediate quantization and dot products
-typedef struct {
-    float   d;              // delta
-    int8_t  qs[QK_K];       // quants
-    int16_t bsums[QK_K/16]; // sum of quants in groups of 16
-} block_q8_K;
-static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding");
-
-// (Almost) "true" 2-bit quantization.
-// Due to the need to use blocks as per ggml dsign, it ends up using
-// 2.0625 bpw because of the 16-bit scale for each block of 256.
-typedef struct {
-    ggml_fp16_t d;
-    uint16_t qs[QK_K/8];
-} block_iq2_xxs;
-static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding");
-
 // Quantization
-void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k);
-void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k);
-void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k);
-void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k);
-void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k);
-void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k);
+void quantize_row_q4_0_reference(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int k);
+void quantize_row_q4_1_reference(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int k);
+void quantize_row_q5_0_reference(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int k);
+void quantize_row_q5_1_reference(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int k);
+void quantize_row_q8_0_reference(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int k);
+void quantize_row_q8_1_reference(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int k);
 
-void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k);
-void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k);
-void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k);
-void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k);
-void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k);
-void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k);
-void quantize_row_iq2_xxs_reference(const float * restrict x, block_iq2_xxs * restrict y, int k);
+void quantize_row_q2_K_reference(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int k);
+void quantize_row_q3_K_reference(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int k);
+void quantize_row_q4_K_reference(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int k);
+void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int k);
+void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int k);
+void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int k);
 
-void quantize_row_q4_0(const float * restrict x, void * restrict y, int k);
-void quantize_row_q4_1(const float * restrict x, void * restrict y, int k);
-void quantize_row_q5_0(const float * restrict x, void * restrict y, int k);
-void quantize_row_q5_1(const float * restrict x, void * restrict y, int k);
-void quantize_row_q8_0(const float * restrict x, void * restrict y, int k);
-void quantize_row_q8_1(const float * restrict x, void * restrict y, int k);
+void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int k);
+void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl  * GGML_RESTRICT y, int k);
+void quantize_row_iq4_xs_reference (const float * GGML_RESTRICT x, block_iq4_xs  * GGML_RESTRICT y, int k);
+void quantize_row_iq3_s_reference  (const float * GGML_RESTRICT x, block_iq3_s   * GGML_RESTRICT y, int k);
+void quantize_row_iq2_s_reference  (const float * GGML_RESTRICT x, block_iq2_s   * GGML_RESTRICT y, int k);
 
-void quantize_row_q2_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q3_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q4_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q5_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q6_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q8_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_iq2_xxs(const float * restrict x, void * restrict y, int k);
+void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q5_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+
+void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+
+void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_iq3_s  (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+void quantize_row_iq2_s  (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
 
 // Dequantization
-void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k);
-void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int k);
-void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int k);
-void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int k);
-void dequantize_row_q8_0(const block_q8_0 * restrict x, float * restrict y, int k);
-//void dequantize_row_q8_1(const block_q8_1 * restrict x, float * restrict y, int k);
+void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q4_1(const block_q4_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q5_0(const block_q5_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q5_1(const block_q5_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+//void dequantize_row_q8_1(const block_q8_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
 
-void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k);
-void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k);
-void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k);
-void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k);
-void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k);
-void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k);
-void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k);
+void dequantize_row_q2_K(const block_q2_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q3_K(const block_q3_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+
+void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_iq2_xs (const block_iq2_xs  * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_iq2_s  (const block_iq2_s   * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_iq1_s  (const block_iq1_s   * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_iq1_m  (const block_iq1_m   * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_iq4_nl (const block_iq4_nl  * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_iq4_xs (const block_iq4_xs  * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+void dequantize_row_iq3_s  (const block_iq3_s   * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
 
 // Dot product
-void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+
+void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+
+void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq2_s_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq1_s_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq1_m_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq4_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq3_s_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+
+// Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization")
+size_t quantize_iq2_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+size_t quantize_iq2_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+size_t quantize_iq2_s  (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+size_t quantize_iq3_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+size_t quantize_iq1_s  (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+size_t quantize_iq1_m  (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+size_t quantize_iq4_nl (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+size_t quantize_iq4_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+size_t quantize_iq3_s  (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+
+size_t quantize_q2_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+size_t quantize_q3_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+size_t quantize_q4_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+size_t quantize_q5_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+size_t quantize_q6_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+size_t quantize_q4_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+size_t quantize_q4_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+size_t quantize_q5_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+size_t quantize_q5_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+
+void iq2xs_init_impl(enum ggml_type type);
+void iq2xs_free_impl(enum ggml_type type);
+void iq3xs_init_impl(int grid_size);
+void iq3xs_free_impl(int grid_size);
+
+#ifdef __cplusplus
+}
+#endif
 
-void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp
new file mode 100644
index 000000000..db3c24f60
--- /dev/null
+++ b/ggml-sycl.cpp
@@ -0,0 +1,17190 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#include <algorithm>
+#include <assert.h>
+#include <atomic>
+#include <cinttypes>
+#include <cstddef>
+#include <cstdint>
+#include <cstdlib>
+#include <float.h>
+#include <limits>
+#include <stdint.h>
+#include <stdio.h>
+#include <vector>
+#include <cmath>
+#include <iostream>
+#include <fstream>
+#include <stdio.h>
+#include <stdlib.h>
+#include <regex>
+
+#include <sycl/sycl.hpp>
+#include <sycl/half_type.hpp>
+
+#include "ggml-sycl.h"
+#include "ggml.h"
+#include "ggml-backend-impl.h"
+
+/*
+Following definition copied from DPCT head files, which are used by ggml-sycl.cpp
+*/
+// COPY from DPCT head files
+#include <sycl/sycl.hpp>
+#include <oneapi/mkl.hpp>
+#include <map>
+
+#if defined(__linux__)
+#include <sys/mman.h>
+#elif defined(_WIN64)
+#ifndef NOMINMAX
+#define NOMINMAX
+#endif
+#include <windows.h>
+#else
+#error "Only support Windows and Linux."
+#endif
+
+#if defined(__linux__)
+#include <unistd.h>
+#include <sys/syscall.h>
+#endif
+#if defined(_WIN64)
+#ifndef NOMINMAX
+#define NOMINMAX
+#endif
+#include <windows.h>
+#endif
+
+#define DPCT_COMPATIBILITY_TEMP (900)
+
+#if defined(_MSC_VER)
+#define __dpct_align__(n) __declspec(align(n))
+#define __dpct_inline__ __forceinline
+#else
+#define __dpct_align__(n) __attribute__((aligned(n)))
+#define __dpct_inline__ __inline__ __attribute__((always_inline))
+#endif
+
+#if defined(_MSC_VER)
+#define __dpct_noinline__ __declspec(noinline)
+#else
+#define __dpct_noinline__ __attribute__((noinline))
+#endif
+
+
+std::string get_device_type_name(const sycl::device &Device) {
+    auto DeviceType = Device.get_info<sycl::info::device::device_type>();
+    switch (DeviceType) {
+    case sycl::info::device_type::cpu:
+        return "cpu";
+    case sycl::info::device_type::gpu:
+        return "gpu";
+    case sycl::info::device_type::host:
+        return "host";
+    case sycl::info::device_type::accelerator:
+        return "acc";
+    default:
+        return "unknown";
+    }
+}
+
+std::string get_device_backend_and_type(const sycl::device &device) {
+    std::stringstream device_type;
+    sycl::backend backend = device.get_backend();
+    device_type <<  backend << ":" << get_device_type_name(device);
+    return device_type.str();
+}
+
+namespace dpct
+{
+    typedef sycl::queue *queue_ptr;
+    typedef sycl::event *event_ptr;
+    typedef char *device_ptr;
+    typedef uint8_t byte_t;
+    typedef sycl::buffer<byte_t> buffer_t;
+
+    /// SYCL default exception handler
+    inline auto exception_handler = [](sycl::exception_list exceptions)
+    {
+        for (std::exception_ptr const &e : exceptions)
+        {
+            try
+            {
+                std::rethrow_exception(e);
+            }
+            catch (sycl::exception const &e)
+            {
+                std::cerr << "Caught asynchronous SYCL exception:" << std::endl
+                          << e.what() << std::endl
+                          << "Exception caught at file:" << __FILE__
+                          << ", line:" << __LINE__ << std::endl;
+            }
+        }
+    };
+
+    enum error_code
+    {
+        success = 0,
+        default_error = 999
+    };
+
+    enum memcpy_direction
+    {
+        host_to_host,
+        host_to_device,
+        device_to_host,
+        device_to_device,
+        automatic
+    };
+
+    enum memory_region
+    {
+        global = 0, // device global memory
+        constant,   // device constant memory
+        local,      // device local memory
+        shared,     // memory which can be accessed by host and device
+    };
+
+    enum class library_data_t : unsigned char
+    {
+        real_float = 0,
+        complex_float,
+        real_double,
+        complex_double,
+        real_half,
+        complex_half,
+        real_bfloat16,
+        complex_bfloat16,
+        real_int4,
+        complex_int4,
+        real_uint4,
+        complex_uint4,
+        real_int8,
+        complex_int8,
+        real_uint8,
+        complex_uint8,
+        real_int16,
+        complex_int16,
+        real_uint16,
+        complex_uint16,
+        real_int32,
+        complex_int32,
+        real_uint32,
+        complex_uint32,
+        real_int64,
+        complex_int64,
+        real_uint64,
+        complex_uint64,
+        real_int8_4,
+        real_int8_32,
+        real_uint8_4,
+        library_data_t_size
+    };
+
+    template <typename T>
+    struct DataType
+    {
+        using T2 = T;
+    };
+    template <typename T>
+    struct DataType<sycl::vec<T, 2>>
+    {
+        using T2 = std::complex<T>;
+    };
+
+    static void destroy_event(event_ptr event)
+    {
+        delete event;
+    }
+
+    static inline unsigned int get_tid()
+    {
+#if defined(__linux__)
+        return syscall(SYS_gettid);
+#elif defined(_WIN64)
+        return GetCurrentThreadId();
+#else
+#error "Only support Windows and Linux."
+#endif
+    }
+
+    namespace detail
+    {
+        static void get_version(const sycl::device &dev, int &major, int &minor)
+        {
+            // Version string has the following format:
+            // a. OpenCL<space><major.minor><space><vendor-specific-information>
+            // b. <major.minor>
+            // c. <AmdGcnArchName> e.g gfx1030
+            std::string ver;
+            ver = dev.get_info<sycl::info::device::version>();
+            std::string::size_type i = 0;
+            while (i < ver.size()) {
+              if (isdigit(ver[i]))
+                break;
+              i++;
+            }
+            major = std::stoi(&(ver[i]));
+            while (i < ver.size()) {
+              if (ver[i] == '.')
+                break;
+              i++;
+            }
+            if (i < ver.size()) {
+              // a. and b.
+              i++;
+              minor = std::stoi(&(ver[i]));
+            } else {
+              // c.
+              minor = 0;
+            }
+        }
+
+        template <typename tag, typename T>
+        class generic_error_type
+        {
+        public:
+            generic_error_type() = default;
+            generic_error_type(T value) : value{value} {}
+            operator T() const { return value; }
+
+        private:
+            T value;
+        };
+
+    } // namespace detail
+
+    /// Pitched 2D/3D memory data.
+    class pitched_data
+    {
+    public:
+        pitched_data() : pitched_data(nullptr, 0, 0, 0) {}
+        pitched_data(void *data, size_t pitch, size_t x, size_t y)
+            : _data(data), _pitch(pitch), _x(x), _y(y) {}
+
+        void *get_data_ptr() { return _data; }
+        void set_data_ptr(void *data) { _data = data; }
+
+        size_t get_pitch() { return _pitch; }
+        void set_pitch(size_t pitch) { _pitch = pitch; }
+
+        size_t get_x() { return _x; }
+        void set_x(size_t x) { _x = x; };
+
+        size_t get_y() { return _y; }
+        void set_y(size_t y) { _y = y; }
+
+    private:
+        void *_data;
+        size_t _pitch, _x, _y;
+    };
+
+    class device_info
+    {
+    public:
+        // get interface
+        const char *get_name() const { return _name; }
+        char *get_name() { return _name; }
+        template <typename WorkItemSizesTy = sycl::range<3>,
+                  std::enable_if_t<std::is_same_v<WorkItemSizesTy, sycl::range<3>> ||
+                                       std::is_same_v<WorkItemSizesTy, int *>,
+                                   int> = 0>
+        auto get_max_work_item_sizes() const
+        {
+            if constexpr (std::is_same_v<WorkItemSizesTy, sycl::range<3>>)
+                return sycl::range<3>(_max_work_item_sizes_i[0],
+                                      _max_work_item_sizes_i[1],
+                                      _max_work_item_sizes_i[2]);
+            else
+            {
+                return _max_work_item_sizes_i;
+            }
+        }
+        template <typename WorkItemSizesTy = sycl::range<3>,
+                  std::enable_if_t<std::is_same_v<WorkItemSizesTy, sycl::range<3>> ||
+                                       std::is_same_v<WorkItemSizesTy, int *>,
+                                   int> = 0>
+        auto get_max_work_item_sizes()
+        {
+            if constexpr (std::is_same_v<WorkItemSizesTy, sycl::range<3>>)
+                return sycl::range<3>(_max_work_item_sizes_i[0],
+                                      _max_work_item_sizes_i[1],
+                                      _max_work_item_sizes_i[2]);
+            else
+            {
+                return _max_work_item_sizes_i;
+            }
+        }
+        bool get_host_unified_memory() const { return _host_unified_memory; }
+        int get_major_version() const { return _major; }
+        int get_minor_version() const { return _minor; }
+        int get_integrated() const { return _integrated; }
+        int get_max_clock_frequency() const { return _frequency; }
+        int get_max_compute_units() const { return _max_compute_units; }
+        int get_max_work_group_size() const { return _max_work_group_size; }
+        int get_max_sub_group_size() const { return _max_sub_group_size; }
+        int get_max_work_items_per_compute_unit() const
+        {
+            return _max_work_items_per_compute_unit;
+        }
+        int get_max_register_size_per_work_group() const
+        {
+            return _max_register_size_per_work_group;
+        }
+        template <typename NDRangeSizeTy = size_t *,
+                  std::enable_if_t<std::is_same_v<NDRangeSizeTy, size_t *> ||
+                                       std::is_same_v<NDRangeSizeTy, int *>,
+                                   int> = 0>
+        auto get_max_nd_range_size() const
+        {
+            if constexpr (std::is_same_v<NDRangeSizeTy, size_t *>)
+                return _max_nd_range_size;
+            else
+                return _max_nd_range_size_i;
+        }
+        template <typename NDRangeSizeTy = size_t *,
+                  std::enable_if_t<std::is_same_v<NDRangeSizeTy, size_t *> ||
+                                       std::is_same_v<NDRangeSizeTy, int *>,
+                                   int> = 0>
+        auto get_max_nd_range_size()
+        {
+            if constexpr (std::is_same_v<NDRangeSizeTy, size_t *>)
+                return _max_nd_range_size;
+            else
+                return _max_nd_range_size_i;
+        }
+        size_t get_global_mem_size() const { return _global_mem_size; }
+        size_t get_local_mem_size() const { return _local_mem_size; }
+        size_t get_max_mem_alloc_size() const { return _max_mem_alloc_size; }
+        /// Returns the maximum clock rate of device's global memory in kHz. If
+        /// compiler does not support this API then returns default value 3200000 kHz.
+        unsigned int get_memory_clock_rate() const { return _memory_clock_rate; }
+        /// Returns the maximum bus width between device and memory in bits. If
+        /// compiler does not support this API then returns default value 64 bits.
+        unsigned int get_memory_bus_width() const { return _memory_bus_width; }
+        uint32_t get_device_id() const { return _device_id; }
+        std::array<unsigned char, 16> get_uuid() const { return _uuid; }
+        /// Returns global memory cache size in bytes.
+        unsigned int get_global_mem_cache_size() const
+        {
+            return _global_mem_cache_size;
+        }
+
+        // set interface
+        void set_name(const char *name)
+        {
+            size_t length = strlen(name);
+            if (length < 256)
+            {
+                std::memcpy(_name, name, length + 1);
+            }
+            else
+            {
+                std::memcpy(_name, name, 255);
+                _name[255] = '\0';
+            }
+        }
+        void set_max_work_item_sizes(const sycl::range<3> max_work_item_sizes)
+        {
+            for (int i = 0; i < 3; ++i)
+                _max_work_item_sizes_i[i] = max_work_item_sizes[i];
+        }
+        [[deprecated]] void
+        set_max_work_item_sizes(const sycl::id<3> max_work_item_sizes)
+        {
+            for (int i = 0; i < 3; ++i)
+            {
+                _max_work_item_sizes_i[i] = max_work_item_sizes[i];
+            }
+        }
+        void set_host_unified_memory(bool host_unified_memory)
+        {
+            _host_unified_memory = host_unified_memory;
+        }
+        void set_major_version(int major) { _major = major; }
+        void set_minor_version(int minor) { _minor = minor; }
+        void set_integrated(int integrated) { _integrated = integrated; }
+        void set_max_clock_frequency(int frequency) { _frequency = frequency; }
+        void set_max_compute_units(int max_compute_units)
+        {
+            _max_compute_units = max_compute_units;
+        }
+        void set_global_mem_size(size_t global_mem_size)
+        {
+            _global_mem_size = global_mem_size;
+        }
+        void set_local_mem_size(size_t local_mem_size)
+        {
+            _local_mem_size = local_mem_size;
+        }
+        void set_max_mem_alloc_size(size_t max_mem_alloc_size)
+        {
+            _max_mem_alloc_size = max_mem_alloc_size;
+        }
+        void set_max_work_group_size(int max_work_group_size)
+        {
+            _max_work_group_size = max_work_group_size;
+        }
+        void set_max_sub_group_size(int max_sub_group_size)
+        {
+            _max_sub_group_size = max_sub_group_size;
+        }
+        void
+        set_max_work_items_per_compute_unit(int max_work_items_per_compute_unit)
+        {
+            _max_work_items_per_compute_unit = max_work_items_per_compute_unit;
+        }
+        void set_max_nd_range_size(int max_nd_range_size[])
+        {
+            for (int i = 0; i < 3; i++)
+            {
+                _max_nd_range_size[i] = max_nd_range_size[i];
+                _max_nd_range_size_i[i] = max_nd_range_size[i];
+            }
+        }
+        void set_memory_clock_rate(unsigned int memory_clock_rate)
+        {
+            _memory_clock_rate = memory_clock_rate;
+        }
+        void set_memory_bus_width(unsigned int memory_bus_width)
+        {
+            _memory_bus_width = memory_bus_width;
+        }
+        void
+        set_max_register_size_per_work_group(int max_register_size_per_work_group)
+        {
+            _max_register_size_per_work_group = max_register_size_per_work_group;
+        }
+        void set_device_id(uint32_t device_id)
+        {
+            _device_id = device_id;
+        }
+        void set_uuid(std::array<unsigned char, 16> uuid)
+        {
+            _uuid = std::move(uuid);
+        }
+        void set_global_mem_cache_size(unsigned int global_mem_cache_size)
+        {
+            _global_mem_cache_size = global_mem_cache_size;
+        }
+
+    private:
+        char _name[256];
+        int _max_work_item_sizes_i[3];
+        bool _host_unified_memory = false;
+        int _major;
+        int _minor;
+        int _integrated = 0;
+        int _frequency;
+        // Set estimated value 3200000 kHz as default value.
+        unsigned int _memory_clock_rate = 3200000;
+        // Set estimated value 64 bits as default value.
+        unsigned int _memory_bus_width = 64;
+        unsigned int _global_mem_cache_size;
+        int _max_compute_units;
+        int _max_work_group_size;
+        int _max_sub_group_size;
+        int _max_work_items_per_compute_unit;
+        int _max_register_size_per_work_group;
+        size_t _global_mem_size;
+        size_t _local_mem_size;
+        size_t _max_mem_alloc_size;
+        size_t _max_nd_range_size[3];
+        int _max_nd_range_size_i[3];
+        uint32_t _device_id;
+        std::array<unsigned char, 16> _uuid;
+    };
+
+    static int get_major_version(const sycl::device &dev)
+    {
+        int major, minor;
+        detail::get_version(dev, major, minor);
+        return major;
+    }
+
+    static int get_minor_version(const sycl::device &dev)
+    {
+        int major, minor;
+        detail::get_version(dev, major, minor);
+        return minor;
+    }
+
+    static void get_device_info(device_info &out, const sycl::device &dev)
+    {
+        device_info prop;
+        prop.set_name(dev.get_info<sycl::info::device::name>().c_str());
+
+        int major, minor;
+        detail::get_version(dev, major, minor);
+        prop.set_major_version(major);
+        prop.set_minor_version(minor);
+
+        prop.set_max_work_item_sizes(
+#if (__SYCL_COMPILER_VERSION && __SYCL_COMPILER_VERSION < 20220902)
+            // oneAPI DPC++ compiler older than 2022/09/02, where max_work_item_sizes
+            // is an enum class element
+            dev.get_info<sycl::info::device::max_work_item_sizes>());
+#else
+            // SYCL 2020-conformant code, max_work_item_sizes is a struct templated by
+            // an int
+            dev.get_info<sycl::info::device::max_work_item_sizes<3>>());
+#endif
+        prop.set_host_unified_memory(dev.has(sycl::aspect::usm_host_allocations));
+
+        prop.set_max_clock_frequency(
+            dev.get_info<sycl::info::device::max_clock_frequency>() * 1000);
+
+        prop.set_max_compute_units(
+            dev.get_info<sycl::info::device::max_compute_units>());
+        prop.set_max_work_group_size(
+            dev.get_info<sycl::info::device::max_work_group_size>());
+        prop.set_global_mem_size(dev.get_info<sycl::info::device::global_mem_size>());
+        prop.set_local_mem_size(dev.get_info<sycl::info::device::local_mem_size>());
+        prop.set_max_mem_alloc_size(dev.get_info<sycl::info::device::max_mem_alloc_size>());
+
+#if (defined(SYCL_EXT_INTEL_DEVICE_INFO) && SYCL_EXT_INTEL_DEVICE_INFO >= 6)
+        if (dev.has(sycl::aspect::ext_intel_memory_clock_rate))
+        {
+            unsigned int tmp =
+                dev.get_info<sycl::ext::intel::info::device::memory_clock_rate>();
+            if (tmp != 0)
+                prop.set_memory_clock_rate(1000 * tmp);
+        }
+        if (dev.has(sycl::aspect::ext_intel_memory_bus_width))
+        {
+            prop.set_memory_bus_width(
+                dev.get_info<sycl::ext::intel::info::device::memory_bus_width>());
+        }
+        if (dev.has(sycl::aspect::ext_intel_device_id))
+        {
+            prop.set_device_id(
+                dev.get_info<sycl::ext::intel::info::device::device_id>());
+        }
+        if (dev.has(sycl::aspect::ext_intel_device_info_uuid))
+        {
+            prop.set_uuid(dev.get_info<sycl::ext::intel::info::device::uuid>());
+        }
+#elif defined(_MSC_VER) && !defined(__clang__)
+#pragma message("get_device_info: querying memory_clock_rate and \
+        memory_bus_width are not supported by the compiler used. \
+        Use 3200000 kHz as memory_clock_rate default value. \
+        Use 64 bits as memory_bus_width default value.")
+#else
+#warning "get_device_info: querying memory_clock_rate and \
+        memory_bus_width are not supported by the compiler used. \
+        Use 3200000 kHz as memory_clock_rate default value. \
+        Use 64 bits as memory_bus_width default value."
+#endif
+
+        size_t max_sub_group_size = 1;
+        std::vector<size_t> sub_group_sizes =
+            dev.get_info<sycl::info::device::sub_group_sizes>();
+
+        for (const auto &sub_group_size : sub_group_sizes)
+        {
+            if (max_sub_group_size < sub_group_size)
+                max_sub_group_size = sub_group_size;
+        }
+
+        prop.set_max_sub_group_size(max_sub_group_size);
+
+        prop.set_max_work_items_per_compute_unit(
+            dev.get_info<sycl::info::device::max_work_group_size>());
+        int max_nd_range_size[] = {0x7FFFFFFF, 0x7FFFFFFF, 0x7FFFFFFF};
+        prop.set_max_nd_range_size(max_nd_range_size);
+
+        // Estimates max register size per work group, feel free to update the value
+        // according to device properties.
+        prop.set_max_register_size_per_work_group(65536);
+
+        prop.set_global_mem_cache_size(
+            dev.get_info<sycl::info::device::global_mem_cache_size>());
+        out = prop;
+    }
+
+    /// dpct device extension
+    class device_ext : public sycl::device
+    {
+        typedef std::mutex mutex_type;
+
+    public:
+        device_ext() : sycl::device(), _ctx(*this) {}
+        ~device_ext()
+        {
+            std::lock_guard<mutex_type> lock(m_mutex);
+            clear_queues();
+        }
+        device_ext(const sycl::device &base) : sycl::device(base), _ctx(*this)
+        {
+            std::lock_guard<mutex_type> lock(m_mutex);
+            init_queues();
+        }
+
+        int is_native_atomic_supported() { return 0; }
+        int get_major_version() const
+        {
+            return dpct::get_major_version(*this);
+        }
+
+        int get_minor_version() const
+        {
+            return dpct::get_minor_version(*this);
+        }
+
+        int get_max_compute_units() const
+        {
+            return get_device_info().get_max_compute_units();
+        }
+
+        /// Return the maximum clock frequency of this device in KHz.
+        int get_max_clock_frequency() const
+        {
+            return get_device_info().get_max_clock_frequency();
+        }
+
+        int get_integrated() const { return get_device_info().get_integrated(); }
+
+        int get_max_sub_group_size() const
+        {
+            return get_device_info().get_max_sub_group_size();
+        }
+
+        int get_max_register_size_per_work_group() const
+        {
+            return get_device_info().get_max_register_size_per_work_group();
+        }
+
+        int get_max_work_group_size() const
+        {
+            return get_device_info().get_max_work_group_size();
+        }
+
+        int get_mem_base_addr_align() const
+        {
+            return get_info<sycl::info::device::mem_base_addr_align>();
+        }
+
+        size_t get_global_mem_size() const
+        {
+            return get_device_info().get_global_mem_size();
+        }
+
+        size_t get_max_mem_alloc_size() const
+        {
+            return get_device_info().get_max_mem_alloc_size();
+        }
+
+        /// Get the number of bytes of free and total memory on the SYCL device.
+        /// \param [out] free_memory The number of bytes of free memory on the SYCL device.
+        /// \param [out] total_memory The number of bytes of total memory on the SYCL device.
+        void get_memory_info(size_t &free_memory, size_t &total_memory)
+        {
+            total_memory = get_device_info().get_global_mem_size();
+            const char *warning_info = "get_memory_info: [warning] ext_intel_free_memory is not "
+                                 "supported (export/set ZES_ENABLE_SYSMAN=1 to support), "
+                                 "use total memory as free memory";
+#if (defined(__SYCL_COMPILER_VERSION) && __SYCL_COMPILER_VERSION >= 20221105)
+            if (!has(sycl::aspect::ext_intel_free_memory))
+            {
+                std::cerr << warning_info << std::endl;
+                free_memory = total_memory;
+            }
+            else
+            {
+                free_memory = get_info<sycl::ext::intel::info::device::free_memory>();
+            }
+#else
+            std::cerr << warning_info << std::endl;
+            free_memory = total_memory;
+#if defined(_MSC_VER) && !defined(__clang__)
+#pragma message("Querying the number of bytes of free memory is not supported")
+#else
+#warning "Querying the number of bytes of free memory is not supported"
+#endif
+#endif
+        }
+
+        void get_device_info(device_info &out) const
+        {
+            dpct::get_device_info(out, *this);
+        }
+
+        device_info get_device_info() const
+        {
+            device_info prop;
+            dpct::get_device_info(prop, *this);
+            return prop;
+        }
+
+        void reset()
+        {
+            std::lock_guard<mutex_type> lock(m_mutex);
+            clear_queues();
+            init_queues();
+        }
+
+        sycl::queue &in_order_queue() { return *_q_in_order; }
+
+        sycl::queue &out_of_order_queue() { return *_q_out_of_order; }
+
+        sycl::queue &default_queue()
+        {
+            return in_order_queue();
+        }
+
+        void queues_wait_and_throw()
+        {
+            std::unique_lock<mutex_type> lock(m_mutex);
+            std::vector<std::shared_ptr<sycl::queue>> current_queues(
+                _queues);
+            lock.unlock();
+            for (const auto &q : current_queues)
+            {
+                q->wait_and_throw();
+            }
+            // Guard the destruct of current_queues to make sure the ref count is safe.
+            lock.lock();
+        }
+
+        sycl::queue *create_queue(bool enable_exception_handler = false)
+        {
+            return create_in_order_queue(enable_exception_handler);
+        }
+
+        sycl::queue *create_queue(sycl::context context, sycl::device device,
+                                bool enable_exception_handler = false) {
+            return create_in_order_queue(context, device, enable_exception_handler);
+        }
+
+        sycl::queue *create_in_order_queue(bool enable_exception_handler = false) {
+            std::lock_guard<mutex_type> lock(m_mutex);
+            return create_queue_impl(enable_exception_handler,
+                                    sycl::property::queue::in_order());
+        }
+
+        sycl::queue *create_in_order_queue(sycl::context context, sycl::device device,
+                                        bool enable_exception_handler = false) {
+            std::lock_guard<mutex_type> lock(m_mutex);
+            return create_queue_impl(context, device, enable_exception_handler,
+                                    sycl::property::queue::in_order());
+        }
+
+        sycl::queue *create_out_of_order_queue(bool enable_exception_handler = false) {
+            std::lock_guard<mutex_type> lock(m_mutex);
+            return create_queue_impl(enable_exception_handler);
+        }
+
+        void destroy_queue(sycl::queue *&queue)
+        {
+            std::lock_guard<mutex_type> lock(m_mutex);
+            _queues.erase(std::remove_if(_queues.begin(), _queues.end(),
+                                         [=](const std::shared_ptr<sycl::queue> &q) -> bool
+                                         {
+                                             return q.get() == queue;
+                                         }),
+                          _queues.end());
+            queue = nullptr;
+        }
+        void set_saved_queue(sycl::queue *q)
+        {
+            std::lock_guard<mutex_type> lock(m_mutex);
+            _saved_queue = q;
+        }
+        sycl::queue *get_saved_queue() const
+        {
+            std::lock_guard<mutex_type> lock(m_mutex);
+            return _saved_queue;
+        }
+        sycl::context get_context() const { return _ctx; }
+
+    private:
+        void clear_queues()
+        {
+            _queues.clear();
+            _q_in_order = _q_out_of_order = _saved_queue = nullptr;
+        }
+
+        void init_queues()
+        {
+            _q_in_order = create_queue_impl(true, sycl::property::queue::in_order());
+            _q_out_of_order = create_queue_impl(true);
+            _saved_queue = &default_queue();
+        }
+
+        /// Caller should acquire resource \p m_mutex before calling this function.
+        template <class... Properties>
+        sycl::queue *create_queue_impl(bool enable_exception_handler,
+                                       Properties... properties)
+        {
+            sycl::async_handler eh = {};
+            if (enable_exception_handler)
+            {
+                eh = exception_handler;
+            }
+            _queues.push_back(std::make_shared<sycl::queue>(
+                _ctx, *this, eh,
+                sycl::property_list(
+#ifdef DPCT_PROFILING_ENABLED
+                    sycl::property::queue::enable_profiling(),
+#endif
+                    properties...)));
+
+            return _queues.back().get();
+        }
+
+        template <class... Properties>
+        sycl::queue *create_queue_impl(sycl::context context, sycl::device device,
+                                    bool enable_exception_handler,
+                                    Properties... properties) {
+            sycl::async_handler eh = {};
+            if (enable_exception_handler) {
+                eh = exception_handler;
+            }
+            _queues.push_back(std::make_shared<sycl::queue>(
+                context, device, eh,
+                sycl::property_list(
+        #ifdef DPCT_PROFILING_ENABLED
+                    sycl::property::queue::enable_profiling(),
+        #endif
+                    properties...)));
+
+            return _queues.back().get();
+        }
+
+        void get_version(int &major, int &minor) const
+        {
+            detail::get_version(*this, major, minor);
+        }
+        sycl::queue *_q_in_order, *_q_out_of_order;
+        sycl::queue *_saved_queue;
+        sycl::context _ctx;
+        std::vector<std::shared_ptr<sycl::queue>> _queues;
+        mutable mutex_type m_mutex;
+    };
+
+    /// device manager
+    class dev_mgr
+    {
+    public:
+        device_ext &current_device()
+        {
+            unsigned int dev_id = current_device_id();
+            check_id(dev_id);
+            return *_devs[dev_id];
+        }
+        device_ext &cpu_device() const
+        {
+            std::lock_guard<std::recursive_mutex> lock(m_mutex);
+            if (_cpu_device == -1)
+            {
+                throw std::runtime_error("no valid cpu device");
+            }
+            else
+            {
+                return *_devs[_cpu_device];
+            }
+        }
+        device_ext &get_device(unsigned int id) const
+        {
+            std::lock_guard<std::recursive_mutex> lock(m_mutex);
+            check_id(id);
+            return *_devs[id];
+        }
+        unsigned int current_device_id() const
+        {
+            std::lock_guard<std::recursive_mutex> lock(m_mutex);
+            auto it = _thread2dev_map.find(get_tid());
+            if (it != _thread2dev_map.end())
+                return it->second;
+            return DEFAULT_DEVICE_ID;
+        }
+
+        /// Select device with a device ID.
+        /// \param [in] id The id of the device which can
+        /// be obtained through get_device_id(const sycl::device).
+        void select_device(unsigned int id)
+        {
+            std::lock_guard<std::recursive_mutex> lock(m_mutex);
+            check_id(id);
+            _thread2dev_map[get_tid()] = id;
+        }
+        unsigned int device_count() { return _devs.size(); }
+
+        unsigned int get_device_id(const sycl::device &dev)
+        {
+            unsigned int id = 0;
+            for (auto dev_item : _devs)
+            {
+                if (*dev_item == dev)
+                {
+                    break;
+                }
+                id++;
+            }
+            return id;
+        }
+
+        template <class DeviceSelector>
+        std::enable_if_t<
+            std::is_invocable_r_v<int, DeviceSelector, const sycl::device &>>
+        select_device(const DeviceSelector &selector = sycl::gpu_selector_v)
+        {
+            sycl::device selected_device = sycl::device(selector);
+            unsigned int selected_device_id = get_device_id(selected_device);
+            select_device(selected_device_id);
+        }
+
+        /// Returns the instance of device manager singleton.
+        static dev_mgr &instance()
+        {
+            static dev_mgr d_m;
+            return d_m;
+        }
+        dev_mgr(const dev_mgr &) = delete;
+        dev_mgr &operator=(const dev_mgr &) = delete;
+        dev_mgr(dev_mgr &&) = delete;
+        dev_mgr &operator=(dev_mgr &&) = delete;
+
+    private:
+        mutable std::recursive_mutex m_mutex;
+        static bool compare_dev(sycl::device &device1, sycl::device &device2)
+        {
+            dpct::device_info prop1;
+            dpct::get_device_info(prop1, device1);
+            dpct::device_info prop2;
+            dpct::get_device_info(prop2, device2);
+            return prop1.get_max_compute_units() > prop2.get_max_compute_units();
+        }
+        static int convert_backend_index(std::string & backend) {
+            if (backend == "ext_oneapi_level_zero:gpu") return 0;
+            if (backend == "opencl:gpu") return 1;
+            if (backend == "ext_oneapi_cuda:gpu") return 2;
+            if (backend == "ext_oneapi_hip:gpu") return 3;
+            if (backend == "opencl:cpu") return 4;
+            if (backend == "opencl:acc") return 5;
+            printf("convert_backend_index: can't handle backend=%s\n", backend.c_str());
+            GGML_ASSERT(false);
+        }
+        static bool compare_backend(std::string &backend1, std::string &backend2) {
+            return convert_backend_index(backend1) < convert_backend_index(backend2);
+        }
+        dev_mgr()
+        {
+            sycl::device default_device =
+                sycl::device(sycl::default_selector_v);
+            _devs.push_back(std::make_shared<device_ext>(default_device));
+
+            std::vector<sycl::device> sycl_all_devs;
+            // Collect other devices except for the default device.
+            if (default_device.is_cpu())
+                _cpu_device = 0;
+
+            auto Platforms = sycl::platform::get_platforms();
+            // Keep track of the number of devices per backend
+            std::map<sycl::backend, size_t> DeviceNums;
+            std::map<std::string, std::vector<sycl::device>> backend_devices;
+
+            while (!Platforms.empty()) {
+                auto Platform = Platforms.back();
+                Platforms.pop_back();
+                auto devices = Platform.get_devices();
+                std::string backend_type = get_device_backend_and_type(devices[0]);
+                for (const auto &device : devices) {
+                    backend_devices[backend_type].push_back(device);
+                }
+            }
+
+            std::vector<std::string> keys;
+            for(auto it = backend_devices.begin(); it != backend_devices.end(); ++it) {
+                keys.push_back(it->first);
+            }
+            std::sort(keys.begin(), keys.end(), compare_backend);
+
+            for (auto &key : keys) {
+                std::vector<sycl::device> devs = backend_devices[key];
+                std::sort(devs.begin(), devs.end(), compare_dev);
+                for (const auto &dev : devs) {
+                    sycl_all_devs.push_back(dev);
+                }
+            }
+
+            for (auto &dev : sycl_all_devs)
+            {
+                if (dev == default_device)
+                {
+                    continue;
+                }
+                _devs.push_back(std::make_shared<device_ext>(dev));
+                if (_cpu_device == -1 && dev.is_cpu())
+                {
+                    _cpu_device = _devs.size() - 1;
+                }
+            }
+        }
+        void check_id(unsigned int id) const
+        {
+            if (id >= _devs.size())
+            {
+                throw std::runtime_error("invalid device id");
+            }
+        }
+        std::vector<std::shared_ptr<device_ext>> _devs;
+        /// DEFAULT_DEVICE_ID is used, if current_device_id() can not find current
+        /// thread id in _thread2dev_map, which means default device should be used
+        /// for the current thread.
+        const unsigned int DEFAULT_DEVICE_ID = 0;
+        /// thread-id to device-id map.
+        std::map<unsigned int, unsigned int> _thread2dev_map;
+        int _cpu_device = -1;
+    };
+
+    static inline sycl::queue &get_default_queue()
+    {
+        return dev_mgr::instance().current_device().default_queue();
+    }
+
+    namespace detail
+    {
+        enum class pointer_access_attribute
+        {
+            host_only = 0,
+            device_only,
+            host_device,
+            end
+        };
+
+        static pointer_access_attribute get_pointer_attribute(sycl::queue &q,
+                                                              const void *ptr)
+        {
+            switch (sycl::get_pointer_type(ptr, q.get_context()))
+            {
+            case sycl::usm::alloc::unknown:
+                return pointer_access_attribute::host_only;
+            case sycl::usm::alloc::device:
+                return pointer_access_attribute::device_only;
+            case sycl::usm::alloc::shared:
+            case sycl::usm::alloc::host:
+                return pointer_access_attribute::host_device;
+            }
+        }
+
+        template <typename ArgT>
+        inline constexpr std::uint64_t get_type_combination_id(ArgT Val)
+        {
+            static_assert((unsigned char)library_data_t::library_data_t_size <=
+                              std::numeric_limits<unsigned char>::max() &&
+                          "library_data_t size exceeds limit.");
+            static_assert(std::is_same_v<ArgT, library_data_t>, "Unsupported ArgT");
+            return (std::uint64_t)Val;
+        }
+
+        template <typename FirstT, typename... RestT>
+        inline constexpr std::uint64_t get_type_combination_id(FirstT FirstVal,
+                                                               RestT... RestVal)
+        {
+            static_assert((std::uint8_t)library_data_t::library_data_t_size <=
+                              std::numeric_limits<unsigned char>::max() &&
+                          "library_data_t size exceeds limit.");
+            static_assert(sizeof...(RestT) <= 8 && "Too many parameters");
+            static_assert(std::is_same_v<FirstT, library_data_t>, "Unsupported FirstT");
+            return get_type_combination_id(RestVal...) << 8 | ((std::uint64_t)FirstVal);
+        }
+
+        class mem_mgr
+        {
+            mem_mgr()
+            {
+                // Reserved address space, no real memory allocation happens here.
+#if defined(__linux__)
+                mapped_address_space =
+                    (byte_t *)mmap(nullptr, mapped_region_size, PROT_NONE,
+                                   MAP_PRIVATE | MAP_ANONYMOUS, -1, 0);
+#elif defined(_WIN64)
+                mapped_address_space = (byte_t *)VirtualAlloc(
+                    NULL,               // NULL specified as the base address parameter
+                    mapped_region_size, // Size of allocation
+                    MEM_RESERVE,        // Allocate reserved pages
+                    PAGE_NOACCESS);     // Protection = no access
+#else
+#error "Only support Windows and Linux."
+#endif
+                next_free = mapped_address_space;
+            };
+
+        public:
+            using buffer_id_t = int;
+
+            struct allocation
+            {
+                buffer_t buffer;
+                byte_t *alloc_ptr;
+                size_t size;
+            };
+
+            ~mem_mgr()
+            {
+#if defined(__linux__)
+                munmap(mapped_address_space, mapped_region_size);
+#elif defined(_WIN64)
+                VirtualFree(mapped_address_space, 0, MEM_RELEASE);
+#else
+#error "Only support Windows and Linux."
+#endif
+            };
+
+            mem_mgr(const mem_mgr &) = delete;
+            mem_mgr &operator=(const mem_mgr &) = delete;
+            mem_mgr(mem_mgr &&) = delete;
+            mem_mgr &operator=(mem_mgr &&) = delete;
+
+            /// Allocate
+            void *mem_alloc(size_t size)
+            {
+                if (!size)
+                    return nullptr;
+                std::lock_guard<std::mutex> lock(m_mutex);
+                if (next_free + size > mapped_address_space + mapped_region_size)
+                {
+                    throw std::runtime_error("dpct_malloc: out of memory for virtual memory pool");
+                }
+                // Allocation
+                sycl::range<1> r(size);
+                buffer_t buf(r);
+                allocation A{buf, next_free, size};
+                // Map allocation to device pointer
+                void *result = next_free;
+                m_map.emplace(next_free + size, A);
+                // Update pointer to the next free space.
+                next_free += (size + extra_padding + alignment - 1) & ~(alignment - 1);
+
+                return result;
+            }
+
+            /// Deallocate
+            void mem_free(const void *ptr)
+            {
+                if (!ptr)
+                    return;
+                std::lock_guard<std::mutex> lock(m_mutex);
+                auto it = get_map_iterator(ptr);
+                m_map.erase(it);
+            }
+
+            /// map: device pointer -> allocation(buffer, alloc_ptr, size)
+            allocation translate_ptr(const void *ptr)
+            {
+                std::lock_guard<std::mutex> lock(m_mutex);
+                auto it = get_map_iterator(ptr);
+                return it->second;
+            }
+
+            /// Check if the pointer represents device pointer or not.
+            bool is_device_ptr(const void *ptr) const
+            {
+                std::lock_guard<std::mutex> lock(m_mutex);
+                return (mapped_address_space <= ptr) &&
+                       (ptr < mapped_address_space + mapped_region_size);
+            }
+
+            /// Returns the instance of memory manager singleton.
+            static mem_mgr &instance()
+            {
+                static mem_mgr m;
+                return m;
+            }
+
+        private:
+            std::map<byte_t *, allocation> m_map;
+            mutable std::mutex m_mutex;
+            byte_t *mapped_address_space;
+            byte_t *next_free;
+            const size_t mapped_region_size = 128ull * 1024 * 1024 * 1024;
+            const size_t alignment = 256;
+            /// This padding may be defined to some positive value to debug
+            /// out of bound accesses.
+            const size_t extra_padding = 0;
+
+            std::map<byte_t *, allocation>::iterator get_map_iterator(const void *ptr)
+            {
+                auto it = m_map.upper_bound((byte_t *)ptr);
+                if (it == m_map.end())
+                {
+                    // Not a virtual pointer.
+                    throw std::runtime_error("can not get buffer from non-virtual pointer");
+                }
+                const allocation &alloc = it->second;
+                if (ptr < alloc.alloc_ptr)
+                {
+                    // Out of bound.
+                    // This may happen if there's a gap between allocations due to alignment
+                    // or extra padding and pointer points to this gap.
+                    throw std::runtime_error("invalid virtual pointer");
+                }
+                return it;
+            }
+        };
+
+        template <class T, memory_region Memory, size_t Dimension>
+        class accessor;
+        template <memory_region Memory, class T = byte_t>
+        class memory_traits
+        {
+        public:
+            static constexpr sycl::access::target target =
+                sycl::access::target::device;
+            static constexpr sycl::access_mode mode =
+                (Memory == constant) ? sycl::access_mode::read
+                                     : sycl::access_mode::read_write;
+            static constexpr size_t type_size = sizeof(T);
+            using element_t =
+                typename std::conditional<Memory == constant, const T, T>::type;
+            using value_t = typename std::remove_cv<T>::type;
+            template <size_t Dimension = 1>
+            using accessor_t = typename std::conditional<
+                Memory == local, sycl::local_accessor<value_t, Dimension>,
+                sycl::accessor<T, Dimension, mode, target>>::type;
+            using pointer_t = T *;
+        };
+
+        static inline void *dpct_malloc(size_t size, sycl::queue &q)
+        {
+            return sycl::malloc_device(size, q.get_device(), q.get_context());
+        }
+
+#define PITCH_DEFAULT_ALIGN(x) (((x) + 31) & ~(0x1F))
+        static inline void *dpct_malloc(size_t &pitch, size_t x, size_t y, size_t z,
+                                        sycl::queue &q)
+        {
+            pitch = PITCH_DEFAULT_ALIGN(x);
+            return dpct_malloc(pitch * y * z, q);
+        }
+
+        /**
+         * @brief Sets \p value to the first \p size elements starting from \p dev_ptr in \p q.
+         * @tparam valueT The type of the element to be set.
+         * @param [in] q The queue in which the operation is done.
+         * @param [in] dev_ptr Pointer to the virtual device memory address.
+         * @param [in] value The value to be set.
+         * @param [in] size Number of elements to be set to the value.
+         * @return An event representing the memset operation.
+         */
+        template <typename valueT>
+        static inline sycl::event dpct_memset(sycl::queue &q, void *dev_ptr,
+                                              valueT value, size_t size)
+        {
+            return q.fill(dev_ptr, value, size);
+        }
+
+        /**
+         * @brief Sets \p value to the 3D memory region pointed by \p data in \p q.
+         * @tparam valueT The type of the element to be set.
+         * @param [in] q The queue in which the operation is done.
+         * @param [in] data Pointer to the pitched device memory region.
+         * @param [in] value The value to be set.
+         * @param [in] size 3D memory region by number of elements.
+         * @return An event list representing the memset operations.
+         */
+        template <typename valueT>
+        static inline std::vector<sycl::event>
+        dpct_memset(sycl::queue &q, pitched_data data, valueT value,
+                    sycl::range<3> size)
+        {
+            std::vector<sycl::event> event_list;
+            size_t slice = data.get_pitch() * data.get_y();
+            unsigned char *data_surface = (unsigned char *)data.get_data_ptr();
+            for (size_t z = 0; z < size.get(2); ++z)
+            {
+                unsigned char *data_ptr = data_surface;
+                for (size_t y = 0; y < size.get(1); ++y)
+                {
+                    event_list.push_back(dpct_memset(q, data_ptr, value, size.get(0)));
+                    data_ptr += data.get_pitch();
+                }
+                data_surface += slice;
+            }
+            return event_list;
+        }
+
+        /**
+         * @brief Sets \p val to the pitched 2D memory region pointed by \p ptr in \p q.
+         * @tparam valueT The type of the element to be set.
+         * @param [in] q The queue in which the operation is done.
+         * @param [in] ptr Pointer to the virtual device memory.
+         * @param [in] pitch The pitch size by number of elements, including padding.
+         * @param [in] val The value to be set.
+         * @param [in] x The width of memory region by number of elements.
+         * @param [in] y The height of memory region by number of elements.
+         * @return An event list representing the memset operations.
+         */
+        template <typename valueT>
+        static inline std::vector<sycl::event>
+        dpct_memset(sycl::queue &q, void *ptr, size_t pitch, valueT val, size_t x,
+                    size_t y)
+        {
+            return dpct_memset(q, pitched_data(ptr, pitch, x, 1), val,
+                               sycl::range<3>(x, y, 1));
+        }
+
+        static memcpy_direction deduce_memcpy_direction(sycl::queue &q, void *to_ptr,
+                                                        const void *from_ptr,
+                                                        memcpy_direction dir)
+        {
+            switch (dir)
+            {
+            case memcpy_direction::host_to_host:
+            case memcpy_direction::host_to_device:
+            case memcpy_direction::device_to_host:
+            case memcpy_direction::device_to_device:
+                return dir;
+            case memcpy_direction::automatic:
+            {
+                // table[to_attribute][from_attribute]
+                static const memcpy_direction
+                    direction_table[static_cast<unsigned>(pointer_access_attribute::end)]
+                                   [static_cast<unsigned>(pointer_access_attribute::end)] =
+                                       {{memcpy_direction::host_to_host,
+                                         memcpy_direction::device_to_host,
+                                         memcpy_direction::host_to_host},
+                                        {memcpy_direction::host_to_device,
+                                         memcpy_direction::device_to_device,
+                                         memcpy_direction::device_to_device},
+                                        {memcpy_direction::host_to_host,
+                                         memcpy_direction::device_to_device,
+                                         memcpy_direction::device_to_device}};
+                return direction_table[static_cast<unsigned>(get_pointer_attribute(
+                    q, to_ptr))][static_cast<unsigned>(get_pointer_attribute(q, from_ptr))];
+            }
+            default:
+                throw std::runtime_error("dpct_memcpy: invalid direction value");
+            }
+        }
+
+        static sycl::event
+        dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, size_t size,
+                    memcpy_direction direction,
+                    const std::vector<sycl::event> &dep_events = {})
+        {
+            if (!size)
+                return sycl::event{};
+            return q.memcpy(to_ptr, from_ptr, size, dep_events);
+            GGML_UNUSED(direction);
+        }
+
+        // Get actual copy range and make sure it will not exceed range.
+        static inline size_t get_copy_range(sycl::range<3> size, size_t slice,
+                                            size_t pitch)
+        {
+            return slice * (size.get(2) - 1) + pitch * (size.get(1) - 1) + size.get(0);
+        }
+
+        static inline size_t get_offset(sycl::id<3> id, size_t slice,
+                                        size_t pitch)
+        {
+            return slice * id.get(2) + pitch * id.get(1) + id.get(0);
+        }
+
+        /// copy 3D matrix specified by \p size from 3D matrix specified by \p from_ptr
+        /// and \p from_range to another specified by \p to_ptr and \p to_range.
+        static inline std::vector<sycl::event>
+        dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr,
+                    sycl::range<3> to_range, sycl::range<3> from_range,
+                    sycl::id<3> to_id, sycl::id<3> from_id,
+                    sycl::range<3> size, memcpy_direction direction,
+                    const std::vector<sycl::event> &dep_events = {})
+        {
+            // RAII for host pointer
+            class host_buffer
+            {
+                void *_buf;
+                size_t _size;
+                sycl::queue &_q;
+                const std::vector<sycl::event> &_deps; // free operation depends
+
+            public:
+                host_buffer(size_t size, sycl::queue &q,
+                            const std::vector<sycl::event> &deps)
+                    : _buf(std::malloc(size)), _size(size), _q(q), _deps(deps) {}
+                void *get_ptr() const { return _buf; }
+                size_t get_size() const { return _size; }
+                ~host_buffer()
+                {
+                    if (_buf)
+                    {
+                        _q.submit([&](sycl::handler &cgh)
+                                  {
+        cgh.depends_on(_deps);
+        cgh.host_task([buf = _buf] { std::free(buf); }); });
+                    }
+                }
+            };
+            std::vector<sycl::event> event_list;
+
+            size_t to_slice = to_range.get(1) * to_range.get(0),
+                   from_slice = from_range.get(1) * from_range.get(0);
+            unsigned char *to_surface =
+                (unsigned char *)to_ptr + get_offset(to_id, to_slice, to_range.get(0));
+            const unsigned char *from_surface =
+                (const unsigned char *)from_ptr +
+                get_offset(from_id, from_slice, from_range.get(0));
+
+            if (to_slice == from_slice && to_slice == size.get(1) * size.get(0))
+            {
+                return {dpct_memcpy(q, to_surface, from_surface, to_slice * size.get(2),
+                                    direction, dep_events)};
+            }
+            direction = deduce_memcpy_direction(q, to_ptr, from_ptr, direction);
+            size_t size_slice = size.get(1) * size.get(0);
+            switch (direction)
+            {
+            case host_to_host:
+                for (size_t z = 0; z < size.get(2); ++z)
+                {
+                    unsigned char *to_ptr = to_surface;
+                    const unsigned char *from_ptr = from_surface;
+                    if (to_range.get(0) == from_range.get(0) &&
+                        to_range.get(0) == size.get(0))
+                    {
+                        event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size_slice,
+                                                         direction, dep_events));
+                    }
+                    else
+                    {
+                        for (size_t y = 0; y < size.get(1); ++y)
+                        {
+                            event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size.get(0),
+                                                             direction, dep_events));
+                            to_ptr += to_range.get(0);
+                            from_ptr += from_range.get(0);
+                        }
+                    }
+                    to_surface += to_slice;
+                    from_surface += from_slice;
+                }
+                break;
+            case host_to_device:
+            {
+                host_buffer buf(get_copy_range(size, to_slice, to_range.get(0)), q,
+                                event_list);
+                std::vector<sycl::event> host_events;
+                if (to_slice == size_slice)
+                {
+                    // Copy host data to a temp host buffer with the shape of target.
+                    host_events =
+                        dpct_memcpy(q, buf.get_ptr(), from_surface, to_range, from_range,
+                                    sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size,
+                                    host_to_host, dep_events);
+                }
+                else
+                {
+                    // Copy host data to a temp host buffer with the shape of target.
+                    host_events = dpct_memcpy(
+                        q, buf.get_ptr(), from_surface, to_range, from_range,
+                        sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size, host_to_host,
+                        // If has padding data, not sure whether it is useless. So fill temp
+                        // buffer with it.
+                        std::vector<sycl::event>{
+                            dpct_memcpy(q, buf.get_ptr(), to_surface, buf.get_size(),
+                                        device_to_host, dep_events)});
+                }
+                // Copy from temp host buffer to device with only one submit.
+                event_list.push_back(dpct_memcpy(q, to_surface, buf.get_ptr(),
+                                                 buf.get_size(), host_to_device,
+                                                 host_events));
+                break;
+            }
+            case device_to_host:
+            {
+                host_buffer buf(get_copy_range(size, from_slice, from_range.get(0)), q,
+                                event_list);
+                // Copy from host temp buffer to host target with reshaping.
+                event_list = dpct_memcpy(
+                    q, to_surface, buf.get_ptr(), to_range, from_range, sycl::id<3>(0, 0, 0),
+                    sycl::id<3>(0, 0, 0), size, host_to_host,
+                    // Copy from device to temp host buffer with only one submit.
+                    std::vector<sycl::event>{dpct_memcpy(q, buf.get_ptr(), from_surface,
+                                                         buf.get_size(),
+                                                         device_to_host, dep_events)});
+                break;
+            }
+            case device_to_device:
+                event_list.push_back(q.submit([&](sycl::handler &cgh){
+                cgh.depends_on(dep_events);
+                cgh.parallel_for<class dpct_memcpy_3d_detail>(
+                    size,
+                    [=](sycl::id<3> id) {
+                        to_surface[get_offset(id, to_slice, to_range.get(0))] =
+                            from_surface[get_offset(id, from_slice, from_range.get(0))];
+                    }); }));
+                break;
+            default:
+                throw std::runtime_error("dpct_memcpy: invalid direction value");
+            }
+            return event_list;
+        }
+
+        /// memcpy 2D/3D matrix specified by pitched_data.
+        static inline std::vector<sycl::event>
+        dpct_memcpy(sycl::queue &q, pitched_data to, sycl::id<3> to_id,
+                    pitched_data from, sycl::id<3> from_id, sycl::range<3> size,
+                    memcpy_direction direction = automatic)
+        {
+            return dpct_memcpy(q, to.get_data_ptr(), from.get_data_ptr(),
+                               sycl::range<3>(to.get_pitch(), to.get_y(), 1),
+                               sycl::range<3>(from.get_pitch(), from.get_y(), 1), to_id, from_id,
+                               size, direction);
+        }
+
+        /// memcpy 2D matrix with pitch.
+        static inline std::vector<sycl::event>
+        dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr,
+                    size_t to_pitch, size_t from_pitch, size_t x, size_t y,
+                    memcpy_direction direction = automatic)
+        {
+            return dpct_memcpy(q, to_ptr, from_ptr, sycl::range<3>(to_pitch, y, 1),
+                               sycl::range<3>(from_pitch, y, 1),
+                               sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0),
+                               sycl::range<3>(x, y, 1), direction);
+        }
+
+        namespace deprecated
+        {
+
+            template <typename T, sycl::usm::alloc AllocKind>
+            class usm_allocator
+            {
+            private:
+                using Alloc = sycl::usm_allocator<T, AllocKind>;
+                Alloc _impl;
+
+            public:
+                using value_type = typename std::allocator_traits<Alloc>::value_type;
+                using pointer = typename std::allocator_traits<Alloc>::pointer;
+                using const_pointer = typename std::allocator_traits<Alloc>::const_pointer;
+                using void_pointer = typename std::allocator_traits<Alloc>::void_pointer;
+                using const_void_pointer =
+                    typename std::allocator_traits<Alloc>::const_void_pointer;
+                using reference = typename std::allocator_traits<Alloc>::value_type &;
+                using const_reference =
+                    const typename std::allocator_traits<Alloc>::value_type &;
+                using difference_type =
+                    typename std::allocator_traits<Alloc>::difference_type;
+                using size_type = typename std::allocator_traits<Alloc>::size_type;
+                using propagate_on_container_copy_assignment = typename std::allocator_traits<
+                    Alloc>::propagate_on_container_copy_assignment;
+                using propagate_on_container_move_assignment = typename std::allocator_traits<
+                    Alloc>::propagate_on_container_move_assignment;
+                using propagate_on_container_swap =
+                    typename std::allocator_traits<Alloc>::propagate_on_container_swap;
+                using is_always_equal =
+                    typename std::allocator_traits<Alloc>::is_always_equal;
+
+                template <typename U>
+                struct rebind
+                {
+                    typedef usm_allocator<U, AllocKind> other;
+                };
+
+                usm_allocator() : _impl(dpct::get_default_queue()) {}
+                ~usm_allocator() {}
+                usm_allocator(const usm_allocator &other) : _impl(other._impl) {}
+                usm_allocator(usm_allocator &&other) : _impl(std::move(other._impl)) {}
+                pointer address(reference r) { return &r; }
+                const_pointer address(const_reference r) { return &r; }
+                pointer allocate(size_type cnt, const_void_pointer hint = nullptr)
+                {
+                    return std::allocator_traits<Alloc>::allocate(_impl, cnt, hint);
+                }
+                void deallocate(pointer p, size_type cnt)
+                {
+                    std::allocator_traits<Alloc>::deallocate(_impl, p, cnt);
+                }
+                size_type max_size() const
+                {
+                    return std::allocator_traits<Alloc>::max_size(_impl);
+                }
+                bool operator==(const usm_allocator &other) const { return _impl == other._impl; }
+                bool operator!=(const usm_allocator &other) const { return _impl != other._impl; }
+            };
+
+        } // namespace deprecated
+
+        inline void dpct_free(void *ptr,
+                              const sycl::queue &q)
+        {
+            if (ptr)
+            {
+                sycl::free(ptr, q.get_context());
+            }
+        }
+
+        template <typename T>
+        inline auto get_memory(const void *x)
+        {
+            T *new_x = reinterpret_cast<T *>(const_cast<void *>(x));
+            return new_x;
+        }
+
+        template <typename T>
+        inline typename DataType<T>::T2 get_value(const T *s, sycl::queue &q)
+        {
+            using Ty = typename DataType<T>::T2;
+            Ty s_h;
+            if (get_pointer_attribute(q, s) == pointer_access_attribute::device_only)
+                detail::dpct_memcpy(q, (void *)&s_h, (const void *)s, sizeof(T), device_to_host)
+                    .wait();
+            else
+                s_h = *reinterpret_cast<const Ty *>(s);
+            return s_h;
+        }
+
+    } // namespace detail
+
+    template <typename T>
+    inline auto get_value(const T *s, sycl::queue &q)
+    {
+        return detail::get_value(s, q);
+    }
+
+    namespace detail
+    {
+        template <class Ta, class Tb, class Tc, class Ts>
+        inline void gemm_impl(sycl::queue &q, oneapi::mkl::transpose a_trans,
+                              oneapi::mkl::transpose b_trans, int m, int n, int k,
+                              const void *alpha, const void *a, int lda, const void *b,
+                              int ldb, const void *beta, void *c, int ldc)
+        {
+            Ts alpha_value = dpct::get_value(reinterpret_cast<const Ts *>(alpha), q);
+            Ts beta_value = dpct::get_value(reinterpret_cast<const Ts *>(beta), q);
+            auto data_a = get_memory<const Ta>(a);
+            auto data_b = get_memory<const Tb>(b);
+            auto data_c = get_memory<Tc>(c);
+            oneapi::mkl::blas::column_major::gemm(
+                q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda,
+                data_b, ldb, beta_value, data_c, ldc);
+        }
+
+        template <typename VecT, class BinaryOperation, class = void>
+        class vectorized_binary
+        {
+        public:
+            inline VecT operator()(VecT a, VecT b, const BinaryOperation binary_op)
+            {
+                VecT v4;
+                for (size_t i = 0; i < v4.size(); ++i)
+                {
+                    v4[i] = binary_op(a[i], b[i]);
+                }
+                return v4;
+            }
+        };
+
+        template <typename VecT, class BinaryOperation>
+        class vectorized_binary<
+            VecT, BinaryOperation,
+            std::void_t<std::invoke_result_t<BinaryOperation, VecT, VecT>>>
+        {
+        public:
+            inline VecT operator()(VecT a, VecT b, const BinaryOperation binary_op)
+            {
+                return binary_op(a, b).template as<VecT>();
+            }
+        };
+
+        template <class Ta, class Tb, class Tc, class Ts>
+        inline void gemm_batch_impl(sycl::queue &q, oneapi::mkl::transpose a_trans,
+                                    oneapi::mkl::transpose b_trans, int m, int n, int k,
+                                    const void *alpha, const void **a, int lda,
+                                    const void **b, int ldb, const void *beta, void **c,
+                                    int ldc, int batch_size)
+        {
+            struct matrix_info_t
+            {
+                oneapi::mkl::transpose transpose_info[2];
+                Ts value_info[2];
+                std::int64_t size_info[3];
+                std::int64_t ld_info[3];
+                std::int64_t groupsize_info;
+            };
+
+            Ts alpha_value = dpct::get_value(reinterpret_cast<const Ts *>(alpha), q);
+            Ts beta_value = dpct::get_value(reinterpret_cast<const Ts *>(beta), q);
+
+            matrix_info_t *matrix_info =
+                (matrix_info_t *)std::malloc(sizeof(matrix_info_t));
+            matrix_info->transpose_info[0] = a_trans;
+            matrix_info->transpose_info[1] = b_trans;
+            matrix_info->value_info[0] = alpha_value;
+            matrix_info->value_info[1] = beta_value;
+            matrix_info->size_info[0] = m;
+            matrix_info->size_info[1] = n;
+            matrix_info->size_info[2] = k;
+            matrix_info->ld_info[0] = lda;
+            matrix_info->ld_info[1] = ldb;
+            matrix_info->ld_info[2] = ldc;
+            matrix_info->groupsize_info = batch_size;
+
+            sycl::event e = oneapi::mkl::blas::column_major::gemm_batch(
+                q, matrix_info->transpose_info, matrix_info->transpose_info + 1,
+                matrix_info->size_info, matrix_info->size_info + 1,
+                matrix_info->size_info + 2, matrix_info->value_info,
+                reinterpret_cast<const Ta **>(a), matrix_info->ld_info,
+                reinterpret_cast<const Tb **>(b), matrix_info->ld_info + 1,
+                matrix_info->value_info + 1, reinterpret_cast<Tc **>(c),
+                matrix_info->ld_info + 2, 1, &(matrix_info->groupsize_info));
+
+            q.submit([&](sycl::handler &cgh)
+                     {
+    cgh.depends_on(e);
+    cgh.host_task([=] { std::free(matrix_info); }); });
+        }
+
+        template <class Ta, class Tb, class Tc, class Ts>
+        inline void
+        gemm_batch_impl(sycl::queue &q, oneapi::mkl::transpose a_trans,
+                        oneapi::mkl::transpose b_trans, int m, int n,
+                        int k, const void *alpha, const void *a, int lda,
+                        long long int stride_a, const void *b, int ldb,
+                        long long int stride_b, const void *beta, void *c,
+                        int ldc, long long int stride_c, int batch_size)
+        {
+            Ts alpha_value = dpct::get_value(reinterpret_cast<const Ts *>(alpha), q);
+            Ts beta_value = dpct::get_value(reinterpret_cast<const Ts *>(beta), q);
+            auto data_a = get_memory<const Ta>(a);
+            auto data_b = get_memory<const Tb>(b);
+            auto data_c = get_memory<Tc>(c);
+            oneapi::mkl::blas::column_major::gemm_batch(
+                q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda,
+                stride_a, data_b, ldb, stride_b, beta_value,
+                data_c, ldc, stride_c, batch_size);
+        }
+
+    } // namespace detail
+
+    template <typename VecT, class BinaryOperation>
+    inline unsigned vectorized_binary(unsigned a, unsigned b,
+                                      const BinaryOperation binary_op)
+    {
+        sycl::vec<unsigned, 1> v0{a}, v1{b};
+        auto v2 = v0.as<VecT>();
+        auto v3 = v1.as<VecT>();
+        auto v4 =
+            detail::vectorized_binary<VecT, BinaryOperation>()(v2, v3, binary_op);
+        v0 = v4.template as<sycl::vec<unsigned, 1>>();
+        return v0;
+    }
+
+    static void async_dpct_memcpy(void *to_ptr, const void *from_ptr, size_t size,
+                                  memcpy_direction direction = automatic,
+                                  sycl::queue &q = dpct::get_default_queue())
+    {
+        detail::dpct_memcpy(q, to_ptr, from_ptr, size, direction);
+    }
+
+    static inline unsigned int select_device(unsigned int id)
+    {
+        dev_mgr::instance().select_device(id);
+        return id;
+    }
+
+    template <typename T>
+    T permute_sub_group_by_xor(sycl::sub_group g, T x, unsigned int mask,
+                               unsigned int logical_sub_group_size = 32)
+    {
+        unsigned int id = g.get_local_linear_id();
+        unsigned int start_index =
+            id / logical_sub_group_size * logical_sub_group_size;
+        unsigned int target_offset = (id % logical_sub_group_size) ^ mask;
+        return sycl::select_from_group(g, x,
+                                       target_offset < logical_sub_group_size
+                                           ? start_index + target_offset
+                                           : id);
+    }
+
+    template <typename T>
+    sycl::vec<T, 4> extract_and_sign_or_zero_extend4(T val)
+    {
+        return sycl::vec<T, 1>(val)
+            .template as<sycl::vec<
+                std::conditional_t<std::is_signed_v<T>, int8_t, uint8_t>, 4>>()
+            .template convert<T>();
+    }
+
+    template <typename T1, typename T2>
+    using dot_product_acc_t =
+        std::conditional_t<std::is_unsigned_v<T1> && std::is_unsigned_v<T2>,
+                           uint32_t, int32_t>;
+
+    template <typename T1, typename T2, typename T3>
+    inline auto dp4a(T1 a, T2 b, T3 c)
+    {
+        dot_product_acc_t<T1, T2> res = c;
+        auto va = extract_and_sign_or_zero_extend4(a);
+        auto vb = extract_and_sign_or_zero_extend4(b);
+        res += va[0] * vb[0];
+        res += va[1] * vb[1];
+        res += va[2] * vb[2];
+        res += va[3] * vb[3];
+        return res;
+    }
+
+    struct sub_sat
+    {
+        template <typename T>
+        auto operator()(const T x, const T y) const
+        {
+            return sycl::sub_sat(x, y);
+        }
+    };
+
+    template <typename S, typename T>
+    inline T vectorized_min(T a, T b)
+    {
+        sycl::vec<T, 1> v0{a}, v1{b};
+        auto v2 = v0.template as<S>();
+        auto v3 = v1.template as<S>();
+        auto v4 = sycl::min(v2, v3);
+        v0 = v4.template as<sycl::vec<T, 1>>();
+        return v0;
+    }
+
+    inline float pow(const float a, const int b) { return sycl::pown(a, b); }
+    inline double pow(const double a, const int b) { return sycl::pown(a, b); }
+    inline float pow(const float a, const float b) { return sycl::pow(a, b); }
+    inline double pow(const double a, const double b) { return sycl::pow(a, b); }
+    template <typename T, typename U>
+    inline typename std::enable_if_t<std::is_floating_point_v<T>, T>
+    pow(const T a, const U b)
+    {
+        return sycl::pow(a, static_cast<T>(b));
+    }
+    template <typename T, typename U>
+    inline typename std::enable_if_t<!std::is_floating_point_v<T>, double>
+    pow(const T a, const U b)
+    {
+        return sycl::pow(static_cast<double>(a), static_cast<double>(b));
+    }
+
+    inline double min(const double a, const float b)
+    {
+        return sycl::fmin(a, static_cast<double>(b));
+    }
+    inline double min(const float a, const double b)
+    {
+        return sycl::fmin(static_cast<double>(a), b);
+    }
+    inline float min(const float a, const float b) { return sycl::fmin(a, b); }
+    inline double min(const double a, const double b) { return sycl::fmin(a, b); }
+    inline std::uint32_t min(const std::uint32_t a, const std::int32_t b)
+    {
+        return sycl::min(a, static_cast<std::uint32_t>(b));
+    }
+    inline std::uint32_t min(const std::int32_t a, const std::uint32_t b)
+    {
+        return sycl::min(static_cast<std::uint32_t>(a), b);
+    }
+    inline std::int32_t min(const std::int32_t a, const std::int32_t b)
+    {
+        return sycl::min(a, b);
+    }
+    inline std::uint32_t min(const std::uint32_t a, const std::uint32_t b)
+    {
+        return sycl::min(a, b);
+    }
+    inline std::uint64_t min(const std::uint64_t a, const std::int64_t b)
+    {
+        return sycl::min(a, static_cast<std::uint64_t>(b));
+    }
+    inline std::uint64_t min(const std::int64_t a, const std::uint64_t b)
+    {
+        return sycl::min(static_cast<std::uint64_t>(a), b);
+    }
+    inline std::int64_t min(const std::int64_t a, const std::int64_t b)
+    {
+        return sycl::min(a, b);
+    }
+    inline std::uint64_t min(const std::uint64_t a, const std::uint64_t b)
+    {
+        return sycl::min(a, b);
+    }
+    inline std::uint64_t min(const std::uint64_t a, const std::int32_t b)
+    {
+        return sycl::min(a, static_cast<std::uint64_t>(b));
+    }
+    inline std::uint64_t min(const std::int32_t a, const std::uint64_t b)
+    {
+        return sycl::min(static_cast<std::uint64_t>(a), b);
+    }
+    inline std::uint64_t min(const std::uint64_t a, const std::uint32_t b)
+    {
+        return sycl::min(a, static_cast<std::uint64_t>(b));
+    }
+    inline std::uint64_t min(const std::uint32_t a, const std::uint64_t b)
+    {
+        return sycl::min(static_cast<std::uint64_t>(a), b);
+    }
+    // max function overloads.
+    // For floating-point types, `float` or `double` arguments are acceptable.
+    // For integer types, `std::uint32_t`, `std::int32_t`, `std::uint64_t` or
+    // `std::int64_t` type arguments are acceptable.
+    inline double max(const double a, const float b)
+    {
+        return sycl::fmax(a, static_cast<double>(b));
+    }
+    inline double max(const float a, const double b)
+    {
+        return sycl::fmax(static_cast<double>(a), b);
+    }
+    inline float max(const float a, const float b) { return sycl::fmax(a, b); }
+    inline double max(const double a, const double b) { return sycl::fmax(a, b); }
+    inline std::uint32_t max(const std::uint32_t a, const std::int32_t b)
+    {
+        return sycl::max(a, static_cast<std::uint32_t>(b));
+    }
+    inline std::uint32_t max(const std::int32_t a, const std::uint32_t b)
+    {
+        return sycl::max(static_cast<std::uint32_t>(a), b);
+    }
+    inline std::int32_t max(const std::int32_t a, const std::int32_t b)
+    {
+        return sycl::max(a, b);
+    }
+    inline std::uint32_t max(const std::uint32_t a, const std::uint32_t b)
+    {
+        return sycl::max(a, b);
+    }
+    inline std::uint64_t max(const std::uint64_t a, const std::int64_t b)
+    {
+        return sycl::max(a, static_cast<std::uint64_t>(b));
+    }
+    inline std::uint64_t max(const std::int64_t a, const std::uint64_t b)
+    {
+        return sycl::max(static_cast<std::uint64_t>(a), b);
+    }
+    inline std::int64_t max(const std::int64_t a, const std::int64_t b)
+    {
+        return sycl::max(a, b);
+    }
+    inline std::uint64_t max(const std::uint64_t a, const std::uint64_t b)
+    {
+        return sycl::max(a, b);
+    }
+    inline std::uint64_t max(const std::uint64_t a, const std::int32_t b)
+    {
+        return sycl::max(a, static_cast<std::uint64_t>(b));
+    }
+    inline std::uint64_t max(const std::int32_t a, const std::uint64_t b)
+    {
+        return sycl::max(static_cast<std::uint64_t>(a), b);
+    }
+    inline std::uint64_t max(const std::uint64_t a, const std::uint32_t b)
+    {
+        return sycl::max(a, static_cast<std::uint64_t>(b));
+    }
+    inline std::uint64_t max(const std::uint32_t a, const std::uint64_t b)
+    {
+        return sycl::max(static_cast<std::uint64_t>(a), b);
+    }
+
+    inline void
+    has_capability_or_fail(const sycl::device &dev,
+                           const std::initializer_list<sycl::aspect> &props)
+    {
+        for (const auto &it : props)
+        {
+            if (dev.has(it))
+                continue;
+            switch (it)
+            {
+            case sycl::aspect::fp64:
+                throw std::runtime_error("'double' is not supported in '" +
+                                         dev.get_info<sycl::info::device::name>() +
+                                         "' device");
+                break;
+            case sycl::aspect::fp16:
+                throw std::runtime_error("'half' is not supported in '" +
+                                         dev.get_info<sycl::info::device::name>() +
+                                         "' device");
+                break;
+            default:
+#define __SYCL_ASPECT(ASPECT, ID) \
+    case sycl::aspect::ASPECT:    \
+        return #ASPECT;
+#define __SYCL_ASPECT_DEPRECATED(ASPECT, ID, MESSAGE) __SYCL_ASPECT(ASPECT, ID)
+#define __SYCL_ASPECT_DEPRECATED_ALIAS(ASPECT, ID, MESSAGE)
+                auto getAspectNameStr = [](sycl::aspect AspectNum) -> std::string
+                {
+                    switch (AspectNum)
+                    {
+#include <sycl/info/aspects.def>
+#include <sycl/info/aspects_deprecated.def>
+                    default:
+                        return "unknown aspect";
+                    }
+                };
+#undef __SYCL_ASPECT_DEPRECATED_ALIAS
+#undef __SYCL_ASPECT_DEPRECATED
+#undef __SYCL_ASPECT
+                throw std::runtime_error(
+                    "'" + getAspectNameStr(it) + "' is not supported in '" +
+                    dev.get_info<sycl::info::device::name>() + "' device");
+            }
+            break;
+        }
+    }
+
+    static inline unsigned int get_current_device_id()
+    {
+        return dev_mgr::instance().current_device_id();
+    }
+
+    static inline device_ext &get_current_device()
+    {
+        return dev_mgr::instance().current_device();
+    }
+
+    static inline sycl::queue &get_in_order_queue()
+    {
+        return dev_mgr::instance().current_device().in_order_queue();
+    }
+
+    static sycl::event
+    dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, size_t size,
+                memcpy_direction direction,
+                const std::vector<sycl::event> &dep_events = {})
+    {
+        if (!size)
+            return sycl::event{};
+        return q.memcpy(to_ptr, from_ptr, size, dep_events);
+        GGML_UNUSED(direction);
+    }
+
+    // Get actual copy range and make sure it will not exceed range.
+    static inline size_t get_copy_range(sycl::range<3> size, size_t slice,
+                                        size_t pitch)
+    {
+        return slice * (size.get(2) - 1) + pitch * (size.get(1) - 1) + size.get(0);
+    }
+
+    static inline size_t get_offset(sycl::id<3> id, size_t slice,
+                                    size_t pitch)
+    {
+        return slice * id.get(2) + pitch * id.get(1) + id.get(0);
+    }
+
+    /// copy 3D matrix specified by \p size from 3D matrix specified by \p from_ptr
+    /// and \p from_range to another specified by \p to_ptr and \p to_range.
+    static inline std::vector<sycl::event>
+    dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr,
+                sycl::range<3> to_range, sycl::range<3> from_range,
+                sycl::id<3> to_id, sycl::id<3> from_id,
+                sycl::range<3> size, memcpy_direction direction,
+                const std::vector<sycl::event> &dep_events = {})
+    {
+        // RAII for host pointer
+        class host_buffer
+        {
+            void *_buf;
+            size_t _size;
+            sycl::queue &_q;
+            const std::vector<sycl::event> &_deps; // free operation depends
+
+        public:
+            host_buffer(size_t size, sycl::queue &q,
+                        const std::vector<sycl::event> &deps)
+                : _buf(std::malloc(size)), _size(size), _q(q), _deps(deps) {}
+            void *get_ptr() const { return _buf; }
+            size_t get_size() const { return _size; }
+            ~host_buffer()
+            {
+                if (_buf)
+                {
+                    _q.submit([&](sycl::handler &cgh)
+                              {
+            cgh.depends_on(_deps);
+            cgh.host_task([buf = _buf] { std::free(buf); }); });
+                }
+            }
+        };
+        std::vector<sycl::event> event_list;
+
+        size_t to_slice = to_range.get(1) * to_range.get(0),
+               from_slice = from_range.get(1) * from_range.get(0);
+        unsigned char *to_surface =
+            (unsigned char *)to_ptr + get_offset(to_id, to_slice, to_range.get(0));
+        const unsigned char *from_surface =
+            (const unsigned char *)from_ptr +
+            get_offset(from_id, from_slice, from_range.get(0));
+
+        if (to_slice == from_slice && to_slice == size.get(1) * size.get(0))
+        {
+            return {dpct_memcpy(q, to_surface, from_surface, to_slice * size.get(2),
+                                direction, dep_events)};
+        }
+        direction = detail::deduce_memcpy_direction(q, to_ptr, from_ptr, direction);
+        size_t size_slice = size.get(1) * size.get(0);
+        switch (direction)
+        {
+        case host_to_host:
+            for (size_t z = 0; z < size.get(2); ++z)
+            {
+                unsigned char *to_ptr = to_surface;
+                const unsigned char *from_ptr = from_surface;
+                if (to_range.get(0) == from_range.get(0) &&
+                    to_range.get(0) == size.get(0))
+                {
+                    event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size_slice,
+                                                     direction, dep_events));
+                }
+                else
+                {
+                    for (size_t y = 0; y < size.get(1); ++y)
+                    {
+                        event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size.get(0),
+                                                         direction, dep_events));
+                        to_ptr += to_range.get(0);
+                        from_ptr += from_range.get(0);
+                    }
+                }
+                to_surface += to_slice;
+                from_surface += from_slice;
+            }
+            break;
+        case host_to_device:
+        {
+            host_buffer buf(get_copy_range(size, to_slice, to_range.get(0)), q,
+                            event_list);
+            std::vector<sycl::event> host_events;
+            if (to_slice == size_slice)
+            {
+                // Copy host data to a temp host buffer with the shape of target.
+                host_events =
+                    dpct_memcpy(q, buf.get_ptr(), from_surface, to_range, from_range,
+                                sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size,
+                                host_to_host, dep_events);
+            }
+            else
+            {
+                // Copy host data to a temp host buffer with the shape of target.
+                host_events = dpct_memcpy(
+                    q, buf.get_ptr(), from_surface, to_range, from_range,
+                    sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size, host_to_host,
+                    // If has padding data, not sure whether it is useless. So fill temp
+                    // buffer with it.
+                    std::vector<sycl::event>{
+                        dpct_memcpy(q, buf.get_ptr(), to_surface, buf.get_size(),
+                                    device_to_host, dep_events)});
+            }
+            // Copy from temp host buffer to device with only one submit.
+            event_list.push_back(dpct_memcpy(q, to_surface, buf.get_ptr(),
+                                             buf.get_size(), host_to_device,
+                                             host_events));
+            break;
+        }
+        case device_to_host:
+        {
+            host_buffer buf(get_copy_range(size, from_slice, from_range.get(0)), q,
+                            event_list);
+            // Copy from host temp buffer to host target with reshaping.
+            event_list = dpct_memcpy(
+                q, to_surface, buf.get_ptr(), to_range, from_range, sycl::id<3>(0, 0, 0),
+                sycl::id<3>(0, 0, 0), size, host_to_host,
+                // Copy from device to temp host buffer with only one submit.
+                std::vector<sycl::event>{dpct_memcpy(q, buf.get_ptr(), from_surface,
+                                                     buf.get_size(),
+                                                     device_to_host, dep_events)});
+            break;
+        }
+        case device_to_device:
+            event_list.push_back(q.submit([&](sycl::handler &cgh)
+                                          {
+        cgh.depends_on(dep_events);
+        cgh.parallel_for<class dpct_memcpy_3d_detail>(
+            size,
+            [=](sycl::id<3> id) {
+                to_surface[get_offset(id, to_slice, to_range.get(0))] =
+                    from_surface[get_offset(id, from_slice, from_range.get(0))];
+            }); }));
+        break;
+        default:
+            throw std::runtime_error("dpct_memcpy: invalid direction value");
+        }
+        return event_list;
+    }
+
+    /// memcpy 2D/3D matrix specified by pitched_data.
+    static inline std::vector<sycl::event>
+    dpct_memcpy(sycl::queue &q, pitched_data to, sycl::id<3> to_id,
+                pitched_data from, sycl::id<3> from_id, sycl::range<3> size,
+                memcpy_direction direction = automatic)
+    {
+        return dpct_memcpy(q, to.get_data_ptr(), from.get_data_ptr(),
+                           sycl::range<3>(to.get_pitch(), to.get_y(), 1),
+                           sycl::range<3>(from.get_pitch(), from.get_y(), 1), to_id, from_id,
+                           size, direction);
+    }
+
+    /// memcpy 2D matrix with pitch.
+    static inline std::vector<sycl::event>
+    dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr,
+                size_t to_pitch, size_t from_pitch, size_t x, size_t y,
+                memcpy_direction direction = automatic)
+    {
+        return dpct_memcpy(q, to_ptr, from_ptr, sycl::range<3>(to_pitch, y, 1),
+                           sycl::range<3>(from_pitch, y, 1),
+                           sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0),
+                           sycl::range<3>(x, y, 1), direction);
+    }
+
+    inline void gemm(sycl::queue &q, oneapi::mkl::transpose a_trans,
+                     oneapi::mkl::transpose b_trans, int m, int n, int k,
+                     const void *alpha, const void *a, library_data_t a_type,
+                     int lda, const void *b, library_data_t b_type, int ldb,
+                     const void *beta, void *c, library_data_t c_type, int ldc,
+                     library_data_t scaling_type)
+    {
+        if (scaling_type == library_data_t::real_float &&
+            c_type == library_data_t::complex_float)
+        {
+            scaling_type = library_data_t::complex_float;
+        }
+        else if (scaling_type == library_data_t::real_double &&
+                 c_type == library_data_t::complex_double)
+        {
+            scaling_type = library_data_t::complex_double;
+        }
+
+        std::uint64_t key =
+            detail::get_type_combination_id(a_type, b_type, c_type, scaling_type);
+        switch (key)
+        {
+        case detail::get_type_combination_id(
+            library_data_t::real_float, library_data_t::real_float,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_impl<float, float, float, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_double, library_data_t::real_double,
+            library_data_t::real_double, library_data_t::real_double):
+        {
+            detail::gemm_impl<double, double, double, double>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::complex_float, library_data_t::complex_float,
+            library_data_t::complex_float, library_data_t::complex_float):
+        {
+            detail::gemm_impl<std::complex<float>, std::complex<float>,
+                              std::complex<float>, std::complex<float>>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::complex_double, library_data_t::complex_double,
+            library_data_t::complex_double, library_data_t::complex_double):
+        {
+            detail::gemm_impl<std::complex<double>, std::complex<double>,
+                              std::complex<double>, std::complex<double>>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_half, library_data_t::real_half,
+            library_data_t::real_half, library_data_t::real_half):
+        {
+            detail::gemm_impl<sycl::half, sycl::half, sycl::half,
+                              sycl::half>(q, a_trans, b_trans, m, n, k, alpha, a,
+                                          lda, b, ldb, beta, c, ldc);
+            break;
+        }
+#ifdef __INTEL_MKL__
+        case detail::get_type_combination_id(
+            library_data_t::real_bfloat16, library_data_t::real_bfloat16,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, float,
+                              float>(q, a_trans, b_trans, m, n, k, alpha, a, lda, b,
+                                     ldb, beta, c, ldc);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_half, library_data_t::real_half,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_impl<sycl::half, sycl::half, float, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_half, library_data_t::real_half,
+            library_data_t::real_half, library_data_t::real_float):
+        {
+            float alpha_value =
+                dpct::get_value(reinterpret_cast<const float *>(alpha), q);
+            float beta_value =
+                dpct::get_value(reinterpret_cast<const float *>(beta), q);
+            sycl::half alpha_half(alpha_value);
+            sycl::half beta_half(beta_value);
+            detail::gemm_impl<sycl::half, sycl::half, sycl::half,
+                              sycl::half>(q, a_trans, b_trans, m, n, k, &alpha_half,
+                                          a, lda, b, ldb, &beta_half, c, ldc);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_int8, library_data_t::real_int8,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_impl<std::int8_t, std::int8_t, float, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_bfloat16, library_data_t::real_bfloat16,
+            library_data_t::real_bfloat16, library_data_t::real_float):
+        {
+            detail::gemm_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16,
+                              oneapi::mkl::bfloat16, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_int8, library_data_t::real_int8,
+            library_data_t::real_int32, library_data_t::real_int32):
+        {
+            float alpha_float =
+                dpct::get_value(reinterpret_cast<const std::int32_t *>(alpha), q);
+            float beta_float =
+                dpct::get_value(reinterpret_cast<const std::int32_t *>(beta), q);
+            detail::gemm_impl<std::int8_t, std::int8_t, std::int32_t, float>(
+                q, a_trans, b_trans, m, n, k, &alpha_float, a, lda, b, ldb, &beta_float, c, ldc);
+            break;
+        }
+#endif // __INTEL_MKL__
+        default:
+            throw std::runtime_error("the combination of data type is unsupported");
+        }
+    } // gemm()
+
+    /// Computes a batch of matrix-matrix product with general matrices.
+    /// \param [in] q The queue where the routine should be executed.
+    /// \param [in] a_trans Specifies the operation applied to A.
+    /// \param [in] b_trans Specifies the operation applied to B.
+    /// \param [in] m Specifies the number of rows of the matrix op(A) and of the matrix C.
+    /// \param [in] n Specifies the number of columns of the matrix op(B) and of the matrix C.
+    /// \param [in] k Specifies the number of columns of the matrix op(A) and the number of rows of the matrix op(B).
+    /// \param [in] alpha Scaling factor for the matrix-matrix product.
+    /// \param [in] a Input matrix A.
+    /// \param [in] a_type Data type of the matrix A.
+    /// \param [in] lda Leading dimension of A.
+    /// \param [in] b Input matrix B.
+    /// \param [in] b_type Data type of the matrix B.
+    /// \param [in] ldb Leading dimension of B.
+    /// \param [in] beta Scaling factor for matrix C.
+    /// \param [in, out] c Input/Output matrix C.
+    /// \param [in] c_type Data type of the matrix C.
+    /// \param [in] ldc Leading dimension of C.
+    /// \param [in] batch_size Specifies the number of matrix multiply operations to perform.
+    /// \param [in] scaling_type Data type of the scaling factors.
+    inline void gemm_batch(sycl::queue &q, oneapi::mkl::transpose a_trans,
+                           oneapi::mkl::transpose b_trans, int m, int n, int k,
+                           const void *alpha, const void *a[],
+                           library_data_t a_type, int lda, const void *b[],
+                           library_data_t b_type, int ldb, const void *beta,
+                           void *c[], library_data_t c_type, int ldc,
+                           int batch_size, library_data_t scaling_type)
+    {
+        if (scaling_type == library_data_t::real_float &&
+            c_type == library_data_t::complex_float)
+        {
+            scaling_type = library_data_t::complex_float;
+        }
+        else if (scaling_type == library_data_t::real_double &&
+                 c_type == library_data_t::complex_double)
+        {
+            scaling_type = library_data_t::complex_double;
+        }
+
+        std::uint64_t key =
+            detail::get_type_combination_id(a_type, b_type, c_type, scaling_type);
+        switch (key)
+        {
+        case detail::get_type_combination_id(
+            library_data_t::real_float, library_data_t::real_float,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<float, float, float, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+                batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_double, library_data_t::real_double,
+            library_data_t::real_double, library_data_t::real_double):
+        {
+            detail::gemm_batch_impl<double, double, double, double>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+                batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::complex_float, library_data_t::complex_float,
+            library_data_t::complex_float, library_data_t::complex_float):
+        {
+            detail::gemm_batch_impl<std::complex<float>, std::complex<float>,
+                                    std::complex<float>, std::complex<float>>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+                batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::complex_double, library_data_t::complex_double,
+            library_data_t::complex_double, library_data_t::complex_double):
+        {
+            detail::gemm_batch_impl<std::complex<double>, std::complex<double>,
+                                    std::complex<double>, std::complex<double>>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+                batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_half, library_data_t::real_half,
+            library_data_t::real_half, library_data_t::real_half):
+        {
+            detail::gemm_batch_impl<sycl::half, sycl::half, sycl::half,
+                                    sycl::half>(q, a_trans, b_trans, m, n, k, alpha,
+                                                a, lda, b, ldb, beta, c, ldc,
+                                                batch_size);
+            break;
+        }
+#ifdef __INTEL_MKL__
+        case detail::get_type_combination_id(
+            library_data_t::real_bfloat16, library_data_t::real_bfloat16,
+            library_data_t::real_bfloat16, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16,
+                                    oneapi::mkl::bfloat16, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+                batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_bfloat16, library_data_t::real_bfloat16,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, float,
+                                    float>(q, a_trans, b_trans, m, n, k, alpha, a, lda,
+                                           b, ldb, beta, c, ldc, batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_int8, library_data_t::real_int8,
+            library_data_t::real_int32, library_data_t::real_int32):
+        {
+            float alpha_float =
+                dpct::get_value(reinterpret_cast<const std::int32_t *>(alpha), q);
+            float beta_float =
+                dpct::get_value(reinterpret_cast<const std::int32_t *>(beta), q);
+            detail::gemm_batch_impl<std::int8_t, std::int8_t, std::int32_t,
+                                    float>(q, a_trans, b_trans, m, n, k, &alpha_float,
+                                           a, lda, b, ldb, &beta_float, c, ldc,
+                                           batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_int8, library_data_t::real_int8,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<std::int8_t, std::int8_t, float, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+                batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_half, library_data_t::real_half,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<sycl::half, sycl::half, float, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc,
+                batch_size);
+            break;
+        }
+#endif
+        case detail::get_type_combination_id(
+            library_data_t::real_half, library_data_t::real_half,
+            library_data_t::real_half, library_data_t::real_float):
+        {
+            float alpha_value =
+                dpct::get_value(reinterpret_cast<const float *>(alpha), q);
+            float beta_value =
+                dpct::get_value(reinterpret_cast<const float *>(beta), q);
+            sycl::half alpha_half(alpha_value);
+            sycl::half beta_half(beta_value);
+            detail::gemm_batch_impl<sycl::half, sycl::half, sycl::half, sycl::half>(
+                q, a_trans, b_trans, m, n, k, &alpha_half, a, lda, b, ldb, &beta_half, c, ldc,
+                batch_size);
+            break;
+        }
+        default:
+            throw std::runtime_error("the combination of data type is unsupported");
+        }
+    }
+
+    /// Computes a batch of matrix-matrix product with general matrices.
+    /// \param [in] q The queue where the routine should be executed.
+    /// \param [in] a_trans Specifies the operation applied to A.
+    /// \param [in] b_trans Specifies the operation applied to B.
+    /// \param [in] m Specifies the number of rows of the matrix op(A) and of the matrix C.
+    /// \param [in] n Specifies the number of columns of the matrix op(B) and of the matrix C.
+    /// \param [in] k Specifies the number of columns of the matrix op(A) and the number of rows of the matrix op(B).
+    /// \param [in] alpha Scaling factor for the matrix-matrix product.
+    /// \param [in] a Input matrix A.
+    /// \param [in] a_type Data type of the matrix A.
+    /// \param [in] lda Leading dimension of A.
+    /// \param [in] stride_a Stride between the different A matrices.
+    /// \param [in] b Input matrix B.
+    /// \param [in] b_type Data type of the matrix B.
+    /// \param [in] ldb Leading dimension of B.
+    /// \param [in] stride_b Stride between the different B matrices.
+    /// \param [in] beta Scaling factor for matrix C.
+    /// \param [in, out] c Input/Output matrix C.
+    /// \param [in] c_type Data type of the matrix C.
+    /// \param [in] ldc Leading dimension of C.
+    /// \param [in] stride_c Stride between the different C matrices.
+    /// \param [in] batch_size Specifies the number of matrix multiply operations to perform.
+    /// \param [in] scaling_type Data type of the scaling factors.
+    inline void gemm_batch(sycl::queue &q, oneapi::mkl::transpose a_trans,
+                           oneapi::mkl::transpose b_trans, int m, int n, int k,
+                           const void *alpha, const void *a, library_data_t a_type,
+                           int lda, long long int stride_a, const void *b,
+                           library_data_t b_type, int ldb, long long int stride_b,
+                           const void *beta, void *c, library_data_t c_type,
+                           int ldc, long long int stride_c, int batch_size,
+                           library_data_t scaling_type)
+    {
+        if (scaling_type == library_data_t::real_float &&
+            c_type == library_data_t::complex_float)
+        {
+            scaling_type = library_data_t::complex_float;
+        }
+        else if (scaling_type == library_data_t::real_double &&
+                 c_type == library_data_t::complex_double)
+        {
+            scaling_type = library_data_t::complex_double;
+        }
+
+        std::uint64_t key =
+            detail::get_type_combination_id(a_type, b_type, c_type, scaling_type);
+        switch (key)
+        {
+        case detail::get_type_combination_id(
+            library_data_t::real_float, library_data_t::real_float,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<float, float, float, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
+                beta, c, ldc, stride_c, batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_double, library_data_t::real_double,
+            library_data_t::real_double, library_data_t::real_double):
+        {
+            detail::gemm_batch_impl<double, double, double, double>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
+                beta, c, ldc, stride_c, batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::complex_float, library_data_t::complex_float,
+            library_data_t::complex_float, library_data_t::complex_float):
+        {
+            detail::gemm_batch_impl<std::complex<float>, std::complex<float>,
+                                    std::complex<float>, std::complex<float>>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
+                beta, c, ldc, stride_c, batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::complex_double, library_data_t::complex_double,
+            library_data_t::complex_double, library_data_t::complex_double):
+        {
+            detail::gemm_batch_impl<std::complex<double>, std::complex<double>,
+                                    std::complex<double>, std::complex<double>>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
+                beta, c, ldc, stride_c, batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_half, library_data_t::real_half,
+            library_data_t::real_half, library_data_t::real_half):
+        {
+            detail::gemm_batch_impl<sycl::half, sycl::half, sycl::half,
+                                    sycl::half>(q, a_trans, b_trans, m, n, k, alpha,
+                                                a, lda, stride_a, b, ldb, stride_b,
+                                                beta, c, ldc, stride_c, batch_size);
+            break;
+        }
+#ifdef __INTEL_MKL__
+        case detail::get_type_combination_id(
+            library_data_t::real_bfloat16, library_data_t::real_bfloat16,
+            library_data_t::real_bfloat16, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16,
+                                    oneapi::mkl::bfloat16, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
+                beta, c, ldc, stride_c, batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_bfloat16, library_data_t::real_bfloat16,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<oneapi::mkl::bfloat16, oneapi::mkl::bfloat16, float,
+                                    float>(q, a_trans, b_trans, m, n, k, alpha, a, lda,
+                                           stride_a, b, ldb, stride_b, beta, c, ldc,
+                                           stride_c, batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_int8, library_data_t::real_int8,
+            library_data_t::real_int32, library_data_t::real_int32):
+        {
+            detail::gemm_batch_impl<std::int8_t, std::int8_t, std::int32_t,
+                                    std::int32_t>(q, a_trans, b_trans, m, n, k, alpha,
+                                                  a, lda, stride_a, b, ldb, stride_b,
+                                                  beta, c, ldc, stride_c, batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_int8, library_data_t::real_int8,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<std::int8_t, std::int8_t, float, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
+                beta, c, ldc, stride_c, batch_size);
+            break;
+        }
+        case detail::get_type_combination_id(
+            library_data_t::real_half, library_data_t::real_half,
+            library_data_t::real_float, library_data_t::real_float):
+        {
+            detail::gemm_batch_impl<sycl::half, sycl::half, float, float>(
+                q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b,
+                beta, c, ldc, stride_c, batch_size);
+            break;
+        }
+#endif
+        case detail::get_type_combination_id(
+            library_data_t::real_half, library_data_t::real_half,
+            library_data_t::real_half, library_data_t::real_float):
+        {
+            float alpha_value =
+                dpct::get_value(reinterpret_cast<const float *>(alpha), q);
+            float beta_value =
+                dpct::get_value(reinterpret_cast<const float *>(beta), q);
+            sycl::half alpha_half(alpha_value);
+            sycl::half beta_half(beta_value);
+            detail::gemm_batch_impl<sycl::half, sycl::half, sycl::half, sycl::half>(
+                q, a_trans, b_trans, m, n, k, &alpha_half, a, lda, stride_a, b, ldb, stride_b,
+                &beta_half, c, ldc, stride_c, batch_size);
+            break;
+        }
+        default:
+            throw std::runtime_error("the combination of data type is unsupported");
+        }
+    }
+
+    static inline void
+    async_dpct_memcpy(void *to_ptr, size_t to_pitch, const void *from_ptr,
+                      size_t from_pitch, size_t x, size_t y,
+                      memcpy_direction direction = automatic,
+                      sycl::queue &q = get_default_queue())
+    {
+        detail::dpct_memcpy(q, to_ptr, from_ptr, to_pitch, from_pitch, x, y,
+                            direction);
+    }
+
+    using err0 = detail::generic_error_type<struct err0_tag, int>;
+    using err1 = detail::generic_error_type<struct err1_tag, int>;
+
+    static inline void dpct_free(void *ptr, sycl::queue &q = get_default_queue()) {
+        detail::dpct_free(ptr, q);
+    }
+
+    /// dpct accessor used as device function parameter.
+    template <class T, memory_region Memory, size_t Dimension> class accessor;
+    template <class T, memory_region Memory> class accessor<T, Memory, 3> {
+    public:
+        using memory_t = detail::memory_traits<Memory, T>;
+        using element_t = typename memory_t::element_t;
+        using pointer_t = typename memory_t::pointer_t;
+        using accessor_t = typename memory_t::template accessor_t<3>;
+        accessor(pointer_t data, const sycl::range<3> &in_range)
+            : _data(data), _range(in_range) {}
+        template <memory_region M = Memory>
+        accessor(typename std::enable_if<M != local, const accessor_t>::type &acc)
+            : accessor(acc, acc.get_range()) {}
+        accessor(const accessor_t &acc, const sycl::range<3> &in_range)
+            : accessor(acc.get_pointer(), in_range) {}
+        accessor<T, Memory, 2> operator[](size_t index) const {
+            sycl::range<2> sub(_range.get(1), _range.get(2));
+            return accessor<T, Memory, 2>(_data + index * sub.size(), sub);
+        }
+
+        pointer_t get_ptr() const { return _data; }
+
+    private:
+        pointer_t _data;
+        sycl::range<3> _range;
+    };
+    template <class T, memory_region Memory> class accessor<T, Memory, 2> {
+    public:
+        using memory_t = detail::memory_traits<Memory, T>;
+        using element_t = typename memory_t::element_t;
+        using pointer_t = typename memory_t::pointer_t;
+        using accessor_t = typename memory_t::template accessor_t<2>;
+        accessor(pointer_t data, const sycl::range<2> &in_range)
+            : _data(data), _range(in_range) {}
+        template <memory_region M = Memory>
+        accessor(typename std::enable_if<M != local, const accessor_t>::type &acc)
+            : accessor(acc, acc.get_range()) {}
+        accessor(const accessor_t &acc, const sycl::range<2> &in_range)
+            : accessor(acc.get_pointer(), in_range) {}
+
+        pointer_t operator[](size_t index) const {
+            return _data + _range.get(1) * index;
+        }
+
+        pointer_t get_ptr() const { return _data; }
+
+    private:
+        pointer_t _data;
+        sycl::range<2> _range;
+    };
+
+    namespace detail {
+        /// Device variable with address space of shared, global or constant.
+        template <class T, memory_region Memory, size_t Dimension> class device_memory {
+        public:
+            using accessor_t =
+                typename detail::memory_traits<Memory,
+                                            T>::template accessor_t<Dimension>;
+            using value_t = typename detail::memory_traits<Memory, T>::value_t;
+            using dpct_accessor_t = dpct::accessor<T, Memory, Dimension>;
+
+            device_memory() : device_memory(sycl::range<Dimension>(1)) {}
+
+            /// Constructor of 1-D array with initializer list
+            device_memory(const sycl::range<Dimension> &in_range,
+                        std::initializer_list<value_t> &&init_list)
+                : device_memory(in_range) {
+                assert(init_list.size() <= in_range.size());
+                _host_ptr = (value_t *)std::malloc(_size);
+                std::memset(_host_ptr, 0, _size);
+                std::memcpy(_host_ptr, init_list.begin(), init_list.size() * sizeof(T));
+            }
+
+            /// Constructor of 2-D array with initializer list
+            template <size_t D = Dimension>
+            device_memory(
+                const typename std::enable_if<D == 2, sycl::range<2>>::type &in_range,
+                std::initializer_list<std::initializer_list<value_t>> &&init_list)
+                : device_memory(in_range) {
+                assert(init_list.size() <= in_range[0]);
+                _host_ptr = (value_t *)std::malloc(_size);
+                std::memset(_host_ptr, 0, _size);
+                auto tmp_data = _host_ptr;
+                for (auto sub_list : init_list) {
+                    assert(sub_list.size() <= in_range[1]);
+                    std::memcpy(tmp_data, sub_list.begin(),
+                                sub_list.size() * sizeof(T));
+                    tmp_data += in_range[1];
+                }
+            }
+
+            /// Constructor with range
+            device_memory(const sycl::range<Dimension> &range_in)
+                : _size(range_in.size() * sizeof(T)), _range(range_in),
+                _reference(false), _host_ptr(nullptr), _device_ptr(nullptr) {
+                static_assert(
+                    (Memory == global) || (Memory == constant) || (Memory == shared),
+                    "device memory region should be global, constant or shared");
+                // Make sure that singleton class mem_mgr and dev_mgr will destruct
+                // later than this.
+                detail::mem_mgr::instance();
+                dev_mgr::instance();
+            }
+
+            /// Constructor with range
+            template <class... Args>
+            device_memory(Args... Arguments)
+                : device_memory(sycl::range<Dimension>(Arguments...)) {}
+
+            ~device_memory() {
+                if (_device_ptr && !_reference)
+                    dpct::dpct_free(_device_ptr);
+                if (_host_ptr)
+                    std::free(_host_ptr);
+            }
+
+            /// Allocate memory with default queue, and init memory if has initial
+            /// value.
+            void init() { init(dpct::get_default_queue()); }
+            /// Allocate memory with specified queue, and init memory if has initial
+            /// value.
+            void init(sycl::queue &q) {
+                if (_device_ptr)
+                    return;
+                if (!_size)
+                    return;
+                allocate_device(q);
+                if (_host_ptr)
+                    detail::dpct_memcpy(q, _device_ptr, _host_ptr, _size,
+                                        host_to_device);
+            }
+
+            /// The variable is assigned to a device pointer.
+            void assign(value_t *src, size_t size) {
+                this->~device_memory();
+                new (this) device_memory(src, size);
+            }
+
+            /// Get memory pointer of the memory object, which is virtual pointer when
+            /// usm is not used, and device pointer when usm is used.
+            value_t *get_ptr() { return get_ptr(get_default_queue()); }
+            /// Get memory pointer of the memory object, which is virtual pointer when
+            /// usm is not used, and device pointer when usm is used.
+            value_t *get_ptr(sycl::queue &q) {
+                init(q);
+                return _device_ptr;
+            }
+
+            /// Get the device memory object size in bytes.
+            size_t get_size() { return _size; }
+
+            template <size_t D = Dimension>
+            typename std::enable_if<D == 1, T>::type &operator[](size_t index) {
+                init();
+                return _device_ptr[index];
+            }
+
+            /// Get dpct::accessor with dimension info for the device memory object
+            /// when usm is used and dimension is greater than 1.
+            template <size_t D = Dimension>
+            typename std::enable_if<D != 1, dpct_accessor_t>::type
+            get_access(sycl::handler &cgh) {
+                return dpct_accessor_t((T *)_device_ptr, _range);
+            }
+
+        private:
+            device_memory(value_t *memory_ptr, size_t size)
+                : _size(size), _range(size / sizeof(T)), _reference(true),
+                _device_ptr(memory_ptr) {}
+
+            void allocate_device(sycl::queue &q) {
+        #ifndef DPCT_USM_LEVEL_NONE
+                if (Memory == shared) {
+                    _device_ptr = (value_t *)sycl::malloc_shared(_size, q.get_device(),
+                                                                q.get_context());
+                    return;
+                }
+        #ifdef SYCL_EXT_ONEAPI_USM_DEVICE_READ_ONLY
+                if (Memory == constant) {
+                    _device_ptr = (value_t *)sycl::malloc_device(
+                        _size, q.get_device(), q.get_context(),
+                        sycl::ext::oneapi::property::usm::device_read_only());
+                    return;
+                }
+        #endif
+        #endif
+                _device_ptr = (value_t *)detail::dpct_malloc(_size, q);
+            }
+
+            size_t _size;
+            sycl::range<Dimension> _range;
+            bool _reference;
+            value_t *_host_ptr;
+            value_t *_device_ptr;
+        };
+        template <class T, memory_region Memory>
+        class device_memory<T, Memory, 0> : public device_memory<T, Memory, 1> {
+        public:
+            using base = device_memory<T, Memory, 1>;
+            using value_t = typename base::value_t;
+            using accessor_t =
+                typename detail::memory_traits<Memory, T>::template accessor_t<0>;
+
+            /// Constructor with initial value.
+            device_memory(const value_t &val) : base(sycl::range<1>(1), {val}) {}
+
+            /// Default constructor
+            device_memory() : base(1) {}
+        };
+        } // namespace detail
+
+    template <class T, size_t Dimension>
+    using global_memory = detail::device_memory<T, global, Dimension>;
+    template <class T, size_t Dimension>
+    using constant_memory = detail::device_memory<T, constant, Dimension>;
+    template <class T, size_t Dimension>
+    using shared_memory = detail::device_memory<T, shared, Dimension>;
+
+
+} // COPY from DPCT head files
+
+#define GGML_COMMON_DECL_SYCL
+#define GGML_COMMON_IMPL_SYCL
+#include "ggml-common.h"
+
+static int g_ggml_sycl_debug=0;
+#define GGML_SYCL_DEBUG(...) do{if(g_ggml_sycl_debug) fprintf(stderr, __VA_ARGS__);}while(0)
+
+#define CHECK_TRY_ERROR(expr)                                                  \
+  [&]() {                                                                      \
+    try {                                                                      \
+      expr;                                                                    \
+      return dpct::success;                                                    \
+    } catch (std::exception const &e) {                                        \
+      std::cerr << e.what()<< "\nException caught at file:" << __FILE__        \
+        << ", line:" << __LINE__ <<", func:"<<__func__<< std::endl;            \
+      return dpct::default_error;                                              \
+    }                                                                          \
+  }()
+
+// #define DEBUG_SYCL_MALLOC
+
+static int g_work_group_size = 0;
+// typedef sycl::half ggml_fp16_t;
+
+#define __SYCL_ARCH__ DPCT_COMPATIBILITY_TEMP
+#define VER_4VEC   610          //todo for hardward optimize.
+#define VER_GEN9      700       //todo for hardward optimize.
+#define VER_GEN12 1000000       //todo for hardward optimize.
+#define VER_GEN13      (VER_GEN12 + 1030)   //todo for hardward optimize.
+
+#define GGML_SYCL_MAX_NODES 8192 //TODO: adapt to hardwares
+
+
+//define for XMX in Intel GPU
+//TODO: currently, it's not used for XMX really.
+#define SYCL_USE_XMX
+
+// max batch size to use MMQ kernels when tensor cores are available
+#define XMX_MAX_BATCH_SIZE 32
+
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+// dmmv = dequantize_mul_mat_vec
+#ifndef GGML_SYCL_DMMV_X
+#define GGML_SYCL_DMMV_X 32
+#endif
+#ifndef GGML_SYCL_MMV_Y
+#define GGML_SYCL_MMV_Y 1
+#endif
+
+enum ggml_sycl_backend_gpu_mode {
+    SYCL_UNSET_GPU_MODE = -1,
+    SYCL_SINGLE_GPU_MODE = 0,
+    SYCL_MUL_GPU_MODE
+};
+
+static_assert(sizeof(sycl::half) == sizeof(ggml_fp16_t), "wrong fp16 size");
+
+static void crash(){
+    int *ptr = NULL;
+    *ptr = 0;
+}
+
+static void ggml_sycl_error(const char * stmt, const char * func, const char * file, const int line, const char * msg) {
+    fprintf(stderr, "SYCL error: %s: %s\n", stmt, msg);
+    fprintf(stderr, "  in function %s at %s:%d\n", func, file, line);
+    GGML_ASSERT(!"SYCL error");
+}
+
+#define SYCL_CHECK(err) do {                                                   \
+    auto err_ = (err); if (err_ != 0) ggml_sycl_error(                         \
+        #err, __func__, __FILE__, __LINE__,                                    \
+        "Meet error in this line code!");   \
+} while (0)
+
+#if DPCT_COMPAT_RT_VERSION >= 11100
+#define GGML_SYCL_ASSUME(x) __builtin_assume(x)
+#else
+#define GGML_SYCL_ASSUME(x)
+#endif // DPCT_COMPAT_RT_VERSION >= 11100
+
+#ifdef GGML_SYCL_F16
+typedef sycl::half dfloat; // dequantize float
+typedef sycl::half2 dfloat2;
+#else
+typedef float dfloat; // dequantize float
+typedef sycl::float2 dfloat2;
+#endif //GGML_SYCL_F16
+
+bool   ggml_sycl_loaded(void);
+void * ggml_sycl_host_malloc(size_t size);
+void   ggml_sycl_host_free(void * ptr);
+bool   ggml_sycl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+void   ggml_sycl_free_data(struct ggml_tensor * tensor);
+void   ggml_sycl_assign_buffers(struct ggml_tensor * tensor);
+void   ggml_sycl_assign_buffers_no_scratch(struct ggml_tensor * tensor);
+void   ggml_sycl_assign_buffers_force_inplace(struct ggml_tensor * tensor);
+void   ggml_sycl_assign_buffers_no_alloc(struct ggml_tensor * tensor);
+void   ggml_sycl_copy_to_device(struct ggml_tensor * tensor);
+void   ggml_sycl_set_main_device(int main_device);
+void   ggml_sycl_set_mul_mat_q(bool mul_mat_q);
+void   ggml_sycl_set_scratch_size(size_t scratch_size);
+void   ggml_sycl_free_scratch(void);
+void   ggml_sycl_get_device_description(int device, char * description, size_t description_size);
+bool   ggml_backend_is_sycl(ggml_backend_t backend);
+int    ggml_backend_sycl_get_device(ggml_backend_t backend);
+int    get_main_device();
+void   print_ggml_tensor(const char*name, struct ggml_tensor *src);
+void   log_tensor_with_cnt(const char* name, struct ggml_tensor * src, int stop_cnt);
+
+void dev2dev_memcpy(sycl::queue &q_dst, sycl::queue &q_src, void *ptr_dst,
+                    const void *ptr_src, size_t size) {
+    char *host_buf = (char *)malloc(size);
+    q_src.memcpy(host_buf, (const char *)ptr_src, size).wait();
+    q_dst.memcpy((char *)ptr_dst, host_buf, size).wait();
+    free(host_buf);
+}
+
+static __dpct_inline__ int get_int_from_int8(const int8_t *x8, const int &i32) {
+    const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
+
+    int x32 = 0;
+    x32 |= x16[0] <<  0;
+    x32 |= x16[1] << 16;
+
+    return x32;
+}
+
+static __dpct_inline__ int get_int_from_uint8(const uint8_t *x8,
+                                              const int &i32) {
+    const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
+
+    int x32 = 0;
+    x32 |= x16[0] <<  0;
+    x32 |= x16[1] << 16;
+
+    return x32;
+}
+
+static __dpct_inline__ int get_int_from_int8_aligned(const int8_t *x8,
+                                                     const int &i32) {
+    return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
+}
+
+static __dpct_inline__ int get_int_from_uint8_aligned(const uint8_t *x8,
+                                                      const int &i32) {
+    return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
+}
+
+template <typename T>
+using to_t_sycl_t = void (*)(const void *__restrict__ x, T *__restrict__ y,
+                             int k, dpct::queue_ptr stream);
+typedef to_t_sycl_t<float> to_fp32_sycl_t;
+typedef to_t_sycl_t<sycl::half> to_fp16_sycl_t;
+
+typedef void (*dequantize_kernel_t)(const void * vx, const int ib, const int iqs, dfloat2 & v);
+typedef void (*dot_kernel_k_t)(const void * __restrict__ vx, const int ib, const int iqs, const float * __restrict__ y, float & v);
+typedef void (*cpy_kernel_t)(const char * cx, char * cdst);
+typedef void (*ggml_sycl_func_t)(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+typedef void (*ggml_sycl_op_mul_mat_t)(
+    const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
+    const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
+    float *dst_dd_i, const int64_t row_low, const int64_t row_high,
+    const int64_t src1_ncols, const int64_t src1_padded_row_size,
+    const dpct::queue_ptr &stream);
+typedef void (*ggml_sycl_op_flatten_t)(const ggml_tensor *src0,
+                                       const ggml_tensor *src1,
+                                       ggml_tensor *dst, const float *src0_dd,
+                                       const float *src1_dd, float *dst_dd,
+                                       const dpct::queue_ptr &main_stream);
+
+typedef float (*vec_dot_q_sycl_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs);
+typedef void (*allocate_tiles_sycl_t)(int **x_ql, sycl::half2 **x_dm,
+                                      int **x_qh, int **x_sc);
+typedef void (*load_tiles_sycl_t)(const void *__restrict__ vx,
+                                  int *__restrict__ x_ql,
+                                  sycl::half2 *__restrict__ x_dm,
+                                  int *__restrict__ x_qh,
+                                  int *__restrict__ x_sc, const int &i_offset,
+                                  const int &i_max, const int &k,
+                                  const int &blocks_per_row);
+typedef float (*vec_dot_q_mul_mat_sycl_t)(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ms,
+    const int &i, const int &j, const int &k);
+
+#define WARP_SIZE 32
+#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
+
+#define SYCL_GELU_BLOCK_SIZE 256
+#define SYCL_SILU_BLOCK_SIZE 256
+#define SYCL_TANH_BLOCK_SIZE 256
+#define SYCL_RELU_BLOCK_SIZE 256
+#define SYCL_HARDSIGMOID_BLOCK_SIZE 256
+#define SYCL_HARDSWISH_BLOCK_SIZE 256
+#define SYCL_SQR_BLOCK_SIZE 256
+#define SYCL_CPY_BLOCK_SIZE 32
+#define SYCL_SCALE_BLOCK_SIZE 256
+#define SYCL_CLAMP_BLOCK_SIZE 256
+#define SYCL_ROPE_BLOCK_SIZE 256
+#define SYCL_SOFT_MAX_BLOCK_SIZE 1024
+#define SYCL_ALIBI_BLOCK_SIZE 32
+#define SYCL_DIAG_MASK_INF_BLOCK_SIZE 32
+#define SYCL_QUANTIZE_BLOCK_SIZE 256
+#define SYCL_DEQUANTIZE_BLOCK_SIZE 256
+#define SYCL_GET_ROWS_BLOCK_SIZE 256
+#define SYCL_UPSCALE_BLOCK_SIZE 256
+#define SYCL_CONCAT_BLOCK_SIZE 256
+#define SYCL_PAD_BLOCK_SIZE 256
+#define SYCL_ACC_BLOCK_SIZE 256
+#define SYCL_IM2COL_BLOCK_SIZE 256
+#define SYCL_POOL2D_BLOCK_SIZE 256
+
+// dmmv = dequantize_mul_mat_vec
+#ifndef GGML_SYCL_DMMV_X
+#define GGML_SYCL_DMMV_X 32
+#endif
+#ifndef GGML_SYCL_MMV_Y
+#define GGML_SYCL_MMV_Y 1
+#endif
+
+#ifndef K_QUANTS_PER_ITERATION
+#define K_QUANTS_PER_ITERATION 2
+#else
+static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
+#endif
+
+#ifndef GGML_SYCL_PEER_MAX_BATCH_SIZE
+#define GGML_SYCL_PEER_MAX_BATCH_SIZE 128
+#endif // GGML_SYCL_PEER_MAX_BATCH_SIZE
+
+#define MUL_MAT_SRC1_COL_STRIDE 128
+
+#define MAX_STREAMS 8
+static dpct::queue_ptr g_syclStreams[GGML_SYCL_MAX_DEVICES][MAX_STREAMS] = {{0}};
+
+struct ggml_tensor_extra_gpu {
+    void * data_device[GGML_SYCL_MAX_DEVICES]; // 1 pointer for each device for split tensors
+    dpct::event_ptr
+        events[GGML_SYCL_MAX_DEVICES]
+              [MAX_STREAMS]; // events for synchronizing multiple GPUs
+};
+
+class sycl_gpu_mgr {
+    public:
+        std::vector<int> gpus;
+        std::vector<sycl::device> devices;
+        sycl::queue *first_queue;
+        sycl::context co_ctx;
+        int max_compute_units = 0;
+        int work_group_size = 0;
+        std::string gpus_list = "";
+
+        /*
+        Use all GPUs with same top max compute units
+        */
+        sycl_gpu_mgr() {
+            detect_sycl_gpu_list_with_max_cu();
+            get_allow_gpus();
+            create_context_with_gpus();
+        }
+
+        /*
+        Only use the assigned GPU
+        */
+        sycl_gpu_mgr(int main_gpu_id) {
+            sycl::device device = dpct::dev_mgr::instance().get_device(main_gpu_id);
+            dpct::device_info prop;
+            dpct::get_device_info(prop, device);
+            gpus.push_back(main_gpu_id);
+            devices.push_back(device);
+            work_group_size = prop.get_max_work_group_size();
+            max_compute_units = prop.get_max_compute_units();
+
+            get_allow_gpus();
+            create_context_with_gpus();
+        }
+
+        void create_context_with_gpus() {
+            sycl::context ctx = sycl::context(devices);
+            assert(gpus.size() > 0);
+            first_queue = dpct::get_current_device().create_queue(ctx, devices[0]);
+            co_ctx = first_queue->get_context();
+        }
+
+        sycl::context &get_co_ctx() { return co_ctx; }
+
+        void get_allow_gpus() {
+            gpus_list = "";
+            for (size_t i = 0; i < gpus.size(); ++i) {
+                gpus_list += std::to_string(gpus[i]);
+                gpus_list += ",";
+            }
+            if (gpus_list.length() > 1) {
+                gpus_list.pop_back();
+            }
+        }
+
+        bool is_allowed_gpu(int device_id) {
+            return std::find(gpus.begin(), gpus.end(), device_id) != gpus.end();
+        }
+
+        void detect_sycl_gpu_list_with_max_cu() try {
+            int device_count = dpct::dev_mgr::instance().device_count();
+
+            for (int id = 0; id < device_count; id++) {
+                sycl::device device = dpct::dev_mgr::instance().get_device(id);
+                if (!device.is_gpu())
+                    continue;
+                dpct::device_info prop;
+                dpct::get_device_info(prop, device);
+                if (max_compute_units < prop.get_max_compute_units())
+                    max_compute_units = prop.get_max_compute_units();
+            }
+
+            for (int id = 0; id < device_count; id++) {
+                sycl::device device = dpct::dev_mgr::instance().get_device(id);
+                if (!device.is_gpu())
+                    continue;
+                dpct::device_info prop;
+                dpct::get_device_info(prop, device);
+                if (max_compute_units == prop.get_max_compute_units() &&
+                    is_ext_oneapi_device(device)) {
+                    gpus.push_back(id);
+                    devices.push_back(device);
+                    work_group_size = prop.get_max_work_group_size();
+                }
+            }
+            return;
+        } catch (sycl::exception const &exc) {
+            std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+                    << ", line:" << __LINE__ << std::endl;
+            std::exit(1);
+        }
+
+        int get_gpu_count() { return (int)gpus.size(); }
+
+        int get_index(int id) {
+            for (int i = 0; i < (int)gpus.size(); i++) {
+                if (gpus[i] == id)
+                    return i;
+            }
+            printf("miss to get device index by id=%d\n", id);
+            GGML_ASSERT(false);
+        }
+
+        int get_next_index(int id) {
+            int cur_index = get_index(id);
+            for (int i = cur_index + 1; i < (int)gpus.size(); i++) {
+                if (gpus[i] == id)
+                    return i;
+            }
+            GGML_ASSERT(false);
+        }
+
+        bool is_ext_oneapi_device(const sycl::device &dev) {
+            sycl::backend dev_backend = dev.get_backend();
+            if (dev_backend == sycl::backend::ext_oneapi_level_zero ||
+                dev_backend == sycl::backend::ext_oneapi_cuda ||
+                dev_backend == sycl::backend::ext_oneapi_hip)
+                return true;
+            return false;
+        }
+};
+
+static sycl_gpu_mgr *g_sycl_gpu_mgr = NULL;
+static int g_device_count = -1;
+static int g_all_sycl_device_count = -1;
+static int g_main_device = -1;
+static int g_main_device_id = -1;
+static bool g_ggml_backend_sycl_buffer_type_initialized = false;
+
+static std::array<float, GGML_SYCL_MAX_DEVICES> g_default_tensor_split = {};
+
+static float g_tensor_split[GGML_SYCL_MAX_DEVICES] = {0};
+
+static ggml_sycl_backend_gpu_mode g_ggml_sycl_backend_gpu_mode = SYCL_UNSET_GPU_MODE;
+
+struct sycl_device_capabilities {
+    int     cc;                 // compute capability
+    bool    vmm;                // virtual memory support
+    size_t  vmm_granularity;    // granularity of virtual memory
+    int device_id;
+};
+
+static sycl_device_capabilities g_device_caps[GGML_SYCL_MAX_DEVICES] = { {0, false, 0, -1} };
+
+struct sycl_device_id2index {
+    int index;
+};
+
+static void * g_scratch_buffer = nullptr;
+static size_t g_scratch_size = 0; // disabled by default
+static size_t g_scratch_offset = 0;
+
+static dpct::queue_ptr g_sycl_handles[GGML_SYCL_MAX_DEVICES] = {nullptr};
+
+int get_main_device(){
+    return g_main_device;
+}
+
+[[noreturn]]
+static void bad_arch(const sycl::stream &stream_ct1) {
+    stream_ct1 << "ERROR: ggml-sycl was compiled without support for the "
+                  "current GPU architecture.\n";
+    // __trap();
+    std::exit(1);
+
+    (void) bad_arch; // suppress unused function warning
+}
+
+/*
+device_index: device index from 0 to n (continue numbers).
+    It is used for device select/set in SYCL backend internal data structure.
+*/
+void check_allow_gpu_index(const int device_index) {
+    if (device_index >= g_device_count) {
+        char error_buf[256];
+        snprintf(error_buf, sizeof(error_buf),
+                 "%s error: device_index:%d is out of range: [0-%d]", __func__,
+                 device_index, g_device_count - 1);
+        fprintf(stderr, "%s\n", error_buf);
+        assert(false);
+    }
+}
+
+/*
+device_id: device ID is shown by ggml_backend_sycl_print_sycl_devices().
+    It is only used to set current working device.
+*/
+void check_allow_gpu_id(const int device_id) {
+    if (!g_sycl_gpu_mgr->is_allowed_gpu(device_id)) {
+        char error_buf[256];
+        snprintf(error_buf, sizeof(error_buf),
+                 "error: cannot set device=%d, which is not allowed. Please "
+                 "set GPU ID in: [%s]",
+                 device_id, g_sycl_gpu_mgr->gpus_list.c_str());
+        fprintf(stderr, "%s\n", error_buf);
+        throw std::invalid_argument(error_buf);
+    }
+}
+
+int get_current_device_id() {
+    return dpct::dev_mgr::instance().current_device_id();
+}
+
+inline dpct::err0 ggml_sycl_set_device(const int device) try {
+
+    int device_id = g_sycl_gpu_mgr->gpus[device];
+    check_allow_gpu_id(device_id);
+
+    int current_device_id;
+    SYCL_CHECK(CHECK_TRY_ERROR(current_device_id = get_current_device_id()));
+
+    // GGML_SYCL_DEBUG("ggml_sycl_set_device device_id=%d,
+    // current_device_id=%d\n", device, current_device);
+    if (device_id == current_device_id) {
+        return 0;
+    }
+
+    return CHECK_TRY_ERROR(dpct::select_device(device_id));
+} catch (sycl::exception const &exc) {
+    std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+              << ", line:" << __LINE__ << std::endl;
+    crash();
+    std::exit(1);
+}
+
+void log_ggml_var_device(const char*name, float *src, size_t total_elements, bool src_on_device){
+    if(!g_ggml_sycl_debug) return;
+    if(!src){
+        printf("GGML Tensor:%s skip to save for NULL pointer\n", name);
+        return;
+    }
+    char filename[1024];
+    sprintf(filename, "%s.txt", name);
+    printf("GGML Tensor:%s save to %s\n", name, filename);
+
+    size_t total_size = total_elements*sizeof(float);
+    float *local_buf = NULL;
+    if(src_on_device) {
+        local_buf = (float *) ggml_sycl_host_malloc(total_size);
+        ggml_sycl_set_device(g_main_device);
+        dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
+        main_stream->memcpy(local_buf, src, total_size).wait();
+    }
+    else {
+        local_buf = (float *)src;
+    }
+
+    std::ofstream logfile;
+    logfile.open(filename);
+    for(size_t i=0; i<total_elements; i++){
+        logfile << local_buf[i] <<" ";
+        if((i+1)%20 ==0) logfile <<std::endl;
+    }
+    logfile <<std::endl;
+    logfile.close();
+
+    if(src_on_device) ggml_sycl_host_free(local_buf);
+}
+
+void log_ggml_var_device_fp16(const char*name, sycl::half *src, size_t total_elements, bool src_on_device){
+    if(!g_ggml_sycl_debug) return;
+    if(!src){
+        printf("GGML Tensor:%s skip to save for NULL pointer\n", name);
+        return;
+    }
+    char filename[1024];
+    sprintf(filename, "%s.txt", name);
+    printf("GGML Tensor:%s save to %s\n", name, filename);
+
+    size_t total_size = total_elements*sizeof(sycl::half);
+    sycl::half *local_buf = NULL;
+    if(src_on_device) {
+        local_buf = (sycl::half *) ggml_sycl_host_malloc(total_size);
+        ggml_sycl_set_device(g_main_device);
+        dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
+        main_stream->memcpy(local_buf, src, total_size).wait();
+    }
+    else {
+        local_buf = (sycl::half *)src;
+    }
+
+    std::ofstream logfile;
+    logfile.open(filename);
+    for(size_t i=0; i<total_elements; i++){
+        logfile << local_buf[i] <<" ";
+        if((i+1)%20 ==0) logfile <<std::endl;
+    }
+    logfile <<std::endl;
+    logfile.close();
+
+    if(src_on_device) ggml_sycl_host_free(local_buf);
+}
+
+//todo: debug for crash in some case
+void print_ggml_tensor(const char*name, struct ggml_tensor *src){
+    if(!g_ggml_sycl_debug) return;
+    if(!src){
+        printf("GGML Tensor:%s skip to save for NULL pointer\n", name);
+        return;
+    }
+
+    size_t total_elements = ggml_nelements(src);
+
+    const bool src_on_device = src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
+    float *src_data =NULL;
+    if(src_on_device) {
+        ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *)  src->extra;
+        src_data = (float*)src_extra->data_device[g_main_device];
+    }
+    else {
+        src_data = (float *)src->data;
+    }
+
+    log_ggml_var_device(name, src_data, total_elements, src_on_device);
+}
+
+static int log_file_name_idx=0;
+void log_tensor_with_cnt(const char* name, struct ggml_tensor * src, int stop_cnt) {
+    stop_cnt = 4;
+    if(log_file_name_idx>=stop_cnt) return;
+    char filename[1280];
+    sprintf(filename, "%s_%07d", name, log_file_name_idx);
+    log_file_name_idx++;
+    print_ggml_tensor(filename, src);
+}
+
+static __dpct_inline__ float warp_reduce_sum(float x,
+                                             const sycl::nd_item<3> &item_ct1) {
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        /*
+        DPCT1096:98: The right-most dimension of the work-group used in the SYCL
+        kernel that calls this function may be less than "32". The function
+        "dpct::permute_sub_group_by_xor" may return an unexpected result on the
+        CPU device. Modify the size of the work-group to ensure that the value
+        of the right-most dimension is a multiple of "32".
+        */
+        x += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), x, mask);
+    }
+    return x;
+}
+
+static __dpct_inline__ sycl::float2
+warp_reduce_sum(sycl::float2 a, const sycl::nd_item<3> &item_ct1) {
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        a.x() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.x(),
+                                                mask);
+        a.y() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.y(),
+                                                mask);
+    }
+    return a;
+}
+
+static __dpct_inline__ float warp_reduce_max(float x,
+                                             const sycl::nd_item<3> &item_ct1) {
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        /*
+        DPCT1096:97: The right-most dimension of the work-group used in the SYCL
+        kernel that calls this function may be less than "32". The function
+        "dpct::permute_sub_group_by_xor" may return an unexpected result on the
+        CPU device. Modify the size of the work-group to ensure that the value
+        of the right-most dimension is a multiple of "32".
+        */
+        x = sycl::fmax(x, dpct::permute_sub_group_by_xor(
+                              item_ct1.get_sub_group(), x, mask));
+    }
+    return x;
+}
+
+static __dpct_inline__ float op_repeat(const float a, const float b) {
+    return b;
+    GGML_UNUSED(a);
+}
+
+static __dpct_inline__ float op_add(const float a, const float b) {
+    return a + b;
+}
+
+static __dpct_inline__ float op_mul(const float a, const float b) {
+    return a * b;
+}
+
+static __dpct_inline__ float op_div(const float a, const float b) {
+    return a / b;
+}
+
+template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
+static void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst,
+        int ne0, int ne1, int ne2, int ne3,
+        int ne10, int ne11, int ne12, int ne13,
+        /*int s0, */ int s1,  int s2,  int s3,
+        /*int s10,*/ int s11, int s12, int s13,
+        const sycl::nd_item<3> &item_ct1) {
+    const int i0s = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                    item_ct1.get_local_id(2);
+    const int i1 = (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                    item_ct1.get_local_id(1));
+    const int i2 = (item_ct1.get_local_range(0) * item_ct1.get_group(0) +
+                    item_ct1.get_local_id(0)) /
+                   ne3;
+    const int i3 = (item_ct1.get_local_range(0) * item_ct1.get_group(0) +
+                    item_ct1.get_local_id(0)) %
+                   ne3;
+
+    if (i0s >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
+        return;
+    }
+
+    const int i11 = i1 % ne11;
+    const int i12 = i2 % ne12;
+    const int i13 = i3 % ne13;
+
+    const size_t i_src0 = i3*s3 + i2*s2 + i1*s1;
+    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
+    const size_t i_dst  = i_src0;
+
+    const src0_t * src0_row = src0 + i_src0;
+    const src1_t * src1_row = src1 + i_src1;
+    dst_t * dst_row = dst + i_dst;
+
+    for (int i0 = i0s; i0 < ne0;
+         i0 += item_ct1.get_local_range(2) * item_ct1.get_group_range(2)) {
+        const int i10 = i0 % ne10;
+        dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
+    }
+}
+
+template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
+static void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t * dst,
+        int ne0, int ne1, int ne2, int ne3,
+        int ne10, int ne11, int ne12, int ne13,
+        /*int s0, */ int s1,  int s2,  int s3,
+        /*int s10,*/ int s11, int s12, int s13,
+        const sycl::nd_item<3> &item_ct1) {
+
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    const int i3 = i/(ne2*ne1*ne0);
+    const int i2 = (i/(ne1*ne0)) % ne2;
+    const int i1 = (i/ne0) % ne1;
+    const int i0 = i % ne0;
+
+    if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
+        return;
+    }
+
+    const int i11 = i1 % ne11;
+    const int i12 = i2 % ne12;
+    const int i13 = i3 % ne13;
+
+    const size_t i_src0 = i3*s3 + i2*s2 + i1*s1;
+    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
+    const size_t i_dst  = i_src0;
+
+    const src0_t * src0_row = src0 + i_src0;
+    const src1_t * src1_row = src1 + i_src1;
+    dst_t * dst_row = dst + i_dst;
+
+    const int i10 = i0 % ne10;
+    dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
+}
+
+static void acc_f32(const float * x, const float * y, float * dst, const int ne,
+    const int ne10, const int ne11, const int ne12,
+    const int nb1, const int nb2, int offset, const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+    if (i >= ne) {
+        return;
+    }
+    int src1_idx = i - offset;
+    int oz = src1_idx / nb2;
+    int oy = (src1_idx - (oz * nb2)) / nb1;
+    int ox = src1_idx % nb1;
+    if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) {
+        dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11];
+    } else {
+        dst[i] = x[i];
+    }
+}
+
+static void gelu_f32(const float * x, float * dst, const int k,
+                     const sycl::nd_item<3> &item_ct1) {
+    const float GELU_COEF_A    = 0.044715f;
+    const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+
+    float xi = x[i];
+    dst[i] = 0.5f * xi *
+             (1.0f +
+              sycl::tanh(SQRT_2_OVER_PI * xi * (1.0f + GELU_COEF_A * xi * xi)));
+}
+
+static void silu_f32(const float * x, float * dst, const int k,
+                     const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = x[i] / (1.0f + sycl::native::exp(-x[i]));
+}
+
+static void gelu_quick_f32(const float *x, float *dst, int k,
+                           const sycl::nd_item<3> &item_ct1) {
+    const float GELU_QUICK_COEF = -1.702f;
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+    if (i >= k) {
+        return;
+    }
+    dst[i] = x[i] * (1.0f / (1.0f + sycl::native::exp(GELU_QUICK_COEF * x[i])));
+}
+
+static void tanh_f32(const float *x, float *dst, int k,
+                     const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+    if (i >= k) {
+        return;
+    }
+    dst[i] = sycl::tanh((float)(x[i]));
+}
+
+static void relu_f32(const float * x, float * dst, const int k,
+                     const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = sycl::fmax((float)(x[i]), (float)0);
+}
+
+static void hardsigmoid_f32(const float * x, float * dst, const int k,
+                            const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f));
+}
+
+static void hardswish_f32(const float * x, float * dst, const int k,
+                          const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = x[i] * sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f));
+}
+
+static void leaky_relu_f32(const float *x, float *dst, const int k, const float negative_slope,
+                           const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+    if (i >= k) {
+        return;
+    }
+    dst[i] = sycl::fmax((float)(x[i]), (float)0) +
+             sycl::fmin((float)(x[i]), 0.0f) * negative_slope;
+}
+
+static void sqr_f32(const float * x, float * dst, const int k,
+                    const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+    dst[i] = x[i] * x[i];
+}
+
+static void norm_f32(const float * x, float * dst, const int ncols, const float eps,
+                     const sycl::nd_item<3> &item_ct1, sycl::float2 *s_sum, int block_size) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+    const int tid = item_ct1.get_local_id(2);
+
+    sycl::float2 mean_var = sycl::float2(0.f, 0.f);
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const float xi = x[row*ncols + col];
+        mean_var.x() += xi;
+        mean_var.y() += xi * xi;
+    }
+
+    // sum up partial sums
+    mean_var = warp_reduce_sum(mean_var, item_ct1);
+    if (block_size > WARP_SIZE) {
+
+        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
+        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = mean_var;
+        }
+        /*
+        DPCT1118:0: SYCL group functions and algorithms must be encountered in
+        converged control flow. You may need to adjust the code.
+        */
+        item_ct1.barrier(sycl::access::fence_space::local_space);
+        mean_var = s_sum[lane_id];
+        mean_var = warp_reduce_sum(mean_var, item_ct1);
+    }
+
+    const float mean = mean_var.x() / ncols;
+    const float var = mean_var.y() / ncols - mean * mean;
+    const float inv_std = sycl::rsqrt(var + eps);
+
+    for (int col = tid; col < ncols; col += block_size) {
+        dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_std;
+    }
+}
+
+static void concat_f32(const float  *x,const float  *y, float *dst, const int ne0, const int ne02,
+                       const sycl::nd_item<3> &item_ct1) {
+    int nidx = item_ct1.get_local_id(2) +
+               item_ct1.get_group(2) * item_ct1.get_local_range(2);
+    if (nidx >= ne0) {
+        return;
+    }
+    // operation
+    int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
+                     item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
+    if (item_ct1.get_group(0) < ne02) { // src0
+        int offset_src =
+            nidx + item_ct1.get_group(1) * ne0 +
+            item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
+            dst[offset_dst] = x[offset_src];
+    } else {
+        int offset_src =
+            nidx + item_ct1.get_group(1) * ne0 +
+            (item_ct1.get_group(0) - ne02) * ne0 * item_ct1.get_group_range(1);
+            dst[offset_dst] = y[offset_src];
+    }
+}
+
+static void upscale_f32(const float  *x, float *dst, const int ne00, const int nb02, const int scale_factor,
+                        const sycl::nd_item<3> &item_ct1) {
+    int ne0 = ne00 * scale_factor;
+    int nidx = item_ct1.get_local_id(2) +
+               item_ct1.get_group(2) * item_ct1.get_local_range(2);
+    if (nidx >= ne0) {
+        return;
+    }
+    // operation
+    int i00 = nidx / scale_factor;
+    int i01 = item_ct1.get_group(1) / scale_factor;
+    int offset_src = i00 + i01 * ne00 + item_ct1.get_group(0) * nb02;
+    int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
+                     item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
+    dst[offset_dst] = x[offset_src];
+}
+
+static void pad_f32(const float  *x, float *dst, const int ne0, const int ne00, const int ne01, const int ne02,
+                    const sycl::nd_item<3> &item_ct1) {
+    int nidx = item_ct1.get_local_id(2) +
+               item_ct1.get_group(2) * item_ct1.get_local_range(2);
+    if (nidx >= ne0) {
+        return;
+    }
+
+    // operation
+    int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
+                     item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
+    if (nidx < ne00 && item_ct1.get_group(1) < ne01 &&
+        item_ct1.get_group(0) < ne02) {
+        int offset_src = nidx + item_ct1.get_group(1) * ne00 +
+                         item_ct1.get_group(0) * ne00 * ne01;
+            dst[offset_dst] = x[offset_src];
+    } else {
+        dst[offset_dst] = 0.0f;
+    }
+}
+
+static void group_norm_f32(const float * x, float * dst, const int group_size, const int ne_elements, const float eps,
+                           const sycl::nd_item<3> &item_ct1, float *s_sum, int block_size) {
+    int start = item_ct1.get_group(2) * group_size;
+    int end = start + group_size;
+
+    start += item_ct1.get_local_id(2);
+
+    if (end >= ne_elements) {
+        end = ne_elements;
+    }
+
+    float tmp = 0.0f; // partial sum for thread in warp
+
+    for (int j = start; j < end; j += block_size) {
+        tmp += x[j];
+    }
+
+    tmp = warp_reduce_sum(tmp, item_ct1);
+    if (block_size > WARP_SIZE) {
+
+        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
+        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        /*
+        DPCT1118:1: SYCL group functions and algorithms must be encountered in
+        converged control flow. You may need to adjust the code.
+        */
+        /*
+        DPCT1065:54: Consider replacing sycl::nd_item::barrier() with
+        sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
+        better performance if there is no access to global memory.
+        */
+        item_ct1.barrier();
+        tmp = s_sum[lane_id];
+        tmp = warp_reduce_sum(tmp, item_ct1);
+    }
+
+    float mean = tmp / group_size;
+    tmp = 0.0f;
+
+    for (int j = start; j < end; j += block_size) {
+        float xi = x[j] - mean;
+        dst[j] = xi;
+        tmp += xi * xi;
+    }
+
+    tmp = warp_reduce_sum(tmp, item_ct1);
+    if (block_size > WARP_SIZE) {
+
+        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
+        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        /*
+        DPCT1118:2: SYCL group functions and algorithms must be encountered in
+        converged control flow. You may need to adjust the code.
+        */
+        /*
+        DPCT1065:55: Consider replacing sycl::nd_item::barrier() with
+        sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
+        better performance if there is no access to global memory.
+        */
+        item_ct1.barrier();
+        tmp = s_sum[lane_id];
+        tmp = warp_reduce_sum(tmp, item_ct1);
+    }
+
+    float variance = tmp / group_size;
+    float scale = sycl::rsqrt(variance + eps);
+    for (int j = start; j < end; j += block_size) {
+        dst[j] *= scale;
+    }
+}
+
+static void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps,
+                         const sycl::nd_item<3> &item_ct1, float *s_sum, int block_size) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+    const int tid = item_ct1.get_local_id(2);
+
+    float tmp = 0.0f; // partial sum for thread in warp
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const float xi = x[row*ncols + col];
+        tmp += xi * xi;
+    }
+
+    // sum up partial sums
+    tmp = warp_reduce_sum(tmp, item_ct1);
+    if (block_size > WARP_SIZE) {
+
+        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
+        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        /*
+        DPCT1118:3: SYCL group functions and algorithms must be encountered in
+        converged control flow. You may need to adjust the code.
+        */
+        item_ct1.barrier(sycl::access::fence_space::local_space);
+        tmp = s_sum[lane_id];
+        tmp = warp_reduce_sum(tmp, item_ct1);
+    }
+
+    const float mean = tmp / ncols;
+    const float scale = sycl::rsqrt(mean + eps);
+
+    for (int col = tid; col < ncols; col += block_size) {
+        dst[row*ncols + col] = scale * x[row*ncols + col];
+    }
+}
+
+static __dpct_inline__ void dequantize_q4_0(const void *vx, const int ib,
+                                            const int iqs, dfloat2 &v) {
+    const block_q4_0 * x = (const block_q4_0 *) vx;
+
+    const dfloat d = x[ib].d;
+
+    const int vui = x[ib].qs[iqs];
+
+    v.x() = vui & 0xF;
+    v.y() = vui >> 4;
+
+#ifdef GGML_SYCL_F16
+    // v = v - {8.0f, 8.0f};
+    // v = v * {d, d};
+    v.s0() = (v.s0() - 8.0f) * d;
+    v.s1() = (v.s1() - 8.0f) * d;
+
+#else
+    v.x() = (v.x() - 8.0f) * d;
+    v.y() = (v.y() - 8.0f) * d;
+#endif // GGML_SYCL_F16
+}
+
+static __dpct_inline__ void dequantize_q4_1(const void *vx, const int ib,
+                                            const int iqs, dfloat2 &v) {
+    const block_q4_1 * x = (const block_q4_1 *) vx;
+
+    const dfloat d = x[ib].dm[0];
+    const dfloat m = x[ib].dm[1];
+
+    const int vui = x[ib].qs[iqs];
+
+    v.x() = vui & 0xF;
+    v.y() = vui >> 4;
+
+#ifdef GGML_SYCL_F16
+    // v = v * {d, d};
+    // v = v + {m, m};
+    v.s0() = (v.s0() * d) + m;
+    v.s1() = (v.s1() * d) + m;
+
+#else
+    v.x() = (v.x() * d) + m;
+    v.y() = (v.y() * d) + m;
+#endif // GGML_SYCL_F16
+}
+
+static __dpct_inline__ void dequantize_q5_0(const void *vx, const int ib,
+                                            const int iqs, dfloat2 &v) {
+    const block_q5_0 * x = (const block_q5_0 *) vx;
+
+    const dfloat d = x[ib].d;
+
+    uint32_t qh;
+    memcpy(&qh, x[ib].qh, sizeof(qh));
+
+    const int xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
+    const int xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
+
+    v.x() = ((x[ib].qs[iqs] & 0xf) | xh_0);
+    v.y() = ((x[ib].qs[iqs] >> 4) | xh_1);
+
+#ifdef GGML_SYCL_F16
+    // v = v - {16.0f, 16.0f};
+    // v = v * {d, d};
+    v.s0() = (v.s0() - 16.0f) * d;
+    v.s1() = (v.s1() - 16.0f) * d;
+
+#else
+    v.x() = (v.x() - 16.0f) * d;
+    v.y() = (v.y() - 16.0f) * d;
+#endif // GGML_SYCL_F16
+}
+
+static __dpct_inline__ void dequantize_q5_1(const void *vx, const int ib,
+                                            const int iqs, dfloat2 &v) {
+    const block_q5_1 * x = (const block_q5_1 *) vx;
+
+    const dfloat d = x[ib].dm[0];
+    const dfloat m = x[ib].dm[1];
+
+    uint32_t qh;
+    memcpy(&qh, x[ib].qh, sizeof(qh));
+
+    const int xh_0 = ((qh >> (iqs +  0)) << 4) & 0x10;
+    const int xh_1 = ((qh >> (iqs + 12))     ) & 0x10;
+
+    v.x() = ((x[ib].qs[iqs] & 0xf) | xh_0);
+    v.y() = ((x[ib].qs[iqs] >> 4) | xh_1);
+
+#ifdef GGML_SYCL_F16
+    // v = v * {d, d};
+    // v = v + {m, m};
+    v.s0() = (v.s0() * d) + m;
+    v.s1() = (v.s1() * d) + m;
+#else
+    v.x() = (v.x() * d) + m;
+    v.y() = (v.y() * d) + m;
+#endif // GGML_SYCL_F16
+}
+
+static __dpct_inline__ void dequantize_q8_0(const void *vx, const int ib,
+                                            const int iqs, dfloat2 &v) {
+    const block_q8_0 * x = (const block_q8_0 *) vx;
+
+    const dfloat d = x[ib].d;
+
+    v.x() = x[ib].qs[iqs + 0];
+    v.y() = x[ib].qs[iqs + 1];
+
+#ifdef GGML_SYCL_F16
+    // v = v * {d, d};
+    v.s0() *= d;
+    v.s1() *= d;
+#else
+    v.x() *= d;
+    v.y() *= d;
+#endif // GGML_SYCL_F16
+}
+
+template<typename dst_t>
+static void dequantize_block_q4_0(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32,
+                                  const sycl::nd_item<3> &item_ct1) {
+
+    const int i = item_ct1.get_group(2);
+
+    // assume 32 threads
+    const int tid = item_ct1.get_local_id(2);
+    const int il  = tid/8;
+    const int ir  = tid%8;
+    const int ib = 8*i + ir;
+    if (ib >= nb32) {
+        return;
+    }
+
+    dst_t * y = yy + 256*i + 32*ir + 4*il;
+
+    const block_q4_0 * x = (const block_q4_0 *)vx + ib;
+    const float d = sycl::vec<sycl::half, 1>(x->d)
+                        .convert<float, sycl::rounding_mode::automatic>()[0];
+    const float dm = -8*d;
+
+    const uint8_t * q = x->qs + 4*il;
+
+    for (int l = 0; l < 4; ++l) {
+        y[l+ 0] = d * (q[l] & 0xF) + dm;
+        y[l+16] = d * (q[l] >>  4) + dm;
+    }
+}
+
+template<typename dst_t>
+static void dequantize_block_q4_1(const void * __restrict__ vx, dst_t * __restrict__ yy, int nb32,
+                                  const sycl::nd_item<3> &item_ct1) {
+
+    const int i = item_ct1.get_group(2);
+
+    // assume 32 threads
+    const int tid = item_ct1.get_local_id(2);
+    const int il  = tid/8;
+    const int ir  = tid%8;
+    const int ib = 8*i + ir;
+    if (ib >= nb32) {
+        return;
+    }
+
+    dst_t * y = yy + 256*i + 32*ir + 4*il;
+
+    const block_q4_1 * x = (const block_q4_1 *)vx + ib;
+    const sycl::float2 d =
+        x->dm.convert<float, sycl::rounding_mode::automatic>();
+
+    const uint8_t * q = x->qs + 4*il;
+
+    for (int l = 0; l < 4; ++l) {
+        y[l + 0] = d.x() * (q[l] & 0xF) + d.y();
+        y[l + 16] = d.x() * (q[l] >> 4) + d.y();
+    }
+}
+
+
+//================================== k-quants
+
+template<typename dst_t>
+static void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                  const sycl::nd_item<3> &item_ct1) {
+
+    const int i = item_ct1.get_group(2);
+    const block_q2_K * x = (const block_q2_K *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int n   = tid/32;
+    const int l   = tid - 32*n;
+    const int is  = 8*n + l/16;
+
+    const uint8_t q = x[i].qs[32*n + l];
+    dst_t * y = yy + i*QK_K + 128*n;
+
+    float dall = x[i].dm[0];
+    float dmin = x[i].dm[1];
+    y[l+ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
+    y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4);
+    y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4);
+    y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4);
+#else
+    const int is = tid/16;  // 0 or 1
+    const int il = tid%16;  // 0...15
+    const uint8_t q = x[i].qs[il] >> (2*is);
+    dst_t * y = yy + i*QK_K + 16*is + il;
+
+    float dall = x[i].dm[0];
+    float dmin = x[i].dm[1];
+    y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
+    y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4);
+#endif
+
+}
+
+template<typename dst_t>
+static void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                  const sycl::nd_item<3> &item_ct1) {
+
+    const int i = item_ct1.get_group(2);
+    const block_q3_K * x = (const block_q3_K *) vx;
+
+#if QK_K == 256
+    const int r = item_ct1.get_local_id(2) / 4;
+    const int tid = r/2;
+    const int is0 = r%2;
+    const int l0 = 16 * is0 + 4 * (item_ct1.get_local_id(2) % 4);
+    const int n = tid / 4;
+    const int j = tid - 4*n;
+
+    uint8_t m = 1 << (4*n + j);
+    int is = 8*n + 2*j + is0;
+    int shift = 2*j;
+
+    int8_t us = is <  4 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+8] >> 0) & 3) << 4) :
+                is <  8 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+4] >> 2) & 3) << 4) :
+                is < 12 ? (x[i].scales[is-8] >>  4) | (((x[i].scales[is+0] >> 4) & 3) << 4) :
+                          (x[i].scales[is-8] >>  4) | (((x[i].scales[is-4] >> 6) & 3) << 4);
+    float d_all = x[i].d;
+    float dl = d_all * (us - 32);
+
+    dst_t * y = yy + i*QK_K + 128*n + 32*j;
+    const uint8_t * q = x[i].qs + 32*n;
+    const uint8_t * hm = x[i].hmask;
+
+    for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
+#else
+    const int tid = item_ct1.get_local_id(2);
+    const int is  = tid/16;  // 0 or 1
+    const int il  = tid%16;  // 0...15
+    const int im  = il/8;    // 0...1
+    const int in  = il%8;    // 0...7
+
+    dst_t * y = yy + i*QK_K + 16*is + il;
+
+    const uint8_t q = x[i].qs[il] >> (2*is);
+    const uint8_t h = x[i].hmask[in] >> (2*is + im);
+    const float   d = (float)x[i].d;
+
+    if (is == 0) {
+        y[ 0] = d * ((x[i].scales[0] & 0xF) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
+        y[32] = d * ((x[i].scales[1] & 0xF) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
+    } else {
+        y[ 0] = d * ((x[i].scales[0] >>  4) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
+        y[32] = d * ((x[i].scales[1] >>  4) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
+    }
+#endif
+
+}
+
+#if QK_K == 256
+static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
+    if (j < 4) {
+        d = q[j] & 63; m = q[j + 4] & 63;
+    } else {
+        d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
+        m = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
+    }
+}
+#endif
+
+template<typename dst_t>
+static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                  const sycl::nd_item<3> &item_ct1) {
+    const block_q4_K * x = (const block_q4_K *) vx;
+
+    const int i = item_ct1.get_group(2);
+
+#if QK_K == 256
+    // assume 32 threads
+    const int tid = item_ct1.get_local_id(2);
+    const int il  = tid/8;
+    const int ir  = tid%8;
+    const int is  = 2*il;
+    const int n   = 4;
+
+    dst_t * y = yy + i*QK_K + 64*il + n*ir;
+
+    const float dall = x[i].dm[0];
+    const float dmin = x[i].dm[1];
+
+    const uint8_t * q = x[i].qs + 32*il + n*ir;
+
+    uint8_t sc, m;
+    get_scale_min_k4(is + 0, x[i].scales, sc, m);
+    const float d1 = dall * sc; const float m1 = dmin * m;
+    get_scale_min_k4(is + 1, x[i].scales, sc, m);
+    const float d2 = dall * sc; const float m2 = dmin * m;
+    for (int l = 0; l < n; ++l) {
+        y[l + 0] = d1 * (q[l] & 0xF) - m1;
+        y[l +32] = d2 * (q[l] >>  4) - m2;
+    }
+#else
+    const int tid = item_ct1.get_local_id(2);
+    const uint8_t * q = x[i].qs;
+    dst_t * y = yy + i*QK_K;
+    const float d = (float)x[i].dm[0];
+    const float m = (float)x[i].dm[1];
+    y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4);
+    y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >>  4) - m * (x[i].scales[1] >> 4);
+#endif
+}
+
+template<typename dst_t>
+static void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                  const sycl::nd_item<3> &item_ct1) {
+    const block_q5_K * x = (const block_q5_K *) vx;
+
+    const int i = item_ct1.get_group(2);
+
+#if QK_K == 256
+    // assume 64 threads - this is very slightly better than the one below
+    const int tid = item_ct1.get_local_id(2);
+    const int il  = tid/16;   // il is in 0...3
+    const int ir  = tid%16;   // ir is in 0...15
+    const int is  = 2*il;     // is is in 0...6
+
+    dst_t * y = yy + i*QK_K + 64*il + 2*ir;
+
+    const float dall = x[i].dm[0];
+    const float dmin = x[i].dm[1];
+
+    const uint8_t * ql = x[i].qs + 32*il + 2*ir;
+    const uint8_t * qh = x[i].qh + 2*ir;
+
+    uint8_t sc, m;
+    get_scale_min_k4(is + 0, x[i].scales, sc, m);
+    const float d1 = dall * sc; const float m1 = dmin * m;
+    get_scale_min_k4(is + 1, x[i].scales, sc, m);
+    const float d2 = dall * sc; const float m2 = dmin * m;
+
+    uint8_t   hm  = 1 << (2*il);
+    y[ 0] = d1 * ((ql[ 0] & 0xF) + (qh[ 0] & hm ? 16 : 0)) - m1;
+    y[ 1] = d1 * ((ql[ 1] & 0xF) + (qh[ 1] & hm ? 16 : 0)) - m1;
+    hm <<= 1;
+    y[32] = d2 * ((ql[ 0] >>  4) + (qh[ 0] & hm ? 16 : 0)) - m2;
+    y[33] = d2 * ((ql[ 1] >>  4) + (qh[ 1] & hm ? 16 : 0)) - m2;
+#else
+    const int tid = item_ct1.get_local_id(2);
+    const uint8_t q = x[i].qs[tid];
+    const int im = tid/8;  // 0...3
+    const int in = tid%8;  // 0...7
+    const int is = tid/16; // 0 or 1
+    const uint8_t h = x[i].qh[in] >> im;
+    const float d = x[i].d;
+    dst_t * y = yy + i*QK_K + tid;
+    y[ 0] = d * x[i].scales[is+0] * ((q & 0xF) - ((h >> 0) & 1 ? 0 : 16));
+    y[32] = d * x[i].scales[is+2] * ((q >>  4) - ((h >> 4) & 1 ? 0 : 16));
+#endif
+}
+
+template<typename dst_t>
+static void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                  const sycl::nd_item<3> &item_ct1) {
+    const block_q6_K * x = (const block_q6_K *) vx;
+
+    const int i = item_ct1.get_group(2);
+#if QK_K == 256
+
+    // assume 64 threads - this is very slightly better than the one below
+    const int tid = item_ct1.get_local_id(2);
+    const int ip  = tid/32;   // ip is 0 or 1
+    const int il  = tid - 32*ip; // 0...32
+    const int is  = 8*ip + il/16;
+
+    dst_t * y = yy + i*QK_K + 128*ip + il;
+
+    const float d = x[i].d;
+
+    const uint8_t * ql = x[i].ql + 64*ip + il;
+    const uint8_t   qh = x[i].qh[32*ip + il];
+    const int8_t  * sc = x[i].scales + is;
+
+    y[ 0] = d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
+    y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32);
+    y[64] = d * sc[4] * ((int8_t)((ql[ 0]  >> 4) | (((qh >> 4) & 3) << 4)) - 32);
+    y[96] = d * sc[6] * ((int8_t)((ql[32]  >> 4) | (((qh >> 6) & 3) << 4)) - 32);
+#else
+
+    // assume 32 threads
+    const int tid = item_ct1.get_local_id(2);
+    const int ip  = tid/16;         // 0 or 1
+    const int il  = tid - 16*ip;    // 0...15
+
+    dst_t * y = yy + i*QK_K + 16*ip + il;
+
+    const float d = x[i].d;
+
+    const uint8_t   ql = x[i].ql[16*ip + il];
+    const uint8_t   qh = x[i].qh[il] >> (2*ip);
+    const int8_t  * sc = x[i].scales;
+
+    y[ 0] = d * sc[ip+0] * ((int8_t)((ql & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
+    y[32] = d * sc[ip+2] * ((int8_t)((ql  >> 4) | (((qh >> 4) & 3) << 4)) - 32);
+#endif
+}
+
+template<typename dst_t>
+static void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                     const sycl::nd_item<3> &item_ct1,
+                                     const uint64_t *iq2xxs_grid_ptr,
+                                     const uint8_t *ksigns_iq2xs_ptr,
+                                     const uint8_t *kmask_iq2xs_ptr) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq2_xxs * x = (const block_iq2_xxs  *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint16_t * q2 = x[i].qs + 4*ib;
+    const uint8_t  * aux8 = (const uint8_t *)q2;
+    const uint8_t  * grid = (const uint8_t *)(iq2xxs_grid_ptr + aux8[il]);
+    const uint32_t aux32 = q2[2] | (q2[3] << 16);
+    const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f;
+    const uint8_t signs = ksigns_iq2xs_ptr[(aux32 >> 7*il) & 127];
+    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs_ptr[j] ? -1.f : 1.f);
+#else
+    assert(false);
+#endif
+
+}
+
+template<typename dst_t>
+static void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                    const sycl::nd_item<3> &item_ct1,
+                                    const uint64_t *iq2xs_grid,
+                                    const uint8_t *ksigns_iq2xs,
+                                    const uint8_t *kmask_iq2xs) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq2_xs * x = (const block_iq2_xs *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint16_t * q2 = x[i].qs + 4*ib;
+    const uint8_t  * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511));
+    const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
+    const uint8_t signs = ksigns_iq2xs[q2[il] >> 9];
+    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
+#else
+    assert(false);
+#endif
+
+}
+
+template<typename dst_t>
+static void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                     const sycl::nd_item<3> &item_ct1,
+                                     const uint32_t *iq3xxs_grid,
+                                     const uint8_t *ksigns_iq2xs,
+                                     const uint8_t *kmask_iq2xs) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq3_xxs * x = (const block_iq3_xxs  *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint8_t  * q3 = x[i].qs + 8*ib;
+    const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib;
+    const uint8_t  * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]);
+    const uint8_t  * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]);
+    const uint32_t aux32 = gas[0] | (gas[1] << 16);
+    const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.5f;
+    const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
+    for (int j = 0; j < 4; ++j) {
+        y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
+        y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
+    }
+#else
+    assert(false);
+#endif
+
+}
+
+template<typename dst_t>
+static void dequantize_block_iq3_s(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                     const sycl::nd_item<3> &item_ct1,
+                                     const uint32_t *iq3s_grid,
+                                     const uint8_t *ksigns_iq2xs,
+                                     const uint8_t *kmask_iq2xs) {
+
+    const int i = item_ct1.get_group(2);
+    const block_iq3_s * x = (const block_iq3_s  *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint8_t  * qs = x[i].qs + 8*ib;
+    const uint8_t  * grid1 = (const uint8_t *)(iq3s_grid + qs[2*il+0]);
+    const uint8_t  * grid2 = (const uint8_t *)(iq3s_grid + qs[2*il+1]);
+    const float d = (float)x[i].d * (1 + 2*((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf));
+    const uint8_t signs = x[i].signs[4*ib + il];
+    for (int j = 0; j < 4; ++j) {
+        y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
+        y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
+    }
+#else
+    assert(false);
+#endif
+
+}
+
+template<typename dst_t>
+static void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                     const sycl::nd_item<3> &item_ct1,
+                                     const uint32_t *iq1s_grid,
+                                     const uint8_t *ksigns_iq2xs,
+                                     const uint8_t *kmask_iq2xs) {
+    const int i = item_ct1.get_group(2);
+    const block_iq1_s * x = (const block_iq1_s  *) vx;
+
+    const int tid = item_ct1.get_local_id(2);
+#if QK_K == 256
+    const int il = tid/8; // 0...3
+    const int ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
+    const uint8_t  * qs = x[i].qs + 8*ib;
+    const uint8_t  * grid1 = (const uint8_t *)(iq1s_grid + qs[2*il+0]);
+    const uint8_t  * grid2 = (const uint8_t *)(iq1s_grid + qs[2*il+1]);
+    const float d = (float)x[i].d * (2*((x[i].qh[ib] >> 12) & 0xf) + 1);
+    const uint8_t signs = ksigns_iq2xs[(x[i].qh[ib] >> 3*il) & 7];
+    for (int j = 0; j < 4; ++j) {
+        y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
+        y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
+    }
+#else
+    assert(false);
+#endif
+
+}
+
+/*
+DPCT1110:4: The total declared local variable size in device function
+dequantize_mul_mat_vec_q2_k exceeds 128 bytes and may cause high register
+pressure. Consult with your hardware vendor to find the total register size
+available and adjust the code, or use smaller sub-group size to avoid high
+register pressure.
+*/
+static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx,
+                                        const float *__restrict__ yy,
+                                        float *__restrict__ dst,
+                                        const int ncols, int nrows,
+                                        const sycl::nd_item<3> &item_ct1) {
+
+    static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
+
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+    if (row > nrows) return;
+
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q2_K * x = (const block_q2_K *)vx + ib0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+#if QK_K == 256
+    const int tid =
+        item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...15
+    const int ix =
+        item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0,1
+
+    const int step = 16/K_QUANTS_PER_ITERATION;
+
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0...15 or 0...7
+
+    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15 or 0...14 in steps of 2
+    const int q_offset = 32*im + l0;
+    const int s_offset = 8*im;
+    const int y_offset = 128*im + l0;
+
+    uint32_t aux[4];
+    const uint8_t * d = (const uint8_t *)aux;
+    const uint8_t * m = (const uint8_t *)(aux + 2);
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y = yy + i * QK_K + y_offset;
+        const uint8_t * q = x[i].qs + q_offset;
+
+        const float dall = x[i].dm[0];
+        const float dmin = x[i].dm[1];
+
+        const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset);
+        aux[0] = a[0] & 0x0f0f0f0f;
+        aux[1] = a[1] & 0x0f0f0f0f;
+        aux[2] = (a[0] >> 4) & 0x0f0f0f0f;
+        aux[3] = (a[1] >> 4) & 0x0f0f0f0f;
+
+        float sum1 = 0, sum2 = 0;
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3)
+                  + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3)
+                  + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3)
+                  + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3)
+                  + y[l+16] * d[1] * ((q[l+16] >> 0) & 3)
+                  + y[l+48] * d[3] * ((q[l+16] >> 2) & 3)
+                  + y[l+80] * d[5] * ((q[l+16] >> 4) & 3)
+                  +y[l+112] * d[7] * ((q[l+16] >> 6) & 3);
+            sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6]
+                  + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7];
+
+        }
+        tmp += dall * sum1 - dmin * sum2;
+
+    }
+#else
+    const int tid = item_ct1.get_local_id(2) /
+                    (2 * K_QUANTS_PER_ITERATION); // 0...15 or 0...7
+    const int ix = item_ct1.get_local_id(2) %
+                   (2 * K_QUANTS_PER_ITERATION); // 0....1 or 0...3
+    const int offset = tid * K_QUANTS_PER_ITERATION;
+
+    uint32_t uaux[2];
+    const uint8_t * d = (const uint8_t *)uaux;
+
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+
+        const float   * y = yy + i * QK_K + offset;
+        const uint8_t * q = x[i].qs + offset;
+        const uint32_t * s = (const uint32_t *)x[i].scales;
+
+        uaux[0] = s[0] & 0x0f0f0f0f;
+        uaux[1] = (s[0] >> 4) & 0x0f0f0f0f;
+
+        const sycl::float2 dall =
+            x[i].dm.convert<float, sycl::rounding_mode::automatic>();
+
+        float sum1 = 0, sum2 = 0;
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            const uint8_t ql = q[l];
+            sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3)
+                  + y[l+16] * d[1] * ((ql >> 2) & 3)
+                  + y[l+32] * d[2] * ((ql >> 4) & 3)
+                  + y[l+48] * d[3] * ((ql >> 6) & 3);
+            sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7];
+        }
+        tmp += dall.x() * sum1 - dall.y() * sum2;
+    }
+
+#endif
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+/*
+DPCT1110:5: The total declared local variable size in device function
+dequantize_mul_mat_vec_q3_k exceeds 128 bytes and may cause high register
+pressure. Consult with your hardware vendor to find the total register size
+available and adjust the code, or use smaller sub-group size to avoid high
+register pressure.
+*/
+static void dequantize_mul_mat_vec_q3_k(const void *__restrict__ vx,
+                                        const float *__restrict__ yy,
+                                        float *__restrict__ dst,
+                                        const int ncols, int nrows,
+                                        const sycl::nd_item<3> &item_ct1) {
+
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+    if (row > nrows) return;
+
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q3_K * x = (const block_q3_K *)vx + ib0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+#if QK_K == 256
+
+    const uint16_t kmask1 = 0x0303;
+    const uint16_t kmask2 = 0x0f0f;
+
+    const int tid =
+        item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...16
+    const int ix =
+        item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0,1
+
+    const int n  = K_QUANTS_PER_ITERATION;               // iterations in the inner loop
+    const int step = 16/K_QUANTS_PER_ITERATION;
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0....15 or 0...7
+
+    const uint8_t m = 1 << (4*im);
+
+    const int l0 = n*in;                                 // 0...15 or 0...14 in steps of 2
+    const int q_offset =  32*im + l0;
+    const int y_offset = 128*im + l0;
+
+    uint16_t utmp[4];
+    const int8_t * s = (const int8_t *)utmp;
+
+    const uint16_t s_shift = 4*im;
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y  = yy + i * QK_K + y_offset;
+        const uint8_t * q = x[i].qs + q_offset;
+        const uint8_t * h = x[i].hmask + l0;
+
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4);
+        utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4);
+        utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4);
+        utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4);
+
+        const float d = x[i].d;
+
+        float sum = 0;
+        for (int l = 0; l < n; ++l) {
+            sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4))
+                 + y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4))
+                 + y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4))
+                 + y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4));
+            sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4))
+                 + y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4))
+                 + y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4))
+                + y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4));
+        }
+        tmp += d * sum;
+
+    }
+#else
+
+    const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION);  // 0...15 or 0...7
+    const int ix  = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);  // 0....1 or 0...3
+    const int offset = tid * K_QUANTS_PER_ITERATION;         // 0...15 or 0...14
+    const int in = offset/8;                                 // 0 or 1
+    const int im = offset%8;                                 // 0...7
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+
+        const float   * y = yy + i * QK_K + offset;
+        const uint8_t * q = x[i].qs + offset;
+        const uint8_t * s = x[i].scales;
+
+        const float dall = (float)x[i].d;
+
+        float sum = 0;
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            const uint8_t hl = x[i].hmask[im+l] >> in;
+            const uint8_t ql = q[l];
+            sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4))
+                 + y[l+16] * dall * ((s[0] >>  4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4))
+                 + y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4))
+                 + y[l+48] * dall * ((s[1] >>  4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4));
+        }
+        tmp += sum;
+    }
+#endif
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+/*
+DPCT1110:6: The total declared local variable size in device function
+dequantize_mul_mat_vec_q4_k exceeds 128 bytes and may cause high register
+pressure. Consult with your hardware vendor to find the total register size
+available and adjust the code, or use smaller sub-group size to avoid high
+register pressure.
+*/
+static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx,
+                                        const float *__restrict__ yy,
+                                        float *__restrict__ dst,
+                                        const int ncols, int nrows,
+                                        const sycl::nd_item<3> &item_ct1) {
+
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+    if (row > nrows) return;
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q4_K * x = (const block_q4_K *)vx + ib0;
+
+#if QK_K == 256
+    const uint16_t kmask1 = 0x3f3f;
+    const uint16_t kmask2 = 0x0f0f;
+    const uint16_t kmask3 = 0xc0c0;
+
+    const int tid =
+        item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...16
+    const int ix =
+        item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0,1
+
+    const int step = 8/K_QUANTS_PER_ITERATION;           // 8 or 4
+
+    const int il  = tid/step;                            // 0...3
+    const int ir  = tid - step*il;                       // 0...7 or 0...3
+    const int n   = 2 * K_QUANTS_PER_ITERATION;          // 2 or 4
+
+    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const int in = il%2;
+
+    const int l0 = n*(2*ir + in);
+    const int q_offset = 32*im + l0;
+    const int y_offset = 64*im + l0;
+
+    uint16_t aux[4];
+    const uint8_t * sc = (const uint8_t *)aux;
+
+#if K_QUANTS_PER_ITERATION == 2
+    uint32_t q32[4];
+    const uint8_t * q4 = (const uint8_t *)q32;
+#else
+    uint16_t q16[4];
+    const uint8_t * q4 = (const uint8_t *)q16;
+#endif
+
+    float tmp = 0; // partial sum for thread in warp
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y1 = yy + i*QK_K + y_offset;
+        const float   * y2 = y1 + 128;
+
+        const float dall = x[i].dm[0];
+        const float dmin = x[i].dm[1];
+
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        aux[0] = a[im+0] & kmask1;
+        aux[1] = a[im+2] & kmask1;
+        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
+        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
+
+#if K_QUANTS_PER_ITERATION == 2
+        const uint32_t * q1 = (const uint32_t *)(x[i].qs + q_offset);
+        const uint32_t * q2 = q1 + 16;
+
+        q32[0] = q1[0] & 0x0f0f0f0f;
+        q32[1] = q1[0] & 0xf0f0f0f0;
+        q32[2] = q2[0] & 0x0f0f0f0f;
+        q32[3] = q2[0] & 0xf0f0f0f0;
+
+        sycl::float4 s = {0.f, 0.f, 0.f, 0.f};
+        float smin = 0;
+        for (int l = 0; l < 4; ++l) {
+            s.x() += y1[l] * q4[l + 0]; s.y() += y1[l + 32] * q4[l + 4];
+            s.z() += y2[l] * q4[l + 8]; s.w() += y2[l + 32] * q4[l + 12];
+            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
+        }
+        tmp += dall * (s.x() * sc[0] + s.y() * sc[1] * 1.f / 16.f +
+                       s.z() * sc[4] + s.w() * sc[5] * 1.f / 16.f) -
+               dmin * smin;
+#else
+        const uint16_t * q1 = (const uint16_t *)(x[i].qs + q_offset);
+        const uint16_t * q2 = q1 + 32;
+
+        q16[0] = q1[0] & 0x0f0f;
+        q16[1] = q1[0] & 0xf0f0;
+        q16[2] = q2[0] & 0x0f0f;
+        q16[3] = q2[0] & 0xf0f0;
+
+        float4 s = {0.f, 0.f, 0.f, 0.f};
+        float smin = 0;
+        for (int l = 0; l < 2; ++l) {
+            s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2];
+            s.z += y2[l] * q4[l+4]; s.w += y2[l+32] * q4[l+6];
+            smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
+        }
+        tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
+#endif
+
+    }
+#else
+    const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION);  // 0...15
+    const int ix  = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);
+
+    const int step = tid * K_QUANTS_PER_ITERATION;
+
+    uint16_t aux16[2];
+    const uint8_t * s = (const uint8_t *)aux16;
+
+    float tmp = 0;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+        const uint8_t * q = x[i].qs + step;
+        const float   * y = yy + i*QK_K + step;
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        aux16[0] = a[0] & 0x0f0f;
+        aux16[1] = (a[0] >> 4) & 0x0f0f;
+        const float d = (float)x[i].dm[0];
+        const float m = (float)x[i].dm[1];
+        float sum = 0.f;
+        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
+            sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2])
+                 + y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2])
+                 + y[j+32] * (d * s[1] * (q[j+ 0] >>  4) - m * s[3])
+                 + y[j+48] * (d * s[1] * (q[j+16] >>  4) - m * s[3]);
+        }
+        tmp += sum;
+    }
+
+#endif
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (tid == 0) {
+        dst[row] = tmp;
+    }
+}
+
+/*
+DPCT1110:7: The total declared local variable size in device function
+dequantize_mul_mat_vec_q5_k exceeds 128 bytes and may cause high register
+pressure. Consult with your hardware vendor to find the total register size
+available and adjust the code, or use smaller sub-group size to avoid high
+register pressure.
+*/
+static void dequantize_mul_mat_vec_q5_k(const void *__restrict__ vx,
+                                        const float *__restrict__ yy,
+                                        float *__restrict__ dst,
+                                        const int ncols,
+                                        const sycl::nd_item<3> &item_ct1) {
+
+    const int row = item_ct1.get_group(2);
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q5_K * x = (const block_q5_K *)vx + ib0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+#if QK_K == 256
+    const uint16_t kmask1 = 0x3f3f;
+    const uint16_t kmask2 = 0x0f0f;
+    const uint16_t kmask3 = 0xc0c0;
+
+    const int tid = item_ct1.get_local_id(2) / 2; // 0...15
+    const int ix = item_ct1.get_local_id(2) % 2;
+
+    const int il  = tid/4;     // 0...3
+    const int ir  = tid - 4*il;// 0...3
+    const int n   = 2;
+
+    const int im = il/2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const int in = il%2;
+
+    const int l0 = n*(2*ir + in);
+    const int q_offset = 32*im + l0;
+    const int y_offset = 64*im + l0;
+
+    const uint8_t hm1  = 1 << (2*im);
+    const uint8_t hm2  = hm1 << 4;
+
+    uint16_t aux[4];
+    const uint8_t * sc = (const uint8_t *)aux;
+
+    uint16_t q16[8];
+    const uint8_t * q4 = (const uint8_t *)q16;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2) {
+
+        const uint8_t * ql1 = x[i].qs + q_offset;
+        const uint8_t * qh  = x[i].qh + l0;
+        const float   * y1  = yy + i*QK_K + y_offset;
+        const float   * y2  = y1 + 128;
+
+        const float dall = x[i].dm[0];
+        const float dmin = x[i].dm[1];
+
+        const uint16_t * a = (const uint16_t *)x[i].scales;
+        aux[0] = a[im+0] & kmask1;
+        aux[1] = a[im+2] & kmask1;
+        aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
+        aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
+
+        sycl::float4 sum = {0.f, 0.f, 0.f, 0.f};
+        float smin = 0;
+        const uint16_t * q1 = (const uint16_t *)ql1;
+        const uint16_t * q2 = q1 + 32;
+        q16[0] = q1[0] & 0x0f0f;
+        q16[1] = q1[8] & 0x0f0f;
+        q16[2] = (q1[0] >> 4) & 0x0f0f;
+        q16[3] = (q1[8] >> 4) & 0x0f0f;
+        q16[4] = q2[0] & 0x0f0f;
+        q16[5] = q2[8] & 0x0f0f;
+        q16[6] = (q2[0] >> 4) & 0x0f0f;
+        q16[7] = (q2[8] >> 4) & 0x0f0f;
+        for (int l = 0; l < n; ++l) {
+            sum.x() +=
+                y1[l + 0] * (q4[l + 0] + (qh[l + 0] & (hm1 << 0) ? 16 : 0)) +
+                y1[l + 16] * (q4[l + 2] + (qh[l + 16] & (hm1 << 0) ? 16 : 0));
+            sum.y() +=
+                y1[l + 32] * (q4[l + 4] + (qh[l + 0] & (hm1 << 1) ? 16 : 0)) +
+                y1[l + 48] * (q4[l + 6] + (qh[l + 16] & (hm1 << 1) ? 16 : 0));
+            sum.z() +=
+                y2[l + 0] * (q4[l + 8] + (qh[l + 0] & (hm2 << 0) ? 16 : 0)) +
+                y2[l + 16] * (q4[l + 10] + (qh[l + 16] & (hm2 << 0) ? 16 : 0));
+            sum.w() +=
+                y2[l + 32] * (q4[l + 12] + (qh[l + 0] & (hm2 << 1) ? 16 : 0)) +
+                y2[l + 48] * (q4[l + 14] + (qh[l + 16] & (hm2 << 1) ? 16 : 0));
+            smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3]
+                  + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7];
+        }
+        tmp += dall * (sum.x() * sc[0] + sum.y() * sc[1] + sum.z() * sc[4] +
+                       sum.w() * sc[5]) -
+               dmin * smin;
+    }
+
+#else
+    const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION);  // 0...15
+    const int ix  = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);
+    const int step = tid * K_QUANTS_PER_ITERATION;
+    const int im = step/8;
+    const int in = step%8;
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+        const uint8_t * q = x[i].qs + step;
+        const int8_t  * s = x[i].scales;
+        const float   * y = yy + i*QK_K + step;
+        const float     d = x[i].d;
+        float sum = 0.f;
+        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
+            const uint8_t h = x[i].qh[in+j] >> im;
+            sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16))
+                 + y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16))
+                 + y[j+32] * d * s[2] * ((q[j+ 0] >>  4) - ((h >> 4) & 1 ? 0 : 16))
+                 + y[j+48] * d * s[3] * ((q[j+16] >>  4) - ((h >> 6) & 1 ? 0 : 16));
+        }
+        tmp += sum;
+    }
+#endif
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+static void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows,
+                                        const sycl::nd_item<3> &item_ct1) {
+
+    static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
+
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+    if (row > nrows) return;
+
+    const int num_blocks_per_row = ncols / QK_K;
+    const int ib0 = row*num_blocks_per_row;
+
+    const block_q6_K * x = (const block_q6_K *)vx + ib0;
+
+#if QK_K == 256
+
+    const int tid =
+        item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...16
+    const int ix =
+        item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0, 1
+
+    const int step = 16/K_QUANTS_PER_ITERATION;          // 16 or 8
+
+    const int im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const int in = tid - step*im;                        // 0...15 or 0...7
+
+#if K_QUANTS_PER_ITERATION == 1
+    const int l0 = K_QUANTS_PER_ITERATION*in;            // 0...15
+    const int is = 0;
+#else
+    const int l0 = 4 * in;                               // 0, 4, 8, ..., 28
+    const int is = in / 4;
+#endif
+    const int ql_offset = 64*im + l0;
+    const int qh_offset = 32*im + l0;
+    const int s_offset  =  8*im + is;
+    const int y_offset = 128*im + l0;
+
+    float tmp = 0; // partial sum for thread in warp
+
+    for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+
+        const float   * y  = yy + i * QK_K + y_offset;
+        const uint8_t * ql = x[i].ql + ql_offset;
+        const uint8_t * qh = x[i].qh + qh_offset;
+        const int8_t  * s  = x[i].scales + s_offset;
+
+        const float d = x[i].d;
+
+#if K_QUANTS_PER_ITERATION == 1
+        float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
+                  + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
+                  + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
+                  + y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
+                  + y[64] * s[4] * d * ((int8_t)((ql[ 0]  >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
+                  + y[80] * s[5] * d * ((int8_t)((ql[16]  >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
+                  + y[96] * s[6] * d * ((int8_t)((ql[32]  >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
+                  +y[112] * s[7] * d * ((int8_t)((ql[48]  >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
+        tmp += sum;
+#else
+        float sum = 0;
+        for (int l = 0; l < 4; ++l) {
+            sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
+                 + y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
+                 + y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0]  >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
+                 + y[l+96] * s[6] * d * ((int8_t)((ql[l+32]  >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
+        }
+        tmp += sum;
+#endif
+
+    }
+
+#else
+
+    const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION);  // 0...7
+    const int ix  = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);  // 0...3
+
+    const int step = tid * K_QUANTS_PER_ITERATION;
+
+    float tmp = 0; // partial sum for thread in warp
+
+    for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
+
+        const float   * y  = yy + i * QK_K + step;
+        const uint8_t * ql = x[i].ql + step;
+        const uint8_t * qh = x[i].qh + step;
+        const int8_t  * s  = x[i].scales;
+
+        const float d = x[i+0].d;
+
+        float sum = 0;
+        for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
+            sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32)
+                 + y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32)
+                 + y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >>  4) | ((qh[j] & 0x30) >> 0)) - 32)
+                 + y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >>  4) | ((qh[j] & 0xc0) >> 2)) - 32);
+        }
+        tmp += sum;
+
+    }
+
+#endif
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (tid == 0) {
+        dst[row] = tmp;
+    }
+}
+
+static void convert_f16(const void * vx, const int ib, const int iqs, dfloat2 & v){
+    const sycl::half *x = (const sycl::half *)vx;
+
+    // automatic half -> float type cast if dfloat == float
+    v.x() = x[ib + iqs + 0];
+    v.y() = x[ib + iqs + 1];
+}
+
+static void convert_f32(const void * vx, const int ib, const int iqs, dfloat2 & v){
+    const float * x = (const float *) vx;
+
+    // automatic half -> float type cast if dfloat == float
+    v.x() = x[ib + iqs + 0];
+    v.y() = x[ib + iqs + 1];
+}
+
+static void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded,
+                          const sycl::nd_item<3> &item_ct1) {
+    const int ix = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                   item_ct1.get_local_id(2);
+
+    if (ix >= kx_padded) {
+        return;
+    }
+
+    const int iy = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                   item_ct1.get_local_id(1);
+
+    const int i_padded = iy*kx_padded + ix;
+
+    block_q8_1 * y = (block_q8_1 *) vy;
+
+    const int ib = i_padded / QK8_1; // block index
+    const int iqs = i_padded % QK8_1; // quant index
+
+    const float xi = ix < kx ? x[iy*kx + ix] : 0.0f;
+    float amax = sycl::fabs((float)xi);
+    float sum = xi;
+
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        amax = sycl::fmax(amax, dpct::permute_sub_group_by_xor(
+                                    item_ct1.get_sub_group(), amax, mask));
+        sum +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), sum, mask);
+    }
+
+    const float d = amax / 127;
+    const int8_t q = amax == 0.0f ? 0 : sycl::round(xi / d);
+
+    y[ib].qs[iqs] = q;
+
+    if (iqs > 0) {
+        return;
+    }
+
+    reinterpret_cast<sycl::half &>(y[ib].ds.x()) = d;
+    reinterpret_cast<sycl::half &>(y[ib].ds.y()) = sum;
+}
+
+template<int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
+static void k_get_rows(
+            const void * src0, const int32_t * src1, dst_t * dst,
+            int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
+            /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
+            /*size_t s0,*/ size_t s1, size_t s2, size_t s3,
+            /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
+            size_t s10, size_t s11, size_t s12,
+            const sycl::nd_item<3> &item_ct1/*, size_t s13*/) {
+
+    const int i00 = (item_ct1.get_group(2) * item_ct1.get_local_range(2) +
+                     item_ct1.get_local_id(2)) *
+                    2;
+    const int i10 = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                    item_ct1.get_local_id(1);
+    const int i11 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) +
+                     item_ct1.get_local_id(0)) /
+                    ne12;
+    const int i12 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) +
+                     item_ct1.get_local_id(0)) %
+                    ne12;
+
+    if (i00 >= ne00) {
+        return;
+    }
+
+    const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
+
+    dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
+    const void * src0_row = (const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03;
+
+    const int ib = i00/qk; // block index
+    const int iqs = (i00%qk)/qr; // quant index
+    const int iybs = i00 - i00%qk; // dst block start index
+    const int y_offset = qr == 1 ? 1 : qk/2;
+
+    // dequantize
+    dfloat2 v;
+    dequantize_kernel(src0_row, ib, iqs, v);
+
+    dst_row[iybs + iqs + 0] = v.x();
+    dst_row[iybs + iqs + y_offset] = v.y();
+}
+
+template<typename src0_t, typename dst_t>
+static void k_get_rows_float(
+            const src0_t * src0, const int32_t * src1, dst_t * dst,
+            int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
+            /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
+            /*size_t s0,*/ size_t s1, size_t s2, size_t s3,
+            /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
+            size_t s10, size_t s11, size_t s12,
+            const sycl::nd_item<3> &item_ct1/*, size_t s13*/) {
+
+    const int i00 = item_ct1.get_group(2) * item_ct1.get_local_range(2) +
+                    item_ct1.get_local_id(2);
+    const int i10 = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                    item_ct1.get_local_id(1);
+    const int i11 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) +
+                     item_ct1.get_local_id(0)) /
+                    ne12;
+    const int i12 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) +
+                     item_ct1.get_local_id(0)) %
+                    ne12;
+
+    if (i00 >= ne00) {
+        return;
+    }
+
+    const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
+
+    dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
+    const src0_t * src0_row = (const src0_t *)((const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03);
+
+    dst_row[i00] = src0_row[i00];
+}
+
+template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
+static void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k,
+                             const sycl::nd_item<3> &item_ct1) {
+    const int i = 2 * (item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                       item_ct1.get_local_id(2));
+
+    if (i >= k) {
+        return;
+    }
+
+    const int ib = i/qk; // block index
+    const int iqs = (i%qk)/qr; // quant index
+    const int iybs = i - i%qk; // y block start index
+    const int y_offset = qr == 1 ? 1 : qk/2;
+
+    // dequantize
+    dfloat2 v;
+    dequantize_kernel(vx, ib, iqs, v);
+
+    y[iybs + iqs + 0] = v.x();
+    y[iybs + iqs + y_offset] = v.y();
+}
+
+template <typename src_t, typename dst_t>
+static void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y, const int k,
+                          const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+
+    const src_t * x = (src_t *) vx;
+
+    y[i] = x[i];
+}
+
+// VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
+// MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
+
+#define VDR_Q4_0_Q8_1_MMVQ 2
+#define VDR_Q4_0_Q8_1_MMQ  4
+
+template <int vdr>
+static __dpct_inline__ float vec_dot_q4_0_q8_1_impl(const int *v, const int *u,
+                                                    const float &d4,
+                                                    const sycl::half2 &ds8) {
+    int sumi = 0;
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
+        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
+
+        // SIMD dot product of quantized values
+        sumi = dpct::dp4a(vi0, u[2 * i + 0], sumi);
+        sumi = dpct::dp4a(vi1, u[2 * i + 1], sumi);
+    }
+
+    const sycl::float2 ds8f =
+        ds8.convert<float, sycl::rounding_mode::automatic>();
+
+    // second part effectively subtracts 8 from each quant value
+    return d4 * (sumi * ds8f.x() - (8 * vdr / QI4_0) * ds8f.y());
+}
+
+#define VDR_Q4_1_Q8_1_MMVQ 2
+#define VDR_Q4_1_Q8_1_MMQ  4
+
+template <int vdr>
+static __dpct_inline__ float vec_dot_q4_1_q8_1_impl(const int *v, const int *u,
+                                                    const sycl::half2 &dm4,
+                                                    const sycl::half2 &ds8) {
+
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
+        const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
+
+        // SIMD dot product of quantized values
+        sumi = dpct::dp4a(vi0, u[2 * i + 0], sumi);
+        sumi = dpct::dp4a(vi1, u[2 * i + 1], sumi);
+    }
+
+#ifdef GGML_SYCL_F16
+    const sycl::float2 tmp =
+        (dm4 * ds8).convert<float, sycl::rounding_mode::automatic>();
+    const float d4d8 = tmp.x();
+    const float m4s8 = tmp.y();
+#else
+    const sycl::float2 dm4f =
+        dm4.convert<float, sycl::rounding_mode::automatic>();
+    const sycl::float2 ds8f =
+        ds8.convert<float, sycl::rounding_mode::automatic>();
+    const float d4d8 = dm4f.x() * ds8f.x();
+    const float m4s8 = dm4f.y() * ds8f.y();
+#endif // GGML_SYCL_F16
+
+    // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it
+    return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1));
+}
+
+#define VDR_Q5_0_Q8_1_MMVQ 2
+#define VDR_Q5_0_Q8_1_MMQ  4
+
+template <int vdr>
+static __dpct_inline__ float
+vec_dot_q5_0_q8_1_impl(const int *vl, const int *vh, const int *u,
+                       const float &d5, const sycl::half2 &ds8) {
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
+        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
+        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
+        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
+        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
+        sumi = dpct::dp4a(vi0, u[2 * i + 0],
+                          sumi); // SIMD dot product of quantized values
+
+        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
+        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
+        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
+        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
+        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
+        sumi = dpct::dp4a(vi1, u[2 * i + 1],
+                          sumi); // SIMD dot product of quantized values
+    }
+
+    const sycl::float2 ds8f =
+        ds8.convert<float, sycl::rounding_mode::automatic>();
+
+    // second part effectively subtracts 16 from each quant value
+    return d5 * (sumi * ds8f.x() - (16 * vdr / QI5_0) * ds8f.y());
+}
+
+#define VDR_Q5_1_Q8_1_MMVQ 2
+#define VDR_Q5_1_Q8_1_MMQ  4
+
+template <int vdr>
+static __dpct_inline__ float
+vec_dot_q5_1_q8_1_impl(const int *vl, const int *vh, const int *u,
+                       const sycl::half2 &dm5, const sycl::half2 &ds8) {
+
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        int vi0 = (vl[i] >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
+        vi0    |= (vh[i] <<  4) & 0x00000010; // 0 ->  4
+        vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
+        vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
+        vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
+        sumi = dpct::dp4a(vi0, u[2 * i + 0],
+                          sumi); // SIMD dot product of quantized values
+
+        int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
+        vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
+        vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
+        vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
+        vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
+        sumi = dpct::dp4a(vi1, u[2 * i + 1],
+                          sumi); // SIMD dot product of quantized values
+    }
+
+#ifdef GGML_SYCL_F16
+     const sycl::float2 tmp =
+        (dm5 * ds8).convert<float, sycl::rounding_mode::automatic>();
+    const float d5d8 = tmp.x();
+    const float m5s8 = tmp.y();
+
+
+#else
+    const sycl::float2 dm5f =
+        dm5.convert<float, sycl::rounding_mode::automatic>();
+    const sycl::float2 ds8f =
+        ds8.convert<float, sycl::rounding_mode::automatic>();
+    const float d5d8 = dm5f.x() * ds8f.x();
+    const float m5s8 = dm5f.y() * ds8f.y();
+#endif // GGML_SYCL_F16
+
+    // scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it
+    return sumi*d5d8 + m5s8 / (QI5_1 / vdr);
+}
+
+#define VDR_Q8_0_Q8_1_MMVQ 2
+#define VDR_Q8_0_Q8_1_MMQ 8
+
+template <int vdr>
+static __dpct_inline__ float vec_dot_q8_0_q8_1_impl(const int *v, const int *u,
+                                                    const float &d8_0,
+                                                    const float &d8_1) {
+
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        // SIMD dot product of quantized values
+        sumi = dpct::dp4a(v[i], u[i], sumi);
+    }
+
+    return d8_0*d8_1 * sumi;
+}
+
+template <int vdr>
+static __dpct_inline__ float vec_dot_q8_1_q8_1_impl(const int *v, const int *u,
+                                                    const sycl::half2 &dm8,
+                                                    const sycl::half2 &ds8) {
+
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+        // SIMD dot product of quantized values
+        sumi = dpct::dp4a(v[i], u[i], sumi);
+    }
+
+#ifdef GGML_SYCL_F16
+    const sycl::float2 tmp =
+        (dm8 * ds8).convert<float, sycl::rounding_mode::automatic>();
+    const float d8d8 = tmp.x();
+    const float m8s8 = tmp.y();
+#else
+    const sycl::float2 dm8f =
+        dm8.convert<float, sycl::rounding_mode::automatic>();
+    const sycl::float2 ds8f =
+        ds8.convert<float, sycl::rounding_mode::automatic>();
+    const float d8d8 = dm8f.x() * ds8f.x();
+    const float m8s8 = dm8f.y() * ds8f.y();
+#endif // GGML_SYCL_F16
+
+    // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it
+    return sumi*d8d8 + m8s8 / (QI8_1 / vdr);
+}
+
+#define VDR_Q2_K_Q8_1_MMVQ 1
+#define VDR_Q2_K_Q8_1_MMQ  2
+
+// contiguous v/x values
+static __dpct_inline__ float vec_dot_q2_K_q8_1_impl_mmvq(
+    const int &v, const int *__restrict__ u, const uint8_t *__restrict__ scales,
+    const sycl::half2 &dm2, const float *__restrict__ d8) {
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR2_K; ++i) {
+        const int sc = scales[2*i];
+
+        const int vi = (v >> (2*i)) & 0x03030303;
+
+        sumf_d +=
+            d8[i] * (dpct::dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product
+
+        // fill int with 4x m
+        int m = sc >> 4;
+        m |= m <<  8;
+        m |= m << 16;
+        sumf_m += d8[i] *
+                  dpct::dp4a(
+                      m, u[i],
+                      0); // multiply constant q2_K part with sum of q8_1 values
+    }
+
+    const sycl::float2 dm2f =
+        dm2.convert<float, sycl::rounding_mode::automatic>();
+
+    return dm2f.x() * sumf_d - dm2f.y() * sumf_m;
+}
+
+// contiguous u/y values
+static __dpct_inline__ float
+vec_dot_q2_K_q8_1_impl_mmq(const int *__restrict__ v, const int *__restrict__ u,
+                           const uint8_t *__restrict__ scales,
+                           const sycl::half2 &dm2, const float &d8) {
+
+    int sumi_d = 0;
+    int sumi_m = 0;
+
+#pragma unroll
+    for (int i0 = 0; i0 < QI8_1; i0 += QI8_1/2) {
+        int sumi_d_sc = 0;
+
+        const int sc = scales[i0 / (QI8_1/2)];
+
+        // fill int with 4x m
+        int m = sc >> 4;
+        m |= m <<  8;
+        m |= m << 16;
+
+#pragma unroll
+        for (int i = i0; i < i0 + QI8_1/2; ++i) {
+            sumi_d_sc = dpct::dp4a(v[i], u[i], sumi_d_sc); // SIMD dot product
+            sumi_m = dpct::dp4a(m, u[i],
+                                sumi_m); // multiply sum of q8_1 values with m
+        }
+
+        sumi_d += sumi_d_sc * (sc & 0xF);
+    }
+
+    const sycl::float2 dm2f =
+        dm2.convert<float, sycl::rounding_mode::automatic>();
+
+    return d8 * (dm2f.x() * sumi_d - dm2f.y() * sumi_m);
+}
+
+#define VDR_Q3_K_Q8_1_MMVQ 1
+#define VDR_Q3_K_Q8_1_MMQ  2
+
+// contiguous v/x values
+static __dpct_inline__ float vec_dot_q3_K_q8_1_impl_mmvq(
+    const int &vl, const int &vh, const int *__restrict__ u,
+    const uint8_t *__restrict__ scales, const int &scale_offset,
+    const float &d3, const float *__restrict__ d8) {
+
+    float sumf = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR3_K; ++i) {
+        const int isc = scale_offset + 2*i;
+
+        const int isc_low = isc % (QK_K/32);
+        const int sc_shift_low = 4 * (isc / (QK_K/32));
+        const int sc_low  = (scales[isc_low] >> sc_shift_low) & 0xF;
+
+        const int isc_high = isc % (QK_K/64);
+        const int sc_shift_high = 2 * (isc / (QK_K/64));
+        const int sc_high = ((scales[(QK_K/32) + isc_high] >> sc_shift_high) & 3) << 4;
+
+        const int sc = (sc_low | sc_high) - 32;
+
+        const int vil = (vl >> (2*i)) & 0x03030303;
+
+        const int vih = ((vh >> i) << 2) & 0x04040404;
+
+        const int vi =
+            dpct::vectorized_binary<sycl::char4>(vil, vih, dpct::sub_sat());
+
+        sumf += d8[i] * (dpct::dp4a(vi, u[i], 0) * sc); // SIMD dot product
+    }
+
+    return d3 * sumf;
+}
+
+// contiguous u/y values
+static __dpct_inline__ float
+vec_dot_q3_K_q8_1_impl_mmq(const int *__restrict__ v, const int *__restrict__ u,
+                           const int8_t *__restrict__ scales, const float &d3,
+                           const float &d8) {
+
+    int sumi = 0;
+
+#pragma unroll
+    for (int i0 = 0; i0 < QR3_K*VDR_Q3_K_Q8_1_MMQ; i0 += QI8_1/2) {
+        int sumi_sc = 0;
+
+        for (int i = i0; i < i0 + QI8_1/2; ++i) {
+            sumi_sc = dpct::dp4a(v[i], u[i], sumi_sc); // SIMD dot product
+        }
+
+        sumi += sumi_sc * scales[i0 / (QI8_1/2)];
+    }
+
+    return d3*d8 * sumi;
+}
+
+#define VDR_Q4_K_Q8_1_MMVQ 2
+#define VDR_Q4_K_Q8_1_MMQ  8
+
+// contiguous v/x values
+static __dpct_inline__ float vec_dot_q4_K_q8_1_impl_vmmq(
+    const int *__restrict__ v, const int *__restrict__ u,
+    const uint8_t *__restrict__ sc, const uint8_t *__restrict__ m,
+    const sycl::half2 &dm4, const float *__restrict__ d8) {
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR4_K; ++i) {
+        const int v0i = (v[0] >> (4*i)) & 0x0F0F0F0F;
+        const int v1i = (v[1] >> (4*i)) & 0x0F0F0F0F;
+
+        const int dot1 =
+            dpct::dp4a(v1i, u[2 * i + 1],
+                       dpct::dp4a(v0i, u[2 * i + 0], 0)); // SIMD dot product
+        const int dot2 =
+            dpct::dp4a(0x01010101, u[2 * i + 1],
+                       dpct::dp4a(0x01010101, u[2 * i + 0], 0)); // sum of u
+
+        sumf_d += d8[i] * (dot1 * sc[i]);
+        sumf_m += d8[i] * (dot2 * m[i]);  // multiply constant part of q4_K with sum of q8_1 values
+    }
+
+    const sycl::float2 dm4f =
+        dm4.convert<float, sycl::rounding_mode::automatic>();
+
+    return dm4f.x() * sumf_d - dm4f.y() * sumf_m;
+}
+
+// contiguous u/y values
+static __dpct_inline__ float vec_dot_q4_K_q8_1_impl_mmq(
+    const int *__restrict__ v, const int *__restrict__ u,
+    const uint8_t *__restrict__ sc, const uint8_t *__restrict__ m,
+    const sycl::half2 &dm4, const sycl::half2 *__restrict__ ds8) {
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR4_K*VDR_Q4_K_Q8_1_MMQ/QI8_1; ++i) {
+        int sumi_d = 0;
+
+#pragma unroll
+        for (int j = 0; j < QI8_1; ++j) {
+            sumi_d = dpct::dp4a((v[j] >> (4 * i)) & 0x0F0F0F0F,
+                                u[i * QI8_1 + j], sumi_d); // SIMD dot product
+        }
+
+        const sycl::float2 ds8f =
+            ds8[i].convert<float, sycl::rounding_mode::automatic>();
+
+        sumf_d += ds8f.x() * (sc[i] * sumi_d);
+        sumf_m += ds8f.y() * m[i]; // sum of q8_1 block * q4_K min val
+    }
+
+    const sycl::float2 dm4f =
+        dm4.convert<float, sycl::rounding_mode::automatic>();
+
+    return dm4f.x() * sumf_d - dm4f.y() * sumf_m;
+}
+
+#define VDR_Q5_K_Q8_1_MMVQ 2
+#define VDR_Q5_K_Q8_1_MMQ  8
+
+// contiguous v/x values
+static __dpct_inline__ float vec_dot_q5_K_q8_1_impl_vmmq(
+    const int *__restrict__ vl, const int *__restrict__ vh,
+    const int *__restrict__ u, const uint8_t *__restrict__ sc,
+    const uint8_t *__restrict__ m, const sycl::half2 &dm5,
+    const float *__restrict__ d8) {
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K; ++i) {
+        const int vl0i = (vl[0] >> (4*i)) & 0x0F0F0F0F;
+        const int vl1i = (vl[1] >> (4*i)) & 0x0F0F0F0F;
+
+        const int vh0i = ((vh[0] >> i) << 4) & 0x10101010;
+        const int vh1i = ((vh[1] >> i) << 4) & 0x10101010;
+
+        const int v0i = vl0i | vh0i;
+        const int v1i = vl1i | vh1i;
+
+        const int dot1 =
+            dpct::dp4a(v0i, u[2 * i + 0],
+                       dpct::dp4a(v1i, u[2 * i + 1], 0)); // SIMD dot product
+        const int dot2 =
+            dpct::dp4a(0x01010101, u[2 * i + 0],
+                       dpct::dp4a(0x01010101, u[2 * i + 1], 0)); // sum of u
+
+        sumf_d += d8[i] * (dot1 * sc[i]);
+        sumf_m += d8[i] * (dot2 * m[i]);
+
+    }
+
+    const sycl::float2 dm5f =
+        dm5.convert<float, sycl::rounding_mode::automatic>();
+
+    return dm5f.x() * sumf_d - dm5f.y() * sumf_m;
+}
+
+// contiguous u/y values
+static __dpct_inline__ float vec_dot_q5_K_q8_1_impl_mmq(
+    const int *__restrict__ v, const int *__restrict__ u,
+    const uint8_t *__restrict__ sc, const uint8_t *__restrict__ m,
+    const sycl::half2 &dm4, const sycl::half2 *__restrict__ ds8) {
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K*VDR_Q5_K_Q8_1_MMQ/QI8_1; ++i) {
+        int sumi_d = 0;
+
+#pragma unroll
+        for (int j = 0; j < QI8_1; ++j) {
+            sumi_d = dpct::dp4a(v[i * QI8_1 + j], u[i * QI8_1 + j],
+                                sumi_d); // SIMD dot product
+        }
+
+        const sycl::float2 ds8f =
+            ds8[i].convert<float, sycl::rounding_mode::automatic>();
+
+        sumf_d += ds8f.x() * (sc[i] * sumi_d);
+        sumf_m += ds8f.y() * m[i]; // sum of q8_1 block * q4_K min val
+    }
+
+    const sycl::float2 dm4f =
+        dm4.convert<float, sycl::rounding_mode::automatic>();
+
+    return dm4f.x() * sumf_d - dm4f.y() * sumf_m;
+}
+
+#define VDR_Q6_K_Q8_1_MMVQ 1
+#define VDR_Q6_K_Q8_1_MMQ  8
+
+// contiguous v/x values
+static __dpct_inline__ float
+vec_dot_q6_K_q8_1_impl_mmvq(const int &vl, const int &vh,
+                            const int *__restrict__ u,
+                            const int8_t *__restrict__ scales, const float &d,
+                            const float *__restrict__ d8) {
+
+    float sumf = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < QR6_K; ++i) {
+        const int sc = scales[4*i];
+
+        const int vil = (vl >> (4*i)) & 0x0F0F0F0F;
+
+        const int vih = ((vh >> (4*i)) << 4) & 0x30303030;
+
+        const int vi = dpct::vectorized_binary<sycl::char4>(
+            (vil | vih), 0x20202020, dpct::sub_sat()); // vi = (vil | vih) - 32
+
+        sumf += d8[i] * (dpct::dp4a(vi, u[i], 0) * sc); // SIMD dot product
+    }
+
+    return d*sumf;
+}
+
+// contiguous u/y values
+static __dpct_inline__ float
+vec_dot_q6_K_q8_1_impl_mmq(const int *__restrict__ v, const int *__restrict__ u,
+                           const int8_t *__restrict__ sc, const float &d6,
+                           const float *__restrict__ d8) {
+
+    float sumf_d = 0.0f;
+
+#pragma unroll
+    for (int i0 = 0; i0 < VDR_Q6_K_Q8_1_MMQ; i0 += 4) {
+        sycl::int2 sumi_d = {0, 0}; // 2 q6_K scales per q8_1 scale
+
+#pragma unroll
+        for (int i = i0; i < i0 + 2; ++i) {
+            sumi_d.x() = dpct::dp4a(v[2 * i + 0], u[2 * i + 0],
+                                    sumi_d.x()); // SIMD dot product
+            sumi_d.x() = dpct::dp4a(v[2 * i + 1], u[2 * i + 1],
+                                    sumi_d.x()); // SIMD dot product
+
+            sumi_d.y() = dpct::dp4a(v[2 * i + 4], u[2 * i + 4],
+                                    sumi_d.y()); // SIMD dot product
+            sumi_d.y() = dpct::dp4a(v[2 * i + 5], u[2 * i + 5],
+                                    sumi_d.y()); // SIMD dot product
+        }
+
+        sumf_d += d8[i0 / 4] *
+                  (sc[i0 / 2 + 0] * sumi_d.x() + sc[i0 / 2 + 1] * sumi_d.y());
+    }
+
+    return d6 * sumf_d;
+}
+
+static __dpct_inline__ float
+vec_dot_q4_0_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq;
+
+    int v[VDR_Q4_0_Q8_1_MMVQ];
+    int u[2*VDR_Q4_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q4_0_Q8_1_MMVQ; ++i) {
+        v[i]     = get_int_from_uint8(bq4_0->qs, iqs + i);
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_0);
+    }
+
+    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMVQ>(v, u, bq4_0->d, bq8_1->ds);
+}
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q4_0(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_qs_q4_0, float *tile_x_d_q4_0) {
+    (void)x_qh; (void)x_sc;
+
+    *x_ql = tile_x_qs_q4_0;
+    *x_dm = (sycl::half2 *)tile_x_d_q4_0;
+}
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q4_0(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+    (void)x_qh; (void)x_sc;
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset <  nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI4_0;
+    const int kqsx = k % QI4_0;
+
+    const block_q4_0 * bx0 = (const block_q4_0 *) vx;
+
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
+        // x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = bxi->d;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_0;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_0) {
+        int i = i0 + i_offset * QI4_0 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = bxi->d;
+    }
+}
+
+static __dpct_inline__ float vec_dot_q4_0_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+    (void)x_qh; (void)x_sc;
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+    const float * x_dmf = (const float *) x_dm;
+
+    int u[2*VDR_Q4_0_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_0) % WARP_SIZE];
+    }
+
+    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
+        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dmf[i * (WARP_SIZE/QI4_0) + i/QI4_0 + k/QI4_0],
+         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+static __dpct_inline__ float
+vec_dot_q4_1_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq;
+
+    int v[VDR_Q4_1_Q8_1_MMVQ];
+    int u[2*VDR_Q4_1_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) {
+        v[i]    = get_int_from_uint8_aligned(bq4_1->qs, iqs + i);
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_1);
+    }
+
+    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, bq4_1->dm, bq8_1->ds);
+}
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q4_1(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_qs_q4_1, sycl::half2 *tile_x_dm_q4_1) {
+    (void)x_qh; (void)x_sc;
+
+    *x_ql = tile_x_qs_q4_1;
+    *x_dm = tile_x_dm_q4_1;
+}
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q4_1(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+    (void)x_qh; (void)x_sc;
+
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset <  nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI4_1;
+    const int kqsx = k % QI4_1;
+
+    const block_q4_1 * bx0 = (const block_q4_1 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_1;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_1) {
+        int i = i0 + i_offset * QI4_1 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm;
+    }
+}
+
+static __dpct_inline__ float vec_dot_q4_1_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+    (void)x_qh; (void)x_sc;
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+
+    int u[2*VDR_Q4_1_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_1) % WARP_SIZE];
+    }
+
+    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMQ>
+        (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dm[i * (WARP_SIZE/QI4_1) + i/QI4_1 + k/QI4_1],
+         y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+static __dpct_inline__ float
+vec_dot_q5_0_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq;
+
+    int vl[VDR_Q5_0_Q8_1_MMVQ];
+    int vh[VDR_Q5_0_Q8_1_MMVQ];
+    int  u[2*VDR_Q5_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q5_0_Q8_1_MMVQ; ++i) {
+        vl[i]    = get_int_from_uint8(bq5_0->qs, iqs + i);
+        vh[i]    = get_int_from_uint8(bq5_0->qh, 0) >> (4 * (iqs + i));
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_0);
+    }
+
+    return vec_dot_q5_0_q8_1_impl<VDR_Q5_0_Q8_1_MMVQ>(vl, vh, u, bq5_0->d, bq8_1->ds);
+}
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q5_0(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_ql_q5_0, float *tile_x_d_q5_0) {
+    (void)x_qh; (void)x_sc;
+
+    *x_ql = tile_x_ql_q5_0;
+    *x_dm = (sycl::half2 *)tile_x_d_q5_0;
+}
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q5_0(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+    (void)x_qh; (void)x_sc;
+
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset <  nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI5_0;
+    const int kqsx = k % QI5_0;
+
+    const block_q5_0 * bx0 = (const block_q5_0 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        const int ql = get_int_from_uint8(bxi->qs, kqsx);
+        const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (k % QI5_0));
+
+        int qs0 = (ql >>  0)   & 0x0F0F0F0F;
+        qs0    |= (qh <<  4)   & 0x00000010;  // 0 ->  4
+        qs0    |= (qh << 11)   & 0x00001000;  // 1 -> 12
+        qs0    |= (qh << 18)   & 0x00100000;  // 2 -> 20
+        qs0    |= (qh << 25)   & 0x10000000;  // 3 -> 28
+        qs0 = dpct::vectorized_binary<sycl::char4>(
+            qs0, 0x10101010, dpct::sub_sat()); // subtract 16
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
+
+        int qs1 = (ql >>  4)   & 0x0F0F0F0F;
+        qs1    |= (qh >> 12)   & 0x00000010;  // 16 ->  4
+        qs1    |= (qh >>  5)   & 0x00001000;  // 17 -> 12
+        qs1    |= (qh <<  2)   & 0x00100000;  // 18 -> 20
+        qs1    |= (qh <<  9)   & 0x10000000;  // 19 -> 28
+        qs1 = dpct::vectorized_binary<sycl::char4>(
+            qs1, 0x10101010, dpct::sub_sat()); // subtract 16
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_0;
+    const int kbxd = k % blocks_per_tile_x_row;
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_0) {
+        int i = i0 + i_offset * QI5_0 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = bxi->d;
+    }
+}
+
+static __dpct_inline__ float vec_dot_q5_0_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+    (void)x_qh; (void)x_sc;
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+    const int index_bx = i * (WARP_SIZE/QI5_0) + i/QI5_0 + k/QI5_0;
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    int u[2*VDR_Q5_0_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE];
+    }
+
+    return vec_dot_q8_0_q8_1_impl<QR5_0*VDR_Q5_0_Q8_1_MMQ>
+        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_df[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+static __dpct_inline__ float
+vec_dot_q5_1_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq;
+
+    int vl[VDR_Q5_1_Q8_1_MMVQ];
+    int vh[VDR_Q5_1_Q8_1_MMVQ];
+    int  u[2*VDR_Q5_1_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) {
+        vl[i]   = get_int_from_uint8_aligned(bq5_1->qs, iqs + i);
+        vh[i]   = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * (iqs + i));
+        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_1);
+    }
+
+    return vec_dot_q5_1_q8_1_impl<VDR_Q5_1_Q8_1_MMVQ>(vl, vh, u, bq5_1->dm, bq8_1->ds);
+}
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q5_1(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_ql_q5_1, sycl::half2 *tile_x_dm_q5_1) {
+    (void)x_qh; (void)x_sc;
+
+    *x_ql = tile_x_ql_q5_1;
+    *x_dm = tile_x_dm_q5_1;
+}
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q5_1(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+    (void)x_qh; (void)x_sc;
+
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset < nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI5_1;
+    const int kqsx = k % QI5_1;
+
+    const block_q5_1 * bx0 = (const block_q5_1 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
+        const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (k % QI5_1));
+
+        int qs0 = (ql >>  0) & 0x0F0F0F0F;
+        qs0    |= (qh <<  4) & 0x00000010; // 0 ->  4
+        qs0    |= (qh << 11) & 0x00001000; // 1 -> 12
+        qs0    |= (qh << 18) & 0x00100000; // 2 -> 20
+        qs0    |= (qh << 25) & 0x10000000; // 3 -> 28
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
+
+        int qs1 = (ql >>  4) & 0x0F0F0F0F;
+        qs1    |= (qh >> 12) & 0x00000010; // 16 ->  4
+        qs1    |= (qh >>  5) & 0x00001000; // 17 -> 12
+        qs1    |= (qh <<  2) & 0x00100000; // 18 -> 20
+        qs1    |= (qh <<  9) & 0x10000000; // 19 -> 28
+
+        x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_1;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_1) {
+        int i = i0 + i_offset * QI5_1 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm;
+    }
+}
+
+static __dpct_inline__ float vec_dot_q5_1_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+    (void)x_qh; (void)x_sc;
+
+    const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
+    const int index_bx = i * (WARP_SIZE/QI5_1) + + i/QI5_1 + k/QI5_1;
+
+    int u[2*VDR_Q5_1_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) {
+        u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l)         % WARP_SIZE];
+        u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_1) % WARP_SIZE];
+    }
+
+    return vec_dot_q8_1_q8_1_impl<QR5_1*VDR_Q5_1_Q8_1_MMQ>
+        (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dm[index_bx], y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
+}
+
+static __dpct_inline__ float
+vec_dot_q8_0_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq;
+
+    int v[VDR_Q8_0_Q8_1_MMVQ];
+    int u[VDR_Q8_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q8_0_Q8_1_MMVQ; ++i) {
+        v[i] = get_int_from_int8(bq8_0->qs, iqs + i);
+        u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+    }
+
+    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d,
+                                                      bq8_1->ds[0]);
+}
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q8_0(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_qs_q8_0, float *tile_x_d_q8_0) {
+    (void)x_qh; (void)x_sc;
+
+    *x_ql = tile_x_qs_q8_0;
+    *x_dm = (sycl::half2 *)tile_x_d_q8_0;
+}
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q8_0(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+    (void)x_qh; (void)x_sc;
+
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset <  nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI8_0;
+    const int kqsx = k % QI8_0;
+    float * x_dmf = (float *) x_dm;
+
+    const block_q8_0 * bx0 = (const block_q8_0 *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_int8(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI8_0;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0) {
+        int i = i0 + i_offset * QI8_0 + k / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbxd] = bxi->d;
+    }
+}
+
+static __dpct_inline__ float vec_dot_q8_0_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+    (void)x_qh; (void)x_sc;
+
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMQ>
+        (&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0],
+         y_df[j * (WARP_SIZE/QI8_1) + k/QI8_1]);
+}
+
+static __dpct_inline__ float
+vec_dot_q2_K_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_q2_K * bq2_K = (const block_q2_K *) vbq;
+
+    const int bq8_offset = QR2_K * (iqs / QI8_1);
+    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
+
+    const uint8_t * scales = bq2_K->scales + scale_offset;
+
+    const int v = get_int_from_uint8_aligned(bq2_K->qs, iqs);
+    int    u[QR2_K];
+    float d8[QR2_K];
+
+#pragma unroll
+    for (int i = 0; i < QR2_K; ++ i) {
+        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
+        d8[i] = bq8_1[bq8_offset + i].ds[0];
+    }
+
+    return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8);
+}
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q2_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_ql_q2_K, sycl::half2 *tile_x_dm_q2_K,
+                    int *tile_x_sc_q2_K) {
+    (void)x_qh;
+
+    *x_ql = tile_x_ql_q2_K;
+    *x_dm = tile_x_dm_q2_K;
+    *x_sc = tile_x_sc_q2_K;
+}
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q2_K(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+    (void)x_qh;
+
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset <  nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI2_K;
+    const int kqsx = k % QI2_K;
+
+    const block_q2_K * bx0 = (const block_q2_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI2_K;
+    const int kbxd = k % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI2_K) {
+        int i = (i0 + i_offset * QI2_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q2_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dm[i * (WARP_SIZE/QI2_K) + i / QI2_K + kbxd] = bxi->dm;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
+        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q2_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI2_K/4);
+
+        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = get_int_from_uint8_aligned(bxi->scales, k % (QI2_K/4));
+    }
+}
+
+static __dpct_inline__ float vec_dot_q2_K_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+    (void)x_qh;
+
+    const int kbx = k / QI2_K;
+    const int ky  = (k % QI2_K) * QR2_K;
+    const float * y_df = (const float *) y_ds;
+
+    int v[QR2_K*VDR_Q2_K_Q8_1_MMQ];
+
+    const int kqsx = i * (WARP_SIZE + 1) + kbx*QI2_K + (QI2_K/2) * (ky/(2*QI2_K)) + ky % (QI2_K/2);
+    const int shift = 2 * ((ky % (2*QI2_K)) / (QI2_K/2));
+
+#pragma unroll
+    for (int l = 0; l < QR2_K*VDR_Q2_K_Q8_1_MMQ; ++l) {
+        v[l] = (x_ql[kqsx + l] >> shift) & 0x03030303;
+    }
+
+    const uint8_t * scales = ((const uint8_t *) &x_sc[i * (WARP_SIZE/4) + i/4 + kbx*4]) + ky/4;
+
+    const int index_y = j * WARP_SIZE + (QR2_K*k) % WARP_SIZE;
+    return vec_dot_q2_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dm[i * (WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[index_y/QI8_1]);
+}
+
+static __dpct_inline__ float
+vec_dot_q3_K_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_q3_K * bq3_K = (const block_q3_K *) vbq;
+
+    const int bq8_offset = QR3_K * (iqs / (QI3_K/2));
+    const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
+
+    const float d = bq3_K->d;
+
+    const int vl = get_int_from_uint8(bq3_K->qs, iqs);
+
+    // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
+    const int vh = ~get_int_from_uint8(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset;
+
+    int    u[QR3_K];
+    float d8[QR3_K];
+
+#pragma unroll
+    for (int i = 0; i < QR3_K; ++i) {
+        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
+        d8[i] = bq8_1[bq8_offset + i].ds[0];
+    }
+
+    return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8);
+}
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q3_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_ql_q3_K, sycl::half2 *tile_x_dm_q3_K,
+                    int *tile_x_qh_q3_K, int *tile_x_sc_q3_K) {
+
+    *x_ql = tile_x_ql_q3_K;
+    *x_dm = tile_x_dm_q3_K;
+    *x_qh = tile_x_qh_q3_K;
+    *x_sc = tile_x_sc_q3_K;
+}
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q3_K(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset <  nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI3_K;
+    const int kqsx = k % QI3_K;
+
+    const block_q3_K * bx0 = (const block_q3_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI3_K;
+    const int kbxd = k % blocks_per_tile_x_row;
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI3_K) {
+        int i = (i0 + i_offset * QI3_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI3_K) + i / QI3_K + kbxd] = bxi->d;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 2) {
+        int i = i0 + i_offset * 2 + k / (WARP_SIZE/2);
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/2)) / (QI3_K/2);
+
+        // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
+        x_qh[i * (WARP_SIZE/2) + i / 2 + k % (WARP_SIZE/2)] = ~get_int_from_uint8(bxi->hmask, k % (QI3_K/2));
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
+        int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI3_K/4);
+
+        const int ksc = k % (QI3_K/4);
+
+        const int ksc_low = ksc % (QI3_K/8);
+        const int shift_low = 4 * (ksc / (QI3_K/8));
+        const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F;
+
+        const int ksc_high = QI3_K/8;
+        const int shift_high = 2 * ksc;
+        const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030;
+
+        const int sc = dpct::vectorized_binary<sycl::char4>(
+            sc_low | sc_high, 0x20202020, dpct::sub_sat());
+
+        x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = sc;
+    }
+}
+
+static __dpct_inline__ float vec_dot_q3_K_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+
+    const int kbx  = k / QI3_K;
+    const int ky  = (k % QI3_K) * QR3_K;
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    const int8_t * scales = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4;
+
+    int v[QR3_K*VDR_Q3_K_Q8_1_MMQ];
+
+#pragma unroll
+    for (int l = 0; l < QR3_K*VDR_Q3_K_Q8_1_MMQ; ++l) {
+        const int kqsx = i * (WARP_SIZE + 1) + kbx*QI3_K + (QI3_K/2) * (ky/(2*QI3_K)) + ky % (QI3_K/2);
+        const int shift = 2 * ((ky % 32) / 8);
+        const int vll = (x_ql[kqsx + l] >> shift) & 0x03030303;
+
+        const int vh = x_qh[i * (WARP_SIZE/2) + i/2 + kbx * (QI3_K/2) + (ky+l)%8] >> ((ky+l) / 8);
+        const int vlh = (vh << 2) & 0x04040404;
+
+        v[l] = dpct::vectorized_binary<sycl::char4>(vll, vlh, dpct::sub_sat());
+    }
+
+    const int index_y = j * WARP_SIZE + (k*QR3_K) % WARP_SIZE;
+    return vec_dot_q3_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dmf[i * (WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[index_y/QI8_1]);
+}
+
+static __dpct_inline__ float
+vec_dot_q4_K_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+#ifndef GGML_QKK_64
+    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
+
+    int    v[2];
+    int    u[2*QR4_K];
+    float d8[QR4_K];
+
+    // iqs is in 0,2..30. bq8_offset = iqs/4 -> bq8_offset = 0, 2, 4, 6
+    const int bq8_offset = QR4_K * ((iqs/2) / (QI8_1/2));
+
+    // iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12
+    // iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44
+    // iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76
+    // iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108
+
+    const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
+    v[0] = q4[0];
+    v[1] = q4[4];
+
+    const uint16_t * scales = (const uint16_t *)bq4_K->scales;
+    uint16_t aux[2];
+    const int j = bq8_offset/2;
+    if (j < 2) {
+        aux[0] = scales[j+0] & 0x3f3f;
+        aux[1] = scales[j+2] & 0x3f3f;
+    } else {
+        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
+        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
+    }
+    const uint8_t * sc = (const uint8_t *)aux;
+    const uint8_t * m  = sc + 2;
+
+    for (int i = 0; i < QR4_K; ++i) {
+        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
+        d8[i] = bq8i->ds[0];
+
+        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
+        u[2*i+0] = q8[0];
+        u[2*i+1] = q8[4];
+    }
+
+    return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8);
+
+#else
+
+#if __SYCL_ARCH__ >= VER_4VEC // lowest compute capability for integer intrinsics
+    const block_q4_K * bq4_K = (const block_q4_K *) vbq;
+
+    float sumf_d = 0.0f;
+    float sumf_m = 0.0f;
+
+    uint16_t aux16[2];
+    const uint8_t * s = (const uint8_t *)aux16;
+
+    const uint16_t * a = (const uint16_t *)bq4_K->scales;
+    aux16[0] = a[0] & 0x0f0f;
+    aux16[1] = (a[0] >> 4) & 0x0f0f;
+
+    const float dall = bq4_K->dm[0];
+    const float dmin = bq4_K->dm[1];
+
+    const float d8_1 = bq8_1[0].ds[0];
+    const float d8_2 = bq8_1[1].ds[1];
+
+    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
+    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
+    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
+    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
+
+    const int * q4 = (const int *)bq4_K->qs + (iqs/2);
+    const int v1 = q4[0];
+    const int v2 = q4[4];
+
+    const int dot1 = dpct::dp4a(ui2, v2 & 0x0f0f0f0f, dpct::dp4a(ui1, v1 & 0x0f0f0f0f, 0));
+    const int dot2 = dpct::dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, dpct::dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0));
+    const int dot3 = dpct::dp4a(0x01010101, ui2, dpct::dp4a(0x01010101, ui1, 0));
+    const int dot4 = dpct::dp4a(0x01010101, ui4, dpct::dp4a(0x01010101, ui3, 0));
+
+    sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]);
+    sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]);
+
+    return dall * sumf_d - dmin * sumf_m;
+
+#else
+    bad_arch();
+#endif // __SYCL_ARCH__ >= VER_4VEC
+
+#endif
+}
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q4_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_ql_q4_K, sycl::half2 *tile_x_dm_q4_K,
+                    int *tile_x_sc_q4_K) {
+    (void)x_qh;
+
+    *x_ql = tile_x_ql_q4_K;
+    *x_dm = tile_x_dm_q4_K;
+    *x_sc = tile_x_sc_q4_K;
+}
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q4_K(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+    (void)x_qh;
+
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset <  nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI4_K; // == 0 if QK_K == 256
+    const int kqsx = k % QI4_K; // == k if QK_K == 256
+
+    const block_q4_K * bx0 = (const block_q4_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbx;
+
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256
+    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_K) {
+        int i = (i0 + i_offset * QI4_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+#if QK_K == 256
+        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm;
+#else
+        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]};
+#endif
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
+        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q4_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI4_K/8);
+
+        const int * scales = (const int *) bxi->scales;
+
+        const int ksc = k % (WARP_SIZE/8);
+
+        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
+        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
+        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
+
+        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
+    }
+}
+
+static __dpct_inline__ float vec_dot_q4_K_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+    (void)x_qh;
+
+    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2*((k % 16) / 8);
+
+    const int index_y = j * WARP_SIZE + (QR4_K*k) % WARP_SIZE;
+    return vec_dot_q4_K_q8_1_impl_mmq(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[index_y], sc, sc+8,
+                                      x_dm[i * (WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[index_y/QI8_1]);
+}
+
+static __dpct_inline__ float
+vec_dot_q5_K_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+#ifndef GGML_QKK_64
+    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
+
+    int   vl[2];
+    int   vh[2];
+    int    u[2*QR5_K];
+    float d8[QR5_K];
+
+    const int bq8_offset = QR5_K * ((iqs/2) / (QI8_1/2));
+    const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
+    const int * qh = (const int *)(bq5_K->qh + 4 * ((iqs/2)%4));
+
+    vl[0] = ql[0];
+    vl[1] = ql[4];
+
+    vh[0] = qh[0] >> bq8_offset;
+    vh[1] = qh[4] >> bq8_offset;
+
+    const uint16_t * scales = (const uint16_t *)bq5_K->scales;
+    uint16_t aux[2];
+    const int j = bq8_offset/2;
+    if (j < 2) {
+        aux[0] = scales[j+0] & 0x3f3f;
+        aux[1] = scales[j+2] & 0x3f3f;
+    } else {
+        aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
+        aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
+    }
+    const uint8_t * sc = (const uint8_t *)aux;
+    const uint8_t * m  = sc + 2;
+
+#pragma unroll
+    for (int i = 0; i < QR5_K; ++i) {
+        const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
+        d8[i] = bq8i->ds[0];
+
+        const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
+        u[2*i+0] = q8[0];
+        u[2*i+1] = q8[4];
+    }
+
+    return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8);
+
+#else
+
+#if __SYCL_ARCH__ >= VER_4VEC // lowest compute capability for integer intrinsics
+    const block_q5_K * bq5_K = (const block_q5_K *) vbq;
+
+    const int8_t * s = bq5_K->scales;
+
+    const float d = bq5_K->d;
+
+    const float d8_1 = bq8_1[0].ds[0];
+    const float d8_2 = bq8_1[1].ds[1];
+
+    const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
+    const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
+    const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
+    const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
+
+    const int * ql = (const int *)bq5_K->qs + (iqs/2);
+    const int vl1 = ql[0];
+    const int vl2 = ql[4];
+
+    const int step = 4 * (iqs/2); // 0, 4, 8, 12
+    const int im = step/8; // = 0 for iqs = 0, 2, = 1 for iqs = 4, 6
+    const int in = step%8; // 0, 4, 0, 4
+    const int vh = (*((const int *)(bq5_K->qh + in))) >> im;
+
+    const int v1 = (((vh << 4) & 0x10101010) ^ 0x10101010) | ((vl1 >> 0) & 0x0f0f0f0f);
+    const int v2 = (((vh << 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 0) & 0x0f0f0f0f);
+    const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f);
+    const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f);
+
+    const float sumf_d = d8_1 * (dpct::dp4a(ui1, v1, 0) * s[0] + dpct::dp4a(ui2, v2, 0) * s[1])
+                       + d8_2 * (dpct::dp4a(ui3, v3, 0) * s[2] + dpct::dp4a(ui4, v4, 0) * s[3]);
+
+    return d * sumf_d;
+
+#else
+    bad_arch();
+#endif // __SYCL_ARCH__ >= VER_4VEC
+
+#endif
+}
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q5_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_ql_q5_K, sycl::half2 *tile_x_dm_q5_K,
+                    int *tile_x_sc_q5_K) {
+    (void)x_qh;
+
+    *x_ql = tile_x_ql_q5_K;
+    *x_dm = tile_x_dm_q5_K;
+    *x_sc = tile_x_sc_q5_K;
+}
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q5_K(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+    (void)x_qh;
+
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset <  nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI5_K; // == 0 if QK_K == 256
+    const int kqsx = k % QI5_K; // == k if QK_K == 256
+
+    const block_q5_K * bx0 = (const block_q5_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbx;
+        const int ky = QR5_K*kqsx;
+
+        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
+        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
+        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
+
+        const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4));
+        const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010;
+        const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010;
+
+        const int kq0 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + 0;
+        const int kq1 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + (QI5_K/4);
+
+        x_ql[i * (2*WARP_SIZE + 1) + kq0] = ql0 | qh0;
+        x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1;
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256
+    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_K) {
+        int i = (i0 + i_offset * QI5_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+#if QK_K == 256
+        x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm;
+#endif
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
+        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q5_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI5_K/8);
+
+        const int * scales = (const int *) bxi->scales;
+
+        const int ksc = k % (WARP_SIZE/8);
+
+        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
+        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
+        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
+
+        x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
+    }
+}
+
+static __dpct_inline__ float vec_dot_q5_K_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+    (void)x_qh;
+
+    const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2 * ((k % 16) / 8);
+
+    const int index_x = i * (QR5_K*WARP_SIZE + 1) +  QR5_K*k;
+    const int index_y = j * WARP_SIZE             + (QR5_K*k) % WARP_SIZE;
+    return vec_dot_q5_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, sc+8,
+                                      x_dm[i * (WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[index_y/QI8_1]);
+}
+
+static __dpct_inline__ float
+vec_dot_q6_K_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_q6_K * bq6_K = (const block_q6_K *) vbq;
+
+    const int bq8_offset = 2 * QR6_K * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/4);
+    const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8);
+    const int vh_shift = 2 * ((iqs % (QI6_K/2)) / (QI6_K/4));
+
+    const int vl = get_int_from_uint8(bq6_K->ql, iqs);
+    const int vh = get_int_from_uint8(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift;
+
+    const int8_t * scales = bq6_K->scales + scale_offset;
+
+    int    u[QR6_K];
+    float d8[QR6_K];
+
+#pragma unroll
+    for (int i = 0; i < QR6_K; ++i) {
+        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1);
+        d8[i] = bq8_1[bq8_offset + 2 * i].ds[0];
+    }
+
+    return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8);
+}
+
+template <int mmq_y>
+static __dpct_inline__ void
+allocate_tiles_q6_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc,
+                    int *tile_x_ql, sycl::half2 *tile_x_dm, int *tile_x_sc) {
+    (void)x_qh;
+
+    *x_ql = tile_x_ql;
+    *x_dm = tile_x_dm;
+    *x_sc = tile_x_sc;
+}
+
+template <int mmq_y, int nwarps, bool need_check>
+static __dpct_inline__ void
+load_tiles_q6_K(const void *__restrict__ vx, int *__restrict__ x_ql,
+                sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh,
+                int *__restrict__ x_sc, const int &i_offset, const int &i_max,
+                const int &k, const int &blocks_per_row) {
+    (void)x_qh;
+
+    GGML_SYCL_ASSUME(i_offset >= 0);
+    GGML_SYCL_ASSUME(i_offset <  nwarps);
+    GGML_SYCL_ASSUME(k >= 0);
+    GGML_SYCL_ASSUME(k <  WARP_SIZE);
+
+    const int kbx  = k / QI6_K; // == 0 if QK_K == 256
+    const int kqsx = k % QI6_K; // == k if QK_K == 256
+
+    const block_q6_K * bx0 = (const block_q6_K *) vx;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + i_offset;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbx;
+        const int ky = QR6_K*kqsx;
+
+        const int ql = get_int_from_uint8(bxi->ql, kqsx);
+        const int ql0 = (ql >> 0) & 0x0F0F0F0F;
+        const int ql1 = (ql >> 4) & 0x0F0F0F0F;
+
+        const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4));
+        const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030;
+        const int qh1 =  (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4))))       & 0x30303030;
+
+        const int kq0 = ky - ky % QI6_K + k % (QI6_K/2) + 0;
+        const int kq1 = ky - ky % QI6_K + k % (QI6_K/2) + (QI6_K/2);
+
+        x_ql[i * (2 * WARP_SIZE + 1) + kq0] =
+            dpct::vectorized_binary<sycl::char4>(ql0 | qh0, 0x20202020,
+                                                 dpct::sub_sat());
+        x_ql[i * (2 * WARP_SIZE + 1) + kq1] =
+            dpct::vectorized_binary<sycl::char4>(ql1 | qh1, 0x20202020,
+                                                 dpct::sub_sat());
+    }
+
+    const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256
+    const int kbxd = k % blocks_per_tile_x_row;          // == 0 if QK_K == 256
+    float * x_dmf = (float *) x_dm;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI6_K) {
+        int i = (i0 + i_offset * QI6_K + k / blocks_per_tile_x_row) % mmq_y;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q6_K * bxi = bx0 + i*blocks_per_row + kbxd;
+
+        x_dmf[i * (WARP_SIZE/QI6_K) + i / QI6_K + kbxd] = bxi->d;
+    }
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
+        int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
+
+        if (need_check) {
+            i = sycl::min(i, i_max);
+        }
+
+        const block_q6_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / 4;
+
+        x_sc[i * (WARP_SIZE/8) + i / 8 + k % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, k % (QI6_K/8));
+    }
+}
+
+static __dpct_inline__ float vec_dot_q6_K_q8_1_mul_mat(
+    const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm,
+    const int *__restrict__ x_qh, const int *__restrict__ x_sc,
+    const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds,
+    const int &i, const int &j, const int &k) {
+    (void)x_qh;
+
+    const float * x_dmf = (const float *) x_dm;
+    const float * y_df  = (const float *) y_ds;
+
+    const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/8]);
+
+    const int index_x = i * (QR6_K*WARP_SIZE + 1) +  QR6_K*k;
+    const int index_y = j * WARP_SIZE             + (QR6_K*k) % WARP_SIZE;
+    return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]);
+}
+
+
+static __dpct_inline__ float
+vec_dot_iq2_xxs_q8_1(const void *__restrict__ vbq,
+                     const block_q8_1 *__restrict__ bq8_1, const int &iqs,
+                     const uint64_t *iq2xxs_grid, const uint8_t *ksigns_iq2xs,
+                     const uint8_t *kmask_iq2xs) {
+#if QK_K == 256
+    const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq;
+
+#if QR2_XXS == 8
+    const int ib32 = iqs;
+    const uint16_t * q2 = bq2->qs + 4*ib32;
+    const uint8_t  * aux8 = (const uint8_t *)q2;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    uint32_t aux32 = q2[2] | (q2[3] << 16);
+    int sumi = 0;
+    for (int l = 0; l < 4; ++l) {
+        const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]);
+        const uint8_t  signs = ksigns_iq2xs[aux32 & 127];
+        for (int j = 0; j < 8; ++j) {
+            sumi += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
+        }
+        q8 += 8;
+        aux32 >>= 7;
+    }
+    const float d = (float)bq2->d * (0.5f + aux32) * bq8_1[ib32].ds[0] * 0.25f;
+    return d * sumi;
+#else
+    // iqs is 0...15
+    const int ib32 = iqs/2;
+    const int il = iqs%2;
+    const uint16_t * q2 = bq2->qs + 4*ib32;
+    const uint8_t  * aux8 = (const uint8_t *)q2;
+    const uint8_t  * grid1 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+0]);
+    const uint8_t  * grid2 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+1]);
+    const uint32_t aux32 = q2[2] | (q2[3] << 16);
+    const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * bq8_1[ib32].ds[0] * 0.25f;
+    const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127];
+    const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127];
+    const int8_t * q8 = bq8_1[ib32].qs + 16*il;
+    int sumi1 = 0, sumi2 = 0;
+    for (int j = 0; j < 8; ++j) {
+        sumi1 += q8[j+0] * grid1[j] * (signs1 & kmask_iq2xs[j] ? -1 : 1);
+        sumi2 += q8[j+8] * grid2[j] * (signs2 & kmask_iq2xs[j] ? -1 : 1);
+    }
+    return d * (sumi1 + sumi2);
+#endif
+#else
+    assert(false);
+    return 0.f;
+#endif
+}
+
+static __dpct_inline__ float
+vec_dot_iq2_xs_q8_1(const void *__restrict__ vbq,
+                    const block_q8_1 *__restrict__ bq8_1, const int &iqs,
+                    const uint64_t *iq2xs_grid, const uint64_t *ksigns64) {
+#if DPCT_COMPATIBILITY_TEMP >=                                                 \
+    MIN_CC_DP4A // lowest compute capability for integer intrinsics
+#if QK_K == 256
+    const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq;
+
+    const int ib32 = iqs;
+    const uint16_t * q2 = bq2->qs + 4*ib32;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    const uint8_t ls1 = bq2->scales[ib32] & 0xf;
+    const uint8_t ls2 = bq2->scales[ib32] >>  4;
+    int sumi1 = 0;
+    for (int l = 0; l < 2; ++l) {
+        const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
+        const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
+        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
+            grid[0] ^ signs[0], signs[0], std::minus<>());
+        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
+            grid[1] ^ signs[1], signs[1], std::minus<>());
+        sumi1 = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi1);
+        sumi1 = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi1);
+        q8 += 8;
+    }
+    int sumi2 = 0;
+    for (int l = 2; l < 4; ++l) {
+        const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
+        const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
+        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
+            grid[0] ^ signs[0], signs[0], std::minus<>());
+        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
+            grid[1] ^ signs[1], signs[1], std::minus<>());
+        sumi2 = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi2);
+        sumi2 = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi2);
+        q8 += 8;
+    }
+    const float d = (float)bq2->d * bq8_1[ib32].ds[0] * 0.25f;
+    return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
+#else
+    assert(false);
+    return 0.f;
+#endif
+#else
+    assert(false);
+    return 0.f;
+#endif
+}
+
+static __dpct_inline__ float
+vec_dot_iq3_xxs_q8_1(const void *__restrict__ vbq,
+                     const block_q8_1 *__restrict__ bq8_1, const int &iqs,
+                     const uint32_t *iq3xxs_grid, const uint64_t *ksigns64) {
+#if DPCT_COMPATIBILITY_TEMP >=                                                 \
+    MIN_CC_DP4A // lowest compute capability for integer intrinsics
+#if QK_K == 256
+    const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq;
+
+    const int ib32 = iqs;
+    const uint8_t  * q3 = bq2->qs + 8*ib32;
+    const uint16_t * gas = (const uint16_t *)(bq2->qs + QK_K/4) + 2*ib32;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    uint32_t aux32 = gas[0] | (gas[1] << 16);
+    int sumi = 0;
+    for (int l = 0; l < 4; ++l) {
+        const uint32_t * grid1 = iq3xxs_grid + q3[2*l+0];
+        const uint32_t * grid2 = iq3xxs_grid + q3[2*l+1];
+        const uint32_t * signs = (const uint32_t *)(ksigns64 + (aux32 & 127));
+        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
+            grid1[0] ^ signs[0], signs[0], std::minus<>());
+        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
+            grid2[0] ^ signs[1], signs[1], std::minus<>());
+        sumi = dpct::dp4a(grid_l, *((int *)q8 + 0), sumi);
+        sumi = dpct::dp4a(grid_h, *((int *)q8 + 1), sumi);
+        q8 += 8;
+        aux32 >>= 7;
+    }
+    const float d = (float)bq2->d * (0.5f + aux32) * bq8_1[ib32].ds[0] * 0.5f;
+    return d * sumi;
+#else
+    assert(false);
+    return 0.f;
+#endif
+#else
+    assert(false);
+    return 0.f;
+#endif
+}
+
+static __dpct_inline__ float
+vec_dot_iq3_s_q8_1(const void *__restrict__ vbq,
+                     const block_q8_1 *__restrict__ bq8_1, const int &iqs,
+                     const uint32_t *iq3s_grid, const uint64_t *ksigns64) {
+#if DPCT_COMPATIBILITY_TEMP >=                                                 \
+    MIN_CC_DP4A // lowest compute capability for integer intrinsics
+#if QK_K == 256
+    const block_iq3_s * bq2 = (const block_iq3_s *) vbq;
+
+    const int ib32 = iqs;
+    const uint8_t  * qs = bq2->qs + 8*ib32;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    int sumi = 0;
+    for (int l = 0; l < 4; ++l) {
+        const uint32_t * grid1 = iq3s_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256));
+        const uint32_t * grid2 = iq3s_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256));
+        uint32_t signs0 = dpct::vectorized_binary<sycl::uchar4>(
+            ((bq2->signs[4*ib32+l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201, std::equal_to<>());
+        uint32_t signs1 = dpct::vectorized_binary<sycl::uchar4>(
+            ((bq2->signs[4*ib32+l] >>  4) * 0x01010101) & 0x08040201, 0x08040201, std::equal_to<>());
+        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
+            grid1[0] ^ signs0, signs0, std::minus<>());
+        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
+            grid2[0] ^ signs1, signs1, std::minus<>());
+        sumi = dpct::dp4a(grid_l, *((int *)q8 + 0), sumi);
+        sumi = dpct::dp4a(grid_h, *((int *)q8 + 1), sumi);
+        q8 += 8;
+    }
+    const float d = (float)bq2->d * (1 + 2*((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * bq8_1[ib32].ds[0];
+    return d * sumi;
+#else
+    assert(false);
+    return 0.f;
+#endif
+#else
+    assert(false);
+    return 0.f;
+#endif
+}
+
+static __dpct_inline__ float
+vec_dot_iq1_s_q8_1(const void *__restrict__ vbq,
+                     const block_q8_1 *__restrict__ bq8_1, const int &iqs,
+                     const uint32_t *iq1s_grid, const uint64_t *ksigns64) {
+#if QK_K == 256
+    const block_iq1_s * bq1 = (const block_iq1_s *) vbq;
+
+    const int ib32 = iqs;
+    const uint8_t  * qs = bq1->qs + 4*ib32;
+    const int8_t   * q8 = bq8_1[ib32].qs;
+    int sumi = 0;
+    for (int l = 0; l < 4; ++l) {
+        const uint32_t * grid = (const uint32_t *)(iq1s_grid + qs[l]);
+        const uint32_t * signs = (const uint32_t *)(ksigns64 + (qs[l] >> 8));
+        const int grid_l = dpct::vectorized_binary<sycl::uchar4>(
+            grid[0] ^ signs[0], signs[0], std::minus<>());
+        const int grid_h = dpct::vectorized_binary<sycl::uchar4>(
+            grid[1] ^ signs[1], signs[1], std::minus<>());
+        sumi = dpct::dp4a(grid_l, *((int *)q8 + 0), sumi);
+        sumi = dpct::dp4a(grid_h, *((int *)q8 + 1), sumi);
+        q8 += 8;
+    }
+    const float d = (float)bq1->d * bq8_1[ib32].ds[0] * 0.25f;
+    return d * sumi;
+#else
+    assert(false);
+    return 0.f;
+#endif
+}
+
+template <int qk, int qr, int qi, bool need_sum, typename block_q_t, int mmq_x,
+          int mmq_y, int nwarps, load_tiles_sycl_t load_tiles, int vdr,
+          vec_dot_q_mul_mat_sycl_t vec_dot>
+/*
+DPCT1110:8: The total declared local variable size in device function mul_mat_q
+exceeds 128 bytes and may cause high register pressure. Consult with your
+hardware vendor to find the total register size available and adjust the code,
+or use smaller sub-group size to avoid high register pressure.
+*/
+static __dpct_inline__ void
+mul_mat_q(const void *__restrict__ vx, const void *__restrict__ vy,
+          float *__restrict__ dst, const int ncols_x, const int nrows_x,
+          const int ncols_y, const int nrows_y, const int nrows_dst,
+          int *tile_x_ql, sycl::half2 *tile_x_dm, int *tile_x_qh,
+          int *tile_x_sc, const sycl::nd_item<3> &item_ct1, int *tile_y_qs,
+          sycl::half2 *tile_y_ds) {
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    const int blocks_per_row_x = ncols_x / qk;
+    const int blocks_per_col_y = nrows_y / QK8_1;
+    const int blocks_per_warp = WARP_SIZE / qi;
+
+    const int & ncols_dst = ncols_y;
+
+    const int row_dst_0 = item_ct1.get_group(2) * mmq_y;
+    const int & row_x_0 = row_dst_0;
+
+    const int col_dst_0 = item_ct1.get_group(1) * mmq_x;
+    const int & col_y_0 = col_dst_0;
+
+    float sum[mmq_y/WARP_SIZE][mmq_x/nwarps] = {{0.0f}};
+
+    for (int ib0 = 0; ib0 < blocks_per_row_x; ib0 += blocks_per_warp) {
+
+        load_tiles(x + row_x_0 * blocks_per_row_x + ib0, tile_x_ql, tile_x_dm,
+                   tile_x_qh, tile_x_sc, item_ct1.get_local_id(1),
+                   nrows_x - row_x_0 - 1, item_ct1.get_local_id(2),
+                   blocks_per_row_x);
+
+#pragma unroll
+        for (int ir = 0; ir < qr; ++ir) {
+            const int kqs = ir * WARP_SIZE + item_ct1.get_local_id(2);
+            const int kbxd = kqs / QI8_1;
+
+#pragma unroll
+            for (int i = 0; i < mmq_x; i += nwarps) {
+                const int col_y_eff = dpct::min(
+                    (unsigned int)(col_y_0 + item_ct1.get_local_id(1) + i),
+                    ncols_y - 1); // to prevent out-of-bounds memory accesses
+
+                const block_q8_1 * by0 = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + kbxd];
+
+                const int index_y = (item_ct1.get_local_id(1) + i) * WARP_SIZE +
+                                    kqs % WARP_SIZE;
+                tile_y_qs[index_y] = get_int_from_int8_aligned(
+                    by0->qs, item_ct1.get_local_id(2) % QI8_1);
+            }
+
+#pragma unroll
+            for (int ids0 = 0; ids0 < mmq_x; ids0 += nwarps * QI8_1) {
+                const int ids =
+                    (ids0 + item_ct1.get_local_id(1) * QI8_1 +
+                     item_ct1.get_local_id(2) / (WARP_SIZE / QI8_1)) %
+                    mmq_x;
+                const int kby = item_ct1.get_local_id(2) % (WARP_SIZE / QI8_1);
+                const int col_y_eff = sycl::min(col_y_0 + ids, ncols_y - 1);
+
+                // if the sum is not needed it's faster to transform the scale to f32 ahead of time
+                const sycl::half2 *dsi_src =
+                    &y[col_y_eff * blocks_per_col_y + ib0 * (qk / QK8_1) +
+                       ir * (WARP_SIZE / QI8_1) + kby]
+                         .ds;
+                sycl::half2 *dsi_dst =
+                    &tile_y_ds[ids * (WARP_SIZE / QI8_1) + kby];
+                if (need_sum) {
+                    *dsi_dst = *dsi_src;
+                } else {
+                    float * dfi_dst = (float *) dsi_dst;
+                    *dfi_dst = (*dsi_src)[0];
+                }
+            }
+
+            /*
+            DPCT1118:9: SYCL group functions and algorithms must be encountered
+            in converged control flow. You may need to adjust the code.
+            */
+            /*
+            DPCT1065:56: Consider replacing sycl::nd_item::barrier() with
+            sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
+            better performance if there is no access to global memory.
+            */
+            item_ct1.barrier();
+
+// #pragma unroll // unrolling this loop causes too much register pressure
+            for (int k = ir*WARP_SIZE/qr; k < (ir+1)*WARP_SIZE/qr; k += vdr) {
+#pragma unroll
+                for (int j = 0; j < mmq_x; j += nwarps) {
+#pragma unroll
+                    for (int i = 0; i < mmq_y; i += WARP_SIZE) {
+                        sum[i / WARP_SIZE][j / nwarps] += vec_dot(
+                            tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc,
+                            tile_y_qs, tile_y_ds, item_ct1.get_local_id(2) + i,
+                            item_ct1.get_local_id(1) + j, k);
+                    }
+                }
+            }
+
+            /*
+            DPCT1118:10: SYCL group functions and algorithms must be encountered
+            in converged control flow. You may need to adjust the code.
+            */
+            /*
+            DPCT1065:57: Consider replacing sycl::nd_item::barrier() with
+            sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
+            better performance if there is no access to global memory.
+            */
+            item_ct1.barrier();
+        }
+    }
+
+#pragma unroll
+    for (int j = 0; j < mmq_x; j += nwarps) {
+        const int col_dst = col_dst_0 + j + item_ct1.get_local_id(1);
+
+        if (col_dst >= ncols_dst) {
+            return;
+        }
+
+#pragma unroll
+        for (int i = 0; i < mmq_y; i += WARP_SIZE) {
+            const int row_dst = row_dst_0 + item_ct1.get_local_id(2) + i;
+
+            if (row_dst >= nrows_dst) {
+                continue;
+            }
+
+            dst[col_dst*nrows_dst + row_dst] = sum[i/WARP_SIZE][j/nwarps];
+        }
+    }
+}
+
+#define  MMQ_X_Q4_0_RDNA2  64
+#define  MMQ_Y_Q4_0_RDNA2  128
+#define NWARPS_Q4_0_RDNA2  8
+#define  MMQ_X_Q4_0_RDNA1  64
+#define  MMQ_Y_Q4_0_RDNA1  64
+#define NWARPS_Q4_0_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q4_0_AMPERE 4
+#define  MMQ_Y_Q4_0_AMPERE 32
+#define NWARPS_Q4_0_AMPERE 4
+#else
+#define  MMQ_X_Q4_0_AMPERE 64
+#define  MMQ_Y_Q4_0_AMPERE 128
+#define NWARPS_Q4_0_AMPERE 4
+#endif
+#define  MMQ_X_Q4_0_PASCAL 64
+#define  MMQ_Y_Q4_0_PASCAL 64
+#define NWARPS_Q4_0_PASCAL 8
+
+template <bool need_check> static void
+    mul_mat_q4_0(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_qs_q4_0, float *tile_x_d_q4_0,
+    int *tile_y_qs, sycl::half2 *tile_y_ds) {
+    int   * tile_x_ql = nullptr;
+    sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+
+    const int mmq_x  =  MMQ_X_Q4_0_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q4_0_AMPERE;
+    const int nwarps = NWARPS_Q4_0_AMPERE;
+    allocate_tiles_q4_0<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_qs_q4_0, tile_x_d_q4_0);
+    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps,
+              load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ,
+              vec_dot_q4_0_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+#define  MMQ_X_Q4_1_RDNA2  64
+#define  MMQ_Y_Q4_1_RDNA2  128
+#define NWARPS_Q4_1_RDNA2  8
+#define  MMQ_X_Q4_1_RDNA1  64
+#define  MMQ_Y_Q4_1_RDNA1  64
+#define NWARPS_Q4_1_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q4_1_AMPERE 4
+#define  MMQ_Y_Q4_1_AMPERE 32
+#define NWARPS_Q4_1_AMPERE 4
+#else
+#define  MMQ_X_Q4_1_AMPERE 64
+#define  MMQ_Y_Q4_1_AMPERE 128
+#define NWARPS_Q4_1_AMPERE 4
+#endif
+#define  MMQ_X_Q4_1_PASCAL 64
+#define  MMQ_Y_Q4_1_PASCAL 64
+#define NWARPS_Q4_1_PASCAL 8
+
+template <bool need_check> static void
+    mul_mat_q4_1(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_qs_q4_1,
+    sycl::half2 *tile_x_dm_q4_1, int *tile_y_qs, sycl::half2 *tile_y_ds) {
+    int   * tile_x_ql = nullptr;
+    sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+    const int mmq_x  =  MMQ_X_Q4_1_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q4_1_AMPERE;
+    const int nwarps = NWARPS_Q4_1_AMPERE;
+    allocate_tiles_q4_1<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_qs_q4_1, tile_x_dm_q4_1);
+    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps,
+              load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ,
+              vec_dot_q4_1_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+#define  MMQ_X_Q5_0_RDNA2  64
+#define  MMQ_Y_Q5_0_RDNA2  128
+#define NWARPS_Q5_0_RDNA2  8
+#define  MMQ_X_Q5_0_RDNA1  64
+#define  MMQ_Y_Q5_0_RDNA1  64
+#define NWARPS_Q5_0_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q5_0_AMPERE 4
+#define  MMQ_Y_Q5_0_AMPERE 32
+#define NWARPS_Q5_0_AMPERE 4
+#else
+#define  MMQ_X_Q5_0_AMPERE 128
+#define  MMQ_Y_Q5_0_AMPERE 64
+#define NWARPS_Q5_0_AMPERE 4
+#endif
+#define  MMQ_X_Q5_0_PASCAL 64
+#define  MMQ_Y_Q5_0_PASCAL 64
+#define NWARPS_Q5_0_PASCAL 8
+
+template <bool need_check> static void
+    mul_mat_q5_0(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q5_0, float *tile_x_d_q5_0,
+    int *tile_y_qs, sycl::half2 *tile_y_ds) {
+    int   * tile_x_ql = nullptr;
+    sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+    const int mmq_x  =  MMQ_X_Q5_0_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q5_0_AMPERE;
+    const int nwarps = NWARPS_Q5_0_AMPERE;
+    allocate_tiles_q5_0<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_ql_q5_0, tile_x_d_q5_0);
+    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps,
+              load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ,
+              vec_dot_q5_0_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+#define  MMQ_X_Q5_1_RDNA2  64
+#define  MMQ_Y_Q5_1_RDNA2  128
+#define NWARPS_Q5_1_RDNA2  8
+#define  MMQ_X_Q5_1_RDNA1  64
+#define  MMQ_Y_Q5_1_RDNA1  64
+#define NWARPS_Q5_1_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q5_1_AMPERE 4
+#define  MMQ_Y_Q5_1_AMPERE 32
+#define NWARPS_Q5_1_AMPERE 4
+#else
+#define  MMQ_X_Q5_1_AMPERE 128
+#define  MMQ_Y_Q5_1_AMPERE 64
+#define NWARPS_Q5_1_AMPERE 4
+#endif
+#define  MMQ_X_Q5_1_PASCAL 64
+#define  MMQ_Y_Q5_1_PASCAL 64
+#define NWARPS_Q5_1_PASCAL 8
+
+template <bool need_check> static void
+mul_mat_q5_1(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q5_1,
+    sycl::half2 *tile_x_dm_q5_1, int *tile_y_qs, sycl::half2 *tile_y_ds) {
+    int   * tile_x_ql = nullptr;
+    sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+    const int mmq_x  =  MMQ_X_Q5_1_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q5_1_AMPERE;
+    const int nwarps = NWARPS_Q5_1_AMPERE;
+    allocate_tiles_q5_1<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_ql_q5_1, tile_x_dm_q5_1);
+    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps,
+              load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ,
+              vec_dot_q5_1_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+#define  MMQ_X_Q8_0_RDNA2  64
+#define  MMQ_Y_Q8_0_RDNA2  128
+#define NWARPS_Q8_0_RDNA2  8
+#define  MMQ_X_Q8_0_RDNA1  64
+#define  MMQ_Y_Q8_0_RDNA1  64
+#define NWARPS_Q8_0_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q8_0_AMPERE 4
+#define  MMQ_Y_Q8_0_AMPERE 32
+#define NWARPS_Q8_0_AMPERE 4
+#else
+#define  MMQ_X_Q8_0_AMPERE 128
+#define  MMQ_Y_Q8_0_AMPERE 64
+#define NWARPS_Q8_0_AMPERE 4
+#endif
+#define  MMQ_X_Q8_0_PASCAL 64
+#define  MMQ_Y_Q8_0_PASCAL 64
+#define NWARPS_Q8_0_PASCAL 8
+
+template <bool need_check> static void
+    mul_mat_q8_0(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_qs_q8_0, float *tile_x_d_q8_0,
+    int *tile_y_qs, sycl::half2 *tile_y_ds) {
+    int   * tile_x_ql = nullptr;
+    sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+    const int mmq_x  =  MMQ_X_Q8_0_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q8_0_AMPERE;
+    const int nwarps = NWARPS_Q8_0_AMPERE;
+    allocate_tiles_q8_0<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_qs_q8_0, tile_x_d_q8_0);
+    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps,
+              load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ,
+              vec_dot_q8_0_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+#define  MMQ_X_Q2_K_RDNA2  64
+#define  MMQ_Y_Q2_K_RDNA2  128
+#define NWARPS_Q2_K_RDNA2  8
+#define  MMQ_X_Q2_K_RDNA1  128
+#define  MMQ_Y_Q2_K_RDNA1  32
+#define NWARPS_Q2_K_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q2_K_AMPERE 4
+#define  MMQ_Y_Q2_K_AMPERE 32
+#define NWARPS_Q2_K_AMPERE 4
+#else
+#define  MMQ_X_Q2_K_AMPERE 64
+#define  MMQ_Y_Q2_K_AMPERE 128
+#define NWARPS_Q2_K_AMPERE 4
+#endif
+#define  MMQ_X_Q2_K_PASCAL 64
+#define  MMQ_Y_Q2_K_PASCAL 64
+#define NWARPS_Q2_K_PASCAL 8
+
+template <bool need_check> static void
+mul_mat_q2_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q2_K,
+    sycl::half2 *tile_x_dm_q2_K, int *tile_x_sc_q2_K, int *tile_y_qs,
+    sycl::half2 *tile_y_ds) {
+    int   * tile_x_ql = nullptr;
+    sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+    const int mmq_x  =  MMQ_X_Q2_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q2_K_AMPERE;
+    const int nwarps = NWARPS_Q2_K_AMPERE;
+    allocate_tiles_q2_K<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_ql_q2_K, tile_x_dm_q2_K, tile_x_sc_q2_K);
+    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps,
+              load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ,
+              vec_dot_q2_K_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+#define  MMQ_X_Q3_K_RDNA2  128
+#define  MMQ_Y_Q3_K_RDNA2  64
+#define NWARPS_Q3_K_RDNA2  8
+#define  MMQ_X_Q3_K_RDNA1  32
+#define  MMQ_Y_Q3_K_RDNA1  128
+#define NWARPS_Q3_K_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q3_K_AMPERE 4
+#define  MMQ_Y_Q3_K_AMPERE 32
+#define NWARPS_Q3_K_AMPERE 4
+#else
+#define  MMQ_X_Q3_K_AMPERE 128
+#define  MMQ_Y_Q3_K_AMPERE 128
+#define NWARPS_Q3_K_AMPERE 4
+#endif
+#define  MMQ_X_Q3_K_PASCAL 64
+#define  MMQ_Y_Q3_K_PASCAL 64
+#define NWARPS_Q3_K_PASCAL 8
+
+template <bool need_check> static void
+mul_mat_q3_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q3_K,
+    sycl::half2 *tile_x_dm_q3_K, int *tile_x_qh_q3_K, int *tile_x_sc_q3_K,
+    int *tile_y_qs, sycl::half2 *tile_y_ds) {
+    int   * tile_x_ql = nullptr;
+    sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+    const int mmq_x  =  MMQ_X_Q3_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q3_K_AMPERE;
+    const int nwarps = NWARPS_Q3_K_AMPERE;
+    allocate_tiles_q3_K<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_ql_q3_K, tile_x_dm_q3_K, tile_x_qh_q3_K,
+                               tile_x_sc_q3_K);
+    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps,
+              load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ,
+              vec_dot_q3_K_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+#define  MMQ_X_Q4_K_RDNA2  64
+#define  MMQ_Y_Q4_K_RDNA2  128
+#define NWARPS_Q4_K_RDNA2  8
+#define  MMQ_X_Q4_K_RDNA1  32
+#define  MMQ_Y_Q4_K_RDNA1  64
+#define NWARPS_Q4_K_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q4_K_AMPERE 4
+#define  MMQ_Y_Q4_K_AMPERE 32
+#define NWARPS_Q4_K_AMPERE 4
+#else
+#define  MMQ_X_Q4_K_AMPERE 64
+#define  MMQ_Y_Q4_K_AMPERE 128
+#define NWARPS_Q4_K_AMPERE 4
+#endif
+#define  MMQ_X_Q4_K_PASCAL 64
+#define  MMQ_Y_Q4_K_PASCAL 64
+#define NWARPS_Q4_K_PASCAL 8
+
+template <bool need_check> static void
+    mul_mat_q4_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q4_K,
+    sycl::half2 *tile_x_dm_q4_K, int *tile_x_sc_q4_K, int *tile_y_qs,
+    sycl::half2 *tile_y_ds) {
+    int   * tile_x_ql = nullptr;
+    sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+    const int mmq_x  =  MMQ_X_Q4_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q4_K_AMPERE;
+    const int nwarps = NWARPS_Q4_K_AMPERE;
+    allocate_tiles_q4_K<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_ql_q4_K, tile_x_dm_q4_K, tile_x_sc_q4_K);
+    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps,
+              load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ,
+              vec_dot_q4_K_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+#define  MMQ_X_Q5_K_RDNA2  64
+#define  MMQ_Y_Q5_K_RDNA2  128
+#define NWARPS_Q5_K_RDNA2  8
+#define  MMQ_X_Q5_K_RDNA1  32
+#define  MMQ_Y_Q5_K_RDNA1  64
+#define NWARPS_Q5_K_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q5_K_AMPERE 4
+#define  MMQ_Y_Q5_K_AMPERE 32
+#define NWARPS_Q5_K_AMPERE 4
+#else
+#define  MMQ_X_Q5_K_AMPERE 64
+#define  MMQ_Y_Q5_K_AMPERE 128
+#define NWARPS_Q5_K_AMPERE 4
+#endif
+#define  MMQ_X_Q5_K_PASCAL 64
+#define  MMQ_Y_Q5_K_PASCAL 64
+#define NWARPS_Q5_K_PASCAL 8
+
+template <bool need_check> static void
+mul_mat_q5_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q5_K,
+    sycl::half2 *tile_x_dm_q5_K, int *tile_x_sc_q5_K, int *tile_y_qs,
+    sycl::half2 *tile_y_ds) {
+    int   * tile_x_ql = nullptr;
+    sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+    const int mmq_x  =  MMQ_X_Q5_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q5_K_AMPERE;
+    const int nwarps = NWARPS_Q5_K_AMPERE;
+    allocate_tiles_q5_K<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_ql_q5_K, tile_x_dm_q5_K, tile_x_sc_q5_K);
+    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps,
+              load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ,
+              vec_dot_q5_K_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+#define  MMQ_X_Q6_K_RDNA2  64
+#define  MMQ_Y_Q6_K_RDNA2  128
+#define NWARPS_Q6_K_RDNA2  8
+#define  MMQ_X_Q6_K_RDNA1  32
+#define  MMQ_Y_Q6_K_RDNA1  64
+#define NWARPS_Q6_K_RDNA1  8
+#if defined(SYCL_USE_XMX)
+#define  MMQ_X_Q6_K_AMPERE 4
+#define  MMQ_Y_Q6_K_AMPERE 32
+#define NWARPS_Q6_K_AMPERE 4
+#else
+#define  MMQ_X_Q6_K_AMPERE 64
+#define  MMQ_Y_Q6_K_AMPERE 64
+#define NWARPS_Q6_K_AMPERE 4
+#endif
+#define  MMQ_X_Q6_K_PASCAL 64
+#define  MMQ_Y_Q6_K_PASCAL 64
+#define NWARPS_Q6_K_PASCAL 8
+
+template <bool need_check> static void
+    mul_mat_q6_K(
+    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst,
+    const sycl::nd_item<3> &item_ct1, int *tile_x_ql, sycl::half2 *tile_x_dm,
+    int *tile_x_sc, int *tile_y_qs, sycl::half2 *tile_y_ds) {
+    // int   * tile_x_ql = nullptr;
+    // sycl::half2 *tile_x_dm = nullptr;
+    int   * tile_x_qh = nullptr;
+    // int   * tile_x_sc = nullptr;
+
+//sycl_todo: change according to hardware
+    const int mmq_x  =  MMQ_X_Q6_K_AMPERE;
+    const int mmq_y  =  MMQ_Y_Q6_K_AMPERE;
+    const int nwarps = NWARPS_Q6_K_AMPERE;
+    allocate_tiles_q6_K<mmq_y>(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc,
+                               tile_x_ql, tile_x_dm, tile_x_sc);
+    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps,
+              load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ,
+              vec_dot_q6_K_q8_1_mul_mat>(
+        vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql,
+        tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds);
+}
+
+template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_sycl_t vec_dot_q_sycl>
+static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows,
+                          const sycl::nd_item<3> &item_ct1,
+                          const uint32_t *iq3xxs_grid_ptr=nullptr, const uint64_t *ksigns64_ptr=nullptr) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_q_sycl(&x[ibx], &y[iby], iqs);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq2_xxs_q8_1(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows,
+                          const sycl::nd_item<3> &item_ct1,
+                          const uint64_t *iq2xxs_grid_ptr, const uint8_t *ksigns_iq2xs_ptr,
+                          const uint8_t *kmask_iq2xs_ptr ) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq2_xxs_q8_1(&x[ibx], &y[iby], iqs, iq2xxs_grid_ptr, ksigns_iq2xs_ptr, kmask_iq2xs_ptr);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq2_xs_q8_1(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows,
+                          const sycl::nd_item<3> &item_ct1,
+                          const uint64_t *iq2xs_grid_ptr, const uint64_t *ksigns64_ptr ) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq2_xs_q8_1(&x[ibx], &y[iby], iqs, iq2xs_grid_ptr, ksigns64_ptr);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq3_xxs_q8_1(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows,
+                          const sycl::nd_item<3> &item_ct1,
+                          const uint32_t *iq3xxs_grid_ptr, const uint64_t *ksigns64_ptr ) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq3_xxs_q8_1(&x[ibx], &y[iby], iqs, iq3xxs_grid_ptr, ksigns64_ptr);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq3_s_q8_1(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows,
+                          const sycl::nd_item<3> &item_ct1,
+                          const uint32_t *iq3s_grid_ptr, const uint64_t *ksigns64_ptr ) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq3_s_q8_1(&x[ibx], &y[iby], iqs, iq3s_grid_ptr, ksigns64_ptr);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qi, typename block_q_t, int vdr>
+static void mul_mat_vec_q_iq1_s_q8_1(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows,
+                          const sycl::nd_item<3> &item_ct1,
+                          const uint32_t *iq1s_grid_ptr, const uint64_t *ksigns64_ptr ) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
+         i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i; // x block index
+
+        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+
+        const int iqs =
+            vdr *
+            (item_ct1.get_local_id(2) %
+             (qi / vdr)); // x block quant index when casting the quants to int
+
+        tmp += vec_dot_iq1_s_q8_1(&x[ibx], &y[iby], iqs, iq1s_grid_ptr, ksigns64_ptr);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[row] = tmp;
+    }
+}
+
+template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
+static void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows,
+                                   const sycl::nd_item<3> &item_ct1) {
+    // qk = quantized weights per x block
+    // qr = number of quantized weights per data value in x block
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+                    item_ct1.get_local_id(1);
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int tid = item_ct1.get_local_id(2);
+
+    const int iter_stride = 2*GGML_SYCL_DMMV_X;
+    const int vals_per_iter = iter_stride / WARP_SIZE; // num quantized vals per thread and i iter
+    const int y_offset = qr == 1 ? 1 : qk/2;
+
+// partial sum for each thread
+#ifdef GGML_SYCL_F16
+    sycl::half2 tmp = {0.0f, 0.0f}; // two sums for f16 to take advantage of half2 intrinsics
+#else
+    float tmp = 0.0f;
+#endif // GGML_SYCL_F16
+
+    for (int i = 0; i < ncols; i += iter_stride) {
+        const int col = i + vals_per_iter*tid;
+        const int ib = (row*ncols + col)/qk; // x block index
+        const int iqs = (col%qk)/qr; // x quant index
+        const int iybs = col - col%qk; // y block start index
+
+// processing >2 values per i iter is faster for fast GPUs
+#pragma unroll
+        for (int j = 0; j < vals_per_iter; j += 2) {
+            // process 2 vals per j iter
+
+            // dequantize
+            // for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val
+            dfloat2 v;
+            dequantize_kernel(vx, ib, iqs + j/qr, v);
+
+            // matrix multiplication
+            // for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2
+#ifdef GGML_SYCL_F16
+            dfloat2 t1{y[iybs + iqs + j / qr + 0],
+                        y[iybs + iqs + j / qr + y_offset]};
+
+            tmp += v * t1;
+#else
+            tmp += v.x() * y[iybs + iqs + j / qr + 0];
+            tmp += v.y() * y[iybs + iqs + j / qr + y_offset];
+#endif // GGML_SYCL_F16
+        }
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (tid == 0) {
+#ifdef GGML_SYCL_F16
+        dst[row] = tmp.x() + tmp.y();
+#else
+        dst[row] = tmp;
+#endif // GGML_SYCL_F16
+    }
+}
+
+static void mul_mat_p021_f16_f32(
+    const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst,
+    const int ncols_x, const int nrows_x, const int nchannels_x, const int nchannels_y,
+    const sycl::nd_item<3> &item_ct1) {
+
+    const sycl::half *x = (const sycl::half *)vx;
+
+    const int row_x = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                      item_ct1.get_local_id(1);
+    const int channel = item_ct1.get_local_range(0) * item_ct1.get_group(0) +
+                        item_ct1.get_local_id(0);
+    const int channel_x = channel / (nchannels_y / nchannels_x);
+
+    const int nrows_y = ncols_x;
+    const int nrows_dst = nrows_x;
+    const int row_dst = row_x;
+
+    float tmp = 0.0f;
+
+    for (int col_x0 = 0; col_x0 < ncols_x;
+         col_x0 += item_ct1.get_local_range(2)) {
+        const int col_x = col_x0 + item_ct1.get_local_id(2);
+
+        if (col_x >= ncols_x) {
+            break;
+        }
+
+        // x is transposed and permuted
+        const int ix = row_x*nchannels_x*ncols_x + channel_x*ncols_x + col_x;
+        const float xi =
+            sycl::vec<sycl::half, 1>(x[ix])
+                .convert<float, sycl::rounding_mode::automatic>()[0];
+
+        const int row_y = col_x;
+
+
+        // y is not transposed but permuted
+        const int iy = channel*nrows_y + row_y;
+
+        tmp += xi * y[iy];
+    }
+
+    // dst is not transposed and not permuted
+    const int idst = channel*nrows_dst + row_dst;
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[idst] = tmp;
+    }
+}
+
+static void mul_mat_vec_nc_f16_f32( // nc == non-contiguous
+    const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst, const int ncols_x, const int nrows_x,
+    const int row_stride_x, const int channel_stride_x, const int channel_x_divisor,
+    const sycl::nd_item<3> &item_ct1) {
+
+    const sycl::half *x = (const sycl::half *)vx;
+
+    const int row_x = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                      item_ct1.get_local_id(1);
+    const int channel = item_ct1.get_local_range(0) * item_ct1.get_group(0) +
+                        item_ct1.get_local_id(0);
+    const int channel_x = channel / channel_x_divisor;
+
+    const int nrows_y   = ncols_x;
+    const int nrows_dst = nrows_x;
+    const int row_dst   = row_x;
+
+    const int idst = channel*nrows_dst + row_dst;
+
+    float tmp = 0.0f;
+
+    for (int col_x0 = 0; col_x0 < ncols_x;
+         col_x0 += item_ct1.get_local_range(2)) {
+        const int col_x = col_x0 + item_ct1.get_local_id(2);
+
+        if (col_x >= ncols_x) {
+            break;
+        }
+
+        const int row_y = col_x;
+
+        const int ix = channel_x*channel_stride_x + row_x*row_stride_x + col_x;
+        const int iy = channel*nrows_y + row_y;
+
+        const float xi =
+            sycl::vec<sycl::half, 1>(x[ix])
+                .convert<float, sycl::rounding_mode::automatic>()[0];
+
+        tmp += xi * y[iy];
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp +=
+            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    }
+
+    if (item_ct1.get_local_id(2) == 0) {
+        dst[idst] = tmp;
+    }
+}
+
+static void cpy_1_f32_f32(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    float * dsti = (float *) cdsti;
+
+    *dsti = *xi;
+}
+
+static void cpy_1_f32_f16(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    sycl::half *dsti = (sycl::half *)cdsti;
+
+    *dsti = sycl::vec<float, 1>(*xi)
+                .convert<sycl::half, sycl::rounding_mode::automatic>()[0];
+}
+
+static void cpy_1_f16_f16(const char * cxi, char * cdsti) {
+    const sycl::half *xi = (const sycl::half *)cxi;
+    sycl::half *dsti = (sycl::half *)cdsti;
+
+    *dsti = *xi;
+}
+
+static void cpy_1_f16_f32(const char * cxi, char * cdsti) {
+    const sycl::half *xi = (const sycl::half *)cxi;
+    float * dsti = (float *) cdsti;
+
+    *dsti = *xi;
+}
+
+static void cpy_1_i16_i16(const char * cxi, char * cdsti) {
+    const int16_t *xi = (const int16_t *)cxi;
+    int16_t *dsti = (int16_t *)cdsti;
+
+    *dsti = *xi;
+}
+
+static void cpy_1_i32_i32(const char * cxi, char * cdsti) {
+    const int32_t *xi = (const int32_t *)cxi;
+    int32_t *dsti = (int32_t *)cdsti;
+
+    *dsti = *xi;
+}
+
+template <cpy_kernel_t cpy_1>
+static void cpy_f32_f16(const char * cx, char * cdst, const int ne,
+                        const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+                        const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                        const int nb12, const int nb13, const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= ne) {
+        return;
+    }
+
+    // determine indices i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor
+    // then combine those indices with the corresponding byte offsets to get the total offsets
+    const int i03 = i/(ne00 * ne01 * ne02);
+    const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
+    const int i01 = (i - i03*ne00*ne01*ne02  -  i02*ne01*ne00) / ne00;
+    const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
+    const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
+
+    const int i13 = i/(ne10 * ne11 * ne12);
+    const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
+    const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
+    const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
+    const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13;
+
+    cpy_1(cx + x_offset, cdst + dst_offset);
+}
+
+static void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    block_q8_0 * dsti = (block_q8_0 *) cdsti;
+
+    float amax = 0.0f; // absolute max
+
+    for (int j = 0; j < QK8_0; j++) {
+        const float v = xi[j];
+        amax = sycl::fmax(amax, sycl::fabs((float)v));
+    }
+
+    const float d = amax / ((1 << 7) - 1);
+    const float id = d ? 1.0f/d : 0.0f;
+
+    dsti->d = d;
+
+    for (int j = 0; j < QK8_0; ++j) {
+        const float x0 = xi[j]*id;
+
+        dsti->qs[j] = sycl::round((float)x0);
+    }
+}
+
+static void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    block_q4_0 * dsti = (block_q4_0 *) cdsti;
+
+    float amax = 0.0f;
+    float vmax = 0.0f;
+
+    for (int j = 0; j < QK4_0; ++j) {
+        const float v = xi[j];
+        if (amax < sycl::fabs((float)v)) {
+            amax = sycl::fabs((float)v);
+            vmax = v;
+        }
+    }
+
+    const float d  = vmax / -8;
+    const float id = d ? 1.0f/d : 0.0f;
+
+    dsti->d = d;
+
+    for (int j = 0; j < QK4_0/2; ++j) {
+        const float x0 = xi[0       + j]*id;
+        const float x1 = xi[QK4_0/2 + j]*id;
+
+        const uint8_t xi0 = dpct::min(15, (int8_t)(x0 + 8.5f));
+        const uint8_t xi1 = dpct::min(15, (int8_t)(x1 + 8.5f));
+
+        dsti->qs[j]  = xi0;
+        dsti->qs[j] |= xi1 << 4;
+    }
+}
+
+static void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) {
+    const float * xi = (const float *) cxi;
+    block_q4_1 * dsti = (block_q4_1 *) cdsti;
+
+    float vmin = FLT_MAX;
+    float vmax = -FLT_MAX;
+
+    for (int j = 0; j < QK4_1; ++j) {
+        const float v = xi[j];
+
+        if (v < vmin) vmin = v;
+        if (v > vmax) vmax = v;
+    }
+
+    const float d  = (vmax - vmin) / ((1 << 4) - 1);
+    const float id = d ? 1.0f/d : 0.0f;
+
+    dsti->dm.x() = d;
+    dsti->dm.y() = vmin;
+
+    for (int j = 0; j < QK4_1/2; ++j) {
+        const float x0 = (xi[0       + j] - vmin)*id;
+        const float x1 = (xi[QK4_1/2 + j] - vmin)*id;
+
+        const uint8_t xi0 = dpct::min(15, (int8_t)(x0 + 0.5f));
+        const uint8_t xi1 = dpct::min(15, (int8_t)(x1 + 0.5f));
+
+        dsti->qs[j]  = xi0;
+        dsti->qs[j] |= xi1 << 4;
+    }
+}
+
+template <cpy_kernel_t cpy_blck, int qk>
+static void cpy_f32_q(const char * cx, char * cdst, const int ne,
+                      const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
+                      const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                      const int nb12, const int nb13, const sycl::nd_item<3> &item_ct1) {
+    const int i = (item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                   item_ct1.get_local_id(2)) *
+                  qk;
+
+    if (i >= ne) {
+        return;
+    }
+
+    const int i03 = i/(ne00 * ne01 * ne02);
+    const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
+    const int i01 = (i - i03*ne00*ne01*ne02  -  i02*ne01*ne00) / ne00;
+    const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
+    const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
+
+    const int i13 = i/(ne10 * ne11 * ne12);
+    const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
+    const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
+    const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
+    const int dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13;
+
+    cpy_blck(cx + x_offset, cdst + dst_offset);
+}
+
+static float rope_yarn_ramp(const float low, const float high, const int i0) {
+    const float y = (i0 / 2 - low) / sycl::max(0.001f, high - low);
+    return 1.0f - sycl::min(1.0f, sycl::max(0.0f, y));
+}
+
+struct rope_corr_dims {
+    float v[4];
+};
+
+// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
+// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
+static void rope_yarn(
+    float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale,
+    float * cos_theta, float * sin_theta
+) {
+    // Get n-d rotational scaling corrected for extrapolation
+    float theta_interp = freq_scale * theta_extrap;
+    float theta = theta_interp;
+    if (ext_factor != 0.0f) {
+        float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor;
+        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+
+        // Get n-d magnitude scaling corrected for interpolation
+        mscale *= 1.0f + 0.1f * sycl::log(1.0f / freq_scale);
+    }
+    *cos_theta = sycl::cos(theta) * mscale;
+    *sin_theta = sycl::sin(theta) * mscale;
+}
+
+// rope == RoPE == rotary positional embedding
+template<typename T, bool has_pos>
+static void rope(
+    const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
+    float ext_factor, float attn_factor, rope_corr_dims corr_dims
+,
+    const sycl::nd_item<3> &item_ct1) {
+    const int col = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                         item_ct1.get_local_id(1));
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                    item_ct1.get_local_id(2);
+    const int i = row*ncols + col;
+    const int i2 = row/p_delta_rows;
+
+    const int p = has_pos ? pos[i2] : 0;
+    const float theta_base = p * dpct::pow(freq_base, -float(col) / ncols);
+
+    float cos_theta, sin_theta;
+    rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + 1];
+
+    dst[i + 0] = x0*cos_theta - x1*sin_theta;
+    dst[i + 1] = x0*sin_theta + x1*cos_theta;
+}
+
+template<typename T, bool has_pos>
+static void rope_neox(
+    const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims
+,
+    const sycl::nd_item<3> &item_ct1) {
+    const int col = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                         item_ct1.get_local_id(1));
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                    item_ct1.get_local_id(2);
+    const int ib = col / n_dims;
+    const int ic = col % n_dims;
+
+    if (ib > 0) {
+        const int i = row*ncols + ib*n_dims + ic;
+
+        dst[i + 0] = x[i + 0];
+        dst[i + 1] = x[i + 1];
+
+        return;
+    }
+
+    const int i  = row*ncols + ib*n_dims + ic/2;
+    const int i2 = row/p_delta_rows;
+
+    float cur_rot = inv_ndims * ic - ib;
+
+    const int p = has_pos ? pos[i2] : 0;
+    const float theta_base =
+        p * freq_scale * dpct::pow(theta_scale, col / 2.0f);
+
+    float cos_theta, sin_theta;
+    rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + n_dims/2];
+
+    dst[i + 0]        = x0*cos_theta - x1*sin_theta;
+    dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
+}
+
+static void rope_glm_f32(
+    const float * x, float * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
+    int n_ctx
+, const sycl::nd_item<3> &item_ct1) {
+    const int col = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                    item_ct1.get_local_id(2);
+    const int half_n_dims = ncols/4;
+
+    if (col >= half_n_dims) {
+        return;
+    }
+
+    const int row = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                    item_ct1.get_local_id(1);
+    const int i = row*ncols + col;
+    const int i2 = row/p_delta_rows;
+
+    const float col_theta_scale = dpct::pow(freq_base, -2.0f * col / ncols);
+     // FIXME: this is likely wrong
+    const int p = pos != nullptr ? pos[i2] : 0;
+
+    const float theta = sycl::min(p, n_ctx - 2) * freq_scale * col_theta_scale;
+    const float sin_theta = sycl::sin((float)theta);
+    const float cos_theta = sycl::cos((float)theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + half_n_dims];
+
+    dst[i + 0]           = x0*cos_theta - x1*sin_theta;
+    dst[i + half_n_dims] = x0*sin_theta + x1*cos_theta;
+
+    const float block_theta =
+        ((float)sycl::max(p - n_ctx - 2, 0)) * col_theta_scale;
+    const float sin_block_theta = sycl::sin((float)block_theta);
+    const float cos_block_theta = sycl::cos((float)block_theta);
+
+    const float x2 = x[i + half_n_dims * 2];
+    const float x3 = x[i + half_n_dims * 3];
+
+    dst[i + half_n_dims * 2] = x2*cos_block_theta - x3*sin_block_theta;
+    dst[i + half_n_dims * 3] = x2*sin_block_theta + x3*cos_block_theta;
+}
+
+static void alibi_f32(const float * x, float * dst, const int ncols, const int k_rows,
+                                 const int n_heads_log2_floor, const float m0, const float m1,
+                                 const sycl::nd_item<3> &item_ct1) {
+    const int col = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                    item_ct1.get_local_id(2);
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int row = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                    item_ct1.get_local_id(1);
+    const int i = row*ncols + col;
+
+    const int k = row/k_rows;
+
+    float m_k;
+    if (k < n_heads_log2_floor) {
+        m_k = dpct::pow(m0, k + 1);
+    } else {
+        m_k = dpct::pow(m1, 2 * (k - n_heads_log2_floor) + 1);
+    }
+
+    dst[i] = col * m_k + x[i];
+}
+
+static void k_sum_rows_f32(const float * x, float * dst, const int ncols,
+                           const sycl::nd_item<3> &item_ct1) {
+    const int row = item_ct1.get_group(1);
+    const int col = item_ct1.get_local_id(2);
+
+    float sum = 0.0f;
+    for (int i = col; i < ncols; i += item_ct1.get_local_range(2)) {
+        sum += x[row * ncols + i];
+    }
+
+    sum = warp_reduce_sum(sum, item_ct1);
+
+    if (col == 0) {
+        dst[row] = sum;
+    }
+}
+
+template<typename T>
+static inline void swap(T & a, T & b) {
+    T tmp = a;
+    a = b;
+    b = tmp;
+}
+
+template<ggml_sort_order order>
+static void k_argsort_f32_i32(const float * x, int * dst, const int ncols,
+                              const sycl::nd_item<3> &item_ct1) {
+    // bitonic sort
+    int col = item_ct1.get_local_id(2);
+    int row = item_ct1.get_group(1);
+
+    if (col >= ncols) return;
+
+    const float * x_row = x + row * ncols;
+    int * dst_row = dst + row * ncols;
+
+    // initialize indices
+    if (col < ncols) {
+        dst_row[col] = col;
+    }
+    /*
+    DPCT1065:58: Consider replacing sycl::nd_item::barrier() with
+    sycl::nd_item::barrier(sycl::access::fence_space::local_space) for better
+    performance if there is no access to global memory.
+    */
+    item_ct1.barrier();
+
+    for (int k = 2; k <= ncols; k *= 2) {
+        for (int j = k / 2; j > 0; j /= 2) {
+            int ixj = col ^ j;
+            if (ixj > col) {
+                if ((col & k) == 0) {
+                    if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) {
+                        swap(dst_row[col], dst_row[ixj]);
+                    }
+                } else {
+                    if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) {
+                        swap(dst_row[col], dst_row[ixj]);
+                    }
+                }
+            }
+            /*
+            DPCT1118:11: SYCL group functions and algorithms must be encountered
+            in converged control flow. You may need to adjust the code.
+            */
+            /*
+            DPCT1065:59: Consider replacing sycl::nd_item::barrier() with
+            sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
+            better performance if there is no access to global memory.
+            */
+            item_ct1.barrier();
+        }
+    }
+}
+
+static void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past,
+                              const sycl::nd_item<3> &item_ct1) {
+    const int col = item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                    item_ct1.get_local_id(1);
+    const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                    item_ct1.get_local_id(2);
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int i = row*ncols + col;
+    //dst[i] = col > (n_past + row % rows_per_channel) ? -INFINITY : x[i];
+    //dst[i] = x[i] - (col > n_past + row % rows_per_channel) * INT_MAX; // equivalent within rounding error but slightly faster on GPU
+    dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX;
+}
+
+
+template <bool vals_smem, int ncols_template, int block_size_template>
+static void soft_max_f32(const float * x, const float * mask, const float *pos, float * dst, const int ncols_par,
+                         const int nrows_y, const float scale, const float max_bias, const float m0,
+                         const float m1, uint32_t n_head_log2, const sycl::nd_item<3> &item_ct1, float *buf) {
+    const int ncols = ncols_template == 0 ? ncols_par : ncols_template;
+
+    const int tid = item_ct1.get_local_id(2);
+    const int rowx = item_ct1.get_group(2);
+    const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension
+
+    const int block_size = block_size_template == 0 ? item_ct1.get_local_range(2) : block_size_template;
+
+    const int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
+    const int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
+
+    float slope = 0.0f;
+
+    // ALiBi
+    if (max_bias > 0.0f) {
+        const uint32_t h = rowx/nrows_y; // head index
+
+        const float base = h < n_head_log2 ? m0 : m1;
+        const int   exp  = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+        slope = sycl::pow(base, float(exp));
+    }
+
+    float * vals = vals_smem ? buf + WARP_SIZE : dst + rowx*ncols;
+    float max_val = -INFINITY;
+
+    for (int col0 = 0; col0 < ncols; col0 += block_size) {
+        const int col = col0 + tid;
+
+        if (ncols_template == 0 && col >= ncols) {
+            break;
+        }
+
+        const int ix = rowx*ncols + col;
+        const int iy = rowy*ncols + col;
+
+        const float val = x[ix]*scale + (mask ? mask[iy] : 0.0f) + (pos ? slope*pos[col] : 0.0f);
+
+        vals[col] = val;
+        max_val = sycl::max(max_val, val);
+    }
+
+    // find the max value in the block
+    max_val = warp_reduce_max(max_val, item_ct1);
+    if (block_size > WARP_SIZE) {
+        if (warp_id == 0) {
+            buf[lane_id] = -INFINITY;
+        }
+        item_ct1.barrier(sycl::access::fence_space::local_space);
+
+        if (lane_id == 0) {
+            buf[warp_id] = max_val;
+        }
+        item_ct1.barrier(sycl::access::fence_space::local_space);
+
+        max_val = buf[lane_id];
+        max_val = warp_reduce_max(max_val, item_ct1);
+    }
+
+    float tmp = 0.f;
+
+#pragma unroll
+    for (int col0 = 0; col0 < ncols; col0 += block_size) {
+        const int col = col0 + tid;
+                if (ncols_template == 0 && col >= ncols) {
+            break;
+        }
+
+        const float val = sycl::native::exp(vals[col] - max_val);
+        tmp += val;
+        vals[col] = val;
+    }
+
+    // find the sum of exps in the block
+    tmp = warp_reduce_sum(tmp, item_ct1);
+    if (block_size > WARP_SIZE) {
+        if (warp_id == 0) {
+            buf[lane_id] = 0.f;
+        }
+        item_ct1.barrier(sycl::access::fence_space::local_space);
+
+        if (lane_id == 0) {
+            buf[warp_id] = tmp;
+        }
+        item_ct1.barrier(sycl::access::fence_space::local_space);
+
+        tmp = buf[lane_id];
+        tmp = warp_reduce_sum(tmp, item_ct1);
+    }
+
+    const float inv_sum = 1.f / tmp;
+
+#pragma unroll
+    for (int col0 = 0; col0 < ncols; col0 += block_size) {
+        const int col = col0 + tid;
+
+        if (ncols_template == 0 && col >= ncols) {
+            return;
+        }
+
+        const int idst = rowx*ncols + col;
+        dst[idst] = vals[col] * inv_sum;
+    }
+}
+
+static void scale_f32(const float * x, float * dst, const float scale, const int k,
+                      const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+
+    dst[i] = scale * x[i];
+}
+
+static void clamp_f32(const float * x, float * dst, const float min, const float max, const int k,
+                      const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                  item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+
+    dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]);
+}
+
+template <typename T>
+static void im2col_kernel(const float *x, T *dst, int offset_delta,
+                           int IW, int IH, int OW, int KW, int KH,
+                           int pelements, int CHW, int s0, int s1, int p0,
+                           int p1, int d0, int d1,
+                           const sycl::nd_item<3> &item_ct1) {
+    const int i = item_ct1.get_local_id(2) +
+                  item_ct1.get_group(2) * item_ct1.get_local_range(2);
+    if (i >= pelements) {
+        return;
+    }
+
+    const int ksize = OW * (KH > 1 ? KW : 1);
+    const int kx = i / ksize;
+    const int kd = kx * ksize;
+    const int ky = (i - kd) / OW;
+    const int ix = i % OW;
+
+    const int64_t iiw = ix * s0 + kx * d0 - p0;
+    const int64_t iih = item_ct1.get_group(1) * s1 + ky * d1 - p1;
+
+    const int64_t offset_dst =
+        (item_ct1.get_group(1) * OW + ix) * CHW +
+        (item_ct1.get_group(0) * (KW * KH) + ky * KW + kx);
+
+    if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
+        dst[offset_dst] =
+            sycl::vec<float, 1>(0.0f)
+                .convert<sycl::half, sycl::rounding_mode::automatic>()[0];
+    } else {
+        const int64_t offset_src = item_ct1.get_group(0) * offset_delta;
+        dst[offset_dst] =
+            sycl::vec<float, 1>(x[offset_src + iih * IW + iiw])
+                .convert<sycl::half, sycl::rounding_mode::automatic>()[0];
+    }
+}
+
+template <typename Ti, typename To>
+static  void pool2d_nchw_kernel(
+        const int ih, const int iw, const int oh, const int ow,
+        const int kh, const int kw, const int sh, const int sw,
+        const int ph, const int pw, const int parallel_elements,
+        const Ti* src, To* dst, const enum ggml_op_pool op,
+        const sycl::nd_item<3> &item_ct1) {
+        int idx = item_ct1.get_local_id(2) +
+                  item_ct1.get_group(2) * item_ct1.get_local_range(2);
+        if (idx >= parallel_elements) {
+            return;
+        }
+
+        const int I_HW = ih * iw;
+        const int O_HW = oh * ow;
+        const int nc = idx / O_HW;
+        const int cur_oh = idx % O_HW / ow;
+        const int cur_ow = idx % O_HW % ow;
+        const Ti* i_ptr = src + nc * I_HW;
+        To* o_ptr = dst + nc * O_HW;
+        const int start_h = cur_oh * sh - ph;
+        const int bh = sycl::max(0, start_h);
+        const int eh = sycl::min(ih, start_h + kh);
+        const int start_w = cur_ow * sw - pw;
+        const int bw = sycl::max(0, start_w);
+        const int ew = sycl::min(iw, start_w + kw);
+
+        To res = 0;
+
+        switch (op) {
+            case GGML_OP_POOL_AVG: res = 0; break;
+            case GGML_OP_POOL_MAX: res = -FLT_MAX; break;
+        }
+
+        for (int i = bh; i < eh; i += 1) {
+            for (int j = bw; j < ew; j += 1) {
+#if DPCT_COMPATIBILITY_TEMP >= 350
+                /*
+                DPCT1098:106: The '*' expression is used instead of the __ldg
+                call. These two expressions do not provide the exact same
+                functionality. Check the generated code for potential precision
+                and/or performance issues.
+                */
+                Ti cur = *(i_ptr + i * iw + j);
+#else
+                Ti cur = i_ptr[i * iw + j];
+#endif
+                switch (op) {
+                    case GGML_OP_POOL_AVG: res += (cur / (kh * kw)); break;
+                    case GGML_OP_POOL_MAX: res = sycl::max(res, (To)cur); break;
+                }
+            }
+        }
+        o_ptr[cur_oh * ow + cur_ow] = res;
+}
+
+template <int qk, int qr, dequantize_kernel_t dq>
+static void get_rows_sycl(const ggml_tensor *src0, const ggml_tensor *src1,
+                          ggml_tensor *dst, const void *src0_dd,
+                          const int32_t *src1_dd, float *dst_dd,
+                          dpct::queue_ptr stream) {
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const sycl::range<3> block_dims(1, 1, SYCL_GET_ROWS_BLOCK_SIZE);
+    const int block_num_x = (ne00 + 2*SYCL_GET_ROWS_BLOCK_SIZE - 1) / (2*SYCL_GET_ROWS_BLOCK_SIZE);
+    const sycl::range<3> block_nums(ne11 * ne12, ne10, block_num_x);
+
+    // strides in elements
+    //const size_t s0 = nb0 / ggml_element_size(dst);
+    const size_t s1 = nb1 / ggml_element_size(dst);
+    const size_t s2 = nb2 / ggml_element_size(dst);
+    const size_t s3 = nb3 / ggml_element_size(dst);
+
+    const size_t s10 = nb10 / ggml_element_size(src1);
+    const size_t s11 = nb11 / ggml_element_size(src1);
+    const size_t s12 = nb12 / ggml_element_size(src1);
+    //const size_t s13 = nb13 / ggml_element_size(src1);
+
+    GGML_ASSERT(ne00 % 2 == 0);
+
+    stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             k_get_rows<qk, qr, dq>(
+                                 src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2,
+                                 s3, nb01, nb02, nb03, s10, s11, s12, item_ct1);
+                         });
+
+    (void) dst;
+}
+
+template <typename src0_t>
+static void get_rows_sycl_float(const ggml_tensor *src0,
+                                const ggml_tensor *src1, ggml_tensor *dst,
+                                const src0_t *src0_dd, const int32_t *src1_dd,
+                                float *dst_dd, dpct::queue_ptr stream) {
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const sycl::range<3> block_dims(1, 1, SYCL_GET_ROWS_BLOCK_SIZE);
+    const int block_num_x = (ne00 + SYCL_GET_ROWS_BLOCK_SIZE - 1) / SYCL_GET_ROWS_BLOCK_SIZE;
+    const sycl::range<3> block_nums(ne11 * ne12, ne10, block_num_x);
+
+    // strides in elements
+    //const size_t s0 = nb0 / ggml_element_size(dst);
+    const size_t s1 = nb1 / ggml_element_size(dst);
+    const size_t s2 = nb2 / ggml_element_size(dst);
+    const size_t s3 = nb3 / ggml_element_size(dst);
+
+    const size_t s10 = nb10 / ggml_element_size(src1);
+    const size_t s11 = nb11 / ggml_element_size(src1);
+    const size_t s12 = nb12 / ggml_element_size(src1);
+    //const size_t s13 = nb13 / ggml_element_size(src1);
+
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
+                k_get_rows_float(src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2,
+                                 s3, nb01, nb02, nb03, s10, s11, s12, item_ct1);
+            });
+    }
+
+    (void) dst;
+}
+
+template<float (*bin_op)(const float, const float)>
+struct bin_bcast_sycl {
+    template <typename src0_t, typename src1_t, typename dst_t>
+    void operator()(const struct ggml_tensor *src0,
+                    const struct ggml_tensor *src1, struct ggml_tensor *dst,
+                    const src0_t *src0_dd, const src1_t *src1_dd, dst_t *dst_dd,
+                    dpct::queue_ptr stream) {
+
+        GGML_TENSOR_BINARY_OP_LOCALS
+
+        int nr0 = ne10/ne0;
+        int nr1 = ne11/ne1;
+        int nr2 = ne12/ne2;
+        int nr3 = ne13/ne3;
+
+        int nr[4] = { nr0, nr1, nr2, nr3 };
+
+        // collapse dimensions until first broadcast dimension
+        int64_t cne0[] = {ne0, ne1, ne2, ne3};
+        int64_t cne1[] = {ne10, ne11, ne12, ne13};
+        size_t cnb0[] = {nb0, nb1, nb2, nb3};
+        size_t cnb1[] = {nb10, nb11, nb12, nb13};
+        auto collapse = [](int64_t cne[]) {
+            cne[0] *= cne[1];
+            cne[1] = cne[2];
+            cne[2] = cne[3];
+            cne[3] = 1;
+        };
+
+        auto collapse_nb = [](size_t cnb[], int64_t cne[]) {
+            cnb[1] *= cne[1];
+            cnb[2] *= cne[2];
+            cnb[3] *= cne[3];
+        };
+
+        for (int i = 0; i < 4; i++) {
+            if (nr[i] != 1) {
+                break;
+            }
+            if (i > 0) {
+                collapse_nb(cnb0, cne0);
+                collapse_nb(cnb1, cne1);
+                collapse(cne0);
+                collapse(cne1);
+            }
+        }
+        {
+            int64_t ne0 = cne0[0];
+            int64_t ne1 = cne0[1];
+            int64_t ne2 = cne0[2];
+            int64_t ne3 = cne0[3];
+
+            int64_t ne10 = cne1[0];
+            int64_t ne11 = cne1[1];
+            int64_t ne12 = cne1[2];
+            int64_t ne13 = cne1[3];
+
+            size_t nb0 = cnb0[0];
+            size_t nb1 = cnb0[1];
+            size_t nb2 = cnb0[2];
+            size_t nb3 = cnb0[3];
+
+            size_t nb10 = cnb1[0];
+            size_t nb11 = cnb1[1];
+            size_t nb12 = cnb1[2];
+            size_t nb13 = cnb1[3];
+
+            size_t s0 = nb0 / sizeof(dst_t);
+            size_t s1 = nb1 / sizeof(dst_t);
+            size_t s2 = nb2 / sizeof(dst_t);
+            size_t s3 = nb3 / sizeof(dst_t);
+
+            size_t s10 = nb10 / sizeof(src1_t);
+            size_t s11 = nb11 / sizeof(src1_t);
+            size_t s12 = nb12 / sizeof(src1_t);
+            size_t s13 = nb13 / sizeof(src1_t);
+
+            GGML_ASSERT(s0 == 1);
+            GGML_ASSERT(s10 == 1);
+
+            const int block_size = 128;
+
+            int64_t hne0 = std::max(ne0/2LL, 1LL);
+
+            sycl::range<3> block_dims(1, 1, 1);
+            block_dims[2] = std::min<unsigned int>(hne0, block_size);
+            block_dims[1] = std::min<unsigned int>(
+                ne1, block_size / (unsigned int)block_dims[2]);
+            block_dims[0] = std::min(
+                std::min<unsigned int>(
+                    ne2 * ne3, block_size / (unsigned int)block_dims[2] /
+                                   (unsigned int)block_dims[1]),
+                64U);
+
+            sycl::range<3> block_nums(
+                (ne2 * ne3 + block_dims[0] - 1) / block_dims[0],
+                (ne1 + block_dims[1] - 1) / block_dims[1],
+                (hne0 + block_dims[2] - 1) / block_dims[2]);
+
+            if (block_nums[0] > 65535) {
+                // this is the maximum number of blocks in z direction, fallback to 1D grid kernel
+                int block_num = (ne0*ne1*ne2*ne3 + block_size - 1) / block_size;
+                {
+                    dpct::has_capability_or_fail(stream->get_device(),
+                                                 {sycl::aspect::fp16});
+
+                    stream->parallel_for(
+                        sycl::nd_range<3>(sycl::range<3>(1, 1, block_num) *
+                                              sycl::range<3>(1, 1, block_size),
+                                          sycl::range<3>(1, 1, block_size)),
+                        [=](sycl::nd_item<3> item_ct1) {
+                            k_bin_bcast_unravel<bin_op>(
+                                src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3,
+                                ne10, ne11, ne12, ne13, s1, s2, s3, s11, s12,
+                                s13, item_ct1);
+                        });
+                }
+            } else {
+                /*
+                DPCT1049:16: The work-group size passed to the SYCL kernel may
+                exceed the limit. To get the device limit, query
+                info::device::max_work_group_size. Adjust the work-group size if
+                needed.
+                */
+                dpct::has_capability_or_fail(stream->get_device(),
+                                             {sycl::aspect::fp16});
+
+                stream->parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        k_bin_bcast<bin_op>(src0_dd, src1_dd, dst_dd, ne0, ne1,
+                                            ne2, ne3, ne10, ne11, ne12, ne13,
+                                            s1, s2, s3, s11, s12, s13,
+                                            item_ct1);
+                    });
+            }
+        }
+    }
+};
+
+static void acc_f32_sycl(const float *x, const float *y, float *dst,
+                         const int n_elements, const int ne10, const int ne11,
+                         const int ne12, const int nb1, const int nb2,
+                         const int offset, dpct::queue_ptr stream) {
+    int num_blocks = (n_elements + SYCL_ACC_BLOCK_SIZE - 1) / SYCL_ACC_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            acc_f32(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset,
+                    item_ct1);
+        });
+}
+
+static void gelu_f32_sycl(const float *x, float *dst, const int k,
+                          dpct::queue_ptr stream) {
+    const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            gelu_f32(x, dst, k, item_ct1);
+        });
+}
+
+static void silu_f32_sycl(const float *x, float *dst, const int k,
+                          dpct::queue_ptr stream) {
+    const int num_blocks = (k + SYCL_SILU_BLOCK_SIZE - 1) / SYCL_SILU_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            silu_f32(x, dst, k, item_ct1);
+        });
+}
+
+static void gelu_quick_f32_sycl(const float *x, float *dst, const int k,
+                                dpct::queue_ptr stream) {
+    const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            gelu_quick_f32(x, dst, k, item_ct1);
+        });
+}
+
+static void tanh_f32_sycl(const float *x, float *dst, const int k,
+                          dpct::queue_ptr stream) {
+    const int num_blocks = (k + SYCL_TANH_BLOCK_SIZE - 1) / SYCL_TANH_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            tanh_f32(x, dst, k, item_ct1);
+        });
+}
+
+static void relu_f32_sycl(const float *x, float *dst, const int k,
+                          dpct::queue_ptr stream) {
+    const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            relu_f32(x, dst, k, item_ct1);
+        });
+}
+
+static void hardsigmoid_f32_sycl(const float *x, float *dst, const int k,
+                                 dpct::queue_ptr stream) {
+    const int num_blocks = (k + SYCL_HARDSIGMOID_BLOCK_SIZE - 1) / SYCL_HARDSIGMOID_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            hardsigmoid_f32(x, dst, k, item_ct1);
+        });
+}
+
+static void hardswish_f32_sycl(const float *x, float *dst, const int k,
+                               dpct::queue_ptr stream) {
+    const int num_blocks = (k + SYCL_HARDSWISH_BLOCK_SIZE - 1) / SYCL_HARDSWISH_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            hardswish_f32(x, dst, k, item_ct1);
+        });
+}
+
+static void leaky_relu_f32_sycl(const float *x, float *dst, const int k,
+                                const float negative_slope,
+                                dpct::queue_ptr stream) {
+    const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            leaky_relu_f32(x, dst, k, negative_slope, item_ct1);
+        });
+}
+
+static void sqr_f32_sycl(const float *x, float *dst, const int k,
+                         dpct::queue_ptr stream) {
+    const int num_blocks = (k + SYCL_SQR_BLOCK_SIZE - 1) / SYCL_SQR_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            sqr_f32(x, dst, k, item_ct1);
+        });
+}
+
+static void norm_f32_sycl(const float *x, float *dst, const int ncols,
+                          const int nrows, const float eps,
+                          dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % WARP_SIZE == 0);
+    if (ncols < 1024) {
+        const sycl::range<3> block_dims(1, 1, WARP_SIZE);
+        stream->submit([&](sycl::handler &cgh) {
+            sycl::local_accessor<sycl::float2, 1> s_sum_acc_ct1(
+                sycl::range<1>(32), cgh);
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
+                                  block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        norm_f32(x, dst, ncols, eps, item_ct1,
+                                            s_sum_acc_ct1.get_pointer(), WARP_SIZE);
+                    });
+        });
+    } else {
+        const int work_group_size = g_work_group_size;
+        const sycl::range<3> block_dims(1, 1, work_group_size);
+        /*
+        DPCT1049:17: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        stream->submit([&](sycl::handler &cgh) {
+            sycl::local_accessor<sycl::float2, 1> s_sum_acc_ct1(
+                sycl::range<1>(32), cgh);
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
+                                  block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        norm_f32(x, dst, ncols, eps, item_ct1,
+                                       s_sum_acc_ct1.get_pointer(), work_group_size);
+                    });
+        });
+    }
+}
+
+static void group_norm_f32_sycl(const float *x, float *dst,
+                                const int num_groups, const int group_size,
+                                const int ne_elements, dpct::queue_ptr stream) {
+    static const float eps = 1e-6f;
+    if (group_size < 1024) {
+        const sycl::range<3> block_dims(1, 1, WARP_SIZE);
+        stream->submit([&](sycl::handler &cgh) {
+            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(32),
+                                                         cgh);
+
+            const float eps_ct4 = eps;
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims,
+                                  block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        group_norm_f32(
+                            x, dst, group_size, ne_elements, eps_ct4, item_ct1,
+                            s_sum_acc_ct1.get_pointer(), WARP_SIZE);
+                    });
+        });
+    } else {
+        const int work_group_size = g_work_group_size;
+        const sycl::range<3> block_dims(1, 1, work_group_size);
+        /*
+        DPCT1049:18: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+
+        stream->submit([&](sycl::handler &cgh) {
+            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(32),
+                                                         cgh);
+
+            const float eps_ct4 = eps;
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims,
+                                  block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        group_norm_f32(x, dst, group_size, ne_elements,
+                                             eps_ct4, item_ct1,
+                                             s_sum_acc_ct1.get_pointer(), work_group_size);
+                    });
+        });
+    }
+}
+
+static void concat_f32_sycl(const float *x, const float *y, float *dst,
+                            const int ne0, int ne1, int ne2, int ne02,
+                            dpct::queue_ptr stream) {
+    int num_blocks = (ne0 + SYCL_CONCAT_BLOCK_SIZE - 1) / SYCL_CONCAT_BLOCK_SIZE;
+    sycl::range<3> gridDim(ne2, ne1, num_blocks);
+    stream->parallel_for(
+        sycl::nd_range<3>(gridDim *
+                              sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            concat_f32(x, y, dst, ne0, ne02, item_ct1);
+        });
+}
+
+static void upscale_f32_sycl(const float *x, float *dst, const int ne00,
+                             const int ne01, const int ne02,
+                             const int scale_factor, dpct::queue_ptr stream) {
+    int ne0 = (ne00 * scale_factor);
+    int num_blocks = (ne0 + SYCL_UPSCALE_BLOCK_SIZE - 1) / SYCL_UPSCALE_BLOCK_SIZE;
+    sycl::range<3> gridDim(ne02, (ne01 * scale_factor), num_blocks);
+    stream->parallel_for(
+        sycl::nd_range<3>(gridDim *
+                              sycl::range<3>(1, 1, SYCL_UPSCALE_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_UPSCALE_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            upscale_f32(x, dst, ne00, ne00 * ne01, scale_factor, item_ct1);
+        });
+}
+
+static void pad_f32_sycl(const float *x, float *dst, const int ne00,
+                         const int ne01, const int ne02, const int ne0,
+                         const int ne1, const int ne2, dpct::queue_ptr stream) {
+    int num_blocks = (ne0 + SYCL_PAD_BLOCK_SIZE - 1) / SYCL_PAD_BLOCK_SIZE;
+    sycl::range<3> gridDim(ne2, ne1, num_blocks);
+    stream->parallel_for(
+        sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            pad_f32(x, dst, ne0, ne00, ne01, ne02, item_ct1);
+        });
+}
+
+static void rms_norm_f32_sycl(const float *x, float *dst, const int ncols,
+                              const int nrows, const float eps,
+                              dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % WARP_SIZE == 0);
+    // printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE);
+    if (ncols < 1024) {
+        const sycl::range<3> block_dims(1, 1, WARP_SIZE);
+        stream->submit([&](sycl::handler &cgh) {
+            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(32),
+                                                         cgh);
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
+                                  block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        rms_norm_f32(x, dst, ncols, eps, item_ct1,
+                                                s_sum_acc_ct1.get_pointer(), WARP_SIZE);
+                    });
+        });
+    } else {
+        const int work_group_size = g_work_group_size;
+        const sycl::range<3> block_dims(1, 1, work_group_size);
+        /*
+        DPCT1049:19: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        stream->submit([&](sycl::handler &cgh) {
+            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(32),
+                                                         cgh);
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
+                                  block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        rms_norm_f32(x, dst, ncols, eps, item_ct1,
+                                           s_sum_acc_ct1.get_pointer(), work_group_size);
+                    });
+        });
+    }
+}
+
+static void quantize_row_q8_1_sycl(const float *x, void *vy, const int kx,
+                                   const int ky, const int kx_padded,
+                                   dpct::queue_ptr stream) {
+    const int block_num_x = (kx_padded + SYCL_QUANTIZE_BLOCK_SIZE - 1) / SYCL_QUANTIZE_BLOCK_SIZE;
+    const sycl::range<3> num_blocks(1, ky, block_num_x);
+    const sycl::range<3> block_size(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(num_blocks * block_size, block_size),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+                quantize_q8_1(x, vy, kx, kx_padded, item_ct1);
+            });
+    }
+}
+
+template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
+static void dequantize_block_sycl(const void *__restrict__ vx,
+                                  dst_t *__restrict__ y, const int k,
+                                  dpct::queue_ptr stream) {
+    const int num_blocks = (k + 2*SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / (2*SYCL_DEQUANTIZE_BLOCK_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+        stream->parallel_for(
+            sycl::nd_range<3>(
+                sycl::range<3>(1, 1, num_blocks) *
+                    sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE),
+                sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                dequantize_block<qk, qr, dequantize_kernel>(vx, y, k, item_ct1);
+            });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_q2_K_sycl(const void *vx, dst_t *y, const int k,
+                                     dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 64),
+                                               sycl::range<3>(1, 1, 64)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q2_K(vx, y, item_ct1);
+                             });
+    }
+#else
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q2_K(vx, y, item_ct1);
+                             });
+    }
+
+#endif
+}
+
+template <typename dst_t>
+static void dequantize_row_q3_K_sycl(const void *vx, dst_t *y, const int k,
+                                     dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 64),
+                                               sycl::range<3>(1, 1, 64)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q3_K(vx, y, item_ct1);
+                             });
+    }
+#else
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q3_K(vx, y, item_ct1);
+                             });
+    }
+#endif
+}
+
+template <typename dst_t>
+static void dequantize_row_q4_0_sycl(const void *vx, dst_t *y, const int k,
+                                     dpct::queue_ptr stream) {
+    const int nb32 = k / 32;
+    const int nb = (k + 255) / 256;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q4_0(vx, y, nb32, item_ct1);
+                             });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_q4_1_sycl(const void *vx, dst_t *y, const int k,
+                                     dpct::queue_ptr stream) {
+    const int nb32 = k / 32;
+    const int nb = (k + 255) / 256;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q4_1(vx, y, nb32, item_ct1);
+                             });
+    }
+}
+
+
+template <typename dst_t>
+static void dequantize_row_q4_K_sycl(const void *vx, dst_t *y, const int k,
+                                     dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q4_K(vx, y, item_ct1);
+                             });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_q5_K_sycl(const void *vx, dst_t *y, const int k,
+                                     dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 64),
+                                               sycl::range<3>(1, 1, 64)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q5_K(vx, y, item_ct1);
+                             });
+    }
+#else
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q5_K(vx, y, item_ct1);
+                             });
+    }
+
+#endif
+}
+
+template <typename dst_t>
+static void dequantize_row_q6_K_sycl(const void *vx, dst_t *y, const int k,
+                                     dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+#if QK_K == 256
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 64),
+                                               sycl::range<3>(1, 1, 64)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q6_K(vx, y, item_ct1);
+                             });
+    }
+#else
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q6_K(vx, y, item_ct1);
+                             });
+    }
+
+#endif
+}
+
+
+template <typename dst_t>
+static void dequantize_row_iq2_xxs_sycl(const void *vx, dst_t *y, const int k,
+                                        dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq2xxs_grid_ptr_ct1 = &iq2xxs_grid[0];
+            auto ksigns_iq2xs_ptr_ct1 = &ksigns_iq2xs[0];
+            auto kmask_iq2xs_ptr_ct1 = &kmask_iq2xs[0];
+
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq2_xxs(
+                                     vx, y, item_ct1, iq2xxs_grid_ptr_ct1,
+                                     ksigns_iq2xs_ptr_ct1, kmask_iq2xs_ptr_ct1);
+                             });
+        });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_iq2_xs_sycl(const void *vx, dst_t *y, const int k,
+                                       dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq2xs_grid_ptr_ct1 = &iq2xs_grid[0];
+            auto ksigns_iq2xs_ptr_ct1 = &ksigns_iq2xs[0];
+            auto kmask_iq2xs_ptr_ct1 = &kmask_iq2xs[0];
+
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq2_xs(
+                                     vx, y, item_ct1, iq2xs_grid_ptr_ct1,
+                                     ksigns_iq2xs_ptr_ct1, kmask_iq2xs_ptr_ct1);
+                             });
+        });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_iq3_xxs_sycl(const void *vx, dst_t *y, const int k,
+                                        dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq3xxs_grid_ptr_ct1 = &iq3xxs_grid[0];
+            auto ksigns_iq2xs_ptr_ct1 = &ksigns_iq2xs[0];
+            auto kmask_iq2xs_ptr_ct1 = &kmask_iq2xs[0];
+
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq3_xxs(
+                                     vx, y, item_ct1, iq3xxs_grid_ptr_ct1,
+                                     ksigns_iq2xs_ptr_ct1, kmask_iq2xs_ptr_ct1);
+                             });
+        });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_iq3_s_sycl(const void *vx, dst_t *y, const int k,
+                                        dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq3s_grid_ptr_ct1 = &iq3s_grid[0];
+            auto ksigns_iq2xs_ptr_ct1 = &ksigns_iq2xs[0];
+            auto kmask_iq2xs_ptr_ct1 = &kmask_iq2xs[0];
+
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq3_s(
+                                     vx, y, item_ct1, iq3s_grid_ptr_ct1,
+                                     ksigns_iq2xs_ptr_ct1, kmask_iq2xs_ptr_ct1);
+                             });
+        });
+    }
+}
+
+template <typename dst_t>
+static void dequantize_row_iq1_s_sycl(const void *vx, dst_t *y, const int k,
+                                        dpct::queue_ptr stream) {
+    const int nb = k / QK_K;
+    {
+
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq1s_grid_ptr_ct1 = &iq1s_grid_gpu[0];
+            auto ksigns_iq2xs_ptr_ct1 = &ksigns_iq2xs[0];
+            auto kmask_iq2xs_ptr_ct1 = &kmask_iq2xs[0];
+
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq1_s(
+                                     vx, y, item_ct1, iq1s_grid_ptr_ct1,
+                                     ksigns_iq2xs_ptr_ct1, kmask_iq2xs_ptr_ct1);
+                             });
+        });
+    }
+}
+
+template <typename src_t, typename dst_t>
+static void convert_unary_sycl(const void *__restrict__ vx,
+                               dst_t *__restrict__ y, const int k,
+                               dpct::queue_ptr stream) {
+    const int num_blocks = (k + SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / SYCL_DEQUANTIZE_BLOCK_SIZE;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(
+                sycl::range<3>(1, 1, num_blocks) *
+                    sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE),
+                sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                convert_unary<src_t>(vx, y, k, item_ct1);
+            });
+    }
+}
+
+
+static to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type) try {
+    int id;
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+            return dequantize_block_sycl<QK4_0, QR4_0, dequantize_q4_0>;
+        case GGML_TYPE_Q4_1:
+            return dequantize_block_sycl<QK4_1, QR4_1, dequantize_q4_1>;
+        case GGML_TYPE_Q5_0:
+            return dequantize_block_sycl<QK5_0, QR5_0, dequantize_q5_0>;
+        case GGML_TYPE_Q5_1:
+            return dequantize_block_sycl<QK5_1, QR5_1, dequantize_q5_1>;
+        case GGML_TYPE_Q8_0:
+            return dequantize_block_sycl<QK8_0, QR8_0, dequantize_q8_0>;
+        case GGML_TYPE_Q2_K:
+            return dequantize_row_q2_K_sycl;
+        case GGML_TYPE_Q3_K:
+            return dequantize_row_q3_K_sycl;
+        case GGML_TYPE_Q4_K:
+            return dequantize_row_q4_K_sycl;
+        case GGML_TYPE_Q5_K:
+            return dequantize_row_q5_K_sycl;
+        case GGML_TYPE_Q6_K:
+            return dequantize_row_q6_K_sycl;
+        case GGML_TYPE_IQ2_XXS:
+            return dequantize_row_iq2_xxs_sycl;
+        case GGML_TYPE_IQ2_XS:
+            return dequantize_row_iq2_xs_sycl;
+        case GGML_TYPE_IQ3_XXS:
+            return dequantize_row_iq3_xxs_sycl;
+        case GGML_TYPE_IQ3_S:
+            return dequantize_row_iq3_s_sycl;
+        case GGML_TYPE_IQ1_S:
+            return dequantize_row_iq1_s_sycl;
+        case GGML_TYPE_F32:
+            return convert_unary_sycl<float>;
+        default:
+            return nullptr;
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+            return dequantize_row_q4_0_sycl;
+        case GGML_TYPE_Q4_1:
+            return dequantize_row_q4_1_sycl;
+        case GGML_TYPE_Q5_0:
+            return dequantize_block_sycl<QK5_0, QR5_0, dequantize_q5_0>;
+        case GGML_TYPE_Q5_1:
+            return dequantize_block_sycl<QK5_1, QR5_1, dequantize_q5_1>;
+        case GGML_TYPE_Q8_0:
+            return dequantize_block_sycl<QK8_0, QR8_0, dequantize_q8_0>;
+        case GGML_TYPE_Q2_K:
+            return dequantize_row_q2_K_sycl;
+        case GGML_TYPE_Q3_K:
+            return dequantize_row_q3_K_sycl;
+        case GGML_TYPE_Q4_K:
+            return dequantize_row_q4_K_sycl;
+        case GGML_TYPE_Q5_K:
+            return dequantize_row_q5_K_sycl;
+        case GGML_TYPE_Q6_K:
+            return dequantize_row_q6_K_sycl;
+        case GGML_TYPE_IQ2_XXS:
+            return dequantize_row_iq2_xxs_sycl;
+        case GGML_TYPE_IQ2_XS:
+            return dequantize_row_iq2_xs_sycl;
+        case GGML_TYPE_IQ3_XXS:
+            return dequantize_row_iq3_xxs_sycl;
+        case GGML_TYPE_IQ3_S:
+            return dequantize_row_iq3_s_sycl;
+        case GGML_TYPE_IQ1_S:
+            return dequantize_row_iq1_s_sycl;
+        case GGML_TYPE_F16:
+            return convert_unary_sycl<sycl::half>;
+        default:
+            return nullptr;
+    }
+}
+
+static void dequantize_mul_mat_vec_q4_0_sycl(const void *vx, const dfloat *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+                dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>(
+                    vx, y, dst, ncols, nrows, item_ct1);
+            });
+    }
+}
+
+static void dequantize_mul_mat_vec_q4_1_sycl(const void *vx, const dfloat *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+                dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>(
+                    vx, y, dst, ncols, nrows, item_ct1);
+            });
+    }
+}
+
+static void dequantize_mul_mat_vec_q5_0_sycl(const void *vx, const dfloat *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+                dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>(
+                    vx, y, dst, ncols, nrows, item_ct1);
+            });
+    }
+}
+
+static void dequantize_mul_mat_vec_q5_1_sycl(const void *vx, const dfloat *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+                dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>(
+                    vx, y, dst, ncols, nrows, item_ct1);
+            });
+    }
+}
+
+static void dequantize_mul_mat_vec_q8_0_sycl(const void *vx, const dfloat *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+                dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>(
+                    vx, y, dst, ncols, nrows, item_ct1);
+            });
+    }
+}
+
+static void dequantize_mul_mat_vec_q2_K_sycl(const void *vx, const float *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, ny, 32);
+    stream->parallel_for(
+        sycl::nd_range<3>(block_nums * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+            dequantize_mul_mat_vec_q2_k(vx, y, dst, ncols, nrows, item_ct1);
+        });
+}
+
+static void dequantize_mul_mat_vec_q3_K_sycl(const void *vx, const float *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2 / K_QUANTS_PER_ITERATION;
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, ny, 32);
+    stream->parallel_for(
+        sycl::nd_range<3>(block_nums * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+            dequantize_mul_mat_vec_q3_k(vx, y, dst, ncols, nrows, item_ct1);
+        });
+}
+
+static void dequantize_mul_mat_vec_q4_K_sycl(const void *vx, const float *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2 / K_QUANTS_PER_ITERATION;
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, ny, 32);
+    stream->parallel_for(
+        sycl::nd_range<3>(block_nums * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+            dequantize_mul_mat_vec_q4_k(vx, y, dst, ncols, nrows, item_ct1);
+        });
+}
+
+static void dequantize_mul_mat_vec_q5_K_sycl(const void *vx, const float *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const sycl::range<3> block_dims(1, 1, 32);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+            dequantize_mul_mat_vec_q5_k(vx, y, dst, ncols, item_ct1);
+        });
+}
+
+static void dequantize_mul_mat_vec_q6_K_sycl(const void *vx, const float *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int ny = 2 / K_QUANTS_PER_ITERATION;
+    const int block_num_y = (nrows + ny - 1) / ny;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, ny, 32);
+    stream->parallel_for(
+        sycl::nd_range<3>(block_nums * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+            dequantize_mul_mat_vec_q6_k(vx, y, dst, ncols, nrows, item_ct1);
+        });
+}
+
+static void convert_mul_mat_vec_f16_sycl(const void *vx, const dfloat *y,
+                                         float *dst, const int ncols,
+                                         const int nrows,
+                                         dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+                dequantize_mul_mat_vec<1, 1, convert_f16>(vx, y, dst, ncols,
+                                                          nrows, item_ct1);
+            });
+    }
+}
+
+
+static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK4_0 == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK4_0, QI4_0, block_q4_0,
+                                      VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK4_1 == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK4_0, QI4_1, block_q4_1,
+                                      VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK5_0 == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK5_0, QI5_0, block_q5_0,
+                                      VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK5_1 == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK5_1, QI5_1, block_q5_1,
+                                      VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK8_0 == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK8_0, QI8_0, block_q8_0,
+                                      VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK_K, QI2_K, block_q2_K,
+                                      VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK_K, QI3_K, block_q3_K,
+                                      VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK_K, QI4_K, block_q4_K,
+                                      VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK_K, QI5_K, block_q5_K,
+                                      VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy,
+                                       float *dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q<QK_K, QI6_K, block_q6_K,
+                                      VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
+        });
+    }
+}
+
+
+static void mul_mat_vec_iq2_xxs_q8_1_sycl(const void *vx, const void *vy,
+                                          float *dst, const int ncols,
+                                          const int nrows,
+                                          dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq2xxs_grid_ptr_ct1 = &iq2xxs_grid[0];
+            auto ksigns_iq2xs_ptr_ct1 = &ksigns_iq2xs[0];
+            auto kmask_iq2xs_ptr_ct1 = &kmask_iq2xs[0];
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q_iq2_xxs_q8_1<QK_K, QI2_XXS, block_iq2_xxs, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1,
+                            iq2xxs_grid_ptr_ct1, ksigns_iq2xs_ptr_ct1, kmask_iq2xs_ptr_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy,
+                                         float *dst, const int ncols,
+                                         const int nrows,
+                                         dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq2xs_grid_ptr_ct1 = &iq2xs_grid[0];
+            auto ksigns64_ptr_ct1 = &ksigns64[0];
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q_iq2_xs_q8_1<QK_K, QI2_XS, block_iq2_xs, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1,
+                            iq2xs_grid_ptr_ct1, ksigns64_ptr_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy,
+                                          float *dst, const int ncols,
+                                          const int nrows,
+                                          dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq3xxs_grid_ptr_ct1 = &iq3xxs_grid[0];
+            auto ksigns64_ptr_ct1 = &ksigns64[0];
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q_iq3_xxs_q8_1<QK_K, QI3_XXS, block_iq3_xxs, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1,
+                            iq3xxs_grid_ptr_ct1, ksigns64_ptr_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy,
+                                          float *dst, const int ncols,
+                                          const int nrows,
+                                          dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq3s_grid_ptr_ct1 = &iq3s_grid[0];
+            auto ksigns64_ptr_ct1 = &ksigns64[0];
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q_iq3_s_q8_1<QK_K, QI3_XS, block_iq3_s, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1,
+                            iq3s_grid_ptr_ct1, ksigns64_ptr_ct1);
+                    });
+        });
+    }
+}
+
+static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy,
+                                          float *dst, const int ncols,
+                                          const int nrows,
+                                          dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_K == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+
+        stream->submit([&](sycl::handler &cgh) {
+            auto iq1s_grid_ptr_ct1 = &iq1s_grid_gpu[0];
+            auto ksigns64_ptr_ct1 = &ksigns64[0];
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[intel::reqd_sub_group_size(32)]] {
+                        mul_mat_vec_q_iq1_s_q8_1<QK_K, QI1_S, block_iq1_s, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1,
+                            iq1s_grid_ptr_ct1, ksigns64_ptr_ct1);
+                    });
+        });
+    }
+}
+
+static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = g_device_caps[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q4_0_RDNA2;
+        mmq_y  =  MMQ_Y_Q4_0_RDNA2;
+        nwarps = NWARPS_Q4_0_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q4_0_RDNA1;
+        mmq_y  =  MMQ_Y_Q4_0_RDNA1;
+        nwarps = NWARPS_Q4_0_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q4_0_AMPERE;
+        mmq_y  =  MMQ_Y_Q4_0_AMPERE;
+        nwarps = NWARPS_Q4_0_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q4_0_PASCAL;
+        mmq_y  =  MMQ_Y_Q4_0_PASCAL;
+        nwarps = NWARPS_Q4_0_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:20: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_qs_q4_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<float, 1> tile_x_d_q4_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI4_0) + mmq_y / QI4_0),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q4_0<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_qs_q4_0_acc_ct1.get_pointer(),
+                            tile_x_d_q4_0_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:21: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_qs_q4_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<float, 1> tile_x_d_q4_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI4_0) + mmq_y / QI4_0),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q4_0<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_qs_q4_0_acc_ct1.get_pointer(),
+                            tile_x_d_q4_0_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = g_device_caps[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q4_1_RDNA2;
+        mmq_y  =  MMQ_Y_Q4_1_RDNA2;
+        nwarps = NWARPS_Q4_1_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q4_1_RDNA1;
+        mmq_y  =  MMQ_Y_Q4_1_RDNA1;
+        nwarps = NWARPS_Q4_1_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q4_1_AMPERE;
+        mmq_y  =  MMQ_Y_Q4_1_AMPERE;
+        nwarps = NWARPS_Q4_1_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q4_1_PASCAL;
+        mmq_y  =  MMQ_Y_Q4_1_PASCAL;
+        nwarps = NWARPS_Q4_1_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:22: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_qs_q4_1_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + +mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_1_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI4_1) + mmq_y / QI4_1),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q4_1<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_qs_q4_1_acc_ct1.get_pointer(),
+                            tile_x_dm_q4_1_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:23: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_qs_q4_1_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + +mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_1_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI4_1) + mmq_y / QI4_1),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q4_1<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_qs_q4_1_acc_ct1.get_pointer(),
+                            tile_x_dm_q4_1_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = g_device_caps[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q5_0_RDNA2;
+        mmq_y  =  MMQ_Y_Q5_0_RDNA2;
+        nwarps = NWARPS_Q5_0_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q5_0_RDNA1;
+        mmq_y  =  MMQ_Y_Q5_0_RDNA1;
+        nwarps = NWARPS_Q5_0_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q5_0_AMPERE;
+        mmq_y  =  MMQ_Y_Q5_0_AMPERE;
+        nwarps = NWARPS_Q5_0_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q5_0_PASCAL;
+        mmq_y  =  MMQ_Y_Q5_0_PASCAL;
+        nwarps = NWARPS_Q5_0_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:24: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q5_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<float, 1> tile_x_d_q5_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI5_0) + mmq_y / QI5_0),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q5_0<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_ql_q5_0_acc_ct1.get_pointer(),
+                            tile_x_d_q5_0_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:25: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q5_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<float, 1> tile_x_d_q5_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI5_0) + mmq_y / QI5_0),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q5_0<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_ql_q5_0_acc_ct1.get_pointer(),
+                            tile_x_d_q5_0_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = g_device_caps[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q5_1_RDNA2;
+        mmq_y  =  MMQ_Y_Q5_1_RDNA2;
+        nwarps = NWARPS_Q5_1_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q5_1_RDNA1;
+        mmq_y  =  MMQ_Y_Q5_1_RDNA1;
+        nwarps = NWARPS_Q5_1_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q5_1_AMPERE;
+        mmq_y  =  MMQ_Y_Q5_1_AMPERE;
+        nwarps = NWARPS_Q5_1_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q5_1_PASCAL;
+        mmq_y  =  MMQ_Y_Q5_1_PASCAL;
+        nwarps = NWARPS_Q5_1_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:26: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q5_1_acc_ct1(
+                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_1_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI5_1) + mmq_y / QI5_1),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q5_1<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_ql_q5_1_acc_ct1.get_pointer(),
+                            tile_x_dm_q5_1_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:27: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q5_1_acc_ct1(
+                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_1_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI5_1) + mmq_y / QI5_1),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q5_1<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_ql_q5_1_acc_ct1.get_pointer(),
+                            tile_x_dm_q5_1_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = g_device_caps[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q8_0_RDNA2;
+        mmq_y  =  MMQ_Y_Q8_0_RDNA2;
+        nwarps = NWARPS_Q8_0_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q8_0_RDNA1;
+        mmq_y  =  MMQ_Y_Q8_0_RDNA1;
+        nwarps = NWARPS_Q8_0_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q8_0_AMPERE;
+        mmq_y  =  MMQ_Y_Q8_0_AMPERE;
+        nwarps = NWARPS_Q8_0_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q8_0_PASCAL;
+        mmq_y  =  MMQ_Y_Q8_0_PASCAL;
+        nwarps = NWARPS_Q8_0_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:28: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_qs_q8_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<float, 1> tile_x_d_q8_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI8_0) + mmq_y / QI8_0),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q8_0<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_qs_q8_0_acc_ct1.get_pointer(),
+                            tile_x_d_q8_0_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:29: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_qs_q8_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<float, 1> tile_x_d_q8_0_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI8_0) + mmq_y / QI8_0),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q8_0<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_qs_q8_0_acc_ct1.get_pointer(),
+                            tile_x_d_q8_0_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = g_device_caps[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q2_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q2_K_RDNA2;
+        nwarps = NWARPS_Q2_K_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q2_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q2_K_RDNA1;
+        nwarps = NWARPS_Q2_K_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q2_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q2_K_AMPERE;
+        nwarps = NWARPS_Q2_K_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q2_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q2_K_PASCAL;
+        nwarps = NWARPS_Q2_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:30: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q2_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q2_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI2_K) + mmq_y / QI2_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_q2_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q2_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_ql_q2_K_acc_ct1.get_pointer(),
+                            tile_x_dm_q2_K_acc_ct1.get_pointer(),
+                            tile_x_sc_q2_K_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:31: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q2_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q2_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI2_K) + mmq_y / QI2_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_q2_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q2_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_ql_q2_K_acc_ct1.get_pointer(),
+                            tile_x_dm_q2_K_acc_ct1.get_pointer(),
+                            tile_x_sc_q2_K_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+#if QK_K == 256
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = g_device_caps[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q3_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q3_K_RDNA2;
+        nwarps = NWARPS_Q3_K_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q3_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q3_K_RDNA1;
+        nwarps = NWARPS_Q3_K_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q3_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q3_K_AMPERE;
+        nwarps = NWARPS_Q3_K_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q3_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q3_K_PASCAL;
+        nwarps = NWARPS_Q3_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:32: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q3_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q3_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI3_K) + mmq_y / QI3_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_qh_q3_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 2) + mmq_y / 2), cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_q3_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q3_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_ql_q3_K_acc_ct1.get_pointer(),
+                            tile_x_dm_q3_K_acc_ct1.get_pointer(),
+                            tile_x_qh_q3_K_acc_ct1.get_pointer(),
+                            tile_x_sc_q3_K_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:33: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q3_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q3_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI3_K) + mmq_y / QI3_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_qh_q3_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 2) + mmq_y / 2), cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_q3_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q3_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_ql_q3_K_acc_ct1.get_pointer(),
+                            tile_x_dm_q3_K_acc_ct1.get_pointer(),
+                            tile_x_qh_q3_K_acc_ct1.get_pointer(),
+                            tile_x_sc_q3_K_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    }
+#endif
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = g_device_caps[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q4_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q4_K_RDNA2;
+        nwarps = NWARPS_Q4_K_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q4_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q4_K_RDNA1;
+        nwarps = NWARPS_Q4_K_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q4_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q4_K_AMPERE;
+        nwarps = NWARPS_Q4_K_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q4_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q4_K_PASCAL;
+        nwarps = NWARPS_Q4_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:34: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q4_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI4_K) + mmq_y / QI4_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_q4_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q4_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_ql_q4_K_acc_ct1.get_pointer(),
+                            tile_x_dm_q4_K_acc_ct1.get_pointer(),
+                            tile_x_sc_q4_K_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:35: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q4_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI4_K) + mmq_y / QI4_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_q4_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q4_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_ql_q4_K_acc_ct1.get_pointer(),
+                            tile_x_dm_q4_K_acc_ct1.get_pointer(),
+                            tile_x_sc_q4_K_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = g_device_caps[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q5_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q5_K_RDNA2;
+        nwarps = NWARPS_Q5_K_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q5_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q5_K_RDNA1;
+        nwarps = NWARPS_Q5_K_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q5_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q5_K_AMPERE;
+        nwarps = NWARPS_Q5_K_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q5_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q5_K_PASCAL;
+        nwarps = NWARPS_Q5_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:36: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q5_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI5_K) + mmq_y / QI5_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_q5_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q5_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_ql_q5_K_acc_ct1.get_pointer(),
+                            tile_x_dm_q5_K_acc_ct1.get_pointer(),
+                            tile_x_sc_q5_K_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:37: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_q5_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI5_K) + mmq_y / QI5_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_q5_K_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q5_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_ql_q5_K_acc_ct1.get_pointer(),
+                            tile_x_dm_q5_K_acc_ct1.get_pointer(),
+                            tile_x_sc_q5_K_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy,
+                                        float *dst, const int ncols_x,
+                                        const int nrows_x, const int ncols_y,
+                                        const int nrows_y, const int nrows_dst,
+                                        dpct::queue_ptr stream) try {
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+    const int compute_capability = g_device_caps[id].cc;
+
+    int mmq_x, mmq_y, nwarps;
+    if (compute_capability >= VER_GEN13) {
+        mmq_x  =  MMQ_X_Q6_K_RDNA2;
+        mmq_y  =  MMQ_Y_Q6_K_RDNA2;
+        nwarps = NWARPS_Q6_K_RDNA2;
+    } else if (compute_capability >= VER_GEN12) {
+        mmq_x  =  MMQ_X_Q6_K_RDNA1;
+        mmq_y  =  MMQ_Y_Q6_K_RDNA1;
+        nwarps = NWARPS_Q6_K_RDNA1;
+    } else if (compute_capability >= VER_GEN9) {
+        mmq_x  =  MMQ_X_Q6_K_AMPERE;
+        mmq_y  =  MMQ_Y_Q6_K_AMPERE;
+        nwarps = NWARPS_Q6_K_AMPERE;
+    } else if (compute_capability >= VER_4VEC) {
+        mmq_x  =  MMQ_X_Q6_K_PASCAL;
+        mmq_y  =  MMQ_Y_Q6_K_PASCAL;
+        nwarps = NWARPS_Q6_K_PASCAL;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+    const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
+    const sycl::range<3> block_nums(1, block_num_y, block_num_x);
+    const sycl::range<3> block_dims(1, nwarps, WARP_SIZE);
+
+    if (nrows_x % mmq_y == 0) {
+        const bool need_check = false;
+        /*
+        DPCT1049:38: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_acc_ct1(
+                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI6_K) + mmq_y / QI6_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q6_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_ql_acc_ct1.get_pointer(),
+                            tile_x_dm_acc_ct1.get_pointer(),
+                            tile_x_sc_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    } else {
+        const bool need_check = true;
+        /*
+        DPCT1049:39: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            stream->submit([&](sycl::handler &cgh) {
+                sycl::local_accessor<int, 1> tile_x_ql_acc_ct1(
+                    sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_x_dm_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / QI6_K) + mmq_y / QI6_K),
+                    cgh);
+                sycl::local_accessor<int, 1> tile_x_sc_acc_ct1(
+                    sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh);
+                sycl::local_accessor<int, 1> tile_y_qs_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE), cgh);
+                sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
+                    sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
+
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
+                        mul_mat_q6_K<need_check>(
+                            vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
+                            nrows_dst, item_ct1,
+                            tile_x_ql_acc_ct1.get_pointer(),
+                            tile_x_dm_acc_ct1.get_pointer(),
+                            tile_x_sc_acc_ct1.get_pointer(),
+                            tile_y_qs_acc_ct1.get_pointer(),
+                            tile_y_ds_acc_ct1.get_pointer());
+                    });
+            });
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_mul_mat_p021_f16_f32_sycl(const void *vx, const float *y,
+                                           float *dst, const int ncols_x,
+                                           const int nrows_x,
+                                           const int nchannels_x,
+                                           const int nchannels_y,
+                                           dpct::queue_ptr stream) {
+
+    const sycl::range<3> block_nums(nchannels_y, nrows_x, 1);
+    const sycl::range<3> block_dims(1, 1, WARP_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+                mul_mat_p021_f16_f32(vx, y, dst, ncols_x, nrows_x, nchannels_x,
+                                     nchannels_y, item_ct1);
+            });
+    }
+}
+
+static void ggml_mul_mat_vec_nc_f16_f32_sycl(
+    const void *vx, const float *y, float *dst, const int ncols_x,
+    const int nrows_x, const int row_stride_x, const int nchannels_x,
+    const int nchannels_y, const int channel_stride_x, dpct::queue_ptr stream) {
+
+    const sycl::range<3> block_nums(nchannels_y, nrows_x, 1);
+    const sycl::range<3> block_dims(1, 1, WARP_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+                mul_mat_vec_nc_f16_f32(vx, y, dst, ncols_x, nrows_x,
+                                       row_stride_x, channel_stride_x,
+                                       nchannels_y / nchannels_x, item_ct1);
+            });
+    }
+}
+
+static void
+ggml_cpy_f16_f32_sycl(const char *cx, char *cdst, const int ne, const int ne00,
+                      const int ne01, const int ne02, const int nb00,
+                      const int nb01, const int nb02, const int nb03,
+                      const int ne10, const int ne11, const int ne12,
+                      const int nb10, const int nb11, const int nb12,
+                      const int nb13, dpct::queue_ptr stream) {
+
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                                  sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                cpy_f32_f16<cpy_1_f16_f32>(cx, cdst, ne, ne00, ne01, ne02, nb00,
+                                           nb01, nb02, nb03, ne10, ne11, ne12,
+                                           nb10, nb11, nb12, nb13, item_ct1);
+            });
+    }
+}
+
+static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne,
+                                  const int ne00, const int ne01,
+                                  const int ne02, const int nb00,
+                                  const int nb01, const int nb02,
+                                  const int nb03, const int ne10,
+                                  const int ne11, const int ne12,
+                                  const int nb10, const int nb11,
+                                  const int nb12, const int nb13,
+                                  dpct::queue_ptr stream) {
+
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                                  sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                cpy_f32_f16<cpy_1_f32_f32>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                           item_ct1);
+            });
+    }
+}
+
+static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne,
+                                  const int ne00, const int ne01,
+                                  const int ne02, const int nb00,
+                                  const int nb01, const int nb02,
+                                  const int nb03, const int ne10,
+                                  const int ne11, const int ne12,
+                                  const int nb10, const int nb11,
+                                  const int nb12, const int nb13,
+                                  dpct::queue_ptr stream) {
+
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                                  sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                cpy_f32_f16<cpy_1_f32_f16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                           item_ct1);
+            });
+    }
+}
+
+static void ggml_cpy_f32_q8_0_sycl(const char *cx, char *cdst, const int ne,
+                                   const int ne00, const int ne01,
+                                   const int ne02, const int nb00,
+                                   const int nb01, const int nb02,
+                                   const int nb03, const int ne10,
+                                   const int ne11, const int ne12,
+                                   const int nb10, const int nb11,
+                                   const int nb12, const int nb13,
+                                   dpct::queue_ptr stream) {
+
+    GGML_ASSERT(ne % QK8_0 == 0);
+    const int num_blocks = ne / QK8_0;
+    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks),
+                                           sycl::range<3>(1, 1, 1)),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>(
+                                 cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                 nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                 item_ct1);
+                         });
+}
+
+static void ggml_cpy_f32_q4_0_sycl(const char *cx, char *cdst, const int ne,
+                                   const int ne00, const int ne01,
+                                   const int ne02, const int nb00,
+                                   const int nb01, const int nb02,
+                                   const int nb03, const int ne10,
+                                   const int ne11, const int ne12,
+                                   const int nb10, const int nb11,
+                                   const int nb12, const int nb13,
+                                   dpct::queue_ptr stream) {
+
+    GGML_ASSERT(ne % QK4_0 == 0);
+    const int num_blocks = ne / QK4_0;
+    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks),
+                                           sycl::range<3>(1, 1, 1)),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>(
+                                 cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                 nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                 item_ct1);
+                         });
+}
+
+static void ggml_cpy_f32_q4_1_sycl(const char *cx, char *cdst, const int ne,
+                                   const int ne00, const int ne01,
+                                   const int ne02, const int nb00,
+                                   const int nb01, const int nb02,
+                                   const int nb03, const int ne10,
+                                   const int ne11, const int ne12,
+                                   const int nb10, const int nb11,
+                                   const int nb12, const int nb13,
+                                   dpct::queue_ptr stream) {
+
+    GGML_ASSERT(ne % QK4_1 == 0);
+    const int num_blocks = ne / QK4_1;
+    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks),
+                                           sycl::range<3>(1, 1, 1)),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             cpy_f32_q<cpy_blck_f32_q4_1, QK4_1>(
+                                 cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                 nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                 item_ct1);
+                         });
+}
+
+static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne,
+                                  const int ne00, const int ne01,
+                                  const int ne02, const int nb00,
+                                  const int nb01, const int nb02,
+                                  const int nb03, const int ne10,
+                                  const int ne11, const int ne12,
+                                  const int nb10, const int nb11,
+                                  const int nb12, const int nb13,
+                                  dpct::queue_ptr stream) {
+
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                                  sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                cpy_f32_f16<cpy_1_f16_f16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                           item_ct1);
+            });
+    }
+}
+
+static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne,
+                                  const int ne00, const int ne01,
+                                  const int ne02, const int nb00,
+                                  const int nb01, const int nb02,
+                                  const int nb03, const int ne10,
+                                  const int ne11, const int ne12,
+                                  const int nb10, const int nb11,
+                                  const int nb12, const int nb13,
+                                  dpct::queue_ptr stream) {
+
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    {
+        // dpct::has_capability_or_fail(stream->get_device(),
+        //                              {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                                  sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                cpy_f32_f16<cpy_1_i16_i16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                           item_ct1);
+            });
+    }
+}
+
+static void ggml_cpy_i32_i32_sycl(const char *cx, char *cdst, const int ne,
+                                  const int ne00, const int ne01,
+                                  const int ne02, const int nb00,
+                                  const int nb01, const int nb02,
+                                  const int nb03, const int ne10,
+                                  const int ne11, const int ne12,
+                                  const int nb10, const int nb11,
+                                  const int nb12, const int nb13,
+                                  dpct::queue_ptr stream) {
+
+    const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE;
+    {
+        // dpct::has_capability_or_fail(stream->get_device(),
+        //                              {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                                  sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                cpy_f32_f16<cpy_1_i32_i32>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
+                                           nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
+                                           item_ct1);
+            });
+    }
+}
+
+static void scale_f32_sycl(const float *x, float *dst, const float scale,
+                           const int k, dpct::queue_ptr stream) {
+    const int num_blocks = (k + SYCL_SCALE_BLOCK_SIZE - 1) / SYCL_SCALE_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_SCALE_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_SCALE_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            scale_f32(x, dst, scale, k, item_ct1);
+        });
+}
+
+static void clamp_f32_sycl(const float *x, float *dst, const float min,
+                           const float max, const int k,
+                           dpct::queue_ptr stream) {
+    const int num_blocks = (k + SYCL_CLAMP_BLOCK_SIZE - 1) / SYCL_CLAMP_BLOCK_SIZE;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_CLAMP_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_CLAMP_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            clamp_f32(x, dst, min, max, k, item_ct1);
+        });
+}
+
+template <typename T>
+static void rope_sycl(const T *x, T *dst, int ncols, int nrows,
+                      const int32_t *pos, float freq_scale, int p_delta_rows,
+                      float freq_base, float ext_factor, float attn_factor,
+                      rope_corr_dims corr_dims, dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % 2 == 0);
+    const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1);
+    const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
+    const sycl::range<3> block_nums(1, num_blocks_x, nrows);
+    if (pos == nullptr) {
+        /*
+        DPCT1049:40: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
+                rope<T, false>(x, dst, ncols, pos, freq_scale, p_delta_rows,
+                               freq_base, ext_factor, attn_factor, corr_dims,
+                               item_ct1);
+            });
+    } else {
+        /*
+        DPCT1049:41: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
+                rope<T, true>(x, dst, ncols, pos, freq_scale, p_delta_rows,
+                              freq_base, ext_factor, attn_factor, corr_dims,
+                              item_ct1);
+            });
+    }
+}
+
+template <typename T>
+static void rope_neox_sycl(const T *x, T *dst, int ncols, int n_dims, int nrows,
+                           const int32_t *pos, float freq_scale,
+                           int p_delta_rows, float freq_base, float ext_factor,
+                           float attn_factor, rope_corr_dims corr_dims,
+                           dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % 2 == 0);
+    const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1);
+    const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
+    const sycl::range<3> block_nums(1, num_blocks_x, nrows);
+
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+    const float inv_ndims = -1.0f / n_dims;
+
+    if (pos == nullptr) {
+        /*
+        DPCT1049:42: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
+                rope_neox<T, false>(x, dst, ncols, n_dims, pos, freq_scale,
+                                    p_delta_rows, ext_factor, attn_factor,
+                                    corr_dims, theta_scale, inv_ndims,
+                                    item_ct1);
+            });
+    } else {
+        /*
+        DPCT1049:43: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
+                rope_neox<T, true>(x, dst, ncols, n_dims, pos, freq_scale,
+                                   p_delta_rows, ext_factor, attn_factor,
+                                   corr_dims, theta_scale, inv_ndims, item_ct1);
+            });
+    }
+}
+
+static void rope_glm_f32_sycl(const float *x, float *dst, int ncols, int nrows,
+                              const int32_t *pos, float freq_scale,
+                              int p_delta_rows, float freq_base, int n_ctx,
+                              dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % 4 == 0);
+    const sycl::range<3> block_dims(1, 1, SYCL_ROPE_BLOCK_SIZE / 4);
+    const int num_blocks_x = (ncols + SYCL_ROPE_BLOCK_SIZE - 1) / SYCL_ROPE_BLOCK_SIZE;
+    const sycl::range<3> block_nums(1, nrows, num_blocks_x);
+    stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             rope_glm_f32(x, dst, ncols, pos, freq_scale,
+                                          p_delta_rows, freq_base, n_ctx,
+                                          item_ct1);
+                         });
+}
+
+static void alibi_f32_sycl(const float *x, float *dst, const int ncols,
+                           const int nrows, const int k_rows,
+                           const int n_heads_log2_floor, const float m0,
+                           const float m1, dpct::queue_ptr stream) {
+    const sycl::range<3> block_dims(1, 1, SYCL_ALIBI_BLOCK_SIZE);
+    const int num_blocks_x = (ncols + SYCL_ALIBI_BLOCK_SIZE - 1) / (SYCL_ALIBI_BLOCK_SIZE);
+    const sycl::range<3> block_nums(1, nrows, num_blocks_x);
+    stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             alibi_f32(x, dst, ncols, k_rows,
+                                       n_heads_log2_floor, m0, m1, item_ct1);
+                         });
+}
+
+static void sum_rows_f32_sycl(const float *x, float *dst, const int ncols,
+                              const int nrows, dpct::queue_ptr stream) {
+    const sycl::range<3> block_dims(1, 1, WARP_SIZE);
+    const sycl::range<3> block_nums(1, nrows, 1);
+    stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                         [=](sycl::nd_item<3> item_ct1)
+                             [[intel::reqd_sub_group_size(32)]] {
+                                 k_sum_rows_f32(x, dst, ncols, item_ct1);
+                             });
+}
+
+static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols,
+                                 const int nrows, ggml_sort_order order,
+                                 dpct::queue_ptr stream) {
+    // bitonic sort requires ncols to be power of 2
+    GGML_ASSERT((ncols & (ncols - 1)) == 0);
+
+    const sycl::range<3> block_dims(1, 1, ncols);
+    const sycl::range<3> block_nums(1, nrows, 1);
+    if (order == GGML_SORT_ORDER_ASC) {
+        /*
+        DPCT1049:44: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
+                k_argsort_f32_i32<GGML_SORT_ORDER_ASC>(x, dst, ncols, item_ct1);
+            });
+    } else if (order == GGML_SORT_ORDER_DESC) {
+        /*
+        DPCT1049:45: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
+                k_argsort_f32_i32<GGML_SORT_ORDER_DESC>(x, dst, ncols, item_ct1);
+            });
+    } else {
+        GGML_ASSERT(false);
+    }
+}
+
+static void diag_mask_inf_f32_sycl(const float *x, float *dst,
+                                   const int ncols_x, const int nrows_x,
+                                   const int rows_per_channel, const int n_past,
+                                   dpct::queue_ptr stream) {
+    const sycl::range<3> block_dims(1, SYCL_DIAG_MASK_INF_BLOCK_SIZE, 1);
+    const int block_num_x = (ncols_x + SYCL_DIAG_MASK_INF_BLOCK_SIZE - 1) / SYCL_DIAG_MASK_INF_BLOCK_SIZE;
+    const sycl::range<3> block_nums(1, block_num_x, nrows_x);
+    stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             diag_mask_inf_f32(x, dst, ncols_x,
+                                               rows_per_channel, n_past,
+                                               item_ct1);
+                         });
+}
+
+template <bool vals_smem, int ncols_template, int block_size_template>
+static void soft_max_f32_submitter(const float * x, const float * mask, const float *pos, float * dst, const int ncols_par,
+                                   const int nrows_y, const float scale, const float max_bias, const float m0,
+                                   const float m1, uint32_t n_head_log2, sycl::range<3> block_nums, sycl::range<3> block_dims,
+                                   const size_t n_local_scratch, dpct::queue_ptr stream) {
+    stream->submit([&](sycl::handler &cgh) {
+        sycl::local_accessor<float, 1> local_buf_acc(n_local_scratch, cgh);
+
+        cgh.parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+                soft_max_f32<vals_smem, ncols_template, block_size_template>(x, mask, pos, dst, ncols_par,
+                                                                             nrows_y, scale, max_bias, m0,
+                                                                             m1, n_head_log2, item_ct1,
+                                                                             local_buf_acc.get_pointer());
+            });
+    });
+}
+
+static void soft_max_f32_sycl(const float * x, const float * mask, const float * pos,
+                              float * dst, const int ncols_x, const int nrows_x,
+                              const int nrows_y, const float scale, const float max_bias,
+                              dpct::queue_ptr stream) {
+    int nth = WARP_SIZE;
+    while (nth < ncols_x && nth < SYCL_SOFT_MAX_BLOCK_SIZE) nth *= 2;
+    const sycl::range<3> block_dims(1, 1, nth);
+    const sycl::range<3> block_nums(1, 1, nrows_x);
+    const size_t n_local_scratch = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE);
+    static_assert(SYCL_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted.");
+
+    const uint32_t n_head_kv   = nrows_x/nrows_y;
+    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
+
+    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+    const size_t local_mem_size = stream->get_device().get_info<sycl::info::device::local_mem_size>();
+    if (n_local_scratch*sizeof(float) < local_mem_size) {
+        switch (ncols_x) {
+            case 32:
+                soft_max_f32_submitter<true, 32, 32>(x, mask, pos, dst, ncols_x, nrows_y, scale,
+                                                     max_bias, m0, m1, n_head_log2, block_nums,
+                                                     block_dims, n_local_scratch, stream);
+                break;
+            case 64:
+                soft_max_f32_submitter<true, 64, 64>(x, mask, pos, dst, ncols_x, nrows_y, scale,
+                                                     max_bias, m0, m1, n_head_log2, block_nums,
+                                                     block_dims, n_local_scratch, stream);
+                break;
+            case 128:
+                soft_max_f32_submitter<true, 128, 128>(x, mask, pos, dst, ncols_x, nrows_y, scale,
+                                                       max_bias, m0, m1, n_head_log2, block_nums,
+                                                       block_dims, n_local_scratch, stream);
+                break;
+            case 256:
+                soft_max_f32_submitter<true, 256, 256>(x, mask, pos, dst, ncols_x, nrows_y, scale,
+                                                       max_bias, m0, m1, n_head_log2, block_nums,
+                                                       block_dims, n_local_scratch, stream);
+                break;
+            case 512:
+                soft_max_f32_submitter<true, 512, 512>(x, mask, pos, dst, ncols_x, nrows_y, scale,
+                                                       max_bias, m0, m1, n_head_log2, block_nums,
+                                                       block_dims, n_local_scratch, stream);
+                break;
+            case 1024:
+                soft_max_f32_submitter<true, 1024, 1024>(x, mask, pos, dst, ncols_x, nrows_y, scale,
+                                                         max_bias, m0, m1, n_head_log2, block_nums,
+                                                         block_dims, n_local_scratch, stream);
+                break;
+            case 2048:
+                soft_max_f32_submitter<true, 2048, 1024>(x, mask, pos, dst, ncols_x, nrows_y, scale,
+                                                         max_bias, m0, m1, n_head_log2, block_nums,
+                                                         block_dims, n_local_scratch, stream);
+                break;
+            case 4096:
+                soft_max_f32_submitter<true, 4096, 1024>(x, mask, pos, dst, ncols_x, nrows_y, scale,
+                                                         max_bias, m0, m1, n_head_log2, block_nums,
+                                                         block_dims, n_local_scratch, stream);
+                break;
+            default:
+                soft_max_f32_submitter<true, 0, 0>(x, mask, pos, dst, ncols_x, nrows_y, scale,
+                                                   max_bias, m0, m1, n_head_log2, block_nums,
+                                                   block_dims, n_local_scratch, stream);
+                break;
+        }
+    } else {
+        soft_max_f32_submitter<false, 0, 0>(x, mask, pos, dst, ncols_x, nrows_y, scale,
+                                            max_bias, m0, m1, n_head_log2, block_nums,
+                                            block_dims, WARP_SIZE, stream);
+    }
+}
+
+template <typename T>
+static void im2col_sycl(const float *x, T *dst, int IW, int IH,
+                                int OW, int OH, int KW, int KH, int IC,
+                                int offset_delta, int s0, int s1, int p0,
+                                int p1, int d0, int d1,
+                                dpct::queue_ptr stream) {
+    const int parallel_elements = OW * KW * KH;
+    const int num_blocks = (parallel_elements + SYCL_IM2COL_BLOCK_SIZE - 1) / SYCL_IM2COL_BLOCK_SIZE;
+    sycl::range<3> block_nums(IC, OH, num_blocks);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums *
+                                  sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                im2col_kernel(x, dst, offset_delta, IW, IH, OW, KW, KH,
+                               parallel_elements, (IC * KH * KW), s0, s1, p0,
+                               p1, d0, d1, item_ct1);
+            });
+    }
+}
+
+// buffer pool for sycl
+#define MAX_SYCL_BUFFERS 256
+
+struct scoped_spin_lock {
+    std::atomic_flag& lock;
+    scoped_spin_lock(std::atomic_flag& lock) : lock(lock) {
+        while (lock.test_and_set(std::memory_order_acquire)) {
+            ; // spin
+        }
+    }
+    ~scoped_spin_lock() {
+        lock.clear(std::memory_order_release);
+    }
+    scoped_spin_lock(const scoped_spin_lock&) = delete;
+    scoped_spin_lock& operator=(const scoped_spin_lock&) = delete;
+};
+
+static std::atomic_flag g_sycl_pool_lock = ATOMIC_FLAG_INIT;
+
+// #define DEBUG_SYCL_MALLOC
+struct sycl_buffer {
+    void * ptr = nullptr;
+    size_t size = 0;
+};
+
+static sycl_buffer g_sycl_buffer_pool[GGML_SYCL_MAX_DEVICES][MAX_SYCL_BUFFERS];
+static size_t g_sycl_pool_size[GGML_SYCL_MAX_DEVICES] = {0};
+
+static void *ggml_sycl_pool_malloc_leg(int device_index, size_t size, size_t *actual_size) try {
+    scoped_spin_lock lock(g_sycl_pool_lock);
+    // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg device_index %d size=%lu\n", device_index, size);
+#ifdef DEBUG_SYCL_MALLOC
+    int nnz = 0;
+    size_t max_size = 0;
+#endif
+    size_t best_diff = 1ull << 36;
+    int ibest = -1;
+    for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) {
+        sycl_buffer& b = g_sycl_buffer_pool[device_index][i];
+        if (b.ptr != nullptr) {
+#ifdef DEBUG_SYCL_MALLOC
+            ++nnz;
+            if (b.size > max_size) max_size = b.size;
+#endif
+            if (b.size >= size) {
+                size_t diff = b.size - size;
+                if (diff < best_diff) {
+                    best_diff = diff;
+                    ibest = i;
+                    if (!best_diff) {
+                        void * ptr = b.ptr;
+                        *actual_size = b.size;
+                        b.ptr = nullptr;
+                        b.size = 0;
+                        // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 1 %p and rm in pool\n", ptr);
+                        return ptr;
+                    }
+                }
+            }
+        }
+    }
+    if (ibest >= 0) {
+        sycl_buffer& b = g_sycl_buffer_pool[device_index][ibest];
+        void * ptr = b.ptr;
+        *actual_size = b.size;
+        b.ptr = nullptr;
+        b.size = 0;
+        // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 2 %p and rm in pool\n", ptr);
+        return ptr;
+    }
+    void * ptr;
+    size_t look_ahead_size = (size_t) (1.05 * size);
+    look_ahead_size = 256 * ((look_ahead_size + 255)/256);
+
+    const dpct::queue_ptr stream = g_syclStreams[device_index][0];
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(ptr = (void *)sycl::malloc_device(
+                             look_ahead_size, *stream)));
+    *actual_size = look_ahead_size;
+    g_sycl_pool_size[device_index] += look_ahead_size;
+
+#ifdef DEBUG_SYCL_MALLOC
+    fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz,
+            (uint32_t)(max_size/1024/1024), (uint32_t)(g_sycl_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024));
+#endif
+    // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg look_ahead_size=%lu, return %p\n", look_ahead_size, ptr);
+    return ptr;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_sycl_pool_free_leg(int device_index, void *ptr, size_t size) try {
+    scoped_spin_lock lock(g_sycl_pool_lock);
+    const dpct::queue_ptr stream = g_syclStreams[device_index][0];
+    for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) {
+        sycl_buffer& b = g_sycl_buffer_pool[device_index][i];
+        if (b.ptr == nullptr) {
+            b.ptr = ptr;
+            b.size = size;
+            return;
+        }
+    }
+    fprintf(stderr, "WARNING: sycl buffer pool full, increase MAX_SYCL_BUFFERS\n");
+    SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *stream)));
+    g_sycl_pool_size[device_index] -= size;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+// pool with virtual memory
+/*
+DPCT1082:64: Migration of CUmemGenericAllocationHandle type is not supported.
+*/
+// static std::vector<CUmemGenericAllocationHandle>
+//     g_sycl_pool_handles[GGML_SYCL_MAX_DEVICES];
+static dpct::device_ptr g_sycl_pool_addr[GGML_SYCL_MAX_DEVICES] = {0};
+static size_t g_sycl_pool_used[GGML_SYCL_MAX_DEVICES] = {0};
+
+static void *ggml_sycl_pool_malloc_vmm(int device_index, size_t size, size_t *actual_size) try {
+    GGML_UNUSED(device_index);
+    GGML_UNUSED(size);
+    GGML_UNUSED(actual_size);
+    return NULL;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_sycl_pool_free_vmm(int device_index, void *ptr, size_t size) try {
+    scoped_spin_lock lock(g_sycl_pool_lock);
+#ifdef DEBUG_SYCL_MALLOC
+    printf("sycl pool[%d]: freed %llu bytes at %llx\n", device_index, (unsigned long long) size, ptr);
+#endif
+
+    g_sycl_pool_used[device_index] -= size;
+
+    // all deallocations must be in reverse order of the allocations
+    GGML_ASSERT(ptr == (void *) (g_sycl_pool_addr[device_index] + g_sycl_pool_used[device_index]));
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void *ggml_sycl_pool_malloc(int device_index, size_t size, size_t *actual_size) try {
+    if (g_device_caps[device_index].vmm) {
+        return ggml_sycl_pool_malloc_vmm(device_index, size, actual_size);
+    } else {
+        return ggml_sycl_pool_malloc_leg(device_index, size, actual_size);
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_sycl_pool_free(int device_index, void *ptr, size_t size) try {
+    if (g_device_caps[device_index].vmm) {
+        ggml_sycl_pool_free_vmm(device_index, ptr, size);
+    } else {
+        ggml_sycl_pool_free_leg(device_index, ptr, size);
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+
+template<typename T>
+struct sycl_pool_alloc {
+    int device_index = -1;
+    int device_id = -1;
+    T * ptr = nullptr;
+    size_t actual_size = 0;
+
+    // size is in number of elements
+    T * alloc(size_t size) {
+        GGML_ASSERT(ptr == nullptr);
+        device_id = get_current_device_id();
+        device_index = g_sycl_gpu_mgr->get_index(device_id);
+        ptr = (T *) ggml_sycl_pool_malloc(device_index, size * sizeof(T), &this->actual_size);
+        // GGML_SYCL_DEBUG("sycl_pool_alloc %lu return %p actual size=%lu\n", size * sizeof(T), ptr, this->actual_size);
+        return ptr;
+    }
+
+    sycl_pool_alloc(size_t size) {
+        alloc(size);
+    }
+
+    ~sycl_pool_alloc() {
+        if (ptr != nullptr) {
+            ggml_sycl_pool_free(device_index, ptr, actual_size);
+        }
+    }
+
+    T * get() {
+        return ptr;
+    }
+
+    sycl_pool_alloc() = default;
+    sycl_pool_alloc(const sycl_pool_alloc &) = delete;
+    sycl_pool_alloc(sycl_pool_alloc &&) = delete;
+    sycl_pool_alloc& operator=(const sycl_pool_alloc &) = delete;
+    sycl_pool_alloc& operator=(sycl_pool_alloc &&) = delete;
+};
+
+static bool g_sycl_loaded = false;
+
+bool ggml_sycl_loaded(void) {
+    return g_sycl_loaded;
+}
+
+void print_device_detail(int id, sycl::device &device, std::string device_type) {
+
+    dpct::device_info prop;
+    SYCL_CHECK(CHECK_TRY_ERROR(
+        dpct::get_device_info(prop, device)));
+
+    std::string version;
+    version += std::to_string(prop.get_major_version());
+    version += ".";
+    version += std::to_string(prop.get_minor_version());
+
+    device_type = std::regex_replace(device_type, std::regex("ext_oneapi_"), "");
+
+    fprintf(stderr, "|%2d|%18s|%45s|%10s|%11d|%8d|%7d|%15lu|\n", id, device_type.c_str(),
+            prop.get_name(), version.c_str(), prop.get_max_compute_units(),
+            prop.get_max_work_group_size(), prop.get_max_sub_group_size(),
+            prop.get_global_mem_size());
+}
+
+void ggml_backend_sycl_print_sycl_devices() {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_print_sycl_devices\n");
+    int device_count = dpct::dev_mgr::instance().device_count();
+    std::map<std::string, size_t> DeviceNums;
+    fprintf(stderr, "found %d SYCL devices:\n", device_count);
+    fprintf(stderr, "|  |                  |                                             |Compute   |Max compute|Max work|Max sub|               |\n");
+    fprintf(stderr, "|ID|       Device Type|                                         Name|capability|units      |group   |group  |Global mem size|\n");
+    fprintf(stderr, "|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|\n");
+    for (int id = 0; id < device_count; ++id) {
+        sycl::device device = dpct::dev_mgr::instance().get_device(id);
+        sycl::backend backend = device.get_backend();
+        std::string backend_type = get_device_backend_and_type(device);
+        int type_id=DeviceNums[backend_type]++;
+        std::stringstream device_type;
+        device_type << "[" <<  backend_type << ":" << std::to_string(type_id) << "]";
+        print_device_detail(id, device, device_type.str());
+    }
+}
+
+void print_gpu_device_list() {
+    GGML_ASSERT(g_sycl_gpu_mgr);
+
+    char* hint=NULL;
+    if (g_ggml_sycl_backend_gpu_mode == SYCL_SINGLE_GPU_MODE) {
+        hint = "use %d SYCL GPUs: [%s] with Max compute units:%d\n";
+    } else {
+        hint = "detect %d SYCL GPUs: [%s] with top Max compute units:%d\n";
+    }
+    fprintf(stderr, hint,
+        g_sycl_gpu_mgr->get_gpu_count(),
+        g_sycl_gpu_mgr->gpus_list.c_str(),
+        g_sycl_gpu_mgr->max_compute_units);
+}
+
+int get_sycl_env(const char *env_name, int default_val) {
+    char *user_device_string = getenv(env_name);
+    int user_number = default_val;
+
+    unsigned n;
+    if (user_device_string != NULL &&
+        sscanf(user_device_string, " %u", &n) == 1) {
+        user_number = (int)n;
+    } else {
+        user_number = default_val;
+    }
+    return user_number;
+}
+
+int get_work_group_size(int user_device_id) {
+    dpct::device_info prop;
+    dpct::get_device_info(prop,
+                          dpct::dev_mgr::instance().get_device(user_device_id));
+    return prop.get_max_work_group_size();
+}
+
+static void ggml_init_sycl() try {
+    static bool initialized = false;
+
+    if (!initialized) {
+        fprintf(stderr, "[SYCL] call ggml_init_sycl\n");
+        g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0);
+
+        fprintf(stderr, "%s: GGML_SYCL_DEBUG: %d\n", __func__, g_ggml_sycl_debug);
+
+#if defined(GGML_SYCL_F16)
+        fprintf(stderr, "%s: GGML_SYCL_F16: yes\n", __func__);
+#else
+        fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__);
+#endif
+
+/* NOT REMOVE, keep it for next optimize for XMX.
+#if defined(SYCL_USE_XMX)
+        fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__);
+#else
+        fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__);
+#endif
+*/
+
+        if (CHECK_TRY_ERROR(g_all_sycl_device_count =
+                            dpct::dev_mgr::instance().device_count()) != 0) {
+            initialized = true;
+            g_sycl_loaded = false;
+            return;
+        }
+        GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES);
+        ggml_backend_sycl_print_sycl_devices();
+        initialized = true;
+        g_sycl_loaded = true;
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+void ggml_init_by_gpus(int device_count) try {
+    g_device_count = device_count;
+    g_work_group_size = g_sycl_gpu_mgr->work_group_size;
+
+    int64_t total_vram = 0;
+
+    print_gpu_device_list();
+
+    for (int id = 0; id < GGML_SYCL_MAX_DEVICES; ++id) {
+        g_device_caps[id].vmm = 0;
+        g_device_caps[id].device_id = -1;
+        g_device_caps[id].cc = 0;
+        g_tensor_split[id] = 0;
+        g_default_tensor_split[id] = 0;
+    }
+
+    for (int i = 0; i < g_device_count; ++i) {
+        int device_id = g_sycl_gpu_mgr->gpus[i];
+        g_device_caps[i].vmm = 0;
+
+        dpct::device_info prop;
+        SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info(
+            prop, dpct::dev_mgr::instance().get_device(device_id))));
+
+        g_default_tensor_split[i] = total_vram;
+        total_vram += prop.get_global_mem_size();
+
+        g_device_caps[i].cc =
+            100 * prop.get_major_version() + 10 * prop.get_minor_version();
+    }
+
+    for (int i = 0; i < g_device_count; ++i) {
+        g_default_tensor_split[i] /= total_vram;
+    }
+
+    for (int i = 0; i < g_device_count; ++i) {
+        SYCL_CHECK(ggml_sycl_set_device(i));
+
+        // create sycl streams
+        for (int is = 0; is < MAX_STREAMS; ++is) {
+            SYCL_CHECK(CHECK_TRY_ERROR(
+                g_syclStreams[i][is] =
+                    dpct::get_current_device().create_queue(
+                        g_sycl_gpu_mgr->get_co_ctx(), dpct::get_current_device())));
+        }
+
+        const dpct::queue_ptr stream = g_syclStreams[i][0];
+        // create sycl handle
+        SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[i] = stream));
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+void *ggml_sycl_host_malloc(size_t size) try {
+    if (getenv("GGML_SYCL_NO_PINNED") != nullptr) {
+        return nullptr;
+    }
+
+    void * ptr = nullptr;
+    //allow to use dpct::get_in_order_queue() for host malloc
+    dpct::err0 err = CHECK_TRY_ERROR(
+        ptr = (void *)sycl::malloc_host(size, dpct::get_in_order_queue()));
+
+    if (err != 0) {
+        // clear the error
+        fprintf(
+            stderr,
+            "WARNING: failed to allocate %.2f MB of pinned memory: %s\n",
+            size / 1024.0 / 1024.0,
+            "syclGetErrorString is not supported");
+        return nullptr;
+    }
+
+    return ptr;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+void ggml_sycl_host_free(void *ptr) try {
+    //allow to use dpct::get_in_order_queue() for host malloc
+    SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, dpct::get_in_order_queue())));
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst,
+                                          const struct ggml_tensor *src,
+                                          int64_t i3, int64_t i2,
+                                          int64_t i1_low, int64_t i1_high,
+                                          dpct::queue_ptr stream) try {
+
+    dpct::memcpy_direction kind;
+    char * src_ptr;
+    if (src->backend == GGML_BACKEND_TYPE_CPU) {
+        kind = dpct::host_to_device;
+        src_ptr = (char *) src->data;
+        // GGML_SYCL_DEBUG("ggml_sycl_cpy_tensor_2d  GGML_BACKEND_TYPE_CPU src_ptr %p\n", src_ptr);
+    } else if (src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
+        GGML_ASSERT(src->backend != GGML_BACKEND_TYPE_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1]));
+        kind = dpct::device_to_device;
+        ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra;
+        int id;
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            id = get_current_device_id()));
+        // GGML_SYCL_DEBUG("current device index %d\n", id);
+        src_ptr = (char *) extra->data_device[id];
+    } else {
+        // GGML_SYCL_DEBUG("GGML_ASSERT(false)\n");
+        GGML_ASSERT(false);
+    }
+    char * dst_ptr = (char *) dst;
+
+    GGML_TENSOR_LOCALS_1(int64_t, ne, src, ne);
+    GGML_TENSOR_LOCALS(int64_t, nb, src, nb);
+    const enum ggml_type type = src->type;
+    const int64_t ts = ggml_type_size(type);
+    const int64_t bs = ggml_blck_size(type);
+    int64_t i1_diff = i1_high - i1_low;
+
+    const char * x = src_ptr + i1_low*nb1 + i2*nb2 + i3*nb3;
+    if (nb0 == ts && nb1 == ts*ne0/bs) {
+        // GGML_SYCL_DEBUG("stream->memcpy: dst_ptr=%p, x=%p, size=%lu\n", dst_ptr, x, i1_diff * nb1);
+        // return CHECK_TRY_ERROR(stream->memcpy(dst_ptr, x, i1_diff * nb1));
+        return CHECK_TRY_ERROR(dpct::async_dpct_memcpy(dst_ptr, x, i1_diff * nb1,
+                                    kind, *stream));
+
+    } else if (nb0 == ts) {
+        return CHECK_TRY_ERROR(
+            dpct::async_dpct_memcpy(dst_ptr, ts * ne0 / bs, x, nb1,
+                                    ts * ne0 / bs, i1_diff, kind, *stream));
+    } else {
+        for (int64_t i1 = 0; i1 < i1_diff; i1++) {
+            const void * rx = (const void *) ((const char *) x + i1*nb1);
+            void * rd = (void *) (dst_ptr + i1*ts*ne0/bs);
+            // pretend the row is a matrix with cols=1
+            dpct::err0 r = CHECK_TRY_ERROR(dpct::async_dpct_memcpy(
+                rd, ts / bs, rx, nb0, ts / bs, ne0, kind, *stream));
+            /*
+            DPCT1001:85: The statement could not be removed.
+            */
+            /*
+            DPCT1000:86: Error handling if-stmt was detected but could not be
+            rewritten.
+            */
+            if (r != 0) return r;
+        }
+        return 0;
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_sycl_op_get_rows(const ggml_tensor *src0,
+                                  const ggml_tensor *src1, ggml_tensor *dst,
+                                  const float *src0_d, const float *src1_d,
+                                  float *dst_d, const dpct::queue_ptr &stream) {
+
+    GGML_ASSERT(src1->type == GGML_TYPE_I32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
+    GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type));
+    GGML_ASSERT(dst->nb[0] == ggml_type_size(dst->type));
+
+    const int32_t * src1_i32 = (const int32_t *) src1_d;
+
+    switch (src0->type) {
+        case GGML_TYPE_F16:
+            get_rows_sycl_float(src0, src1, dst, (const sycl::half *)src0_d,
+                                src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_F32:
+            get_rows_sycl_float(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q4_0:
+            get_rows_sycl<QK4_0, QR4_0, dequantize_q4_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            get_rows_sycl<QK4_1, QR4_1, dequantize_q4_1>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            get_rows_sycl<QK5_0, QR5_0, dequantize_q5_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            get_rows_sycl<QK5_1, QR5_1, dequantize_q5_1>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            get_rows_sycl<QK8_0, QR8_0, dequantize_q8_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
+            break;
+        default:
+            // TODO: k-quants
+            fprintf(stderr, "%s: unsupported type: %s\n", __func__, ggml_type_name(src0->type));
+            GGML_ASSERT(false);
+            break;
+    }
+}
+
+template <class op>
+inline void ggml_sycl_op_bin_bcast(const ggml_tensor *src0,
+                                   const ggml_tensor *src1, ggml_tensor *dst,
+                                   const float *src0_dd, const float *src1_dd,
+                                   float *dst_dd,
+                                   const dpct::queue_ptr &main_stream) {
+
+    if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+        op()(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
+        op()(src0, src1, dst, (const sycl::half *)src0_dd, src1_dd,
+             (sycl::half *)dst_dd, main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
+        op()(src0, src1, dst, (const sycl::half *)src0_dd, src1_dd, dst_dd,
+             main_stream);
+    } else if (src0->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_I32) {
+        op()(src0, src1, dst, (const int32_t *)src0_dd, (const int32_t *)src1_dd, (int32_t *)dst_dd,
+             main_stream);
+    } else if (src0->type == GGML_TYPE_I16 && dst->type == GGML_TYPE_I16) {
+        op()(src0, src1, dst, (const int16_t *)src0_dd, (const int16_t *)src1_dd, (int16_t *)dst_dd,
+             main_stream);
+    } else {
+        fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__,
+            ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type));
+        GGML_ASSERT(false);
+    }
+}
+
+static void ggml_sycl_op_repeat(const ggml_tensor *src0,
+                                const ggml_tensor *src1, ggml_tensor *dst,
+                                const float *src0_d, const float *src1_d,
+                                float *dst_d,
+                                const dpct::queue_ptr &main_stream) {
+
+    ggml_sycl_op_bin_bcast<bin_bcast_sycl<op_repeat>>(dst, src0, dst, nullptr, src0_d, dst_d, main_stream);
+
+    (void) src1;
+    (void) src1_d;
+}
+
+inline void ggml_sycl_op_add(const ggml_tensor *src0, const ggml_tensor *src1,
+                             ggml_tensor *dst, const float *src0_dd,
+                             const float *src1_dd, float *dst_dd,
+                             const dpct::queue_ptr &main_stream) {
+
+    ggml_sycl_op_bin_bcast<bin_bcast_sycl<op_add>>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
+}
+
+inline void ggml_sycl_op_acc(const ggml_tensor *src0, const ggml_tensor *src1,
+                             ggml_tensor *dst, const float *src0_dd,
+                             const float *src1_dd, float *dst_dd,
+                             const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported
+
+    int nb1 = dst->op_params[0] / 4; // 4 bytes of float32
+    int nb2 = dst->op_params[1] / 4; // 4 bytes of float32
+    // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused
+    int offset = dst->op_params[3] / 4; // offset in bytes
+
+    acc_f32_sycl(src0_dd, src1_dd, dst_dd, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, main_stream);
+
+    (void) dst;
+}
+
+inline void ggml_sycl_op_mul(const ggml_tensor *src0, const ggml_tensor *src1,
+                             ggml_tensor *dst, const float *src0_dd,
+                             const float *src1_dd, float *dst_dd,
+                             const dpct::queue_ptr &main_stream) {
+
+    ggml_sycl_op_bin_bcast<bin_bcast_sycl<op_mul>>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
+}
+
+inline void ggml_sycl_op_div(const ggml_tensor *src0, const ggml_tensor *src1,
+                             ggml_tensor *dst, const float *src0_dd,
+                             const float *src1_dd, float *dst_dd,
+                             const dpct::queue_ptr &main_stream) {
+
+    ggml_sycl_op_bin_bcast<bin_bcast_sycl<op_div>>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
+}
+
+inline void ggml_sycl_op_gelu(const ggml_tensor *src0, const ggml_tensor *src1,
+                              ggml_tensor *dst, const float *src0_dd,
+                              const float *src1_dd, float *dst_dd,
+                              const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    gelu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_silu(const ggml_tensor *src0, const ggml_tensor *src1,
+                              ggml_tensor *dst, const float *src0_dd,
+                              const float *src1_dd, float *dst_dd,
+                              const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    silu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_gelu_quick(const ggml_tensor *src0,
+                                    const ggml_tensor *src1, ggml_tensor *dst,
+                                    const float *src0_dd, const float *src1_dd,
+                                    float *dst_dd,
+                                    const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    gelu_quick_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_tanh(const ggml_tensor *src0, const ggml_tensor *src1,
+                              ggml_tensor *dst, const float *src0_dd,
+                              const float *src1_dd, float *dst_dd,
+                              const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+    tanh_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_relu(const ggml_tensor *src0, const ggml_tensor *src1,
+                              ggml_tensor *dst, const float *src0_dd,
+                              const float *src1_dd, float *dst_dd,
+                              const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+static void ggml_sycl_op_hardsigmoid(const ggml_tensor *src0,
+                                     const ggml_tensor *src1, ggml_tensor *dst,
+                                     const float *src0_dd, const float *src1_dd,
+                                     float *dst_dd,
+                                     const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    hardsigmoid_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+static void ggml_sycl_op_hardswish(const ggml_tensor *src0,
+                                   const ggml_tensor *src1, ggml_tensor *dst,
+                                   const float *src0_dd, const float *src1_dd,
+                                   float *dst_dd, const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    hardswish_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_leaky_relu(const ggml_tensor *src0,
+                                    const ggml_tensor *src1, ggml_tensor *dst,
+                                    const float *src0_dd, const float *src1_dd,
+                                    float *dst_dd,
+                                    const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    float negative_slope;
+    memcpy(&negative_slope, dst->op_params, sizeof(float));
+
+    leaky_relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), negative_slope, main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_sqr(const ggml_tensor *src0, const ggml_tensor *src1,
+                             ggml_tensor *dst, const float *src0_dd,
+                             const float *src1_dd, float *dst_dd,
+                             const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    sqr_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_norm(const ggml_tensor *src0, const ggml_tensor *src1,
+                              ggml_tensor *dst, const float *src0_dd,
+                              const float *src1_dd, float *dst_dd,
+                              const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_group_norm(const ggml_tensor *src0,
+                                    const ggml_tensor *src1, ggml_tensor *dst,
+                                    const float *src0_dd, const float *src1_dd,
+                                    float *dst_dd,
+                                    const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    int num_groups = dst->op_params[0];
+    int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups);
+    group_norm_f32_sycl(src0_dd, dst_dd, num_groups, group_size, src0->ne[0] * src0->ne[1] * src0->ne[2], main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_concat(const ggml_tensor *src0,
+                                const ggml_tensor *src1, ggml_tensor *dst,
+                                const float *src0_dd, const float *src1_dd,
+                                float *dst_dd,
+                                const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    for (int i3 = 0; i3 < dst->ne[3]; i3++) {
+        concat_f32_sycl(src0_dd + i3 * (src0->nb[3] / 4), src1_dd + i3 * (src1->nb[3] / 4), dst_dd + i3 * (dst->nb[3] / 4), dst->ne[0], dst->ne[1], dst->ne[2], src0->ne[2], main_stream);
+    }
+
+    (void) src1;
+    (void) dst;
+}
+
+inline void ggml_sycl_op_upscale(const ggml_tensor *src0,
+                                 const ggml_tensor *src1, ggml_tensor *dst,
+                                 const float *src0_dd, const float *src1_dd,
+                                 float *dst_dd,
+                                 const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
+
+    const int scale_factor = dst->op_params[0];
+
+    upscale_f32_sycl(src0_dd, dst_dd, src0->ne[0], src0->ne[1], src0->ne[2], scale_factor, main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_pad(const ggml_tensor *src0, const ggml_tensor *src1,
+                             ggml_tensor *dst, const float *src0_dd,
+                             const float *src1_dd, float *dst_dd,
+                             const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
+
+    pad_f32_sycl(src0_dd, dst_dd,
+        src0->ne[0], src0->ne[1], src0->ne[2],
+        dst->ne[0], dst->ne[1], dst->ne[2], main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_rms_norm(const ggml_tensor *src0,
+                                  const ggml_tensor *src1, ggml_tensor *dst,
+                                  const float *src0_dd, const float *src1_dd,
+                                  float *dst_dd,
+                                  const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    rms_norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_mul_mat_q(
+    const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
+    const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
+    float *dst_dd_i, const int64_t row_low, const int64_t row_high,
+    const int64_t src1_ncols, const int64_t src1_padded_row_size,
+    const dpct::queue_ptr &stream) try {
+
+    const int64_t ne00 = src0->ne[0];
+
+    const int64_t ne10 = src1->ne[0];
+    GGML_ASSERT(ne10 % QK8_1 == 0);
+
+    const int64_t ne0 = dst->ne[0];
+
+    const int64_t row_diff = row_high - row_low;
+
+    int device_id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(device_id = get_current_device_id()));
+
+    // the main device has a larger memory buffer to hold the results from all GPUs
+    // nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into
+    const int64_t nrows_dst = dst->backend == GGML_BACKEND_TYPE_GPU && device_id == g_main_device ? ne0 : row_diff;
+
+    switch (src0->type) {
+        case GGML_TYPE_Q4_0:
+            ggml_mul_mat_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            ggml_mul_mat_q4_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            ggml_mul_mat_q5_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            ggml_mul_mat_q5_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            ggml_mul_mat_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q2_K:
+            ggml_mul_mat_q2_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q3_K:
+            ggml_mul_mat_q3_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q4_K:
+            ggml_mul_mat_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q5_K:
+            ggml_mul_mat_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        case GGML_TYPE_Q6_K:
+            ggml_mul_mat_q6_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+
+    (void) src1;
+    (void) dst;
+    (void) src1_ddf_i;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_SYCL_MAX_DEVICES> & tensor_split) {
+    int64_t min_compute_capability = INT_MAX;
+    int64_t max_compute_capability = INT_MIN;
+    for (int i = 0; i < g_device_count; ++i) {
+        if (tensor_split[i] < (i + 1 < g_device_count ? tensor_split[i + 1] : 1.0f)) {
+            if (min_compute_capability > g_device_caps[i].cc) {
+                min_compute_capability = g_device_caps[i].cc;
+            }
+            if (max_compute_capability < g_device_caps[i].cc) {
+                max_compute_capability = g_device_caps[i].cc;
+            }
+        }
+    }
+
+    switch(type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+            return max_compute_capability >= VER_GEN9 ? 128 : 64;
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+            return 64;
+        case GGML_TYPE_F16:
+        case GGML_TYPE_F32:
+            return 1;
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ3_XXS:
+            return max_compute_capability >= VER_GEN9 ? 128 : 64;
+        case GGML_TYPE_IQ3_S:
+            return max_compute_capability >= VER_GEN9 ? 128 : 64;
+        case GGML_TYPE_Q6_K:
+            return 64;
+        default:
+            GGML_ASSERT(false);
+    }
+
+}
+
+inline void ggml_sycl_op_mul_mat_vec_q(
+    const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
+    const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
+    float *dst_dd_i, const int64_t row_low, const int64_t row_high,
+    const int64_t src1_ncols, const int64_t src1_padded_row_size,
+    const dpct::queue_ptr &stream) {
+
+    GGML_ASSERT(ggml_nrows(src1) == 1);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t row_diff = row_high - row_low;
+
+    switch (src0->type) {
+        case GGML_TYPE_Q4_0:
+            mul_mat_vec_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            mul_mat_vec_q4_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            mul_mat_vec_q5_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            mul_mat_vec_q5_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            mul_mat_vec_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q2_K:
+            mul_mat_vec_q2_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q3_K:
+            mul_mat_vec_q3_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q4_K:
+            mul_mat_vec_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_K:
+            mul_mat_vec_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q6_K:
+            mul_mat_vec_q6_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ2_XXS:
+            mul_mat_vec_iq2_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ2_XS:
+            mul_mat_vec_iq2_xs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ3_XXS:
+            mul_mat_vec_iq3_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ3_S:
+            mul_mat_vec_iq3_s_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_IQ1_S:
+            mul_mat_vec_iq1_s_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+
+    (void) src1;
+    (void) dst;
+    (void) src1_ddf_i;
+    (void) src1_ncols;
+    (void) src1_padded_row_size;
+}
+
+
+inline void ggml_sycl_op_dequantize_mul_mat_vec(
+    const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
+    const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
+    float *dst_dd_i, const int64_t row_low, const int64_t row_high,
+    const int64_t src1_ncols, const int64_t src1_padded_row_size,
+    const dpct::queue_ptr &stream) {
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t row_diff = row_high - row_low;
+
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
+#ifdef GGML_SYCL_F16
+    sycl_pool_alloc<sycl::half> src1_dfloat_a;
+    sycl::half *src1_dfloat = nullptr; // dfloat == half
+
+    bool src1_convert_f16 =
+        src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 ||
+        src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 ||
+        src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16;
+
+    if (src1_convert_f16) {
+        src1_dfloat = src1_dfloat_a.alloc(ne00);
+        const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type);
+        GGML_ASSERT(to_fp16_sycl != nullptr);
+        to_fp16_sycl(src1_ddf_i, src1_dfloat, ne00, stream);
+    }
+#else
+    const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion
+#endif // GGML_SYCL_F16
+
+    switch (src0->type) {
+        case GGML_TYPE_Q4_0:
+            dequantize_mul_mat_vec_q4_0_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q4_1:
+            dequantize_mul_mat_vec_q4_1_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_0:
+            dequantize_mul_mat_vec_q5_0_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_1:
+            dequantize_mul_mat_vec_q5_1_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q8_0:
+            dequantize_mul_mat_vec_q8_0_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q2_K:
+            dequantize_mul_mat_vec_q2_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q3_K:
+            dequantize_mul_mat_vec_q3_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q4_K:
+            dequantize_mul_mat_vec_q4_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q5_K:
+            dequantize_mul_mat_vec_q5_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_Q6_K:
+            dequantize_mul_mat_vec_q6_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
+            break;
+        case GGML_TYPE_F16:
+            convert_mul_mat_vec_f16_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+
+    (void) src1;
+    (void) dst;
+    (void) src1_ddq_i;
+    (void) src1_ncols;
+    (void) src1_padded_row_size;
+}
+
+inline void ggml_sycl_op_mul_mat_sycl(
+    const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
+    const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
+    float *dst_dd_i, const int64_t row_low, const int64_t row_high,
+    const int64_t src1_ncols, const int64_t src1_padded_row_size,
+    const dpct::queue_ptr &stream) try {
+
+    GGML_ASSERT(src0_dd_i  != nullptr);
+    GGML_ASSERT(src1_ddf_i != nullptr);
+    GGML_ASSERT(dst_dd_i   != nullptr);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne10 = src1->ne[0];
+
+    const int64_t ne0 = dst->ne[0];
+
+    const int64_t row_diff = row_high - row_low;
+
+    int id;
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(id = get_current_device_id()));
+
+    // the main device has a larger memory buffer to hold the results from all GPUs
+    // ldc == nrows of the matrix that cuBLAS writes into
+    int ldc = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne0 : row_diff;
+
+#ifdef GGML_SYCL_F16
+    bool use_fp16 = true;  // TODO(Yu) SYCL capability check
+#else
+    bool use_fp16 = false;
+#endif
+    if ((src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
+        use_fp16 && ggml_is_contiguous(src0) && row_diff == src0->ne[1] &&
+        dst->op_params[0] == GGML_PREC_DEFAULT) {
+
+        // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp16 path\n");
+        sycl_pool_alloc<sycl::half> src0_as_f16;
+        if (src0->type != GGML_TYPE_F16) {
+            const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src0->type);
+            GGML_ASSERT(to_fp16_sycl != nullptr);
+            size_t ne = row_diff*ne00;
+            src0_as_f16.alloc(ne);
+            to_fp16_sycl(src0_dd_i, src0_as_f16.get(), ne, stream);
+        }
+        const sycl::half *src0_ptr = src0->type == GGML_TYPE_F16
+                                         ? (const sycl::half *)src0_dd_i
+                                         : src0_as_f16.get();
+
+        sycl_pool_alloc<sycl::half> src1_as_f16;
+        if (src1->type != GGML_TYPE_F16) {
+            const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type);
+            GGML_ASSERT(to_fp16_sycl != nullptr);
+            size_t ne = src1_ncols*ne10;
+            src1_as_f16.alloc(ne);
+            to_fp16_sycl(src1_ddf_i, src1_as_f16.get(), ne, stream);
+        }
+        const sycl::half *src1_ptr = src1->type == GGML_TYPE_F16
+                ? (const sycl::half *)src1->data + src1_padded_row_size
+                                         : src1_as_f16.get();
+        sycl_pool_alloc<sycl::half> dst_f16(row_diff * src1_ncols);
+
+        const sycl::half alpha_f16 = 1.0f;
+        const sycl::half beta_f16 = 0.0f;
+        SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[id] = stream));
+        SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm(
+            *g_sycl_handles[id], oneapi::mkl::transpose::trans,
+            oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10,
+            &alpha_f16, src0_ptr, dpct::library_data_t::real_half, ne00,
+            src1_ptr, dpct::library_data_t::real_half, ne10, &beta_f16,
+            dst_f16.get(), dpct::library_data_t::real_half, ldc,
+            dpct::library_data_t::real_half)));
+        g_sycl_handles[id]->wait();
+        const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
+        to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream);
+    }
+    else {
+        // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp32 path\n");
+        sycl_pool_alloc<float> src0_ddq_as_f32;
+        sycl_pool_alloc<float> src1_ddq_as_f32;
+        if (src0->type != GGML_TYPE_F32) {
+            const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src0->type);
+            GGML_ASSERT(to_fp32_sycl != nullptr);
+            src0_ddq_as_f32.alloc(row_diff*ne00);
+            to_fp32_sycl(src0_dd_i, src0_ddq_as_f32.get(), row_diff*ne00, stream);
+        }
+        if (src1->type != GGML_TYPE_F32) {
+            const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src1->type);
+            GGML_ASSERT(to_fp32_sycl != nullptr);
+            src1_ddq_as_f32.alloc(src1_ncols*ne10);
+            to_fp32_sycl(src1_ddf_i, src1_ddq_as_f32.get(), src1_ncols*ne10, stream);
+        }
+        const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32.get();
+        const float * src1_ddf1_i = src1->type == GGML_TYPE_F32 ? (const float *) src1_ddf_i : src1_ddq_as_f32.get();
+
+        const float alpha = 1.0f;
+        const float beta = 0.0f;
+
+        SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[id] = stream));
+        SYCL_CHECK(CHECK_TRY_ERROR(oneapi::mkl::blas::column_major::gemm(
+            *g_sycl_handles[id], oneapi::mkl::transpose::trans,
+            oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10,
+            dpct::get_value(&alpha, *g_sycl_handles[id]), src0_ddf_i, ne00,
+            src1_ddf1_i, ne10, dpct::get_value(&beta, *g_sycl_handles[id]),
+            dst_dd_i, ldc)));
+        g_sycl_handles[id]->wait();
+    }
+    (void) dst;
+    (void) src1_ddq_i;
+    (void) src1_padded_row_size;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1,
+                              ggml_tensor *dst, const float *src0_dd,
+                              const float *src1_dd, float *dst_dd,
+                              const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32 ||  dst->type == GGML_TYPE_F16);
+    GGML_ASSERT(src0->type == dst->type);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne2 = dst->ne[2];
+    const int64_t nrows = ggml_nrows(src0);
+
+    //const int n_past      = ((int32_t *) dst->op_params)[0];
+    const int n_dims      = ((int32_t *) dst->op_params)[1];
+    const int mode        = ((int32_t *) dst->op_params)[2];
+    const int n_ctx       = ((int32_t *) dst->op_params)[3];
+    const int n_orig_ctx  = ((int32_t *) dst->op_params)[4];
+
+    // RoPE alteration for extended context
+    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
+    memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
+    memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
+    memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
+    memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
+    memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
+    memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
+
+    const int32_t * pos = nullptr;
+    if ((mode & 1) == 0) {
+        GGML_ASSERT(src1->type == GGML_TYPE_I32);
+        GGML_ASSERT(src1->ne[0] == ne2);
+        pos = (const int32_t *) src1_dd;
+    }
+
+    const bool is_neox = mode & 2;
+    const bool is_glm  = mode & 4;
+
+    rope_corr_dims corr_dims;
+    ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v);
+
+    // compute
+    if (is_glm) {
+        GGML_ASSERT(false);
+        rope_glm_f32_sycl(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, n_ctx, main_stream);
+    } else if (is_neox) {
+        if (src0->type == GGML_TYPE_F32) {
+            rope_neox_sycl(
+                (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, main_stream
+            );
+        } else if (src0->type == GGML_TYPE_F16) {
+            rope_neox_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd,
+                           ne00, n_dims, nrows, pos, freq_scale, ne01,
+                           freq_base, ext_factor, attn_factor, corr_dims,
+                           main_stream);
+        } else {
+            GGML_ASSERT(false);
+        }
+    } else {
+        if (src0->type == GGML_TYPE_F32) {
+            rope_sycl(
+                (const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, main_stream
+            );
+        } else if (src0->type == GGML_TYPE_F16) {
+            rope_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd, ne00,
+                      nrows, pos, freq_scale, ne01, freq_base, ext_factor,
+                      attn_factor, corr_dims, main_stream);
+        } else {
+            GGML_ASSERT(false);
+        }
+    }
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_alibi(const ggml_tensor *src0, const ggml_tensor *src1,
+                               ggml_tensor *dst, const float *src0_dd,
+                               const float *src1_dd, float *dst_dd,
+                               const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    GGML_TENSOR_LOCALS_3(int64_t, ne0, src0, ne);
+    const int64_t nrows = ggml_nrows(src0);
+
+    //const int n_past = ((int32_t *) dst->op_params)[0];
+    const int n_head = ((int32_t *) dst->op_params)[1];
+    float max_bias;
+    memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
+
+    //GGML_ASSERT(ne01 + n_past == ne00);
+    GGML_ASSERT(n_head == ne02);
+
+    const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
+
+    const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
+
+    alibi_f32_sycl(src0_dd, dst_dd, ne00, nrows, ne01, n_heads_log2_floor, m0, m1, main_stream);
+
+    (void) src1;
+    (void) src1_dd;
+}
+
+static void ggml_sycl_op_pool2d(const ggml_tensor *src0,
+                                const ggml_tensor *src1, ggml_tensor *dst,
+                                const float *src0_dd, const float *src1_dd,
+                                float *dst_dd, const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int32_t * opts = (const int32_t *)dst->op_params;
+    enum ggml_op_pool op = static_cast<ggml_op_pool>(opts[0]);
+    const int k0 = opts[1];
+    const int k1 = opts[2];
+    const int s0 = opts[3];
+    const int s1 = opts[4];
+    const int p0 = opts[5];
+    const int p1 = opts[6];
+
+    const int64_t IH = src0->ne[1];
+    const int64_t IW = src0->ne[0];
+
+    const int64_t N = dst->ne[3];
+    const int64_t OC = dst->ne[2];
+    const int64_t OH = dst->ne[1];
+    const int64_t OW = dst->ne[0];
+
+    const int parallel_elements = N * OC * OH * OW;
+    const int num_blocks = (parallel_elements + SYCL_POOL2D_BLOCK_SIZE - 1) / SYCL_POOL2D_BLOCK_SIZE;
+    sycl::range<3> block_nums(1, 1, num_blocks);
+    main_stream->parallel_for(
+        sycl::nd_range<3>(block_nums *
+                              sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            pool2d_nchw_kernel(IH, IW, OH, OW, k1, k0, s1, s0, p1, p0,
+                               parallel_elements, src0_dd, dst_dd, op,
+                               item_ct1);
+        });
+
+    (void) src1;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_im2col(const ggml_tensor *src0,
+                                const ggml_tensor *src1, ggml_tensor *dst,
+                                const float *src0_dd, const float *src1_dd,
+                                float *dst_dd,
+                                const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
+
+    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
+    const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
+    const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
+    const int32_t p1 = ((const int32_t*)(dst->op_params))[3];
+    const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
+    const int32_t d1 = ((const int32_t*)(dst->op_params))[5];
+
+    const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1;
+
+    const int64_t IC = src1->ne[is_2D ? 2 : 1];
+    const int64_t IH = is_2D ? src1->ne[1] : 1;
+    const int64_t IW =         src1->ne[0];
+
+    const int64_t KH = is_2D ? src0->ne[1] : 1;
+    const int64_t KW =         src0->ne[0];
+
+    const int64_t OH = is_2D ? dst->ne[2] : 1;
+    const int64_t OW =         dst->ne[1];
+
+    const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
+
+    if (dst->type == GGML_TYPE_F16) {
+        im2col_sycl(src1_dd, (sycl::half *)dst_dd, IW, IH, OW, OH, KW, KH, IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream);
+    } else {
+        im2col_sycl(src1_dd, (float *)dst_dd, IW, IH, OW, OH, KW, KH, IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream);
+    }
+
+    (void) src0;
+    (void) src0_dd;
+}
+
+inline void ggml_sycl_op_sum_rows(const ggml_tensor *src0,
+                                  const ggml_tensor *src1, ggml_tensor *dst,
+                                  const float *src0_dd, const float *src1_dd,
+                                  float *dst_dd,
+                                  const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ncols = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    sum_rows_f32_sycl(src0_dd, dst_dd, ncols, nrows, main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_argsort(const ggml_tensor *src0,
+                                 const ggml_tensor *src1, ggml_tensor *dst,
+                                 const float *src0_dd, const float *src1_dd,
+                                 float *dst_dd,
+                                 const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_I32);
+
+    const int64_t ncols = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0];
+
+    argsort_f32_i32_sycl(src0_dd, (int *)dst_dd, ncols, nrows, order, main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_diag_mask_inf(const ggml_tensor *src0,
+                                       const ggml_tensor *src1,
+                                       ggml_tensor *dst, const float *src0_dd,
+                                       const float *src1_dd, float *dst_dd,
+                                       const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int nrows0 = ggml_nrows(src0);
+
+    const int n_past = ((int32_t *) dst->op_params)[0];
+
+    diag_mask_inf_f32_sycl(src0_dd, dst_dd, ne00, nrows0, ne01, n_past, main_stream);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_soft_max(const ggml_tensor *src0,
+                                  const ggml_tensor *src1, ggml_tensor *dst,
+                                  const float *src0_dd, const float *src1_dd,
+                                  float *dst_dd,
+                                  const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t nrows_x = ggml_nrows(src0);
+    const int64_t nrows_y = src0->ne[1];
+
+    float scale = 1.0f;
+    float max_bias = 0.0f;
+
+    memcpy(&scale, dst->op_params + 0, sizeof(float));
+    memcpy(&max_bias, dst->op_params + 1, sizeof(float));
+
+    // positions tensor
+    float * src2_dd = nullptr;
+    sycl_pool_alloc<float> src2_f;
+
+    ggml_tensor * src2 = dst->src[2];
+    const bool use_src2 = src2 != nullptr;
+
+    if (use_src2) {
+        const bool src2_on_device = src2->backend == GGML_BACKEND_TYPE_GPU;
+
+        if (src2_on_device) {
+            ggml_tensor_extra_gpu * src2_extra = (ggml_tensor_extra_gpu *) src2->extra;
+            src2_dd = (float *) src2_extra->data_device[g_main_device];
+        } else {
+            src2_dd = src2_f.alloc(ggml_nelements(src2));
+            SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src2_dd, src2, 0, 0, 0, 1, main_stream));
+        }
+    }
+
+    soft_max_f32_sycl(src0_dd, src1 ? src1_dd : nullptr, src2_dd, dst_dd, ne00,
+                      nrows_x, nrows_y, scale, max_bias, main_stream);
+}
+
+inline void ggml_sycl_op_scale(const ggml_tensor *src0, const ggml_tensor *src1,
+                               ggml_tensor *dst, const float *src0_dd,
+                               const float *src1_dd, float *dst_dd,
+                               const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    float scale;
+    memcpy(&scale, dst->op_params, sizeof(float));
+
+    scale_f32_sycl(src0_dd, dst_dd, scale, ggml_nelements(src0), main_stream);
+    /*
+    DPCT1010:87: SYCL uses exceptions to report errors and does not use the
+    error codes. The call was replaced with 0. You need to rewrite this code.
+    */
+    SYCL_CHECK(0);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+inline void ggml_sycl_op_clamp(const ggml_tensor *src0, const ggml_tensor *src1,
+                               ggml_tensor *dst, const float *src0_dd,
+                               const float *src1_dd, float *dst_dd,
+                               const dpct::queue_ptr &main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    float min;
+    float max;
+    memcpy(&min, dst->op_params, sizeof(float));
+    memcpy(&max, (float *) dst->op_params + 1, sizeof(float));
+
+    clamp_f32_sycl(src0_dd, dst_dd, min, max, ggml_nelements(src0), main_stream);
+    /*
+    DPCT1010:88: SYCL uses exceptions to report errors and does not use the
+    error codes. The call was replaced with 0. You need to rewrite this code.
+    */
+    SYCL_CHECK(0);
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
+static void ggml_sycl_op_flatten(const ggml_tensor *src0,
+                                 const ggml_tensor *src1, ggml_tensor *dst,
+                                 const ggml_sycl_op_flatten_t op) try {
+    const int64_t nrows0 = ggml_nrows(src0);
+
+    const bool use_src1 = src1 != nullptr;
+    const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1;
+
+    GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(              dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+
+    ggml_tensor_extra_gpu * src0_extra =            (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
+    ggml_tensor_extra_gpu * dst_extra  =            (ggml_tensor_extra_gpu *)  dst->extra;
+
+    const bool src0_on_device =             src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
+    const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU;
+    const bool  dst_on_device =              dst->backend == GGML_BACKEND_TYPE_GPU;
+
+    // dd = data device
+    float * src0_ddf = nullptr;
+    float * src1_ddf = nullptr;
+    float *  dst_ddf = nullptr;
+
+    sycl_pool_alloc<float> src0_f;
+    sycl_pool_alloc<float> src1_f;
+    sycl_pool_alloc<float>  dst_f;
+
+    ggml_sycl_set_device(g_main_device);
+    dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
+    // GGML_SYCL_DEBUG("g_main_device=%d, main_stream=%p src0_on_device=%d, src1_on_device=%d, dst_on_device=%d\n",
+        // g_main_device, main_stream, src0_on_device, src1_on_device, dst_on_device);
+
+    if (src0_on_device) {
+        src0_ddf = (float *) src0_extra->data_device[g_main_device];
+    } else {
+        src0_ddf = src0_f.alloc(ggml_nelements(src0));
+        // GGML_SYCL_DEBUG("before ggml_sycl_cpy_tensor_2d src0_ddf=%p, src0=%p\n", src0_ddf, src0);
+        SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src0_ddf, src0, 0, 0, 0, nrows0, main_stream));
+    }
+
+    if (use_src1) {
+        if (src1_on_device) {
+            src1_ddf = (float *) src1_extra->data_device[g_main_device];
+        } else {
+            src1_ddf = src1_f.alloc(ggml_nelements(src1));
+            SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src1_ddf, src1, 0, 0, 0, nrows1, main_stream));
+        }
+    }
+    if (dst_on_device) {
+        dst_ddf = (float *) dst_extra->data_device[g_main_device];
+    } else {
+        dst_ddf = dst_f.alloc(ggml_nelements(dst));
+    }
+
+    // GGML_SYCL_DEBUG("op src0=%p, src1=%p, dst=%p, src0_ddf=%p, src1_ddf=%p, dst_ddf=%p, main_stream=%p\n",
+        // src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream);
+    // do the computation
+    op(src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream);
+    /*
+    DPCT1010:89: SYCL uses exceptions to report errors and does not use the
+    error codes. The call was replaced with 0. You need to rewrite this code.
+    */
+    SYCL_CHECK(0);
+
+    // copy dst to host if necessary
+    if (!dst_on_device) {
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            main_stream->memcpy(dst->data, dst_ddf, ggml_nbytes(dst)).wait()));
+    }
+
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            dpct::get_current_device().queues_wait_and_throw()));
+    }
+    // print_ggml_tensor("tensor", dst);
+}
+catch (sycl::exception const &exc) {
+
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_sycl_set_peer_access(const int n_tokens) {
+    static bool peer_access_enabled = false;
+
+    const bool enable_peer_access = n_tokens <= GGML_SYCL_PEER_MAX_BATCH_SIZE;
+
+    if (peer_access_enabled == enable_peer_access) {
+        return;
+    }
+
+#ifdef NDEBUG
+    for (int i = 0; i < g_device_count; ++i) {
+        SYCL_CHECK(ggml_sycl_set_device(i));
+        // SYCL_CHECK(syclDeviceSynchronize());
+    }
+
+    for (int i = 0; i < g_device_count; ++i) {
+        SYCL_CHECK(ggml_sycl_set_device(i));
+
+        for (int id_other = 0; id_other < g_device_count; ++id_other) {
+            if (i == id_other) {
+                continue;
+            }
+            if (i != g_main_device && id_other != g_main_device) {
+                continue;
+            }
+
+            // int can_access_peer;
+            // SYCL_CHECK(syclDeviceCanAccessPeer(&can_access_peer, id, id_other));
+            // if (can_access_peer) {
+            //     if (enable_peer_access) {
+            //         SYCL_CHECK(syclDeviceEnablePeerAccess(id_other, 0));
+            //     } else {
+            //         SYCL_CHECK(syclDeviceDisablePeerAccess(id_other));
+            //     }
+            // }
+        }
+    }
+#endif // NDEBUG
+
+    peer_access_enabled = enable_peer_access;
+}
+
+struct ggml_backend_sycl_split_buffer_type_context {
+    std::array<float, GGML_SYCL_MAX_DEVICES> tensor_split;
+};
+
+static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
+                                 const ggml_tensor *src1, ggml_tensor *dst,
+                                 ggml_sycl_op_mul_mat_t op,
+                                 const bool convert_src1_to_q8_1) try {
+
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne);
+
+    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);
+    const int64_t nrows1 = ggml_nrows(src1);
+
+    GGML_ASSERT(ne03 == ne13);
+
+    const int64_t ne0 = dst->ne[0];
+    const int64_t ne1 = dst->ne[1];
+
+    const int nb2 = dst->nb[2];
+    const int nb3 = dst->nb[3];
+
+    GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1));
+
+    GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0);
+
+    const int64_t i02_divisor = ne12 / ne02;
+
+    const size_t src0_ts = ggml_type_size(src0->type);
+    const size_t src0_bs = ggml_blck_size(src0->type);
+    const size_t q8_1_ts = sizeof(block_q8_1);
+    const size_t q8_1_bs = QK8_1;
+
+    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
+    ggml_tensor_extra_gpu *  dst_extra = (ggml_tensor_extra_gpu *)  dst->extra;
+
+    const bool src0_on_device = src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
+    const bool src0_is_contiguous = ggml_is_contiguous(src0);
+    const bool src1_is_contiguous = ggml_is_contiguous(src1);
+
+    int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING);
+
+    const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
+    GGML_ASSERT(!(split && ne02 > 1));
+    GGML_ASSERT(!(split && ne03 > 1));
+    GGML_ASSERT(!(split && ne02 < ne12));
+
+    std::array<float, GGML_SYCL_MAX_DEVICES> tensor_split;
+    if (split) {
+        // TODO: check that src0->buffer->buft is a split buffer type, replace GGML_BACKEND_TYPE_GPU_SPLIT check
+        // GGML_ASSERT(src0->buffer != nullptr && src0->buffer->buft == ...);
+        ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *) src0->buffer->buft->context;
+        tensor_split = buft_ctx->tensor_split;
+    }
+
+    struct dev_data {
+        sycl_pool_alloc<char> src0_dd_alloc;
+        sycl_pool_alloc<float> src1_ddf_alloc;
+        sycl_pool_alloc<char> src1_ddq_alloc;
+        sycl_pool_alloc<float> dst_dd_alloc;
+
+        char *src0_dd = nullptr;
+        float *src1_ddf = nullptr; // float
+        char *src1_ddq = nullptr;  // q8_1
+        float *dst_dd = nullptr;
+
+        int64_t row_low;
+        int64_t row_high;
+    };
+
+    dev_data dev[GGML_SYCL_MAX_DEVICES];
+
+    int used_devices = 0;
+    dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
+
+    for (int i = 0; i < g_device_count; ++i) {
+        // by default, use all rows
+        dev[i].row_low  = 0;
+        dev[i].row_high = ne01;
+
+        // for multi GPU, get the row boundaries from tensor split
+        // and round to mul_mat_q tile sizes
+        if (split) {
+            const int64_t rounding = get_row_rounding(src0->type, tensor_split);
+
+            if (i != 0) {
+                dev[i].row_low  = ne01*tensor_split[i];
+                if (dev[i].row_low < ne01) {
+                    dev[i].row_low -= dev[i].row_low % rounding;
+                }
+            }
+
+            if (i != g_device_count - 1) {
+                dev[i].row_high  = ne01*tensor_split[i + 1];
+                if (dev[i].row_high < ne01) {
+                    dev[i].row_high -= dev[i].row_high % rounding;
+                }
+            }
+        }
+    }
+
+    for (int i = 0; i < g_device_count; ++i) {
+        if ((!split && i != g_main_device) || dev[i].row_low == dev[i].row_high) {
+            continue;
+        }
+
+        used_devices++;
+
+        const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device;
+        const bool  dst_on_device =  dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device;
+
+        ggml_sycl_set_device(i);
+        dpct::queue_ptr stream = g_syclStreams[i][0];
+
+        if (src0_on_device && src0_is_contiguous) {
+            dev[i].src0_dd = (char *) src0_extra->data_device[i];
+        } else {
+            dev[i].src0_dd = dev[i].src0_dd_alloc.alloc(ggml_nbytes(src0));
+        }
+
+        if (src1_on_device && src1_is_contiguous) {
+            dev[i].src1_ddf = (float *) src1_extra->data_device[i];
+        } else {
+            dev[i].src1_ddf = dev[i].src1_ddf_alloc.alloc(ggml_nelements(src1));
+        }
+
+        if (convert_src1_to_q8_1) {
+            dev[i].src1_ddq = dev[i].src1_ddq_alloc.alloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs);
+
+            if (src1_on_device && src1_is_contiguous) {
+                quantize_row_q8_1_sycl(dev[i].src1_ddf, dev[i].src1_ddq, ne10, nrows1, src1_padded_col_size, stream);
+                /*
+                DPCT1010:90: SYCL uses exceptions to report errors and does not
+                use the error codes. The call was replaced with 0. You need to
+                rewrite this code.
+                */
+                SYCL_CHECK(0);
+            }
+        }
+
+        if (dst_on_device) {
+            dev[i].dst_dd = (float *) dst_extra->data_device[i];
+        } else {
+            const size_t size_dst_ddf = split ? (dev[i].row_high - dev[i].row_low)*ne1 : ggml_nelements(dst);
+            dev[i].dst_dd = dev[i].dst_dd_alloc.alloc(size_dst_ddf);
+        }
+    }
+
+    // if multiple devices are used they need to wait for the main device
+    // here an event is recorded that signals that the main device has finished calculating the input data
+    if (split && used_devices > 1) {
+        ggml_sycl_set_device(g_main_device);
+        /*
+        DPCT1024:91: The original code returned the error code that was further
+        consumed by the program logic. This original code was replaced with 0.
+        You may need to rewrite the program logic consuming the error code.
+        */
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            *src0_extra->events[g_main_device][0] =
+                g_syclStreams[g_main_device][0]->ext_oneapi_submit_barrier()));
+    }
+
+    const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11;
+    for (int64_t src1_col_0 = 0; src1_col_0 < ne11; src1_col_0 += src1_col_stride) {
+        const int64_t is = split ? (src1_col_0/src1_col_stride) % MAX_STREAMS : 0;
+        const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride;
+
+        for (int i = 0; i < g_device_count; ++i) {
+            if ((!split && i != g_main_device) || dev[i].row_low == dev[i].row_high) {
+                continue;
+            }
+
+            const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device;
+            const bool  dst_on_device =  dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device;
+            const int64_t row_diff = dev[i].row_high - dev[i].row_low;
+
+            ggml_sycl_set_device(i);
+            dpct::queue_ptr stream = g_syclStreams[i][is];
+
+            // wait for main GPU data if necessary
+            if (split && (i != g_main_device || is != 0)) {
+                /*
+                DPCT1009:163: SYCL uses exceptions to report errors and does not
+                use the error codes. The original code was commented out and a
+                warning string was inserted. You need to rewrite this code.
+                */
+                SYCL_CHECK(CHECK_TRY_ERROR(stream->ext_oneapi_submit_barrier(
+                    {*src0_extra->events[g_main_device][0]})));
+            }
+
+            for (int64_t i0 = 0; i0 < ne13*ne12; ++i0) {
+                const int64_t i03 = i0 / ne12;
+                const int64_t i02 = i0 % ne12;
+
+                const size_t src1_ddq_i_offset = (i0*ne11 + src1_col_0) * src1_padded_col_size*q8_1_ts/q8_1_bs;
+
+                // for split tensors the data begins at i0 == i0_offset_low
+                char  *  src0_dd_i =  dev[i].src0_dd + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs;
+                float * src1_ddf_i = dev[i].src1_ddf + (i0*ne11 + src1_col_0) * ne10;
+                char  * src1_ddq_i = dev[i].src1_ddq +  src1_ddq_i_offset;
+                float *   dst_dd_i =   dev[i].dst_dd + (i0*ne1  + src1_col_0) * (dst_on_device ? ne0 : row_diff);
+
+                // the main device memory buffer can be on VRAM scratch, with space for all partial results
+                // in that case an offset on dst_ddf_i is needed
+                if (dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device) {
+                    dst_dd_i += dev[i].row_low; // offset is 0 if no tensor split
+                }
+
+                // copy src0, src1 to device if necessary
+                if (src1->backend == GGML_BACKEND_TYPE_GPU && src1_is_contiguous) {
+                    if (i != g_main_device) {
+                        if (convert_src1_to_q8_1) {
+                            char * src1_ddq_i_source = dev[g_main_device].src1_ddq + src1_ddq_i_offset;
+                          SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(
+                                src1_ddq_i, src1_ddq_i_source,
+                                src1_ncols * src1_padded_col_size * q8_1_ts /
+                                    q8_1_bs).wait()));
+                        } else {
+
+                            float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device];
+                            src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10;
+
+                            SYCL_CHECK(CHECK_TRY_ERROR(dev2dev_memcpy(*stream, *main_stream,
+                                src1_ddf_i, src1_ddf_i_source,
+                                src1_ncols * ne10 * sizeof(float))));
+                        }
+                    }
+                } else if (src1->backend == GGML_BACKEND_TYPE_CPU || (src1_on_device && !src1_is_contiguous)) {
+                    SYCL_CHECK(ggml_sycl_cpy_tensor_2d(
+                                   src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream));
+                } else {
+                    GGML_ASSERT(false);
+                }
+
+                if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_TYPE_CPU || !src1_is_contiguous)) {
+                    quantize_row_q8_1_sycl(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream);
+                    /*
+                    DPCT1010:92: SYCL uses exceptions to report errors and does
+                    not use the error codes. The call was replaced with 0. You
+                    need to rewrite this code.
+                    */
+                    SYCL_CHECK(0);
+                }
+
+                if (src1_col_0 == 0 && (!src0_on_device || !src0_is_contiguous) && i02 % i02_divisor == 0) {
+                    SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, dev[i].row_low, dev[i].row_high, stream));
+                }
+                if (src1->type == GGML_TYPE_F16) {
+                    src1_padded_col_size = (i0 * ne11 + src1_col_0) * ne10;
+                }
+                // do the computation
+                op(src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i,
+                    dev[i].row_low, dev[i].row_high, src1_ncols, src1_padded_col_size, stream);
+                /*
+                DPCT1010:93: SYCL uses exceptions to report errors and does not
+                use the error codes. The call was replaced with 0. You need to
+                rewrite this code.
+                */
+                SYCL_CHECK(0);
+
+                // copy dst to host or other device if necessary
+                if (!dst_on_device) {
+                    void * dst_off_device;
+                    dpct::memcpy_direction kind;
+                    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
+                        dst_off_device = dst->data;
+                        kind = dpct::device_to_host;
+                    } else if (dst->backend == GGML_BACKEND_TYPE_GPU) {
+                        dst_off_device = dst_extra->data_device[g_main_device];
+                        kind = dpct::device_to_device;
+                    } else {
+                        GGML_ASSERT(false);
+                    }
+                    if (split) {
+                        // src0 = weight matrix is saved as a transposed matrix for better memory layout.
+                        // dst is NOT transposed.
+                        // The outputs of matrix matrix multiplications can therefore NOT simply be concatenated for >1 GPU.
+                        // Instead they need to be copied to the correct slice in ne0 = dst row index.
+                        // If dst is a vector with ne0 == 1 then you don't have to do this but it still produces correct results.
+                        float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
+                        GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
+                        dhf_dst_i += src1_col_0*ne0 + dev[i].row_low;
+
+                        //todo, dirty solution. Need be updated when device2device memcpy() is supported.
+                        if (kind == dpct::device_to_device) {
+                            size_t dst_size = ggml_nbytes_pad(dst);
+                            float *host_buf = (float *)malloc(dst_size);
+                            SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy(
+                                host_buf, ne0 * sizeof(float), dst_dd_i,
+                                row_diff * sizeof(float), row_diff * sizeof(float),
+                                src1_ncols, dpct::device_to_host, *stream)));
+                            dpct::dev_mgr::instance().get_device(g_sycl_gpu_mgr->gpus[i]).queues_wait_and_throw();
+                            SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy(
+                                dhf_dst_i, ne0 * sizeof(float), host_buf,
+                                row_diff * sizeof(float), row_diff * sizeof(float),
+                                src1_ncols, dpct::host_to_device, *main_stream)));
+                            dpct::dev_mgr::instance().get_device(g_sycl_gpu_mgr->gpus[g_main_device]).queues_wait_and_throw();
+                            free(host_buf);
+                        } else {
+                            SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy(
+                                dhf_dst_i, ne0 * sizeof(float), dst_dd_i,
+                                row_diff * sizeof(float), row_diff * sizeof(float),
+                                src1_ncols, kind, *stream)));
+                        }
+                    } else {
+                        float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
+                        GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
+                        dhf_dst_i += src1_col_0*ne0;
+                        SYCL_CHECK(CHECK_TRY_ERROR(
+                            stream->memcpy(dhf_dst_i, dst_dd_i,
+                                           src1_ncols * ne0 * sizeof(float)).wait()));
+                    }
+                }
+
+                // add event for the main device to wait on until other device is done
+                if (split && (i != g_main_device || is != 0)) {
+                    /*
+                    DPCT1024:94: The original code returned the error code that
+                    was further consumed by the program logic. This original
+                    code was replaced with 0. You may need to rewrite the
+                    program logic consuming the error code.
+                    */
+                    SYCL_CHECK(CHECK_TRY_ERROR(
+                        *src0_extra->events[i][is] =
+                            stream->ext_oneapi_submit_barrier()));
+                }
+            }
+        }
+    }
+
+    // main device waits for all other devices to be finished
+    if (split && g_device_count > 1) {
+        int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE;
+        is_max = is_max <= MAX_STREAMS ? is_max : MAX_STREAMS;
+
+        ggml_sycl_set_device(g_main_device);
+        for (int i = 0; i < g_device_count; ++i) {
+            if (dev[i].row_low == dev[i].row_high) {
+                continue;
+            }
+            for (int64_t is = 0; is < is_max; ++is) {
+                SYCL_CHECK(CHECK_TRY_ERROR(
+                    g_syclStreams[g_main_device][0]->ext_oneapi_submit_barrier(
+                        {*src0_extra->events[i][is]})));
+            }
+        }
+    }
+
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
+        SYCL_CHECK(ggml_sycl_set_device(g_main_device));
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            dpct::get_current_device().queues_wait_and_throw()));
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+
+static void ggml_sycl_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_repeat);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_get_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_get_rows);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_add);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_acc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_acc);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_mul(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_mul);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_div(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_div);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_gelu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_gelu);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_silu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_silu);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_gelu_quick(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_gelu_quick);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_tanh(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_tanh);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_relu);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_hardsigmoid(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_hardsigmoid);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_hardswish(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_hardswish);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_leaky_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_leaky_relu);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_sqr(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_sqr);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_norm);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_group_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_group_norm);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_concat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_concat);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_upscale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_upscale);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+static void ggml_sycl_pad(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_pad);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+
+static void ggml_sycl_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_SYCL_DEBUG("call %s\n", __func__);
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_rms_norm);
+    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+bool ggml_sycl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
+    if (!g_sycl_loaded) return false;
+
+    const int64_t ne10 = src1->ne[0];
+
+    const int64_t ne0 = dst->ne[0];
+    const int64_t ne1 = dst->ne[1];
+
+    // TODO: find the optimal values for these
+    return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
+            src1->type == GGML_TYPE_F32 &&
+             dst->type == GGML_TYPE_F32 &&
+            (ne0 >= 32 && ne1 >= 32 && ne10 >= 32);
+}
+
+static void ggml_sycl_mul_mat_vec_p021(const ggml_tensor *src0,
+                                       const ggml_tensor *src1,
+                                       ggml_tensor *dst) try {
+    GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
+    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation
+    GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+
+    const int64_t ne12 = src1->ne[2];
+
+    SYCL_CHECK(ggml_sycl_set_device(g_main_device));
+    dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
+
+    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
+    void * src0_ddq = src0_extra->data_device[g_main_device];
+
+    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
+    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
+
+    ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
+    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
+
+    ggml_mul_mat_p021_f16_f32_sycl(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream);
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_sycl_mul_mat_vec_nc(const ggml_tensor *src0,
+                                     const ggml_tensor *src1,
+                                     ggml_tensor *dst) try {
+    GGML_ASSERT(!ggml_is_transposed(src0));
+    GGML_ASSERT(!ggml_is_transposed(src1));
+    GGML_ASSERT(!ggml_is_permuted(src0));
+    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+
+    const int64_t nb01 = src0->nb[1];
+    const int64_t nb02 = src0->nb[2];
+
+    const int64_t ne12 = src1->ne[2];
+
+    SYCL_CHECK(ggml_sycl_set_device(g_main_device));
+    dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
+
+    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
+    void * src0_ddq = src0_extra->data_device[g_main_device];
+
+    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
+    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
+
+    ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
+    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
+
+    const int64_t row_stride_x = nb01 / sizeof(sycl::half);
+    const int64_t channel_stride_x = nb02 / sizeof(sycl::half);
+
+    ggml_mul_mat_vec_nc_f16_f32_sycl(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream);
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void k_compute_batched_ptrs(const sycl::half *src0_as_f16,
+                                   const sycl::half *src1_as_f16, char *dst,
+                                   const void **ptrs_src, void **ptrs_dst,
+                                   int64_t ne12, int64_t ne13, int64_t ne23,
+                                   size_t nb02, size_t nb03, size_t nb12,
+                                   size_t nb13, size_t nbd2, size_t nbd3,
+                                   int64_t r2, int64_t r3,
+                                   const sycl::nd_item<3> &item_ct1) {
+    int64_t i13 = item_ct1.get_group(2) * item_ct1.get_local_range(2) +
+                  item_ct1.get_local_id(2);
+    int64_t i12 = item_ct1.get_group(1) * item_ct1.get_local_range(1) +
+                  item_ct1.get_local_id(1);
+
+    if (i13 >= ne13 || i12 >= ne12) {
+        return;
+    }
+
+    int64_t i03 = i13 / r3;
+    int64_t i02 = i12 / r2;
+
+    ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03;
+    ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12 + i13*nb13;
+    ptrs_dst[0*ne23 + i12 + i13*ne12] = (      char *)         dst + i12*nbd2 + i13*nbd3;
+}
+
+static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0,
+                                             const ggml_tensor *src1,
+                                             ggml_tensor *dst) try {
+    GGML_ASSERT(!ggml_is_transposed(src0));
+    GGML_ASSERT(!ggml_is_transposed(src1));
+    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int64_t ne_dst = ggml_nelements(dst);
+
+    SYCL_CHECK(ggml_sycl_set_device(g_main_device));
+    dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
+
+    bool no_mixed_dtypes = main_stream->get_backend() == sycl::backend::ext_oneapi_cuda ||
+                           main_stream->get_backend() == sycl::backend::ext_oneapi_hip;
+
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(g_sycl_handles[g_main_device] = main_stream));
+
+    ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
+    void * src0_ddq = src0_extra->data_device[g_main_device];
+    sycl::half *src0_as_f16 = (sycl::half *)src0_ddq;
+
+    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
+    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
+
+    ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
+    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
+
+    // convert src1 to fp16
+    sycl_pool_alloc<sycl::half> src1_f16_alloc;
+    if (src1->type != GGML_TYPE_F16) {
+        const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type);
+        const int64_t ne_src1 = ggml_nelements(src1);
+        src1_f16_alloc.alloc(ne_src1);
+        GGML_ASSERT(to_fp16_sycl != nullptr);
+        to_fp16_sycl(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream);
+    }
+    sycl::half *src1_f16 = src1->type == GGML_TYPE_F16 ? (sycl::half *)src1_ddf
+                                                       : src1_f16_alloc.get();
+
+    sycl_pool_alloc<sycl::half> dst_f16;
+    char * dst_t;
+
+    dpct::library_data_t cu_compute_type = dpct::library_data_t::real_float;
+    dpct::library_data_t cu_data_type = dpct::library_data_t::real_float;
+    if (no_mixed_dtypes) {
+        cu_compute_type = dpct::library_data_t::real_half;
+        cu_data_type = dpct::library_data_t::real_half;
+    }
+
+    // dst strides
+    size_t nbd2 = dst->nb[2];
+    size_t nbd3 = dst->nb[3];
+
+    const float alpha_f32 = 1.0f;
+    const float beta_f32 = 0.0f;
+
+    const sycl::half alpha_f16 = 1.0f;
+    const sycl::half beta_f16 = 0.0f;
+
+    const void * alpha = &alpha_f32;
+    const void * beta  = &beta_f32;
+    if (no_mixed_dtypes) {
+        alpha = &alpha_f16;
+        beta  = &beta_f16;
+    }
+
+    // TODO: Renable (dst->op_params[0] =! GGML_PREC_DEFAULT) pathway
+    // when oneMKL open source supports half, half, float, float: datatypes
+
+    dst_t = (char *) dst_ddf;
+    if (no_mixed_dtypes) {
+        dst_t = (char *) dst_f16.alloc(ne_dst);
+
+        nbd2 /= sizeof(float) / sizeof(sycl::half);
+        nbd3 /= sizeof(float) / sizeof(sycl::half);
+    }
+
+    GGML_ASSERT(ne12 % ne02 == 0);
+    GGML_ASSERT(ne13 % ne03 == 0);
+
+    // broadcast factors
+    const int64_t r2 = ne12/ne02;
+    const int64_t r3 = ne13/ne03;
+
+#if 0
+    // use syclGemmEx
+    {
+        for (int i13 = 0; i13 < ne13; ++i13) {
+            for (int i12 = 0; i12 < ne12; ++i12) {
+                int i03 = i13 / r3;
+                int i02 = i12 / r2;
+
+                SYCL_CHECK(
+                        syclGemmEx(g_sycl_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
+                            ne01, ne11, ne10,
+                            alpha, (const char *) src0_as_f16 + i02*src0->nb[2]   + i03*src0->nb[3]  , SYCL_R_16F,   nb01/sizeof(half),
+                                   (const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, SYCL_R_16F,   nb11/sizeof(float),
+                            beta,  (      char *)       dst_t + i12*nbd2          + i13*nbd3,          cu_data_type, ne01,
+                            cu_compute_type,
+                            CUBLAS_GEMM_DEFAULT_TENSOR_OP));
+            }
+        }
+    }
+#else
+    if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) {
+        // there is no broadcast and src0, src1 are contiguous across dims 2, 3
+        SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(
+            *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans,
+            oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha,
+            (const char *)src0_as_f16, dpct::library_data_t::real_half,
+            nb01 / nb00, nb02 / nb00,
+            (const char *)src1_f16, dpct::library_data_t::real_half,
+            nb11 / nb10, nb12 / nb10, beta,
+            (char *)dst_t, cu_data_type, ne01, nb2 / nb0,
+            ne12 * ne13, cu_compute_type)));
+        g_sycl_handles[g_main_device]->wait();
+    } else {
+        const int ne23 = ne12*ne13;
+
+        sycl_pool_alloc<const void *> ptrs_src(2*ne23);
+        sycl_pool_alloc<      void *> ptrs_dst(1*ne23);
+
+        sycl::range<3> block_dims(1, ne12, ne13);
+        /*
+        DPCT1049:47: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        {
+            dpct::has_capability_or_fail(main_stream->get_device(),
+                                         {sycl::aspect::fp16});
+
+            main_stream->submit([&](sycl::handler &cgh) {
+                const void **ptrs_src_get = ptrs_src.get();
+                void **ptrs_dst_get = ptrs_dst.get();
+                size_t nb12_scaled = src1->type == GGML_TYPE_F16 ? nb12 : nb12 / 2;
+                size_t nb13_scaled = src1->type == GGML_TYPE_F16 ? nb13 : nb13 / 2;
+                cgh.parallel_for(sycl::nd_range<3>(block_dims, block_dims),
+                                 [=](sycl::nd_item<3> item_ct1) {
+                                     k_compute_batched_ptrs(
+                                         src0_as_f16, src1_f16,
+                                         dst_t, ptrs_src_get,
+                                         ptrs_dst_get, ne12, ne13, ne23,
+                                         nb02, nb03, nb12_scaled, nb13_scaled,
+                                         nbd2, nbd3, r2, r3, item_ct1);
+                                 });
+            }).wait();
+        }
+        SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(
+            *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans,
+            oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha,
+            (const void **)(ptrs_src.get() + 0 * ne23),
+            dpct::library_data_t::real_half, nb01 / nb00,
+            (const void **)(ptrs_src.get() + 1 * ne23),
+            dpct::library_data_t::real_half, nb11 / nb10, beta,
+            (void **)(ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23,
+            cu_compute_type)));
+        g_sycl_handles[g_main_device]->wait();
+    }
+#endif
+
+    if (no_mixed_dtypes) {
+        const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
+        to_fp32_sycl(dst_f16.get(), dst_ddf, ne_dst, main_stream);
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+
+static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    const bool all_on_device =
+        (src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT) &&
+        (src1->backend == GGML_BACKEND_TYPE_GPU) &&
+        ( dst->backend == GGML_BACKEND_TYPE_GPU);
+
+    const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
+
+    int64_t min_compute_capability = INT_MAX;
+    for (int i = 0; i < g_device_count; ++i) {
+        if (min_compute_capability > g_device_caps[i].cc && g_tensor_split[i] < (i + 1 < g_device_count ? g_tensor_split[i + 1] : 1.0f)) {
+            min_compute_capability = g_device_caps[i].cc;
+        }
+    }
+
+#ifdef SYCL_USE_XMX
+    const bool use_xmx = true;
+#else
+    const bool use_xmx = false;
+#endif
+
+    // debug helpers
+    //printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]);
+    //printf("      %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]);
+    //printf("src1: %8d %8d %8d %8d\n", src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]);
+    //printf("      %8d %8d %8d %8d\n", src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3]);
+    //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
+    //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
+
+    if (!split && all_on_device && !use_xmx && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
+        // KQ single-batch
+        // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_p021\n");
+        ggml_sycl_mul_mat_vec_p021(src0, src1, dst);
+    } else if (!split && all_on_device && !use_xmx && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
+        // KQV single-batch
+        // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_nc\n");
+        ggml_sycl_mul_mat_vec_nc(src0, src1, dst);
+    } else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
+        // KQ + KQV multi-batch
+        // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_batched_sycl\n");
+        ggml_sycl_mul_mat_batched_sycl(src0, src1, dst);
+    } else if (src0->type == GGML_TYPE_F32) {
+        // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat\n");
+        ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false);
+    } else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) {
+        // GGML_SYCL_DEBUG("ggml_is_quantized or GGML_TYPE_F16\n");
+        if (src1->ne[1] == 1 && src0->ne[0] % GGML_SYCL_DMMV_X == 0) {
+#ifdef GGML_SYCL_FORCE_DMMV
+            const bool use_mul_mat_vec_q = false;
+#else
+            const bool use_mul_mat_vec_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type) && ggml_nrows(src1) == 1;
+#endif // GGML_SYCL_FORCE_DMMV
+
+            if (use_mul_mat_vec_q) {
+                // NOTE: this kernel does not support ggml_nrows(src1) > 1
+                // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_vec_q path\n");
+                ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_vec_q, true);
+            } else {
+                // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_dequantize_mul_mat_vec path\n");
+                ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_dequantize_mul_mat_vec, false);
+            }
+        } else {
+            bool use_mul_mat_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type);
+
+            if (use_xmx && min_compute_capability >= VER_GEN9 && src1->ne[1] > XMX_MAX_BATCH_SIZE) {
+                use_mul_mat_q = false;
+            }
+
+            if (use_mul_mat_q) {
+                // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_q path\n");
+                ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_q, true);
+            } else {
+                // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_sycl path\n");
+                ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false);
+            }
+        }
+    } else {
+        GGML_ASSERT(false);
+    }
+}
+
+#if 0
+template<typename ... Srcs>
+static __global__ void k_compute_batched_ptrs_id(
+        const void ** ptrs_src, void ** ptrs_dst,
+        int ne12, int ne13,
+        int ne23,
+        int nb02, int nb03,
+        int nb12, int nb13,
+        int nb2, int nb3,
+        int r2, int r3,
+        ggml_type src0_type, half * src0_as_f16, int64_t src0_ne,
+        const half * src1_f16, half * dst_f16,
+        const int32_t * ids, const int id,
+        Srcs... src0s) {
+
+    int i = ids[id];
+
+    half * src0_f16;
+    const void * srcs_ar[] = { (const half *) src0s... };
+    if (src0_type == GGML_TYPE_F16) {
+        src0_f16 = (half *) srcs_ar[i];
+    } else {
+        src0_f16 = src0_as_f16;
+        if (item_ct1.get_local_id(2) == 0 && threadIdx.y == 0) {
+            const to_fp16_sycl_t to_fp16 = ggml_get_to_fp16_sycl(src0_type);
+            to_fp16(srcs_ar[i], src0_f16, src0_ne, syclStreamFireAndForget);
+        }
+    }
+
+    int i13 = blockIdx.x * blockDim.x + item_ct1.get_local_id(2);
+    int i12 = blockIdx.y * blockDim.y + threadIdx.y;
+
+    if (i13 >= ne13 || i12 >= ne12) {
+        return;
+    }
+
+    int i03 = i13 / r3;
+    int i02 = i12 / r2;
+
+    ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_f16 + i02*nb02   + i03*nb03;
+    ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_f16 + i12*nb12/2 + i13*nb13/2;
+    ptrs_dst[0*ne23 + i12 + i13*ne12] = (      char *)  dst_f16 + i12* nb2/2 + i13* nb3/2;
+}
+
+static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) {
+    const struct ggml_tensor * ids = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+    const struct ggml_tensor * src00 = dst->src[2];
+
+    const int id = dst->op_params[0];
+
+    GGML_ASSERT(!ggml_is_transposed(src00));
+    GGML_ASSERT(!ggml_is_transposed(src1));
+
+    GGML_ASSERT(src00->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    GGML_TENSOR_LOCALS(int64_t, ne0, src00, ne);
+
+    //const int64_t nb01 = src00->nb[1];
+    GGML_TENSOR_LOCALS(int64_t, nb0, src00, nb);
+
+    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);
+
+    GGML_TENSOR_LOCALS(int64_t, nb1, src1, nb);
+    //const int64_t nb11 = src1->nb[1];
+
+    const int64_t ne1 = ggml_nelements(src1);
+    const int64_t ne  = ggml_nelements(dst);
+
+    SYCL_CHECK(ggml_sycl_set_device(g_main_device));
+    syclStream_t main_stream = g_syclStreams[g_main_device][0];
+
+    SYCL_CHECK(syclSetStream(g_sycl_handles[g_main_device], main_stream));
+
+    //ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
+    //void * src0_ddq = src0_extra->data_device[g_main_device];
+    //half * src0_as_f16 = (half *) src0_ddq;
+
+    ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
+    float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
+
+    ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
+    float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
+
+    // convert src1 to fp16
+    const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type);
+    GGML_ASSERT(to_fp16_sycl != nullptr);
+
+    size_t src1_as = 0;
+    half * src1_as_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, ne1 * sizeof(half), &src1_as);
+    to_fp16_sycl(src1_ddf, src1_as_f16, ne1, main_stream);
+
+    size_t dst_as = 0;
+    half * dst_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, ne * sizeof(half), &dst_as);
+
+    GGML_ASSERT(ne12 % ne02 == 0);
+    GGML_ASSERT(ne13 % ne03 == 0);
+
+    // broadcast factors
+    const int64_t r2 = ne12/ne02;
+    const int64_t r3 = ne13/ne03;
+
+    const half alpha_f16 = 1.0f;
+    const half beta_f16  = 0.0f;
+
+    // use syclGemmBatchedEx
+    const int ne23 = ne12*ne13;
+
+    const void ** ptrs_src = nullptr;
+          void ** ptrs_dst = nullptr;
+
+    size_t ptrs_src_s = 0;
+    size_t ptrs_dst_s = 0;
+
+    ptrs_src = (const void **) ggml_sycl_pool_malloc(g_main_device, 2*ne23*sizeof(void *), &ptrs_src_s);
+    ptrs_dst = (      void **) ggml_sycl_pool_malloc(g_main_device, 1*ne23*sizeof(void *), &ptrs_dst_s);
+
+    int64_t src0_ne = ggml_nelements(src00);
+    half * src0_as_f16 = nullptr;
+    size_t src0_as = 0;
+    if (src00->type != GGML_TYPE_F16) {
+        src0_as_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, src0_ne * sizeof(half), &src0_as);
+    }
+
+    static_assert(GGML_MAX_SRC == 6, "GGML_MAX_SRC == 6");
+    dim3 block_dims(ne13, ne12);
+    k_compute_batched_ptrs_id<<<1, block_dims, 0, main_stream>>>(
+            ptrs_src, ptrs_dst,
+            ne12, ne13,
+            ne23,
+            ne00*ne01*sizeof(half), ne00*ne01*ne02*sizeof(half),
+            nb12, nb13,
+            dst->nb[2], dst->nb[3],
+            r2, r3,
+            src00->type, src0_as_f16, src0_ne,
+            src1_as_f16, dst_f16,
+            (const int *)((ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device], id,
+            dst->src[2] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[2]->extra)->data_device[g_main_device] : nullptr,
+            dst->src[3] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[3]->extra)->data_device[g_main_device] : nullptr,
+            dst->src[4] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[4]->extra)->data_device[g_main_device] : nullptr,
+            dst->src[5] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[5]->extra)->data_device[g_main_device] : nullptr
+    );
+    SYCL_CHECK(syclGetLastError());
+
+    SYCL_CHECK(
+    syclGemmBatchedEx(g_sycl_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
+            ne01, ne11, ne10,
+            &alpha_f16, (const void **) (ptrs_src + 0*ne23), SYCL_R_16F, ne00,
+                        (const void **) (ptrs_src + 1*ne23), SYCL_R_16F, ne10,
+            &beta_f16,  (      void **) (ptrs_dst + 0*ne23), SYCL_R_16F, ne01,
+            ne23,
+            CUBLAS_COMPUTE_16F,
+            CUBLAS_GEMM_DEFAULT_TENSOR_OP));
+
+    if (src0_as != 0) {
+        ggml_sycl_pool_free(g_main_device, src0_as_f16, src0_as);
+    }
+    if (ptrs_src_s != 0) {
+        ggml_sycl_pool_free(g_main_device, ptrs_src, ptrs_src_s);
+    }
+    if (ptrs_dst_s != 0) {
+        ggml_sycl_pool_free(g_main_device, ptrs_dst, ptrs_dst_s);
+    }
+
+    const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
+    to_fp32_sycl(dst_f16, dst_ddf, ne, main_stream);
+
+    ggml_sycl_pool_free(g_main_device, src1_as_f16, src1_as);
+    ggml_sycl_pool_free(g_main_device, dst_f16, dst_as);
+}
+#endif
+
+static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
+                                 const ggml_tensor *src1,
+                                 ggml_tensor *dst) try {
+#if 0
+    ggml_sycl_mul_mat_id_sycl(dst);
+    // TODO: mmq/mmv support
+#endif
+
+    const int64_t nb11 = src1->nb[1];
+    const int64_t nb1  =  dst->nb[1];
+
+    const struct ggml_tensor * ids = src0;
+    const int32_t id = ((int32_t *) dst->op_params)[0];
+    const int32_t n_as = ((int32_t *) dst->op_params)[1];
+
+    std::vector<char> ids_host(ggml_nbytes(ids));
+
+    const dpct::queue_ptr stream = g_syclStreams[g_main_device][0];
+
+    if (ids->backend == GGML_BACKEND_TYPE_GPU) {
+        const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device];
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids)).wait()));
+        // SYCL_CHECK(CHECK_TRY_ERROR(stream->wait()));
+    } else {
+        memcpy(ids_host.data(), ids->data, ggml_nbytes(ids));
+    }
+
+    const ggml_tensor_extra_gpu * src1_extra = (const ggml_tensor_extra_gpu *) src1->extra;
+    const ggml_tensor_extra_gpu * dst_extra = (const ggml_tensor_extra_gpu *) dst->extra;
+
+    ggml_tensor_extra_gpu src1_row_extra;
+    ggml_tensor_extra_gpu dst_row_extra;
+
+    ggml_tensor src1_row = *src1;
+    ggml_tensor dst_row = *dst;
+
+    src1_row.backend = GGML_BACKEND_TYPE_GPU;
+    dst_row.backend  = GGML_BACKEND_TYPE_GPU;
+
+    src1_row.extra = &src1_row_extra;
+    dst_row.extra = &dst_row_extra;
+
+    char * src1_original = src1->backend == GGML_BACKEND_TYPE_CPU ?
+        (char *) src1->data : (char *) src1_extra->data_device[g_main_device];
+    char * dst_original  =  dst->backend == GGML_BACKEND_TYPE_CPU ?
+        (char *)  dst->data : (char *)  dst_extra->data_device[g_main_device];
+
+    if (src1->ne[1] == 1) {
+        GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
+        GGML_ASSERT(dst->backend  == GGML_BACKEND_TYPE_GPU);
+
+        for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
+            //int32_t row_id;
+            //SYCL_CHECK(syclMemcpyAsync(&row_id, ids_dev + i01*ids->nb[1] + id*ids->nb[0], sizeof(int32_t), syclMemcpyDeviceToHost, g_syclStreams[g_main_device][0]));
+            //SYCL_CHECK(syclStreamSynchronize(g_syclStreams[g_main_device][0]));
+
+            const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
+
+            GGML_ASSERT(row_id >= 0 && row_id < n_as);
+
+            const struct ggml_tensor * src0_row = dst->src[row_id + 2];
+
+            src1_row_extra.data_device[g_main_device] = src1_original + i01*src1->nb[1];
+            src1_row.data = (char *) src1->data + i01*src1->nb[1]; // TODO why is this set?
+
+            dst_row_extra.data_device[g_main_device] = dst_original + i01*dst->nb[1];
+            dst_row.data = (char *) dst->data + i01*dst->nb[1]; // TODO why is this set?
+
+            ggml_sycl_mul_mat(src0_row, &src1_row, &dst_row);
+        }
+    } else {
+        sycl_pool_alloc<char> src1_contiguous(sizeof(float)*ggml_nelements(src1));
+        sycl_pool_alloc<char>  dst_contiguous(sizeof(float)*ggml_nelements(dst));
+
+        src1_row_extra.data_device[g_main_device] = src1_contiguous.get();
+        dst_row_extra.data_device[g_main_device]  =  dst_contiguous.get();
+
+        for (int32_t row_id = 0; row_id < n_as; ++row_id) {
+            const struct ggml_tensor * src0_row = dst->src[row_id + 2];
+
+            int64_t num_src1_rows = 0;
+            for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
+                const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
+
+                if (row_id_i != row_id) {
+                    continue;
+                }
+
+                GGML_ASSERT(row_id >= 0 && row_id < n_as);
+
+                SYCL_CHECK(CHECK_TRY_ERROR(
+                    stream->memcpy(src1_contiguous.get() + num_src1_rows * nb11,
+                                   src1_original + i01 * nb11, nb11).wait()));
+                num_src1_rows++;
+            }
+
+            if (num_src1_rows == 0) {
+                continue;
+            }
+
+            src1_row.ne[1] = num_src1_rows;
+            dst_row.ne[1] = num_src1_rows;
+
+            src1_row.nb[1] = nb11;
+            src1_row.nb[2] = num_src1_rows*nb11;
+            src1_row.nb[3] = num_src1_rows*nb11;
+
+            dst_row.nb[1] = nb1;
+            dst_row.nb[2] = num_src1_rows*nb1;
+            dst_row.nb[3] = num_src1_rows*nb1;
+
+            ggml_sycl_mul_mat(src0_row, &src1_row, &dst_row);
+
+            num_src1_rows = 0;
+            for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
+                const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
+
+                if (row_id_i != row_id) {
+                    continue;
+                }
+
+                GGML_ASSERT(row_id >= 0 && row_id < n_as);
+
+                SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(
+                    dst_original + i01 * nb1,
+                    dst_contiguous.get() + num_src1_rows * nb1, nb1).wait()));
+                num_src1_rows++;
+            }
+        }
+    }
+
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
+        SYCL_CHECK(CHECK_TRY_ERROR(stream->wait()));
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_sycl_scale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_scale);
+}
+
+static void ggml_sycl_clamp(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_clamp);
+}
+
+static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1,
+                          ggml_tensor *dst) try {
+    const int64_t ne = ggml_nelements(src0);
+    GGML_ASSERT(ne == ggml_nelements(src1));
+
+    GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
+    GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
+
+    GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
+    GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);
+
+    GGML_TENSOR_BINARY_OP_LOCALS;
+
+    SYCL_CHECK(ggml_sycl_set_device(g_main_device));
+    dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
+
+    const ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
+    const ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
+
+    char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
+    char * src1_ddc = (char *) src1_extra->data_device[g_main_device];
+
+    if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
+        ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
+        ggml_cpy_f32_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
+        ggml_cpy_f32_q8_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
+        ggml_cpy_f32_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
+        ggml_cpy_f32_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
+        ggml_cpy_f16_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
+        ggml_cpy_f16_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_I16 && src1->type == GGML_TYPE_I16) {
+        ggml_cpy_i16_i16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32) {
+        ggml_cpy_i32_i32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else {
+        fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
+                ggml_type_name(src0->type), ggml_type_name(src1->type));
+        GGML_ASSERT(false);
+    }
+
+    (void) dst;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_sycl_dup(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    // TODO: why do we pass dst as src1 here?
+    ggml_sycl_cpy(src0, dst, nullptr);
+    (void) src1;
+}
+
+static void ggml_sycl_diag_mask_inf(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_diag_mask_inf);
+}
+
+static void ggml_sycl_soft_max(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_soft_max);
+}
+
+static void ggml_sycl_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_rope);
+}
+
+static void ggml_sycl_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_alibi);
+}
+
+static void ggml_sycl_pool2d(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_pool2d);
+}
+
+static void ggml_sycl_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_im2col);
+}
+
+static void ggml_sycl_sum_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_sum_rows);
+}
+
+static void ggml_sycl_argsort(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_argsort);
+}
+
+static void ggml_sycl_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    (void) src0;
+    (void) src1;
+    (void) dst;
+}
+
+static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+
+    return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]);
+}
+
+void ggml_sycl_free_data(struct ggml_tensor *tensor) try {
+    if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_TYPE_GPU && tensor->backend != GGML_BACKEND_TYPE_GPU_SPLIT) ) {
+        return;
+    }
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
+
+    for (int i = 0; i < g_device_count; ++i) {
+        const dpct::queue_ptr stream = g_syclStreams[i][0];
+        if (extra->data_device[i] != nullptr) {
+            SYCL_CHECK(ggml_sycl_set_device(i));
+            SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(extra->data_device[i], *stream)));
+        }
+
+        for (int64_t is = 0; is < MAX_STREAMS; ++is) {
+            if (extra->events[i][is] != nullptr) {
+                SYCL_CHECK(ggml_sycl_set_device(i));
+                SYCL_CHECK(CHECK_TRY_ERROR(
+                    dpct::destroy_event(extra->events[i][is])));
+            }
+        }
+    }
+
+    delete extra;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static ggml_tensor_extra_gpu * g_temp_tensor_extras = nullptr;
+static size_t g_temp_tensor_extra_index = 0;
+
+static ggml_tensor_extra_gpu * ggml_sycl_alloc_temp_tensor_extra() {
+    if (g_temp_tensor_extras == nullptr) {
+        g_temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_SYCL_MAX_NODES];
+    }
+
+    size_t alloc_index = g_temp_tensor_extra_index;
+    g_temp_tensor_extra_index = (g_temp_tensor_extra_index + 1) % GGML_SYCL_MAX_NODES;
+    ggml_tensor_extra_gpu * extra = &g_temp_tensor_extras[alloc_index];
+    memset(extra, 0, sizeof(*extra));
+
+    return extra;
+}
+
+static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor,
+                                          bool scratch, bool force_inplace,
+                                          bool no_alloc) try {
+    if (scratch && g_scratch_size == 0) {
+        return;
+    }
+
+    tensor->backend = GGML_BACKEND_TYPE_GPU;
+
+    if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_TYPE_CPU) {
+        const ggml_op src0_op = tensor->src[0]->op;
+        if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW || src0_op == GGML_OP_PERMUTE) {
+            ggml_sycl_assign_buffers_impl(tensor->src[0], scratch, force_inplace, no_alloc);
+        }
+    }
+    if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_TYPE_CPU) {
+        ggml_sycl_assign_buffers_impl(tensor->src[1], scratch, force_inplace, no_alloc);
+    }
+
+    if (scratch && no_alloc) {
+        return;
+    }
+
+    ggml_tensor_extra_gpu * extra;
+
+    const bool inplace = (tensor->src[0] != nullptr && tensor->src[0]->data == tensor->data) ||
+        tensor->op == GGML_OP_VIEW ||
+        force_inplace;
+    const size_t size = ggml_nbytes(tensor);
+
+    SYCL_CHECK(ggml_sycl_set_device(g_main_device));
+    const dpct::queue_ptr stream = g_syclStreams[g_main_device][0];
+
+    if (inplace && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) {
+        ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra;
+        char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
+        size_t offset = 0;
+        if (tensor->op == GGML_OP_VIEW) {
+            memcpy(&offset, tensor->op_params, sizeof(size_t));
+        }
+        extra = ggml_sycl_alloc_temp_tensor_extra();
+        extra->data_device[g_main_device] = src0_ddc + offset;
+    } else if (tensor->op == GGML_OP_CPY) {
+        ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu * ) tensor->src[1]->extra;
+        void * src1_ddv = src1_extra->data_device[g_main_device];
+        extra = ggml_sycl_alloc_temp_tensor_extra();
+        extra->data_device[g_main_device] = src1_ddv;
+    } else if (scratch) {
+        GGML_ASSERT(size <= g_scratch_size);
+        if (g_scratch_offset + size > g_scratch_size) {
+            g_scratch_offset = 0;
+        }
+
+        char * data = (char *) g_scratch_buffer;
+        if (data == nullptr) {
+            SYCL_CHECK(CHECK_TRY_ERROR(
+                data = (char *)sycl::malloc_device(
+                    g_scratch_size, *stream)));
+            g_scratch_buffer = data;
+        }
+        extra = ggml_sycl_alloc_temp_tensor_extra();
+        extra->data_device[g_main_device] = data + g_scratch_offset;
+
+        g_scratch_offset += size;
+
+        GGML_ASSERT(g_scratch_offset <= g_scratch_size);
+    } else { // allocate new buffers outside of scratch
+        void * data;
+        SYCL_CHECK(CHECK_TRY_ERROR(data = (void *)sycl::malloc_device(
+                                        size, *stream)));
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            (*stream).memset(data, 0, size).wait()));
+        extra = new ggml_tensor_extra_gpu;
+        memset(extra, 0, sizeof(*extra));
+        extra->data_device[g_main_device] = data;
+    }
+
+    tensor->extra = extra;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+void ggml_sycl_copy_to_device(struct ggml_tensor *tensor) try {
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
+    GGML_ASSERT(ggml_is_contiguous(tensor));
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
+    SYCL_CHECK(ggml_sycl_set_device(g_main_device));
+    const dpct::queue_ptr stream = g_syclStreams[g_main_device][0];
+    SYCL_CHECK(CHECK_TRY_ERROR((*stream)
+                                    .memcpy(extra->data_device[g_main_device],
+                                            tensor->data, ggml_nbytes(tensor))
+                                    .wait()));
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+void ggml_sycl_assign_buffers(struct ggml_tensor * tensor) {
+    ggml_sycl_assign_buffers_impl(tensor, true, false, false);
+}
+
+void ggml_sycl_assign_buffers_no_alloc(struct ggml_tensor * tensor) {
+    ggml_sycl_assign_buffers_impl(tensor, true, false, true);
+}
+
+void ggml_sycl_assign_buffers_no_scratch(struct ggml_tensor * tensor) {
+    ggml_sycl_assign_buffers_impl(tensor, false, false, false);
+}
+
+void ggml_sycl_assign_buffers_force_inplace(struct ggml_tensor * tensor) {
+    ggml_sycl_assign_buffers_impl(tensor, false, true, false);
+}
+
+void ggml_sycl_set_main_device(const int main_device) try {
+    if (g_main_device == main_device) return;
+    check_allow_gpu_index(main_device);
+    g_main_device = main_device;
+    g_main_device_id = g_sycl_gpu_mgr->gpus[main_device];
+
+    if (g_ggml_sycl_debug) {
+        dpct::device_info prop;
+        SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info(
+            prop, dpct::dev_mgr::instance().get_device(g_main_device_id))));
+        fprintf(stderr, "Using device %d (%s) as main device\n",
+                g_main_device_id, prop.get_name());
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+void ggml_sycl_set_scratch_size(const size_t scratch_size) {
+    // this is a hack to not completely break llama.cpp when using multiple models or contexts simultaneously
+    // it still won't always work as expected, but it's better than nothing
+    if (scratch_size > g_scratch_size) {
+        ggml_sycl_free_scratch();
+    }
+    g_scratch_size = std::max(g_scratch_size, scratch_size);
+}
+
+void ggml_sycl_free_scratch() try {
+    if (g_scratch_buffer == nullptr) {
+        return;
+    }
+    ggml_sycl_set_device(g_main_device);
+    const dpct::queue_ptr stream = g_syclStreams[g_main_device][0];
+
+    SYCL_CHECK(CHECK_TRY_ERROR(
+        sycl::free(g_scratch_buffer, *stream)));
+    g_scratch_buffer = nullptr;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
+    if (!g_sycl_loaded) return false;
+
+    ggml_sycl_func_t func;
+    const bool any_on_device = tensor->backend == GGML_BACKEND_TYPE_GPU
+        || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT))
+        || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_TYPE_GPU);
+
+    if (!any_on_device && tensor->op != GGML_OP_MUL_MAT && tensor->op != GGML_OP_MUL_MAT_ID) {
+        return false;
+    }
+
+    if (tensor->op == GGML_OP_MUL_MAT) {
+        if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) {
+#ifndef NDEBUG
+            fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, tensor->name, tensor->src[0]->ne[3], tensor->src[1]->ne[3]);
+#endif
+            return false;
+        }
+    }
+
+    switch (tensor->op) {
+        case GGML_OP_REPEAT:
+            func = ggml_sycl_repeat;
+            break;
+        case GGML_OP_GET_ROWS:
+            func = ggml_sycl_get_rows;
+            break;
+        case GGML_OP_DUP:
+            func = ggml_sycl_dup;
+            break;
+        case GGML_OP_ADD:
+            func = ggml_sycl_add;
+            break;
+        case GGML_OP_ACC:
+            func = ggml_sycl_acc;
+            break;
+        case GGML_OP_MUL:
+            func = ggml_sycl_mul;
+            break;
+        case GGML_OP_DIV:
+            func = ggml_sycl_div;
+            break;
+        case GGML_OP_UNARY:
+            switch (ggml_get_unary_op(tensor)) {
+                case GGML_UNARY_OP_GELU:
+                    func = ggml_sycl_gelu;
+                    break;
+                case GGML_UNARY_OP_SILU:
+                    func = ggml_sycl_silu;
+                    break;
+                case GGML_UNARY_OP_GELU_QUICK:
+                    func = ggml_sycl_gelu_quick;
+                    break;
+                case GGML_UNARY_OP_TANH:
+                    func = ggml_sycl_tanh;
+                    break;
+                case GGML_UNARY_OP_RELU:
+                    func = ggml_sycl_relu;
+                    break;
+                case GGML_UNARY_OP_HARDSIGMOID:
+                    func = ggml_sycl_hardsigmoid;
+                    break;
+                case GGML_UNARY_OP_HARDSWISH:
+                    func = ggml_sycl_hardswish;
+                    break;
+                default:
+                    return false;
+            }
+            break;
+        case GGML_OP_NORM:
+            func = ggml_sycl_norm;
+            break;
+        case GGML_OP_GROUP_NORM:
+            func = ggml_sycl_group_norm;
+            break;
+        case GGML_OP_CONCAT:
+            func = ggml_sycl_concat;
+            break;
+        case GGML_OP_UPSCALE:
+            func = ggml_sycl_upscale;
+            break;
+        case GGML_OP_PAD:
+            func = ggml_sycl_pad;
+            break;
+        case GGML_OP_LEAKY_RELU:
+            func = ggml_sycl_leaky_relu;
+            break;
+        case GGML_OP_RMS_NORM:
+            func = ggml_sycl_rms_norm;
+            break;
+        case GGML_OP_MUL_MAT:
+            if (!any_on_device && !ggml_sycl_can_mul_mat(tensor->src[0], tensor->src[1], tensor)) {
+                return false;
+            }
+            func = ggml_sycl_mul_mat;
+            break;
+        case GGML_OP_MUL_MAT_ID:
+            if (!any_on_device && !ggml_sycl_can_mul_mat(tensor->src[2], tensor->src[1], tensor)) {
+                return false;
+            }
+            func = ggml_sycl_mul_mat_id;
+            break;
+        case GGML_OP_SCALE:
+            func = ggml_sycl_scale;
+            break;
+        case GGML_OP_SQR:
+            func = ggml_sycl_sqr;
+            break;
+        case GGML_OP_CLAMP:
+            func = ggml_sycl_clamp;
+            break;
+        case GGML_OP_CPY:
+            func = ggml_sycl_cpy;
+            break;
+        case GGML_OP_CONT:
+            func = ggml_sycl_dup;
+            break;
+        case GGML_OP_NONE:
+        case GGML_OP_RESHAPE:
+        case GGML_OP_VIEW:
+        case GGML_OP_PERMUTE:
+        case GGML_OP_TRANSPOSE:
+            func = ggml_sycl_nop;
+            break;
+        case GGML_OP_DIAG_MASK_INF:
+            func = ggml_sycl_diag_mask_inf;
+            break;
+        case GGML_OP_SOFT_MAX:
+            func = ggml_sycl_soft_max;
+            break;
+        case GGML_OP_ROPE:
+            func = ggml_sycl_rope;
+            break;
+        case GGML_OP_ALIBI:
+            func = ggml_sycl_alibi;
+            break;
+        case GGML_OP_IM2COL:
+            func = ggml_sycl_im2col;
+            break;
+        case GGML_OP_POOL_2D:
+            func = ggml_sycl_pool2d;
+            break;
+        case GGML_OP_SUM_ROWS:
+            func = ggml_sycl_sum_rows;
+            break;
+        case GGML_OP_ARGSORT:
+            func = ggml_sycl_argsort;
+            break;
+        default:
+            return false;
+    }
+
+    if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
+        ggml_sycl_set_peer_access(tensor->src[1]->ne[1]);
+    }
+
+    if (params->ith != 0) {
+        return true;
+    }
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return true;
+    }
+    func(tensor->src[0], tensor->src[1], tensor);
+    return true;
+}
+
+GGML_API GGML_CALL void   ggml_sycl_get_gpu_list(int *id_list, int max_len) try {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_sycl_get_gpu_list\n");
+    for(int i=0;i<max_len;i++) id_list[i] = -1;
+
+    if (!g_sycl_gpu_mgr) {
+        g_sycl_gpu_mgr = new sycl_gpu_mgr();
+    }
+
+    for (int i=0;i< g_sycl_gpu_mgr->gpus.size();i++){
+        if (i>=max_len) break;
+        id_list[i] = g_sycl_gpu_mgr->gpus[i];
+    }
+    return;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+int ggml_sycl_get_device_count() try {
+    int device_count;
+    if (CHECK_TRY_ERROR(device_count =
+                             dpct::dev_mgr::instance().device_count()) != 0) {
+        return 0;
+    }
+    return device_count;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_API GGML_CALL void ggml_sycl_get_device_description(int device, char *description,
+                                      size_t description_size) try {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_sycl_get_device_description\n");
+    dpct::device_info prop;
+    int device_id = g_sycl_gpu_mgr->gpus[device];
+    SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info(
+        prop, dpct::dev_mgr::instance().get_device(device_id))));
+    snprintf(description, description_size, "%s", prop.get_name());
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free,
+                                                   size_t *total) try {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_memory\n");
+    ggml_sycl_set_device(device);
+
+    /*
+    DPCT1009:218: SYCL uses exceptions to report errors and does not use the
+    error codes. The original code was commented out and a warning string was
+    inserted. You need to rewrite this code.
+    */
+    /*
+    DPCT1106:217: 'cudaMemGetInfo' was migrated with the Intel extensions for
+    device information which may not be supported by all compilers or runtimes.
+    You may need to adjust the code.
+    */
+   int device_id = g_sycl_gpu_mgr->gpus[device];
+    SYCL_CHECK(CHECK_TRY_ERROR(
+        dpct::dev_mgr::instance().get_device(device_id).get_memory_info(*free, *total)));
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
+// backend interface
+
+#define UNUSED GGML_UNUSED
+
+// sycl buffer
+
+struct ggml_backend_sycl_buffer_context {
+    int device;
+    void * dev_ptr = nullptr;
+    ggml_tensor_extra_gpu * temp_tensor_extras = nullptr;
+    size_t temp_tensor_extra_index = 0;
+    std::string name;
+
+     ggml_backend_sycl_buffer_context(int device, void * dev_ptr) :
+        device(device), dev_ptr(dev_ptr) {
+            check_allow_gpu_index(device);
+            int id = g_sycl_gpu_mgr->gpus[device];
+            name = (GGML_SYCL_NAME + std::to_string(id));
+        }
+
+    ~ ggml_backend_sycl_buffer_context() {
+        delete[] temp_tensor_extras;
+    }
+
+    ggml_tensor_extra_gpu * ggml_sycl_alloc_temp_tensor_extra() {
+        if (temp_tensor_extras == nullptr) {
+            temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_SYCL_MAX_NODES];
+        }
+
+        size_t alloc_index = temp_tensor_extra_index;
+        temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_SYCL_MAX_NODES;
+        ggml_tensor_extra_gpu * extra = &temp_tensor_extras[alloc_index];
+        memset(extra, 0, sizeof(*extra));
+
+        return extra;
+    }
+};
+
+GGML_CALL static const char * ggml_backend_sycl_buffer_get_name(ggml_backend_buffer_t buffer) {
+    ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context;
+    return ctx->name.c_str();
+}
+
+GGML_CALL static bool ggml_backend_buffer_is_sycl(ggml_backend_buffer_t buffer) {
+    return buffer->iface.get_name == ggml_backend_sycl_buffer_get_name;
+}
+
+static void
+ggml_backend_sycl_buffer_free_buffer(ggml_backend_buffer_t buffer) try {
+    ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
+    ggml_sycl_set_device(ctx->device);
+    const dpct::queue_ptr stream = g_syclStreams[ctx->device][0];
+
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(sycl::free(ctx->dev_ptr, *stream)));
+    delete ctx;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void * ggml_backend_sycl_buffer_get_base(ggml_backend_buffer_t buffer) {
+    ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
+    return ctx->dev_ptr;
+}
+
+GGML_CALL static void
+ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer,
+                                     ggml_tensor *tensor) try {
+    ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context;
+
+    if (tensor->view_src != NULL && tensor->view_offs == 0) {
+        assert(tensor->view_src->buffer->buft == buffer->buft);
+        tensor->backend = tensor->view_src->backend;
+        tensor->extra = tensor->view_src->extra;
+        return;
+    }
+
+    ggml_tensor_extra_gpu * extra = ctx->ggml_sycl_alloc_temp_tensor_extra();
+
+    extra->data_device[ctx->device] = tensor->data;
+    tensor->backend = GGML_BACKEND_TYPE_GPU;
+    tensor->extra = extra;
+
+    if (ggml_is_quantized(tensor->type)) {
+        // initialize padding to 0 to avoid possible NaN values
+        size_t original_size = ggml_nbytes(tensor);
+        size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor);
+
+        if (padded_size > original_size && tensor->view_src == nullptr) {
+            SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[ctx->device][0]->memset(
+                (char *)tensor->data + original_size, 0,
+                padded_size - original_size).wait()));
+        }
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer,
+                                                ggml_tensor *tensor,
+                                                const void *data, size_t offset,
+                                                size_t size) try {
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
+
+    ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
+
+    ggml_sycl_set_device(ctx->device);
+    const dpct::queue_ptr stream = g_syclStreams[ctx->device][0];
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw()));
+
+    SYCL_CHECK(
+        CHECK_TRY_ERROR((*stream)
+                             .memcpy((char *)tensor->data + offset, data, size)
+                             .wait()));
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_backend_sycl_buffer_get_tensor(ggml_backend_buffer_t buffer,
+                                                const ggml_tensor *tensor,
+                                                void *data, size_t offset,
+                                                size_t size) try {
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
+
+    ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
+
+    ggml_sycl_set_device(ctx->device);
+    const dpct::queue_ptr stream = g_syclStreams[ctx->device][0];
+
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw()));
+
+    SYCL_CHECK(CHECK_TRY_ERROR(
+        (*stream)
+            .memcpy(data, (const char *)tensor->data + offset, size)
+            .wait()));
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static bool
+ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer,
+                                    const ggml_tensor *src,
+                                    ggml_tensor *dst) try {
+    if (ggml_backend_buffer_is_sycl(src->buffer)) {
+        ggml_backend_sycl_buffer_context * src_ctx = (ggml_backend_sycl_buffer_context *)src->buffer->context;
+        ggml_backend_sycl_buffer_context * dst_ctx = (ggml_backend_sycl_buffer_context *)buffer->context;
+
+        ggml_sycl_set_device(src_ctx->device);
+        /*
+        DPCT1009:198: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            dpct::dev_mgr::instance().get_device(src_ctx->device).queues_wait_and_throw()));
+        ggml_sycl_set_device(dst_ctx->device);
+        /*
+        DPCT1009:199: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw()));
+        /*
+        DPCT1009:200: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+
+        dpct::queue_ptr stream_dst = g_syclStreams[dst_ctx->device][0];
+        dpct::queue_ptr stream_src = g_syclStreams[src_ctx->device][0];
+        size_t size = ggml_nbytes(src);
+
+        //todo. it's dirty solutino to walkaroud known issue:device2device cross GPUs.
+        dev2dev_memcpy(*stream_dst, *stream_src, dst->data, src->data, size);
+
+//todo, it's known issue：error in device2device cross GPUs. reused when the issue is fixed. DON"T remove
+#if 0
+        SYCL_CHECK(CHECK_TRY_ERROR((*stream).memcpy(
+            (char *)dst->data, (const char *)src->data, size).wait()));
+
+        /*
+        DPCT1009:201: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw()));
+#endif
+        return true;
+    }
+    return false;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+
+static void ggml_backend_sycl_buffer_clear(ggml_backend_buffer_t buffer,
+                                           uint8_t value) try {
+     ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
+
+    ggml_sycl_set_device(ctx->device);
+    const dpct::queue_ptr stream = g_syclStreams[ctx->device][0];
+    SYCL_CHECK(
+        CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw()));
+
+    SYCL_CHECK(CHECK_TRY_ERROR((*stream)
+                                    .memset(ctx->dev_ptr, value, buffer->size)
+                                    .wait()));
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static struct ggml_backend_buffer_i ggml_backend_sycl_buffer_interface = {
+    /* .get_name        = */ ggml_backend_sycl_buffer_get_name,
+    /* .free_buffer     = */ ggml_backend_sycl_buffer_free_buffer,
+    /* .get_base        = */ ggml_backend_sycl_buffer_get_base,
+    /* .init_tensor     = */ ggml_backend_sycl_buffer_init_tensor,
+    /* .set_tensor      = */ ggml_backend_sycl_buffer_set_tensor,
+    /* .get_tensor      = */ ggml_backend_sycl_buffer_get_tensor,
+    /* .cpy_tensor      = */ ggml_backend_sycl_buffer_cpy_tensor,
+    /* .clear           = */ ggml_backend_sycl_buffer_clear,
+    /* .reset           = */ NULL,
+};
+
+// sycl buffer type
+struct ggml_backend_sycl_buffer_type_context {
+    int device;
+    std::string name;
+};
+
+struct ggml_backend_sycl_context {
+    int device;
+    std::string name;
+};
+
+GGML_CALL static const char * ggml_backend_sycl_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    ggml_backend_sycl_buffer_type_context * ctx = (ggml_backend_sycl_buffer_type_context *)buft->context;
+
+    return ctx->name.c_str();
+}
+GGML_CALL static ggml_backend_buffer_t
+ggml_backend_sycl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft,
+                                           size_t size) try {
+    ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context;
+    ggml_sycl_set_device(buft_ctx->device);
+    const dpct::queue_ptr stream = g_syclStreams[buft_ctx->device][0];
+    size = std::max(size, (size_t)1); // syclMalloc returns null for size 0
+
+    void * dev_ptr;
+    SYCL_CHECK(CHECK_TRY_ERROR(dev_ptr = (void *)sycl::malloc_device(
+                                    size, *stream)));
+    ggml_backend_sycl_buffer_context * ctx = new  ggml_backend_sycl_buffer_context(buft_ctx->device, dev_ptr);
+    return ggml_backend_buffer_init(buft, ggml_backend_sycl_buffer_interface, ctx, size);
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+    return 128;
+    UNUSED(buft);
+}
+
+static size_t ggml_backend_sycl_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
+    return dpct::get_current_device().get_max_mem_alloc_size();
+
+    UNUSED(buft);
+}
+
+GGML_CALL static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
+    size_t size = ggml_nbytes(tensor);
+    int64_t ne0 = tensor->ne[0];
+
+    if (ggml_is_quantized(tensor->type)) {
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
+    }
+
+    return size;
+
+    UNUSED(buft);
+}
+
+GGML_CALL static bool ggml_backend_sycl_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
+    if (!ggml_backend_is_sycl(backend)) {
+        return false;
+    }
+    ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context;
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+    return buft_ctx->device == sycl_ctx->device;
+}
+
+static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = {
+    /* .get_name         = */ ggml_backend_sycl_buffer_type_name,
+    /* .alloc_buffer     = */ ggml_backend_sycl_buffer_type_alloc_buffer,
+    /* .get_alignment    = */ ggml_backend_sycl_buffer_type_get_alignment,
+    /* .get_max_size     = */ ggml_backend_sycl_buffer_type_get_max_size,
+    /* .get_alloc_size   = */ ggml_backend_sycl_buffer_type_get_alloc_size,
+    /* .supports_backend = */ ggml_backend_sycl_buffer_type_supports_backend,
+    /* .is_host          = */ nullptr,
+};
+
+ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device_index) {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_buffer_type\n");
+
+    if (device_index>=g_device_count or device_index<0) {
+        printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n",
+            device_index, g_device_count-1);
+        GGML_ASSERT(device_index<g_device_count);
+    }
+    static struct ggml_backend_buffer_type ggml_backend_sycl_buffer_types[GGML_SYCL_MAX_DEVICES];
+
+    if (!g_ggml_backend_sycl_buffer_type_initialized) {
+        for (int i = 0; i < g_device_count; i++) {
+            ggml_backend_sycl_buffer_types[i] = {
+                /* .iface    = */ ggml_backend_sycl_buffer_type_interface,
+                /* .context  = */ new ggml_backend_sycl_buffer_type_context{i, GGML_SYCL_NAME + std::to_string(g_sycl_gpu_mgr->gpus[i])},
+            };
+        }
+        g_ggml_backend_sycl_buffer_type_initialized = true;
+    }
+    return &ggml_backend_sycl_buffer_types[device_index];
+}
+
+// sycl split buffer type
+static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array<float, GGML_SYCL_MAX_DEVICES> & tensor_split, int id) {
+    const int64_t nrows = ggml_nrows(tensor);
+    const int64_t rounding = get_row_rounding(tensor->type, tensor_split);
+
+    *row_low = id == 0 ? 0 : nrows*tensor_split[id];
+    *row_low -= *row_low % rounding;
+    if (id == g_device_count - 1) {
+        *row_high = nrows;
+    } else {
+        *row_high = nrows*tensor_split[id + 1];
+        *row_high -= *row_high % rounding;
+    }
+}
+
+struct ggml_backend_sycl_split_buffer_context {
+    ~ggml_backend_sycl_split_buffer_context() try {
+        for (ggml_tensor_extra_gpu * extra : tensor_extras) {
+            for (int i = 0; i < g_device_count; ++i) {
+                // int id = g_sycl_gpu_mgr->gpus[i];
+                for (int64_t is = 0; is < MAX_STREAMS; ++is) {
+                    if (extra->events[i][is] != nullptr) {
+                        /*
+                        DPCT1009:206: SYCL uses exceptions to report errors and
+                        does not use the error codes. The original code was
+                        commented out and a warning string was inserted. You
+                        need to rewrite this code.
+                        */
+                        SYCL_CHECK(CHECK_TRY_ERROR(
+                            dpct::destroy_event(extra->events[i][is])));
+                    }
+                }
+                if (extra->data_device[i] != nullptr) {
+                    /*
+                    DPCT1009:207: SYCL uses exceptions to report errors and does
+                    not use the error codes. The original code was commented out
+                    and a warning string was inserted. You need to rewrite this
+                    code.
+                    */
+                    ggml_sycl_set_device(i);
+                    SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(
+                        extra->data_device[i], *g_syclStreams[i][0])));
+                }
+            }
+            delete extra;
+        }
+    }
+    catch (sycl::exception const &exc) {
+      std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+                << ", line:" << __LINE__ << std::endl;
+      std::exit(1);
+    }
+
+    std::vector<ggml_tensor_extra_gpu *> tensor_extras;
+};
+
+GGML_CALL static const char * ggml_backend_sycl_split_buffer_get_name(ggml_backend_buffer_t buffer) {
+    return GGML_SYCL_NAME "_Split";
+
+    UNUSED(buffer);
+}
+
+// unused at the moment
+//static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) {
+//    return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name;
+//}
+
+GGML_CALL static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context;
+    delete ctx;
+}
+
+GGML_CALL static void * ggml_backend_sycl_split_buffer_get_base(ggml_backend_buffer_t buffer) {
+    // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced
+    return (void *)0x1000;
+
+    UNUSED(buffer);
+}
+
+GGML_CALL static void
+ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer,
+                                           ggml_tensor *tensor) try {
+    GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported
+
+    ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context;
+    ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context;
+
+    const int64_t ne0 = tensor->ne[0];
+
+    ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{};
+
+    ctx->tensor_extras.push_back(extra);
+
+    for (int i = 0; i < g_device_count; ++i) {
+        // int id = g_sycl_gpu_mgr->gpus[i];
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i);
+
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
+        }
+
+        size_t size = ggml_nbytes_split(tensor, nrows_split);
+        const size_t original_size = size;
+
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
+
+        // FIXME: do not crash if cudaMalloc fails
+        // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first
+        ggml_sycl_set_device(i);
+        char * buf;
+        /*
+        DPCT1009:208: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)sycl::malloc_device(
+                                        size, *g_syclStreams[i][0])));
+
+        // set padding to 0 to avoid possible NaN values
+        if (size > original_size) {
+            /*
+            DPCT1009:209: SYCL uses exceptions to report errors and does not use
+            the error codes. The original code was commented out and a warning
+            string was inserted. You need to rewrite this code.
+            */
+            SYCL_CHECK(CHECK_TRY_ERROR(
+                (*g_syclStreams[i][0])
+                    .memset(buf + original_size, 0, size - original_size)
+                    .wait()));
+        }
+
+        extra->data_device[i] = buf;
+
+        for (int64_t is = 0; is < MAX_STREAMS; ++is) {
+            /*
+            DPCT1009:210: SYCL uses exceptions to report errors and does not use
+            the error codes. The original code was commented out and a warning
+            string was inserted. You need to rewrite this code.
+            */
+            SYCL_CHECK(
+                CHECK_TRY_ERROR(extra->events[i][is] = new sycl::event()));
+        }
+    }
+    tensor->backend = GGML_BACKEND_TYPE_GPU_SPLIT;
+    tensor->extra = extra;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static void
+ggml_backend_sycl_split_buffer_set_tensor(ggml_backend_buffer_t buffer,
+                                          ggml_tensor *tensor, const void *data,
+                                          size_t offset, size_t size) try {
+    // split tensors must always be set in their entirety at once
+    GGML_ASSERT(offset == 0);
+    GGML_ASSERT(size == ggml_nbytes(tensor));
+
+    ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context;
+
+    const int64_t ne0 = tensor->ne[0];
+    const size_t nb1 = tensor->nb[1];
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra;
+
+    for (int i = 0; i < g_device_count; ++i) {
+        // int id = g_sycl_gpu_mgr->gpus[i];
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i);
+
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
+        }
+
+        const size_t offset_split = row_low*nb1;
+        size_t size = ggml_nbytes_split(tensor, nrows_split);
+        const size_t original_size = size;
+
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
+
+        const char * buf_host = (const char *)data + offset_split;
+        /*
+        DPCT1009:211: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        ggml_sycl_set_device(i);
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            (*g_syclStreams[i][0])
+                .memcpy(extra->data_device[i], buf_host, original_size)
+                .wait()));
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static void
+ggml_backend_sycl_split_buffer_get_tensor(ggml_backend_buffer_t buffer,
+                                          const ggml_tensor *tensor, void *data,
+                                          size_t offset, size_t size) try {
+    // split tensors must always be set in their entirety at once
+    GGML_ASSERT(offset == 0);
+    GGML_ASSERT(size == ggml_nbytes(tensor));
+
+    ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context;
+
+    const int64_t ne0 = tensor->ne[0];
+    const size_t nb1 = tensor->nb[1];
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra;
+
+    for (int i = 0; i < g_device_count; ++i) {
+        // int id = g_sycl_gpu_mgr->gpus[i];
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i);
+
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
+        }
+
+        const size_t offset_split = row_low*nb1;
+        size_t size = ggml_nbytes_split(tensor, nrows_split);
+        const size_t original_size = size;
+
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
+
+        char * buf_host = (char *)data + offset_split;
+        /*
+        DPCT1009:212: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        ggml_sycl_set_device(i);
+        SYCL_CHECK(CHECK_TRY_ERROR(
+            (*g_syclStreams[i][0])
+                .memcpy(buf_host, extra->data_device[i], original_size)
+                .wait()));
+    }
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static void ggml_backend_sycl_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    UNUSED(buffer);
+    UNUSED(value);
+}
+
+static struct ggml_backend_buffer_i ggml_backend_sycl_split_buffer_interface = {
+    /* .get_name        = */ ggml_backend_sycl_split_buffer_get_name,
+    /* .free_buffer     = */ ggml_backend_sycl_split_buffer_free_buffer,
+    /* .get_base        = */ ggml_backend_sycl_split_buffer_get_base,
+    /* .init_tensor     = */ ggml_backend_sycl_split_buffer_init_tensor,
+    /* .set_tensor      = */ ggml_backend_sycl_split_buffer_set_tensor,
+    /* .get_tensor      = */ ggml_backend_sycl_split_buffer_get_tensor,
+    /* .cpy_tensor      = */ NULL,
+    /* .clear           = */ ggml_backend_sycl_split_buffer_clear,
+    /* .reset           = */ NULL,
+};
+
+GGML_CALL static const char * ggml_backend_sycl_split_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    return GGML_SYCL_NAME "_Split";
+
+    UNUSED(buft);
+}
+
+GGML_CALL static ggml_backend_buffer_t ggml_backend_sycl_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point
+    // instead, we allocate them for each tensor separately in init_tensor
+    // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated,
+    // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct.
+    ggml_backend_sycl_split_buffer_context * ctx = new ggml_backend_sycl_split_buffer_context();
+
+    return ggml_backend_buffer_init(buft, ggml_backend_sycl_split_buffer_interface, ctx, size);
+}
+
+GGML_CALL static size_t ggml_backend_sycl_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+    return 128;
+    UNUSED(buft);
+}
+
+GGML_CALL static size_t ggml_backend_sycl_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
+    ggml_backend_sycl_split_buffer_type_context * ctx = (ggml_backend_sycl_split_buffer_type_context *)buft->context;
+
+    size_t total_size = 0;
+
+    const int64_t ne0 = tensor->ne[0];
+
+    for (int i = 0; i < g_device_count; ++i) {
+        // int id = g_sycl_gpu_mgr->gpus[i];
+        int64_t row_low, row_high;
+        get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, i);
+
+        int64_t nrows_split = row_high - row_low;
+        if (nrows_split == 0) {
+            continue;
+        }
+
+        total_size += ggml_nbytes_split(tensor, nrows_split);
+
+        // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
+    }
+
+    return total_size;
+}
+
+GGML_CALL static bool ggml_backend_sycl_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
+    return ggml_backend_is_sycl(backend);
+
+    UNUSED(buft);
+}
+
+GGML_CALL static bool ggml_backend_sycl_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
+    return false;
+
+    UNUSED(buft);
+}
+
+static ggml_backend_buffer_type_i ggml_backend_sycl_split_buffer_type_interface = {
+    /* .get_name         = */ ggml_backend_sycl_split_buffer_type_name,
+    /* .alloc_buffer     = */ ggml_backend_sycl_split_buffer_type_alloc_buffer,
+    /* .get_alignment    = */ ggml_backend_sycl_split_buffer_type_get_alignment,
+    /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
+    /* .get_alloc_size   = */ ggml_backend_sycl_split_buffer_type_get_alloc_size,
+    /* .supports_backend = */ ggml_backend_sycl_split_buffer_type_supports_backend,
+    /* .is_host          = */ ggml_backend_sycl_split_buffer_type_is_host,
+};
+
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split) {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_split_buffer_type\n");
+    ggml_init_sycl();
+    // FIXME: this is not thread safe
+    static std::map<std::array<float, GGML_SYCL_MAX_DEVICES>, struct ggml_backend_buffer_type> buft_map;
+
+    std::array<float, GGML_SYCL_MAX_DEVICES> tensor_split_arr = {};
+
+    bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_SYCL_MAX_DEVICES, [](float x) { return x == 0.0f; });
+    if (all_zero) {
+        tensor_split_arr = g_default_tensor_split;
+    } else {
+        float split_sum = 0.0f;
+        for (int i = 0; i < g_device_count; ++i) {
+            // int id = g_sycl_gpu_mgr->gpus[i];
+            tensor_split_arr[i] = split_sum;
+            split_sum += tensor_split[i];
+        }
+        for (int i = 0; i < g_device_count; ++i) {
+            // int id = g_sycl_gpu_mgr->gpus[i];
+            tensor_split_arr[i] /= split_sum;
+        }
+    }
+
+    auto it = buft_map.find(tensor_split_arr);
+    if (it != buft_map.end()) {
+        return &it->second;
+    }
+
+    struct ggml_backend_buffer_type buft {
+        /* .iface   = */ ggml_backend_sycl_split_buffer_type_interface,
+        /* .context = */ new ggml_backend_sycl_split_buffer_type_context{tensor_split_arr},
+    };
+
+    auto result = buft_map.emplace(tensor_split_arr, buft);
+    return &result.first->second;
+}
+
+// host buffer type
+
+GGML_CALL static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    return GGML_SYCL_NAME "_Host";
+
+    UNUSED(buft);
+}
+
+GGML_CALL static const char * ggml_backend_sycl_host_buffer_name(ggml_backend_buffer_t buffer) {
+    return GGML_SYCL_NAME "_Host";
+
+    UNUSED(buffer);
+}
+
+static void ggml_backend_sycl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    ggml_sycl_host_free(buffer->context);
+}
+
+static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    void * ptr = ggml_sycl_host_malloc(size);
+
+    if (ptr == nullptr) {
+        // fallback to cpu buffer
+        return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size);
+    }
+
+    // FIXME: this is a hack to avoid having to implement a new buffer type
+    ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
+    buffer->buft = buft;
+    buffer->iface.get_name = ggml_backend_sycl_host_buffer_name;
+    buffer->iface.free_buffer = ggml_backend_sycl_host_buffer_free_buffer;
+
+    return buffer;
+}
+
+ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type() {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_host_buffer_type\n");
+    static struct ggml_backend_buffer_type ggml_backend_sycl_buffer_type_host = {
+        /* .iface    = */ {
+            /* .get_name         = */ ggml_backend_sycl_host_buffer_type_name,
+            /* .alloc_buffer     = */ ggml_backend_sycl_host_buffer_type_alloc_buffer,
+            /* .get_alignment    = */ ggml_backend_cpu_buffer_type()->iface.get_alignment,
+            /* .get_max_size     = */ NULL, // TODO: return device.maxBufferLength
+            /* .get_alloc_size   = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
+            /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend,
+            /* .is_host          = */ ggml_backend_cpu_buffer_type()->iface.is_host,
+        },
+        /* .context  = */ nullptr,
+    };
+
+    return &ggml_backend_sycl_buffer_type_host;
+}
+
+// backend
+
+GGML_CALL static const char * ggml_backend_sycl_name(ggml_backend_t backend) {
+
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+
+    return sycl_ctx->name.c_str();
+}
+
+GGML_CALL static void ggml_backend_sycl_free(ggml_backend_t backend) {
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+
+    delete sycl_ctx;
+    delete backend;
+}
+
+
+GGML_CALL static ggml_backend_buffer_type_t ggml_backend_sycl_get_default_buffer_type(ggml_backend_t backend) {
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+    return ggml_backend_sycl_buffer_type(sycl_ctx->device);
+}
+
+GGML_CALL static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend,
+                                               ggml_tensor *tensor,
+                                               const void *data, size_t offset,
+                                               size_t size) try {
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+    GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type");
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
+    SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy(
+        (char *)tensor->data + offset, data, size).wait()));
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend,
+                                               const ggml_tensor *tensor,
+                                               void *data, size_t offset,
+                                               size_t size) try {
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+    GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type");
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
+    SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy(
+        data, (const char *)tensor->data + offset, size).wait()));
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static bool ggml_backend_sycl_cpy_tensor_async(ggml_backend_t backend,
+                                                         const ggml_tensor *src,
+                                                         ggml_tensor *dst) try {
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+    if (dst->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && ggml_backend_buffer_is_sycl(src->buffer)) {
+        /*
+        DPCT1009:215: SYCL uses exceptions to report errors and does not use the
+        error codes. The original code was commented out and a warning string
+        was inserted. You need to rewrite this code.
+        */
+        SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy(
+            dst->data, src->data, ggml_nbytes(dst)).wait()));
+        return true;
+    }
+
+    return false;
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+static void ggml_backend_sycl_synchronize(ggml_backend_t backend) try {
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+    SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->wait()));
+
+    UNUSED(backend);
+}
+catch (sycl::exception const &exc) {
+  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+            << ", line:" << __LINE__ << std::endl;
+  std::exit(1);
+}
+
+GGML_CALL static ggml_status ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
+    ggml_sycl_set_main_device(sycl_ctx->device);
+
+    ggml_compute_params params = {};
+    params.type = GGML_TASK_TYPE_COMPUTE;
+    params.ith = 0;
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        ggml_tensor * node = cgraph->nodes[i];
+        if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
+            continue;
+        }
+#ifndef NDEBUG
+        assert(node->backend == GGML_BACKEND_TYPE_GPU || node->backend == GGML_BACKEND_TYPE_GPU_SPLIT);
+        assert(node->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device));
+        assert(node->extra != nullptr);
+
+        for (int j = 0; j < GGML_MAX_SRC; j++) {
+            if (node->src[j] != nullptr) {
+                assert(node->src[j]->backend == GGML_BACKEND_TYPE_GPU || node->src[j]->backend == GGML_BACKEND_TYPE_GPU_SPLIT);
+                assert(node->src[j]->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device));
+                assert(node->src[j]->extra != nullptr);
+            }
+        }
+#endif
+        bool ok = ggml_sycl_compute_forward(&params, node);
+        if (!ok) {
+            fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
+        }
+        GGML_ASSERT(ok);
+    }
+
+    return GGML_STATUS_SUCCESS;
+}
+
+GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
+    switch (op->op) {
+        case GGML_OP_UNARY:
+            switch (ggml_get_unary_op(op)) {
+                case GGML_UNARY_OP_GELU:
+                case GGML_UNARY_OP_SILU:
+                case GGML_UNARY_OP_RELU:
+                case GGML_UNARY_OP_HARDSIGMOID:
+                case GGML_UNARY_OP_HARDSWISH:
+                case GGML_UNARY_OP_GELU_QUICK:
+                case GGML_UNARY_OP_TANH:
+                    return true;
+                default:
+                    return false;
+            }
+            break;
+        case GGML_OP_MUL_MAT:
+        case GGML_OP_MUL_MAT_ID:
+            {
+                struct ggml_tensor * a;
+                struct ggml_tensor * b;
+                if (op->op == GGML_OP_MUL_MAT) {
+                    a = op->src[0];
+                    b = op->src[1];
+                } else {
+                    a = op->src[2];
+                    b = op->src[1];
+                }
+                if (a->ne[3] != b->ne[3]) {
+                    return false;
+                }
+                ggml_type a_type = a->type;
+                if (a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ2_S ||
+                    a_type == GGML_TYPE_IQ4_XS) {
+                    return false;
+                }
+                return true;
+            } break;
+        case GGML_OP_GET_ROWS:
+            {
+                switch (op->src[0]->type) {
+                    case GGML_TYPE_F16:
+                    case GGML_TYPE_F32:
+                    case GGML_TYPE_Q4_0:
+                    case GGML_TYPE_Q4_1:
+                    case GGML_TYPE_Q5_0:
+                    case GGML_TYPE_Q5_1:
+                    case GGML_TYPE_Q8_0:
+                        return true;
+                    default:
+                        return false;
+                }
+            } break;
+        case GGML_OP_CPY:
+            {
+                ggml_type src0_type = op->src[0]->type;
+                ggml_type src1_type = op->src[1]->type;
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q8_0) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_0) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_1) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
+                    return true;
+                }
+                return false;
+            } break;
+        case GGML_OP_CONCAT:
+            {
+                ggml_type src0_type = op->src[0]->type;
+                return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
+            } break;
+        case GGML_OP_DUP:
+        case GGML_OP_NONE:
+        case GGML_OP_RESHAPE:
+        case GGML_OP_REPEAT:
+        case GGML_OP_VIEW:
+        case GGML_OP_PERMUTE:
+        case GGML_OP_TRANSPOSE:
+        case GGML_OP_NORM:
+        case GGML_OP_ADD:
+        case GGML_OP_MUL:
+        case GGML_OP_DIV:
+        case GGML_OP_RMS_NORM:
+        case GGML_OP_SCALE:
+        case GGML_OP_SQR:
+        case GGML_OP_CLAMP:
+        case GGML_OP_CONT:
+        case GGML_OP_DIAG_MASK_INF:
+        case GGML_OP_SOFT_MAX:
+        case GGML_OP_ROPE:
+        case GGML_OP_ALIBI:
+        case GGML_OP_IM2COL:
+        case GGML_OP_POOL_2D:
+        case GGML_OP_SUM_ROWS:
+        case GGML_OP_ARGSORT:
+        case GGML_OP_ACC:
+        case GGML_OP_GROUP_NORM:
+        case GGML_OP_UPSCALE:
+        case GGML_OP_PAD:
+        case GGML_OP_LEAKY_RELU:
+            return true;
+        default:
+            return false;
+    }
+
+    UNUSED(backend);
+}
+
+GGML_CALL static bool ggml_backend_sycl_offload_op(ggml_backend_t backend, const ggml_tensor * op) {
+    const int min_batch_size = 32;
+    return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS;
+    GGML_UNUSED(backend);
+}
+
+
+static ggml_backend_i ggml_backend_sycl_interface = {
+    /* .get_name                = */ ggml_backend_sycl_name,
+    /* .free                    = */ ggml_backend_sycl_free,
+    /* .get_default_buffer_type = */ ggml_backend_sycl_get_default_buffer_type,
+    /* .set_tensor_async        = */ ggml_backend_sycl_set_tensor_async,
+    /* .get_tensor_async        = */ ggml_backend_sycl_get_tensor_async,
+    /* .cpy_tensor_async        = */ NULL, //ggml_backend_sycl_cpy_tensor_async, // TODO: update for the new interface
+    /* .synchronize             = */ ggml_backend_sycl_synchronize,
+    /* .graph_plan_create       = */ NULL,
+    /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_compute      = */ NULL,
+    /* .graph_compute           = */ ggml_backend_sycl_graph_compute,
+    /* .supports_op             = */ ggml_backend_sycl_supports_op,
+    /* .offload_op              = */ ggml_backend_sycl_offload_op,
+    /* .event_new               = */ NULL,
+    /* .event_free              = */ NULL,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .event_synchronize       = */ NULL,
+};
+
+static ggml_guid_t ggml_backend_sycl_guid() {
+    static ggml_guid guid = { 0x58, 0x05, 0x13, 0x8f, 0xcd, 0x3a, 0x61, 0x9d, 0xe7, 0xcd, 0x98, 0xa9, 0x03, 0xfd, 0x7c, 0x53 };
+    return &guid;
+}
+
+GGML_CALL ggml_backend_t ggml_backend_sycl_init(int device) {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_init\n");
+    ggml_init_sycl();
+
+    check_allow_gpu_index(device);
+
+    // not strictly necessary, but it may reduce the overhead of the first graph_compute
+    ggml_sycl_set_main_device(device);
+    int id = g_sycl_gpu_mgr->gpus[device];
+    ggml_backend_sycl_context * ctx = new ggml_backend_sycl_context {
+        /* .device = */ device,
+        /* .name   = */ GGML_SYCL_NAME + std::to_string(id),
+    };
+
+    ggml_backend_t sycl_backend = new ggml_backend {
+        /* .guid      = */ ggml_backend_sycl_guid(),
+        /* .interface = */ ggml_backend_sycl_interface,
+        /* .context   = */ ctx
+    };
+
+    return sycl_backend;
+}
+
+bool ggml_backend_is_sycl(ggml_backend_t backend) {
+    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_sycl_guid());
+}
+
+GGML_CALL int ggml_backend_sycl_get_device_count() {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_count\n");
+    if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr();
+    return g_sycl_gpu_mgr->get_gpu_count();
+}
+
+GGML_CALL static ggml_backend_t ggml_backend_reg_sycl_init(const char * params, void * user_data) {
+    ggml_backend_t sycl_backend = ggml_backend_sycl_init((int) (intptr_t) user_data);
+    return sycl_backend;
+
+    UNUSED(params);
+}
+
+GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id) {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_index\n");
+    return g_sycl_gpu_mgr->get_index(device_id);
+}
+
+GGML_API GGML_CALL int ggml_backend_sycl_get_device_id(int device_index) {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_id\n");
+    return g_sycl_gpu_mgr->gpus[device_index];
+}
+
+GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id) {
+    ggml_init_sycl();
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_set_single_device_mode\n");
+    fprintf(stderr, "ggml_backend_sycl_set_single_device: use single device: [%d]\n", main_gpu_id);
+    GGML_ASSERT(main_gpu_id<g_all_sycl_device_count);
+
+    if (g_sycl_gpu_mgr) {
+        delete g_sycl_gpu_mgr;
+    }
+    g_sycl_gpu_mgr = new sycl_gpu_mgr(main_gpu_id);
+    g_ggml_sycl_backend_gpu_mode = SYCL_SINGLE_GPU_MODE;
+    ggml_init_by_gpus(g_sycl_gpu_mgr->get_gpu_count());
+    g_ggml_backend_sycl_buffer_type_initialized = false;
+}
+
+GGML_API GGML_CALL void ggml_backend_sycl_set_mul_device_mode() {
+    ggml_init_sycl();
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_set_mul_device_mode\n");
+
+    if (g_ggml_sycl_backend_gpu_mode == SYCL_MUL_GPU_MODE) {
+        return;
+    }
+
+    fprintf(stderr, "ggml_backend_sycl_set_mul_device_mode: true\n");
+
+    if (g_sycl_gpu_mgr) {
+        delete g_sycl_gpu_mgr;
+    }
+    g_sycl_gpu_mgr = new sycl_gpu_mgr();
+    g_ggml_sycl_backend_gpu_mode = SYCL_MUL_GPU_MODE;
+    ggml_init_by_gpus(g_sycl_gpu_mgr->get_gpu_count());
+    g_ggml_backend_sycl_buffer_type_initialized = false;
+}
+
+extern "C" int ggml_backend_sycl_reg_devices();
+
+int ggml_backend_sycl_reg_devices() {
+    ggml_backend_sycl_set_mul_device_mode();
+    assert(g_device_count>0);
+    for (int i = 0; i < g_device_count; i++) {
+        int id = g_sycl_gpu_mgr->gpus[i];
+        char name[128];
+        snprintf(name, sizeof(name), "%s%d", GGML_SYCL_NAME, id);
+        ggml_backend_register(name, ggml_backend_reg_sycl_init, ggml_backend_sycl_buffer_type(i), (void *) (intptr_t) i);
+    }
+    return g_device_count;
+}
diff --git a/ggml-sycl.h b/ggml-sycl.h
new file mode 100644
index 000000000..a9f776fc1
--- /dev/null
+++ b/ggml-sycl.h
@@ -0,0 +1,49 @@
+//
+//  MIT license
+//  Copyright (C) 2024 Intel Corporation
+//  SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+#define GGML_SYCL_MAX_DEVICES       48
+#define GGML_SYCL_NAME "SYCL"
+
+// backend API
+GGML_API ggml_backend_t ggml_backend_sycl_init(int device);
+
+// devide buffer
+GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device);
+
+// split tensor buffer that splits matrices by rows across multiple devices
+GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split);
+
+// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
+GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void);
+
+GGML_API void   ggml_backend_sycl_print_sycl_devices(void);
+GGML_API GGML_CALL void   ggml_sycl_get_gpu_list(int *id_list, int max_len);
+GGML_API GGML_CALL void   ggml_sycl_get_device_description(int device, char *description, size_t description_size);
+GGML_API GGML_CALL int   ggml_backend_sycl_get_device_count();
+GGML_API GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total);
+GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id);
+
+// TODO: these are temporary
+//       ref: https://github.com/ggerganov/llama.cpp/pull/6022#issuecomment-1992615670
+GGML_API GGML_CALL int ggml_backend_sycl_get_device_id(int device_index);
+GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id);
+GGML_API GGML_CALL void ggml_backend_sycl_set_mul_device_mode();
+
+// SYCL doesn't support registering host memory, keep here for reference
+// GGML_API GGML_CALL bool ggml_backend_sycl_register_host_buffer(void * buffer, size_t size);
+// GGML_API GGML_CALL void ggml_backend_sycl_unregister_host_buffer(void * buffer);
+#ifdef  __cplusplus
+}
+#endif
diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp
new file mode 100644
index 000000000..fcb6f384b
--- /dev/null
+++ b/ggml-vulkan-shaders.hpp
@@ -0,0 +1,69849 @@
+#include <cstdint>
+
+unsigned char add_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
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+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xc4,0x01,0x00,0x00,
+0x17,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xc5,0x01,0x00,0x00,0xc4,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc6,0x01,0x00,0x00,
+0xc3,0x01,0x00,0x00,0xc5,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0xc7,0x01,0x00,0x00,0x17,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xc8,0x01,0x00,0x00,0xc7,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc9,0x01,0x00,0x00,0xc6,0x01,0x00,0x00,
+0xc8,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xca,0x01,0x00,0x00,0x33,0x01,0x00,0x00,0xc9,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xce,0x01,0x00,0x00,
+0xca,0x01,0x00,0x00,0xc3,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd1,0x01,0x00,0x00,0xce,0x01,0x00,0x00,
+0xc5,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd4,0x01,0x00,0x00,0xd1,0x01,0x00,0x00,0xc8,0x01,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd7,0x01,0x00,0x00,
+0x33,0x01,0x00,0x00,0xd4,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdc,0x01,0x00,0x00,0xc5,0x01,0x00,0x00,
+0xc8,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xdd,0x01,0x00,0x00,0xd7,0x01,0x00,0x00,0xdc,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe1,0x01,0x00,0x00,
+0xdd,0x01,0x00,0x00,0xc5,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe4,0x01,0x00,0x00,0xe1,0x01,0x00,0x00,
+0xc8,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe9,0x01,0x00,0x00,0xd7,0x01,0x00,0x00,0xe4,0x01,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x01,0x00,0x00,
+0xe9,0x01,0x00,0x00,0xc8,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf5,0x01,0x00,0x00,0xec,0x01,0x00,0x00,
+0xc8,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf6,0x01,0x00,0x00,0xe9,0x01,0x00,0x00,0xf5,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xf8,0x01,0x00,0x00,
+0x17,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xf9,0x01,0x00,0x00,0xf8,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfa,0x01,0x00,0x00,
+0xca,0x01,0x00,0x00,0xf9,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0xfc,0x01,0x00,0x00,0x17,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xfd,0x01,0x00,0x00,0xfc,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xfe,0x01,0x00,0x00,0xdd,0x01,0x00,0x00,
+0xfd,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xff,0x01,0x00,0x00,0xfa,0x01,0x00,0x00,0xfe,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x01,0x02,0x00,0x00,
+0x17,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x02,0x02,0x00,0x00,0x01,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x03,0x02,0x00,0x00,
+0xec,0x01,0x00,0x00,0x02,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x04,0x02,0x00,0x00,0xff,0x01,0x00,0x00,
+0x03,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x06,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x07,0x02,0x00,0x00,
+0x06,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x08,0x02,0x00,0x00,0xf6,0x01,0x00,0x00,0x07,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x09,0x02,0x00,0x00,
+0x04,0x02,0x00,0x00,0x08,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x50,0x01,0x00,0x00,0x51,0x01,0x00,0x00,0x4b,0x01,0x00,0x00,
+0x34,0x01,0x00,0x00,0x09,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x52,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x49,0x02,0x00,0x00,
+0x17,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,0x49,0x02,0x00,0x00,
+0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4b,0x02,0x00,0x00,
+0xca,0x01,0x00,0x00,0x4a,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x4c,0x02,0x00,0x00,0x17,0x00,0x00,0x00,
+0xb1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x4d,0x02,0x00,0x00,0x4c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x4e,0x02,0x00,0x00,0x4b,0x02,0x00,0x00,
+0x4d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x50,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x51,0x02,0x00,0x00,
+0x50,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x52,0x02,0x00,0x00,0xdd,0x01,0x00,0x00,0x51,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x53,0x02,0x00,0x00,
+0x17,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x54,0x02,0x00,0x00,0x53,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x55,0x02,0x00,0x00,
+0x52,0x02,0x00,0x00,0x54,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x56,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,
+0x55,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x58,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x59,0x02,0x00,0x00,
+0x58,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5a,0x02,0x00,0x00,0xec,0x01,0x00,0x00,0x59,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x5b,0x02,0x00,0x00,
+0x17,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x02,0x00,0x00,
+0x5a,0x02,0x00,0x00,0x5c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5e,0x02,0x00,0x00,0x56,0x02,0x00,0x00,
+0x5d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x60,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,
+0x60,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x62,0x02,0x00,0x00,0xf6,0x01,0x00,0x00,0x61,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x63,0x02,0x00,0x00,
+0x17,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x63,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x65,0x02,0x00,0x00,
+0x62,0x02,0x00,0x00,0x64,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x66,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,
+0x65,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x01,0x00,0x00,
+0x5b,0x01,0x00,0x00,0x56,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
+0x66,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x5c,0x01,0x00,0x00,0x5b,0x01,0x00,0x00,0x81,0x00,0x05,0x00,
+0x14,0x00,0x00,0x00,0x5d,0x01,0x00,0x00,0x52,0x01,0x00,0x00,
+0x5c,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x01,0x00,0x00,
+0x5e,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
+0x47,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x5e,0x01,0x00,0x00,
+0x5d,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x62,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x62,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t add_f32_len = 4276;
+
+unsigned char argsort_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x2b,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x33,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x04,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x24,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x2b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x31,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x31,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x31,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x81,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x82,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x82,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x82,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x84,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x84,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x11,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
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+0x2a,0x01,0x00,0x00,0x9b,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xb5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x2a,0x01,0x00,0x00,0xb4,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb4,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x16,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x10,0x01,0x00,0x00,
+0x0f,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x16,0x00,0x00,0x00,
+0x13,0x01,0x00,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x14,0x01,0x00,0x00,0x13,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x0f,0x01,0x00,0x00,0x14,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x13,0x01,0x00,0x00,0x10,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xb5,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x77,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xc0,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x34,0x00,0x00,0x00,
+0xc1,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
+0xc1,0x00,0x00,0x00,0xab,0x00,0x05,0x00,0x37,0x00,0x00,0x00,
+0xc3,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xc6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
+0xdd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xc5,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x16,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
+0xcd,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x8e,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0x84,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x80,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd5,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x16,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xd8,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0xd8,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x8e,0x00,0x00,0x00,
+0xda,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0xd9,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x80,0x00,0x00,0x00,
+0xdb,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0xb8,0x00,0x05,0x00,
+0x37,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,
+0xdb,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc6,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xdd,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x16,0x00,0x00,0x00,
+0xe3,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0xe2,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0xe4,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x8e,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x80,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x16,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0xee,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x8e,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
+0x84,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x80,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
+0xf1,0x00,0x00,0x00,0xba,0x00,0x05,0x00,0x37,0x00,0x00,0x00,
+0xf3,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xc6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xc6,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x37,0x00,0x00,0x00,
+0x29,0x01,0x00,0x00,0xdc,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
+0xf3,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xf6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x29,0x01,0x00,0x00,0xf5,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xfd,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x16,0x00,0x00,0x00,0x1c,0x01,0x00,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,
+0x1c,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x16,0x00,0x00,0x00,
+0x20,0x01,0x00,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0xfd,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x21,0x01,0x00,0x00,0x20,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x1c,0x01,0x00,0x00,0x21,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x20,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xf6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf6,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x77,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x77,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x70,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x70,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x50,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x62,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x62,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x02,0x01,0x00,0x00,0x27,0x01,0x00,0x00,0x50,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x61,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x56,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x56,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x26,0x01,0x00,0x00,
+0x50,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x53,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x55,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x07,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x07,0x01,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t argsort_f32_len = 3752;
+
+unsigned char clamp_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xb6,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
+0xe4,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
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+0x60,0x01,0x00,0x00,0x4e,0x01,0x00,0x00,0x5f,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x61,0x01,0x00,0x00,
+0x5c,0x01,0x00,0x00,0x60,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0xde,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0x61,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x6b,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x6c,0x01,0x00,0x00,0x6b,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x6d,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x6e,0x01,0x00,0x00,0x6d,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6f,0x01,0x00,0x00,
+0x6c,0x01,0x00,0x00,0x6e,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x70,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x71,0x01,0x00,0x00,0x70,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x72,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,
+0x71,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x73,0x01,0x00,0x00,0xcb,0x00,0x00,0x00,0x72,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x77,0x01,0x00,0x00,
+0x73,0x01,0x00,0x00,0x6c,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x7a,0x01,0x00,0x00,0x77,0x01,0x00,0x00,
+0x6e,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x7d,0x01,0x00,0x00,0x7a,0x01,0x00,0x00,0x71,0x01,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x80,0x01,0x00,0x00,
+0xcb,0x00,0x00,0x00,0x7d,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x85,0x01,0x00,0x00,0x6e,0x01,0x00,0x00,
+0x71,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x86,0x01,0x00,0x00,0x80,0x01,0x00,0x00,0x85,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8a,0x01,0x00,0x00,
+0x86,0x01,0x00,0x00,0x6e,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,
+0x71,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x92,0x01,0x00,0x00,0x80,0x01,0x00,0x00,0x8d,0x01,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,
+0x92,0x01,0x00,0x00,0x71,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9e,0x01,0x00,0x00,0x95,0x01,0x00,0x00,
+0x71,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9f,0x01,0x00,0x00,0x92,0x01,0x00,0x00,0x9e,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xa2,0x01,0x00,0x00,0xa1,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,
+0x73,0x01,0x00,0x00,0xa2,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0xa5,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xa6,0x01,0x00,0x00,0xa5,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa7,0x01,0x00,0x00,0x86,0x01,0x00,0x00,
+0xa6,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa8,0x01,0x00,0x00,0xa3,0x01,0x00,0x00,0xa7,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xaa,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xab,0x01,0x00,0x00,0xaa,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xac,0x01,0x00,0x00,
+0x95,0x01,0x00,0x00,0xab,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xad,0x01,0x00,0x00,0xa8,0x01,0x00,0x00,
+0xac,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0xaf,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb0,0x01,0x00,0x00,
+0xaf,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb1,0x01,0x00,0x00,0x9f,0x01,0x00,0x00,0xb0,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x01,0x00,0x00,
+0xad,0x01,0x00,0x00,0xb1,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
+0xb2,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0xef,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0xb8,0x00,0x05,0x00,0xcf,0x00,0x00,0x00,
+0xf2,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xf5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xf2,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
+0xf8,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf8,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0xef,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0xba,0x00,0x05,0x00,0xcf,0x00,0x00,0x00,
+0xfd,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
+0xa9,0x00,0x06,0x00,0x11,0x00,0x00,0x00,0xb5,0x01,0x00,0x00,
+0xfd,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf5,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x11,0x00,0x00,0x00,
+0xb4,0x01,0x00,0x00,0xf1,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
+0xb5,0x01,0x00,0x00,0xf8,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0xde,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0xe4,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x07,0x01,0x00,0x00,0xb4,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0b,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0b,0x01,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t clamp_f32_len = 3512;
+
+unsigned char cpy_f16_f16_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x98,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc7,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xd5,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xd6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xd6,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xd6,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xd8,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd8,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xe1,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xe2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xe2,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xe2,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xe4,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
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+0x58,0x01,0x00,0x00,0x51,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5f,0x01,0x00,0x00,0x5c,0x01,0x00,0x00,
+0x53,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x62,0x01,0x00,0x00,0x5f,0x01,0x00,0x00,0x56,0x01,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x65,0x01,0x00,0x00,
+0xcb,0x00,0x00,0x00,0x62,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6a,0x01,0x00,0x00,0x53,0x01,0x00,0x00,
+0x56,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6b,0x01,0x00,0x00,0x65,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6f,0x01,0x00,0x00,
+0x6b,0x01,0x00,0x00,0x53,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x72,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,
+0x56,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x77,0x01,0x00,0x00,0x65,0x01,0x00,0x00,0x72,0x01,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7a,0x01,0x00,0x00,
+0x77,0x01,0x00,0x00,0x56,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x83,0x01,0x00,0x00,0x7a,0x01,0x00,0x00,
+0x56,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x84,0x01,0x00,0x00,0x77,0x01,0x00,0x00,0x83,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x86,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x87,0x01,0x00,0x00,0x86,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x88,0x01,0x00,0x00,
+0x58,0x01,0x00,0x00,0x87,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x8a,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x8b,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8c,0x01,0x00,0x00,0x6b,0x01,0x00,0x00,
+0x8b,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8d,0x01,0x00,0x00,0x88,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x90,0x01,0x00,0x00,0x8f,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x91,0x01,0x00,0x00,
+0x7a,0x01,0x00,0x00,0x90,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x92,0x01,0x00,0x00,0x8d,0x01,0x00,0x00,
+0x91,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x94,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,
+0x94,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x96,0x01,0x00,0x00,0x84,0x01,0x00,0x00,0x95,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x97,0x01,0x00,0x00,
+0x92,0x01,0x00,0x00,0x96,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0xe9,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0x97,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xd4,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xe9,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
+0xd8,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xf0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf0,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t cpy_f16_f16_len = 3252;
+
+unsigned char cpy_f32_f16_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x9a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc7,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xd5,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xd6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xd6,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xd6,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xd8,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd8,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xe1,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xe2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xe2,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xe2,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xe4,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe4,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xf1,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x1e,0x00,0x16,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x60,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x72,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0xc5,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0xc6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
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+0x06,0x00,0x00,0x00,0x98,0x01,0x00,0x00,0x86,0x01,0x00,0x00,
+0x97,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x99,0x01,0x00,0x00,0x94,0x01,0x00,0x00,0x98,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0xe9,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
+0xe4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x99,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
+0xea,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0xd4,0x00,0x00,0x00,
+0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0xed,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xee,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xf2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf2,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t cpy_f32_f16_len = 3284;
+
+unsigned char cpy_f32_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x97,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
+0xe3,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xc7,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xd4,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xd5,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xd5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xd5,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd7,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xd7,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xe0,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xe1,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xe1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xe1,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe3,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xe3,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xee,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x1e,0x00,0x16,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x6f,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0xad,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0xbe,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0xc5,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0xc6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0xc5,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0xc6,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0xc9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
+0xcf,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xd4,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xd5,0x00,0x00,0x00,
+0xd4,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xd6,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0xd6,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xe0,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xe1,0x00,0x00,0x00,
+0xe0,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xe2,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0xe2,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0xe8,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xec,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2c,0x00,0x06,0x00,0xc5,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
+0xec,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
+0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xef,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,0xc8,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf0,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0xc9,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
+0xae,0x00,0x05,0x00,0xcf,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xcb,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xd2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xd0,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd1,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xef,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd2,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x01,0x01,0x00,0x00,0x00,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x02,0x01,0x00,0x00,
+0xff,0x00,0x00,0x00,0x01,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x04,0x01,0x00,0x00,0x03,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x02,0x01,0x00,0x00,
+0x04,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x06,0x01,0x00,0x00,0xcb,0x00,0x00,0x00,0x05,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,
+0x06,0x01,0x00,0x00,0xff,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,0x0a,0x01,0x00,0x00,
+0x01,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x10,0x01,0x00,0x00,0x0d,0x01,0x00,0x00,0x04,0x01,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x13,0x01,0x00,0x00,
+0xcb,0x00,0x00,0x00,0x10,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x18,0x01,0x00,0x00,0x01,0x01,0x00,0x00,
+0x04,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x19,0x01,0x00,0x00,0x13,0x01,0x00,0x00,0x18,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,
+0x19,0x01,0x00,0x00,0x01,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x20,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,
+0x04,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x25,0x01,0x00,0x00,0x13,0x01,0x00,0x00,0x20,0x01,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x28,0x01,0x00,0x00,
+0x25,0x01,0x00,0x00,0x04,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x31,0x01,0x00,0x00,0x28,0x01,0x00,0x00,
+0x04,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x32,0x01,0x00,0x00,0x25,0x01,0x00,0x00,0x31,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x34,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x35,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x36,0x01,0x00,0x00,
+0x06,0x01,0x00,0x00,0x35,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x38,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x39,0x01,0x00,0x00,0x38,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,0x19,0x01,0x00,0x00,
+0x39,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x3b,0x01,0x00,0x00,0x36,0x01,0x00,0x00,0x3a,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x3e,0x01,0x00,0x00,0x3d,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3f,0x01,0x00,0x00,
+0x28,0x01,0x00,0x00,0x3e,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x40,0x01,0x00,0x00,0x3b,0x01,0x00,0x00,
+0x3f,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x42,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x43,0x01,0x00,0x00,
+0x42,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x44,0x01,0x00,0x00,0x32,0x01,0x00,0x00,0x43,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x45,0x01,0x00,0x00,
+0x40,0x01,0x00,0x00,0x44,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
+0x45,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x4f,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x50,0x01,0x00,0x00,
+0x4f,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x51,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x52,0x01,0x00,0x00,
+0x51,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x53,0x01,0x00,0x00,0x50,0x01,0x00,0x00,0x52,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x54,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x55,0x01,0x00,0x00,0x54,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x56,0x01,0x00,0x00,
+0x53,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x57,0x01,0x00,0x00,0xcb,0x00,0x00,0x00,
+0x56,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5b,0x01,0x00,0x00,0x57,0x01,0x00,0x00,0x50,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5e,0x01,0x00,0x00,
+0x5b,0x01,0x00,0x00,0x52,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x61,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,
+0x55,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x64,0x01,0x00,0x00,0xcb,0x00,0x00,0x00,0x61,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x01,0x00,0x00,
+0x52,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6a,0x01,0x00,0x00,0x64,0x01,0x00,0x00,
+0x69,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6e,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,0x52,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x71,0x01,0x00,0x00,
+0x6e,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x82,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x76,0x01,0x00,0x00,0x64,0x01,0x00,0x00,
+0x71,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x79,0x01,0x00,0x00,0x76,0x01,0x00,0x00,0x55,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x82,0x01,0x00,0x00,
+0x79,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x82,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x83,0x01,0x00,0x00,0x76,0x01,0x00,0x00,
+0x82,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x85,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x86,0x01,0x00,0x00,
+0x85,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x87,0x01,0x00,0x00,0x57,0x01,0x00,0x00,0x86,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x89,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x8a,0x01,0x00,0x00,0x89,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,
+0x6a,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8c,0x01,0x00,0x00,0x87,0x01,0x00,0x00,
+0x8b,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x8e,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,
+0x8e,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x90,0x01,0x00,0x00,0x79,0x01,0x00,0x00,0x8f,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x91,0x01,0x00,0x00,
+0x8c,0x01,0x00,0x00,0x90,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x93,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0x66,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x94,0x01,0x00,0x00,0x93,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,0x83,0x01,0x00,0x00,
+0x94,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x96,0x01,0x00,0x00,0x91,0x01,0x00,0x00,0x95,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0xe8,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
+0xe3,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x96,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
+0xe9,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xe8,0x00,0x00,0x00,
+0xeb,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
+0xdf,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xeb,0x00,0x00,0x00,
+0xea,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xef,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xef,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t cpy_f32_f32_len = 3232;
+
+unsigned char dequant_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x1f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x13,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x13,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x2a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
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+0xa0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,
+0xa3,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x28,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xa5,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x17,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x39,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x31,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
+0xb0,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0xad,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xb2,0x00,0x00,0x00,
+0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xba,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x18,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
+0xbe,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x3d,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xbf,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x19,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,
+0xc9,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,
+0xca,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x28,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xcc,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x39,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x31,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
+0xd7,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,
+0xd9,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0xd4,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xd9,0x00,0x00,0x00,
+0xd8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe1,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1b,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
+0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
+0xe3,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
+0xe5,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x3d,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xe6,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x1c,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0xee,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,
+0xf1,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x28,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xf3,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x39,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x31,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,0xff,0x00,0x00,0x00,
+0xfe,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x00,0x01,0x00,0x00,
+0xff,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x08,0x01,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,
+0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,
+0x0a,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
+0x0c,0x01,0x00,0x00,0x0b,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x3d,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x0d,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x45,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x45,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t dequant_f32_len = 3200;
+
+unsigned char dequant_q2_K_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x10,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x5e,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x30,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x55,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x5a,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x5a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x5a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5b,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x5c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x5c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x5c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x5e,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x5e,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x76,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x77,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x77,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x77,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x79,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x79,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x00,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x14,0x00,0x02,0x00,0x11,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x1e,0x00,0x07,0x00,0x1e,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x1f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
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+0x06,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,
+0xa5,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0xa7,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x52,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,
+0x66,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
+0xaa,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0xac,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x21,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
+0xa7,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,
+0x67,0x00,0x00,0x00,0xae,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x52,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,
+0xa3,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x52,0x00,0x00,0x00,
+0xb6,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
+0xb6,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x67,0x00,0x00,0x00,
+0xb8,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
+0x7f,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,
+0xb8,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x67,0x00,0x00,0x00,
+0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x6f,0x00,0x00,0x00,0xae,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
+0xb9,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x6c,0x00,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x9d,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xbb,0x00,0x00,0x00,
+0xba,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbd,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
+0x43,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
+0x64,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0xc2,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
+0xc4,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
+0xc5,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x52,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
+0x66,0x00,0x00,0x00,0x95,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
+0xca,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x21,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,
+0x67,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x52,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
+0xc3,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x52,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x67,0x00,0x00,0x00,
+0xd8,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
+0x7f,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x0e,0x01,0x00,0x00,
+0xd8,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x67,0x00,0x00,0x00,
+0xd9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x6f,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x0e,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
+0xd9,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x6c,0x00,0x00,0x00,
+0xdb,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0xbd,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xdb,0x00,0x00,0x00,
+0xda,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xde,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
+0x43,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
+0x64,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0xe3,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
+0xe5,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
+0xe6,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0xe8,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x52,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
+0x66,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
+0xec,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x21,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0xe8,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,
+0x67,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x52,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
+0xe4,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x52,0x00,0x00,0x00,
+0xf8,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
+0xf8,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x67,0x00,0x00,0x00,
+0xfa,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
+0x7f,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,
+0xfa,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x67,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x6f,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x6c,0x00,0x00,0x00,
+0xfd,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0xde,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xfd,0x00,0x00,0x00,
+0xfc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0x09,0x01,0x00,0x00,
+0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0a,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x11,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x03,0x01,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x07,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x0a,0x01,0x00,0x00,0x01,0x01,0x00,0x00,
+0x07,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x07,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x01,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x01,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t dequant_q2_K_len = 4032;
+
+unsigned char dequant_q3_K_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x3f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x77,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x30,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x71,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x73,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x73,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x73,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x73,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x74,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x75,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x75,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x75,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x77,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
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+0x06,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0x12,0x01,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x14,0x01,0x00,0x00,
+0x13,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0x15,0x01,0x00,0x00,0x14,0x01,0x00,0x00,0x8b,0x00,0x00,0x00,
+0x72,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x16,0x01,0x00,0x00,
+0x15,0x01,0x00,0x00,0x72,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0x17,0x01,0x00,0x00,0x16,0x01,0x00,0x00,0x41,0x00,0x08,0x00,
+0x7c,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,0x77,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x39,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0x1b,0x01,0x00,0x00,0x1a,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
+0x4d,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0x1b,0x01,0x00,0x00,
+0x57,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,
+0xab,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x20,0x01,0x00,0x00,
+0x1f,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0xa9,0x00,0x06,0x00,
+0x21,0x00,0x00,0x00,0x21,0x01,0x00,0x00,0x20,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
+0x21,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x17,0x01,0x00,0x00,
+0x21,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,0xdb,0x00,0x00,0x00,
+0x23,0x01,0x00,0x00,0x22,0x01,0x00,0x00,0x85,0x00,0x05,0x00,
+0xdb,0x00,0x00,0x00,0x24,0x01,0x00,0x00,0xea,0x00,0x00,0x00,
+0x23,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x72,0x00,0x00,0x00,
+0x25,0x01,0x00,0x00,0x24,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0xdf,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0x06,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x26,0x01,0x00,0x00,0x25,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0x39,0x01,0x00,0x00,
+0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xfa,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xfc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2a,0x01,0x00,0x00,
+0x35,0x01,0x00,0x00,0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x11,0x00,0x00,0x00,0x3e,0x01,0x00,0x00,
+0x2f,0x01,0x00,0x00,0x0a,0x00,0x00,0x00,0x32,0x01,0x00,0x00,
+0x2c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x33,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x3e,0x01,0x00,0x00,
+0x2d,0x01,0x00,0x00,0x33,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x33,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x2d,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x2d,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t dequant_q3_K_len = 4804;
+
+unsigned char dequant_q4_0_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x83,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x48,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x49,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x49,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x4b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x64,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x64,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x64,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x66,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x66,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x94,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1e,0x00,0x07,0x00,
+0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x29,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x2c,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
+0x32,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x00,0x04,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x42,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x45,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
+0x48,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
+0x1e,0x00,0x04,0x00,0x49,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
+0x48,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x4a,0x00,0x00,0x00,
+0x49,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x4b,0x00,0x00,0x00,
+0x4a,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x42,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x00,0x00,0x00,0xc1,
+0x1d,0x00,0x03,0x00,0x63,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x64,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x64,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x65,0x00,0x00,0x00,
+0x66,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x2c,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x71,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x46,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x93,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,
+0x94,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x93,0x00,0x00,0x00,
+0x93,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x77,0x01,0x00,0x00,0x11,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x78,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x79,0x01,0x00,0x00,
+0x12,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x7a,0x01,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x7b,0x01,0x00,0x00,0x13,0x00,0x00,0x00,
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+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x6f,0x01,0x00,0x00,0x57,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x45,0x00,0x00,0x00,0x72,0x01,0x00,0x00,0x71,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,0x73,0x01,0x00,0x00,
+0x66,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x6a,0x01,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x73,0x01,0x00,0x00,0x72,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x95,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x95,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t dequant_q4_0_len = 5184;
+
+unsigned char dequant_q4_1_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x85,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x48,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x49,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x49,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x49,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x4b,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x66,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x66,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x66,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x68,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x68,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x96,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x1e,0x00,0x07,0x00,0x29,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x2a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x32,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x00,0x04,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x42,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x45,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x46,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x48,0x00,0x00,0x00,
+0x46,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x1e,0x00,0x05,0x00,
+0x49,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
+0x48,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x4a,0x00,0x00,0x00,
+0x49,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x4b,0x00,0x00,0x00,
+0x4a,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x2c,0x00,0x00,0x00,0x56,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x65,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x66,0x00,0x00,0x00,0x65,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x66,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x67,0x00,0x00,0x00,
+0x68,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x2c,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x73,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x46,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
+0x78,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,
+0x96,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
+0x95,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x79,0x01,0x00,0x00,0x11,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x7a,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x7b,0x01,0x00,0x00,
+0x12,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x7c,0x01,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x7d,0x01,0x00,0x00,0x13,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x7e,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x7f,0x01,0x00,0x00,0x05,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x80,0x01,0x00,0x00,0x15,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x81,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x82,0x01,0x00,0x00,0x16,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x83,0x01,0x00,0x00,0x07,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x84,0x01,0x00,0x00,
+0x17,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x97,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,
+0x0c,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x98,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x1e,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x25,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
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+0xf0,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xf9,0x00,0x00,0x00,
+0xf8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x03,0x01,0x00,0x00,
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+0x7c,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x07,0x01,0x00,0x00,
+0x06,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x2c,0x00,0x00,0x00,
+0x08,0x01,0x00,0x00,0x07,0x01,0x00,0x00,0x78,0x00,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x09,0x01,0x00,0x00,
+0x08,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,
+0x0b,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x59,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,
+0x0b,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,
+0x0d,0x01,0x00,0x00,0x68,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x01,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x0d,0x01,0x00,0x00,
+0x0c,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x0f,0x01,0x00,0x00,0x41,0x00,0x00,0x00,0x7e,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0x12,0x01,0x00,0x00,
+0x04,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x46,0x00,0x00,0x00,
+0x13,0x01,0x00,0x00,0x12,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x14,0x01,0x00,0x00,
+0x13,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,
+0x16,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x14,0x01,0x00,0x00,0x59,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x17,0x01,0x00,0x00,
+0x16,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,
+0x18,0x01,0x00,0x00,0x68,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x0f,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x18,0x01,0x00,0x00,
+0x17,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x20,0x01,0x00,0x00,0x41,0x00,0x00,0x00,0x7f,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x22,0x01,0x00,0x00,
+0x40,0x00,0x00,0x00,0x7f,0x01,0x00,0x00,0x41,0x00,0x08,0x00,
+0x73,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x4e,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
+0x22,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,
+0x24,0x01,0x00,0x00,0x23,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x25,0x01,0x00,0x00,0x24,0x01,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x26,0x01,0x00,0x00,
+0x25,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x2c,0x00,0x00,0x00,
+0x27,0x01,0x00,0x00,0x26,0x01,0x00,0x00,0x78,0x00,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x28,0x01,0x00,0x00,
+0x27,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,
+0x2a,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0x59,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,
+0x2a,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,
+0x2c,0x01,0x00,0x00,0x68,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x20,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x2c,0x01,0x00,0x00,
+0x2b,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x2e,0x01,0x00,0x00,0x41,0x00,0x00,0x00,0x80,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0x31,0x01,0x00,0x00,
+0x23,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x46,0x00,0x00,0x00,
+0x32,0x01,0x00,0x00,0x31,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x33,0x01,0x00,0x00,
+0x32,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,
+0x35,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x33,0x01,0x00,0x00,0x59,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x36,0x01,0x00,0x00,
+0x35,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,
+0x37,0x01,0x00,0x00,0x68,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x2e,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x37,0x01,0x00,0x00,
+0x36,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x3f,0x01,0x00,0x00,0x41,0x00,0x00,0x00,0x81,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x41,0x01,0x00,0x00,
+0x40,0x00,0x00,0x00,0x81,0x01,0x00,0x00,0x41,0x00,0x08,0x00,
+0x73,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x4e,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
+0x41,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,
+0x43,0x01,0x00,0x00,0x42,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x44,0x01,0x00,0x00,0x43,0x01,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x45,0x01,0x00,0x00,
+0x44,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x2c,0x00,0x00,0x00,
+0x46,0x01,0x00,0x00,0x45,0x01,0x00,0x00,0x78,0x00,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x47,0x01,0x00,0x00,
+0x46,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,
+0x49,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x47,0x01,0x00,0x00,0x59,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,
+0x49,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,
+0x4b,0x01,0x00,0x00,0x68,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x3f,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x4b,0x01,0x00,0x00,
+0x4a,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x4d,0x01,0x00,0x00,0x41,0x00,0x00,0x00,0x82,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0x50,0x01,0x00,0x00,
+0x42,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x46,0x00,0x00,0x00,
+0x51,0x01,0x00,0x00,0x50,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x52,0x01,0x00,0x00,
+0x51,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,
+0x54,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x52,0x01,0x00,0x00,0x59,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x55,0x01,0x00,0x00,
+0x54,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,
+0x56,0x01,0x00,0x00,0x68,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x4d,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x56,0x01,0x00,0x00,
+0x55,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5e,0x01,0x00,0x00,0x41,0x00,0x00,0x00,0x83,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x60,0x01,0x00,0x00,
+0x40,0x00,0x00,0x00,0x83,0x01,0x00,0x00,0x41,0x00,0x08,0x00,
+0x73,0x00,0x00,0x00,0x61,0x01,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x4e,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
+0x60,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,
+0x62,0x01,0x00,0x00,0x61,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x63,0x01,0x00,0x00,0x62,0x01,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x64,0x01,0x00,0x00,
+0x63,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x2c,0x00,0x00,0x00,
+0x65,0x01,0x00,0x00,0x64,0x01,0x00,0x00,0x78,0x00,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x66,0x01,0x00,0x00,
+0x65,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,
+0x68,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x66,0x01,0x00,0x00,0x59,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x69,0x01,0x00,0x00,
+0x68,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,
+0x6a,0x01,0x00,0x00,0x68,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x5e,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x6a,0x01,0x00,0x00,
+0x69,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6c,0x01,0x00,0x00,0x41,0x00,0x00,0x00,0x84,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0x6f,0x01,0x00,0x00,
+0x61,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x46,0x00,0x00,0x00,
+0x70,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x71,0x01,0x00,0x00,
+0x70,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,
+0x73,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x71,0x01,0x00,0x00,0x59,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x74,0x01,0x00,0x00,
+0x73,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,
+0x75,0x01,0x00,0x00,0x68,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x6c,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x75,0x01,0x00,0x00,
+0x74,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x97,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x97,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t dequant_q4_1_len = 5248;
+
+unsigned char dequant_q4_K_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xae,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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+0x11,0x00,0x00,0x00,0x45,0x01,0x00,0x00,0x36,0x01,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x39,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x3a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x45,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
+0x3a,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x3a,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x34,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x34,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t dequant_q4_K_len = 5916;
+
+unsigned char dequant_q5_0_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xb7,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x48,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4e,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x4e,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x7c,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x7d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x7f,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7f,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xab,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x1e,0x00,0x07,0x00,0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x2a,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x2e,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x14,0x00,0x02,0x00,0x32,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x00,0x04,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x42,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x45,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x46,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x1c,0x00,0x04,0x00,0x48,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x49,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x1c,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
+0x4a,0x00,0x00,0x00,0x1e,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,
+0x45,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x4d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x4e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x4e,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x2c,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x45,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x5a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x78,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x7c,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x7d,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x7e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x7d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x7e,0x00,0x00,0x00,
+0x7f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,
+0x00,0x00,0x80,0x41,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x9d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,
+0xab,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
+0xaa,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xaa,0x01,0x00,0x00,0x11,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xab,0x01,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xac,0x01,0x00,0x00,
+0x12,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xad,0x01,0x00,0x00,0x0e,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xae,0x01,0x00,0x00,0x03,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xaf,0x01,0x00,0x00,
+0x13,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xb0,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xb1,0x01,0x00,0x00,0x05,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb2,0x01,0x00,0x00,
+0x15,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xb3,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xb4,0x01,0x00,0x00,0x16,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb5,0x01,0x00,0x00,
+0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xb6,0x01,0x00,0x00,0x17,0x00,0x00,0x00,0x36,0x00,0x05,0x00,
+0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xac,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xad,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
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+0x53,0x00,0x00,0x00,0x60,0x01,0x00,0x00,0x7f,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x56,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x60,0x01,0x00,0x00,0x5f,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x68,0x01,0x00,0x00,0x40,0x00,0x00,0x00,
+0xb3,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x78,0x00,0x00,0x00,
+0x69,0x01,0x00,0x00,0x50,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x68,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x49,0x00,0x00,0x00,0x6a,0x01,0x00,0x00,
+0x69,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x6b,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6c,0x01,0x00,0x00,0x41,0x00,0x00,0x00,
+0xb3,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6e,0x01,0x00,0x00,0x6b,0x01,0x00,0x00,0x86,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6f,0x01,0x00,0x00,
+0x64,0x00,0x00,0x00,0x68,0x01,0x00,0x00,0xc4,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x70,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,
+0x2d,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x71,0x01,0x00,0x00,0x70,0x01,0x00,0x00,0x4a,0x00,0x00,0x00,
+0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x72,0x01,0x00,0x00,
+0x6e,0x01,0x00,0x00,0x71,0x01,0x00,0x00,0x70,0x00,0x04,0x00,
+0x42,0x00,0x00,0x00,0x73,0x01,0x00,0x00,0x72,0x01,0x00,0x00,
+0x83,0x00,0x05,0x00,0x42,0x00,0x00,0x00,0x74,0x01,0x00,0x00,
+0x73,0x01,0x00,0x00,0x8f,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
+0x42,0x00,0x00,0x00,0x75,0x01,0x00,0x00,0x56,0x00,0x00,0x00,
+0x74,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
+0x76,0x01,0x00,0x00,0x75,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x53,0x00,0x00,0x00,0x77,0x01,0x00,0x00,0x7f,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x6c,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x77,0x01,0x00,0x00,0x76,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x79,0x01,0x00,0x00,0x41,0x00,0x00,0x00,
+0xb4,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x7a,0x01,0x00,0x00,0x6b,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7b,0x01,0x00,0x00,
+0x40,0x00,0x00,0x00,0xac,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x7c,0x01,0x00,0x00,0x64,0x00,0x00,0x00,
+0x7b,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x7d,0x01,0x00,0x00,0x7c,0x01,0x00,0x00,0x4a,0x00,0x00,0x00,
+0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x01,0x00,0x00,
+0x7a,0x01,0x00,0x00,0x7d,0x01,0x00,0x00,0x70,0x00,0x04,0x00,
+0x42,0x00,0x00,0x00,0x7f,0x01,0x00,0x00,0x7e,0x01,0x00,0x00,
+0x83,0x00,0x05,0x00,0x42,0x00,0x00,0x00,0x80,0x01,0x00,0x00,
+0x7f,0x01,0x00,0x00,0x8f,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
+0x42,0x00,0x00,0x00,0x81,0x01,0x00,0x00,0x56,0x00,0x00,0x00,
+0x80,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
+0x82,0x01,0x00,0x00,0x81,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x53,0x00,0x00,0x00,0x83,0x01,0x00,0x00,0x7f,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x79,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x83,0x01,0x00,0x00,0x82,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,0x40,0x00,0x00,0x00,
+0xb5,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x78,0x00,0x00,0x00,
+0x8c,0x01,0x00,0x00,0x50,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x49,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,
+0x8c,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x8e,0x01,0x00,0x00,0x8d,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,0x41,0x00,0x00,0x00,
+0xb5,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x91,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x86,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x92,0x01,0x00,0x00,
+0x64,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,0xc4,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x93,0x01,0x00,0x00,0x92,0x01,0x00,0x00,
+0x2d,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x94,0x01,0x00,0x00,0x93,0x01,0x00,0x00,0x4a,0x00,0x00,0x00,
+0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,
+0x91,0x01,0x00,0x00,0x94,0x01,0x00,0x00,0x70,0x00,0x04,0x00,
+0x42,0x00,0x00,0x00,0x96,0x01,0x00,0x00,0x95,0x01,0x00,0x00,
+0x83,0x00,0x05,0x00,0x42,0x00,0x00,0x00,0x97,0x01,0x00,0x00,
+0x96,0x01,0x00,0x00,0x8f,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
+0x42,0x00,0x00,0x00,0x98,0x01,0x00,0x00,0x56,0x00,0x00,0x00,
+0x97,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
+0x99,0x01,0x00,0x00,0x98,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x53,0x00,0x00,0x00,0x9a,0x01,0x00,0x00,0x7f,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x9a,0x01,0x00,0x00,0x99,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9c,0x01,0x00,0x00,0x41,0x00,0x00,0x00,
+0xb6,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9d,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x01,0x00,0x00,
+0x40,0x00,0x00,0x00,0xaf,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9f,0x01,0x00,0x00,0x64,0x00,0x00,0x00,
+0x9e,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa0,0x01,0x00,0x00,0x9f,0x01,0x00,0x00,0x4a,0x00,0x00,0x00,
+0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,
+0x9d,0x01,0x00,0x00,0xa0,0x01,0x00,0x00,0x70,0x00,0x04,0x00,
+0x42,0x00,0x00,0x00,0xa2,0x01,0x00,0x00,0xa1,0x01,0x00,0x00,
+0x83,0x00,0x05,0x00,0x42,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,
+0xa2,0x01,0x00,0x00,0x8f,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
+0x42,0x00,0x00,0x00,0xa4,0x01,0x00,0x00,0x56,0x00,0x00,0x00,
+0xa3,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
+0xa5,0x01,0x00,0x00,0xa4,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x53,0x00,0x00,0x00,0xa6,0x01,0x00,0x00,0x7f,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x9c,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xa6,0x01,0x00,0x00,0xa5,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xac,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xac,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t dequant_q5_0_len = 6644;
+
+unsigned char dequant_q5_1_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xb4,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x48,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x49,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x49,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x49,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x49,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x4b,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x78,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x78,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x78,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7a,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x7a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
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+0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,
+0xa0,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,
+0xa2,0x01,0x00,0x00,0x7a,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x98,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0xa2,0x01,0x00,0x00,
+0xa1,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xa8,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa8,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t dequant_q5_1_len = 6412;
+
+unsigned char dequant_q5_K_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x9e,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x4e,0x00,0x00,0x00,0x11,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x30,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x45,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x47,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x49,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4a,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x4c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4e,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0e,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x0f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x0f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x0f,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x11,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x11,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x87,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x14,0x00,0x02,0x00,0x11,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x1e,0x00,0x07,0x00,0x1e,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x1f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0x26,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x3e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x41,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x42,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x43,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x44,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
+0x45,0x00,0x00,0x00,0x43,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x47,0x00,0x00,0x00,
+0x43,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
+0x1c,0x00,0x04,0x00,0x49,0x00,0x00,0x00,0x43,0x00,0x00,0x00,
+0x48,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x4a,0x00,0x00,0x00,
+0x42,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
+0x49,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x4b,0x00,0x00,0x00,
+0x4a,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x4c,0x00,0x00,0x00,
+0x4b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x41,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x56,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x76,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x43,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0x93,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xca,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xde,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x0e,0x01,0x00,0x00,0x41,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x0f,0x01,0x00,0x00,0x0e,0x01,0x00,0x00,
+0x20,0x00,0x04,0x00,0x10,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x0f,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x10,0x01,0x00,0x00,
+0x11,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0x15,0x01,0x00,0x00,0x03,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0x26,0x01,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x68,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x2c,0x00,0x06,0x00,0x14,0x00,0x00,0x00,0x87,0x01,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x56,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
+0x2a,0x00,0x03,0x00,0x11,0x00,0x00,0x00,0x8a,0x01,0x00,0x00,
+0x29,0x00,0x03,0x00,0x11,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,
+0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x88,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,0x09,0x00,0x00,0x00,
+0x89,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x89,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x0a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x0a,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x90,0x01,0x00,0x00,0x09,0x00,0x00,0x00,0x89,0x01,0x00,0x00,
+0x86,0x01,0x00,0x00,0x0d,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
+0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x90,0x01,0x00,0x00,
+0x10,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x0b,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
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+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x01,0x00,0x00,
+0x63,0x00,0x00,0x00,0x68,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x43,0x00,0x00,0x00,0x6f,0x01,0x00,0x00,0x35,0x01,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x43,0x00,0x00,0x00,0x70,0x01,0x00,0x00,
+0x6f,0x01,0x00,0x00,0x9c,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x71,0x01,0x00,0x00,0x70,0x01,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x72,0x01,0x00,0x00,
+0x71,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x43,0x00,0x00,0x00,
+0x77,0x01,0x00,0x00,0x3d,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
+0x43,0x00,0x00,0x00,0x79,0x01,0x00,0x00,0x77,0x01,0x00,0x00,
+0x0d,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x7a,0x01,0x00,0x00,0x79,0x01,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0x7b,0x01,0x00,0x00,0x7a,0x01,0x00,0x00,
+0xab,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x7c,0x01,0x00,0x00,
+0x7b,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0xa9,0x00,0x06,0x00,
+0x21,0x00,0x00,0x00,0x7d,0x01,0x00,0x00,0x7c,0x01,0x00,0x00,
+0x26,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x21,0x00,0x00,0x00,0x7e,0x01,0x00,0x00,0x72,0x01,0x00,0x00,
+0x7d,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
+0x7f,0x01,0x00,0x00,0x7e,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x3e,0x00,0x00,0x00,0x82,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x7f,0x01,0x00,0x00,
+0x9d,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x41,0x00,0x00,0x00,
+0x83,0x01,0x00,0x00,0x82,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x50,0x00,0x00,0x00,0x84,0x01,0x00,0x00,0x11,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x69,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x84,0x01,0x00,0x00,0x83,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x86,0x01,0x00,0x00,
+0x90,0x01,0x00,0x00,0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x11,0x00,0x00,0x00,0x97,0x01,0x00,0x00,
+0x8a,0x01,0x00,0x00,0x0a,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,
+0x2c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x8e,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x97,0x01,0x00,0x00,
+0x88,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8e,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x88,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x88,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t dequant_q5_K_len = 5980;
+
+unsigned char dequant_q6_K_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x0d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x5f,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x30,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x56,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x59,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x5b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x5b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x5b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x5b,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x5d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x5d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x5d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x5f,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x5f,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x71,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x72,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x72,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x72,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x74,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x74,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x02,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x14,0x00,0x02,0x00,0x11,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x1e,0x00,0x07,0x00,0x1e,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x1f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0x26,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
+0x80,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x4e,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x53,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x1c,0x00,0x04,0x00,0x56,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x57,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x58,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x1c,0x00,0x04,0x00,0x59,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
+0x41,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x5a,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x5b,0x00,0x00,0x00,
+0x56,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x59,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x5c,0x00,0x00,0x00,
+0x5b,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x5d,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x5e,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x5e,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x68,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x71,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x72,0x00,0x00,0x00,0x71,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x73,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x72,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x73,0x00,0x00,0x00,
+0x74,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x7c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x58,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0x87,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x93,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
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+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,
+0x43,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
+0x7c,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
+0xc1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x58,0x00,0x00,0x00,
+0xc3,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0x72,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
+0x83,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x53,0x00,0x00,0x00,
+0xca,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,
+0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
+0xca,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x53,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
+0x8e,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
+0x21,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,
+0x8e,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0xd3,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0x72,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
+0xd3,0x00,0x00,0x00,0x72,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0xd5,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
+0x21,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
+0x93,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0xd7,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x68,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
+0xd7,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x68,0x00,0x00,0x00,
+0xd9,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
+0xd9,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x6d,0x00,0x00,0x00,
+0xdb,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xdb,0x00,0x00,0x00,
+0xda,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xde,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
+0x43,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
+0x7c,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
+0xe3,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x58,0x00,0x00,0x00,
+0xe5,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x72,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
+0xa7,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x53,0x00,0x00,0x00,
+0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,
+0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
+0xec,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x53,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xf2,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
+0xf3,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
+0x21,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
+0x8e,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0xf6,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,
+0x72,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
+0xf6,0x00,0x00,0x00,0x72,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0xf8,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
+0x21,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
+0x93,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
+0xfa,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x68,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
+0xfa,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x68,0x00,0x00,0x00,
+0xfc,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
+0xfc,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x6d,0x00,0x00,0x00,
+0xfe,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0xde,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xfe,0x00,0x00,0x00,
+0xfd,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x0b,0x01,0x00,0x00,
+0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0a,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x11,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,0x05,0x01,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x09,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x0c,0x01,0x00,0x00,0x03,0x01,0x00,0x00,
+0x09,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x09,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x03,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x03,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t dequant_q6_K_len = 4272;
+
+unsigned char dequant_q8_0_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x4c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x47,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x48,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x48,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x49,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x4a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x4a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5e,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x5f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x5f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x5f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x61,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x87,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1e,0x00,0x07,0x00,
+0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x29,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x2c,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
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+0x32,0x01,0x00,0x00,0x31,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x34,0x01,0x00,0x00,0x41,0x00,0x00,0x00,
+0x4b,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x36,0x01,0x00,0x00,0x40,0x00,0x00,0x00,0x4b,0x01,0x00,0x00,
+0x41,0x00,0x08,0x00,0x6b,0x00,0x00,0x00,0x37,0x01,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x67,0x00,0x00,0x00,0x36,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x46,0x00,0x00,0x00,0x38,0x01,0x00,0x00,0x37,0x01,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x39,0x01,0x00,0x00,
+0x38,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x42,0x00,0x00,0x00,
+0x3a,0x01,0x00,0x00,0x52,0x00,0x00,0x00,0x39,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x3b,0x01,0x00,0x00,
+0x3a,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x4f,0x00,0x00,0x00,
+0x3c,0x01,0x00,0x00,0x61,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x34,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x3c,0x01,0x00,0x00,
+0x3b,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x88,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x88,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t dequant_q8_0_len = 4780;
+
+unsigned char diag_mask_inf_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x4c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x15,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x2a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2b,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x2b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x2f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x30,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x30,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x30,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x32,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x32,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x49,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x1e,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x18,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x1d,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x29,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x2a,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x2f,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x30,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x31,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x34,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x18,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x43,0x00,0x00,0x00,0xff,0xff,0xff,0xff,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
+0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
+0x48,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x4a,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
+0x4b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x4b,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x17,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0xae,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x1e,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x1f,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x4a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x20,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x34,0x00,0x00,0x00,
+0x35,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x29,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
+0x3c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
+0xac,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0xa9,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
+0x43,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x29,0x00,0x00,0x00,
+0x45,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x83,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
+0x45,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x47,0x00,0x00,0x00,
+0x46,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4a,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x4a,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t diag_mask_inf_f32_len = 1480;
+
+unsigned char gelu_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x22,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x24,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x24,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x35,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x36,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x36,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x36,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x38,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x48,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x14,0x00,0x02,0x00,0x1a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x21,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x22,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x26,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x2a,0x42,0x4c,0x3f,
+0x2b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x00,0x00,0x80,0x3f,0x2b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x2e,0x00,0x00,0x00,0x13,0x27,0x37,0x3d,0x1d,0x00,0x03,0x00,
+0x35,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x36,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x37,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x00,0x00,0x00,0x3f,0x2b,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x00,0x00,0x00,0x40,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,
+0x09,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x36,0x00,0x05,0x00,
+0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x49,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x4a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x1c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x49,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x1d,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x2a,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
+0x11,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x11,0x00,0x00,0x00,
+0x33,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
+0x11,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x0c,0x00,0x06,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x88,0x00,0x05,0x00,
+0x11,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x41,0x00,0x00,0x00,0x83,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
+0x43,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
+0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
+0x3c,0x00,0x00,0x00,0x43,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x26,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x45,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x49,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x49,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t gelu_f32_len = 1484;
+
+unsigned char get_rows_f16_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x87,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
+0x7b,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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+0x36,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x1c,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
+0x4a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x4f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
+0x4f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
+0x56,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x59,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
+0x57,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x66,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x65,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
+0x66,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x68,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
+0x63,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x6b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x6d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x6d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6f,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x77,0x00,0x00,0x00,
+0x6f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x7f,0x00,0x00,0x00,
+0x80,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
+0x7e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x70,0x00,0x00,0x00,
+0x81,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x7f,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x82,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x85,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x85,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_f16_len = 2996;
+
+unsigned char get_rows_f16_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x89,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
+0x7b,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x19,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x32,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x33,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x33,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x33,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x35,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x35,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x70,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x71,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x71,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x71,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x73,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x73,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x78,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x7b,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x86,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x1e,0x00,0x1e,0x00,0x19,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x1c,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1c,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x14,0x00,0x02,0x00,0x2c,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x32,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x33,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x34,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x34,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1c,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
+0x43,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x48,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1c,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
+0x65,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1c,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x70,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x71,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x72,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x71,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x72,0x00,0x00,0x00,
+0x73,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x77,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x78,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x79,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x7a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x7a,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x7f,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x83,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x85,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x85,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x87,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,
+0x0c,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x88,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x1f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x1f,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x17,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
+0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x2f,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
+0x2e,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x2e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x2f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
+0x3b,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x43,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x45,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x45,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
+0x35,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
+0x49,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x4b,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x4e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x56,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x57,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x56,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x5b,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x5b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x65,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x67,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x67,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x69,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
+0x6e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x7f,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x77,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
+0x81,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x83,0x00,0x00,0x00,
+0x84,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x84,0x00,0x00,0x00,
+0x82,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x87,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_f16_f32_len = 3028;
+
+unsigned char get_rows_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x89,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
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+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x1f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x1f,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x17,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
+0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x2f,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
+0x2e,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x2e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x2f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
+0x3b,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x43,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x45,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x45,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
+0x35,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
+0x49,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x4b,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x4e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x56,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x57,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x56,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x5b,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x5b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x65,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x67,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x67,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x69,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
+0x6e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x77,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x7f,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x18,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x70,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
+0x81,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x83,0x00,0x00,0x00,
+0x84,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
+0x77,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x84,0x00,0x00,0x00,
+0x82,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x87,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_f32_len = 3028;
+
+unsigned char get_rows_f32_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x86,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
+0x73,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x32,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x33,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x33,0x00,0x00,0x00,
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+0x1b,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x65,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x67,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x67,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x69,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
+0x6e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x7e,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x18,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x7e,0x00,0x00,0x00,0x81,0x00,0x00,0x00,
+0x73,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x81,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x84,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x84,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t get_rows_f32_f32_len = 2976;
+
+unsigned char get_rows_q4_0_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xcb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x33,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x34,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x34,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x36,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x36,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x85,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x86,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x86,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x87,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x88,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x88,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x88,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x8a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8a,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa9,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xaa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xaa,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xaa,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xac,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xac,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xc1,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x19,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x1e,0x00,0x1e,0x00,0x1a,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,
+0x1e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x1f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x2d,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x33,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x34,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
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+0x06,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0x96,0x00,0x00,0x00,
+0x9f,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x19,0x00,0x00,0x00,
+0xa1,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0x50,0x00,0x05,0x00,
+0x97,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,
+0xa1,0x00,0x00,0x00,0x83,0x00,0x05,0x00,0x97,0x00,0x00,0x00,
+0xa6,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
+0x8e,0x00,0x05,0x00,0x97,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
+0xa6,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
+0x7f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb1,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
+0x51,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,
+0xa8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x82,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x8c,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,
+0xac,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xb5,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
+0xb1,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x82,0x00,0x00,0x00,
+0xbe,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x8c,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xbf,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xc2,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_q4_0_len = 3704;
+
+unsigned char get_rows_q4_0_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xca,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x33,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x34,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x34,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x36,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x36,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x85,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x86,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x86,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x87,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x88,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x88,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x88,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x8a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8a,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa9,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xaa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xaa,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xaa,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xac,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xac,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xc0,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x19,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x1e,0x00,0x1e,0x00,0x1a,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,
+0x1e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x1f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x2d,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x33,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x34,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x35,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
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+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,
+0x96,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
+0x19,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,
+0x50,0x00,0x05,0x00,0x97,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,
+0x9d,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x83,0x00,0x05,0x00,
+0x97,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,
+0xc9,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,0x97,0x00,0x00,0x00,
+0xa8,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
+0x70,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
+0x7a,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0xb3,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xb4,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,
+0xac,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xb5,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
+0xb1,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xb4,0x00,0x00,0x00,
+0xbe,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xbe,0x00,0x00,0x00,
+0xbd,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc1,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc1,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_q4_0_f32_len = 3688;
+
+unsigned char get_rows_q4_1_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xca,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x33,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x34,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x34,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x36,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x36,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x85,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x86,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x86,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x87,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x88,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x88,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x88,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x8a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x8a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xaf,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0xb0,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xb0,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0xb0,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb2,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb2,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc7,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x19,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x1e,0x00,0x1e,0x00,0x1a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x1d,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1d,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x14,0x00,0x02,0x00,0x2d,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x33,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x34,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x35,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x35,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
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+0x19,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,
+0x9c,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
+0x19,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,
+0x50,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
+0xa3,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,
+0x9d,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
+0x8f,0x00,0x00,0x00,0x50,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,
+0xad,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x94,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
+0xab,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
+0x7f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb7,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
+0x51,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,
+0xae,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x82,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x8c,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xbb,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,
+0xb7,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x82,0x00,0x00,0x00,
+0xc4,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x8c,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xc5,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc8,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xc8,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_q4_1_len = 3788;
+
+unsigned char get_rows_q4_1_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xc9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x33,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x34,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x34,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x36,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x36,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x85,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x86,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x86,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x87,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x88,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x88,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x88,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x8a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x8a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xaf,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0xb0,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xb0,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0xb0,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb2,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb2,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc6,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x19,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x1e,0x00,0x1e,0x00,0x1a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x1d,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
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+0x41,0x00,0x08,0x00,0x99,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,
+0x8a,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
+0x97,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x83,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,
+0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,
+0x9b,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa2,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,
+0xa2,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa6,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,
+0xa6,0x00,0x00,0x00,0x50,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,
+0xa8,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,
+0x8e,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
+0xa8,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,0x50,0x00,0x05,0x00,
+0x9d,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x94,0x00,0x00,0x00,
+0x94,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,
+0xae,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,
+0x70,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,
+0x7a,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0xb9,0x00,0x00,0x00,0xae,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xba,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xbb,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,
+0xb7,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xba,0x00,0x00,0x00,
+0xc4,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0xc1,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xc4,0x00,0x00,0x00,
+0xc3,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc7,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc7,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_q4_1_f32_len = 3772;
+
+unsigned char get_rows_q5_0_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xf7,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x33,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x34,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x34,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x36,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x36,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x84,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x87,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x88,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x88,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x88,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x89,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x8a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x8a,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x8a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x8c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8c,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xd3,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xd4,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xd4,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xd4,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xd6,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd6,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xeb,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x19,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x1e,0x00,0x1e,0x00,0x1a,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
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+0x74,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x7a,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x41,0x00,0x07,0x00,
+0x8e,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x82,0x00,0x00,0x00,0x90,0x00,0x00,0x00,
+0x8f,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x19,0x00,0x00,0x00,
+0x91,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
+0x94,0x00,0x00,0x00,0x95,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
+0x2a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,
+0x96,0x00,0x00,0x00,0x95,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x96,0x00,0x00,0x00,
+0xc4,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x99,0x00,0x00,0x00,
+0x97,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
+0x94,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x83,0x00,0x00,0x00,
+0x9c,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,
+0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
+0x99,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
+0x7a,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa6,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,
+0xa6,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x1d,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
+0x7a,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
+0xac,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xae,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
+0xae,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0xb5,0x00,0x00,0x00,
+0xb6,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x85,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
+0xb6,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xb8,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
+0xbd,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xc2,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
+0xc2,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x19,0x00,0x00,0x00,
+0xc4,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
+0xa5,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xc9,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
+0xc9,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x19,0x00,0x00,0x00,
+0xcb,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0x50,0x00,0x05,0x00,
+0xb9,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
+0xcb,0x00,0x00,0x00,0x83,0x00,0x05,0x00,0xb9,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,
+0x8e,0x00,0x05,0x00,0xb9,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
+0x7f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xdb,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
+0x51,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
+0xd2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x82,0x00,0x00,0x00,0xde,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x8e,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xdf,0x00,0x00,0x00,0xde,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
+0xdb,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x82,0x00,0x00,0x00,
+0xe8,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x8e,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xe9,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xec,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xec,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_q5_0_len = 4232;
+
+unsigned char get_rows_q5_0_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xf6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x33,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x34,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x34,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x36,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x36,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x84,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x87,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x88,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x88,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x88,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
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+0x47,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x49,0x00,0x00,0x00,
+0x4a,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0x48,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,
+0x4b,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1f,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x52,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1f,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x56,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x56,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x58,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x57,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1f,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1f,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x64,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1f,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
+0x69,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1f,0x00,0x00,0x00,
+0x6d,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
+0x6d,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6f,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
+0x6a,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x74,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
+0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x7f,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
+0x41,0x00,0x07,0x00,0x8e,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,
+0x8c,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x82,0x00,0x00,0x00,
+0x90,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x19,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x90,0x00,0x00,0x00,
+0x41,0x00,0x08,0x00,0x94,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
+0x8c,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
+0x2a,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x83,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
+0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x97,0x00,0x00,0x00,
+0x96,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x99,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
+0x41,0x00,0x08,0x00,0x94,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,
+0x8c,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
+0x2a,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x83,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,
+0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,
+0x9c,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9e,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,
+0x9e,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,
+0xa5,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa7,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
+0xa7,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xac,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
+0x9e,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xae,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,
+0xaf,0x00,0x00,0x00,0xae,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
+0xb5,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,
+0x7a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x85,0x00,0x00,0x00,
+0xb7,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
+0xb8,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
+0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
+0xbe,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
+0x19,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
+0xb8,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
+0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
+0xc6,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
+0x19,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
+0x50,0x00,0x05,0x00,0xb9,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
+0xc4,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0x83,0x00,0x05,0x00,
+0xb9,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
+0xf5,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,0xb9,0x00,0x00,0x00,
+0xd2,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x91,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
+0x70,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
+0x7a,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0xdd,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xde,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xdf,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
+0xdb,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xde,0x00,0x00,0x00,
+0xe8,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0xe5,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xe8,0x00,0x00,0x00,
+0xe7,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xeb,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xeb,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_q5_0_f32_len = 4216;
+
+unsigned char get_rows_q5_1_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xea,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
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+0x14,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0x17,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x17,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1f,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1f,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x2a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
+0x2d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x30,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,
+0x2f,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x2f,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe8,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x30,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1f,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x39,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3b,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x3b,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1f,0x00,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x3f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x41,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
+0x3c,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1f,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x44,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x46,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x46,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x48,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x49,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
+0x4a,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1f,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x4f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1f,0x00,0x00,0x00,
+0x55,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
+0x55,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x57,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
+0x52,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1f,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5e,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1f,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1f,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x66,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x68,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x68,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6a,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1f,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,
+0x6f,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x75,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
+0x5e,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x74,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x7a,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x41,0x00,0x07,0x00,
+0x8c,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x82,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,
+0x8d,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x19,0x00,0x00,0x00,
+0x8f,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,0x41,0x00,0x07,0x00,
+0x8c,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x82,0x00,0x00,0x00,0x93,0x00,0x00,0x00,
+0x92,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x19,0x00,0x00,0x00,
+0x94,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x41,0x00,0x07,0x00,
+0x98,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x97,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,
+0x99,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa0,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
+0xc4,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,
+0xa0,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,
+0x84,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,
+0xa4,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
+0xa7,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa9,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
+0xa9,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x1d,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
+0x41,0x00,0x08,0x00,0xb1,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
+0x8a,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
+0xaf,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x83,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
+0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,
+0xb3,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xba,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
+0xa4,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbf,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
+0xbf,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc2,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
+0xab,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc6,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
+0xc6,0x00,0x00,0x00,0x50,0x00,0x05,0x00,0xb5,0x00,0x00,0x00,
+0xc8,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
+0x8e,0x00,0x05,0x00,0xb5,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
+0xc8,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,0x50,0x00,0x05,0x00,
+0xb5,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0x94,0x00,0x00,0x00,
+0x94,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0xb5,0x00,0x00,0x00,
+0xce,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
+0x70,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
+0x7a,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0xd9,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x82,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
+0xd9,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x8c,0x00,0x00,0x00,
+0xdb,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0xd7,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xdb,0x00,0x00,0x00,
+0xda,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe1,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
+0x51,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
+0xce,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x82,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x8c,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
+0xd2,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xe5,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe8,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe8,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t get_rows_q5_1_len = 4128;
+
+unsigned char get_rows_q5_1_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xe9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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+0xbe,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x19,0x00,0x00,0x00,
+0xc0,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,
+0xa1,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xc5,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
+0xc5,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x19,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x50,0x00,0x05,0x00,
+0xb5,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,0xb5,0x00,0x00,0x00,
+0xcb,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,
+0x50,0x00,0x05,0x00,0xb5,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
+0x94,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
+0xb5,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
+0xcd,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd5,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
+0xd5,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xda,0x00,0x00,0x00,
+0xdb,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0xd7,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xdb,0x00,0x00,0x00,
+0xd9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe1,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
+0x51,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
+0xce,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0xda,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xe4,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe7,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe7,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_q5_1_f32_len = 4112;
+
+unsigned char get_rows_q8_0_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xbf,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x89,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x33,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x34,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x34,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x36,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x36,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x84,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x85,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x85,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x86,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x87,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x87,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x89,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x89,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa4,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xa5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xa5,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xa5,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbc,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x19,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x1e,0x00,0x1e,0x00,0x1a,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,
+0x1e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x1f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
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+0x1d,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,
+0x9e,0x00,0x00,0x00,0x50,0x00,0x05,0x00,0x8f,0x00,0x00,0x00,
+0xa0,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,
+0x8e,0x00,0x05,0x00,0x8f,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,
+0xa0,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
+0x7f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xac,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
+0x51,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
+0xa3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x82,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x8b,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
+0xa7,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xb0,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
+0xac,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x82,0x00,0x00,0x00,
+0xb9,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x8b,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xba,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xbd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xbd,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_q8_0_len = 3644;
+
+unsigned char get_rows_q8_0_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xbe,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x89,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x33,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x34,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x34,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x36,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x36,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x84,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x85,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x85,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x86,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x87,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x87,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x89,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x89,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa4,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xa5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xa5,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xa5,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbb,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x19,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x1e,0x00,0x1e,0x00,0x1a,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,
+0x1e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x1f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x2d,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x33,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x34,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x35,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x35,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
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+0xa3,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
+0x70,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
+0x7a,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
+0xae,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xaf,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
+0xa7,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xb0,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
+0xac,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xaf,0x00,0x00,0x00,
+0xb9,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0xb6,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xb9,0x00,0x00,0x00,
+0xb8,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xbc,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xbc,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t get_rows_q8_0_f32_len = 3628;
+
+unsigned char matmul_f16_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
+0x06,0x01,0x00,0x00,0x46,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
+0x39,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x04,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x04,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x06,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x20,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4e,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x4f,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x39,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x7f,0x02,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x80,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x80,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x80,0x02,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x82,0x02,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x82,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x10,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x39,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x58,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x60,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x6d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x72,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
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+0xaa,0x02,0x00,0x00,0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x52,0x02,0x00,0x00,0x27,0x02,0x00,0x00,
+0x51,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x54,0x02,0x00,0x00,0x64,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x55,0x02,0x00,0x00,
+0x52,0x02,0x00,0x00,0x54,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x59,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
+0xc5,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5a,0x02,0x00,0x00,0x2d,0x02,0x00,0x00,0x59,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,
+0x68,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5d,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,
+0x5c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x48,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x62,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x65,0x02,0x00,0x00,
+0xac,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x61,0x02,0x00,0x00,0x62,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x65,0x02,0x00,0x00,0x60,0x02,0x00,0x00,
+0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x60,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x67,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x67,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xae,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0x60,0x02,0x00,0x00,
+0xa0,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x69,0x02,0x00,0x00,
+0x6a,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x6d,0x02,0x00,0x00,0x68,0x02,0x00,0x00,0x69,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x68,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0x55,0x02,0x00,0x00,
+0xae,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x73,0x02,0x00,0x00,0x70,0x02,0x00,0x00,0x36,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x75,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x73,0x02,0x00,0x00,0x74,0x02,0x00,0x00,
+0x75,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x74,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x78,0x02,0x00,0x00,
+0x5d,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x79,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x7a,0x02,0x00,0x00,0x79,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,0x78,0x02,0x00,0x00,
+0x7a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x75,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x75,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0xc1,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,0x73,0x02,0x00,0x00,
+0x68,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,0x74,0x02,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x7e,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x7c,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,
+0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,
+0x5d,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
+0x87,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x89,0x02,0x00,0x00,0x88,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,0x86,0x02,0x00,0x00,
+0x89,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8b,0x02,0x00,0x00,0x3d,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
+0x8b,0x02,0x00,0x00,0x55,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
+0xae,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x91,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,
+0x91,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,0x93,0x02,0x00,0x00,
+0x94,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x97,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
+0x95,0x02,0x00,0x00,0x97,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,0x98,0x02,0x00,0x00,
+0xae,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,
+0x9b,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,
+0x9b,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x9d,0x02,0x00,0x00,
+0x9e,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x34,0x00,0x00,0x00,
+0x8f,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x9e,0x02,0x00,0x00,
+0x9c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x6a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6a,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
+0xae,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x67,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x69,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x62,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x62,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa2,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x61,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x4a,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x4a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x47,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x49,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x42,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x42,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,
+0xa9,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x3f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x41,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_f16_len = 10316;
+
+unsigned char matmul_f16_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x09,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
+0x05,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x69,0x01,0x00,0x00,
+0x69,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x03,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
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+0x06,0x00,0x00,0x00,0x85,0x02,0x00,0x00,0x82,0x02,0x00,0x00,
+0x84,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x89,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,0xf5,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
+0x5d,0x02,0x00,0x00,0x89,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,0x68,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8d,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xdc,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0x78,0x02,0x00,0x00,
+0xd2,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x95,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
+0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x91,0x02,0x00,0x00,
+0x92,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x95,0x02,0x00,0x00,0x90,0x02,0x00,0x00,0x91,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x90,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x97,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x97,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xde,0x02,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x90,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,
+0x9a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x9d,0x02,0x00,0x00,0xde,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x99,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x9d,0x02,0x00,0x00,
+0x98,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x98,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa0,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0xde,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,
+0xa0,0x02,0x00,0x00,0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xa5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xa3,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
+0xdc,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0xa9,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,
+0xa9,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xab,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa5,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,
+0xac,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0x98,0x02,0x00,0x00,
+0xab,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xae,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xac,0x02,0x00,0x00,0xad,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
+0xdc,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0xb8,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,
+0xb8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xba,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,
+0x6d,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
+0x85,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbf,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,0xde,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,
+0xd9,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
+0xdc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc5,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
+0xda,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
+0xc7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xca,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0xde,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,
+0xca,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc4,0x00,0x00,0x00,0xcc,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0xcd,0x02,0x00,0x00,0xce,0x02,0x00,0x00,
+0xb2,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xce,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xae,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xae,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,0xde,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x97,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x92,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x92,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,
+0xdc,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x8f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x91,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd4,0x02,0x00,0x00,0xda,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x79,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x72,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_f16_aligned_len = 11200;
+
+unsigned char matmul_f16_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0xfd,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x4a,0x01,0x00,0x00,
+0x50,0x01,0x00,0x00,0x39,0x02,0x00,0x00,0x82,0x02,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x01,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x02,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x02,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x02,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x04,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x22,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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+0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x61,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x4a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x4a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa4,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x47,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x49,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x42,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x42,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x41,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_f16_aligned_fp32_len = 10288;
+
+unsigned char matmul_f16_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xda,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
+0x06,0x01,0x00,0x00,0x46,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
+0x3b,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x04,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x04,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x06,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x20,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4e,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x4f,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3b,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x81,0x02,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x82,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x82,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x82,0x02,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x84,0x02,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x84,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x10,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x39,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x58,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x60,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x6d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x72,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x77,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x7c,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x80,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x85,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
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+0xaa,0x02,0x00,0x00,0xc7,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,0x2f,0x02,0x00,0x00,
+0x5b,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5e,0x02,0x00,0x00,0x68,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5f,0x02,0x00,0x00,
+0x5c,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x61,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xad,0x02,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
+0x64,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x67,0x02,0x00,0x00,0xad,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x63,0x02,0x00,0x00,0x64,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x67,0x02,0x00,0x00,
+0x62,0x02,0x00,0x00,0x63,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x62,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x69,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x69,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x62,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,
+0xaf,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x6b,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x6f,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,
+0x6b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6a,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x72,0x02,0x00,0x00,
+0x57,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x75,0x02,0x00,0x00,0x72,0x02,0x00,0x00,
+0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x77,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x75,0x02,0x00,0x00,
+0x76,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x76,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x7a,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x7d,0x02,0x00,0x00,
+0x7a,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0x7e,0x02,0x00,0x00,
+0x75,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,
+0x76,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x80,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x7e,0x02,0x00,0x00,
+0x7f,0x02,0x00,0x00,0x80,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x88,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0x89,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
+0x88,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,0x3f,0x02,0x00,0x00,
+0x8c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8f,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0x57,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x91,0x02,0x00,0x00,
+0x8f,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x95,0x02,0x00,0x00,0x93,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x97,0x02,0x00,0x00,
+0x95,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9a,0x02,0x00,0x00,0x97,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,
+0x9a,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
+0x9c,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0x9e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x5c,0x01,0x00,0x00,0x9f,0x02,0x00,0x00,0x84,0x02,0x00,0x00,
+0x34,0x00,0x00,0x00,0x91,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x9f,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x80,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x6c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x6c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa1,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x69,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x6b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x61,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x4c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4c,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
+0xab,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x49,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4b,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x44,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x44,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa7,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x41,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x43,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_f16_f32_len = 10332;
+
+unsigned char matmul_f16_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x13,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
+0x05,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x6b,0x01,0x00,0x00,
+0x74,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x03,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x03,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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+0x84,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x88,0x02,0x00,0x00,0x83,0x02,0x00,0x00,
+0x84,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
+0xe4,0x02,0x00,0x00,0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,0x62,0x02,0x00,0x00,
+0x8c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8f,0x02,0x00,0x00,0x64,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x90,0x02,0x00,0x00,
+0x8d,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
+0x00,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x95,0x02,0x00,0x00,0x68,0x02,0x00,0x00,0x94,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x97,0x02,0x00,0x00,
+0x68,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,0x95,0x02,0x00,0x00,
+0x97,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xe6,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x83,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
+0xe6,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x9c,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xa0,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
+0x9c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa2,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xe8,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
+0xda,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xa4,0x02,0x00,0x00,
+0xa5,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xa8,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0x90,0x02,0x00,0x00,
+0xe8,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xae,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0x36,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xb0,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xae,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
+0xb0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,
+0x98,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xb5,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,
+0xb5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xb0,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb0,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0xc2,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
+0xa3,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xb9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xb7,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,
+0xb9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb8,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,
+0x98,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
+0xc2,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xc4,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
+0xc4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc6,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,
+0xc6,0x02,0x00,0x00,0x90,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,
+0xe8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcc,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xce,0x02,0x00,0x00,
+0xcc,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,0xce,0x02,0x00,0x00,
+0xcf,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd2,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,
+0xd0,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,
+0xe8,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
+0xd6,0x02,0x00,0x00,0xca,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,
+0xd6,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x6d,0x01,0x00,0x00,
+0xd8,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,0x34,0x00,0x00,0x00,
+0xca,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xd8,0x02,0x00,0x00,
+0xd7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xb9,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xda,0x02,0x00,0x00,
+0xe8,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x9d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x9d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xdc,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x9c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x85,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x85,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xde,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,
+0xe3,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_f16_f32_aligned_len = 11360;
+
+unsigned char matmul_f16_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xd6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0xfd,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x4a,0x01,0x00,0x00,
+0x51,0x01,0x00,0x00,0x37,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x01,0x01,0x00,0x00,
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+0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x65,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x67,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x60,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x60,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
+0xa9,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x48,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x48,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa1,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x45,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x47,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x40,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x40,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3d,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_f16_f32_aligned_fp32_len = 10240;
+
+unsigned char matmul_f16_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xd6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0xfa,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x46,0x01,0x00,0x00,
+0x51,0x01,0x00,0x00,0x37,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x04,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x06,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x20,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x21,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4e,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x4f,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x51,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x37,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7d,0x02,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x7e,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x7e,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x7e,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x80,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x80,0x02,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x00,0x10,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x63,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x67,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
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+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x57,0x02,0x00,0x00,
+0xa6,0x02,0x00,0x00,0xc5,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x58,0x02,0x00,0x00,0x2b,0x02,0x00,0x00,
+0x57,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5a,0x02,0x00,0x00,0x68,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5b,0x02,0x00,0x00,
+0x58,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x46,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,
+0x60,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x63,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x5f,0x02,0x00,0x00,0x60,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x63,0x02,0x00,0x00,
+0x5e,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x65,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x65,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x5e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0x68,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,
+0xab,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x67,0x02,0x00,0x00,0x68,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x6b,0x02,0x00,0x00,0x66,0x02,0x00,0x00,
+0x67,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x66,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,
+0x53,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x71,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
+0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x73,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x71,0x02,0x00,0x00,
+0x72,0x02,0x00,0x00,0x73,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x72,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x76,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x77,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x78,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x79,0x02,0x00,0x00,
+0x76,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x73,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,
+0x71,0x02,0x00,0x00,0x66,0x02,0x00,0x00,0x79,0x02,0x00,0x00,
+0x72,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x7c,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x7a,0x02,0x00,0x00,
+0x7b,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x84,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x86,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0x85,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x87,0x02,0x00,0x00,0x86,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,
+0x84,0x02,0x00,0x00,0x87,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x89,0x02,0x00,0x00,0x3b,0x02,0x00,0x00,
+0x88,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8b,0x02,0x00,0x00,0x89,0x02,0x00,0x00,0x53,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
+0x8b,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x91,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,
+0x91,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x96,0x02,0x00,0x00,0x93,0x02,0x00,0x00,0x95,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
+0x96,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
+0x98,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0x9a,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x5c,0x01,0x00,0x00,0x9b,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
+0x34,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x9b,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x68,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x68,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9d,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x65,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x67,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x60,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x60,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x48,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x48,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,
+0xa7,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x45,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x47,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x40,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x40,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa3,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x3d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x3f,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_f16_f32_fp32_len = 10260;
+
+unsigned char matmul_f16_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xd7,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0xfa,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x46,0x01,0x00,0x00,
+0x51,0x01,0x00,0x00,0x37,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x04,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x06,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x20,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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+0x48,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa2,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x45,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x47,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x40,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x40,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3d,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_f16_fp32_len = 10276;
+
+unsigned char matmul_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xda,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0xfb,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x47,0x01,0x00,0x00,
+0x52,0x01,0x00,0x00,0x3b,0x02,0x00,0x00,0x84,0x02,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x04,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x06,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x22,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x50,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x50,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x50,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x52,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x52,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3b,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x81,0x02,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x82,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x82,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x82,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x84,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x84,0x02,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x00,0x10,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x63,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x67,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x85,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x90,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
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+0x2f,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5e,0x02,0x00,0x00,0x68,0x00,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5f,0x02,0x00,0x00,0x5c,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x61,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xad,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,
+0xa3,0x02,0x00,0x00,0x64,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x67,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
+0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x63,0x02,0x00,0x00,
+0x64,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x67,0x02,0x00,0x00,0x62,0x02,0x00,0x00,0x63,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x62,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x69,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x69,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x62,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
+0x6c,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x6f,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x6b,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x6f,0x02,0x00,0x00,
+0x6a,0x02,0x00,0x00,0x6b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x6a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x72,0x02,0x00,0x00,0x57,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x75,0x02,0x00,0x00,
+0x72,0x02,0x00,0x00,0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x77,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x75,0x02,0x00,0x00,0x76,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x76,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,
+0xad,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x7b,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,
+0x7b,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x7d,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x77,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,
+0x7e,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,
+0x7d,0x02,0x00,0x00,0x76,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x80,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x7e,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,
+0xad,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x8a,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0x89,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,
+0x8a,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8c,0x02,0x00,0x00,0x88,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
+0x3f,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
+0x57,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x91,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,
+0xaa,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,0x93,0x02,0x00,0x00,
+0xad,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x97,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,
+0xab,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,0x97,0x02,0x00,0x00,
+0x99,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9c,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,
+0xc9,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc3,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0x11,0x01,0x00,0x00,0x9f,0x02,0x00,0x00,
+0x84,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0x91,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x9f,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x80,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x80,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x6c,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x6c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x69,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x6b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,
+0xad,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x4c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x4c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa5,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x49,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x4b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x44,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x44,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x41,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x43,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_f32_len = 10324;
+
+unsigned char matmul_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x18,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0xfe,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x6b,0x01,0x00,0x00,
+0x71,0x01,0x00,0x00,0x79,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x02,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x03,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x05,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x43,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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+0x06,0x00,0x00,0x00,0xe9,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x80,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
+0xe9,0x02,0x00,0x00,0x60,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x89,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x8d,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
+0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x88,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x91,0x02,0x00,0x00,
+0xe9,0x02,0x00,0x00,0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,0x67,0x02,0x00,0x00,
+0x91,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x94,0x02,0x00,0x00,0x64,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
+0x92,0x02,0x00,0x00,0x94,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,
+0x05,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9a,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,
+0x68,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
+0x9c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xeb,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x88,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
+0xeb,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xa1,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xa5,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
+0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa7,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xed,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
+0xdf,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xad,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xa9,0x02,0x00,0x00,
+0xaa,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xad,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,0x95,0x02,0x00,0x00,
+0xed,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xb3,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,0x36,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xb5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xb3,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,
+0xb5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb4,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,
+0x9d,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xba,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,
+0xba,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xb5,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb5,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0xc2,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xbe,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xbc,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
+0xbe,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc6,0x02,0x00,0x00,
+0x9d,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
+0xc7,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xc9,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
+0xc9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcb,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
+0xcb,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,
+0xed,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd1,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,
+0xd1,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,
+0xd4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd7,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,
+0xd5,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xda,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,
+0xed,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
+0xdb,0x02,0x00,0x00,0xca,0x00,0x00,0x00,0xda,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,
+0xdb,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x07,0x01,0x00,0x00,
+0xdd,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0x34,0x00,0x00,0x00,
+0xcf,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xdd,0x02,0x00,0x00,
+0xdc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xbe,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xbe,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xaa,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
+0xed,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe1,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x8a,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,
+0xe8,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_f32_aligned_len = 11432;
+
+unsigned char matmul_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xcf,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0x03,0x01,0x00,0x00,
+0x45,0x01,0x00,0x00,0x4b,0x01,0x00,0x00,0x30,0x02,0x00,0x00,
+0x79,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
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+0x58,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x41,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x41,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3e,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x40,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x39,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x39,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,
+0x9f,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x36,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x38,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_f32_aligned_fp32_len = 10124;
+
+unsigned char matmul_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xd3,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x05,0x01,0x00,0x00,
+0x44,0x01,0x00,0x00,0x4f,0x01,0x00,0x00,0x34,0x02,0x00,0x00,
+0x7d,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x03,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1e,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1f,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x4d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x4d,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4f,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x34,0x02,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x7a,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x7b,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7b,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x7b,0x02,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x7d,0x02,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7d,0x02,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x1e,0x00,0x10,0x00,0x10,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x39,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x4f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x61,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x60,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x61,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x61,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x77,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x85,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x96,0x00,0x00,0x00,
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+0x5c,0x02,0x00,0x00,0x5d,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x60,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,
+0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5b,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x62,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x62,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xa8,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0x5b,0x02,0x00,0x00,
+0x9a,0x02,0x00,0x00,0x65,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x68,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x64,0x02,0x00,0x00,
+0x65,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x68,0x02,0x00,0x00,0x63,0x02,0x00,0x00,0x64,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,0x50,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x6e,0x02,0x00,0x00,0x6b,0x02,0x00,0x00,0x36,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x70,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x6e,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,
+0x70,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x73,0x02,0x00,0x00,
+0x58,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x74,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x75,0x02,0x00,0x00,0x74,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x76,0x02,0x00,0x00,0x73,0x02,0x00,0x00,
+0x75,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x70,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x70,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0xc1,0x00,0x00,0x00,0x77,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
+0x63,0x02,0x00,0x00,0x76,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x79,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x77,0x02,0x00,0x00,0x78,0x02,0x00,0x00,
+0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x81,0x02,0x00,0x00,
+0x58,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x83,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
+0x82,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x84,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x85,0x02,0x00,0x00,0x81,0x02,0x00,0x00,
+0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x86,0x02,0x00,0x00,0x38,0x02,0x00,0x00,0x85,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,
+0x86,0x02,0x00,0x00,0x50,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8c,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,
+0x8c,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x90,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,
+0x8f,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x92,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,
+0x90,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,0x93,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,
+0x96,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0x97,0x02,0x00,0x00,
+0x96,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x10,0x01,0x00,0x00,
+0x98,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,0x34,0x00,0x00,0x00,
+0x8a,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x98,0x02,0x00,0x00,
+0x97,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x65,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x65,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x62,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9c,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x45,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x45,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x42,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x44,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x3d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3d,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
+0xa3,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x3a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_f32_fp32_len = 10208;
+
+unsigned char matmul_q2_k_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x4f,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,0x58,0x01,0x00,0x00,
+0x8b,0x01,0x00,0x00,0x96,0x01,0x00,0x00,0x81,0x02,0x00,0x00,
+0xca,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x17,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x18,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1b,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1b,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1c,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x1d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x1d,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1f,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1f,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x65,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x66,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x93,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x94,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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+0xfa,0x00,0x04,0x00,0xad,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
+0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xaf,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xf9,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
+0xe7,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xf9,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xb1,0x02,0x00,0x00,
+0xb2,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xb5,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
+0xf9,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0xbb,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,0x36,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xbd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xbb,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
+0xbd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbc,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
+0xa5,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xc2,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
+0xc2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0xc1,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
+0xb0,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xc6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xc4,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
+0xc6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xce,0x02,0x00,0x00,
+0xa5,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
+0xcf,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xd1,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,0xce,0x02,0x00,0x00,
+0xd1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd3,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,
+0xd3,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,0xd5,0x02,0x00,0x00,
+0xf9,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd9,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,
+0xd9,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,
+0xdc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xdf,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,
+0xdd,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,
+0xf9,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,
+0xe3,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,
+0xe3,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0xa1,0x01,0x00,0x00,
+0xe5,0x02,0x00,0x00,0xca,0x02,0x00,0x00,0x34,0x00,0x00,0x00,
+0xd7,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xe5,0x02,0x00,0x00,
+0xe4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc6,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb2,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe7,0x02,0x00,0x00,
+0xf9,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xaf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb1,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xaa,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe9,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x92,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x92,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xeb,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x91,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x8a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8a,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xed,0x02,0x00,0x00,
+0xf4,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x87,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q2_k_f32_len = 11240;
+
+unsigned char matmul_q2_k_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x68,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,0x58,0x01,0x00,0x00,
+0x8d,0x01,0x00,0x00,0x95,0x01,0x00,0x00,0xa2,0x02,0x00,0x00,
+0xeb,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x17,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x18,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1b,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1b,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1c,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x1d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x1d,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1f,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1f,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x65,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x66,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x92,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x93,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x93,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
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+0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x9c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa1,0x02,0x00,0x00,0x47,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,
+0xa2,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
+0xa1,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
+0xa5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x15,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0x0e,0x03,0x00,0x00,0xab,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xae,0x02,0x00,0x00,
+0x15,0x03,0x00,0x00,0xbf,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xaa,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xae,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
+0xaa,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb0,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x16,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,
+0x0c,0x03,0x00,0x00,0xb3,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0x16,0x03,0x00,0x00,
+0x60,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xb2,0x02,0x00,0x00,
+0xb3,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xb6,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb1,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0x16,0x03,0x00,0x00,
+0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbb,0x02,0x00,0x00,0x90,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,
+0x64,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
+0xbd,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc2,0x02,0x00,0x00,0x15,0x03,0x00,0x00,0x2e,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,
+0x96,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,0x68,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc6,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xc8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xc8,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x18,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,
+0x0a,0x03,0x00,0x00,0xcb,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xce,0x02,0x00,0x00,0x18,0x03,0x00,0x00,
+0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xca,0x02,0x00,0x00,
+0xcb,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xce,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd0,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x1a,0x03,0x00,0x00,
+0x3e,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0x08,0x03,0x00,0x00,
+0xd3,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xd6,0x02,0x00,0x00,0x1a,0x03,0x00,0x00,0x62,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xd2,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xd6,0x02,0x00,0x00,
+0xd1,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xd1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd9,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0x1a,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,
+0xd9,0x02,0x00,0x00,0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xde,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xdc,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,0xde,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xdd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe1,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
+0x18,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0xe2,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,
+0xe2,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xe4,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xde,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xde,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,
+0xe5,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,
+0xe4,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xe7,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xe5,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xef,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
+0x18,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0xf1,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xf0,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf2,0x02,0x00,0x00,
+0xf1,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf3,0x02,0x00,0x00,0xef,0x02,0x00,0x00,0xf2,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,
+0xa6,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf6,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,
+0xbe,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf8,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,0x1a,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,
+0x15,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xfc,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,
+0x18,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xfe,0x02,0x00,0x00,0xfc,0x02,0x00,0x00,0xfd,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x00,0x03,0x00,0x00,
+0x16,0x03,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x01,0x03,0x00,0x00,0xfe,0x02,0x00,0x00,
+0x00,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x03,0x03,0x00,0x00,0x01,0x03,0x00,0x00,0x1a,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0x04,0x03,0x00,0x00,
+0xca,0x00,0x00,0x00,0x03,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc4,0x00,0x00,0x00,0x05,0x03,0x00,0x00,0x04,0x03,0x00,0x00,
+0x41,0x00,0x06,0x00,0x97,0x01,0x00,0x00,0x06,0x03,0x00,0x00,
+0xeb,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0xf8,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x06,0x03,0x00,0x00,0x05,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xd3,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x08,0x03,0x00,0x00,0x1a,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd0,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xcb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0a,0x03,0x00,0x00,
+0x18,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xca,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x03,0x00,0x00,0x16,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xab,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xab,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x0e,0x03,0x00,0x00,0x15,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q2_k_f32_aligned_len = 11800;
+
+unsigned char matmul_q2_k_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x3f,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x20,0x01,0x00,0x00,
+0x59,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,0x93,0x01,0x00,0x00,
+0x79,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
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+0x81,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x85,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
+0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x87,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x87,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xed,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0x80,0x02,0x00,0x00,
+0xe3,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
+0x60,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x89,0x02,0x00,0x00,
+0x8a,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x8d,0x02,0x00,0x00,0x88,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x88,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x91,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
+0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x92,0x02,0x00,0x00,0x67,0x02,0x00,0x00,0x91,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
+0x64,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,0x92,0x02,0x00,0x00,
+0x94,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x99,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0x07,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
+0x6d,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x68,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9d,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x9f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xef,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0x88,0x02,0x00,0x00,
+0xe1,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,0xef,0x02,0x00,0x00,
+0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xa1,0x02,0x00,0x00,
+0xa2,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xa5,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xf1,0x02,0x00,0x00,
+0x3e,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,
+0xaa,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xad,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xa9,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xad,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb0,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,
+0xb0,0x02,0x00,0x00,0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xb5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xb3,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
+0xef,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0xb9,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,
+0xb9,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xbb,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb5,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,
+0xbc,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
+0xbb,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xbe,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xbc,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc6,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
+0xef,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0xc8,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,
+0xc8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xca,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,
+0x7d,0x02,0x00,0x00,0xca,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,
+0x95,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcf,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,
+0xec,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,
+0xef,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd5,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,
+0xed,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,0xd5,0x02,0x00,0x00,
+0xd7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xda,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,
+0xca,0x00,0x00,0x00,0xda,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc4,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0x95,0x01,0x00,0x00,0xdd,0x02,0x00,0x00,
+0xc2,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xdd,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xbe,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xbe,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe1,0x02,0x00,0x00,
+0xef,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x9f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x8a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe3,0x02,0x00,0x00,0xed,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x87,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x89,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,0xec,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q2_k_f32_aligned_fp32_len = 11068;
+
+unsigned char matmul_q2_k_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x48,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,
+0x58,0x01,0x00,0x00,0x89,0x01,0x00,0x00,0x94,0x01,0x00,0x00,
+0x7a,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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+0x8a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,
+0xee,0x02,0x00,0x00,0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,0x68,0x02,0x00,0x00,
+0x92,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x95,0x02,0x00,0x00,0x64,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
+0x93,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
+0x08,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9b,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,
+0x68,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
+0x9d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xf0,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x89,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,
+0xf0,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xa2,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xa6,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
+0xa2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa8,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xf2,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,
+0xe0,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0xf2,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xaa,0x02,0x00,0x00,
+0xab,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xae,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
+0xf2,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0xb4,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,0x36,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xb6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xb4,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,
+0xb6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb5,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,
+0x9e,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xbb,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
+0xbb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xb6,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb6,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0xc1,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,
+0xa9,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xbf,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xbd,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
+0xbf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbe,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
+0x9e,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
+0xc8,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xca,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,
+0xca,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcc,0x02,0x00,0x00,0x7e,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xce,0x02,0x00,0x00,
+0xcc,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,0xce,0x02,0x00,0x00,
+0xf2,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd2,0x02,0x00,0x00,0xed,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,
+0xd2,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,
+0xd5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd8,0x02,0x00,0x00,0xee,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,
+0xd6,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,
+0xf2,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,
+0xdc,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,
+0xdc,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x9f,0x01,0x00,0x00,
+0xde,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0x34,0x00,0x00,0x00,
+0xd0,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xde,0x02,0x00,0x00,
+0xdd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xbf,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xbf,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xab,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xab,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,
+0xf2,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe2,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x8b,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x88,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x83,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe6,0x02,0x00,0x00,
+0xed,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x80,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q2_k_f32_fp32_len = 11120;
+
+unsigned char matmul_q3_k_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xa8,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x3a,0x01,0x00,0x00,0xaa,0x01,0x00,0x00,0x0b,0x02,0x00,0x00,
+0x16,0x02,0x00,0x00,0x01,0x03,0x00,0x00,0x4a,0x03,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x31,0x01,0x00,0x00,
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+0x12,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x15,0x03,0x00,0x00,0x10,0x03,0x00,0x00,0x11,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x10,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x19,0x03,0x00,0x00,0x71,0x03,0x00,0x00,
+0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1a,0x03,0x00,0x00,0xef,0x02,0x00,0x00,0x19,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,
+0x64,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x1d,0x03,0x00,0x00,0x1a,0x03,0x00,0x00,
+0x1c,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x21,0x03,0x00,0x00,0x70,0x03,0x00,0x00,0x8d,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x22,0x03,0x00,0x00,
+0xf5,0x02,0x00,0x00,0x21,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x24,0x03,0x00,0x00,0x68,0x00,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x25,0x03,0x00,0x00,0x22,0x03,0x00,0x00,0x24,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x27,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x27,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x73,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0x10,0x03,0x00,0x00,
+0x69,0x03,0x00,0x00,0x2a,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x2d,0x03,0x00,0x00,0x73,0x03,0x00,0x00,
+0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x29,0x03,0x00,0x00,
+0x2a,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x2d,0x03,0x00,0x00,0x28,0x03,0x00,0x00,0x29,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x28,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x2f,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x2f,0x03,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x75,0x03,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x28,0x03,0x00,0x00,0x67,0x03,0x00,0x00,
+0x32,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x35,0x03,0x00,0x00,0x75,0x03,0x00,0x00,0x62,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x31,0x03,0x00,0x00,0x32,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x35,0x03,0x00,0x00,
+0x30,0x03,0x00,0x00,0x31,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x30,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x38,0x03,0x00,0x00,0x1d,0x03,0x00,0x00,0x75,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x3b,0x03,0x00,0x00,
+0x38,0x03,0x00,0x00,0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x3d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x3b,0x03,0x00,0x00,0x3c,0x03,0x00,0x00,0x3d,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x3c,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x40,0x03,0x00,0x00,0x25,0x03,0x00,0x00,
+0x73,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x41,0x03,0x00,0x00,0x12,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x42,0x03,0x00,0x00,
+0x41,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x43,0x03,0x00,0x00,0x40,0x03,0x00,0x00,0x42,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x3d,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x3d,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,
+0x44,0x03,0x00,0x00,0x3b,0x03,0x00,0x00,0x30,0x03,0x00,0x00,
+0x43,0x03,0x00,0x00,0x3c,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x46,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x44,0x03,0x00,0x00,0x45,0x03,0x00,0x00,0x46,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x45,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x4e,0x03,0x00,0x00,0x25,0x03,0x00,0x00,
+0x73,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x50,0x03,0x00,0x00,0x12,0x00,0x00,0x00,0x4f,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x51,0x03,0x00,0x00,
+0x50,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x52,0x03,0x00,0x00,0x4e,0x03,0x00,0x00,0x51,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x53,0x03,0x00,0x00,
+0x05,0x03,0x00,0x00,0x52,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x55,0x03,0x00,0x00,0x53,0x03,0x00,0x00,
+0x1d,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x57,0x03,0x00,0x00,0x55,0x03,0x00,0x00,0x75,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x59,0x03,0x00,0x00,
+0x70,0x03,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5b,0x03,0x00,0x00,0x59,0x03,0x00,0x00,
+0x73,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5d,0x03,0x00,0x00,0x5b,0x03,0x00,0x00,0x5c,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5f,0x03,0x00,0x00,
+0x71,0x03,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x60,0x03,0x00,0x00,0x5d,0x03,0x00,0x00,
+0x5f,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x62,0x03,0x00,0x00,0x60,0x03,0x00,0x00,0x75,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0x63,0x03,0x00,0x00,
+0xc9,0x00,0x00,0x00,0x62,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc3,0x00,0x00,0x00,0x64,0x03,0x00,0x00,0x63,0x03,0x00,0x00,
+0x41,0x00,0x06,0x00,0x21,0x02,0x00,0x00,0x65,0x03,0x00,0x00,
+0x4a,0x03,0x00,0x00,0x34,0x00,0x00,0x00,0x57,0x03,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x65,0x03,0x00,0x00,0x64,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x46,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x46,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x32,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x32,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x67,0x03,0x00,0x00,0x75,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x2f,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x31,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x2a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x2a,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x03,0x00,0x00,
+0x73,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x27,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x29,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x12,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x12,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6b,0x03,0x00,0x00,0x71,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x0f,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x11,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x0a,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x0a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6d,0x03,0x00,0x00,0x70,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x07,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x09,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q3_k_f32_len = 13144;
+
+unsigned char matmul_q3_k_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xc6,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x3a,0x01,0x00,0x00,0xaa,0x01,0x00,0x00,0x0d,0x02,0x00,0x00,
+0x15,0x02,0x00,0x00,0x22,0x03,0x00,0x00,0x6b,0x03,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
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+0xc2,0x00,0x00,0x00,0x2e,0x03,0x00,0x00,0x91,0x03,0x00,0x00,
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+0x48,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x48,0x03,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x94,0x03,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x31,0x03,0x00,0x00,0x8a,0x03,0x00,0x00,
+0x4b,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x4e,0x03,0x00,0x00,0x94,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x4a,0x03,0x00,0x00,0x4b,0x03,0x00,0x00,
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+0x49,0x03,0x00,0x00,0x4a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x49,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x50,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x50,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x96,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x49,0x03,0x00,0x00,0x88,0x03,0x00,0x00,0x53,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x56,0x03,0x00,0x00,
+0x96,0x03,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x52,0x03,0x00,0x00,0x53,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x56,0x03,0x00,0x00,0x51,0x03,0x00,0x00,
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+0xc2,0x00,0x00,0x00,0x5c,0x03,0x00,0x00,0x59,0x03,0x00,0x00,
+0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x5e,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x5c,0x03,0x00,0x00,
+0x5d,0x03,0x00,0x00,0x5e,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5d,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x61,0x03,0x00,0x00,0x46,0x03,0x00,0x00,0x94,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x62,0x03,0x00,0x00,
+0x12,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x63,0x03,0x00,0x00,0x62,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x64,0x03,0x00,0x00,
+0x61,0x03,0x00,0x00,0x63,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5e,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x5e,0x03,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,0x65,0x03,0x00,0x00,
+0x5c,0x03,0x00,0x00,0x51,0x03,0x00,0x00,0x64,0x03,0x00,0x00,
+0x5d,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,0x67,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x65,0x03,0x00,0x00,
+0x66,0x03,0x00,0x00,0x67,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x66,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6f,0x03,0x00,0x00,0x46,0x03,0x00,0x00,0x94,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x71,0x03,0x00,0x00,
+0x12,0x00,0x00,0x00,0x70,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x72,0x03,0x00,0x00,0x71,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x73,0x03,0x00,0x00,
+0x6f,0x03,0x00,0x00,0x72,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x74,0x03,0x00,0x00,0x26,0x03,0x00,0x00,
+0x73,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x76,0x03,0x00,0x00,0x74,0x03,0x00,0x00,0x3e,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x78,0x03,0x00,0x00,
+0x76,0x03,0x00,0x00,0x96,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x7a,0x03,0x00,0x00,0x91,0x03,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x7c,0x03,0x00,0x00,0x7a,0x03,0x00,0x00,0x94,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x03,0x00,0x00,
+0x7c,0x03,0x00,0x00,0x7d,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x80,0x03,0x00,0x00,0x92,0x03,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x81,0x03,0x00,0x00,0x7e,0x03,0x00,0x00,0x80,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x83,0x03,0x00,0x00,
+0x81,0x03,0x00,0x00,0x96,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcd,0x00,0x00,0x00,0x84,0x03,0x00,0x00,0xca,0x00,0x00,0x00,
+0x83,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0x85,0x03,0x00,0x00,0x84,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
+0x17,0x02,0x00,0x00,0x86,0x03,0x00,0x00,0x6b,0x03,0x00,0x00,
+0x34,0x00,0x00,0x00,0x78,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x86,0x03,0x00,0x00,0x85,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x67,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x67,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x53,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x53,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x88,0x03,0x00,0x00,0x96,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x50,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x52,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x4b,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x4b,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8a,0x03,0x00,0x00,0x94,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x48,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x4a,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x33,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x33,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x03,0x00,0x00,
+0x92,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x30,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x32,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x2b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x2b,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8e,0x03,0x00,0x00,0x91,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x28,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x2a,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q3_k_f32_aligned_len = 13704;
+
+unsigned char matmul_q3_k_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x9d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
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+0x06,0x00,0x00,0x00,0xe1,0x02,0x00,0x00,0x67,0x03,0x00,0x00,
+0x6c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd4,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe6,0x02,0x00,0x00,0x55,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe7,0x02,0x00,0x00,0x96,0x00,0x00,0x00,0xe6,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x02,0x00,0x00,
+0x59,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xed,0x02,0x00,0x00,0xa8,0x00,0x00,0x00,
+0xec,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0xf2,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xf1,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,
+0xf2,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf4,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf8,0x02,0x00,0x00,
+0x47,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x0d,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,0xf9,0x02,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xfb,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xfc,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,
+0xfb,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xfd,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,0xfc,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xff,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xff,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x68,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
+0x65,0x03,0x00,0x00,0x02,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x05,0x03,0x00,0x00,0x68,0x03,0x00,0x00,
+0xbf,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x01,0x03,0x00,0x00,
+0x02,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x05,0x03,0x00,0x00,0x00,0x03,0x00,0x00,0x01,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x00,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x07,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x07,0x03,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x69,0x03,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x63,0x03,0x00,0x00,
+0x0a,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x0d,0x03,0x00,0x00,0x69,0x03,0x00,0x00,0x60,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x09,0x03,0x00,0x00,0x0a,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x0d,0x03,0x00,0x00,
+0x08,0x03,0x00,0x00,0x09,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x08,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x11,0x03,0x00,0x00,0x69,0x03,0x00,0x00,0x61,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x12,0x03,0x00,0x00,
+0xe7,0x02,0x00,0x00,0x11,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x14,0x03,0x00,0x00,0x64,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x15,0x03,0x00,0x00,0x12,0x03,0x00,0x00,0x14,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x19,0x03,0x00,0x00,
+0x68,0x03,0x00,0x00,0x87,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x1a,0x03,0x00,0x00,0xed,0x02,0x00,0x00,
+0x19,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1c,0x03,0x00,0x00,0x68,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1d,0x03,0x00,0x00,
+0x1a,0x03,0x00,0x00,0x1c,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x1f,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x1f,0x03,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x6b,0x03,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x08,0x03,0x00,0x00,0x61,0x03,0x00,0x00,
+0x22,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x25,0x03,0x00,0x00,0x6b,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x21,0x03,0x00,0x00,0x22,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x25,0x03,0x00,0x00,
+0x20,0x03,0x00,0x00,0x21,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x20,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x27,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x27,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x6d,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x20,0x03,0x00,0x00,0x5f,0x03,0x00,0x00,0x2a,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x2d,0x03,0x00,0x00,
+0x6d,0x03,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x29,0x03,0x00,0x00,0x2a,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x2d,0x03,0x00,0x00,0x28,0x03,0x00,0x00,
+0x29,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x28,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x30,0x03,0x00,0x00,
+0x15,0x03,0x00,0x00,0x6d,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x33,0x03,0x00,0x00,0x30,0x03,0x00,0x00,
+0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x35,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x33,0x03,0x00,0x00,
+0x34,0x03,0x00,0x00,0x35,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x34,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x38,0x03,0x00,0x00,0x1d,0x03,0x00,0x00,0x6b,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x39,0x03,0x00,0x00,
+0x12,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x3a,0x03,0x00,0x00,0x39,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x3b,0x03,0x00,0x00,
+0x38,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x35,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x35,0x03,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,0x3c,0x03,0x00,0x00,
+0x33,0x03,0x00,0x00,0x28,0x03,0x00,0x00,0x3b,0x03,0x00,0x00,
+0x34,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,0x3e,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x3c,0x03,0x00,0x00,
+0x3d,0x03,0x00,0x00,0x3e,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x3d,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x46,0x03,0x00,0x00,0x1d,0x03,0x00,0x00,0x6b,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x48,0x03,0x00,0x00,
+0x12,0x00,0x00,0x00,0x47,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x49,0x03,0x00,0x00,0x48,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4a,0x03,0x00,0x00,
+0x46,0x03,0x00,0x00,0x49,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x4b,0x03,0x00,0x00,0xfd,0x02,0x00,0x00,
+0x4a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x4d,0x03,0x00,0x00,0x4b,0x03,0x00,0x00,0x15,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4f,0x03,0x00,0x00,
+0x4d,0x03,0x00,0x00,0x6d,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x51,0x03,0x00,0x00,0x68,0x03,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x53,0x03,0x00,0x00,0x51,0x03,0x00,0x00,0x6b,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x55,0x03,0x00,0x00,
+0x53,0x03,0x00,0x00,0x54,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x57,0x03,0x00,0x00,0x69,0x03,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x58,0x03,0x00,0x00,0x55,0x03,0x00,0x00,0x57,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5a,0x03,0x00,0x00,
+0x58,0x03,0x00,0x00,0x6d,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcd,0x00,0x00,0x00,0x5b,0x03,0x00,0x00,0xca,0x00,0x00,0x00,
+0x5a,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0x5c,0x03,0x00,0x00,0x5b,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
+0x14,0x02,0x00,0x00,0x5d,0x03,0x00,0x00,0x42,0x03,0x00,0x00,
+0x34,0x00,0x00,0x00,0x4f,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x5d,0x03,0x00,0x00,0x5c,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x3e,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x3e,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x2a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x2a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5f,0x03,0x00,0x00,0x6d,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x27,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x29,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x22,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x22,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x61,0x03,0x00,0x00,0x6b,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x1f,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x21,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0a,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x63,0x03,0x00,0x00,
+0x69,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x07,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x09,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x02,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x02,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x65,0x03,0x00,0x00,0x68,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xff,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x01,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q3_k_f32_aligned_fp32_len = 12972;
+
+unsigned char matmul_q3_k_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xa1,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,0xaa,0x01,0x00,0x00,
+0x09,0x02,0x00,0x00,0x14,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,
+0x43,0x03,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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+0x15,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
+0xf2,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xf4,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,
+0xf4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf9,0x02,0x00,0x00,0x47,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0xfb,0x02,0x00,0x00,
+0xfa,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xfc,0x02,0x00,0x00,0xfb,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,
+0xf9,0x02,0x00,0x00,0xfc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,
+0xfd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x00,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x00,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x69,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
+0xd5,0x00,0x00,0x00,0x66,0x03,0x00,0x00,0x03,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x06,0x03,0x00,0x00,
+0x69,0x03,0x00,0x00,0xbe,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x02,0x03,0x00,0x00,0x03,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x06,0x03,0x00,0x00,0x01,0x03,0x00,0x00,
+0x02,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x01,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x08,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x08,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x6a,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0x01,0x03,0x00,0x00,
+0x64,0x03,0x00,0x00,0x0b,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x0e,0x03,0x00,0x00,0x6a,0x03,0x00,0x00,
+0x60,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x0a,0x03,0x00,0x00,
+0x0b,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x0e,0x03,0x00,0x00,0x09,0x03,0x00,0x00,0x0a,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x09,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x12,0x03,0x00,0x00,0x6a,0x03,0x00,0x00,
+0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x13,0x03,0x00,0x00,0xe8,0x02,0x00,0x00,0x12,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x15,0x03,0x00,0x00,
+0x64,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x16,0x03,0x00,0x00,0x13,0x03,0x00,0x00,
+0x15,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1a,0x03,0x00,0x00,0x69,0x03,0x00,0x00,0x88,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1b,0x03,0x00,0x00,
+0xee,0x02,0x00,0x00,0x1a,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x1d,0x03,0x00,0x00,0x68,0x00,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x03,0x00,0x00,0x1b,0x03,0x00,0x00,0x1d,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x20,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x20,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x6c,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0x09,0x03,0x00,0x00,
+0x62,0x03,0x00,0x00,0x23,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x26,0x03,0x00,0x00,0x6c,0x03,0x00,0x00,
+0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x22,0x03,0x00,0x00,
+0x23,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x26,0x03,0x00,0x00,0x21,0x03,0x00,0x00,0x22,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x21,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x28,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x28,0x03,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x6e,0x03,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x21,0x03,0x00,0x00,0x60,0x03,0x00,0x00,
+0x2b,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x2e,0x03,0x00,0x00,0x6e,0x03,0x00,0x00,0x62,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x2a,0x03,0x00,0x00,0x2b,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x2e,0x03,0x00,0x00,
+0x29,0x03,0x00,0x00,0x2a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x29,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x31,0x03,0x00,0x00,0x16,0x03,0x00,0x00,0x6e,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x34,0x03,0x00,0x00,
+0x31,0x03,0x00,0x00,0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x36,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x34,0x03,0x00,0x00,0x35,0x03,0x00,0x00,0x36,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x35,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x39,0x03,0x00,0x00,0x1e,0x03,0x00,0x00,
+0x6c,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x3a,0x03,0x00,0x00,0x12,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3b,0x03,0x00,0x00,
+0x3a,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x3c,0x03,0x00,0x00,0x39,0x03,0x00,0x00,0x3b,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x36,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x36,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,
+0x3d,0x03,0x00,0x00,0x34,0x03,0x00,0x00,0x29,0x03,0x00,0x00,
+0x3c,0x03,0x00,0x00,0x35,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x3f,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x3d,0x03,0x00,0x00,0x3e,0x03,0x00,0x00,0x3f,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x3e,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x47,0x03,0x00,0x00,0x1e,0x03,0x00,0x00,
+0x6c,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x49,0x03,0x00,0x00,0x12,0x00,0x00,0x00,0x48,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x4a,0x03,0x00,0x00,
+0x49,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x4b,0x03,0x00,0x00,0x47,0x03,0x00,0x00,0x4a,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4c,0x03,0x00,0x00,
+0xfe,0x02,0x00,0x00,0x4b,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x4e,0x03,0x00,0x00,0x4c,0x03,0x00,0x00,
+0x16,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x50,0x03,0x00,0x00,0x4e,0x03,0x00,0x00,0x6e,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x52,0x03,0x00,0x00,
+0x69,0x03,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x54,0x03,0x00,0x00,0x52,0x03,0x00,0x00,
+0x6c,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x56,0x03,0x00,0x00,0x54,0x03,0x00,0x00,0x55,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x58,0x03,0x00,0x00,
+0x6a,0x03,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x59,0x03,0x00,0x00,0x56,0x03,0x00,0x00,
+0x58,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5b,0x03,0x00,0x00,0x59,0x03,0x00,0x00,0x6e,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0x5c,0x03,0x00,0x00,
+0xc9,0x00,0x00,0x00,0x5b,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc3,0x00,0x00,0x00,0x5d,0x03,0x00,0x00,0x5c,0x03,0x00,0x00,
+0x41,0x00,0x06,0x00,0x1f,0x02,0x00,0x00,0x5e,0x03,0x00,0x00,
+0x43,0x03,0x00,0x00,0x34,0x00,0x00,0x00,0x50,0x03,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x5e,0x03,0x00,0x00,0x5d,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x3f,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x3f,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x2b,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x2b,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x60,0x03,0x00,0x00,0x6e,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x28,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x2a,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x23,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x23,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x62,0x03,0x00,0x00,
+0x6c,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x20,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x22,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x0b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x0b,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x64,0x03,0x00,0x00,0x6a,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x08,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x0a,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x03,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x03,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x66,0x03,0x00,0x00,0x69,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x00,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x02,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q3_k_f32_fp32_len = 13024;
+
+unsigned char matmul_q4_0_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x1c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x27,0x01,0x00,0x00,
+0x5a,0x01,0x00,0x00,0x65,0x01,0x00,0x00,0x50,0x02,0x00,0x00,
+0x99,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x84,0x02,0x00,0x00,
+0x7f,0x02,0x00,0x00,0x80,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x87,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
+0x87,0x02,0x00,0x00,0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x8c,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x8a,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,0x74,0x02,0x00,0x00,
+0xc4,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x90,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x91,0x02,0x00,0x00,
+0x90,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x92,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0x91,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x8c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8c,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,
+0x93,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
+0x92,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x95,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x93,0x02,0x00,0x00,0x94,0x02,0x00,0x00,0x95,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x94,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0x74,0x02,0x00,0x00,
+0xc4,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x9f,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
+0x9f,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa1,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
+0x54,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
+0x6c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa6,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
+0xc1,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
+0xc4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xac,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,
+0xc2,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
+0xae,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb1,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,
+0xc9,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc3,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0x70,0x01,0x00,0x00,0xb4,0x02,0x00,0x00,
+0x99,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xb4,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x95,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x95,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,
+0xc4,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x76,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x61,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xba,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x60,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q4_0_f32_len = 10564;
+
+unsigned char matmul_q4_0_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x37,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x28,0x01,0x00,0x00,
+0x5d,0x01,0x00,0x00,0x65,0x01,0x00,0x00,0x73,0x02,0x00,0x00,
+0xbc,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x03,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x04,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x06,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x06,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x06,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x08,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x08,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x35,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x36,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x62,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x63,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x63,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x63,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x63,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x63,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x65,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x65,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x73,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x02,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0xba,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
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+0xfa,0x00,0x04,0x00,0xad,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
+0xaf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xae,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,
+0x97,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xb4,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,
+0xb4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0xc2,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
+0xa2,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xb8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xb6,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,
+0xb8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb7,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
+0x97,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
+0xc1,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xc3,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
+0xc3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc5,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
+0xc5,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,
+0xe9,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcb,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
+0xcb,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,
+0xce,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd1,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,
+0xcf,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,
+0xe9,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
+0xd5,0x02,0x00,0x00,0xca,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
+0xd5,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x67,0x01,0x00,0x00,
+0xd7,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x34,0x00,0x00,0x00,
+0xc9,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xd7,0x02,0x00,0x00,
+0xd6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xb8,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,
+0xe9,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x9c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xdb,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x9b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x84,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
+0xe4,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q4_0_f32_aligned_len = 11156;
+
+unsigned char matmul_q4_0_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x0d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
+0x28,0x01,0x00,0x00,0x5b,0x01,0x00,0x00,0x62,0x01,0x00,0x00,
+0x49,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x05,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x06,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x06,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x06,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x08,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x08,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x33,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x34,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5f,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x60,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x60,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x60,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x62,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x62,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x49,0x02,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x8f,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x90,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x90,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x90,0x02,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x92,0x02,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x92,0x02,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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+0xf8,0x00,0x02,0x00,0xf5,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xee,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xee,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2d,0x02,0x00,0x00,
+0xd9,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xeb,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xed,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x8b,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8b,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x2f,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x88,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8a,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x80,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd7,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd7,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x31,0x02,0x00,0x00,
+0xb9,0x02,0x00,0x00,0x6c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x36,0x02,0x00,0x00,
+0x55,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x37,0x02,0x00,0x00,0x96,0x00,0x00,0x00,
+0x36,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x3c,0x02,0x00,0x00,0x59,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3d,0x02,0x00,0x00,
+0xa8,0x00,0x00,0x00,0x3c,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x42,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
+0x41,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x43,0x02,0x00,0x00,0x42,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x44,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x43,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x48,0x02,0x00,0x00,0x47,0x00,0x00,0x00,0x43,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,
+0x49,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x4b,0x02,0x00,0x00,0x4a,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4c,0x02,0x00,0x00,
+0x48,0x02,0x00,0x00,0x4b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x4d,0x02,0x00,0x00,0x44,0x02,0x00,0x00,
+0x4c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0x52,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x55,0x02,0x00,0x00,
+0xba,0x02,0x00,0x00,0xbf,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x51,0x02,0x00,0x00,0x52,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x55,0x02,0x00,0x00,0x50,0x02,0x00,0x00,
+0x51,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x50,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x57,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xbb,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0x50,0x02,0x00,0x00,
+0xb3,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x5d,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
+0x60,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x59,0x02,0x00,0x00,
+0x5a,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x5d,0x02,0x00,0x00,0x58,0x02,0x00,0x00,0x59,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
+0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x62,0x02,0x00,0x00,0x37,0x02,0x00,0x00,0x61,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x64,0x02,0x00,0x00,
+0x64,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x65,0x02,0x00,0x00,0x62,0x02,0x00,0x00,
+0x64,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x69,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xd7,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6a,0x02,0x00,0x00,
+0x3d,0x02,0x00,0x00,0x69,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6c,0x02,0x00,0x00,0x68,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6d,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x6f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xbd,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0x58,0x02,0x00,0x00,
+0xb1,0x02,0x00,0x00,0x72,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x75,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
+0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x71,0x02,0x00,0x00,
+0x72,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x75,0x02,0x00,0x00,0x70,0x02,0x00,0x00,0x71,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x70,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
+0x7a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x7d,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x79,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x7d,0x02,0x00,0x00,
+0x78,0x02,0x00,0x00,0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x78,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x80,0x02,0x00,0x00,0x65,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x83,0x02,0x00,0x00,
+0x80,0x02,0x00,0x00,0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x85,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x83,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0x85,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
+0xbd,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x89,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
+0x89,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x8b,0x02,0x00,0x00,0x88,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x85,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x85,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,
+0x8c,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0x78,0x02,0x00,0x00,
+0x8b,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x8e,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x8c,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
+0xbd,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x98,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0x97,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,
+0x98,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9a,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
+0x4d,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
+0x65,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9f,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,
+0xba,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
+0xbd,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa5,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,
+0xbb,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
+0xa7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xaa,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0xab,0x02,0x00,0x00,
+0xca,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc4,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0x64,0x01,0x00,0x00,0xad,0x02,0x00,0x00,
+0x92,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xad,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,
+0xbd,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x6f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb3,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x59,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x52,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x52,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x51,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q4_0_f32_aligned_fp32_len = 10408;
+
+unsigned char matmul_q4_0_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x15,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
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+0xbd,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0x58,0x02,0x00,0x00,
+0xb1,0x02,0x00,0x00,0x72,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x75,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
+0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x71,0x02,0x00,0x00,
+0x72,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x75,0x02,0x00,0x00,0x70,0x02,0x00,0x00,0x71,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x70,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
+0x7a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x7d,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x79,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x7d,0x02,0x00,0x00,
+0x78,0x02,0x00,0x00,0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x78,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x80,0x02,0x00,0x00,0x65,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x83,0x02,0x00,0x00,
+0x80,0x02,0x00,0x00,0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x85,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x83,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0x85,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
+0xbd,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x89,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
+0x89,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x8b,0x02,0x00,0x00,0x88,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x85,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x85,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,
+0x8c,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0x78,0x02,0x00,0x00,
+0x8b,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x8e,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x8c,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
+0xbd,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x98,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0x97,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,
+0x98,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9a,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
+0x4d,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
+0x65,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9f,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,
+0xba,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
+0xbd,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa5,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,
+0xbb,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
+0xa7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xaa,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0xab,0x02,0x00,0x00,
+0xc9,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc3,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0x6e,0x01,0x00,0x00,0xad,0x02,0x00,0x00,
+0x92,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xad,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,
+0xbd,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x6f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb3,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x59,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x52,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x52,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x51,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q4_0_f32_fp32_len = 10444;
+
+unsigned char matmul_q4_1_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xf6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,
+0x5f,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,0x55,0x02,0x00,0x00,
+0x9e,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x03,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x05,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x05,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x07,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x39,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3a,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb4,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,
+0xb4,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
+0xb6,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0xb8,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x75,0x01,0x00,0x00,0xb9,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
+0x34,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xb9,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x9a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x86,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbb,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x83,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x85,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x66,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,
+0xc7,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x63,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x65,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc1,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5d,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q4_1_f32_len = 10632;
+
+unsigned char matmul_q4_1_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x19,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x2d,0x01,0x00,0x00,
+0x62,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,0x78,0x02,0x00,0x00,
+0xc1,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x03,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x04,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x06,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x06,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x06,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x08,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x08,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3a,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3b,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x67,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x68,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x68,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x68,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x68,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x68,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6a,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6a,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x78,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbe,0x02,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xbf,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xbf,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xbf,0x02,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc1,0x02,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xc1,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x10,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x39,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
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+0xd6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd9,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0xda,0x02,0x00,0x00,
+0xca,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc4,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,0xda,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0x6c,0x01,0x00,0x00,0xdc,0x02,0x00,0x00,
+0xc1,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0xce,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xdc,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xbd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xde,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,
+0xec,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x9e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x89,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe2,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x88,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q4_1_f32_aligned_len = 11224;
+
+unsigned char matmul_q4_1_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xef,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
+0x2d,0x01,0x00,0x00,0x60,0x01,0x00,0x00,0x67,0x01,0x00,0x00,
+0x4e,0x02,0x00,0x00,0x97,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x04,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x06,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x06,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x06,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x08,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x08,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x39,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x64,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x65,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x65,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x65,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x67,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x67,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4e,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x94,0x02,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x95,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x95,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x95,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x97,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x97,0x02,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x00,0x10,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
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+0x0d,0x00,0x00,0x00,0x4f,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x50,0x02,0x00,0x00,0x4f,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x51,0x02,0x00,0x00,0x4d,0x02,0x00,0x00,
+0x50,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x52,0x02,0x00,0x00,0x49,0x02,0x00,0x00,0x51,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x54,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x54,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xbf,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
+0xba,0x02,0x00,0x00,0x57,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x5a,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
+0xbf,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x56,0x02,0x00,0x00,
+0x57,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x5a,0x02,0x00,0x00,0x55,0x02,0x00,0x00,0x56,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x55,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x55,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,
+0x5f,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x62,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0x60,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x5e,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x62,0x02,0x00,0x00,
+0x5d,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5d,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x66,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0x61,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x67,0x02,0x00,0x00,
+0x3c,0x02,0x00,0x00,0x66,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x69,0x02,0x00,0x00,0x64,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6a,0x02,0x00,0x00,0x67,0x02,0x00,0x00,0x69,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,
+0xbf,0x02,0x00,0x00,0xdc,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,0x42,0x02,0x00,0x00,
+0x6e,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x71,0x02,0x00,0x00,0x68,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x72,0x02,0x00,0x00,
+0x6f,0x02,0x00,0x00,0x71,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x74,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x74,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x5d,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,
+0x77,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x7a,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x76,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x7a,0x02,0x00,0x00,
+0x75,0x02,0x00,0x00,0x76,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x75,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x75,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x82,0x02,0x00,0x00,
+0xc4,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x7e,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x82,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,
+0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x85,0x02,0x00,0x00,
+0x6a,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x85,0x02,0x00,0x00,
+0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x8a,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x88,0x02,0x00,0x00,
+0x89,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x89,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8d,0x02,0x00,0x00,0x72,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x90,0x02,0x00,0x00,
+0x8d,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x8a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8a,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,0x91,0x02,0x00,0x00,
+0x88,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,0x90,0x02,0x00,0x00,
+0x89,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x93,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x91,0x02,0x00,0x00,
+0x92,0x02,0x00,0x00,0x93,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x92,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9b,0x02,0x00,0x00,0x72,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
+0x9b,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,0x52,0x02,0x00,0x00,
+0x9f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa2,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,
+0xa2,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xad,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,
+0xad,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcd,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,0xca,0x00,0x00,0x00,
+0xaf,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0xb1,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x69,0x01,0x00,0x00,0xb2,0x02,0x00,0x00,0x97,0x02,0x00,0x00,
+0x34,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xb2,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x93,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x93,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb4,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x74,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x76,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,
+0xc0,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x57,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xba,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x54,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x56,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q4_1_f32_aligned_fp32_len = 10476;
+
+unsigned char matmul_q4_1_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xef,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x07,0x01,0x00,0x00,
+0x2c,0x01,0x00,0x00,0x5d,0x01,0x00,0x00,0x68,0x01,0x00,0x00,
+0x4e,0x02,0x00,0x00,0x97,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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+0x12,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x90,0x02,0x00,0x00,
+0x8d,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x8a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8a,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0x91,0x02,0x00,0x00,
+0x88,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,0x90,0x02,0x00,0x00,
+0x89,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x93,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x91,0x02,0x00,0x00,
+0x92,0x02,0x00,0x00,0x93,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x92,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9b,0x02,0x00,0x00,0x72,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
+0x9b,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,0x52,0x02,0x00,0x00,
+0x9f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa2,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,
+0xa2,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xad,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,
+0xad,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
+0xaf,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0xb1,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x73,0x01,0x00,0x00,0xb2,0x02,0x00,0x00,0x97,0x02,0x00,0x00,
+0x34,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xb2,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x93,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x93,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb4,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x74,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x76,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,
+0xc0,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x57,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xba,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x54,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x56,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q4_1_f32_fp32_len = 10512;
+
+unsigned char matmul_q4_k_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x8e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x21,0x01,0x00,0x00,0x78,0x01,0x00,0x00,0xc6,0x01,0x00,0x00,
+0xd1,0x01,0x00,0x00,0xbc,0x02,0x00,0x00,0x05,0x03,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1b,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1c,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1d,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1e,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x90,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x1f,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1f,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x21,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa0,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa1,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xce,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xcf,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xcf,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xcf,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xd1,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd1,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbc,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x02,0x03,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x03,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x03,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x03,0x03,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x05,0x03,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x05,0x03,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
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+0xaf,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0xb5,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,
+0xb5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb7,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,
+0x47,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x0d,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xbe,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
+0xbe,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc0,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xc2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xc2,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x2d,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
+0x28,0x03,0x00,0x00,0xc5,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,0x2d,0x03,0x00,0x00,
+0xbe,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xc4,0x02,0x00,0x00,
+0xc5,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xc8,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xca,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xca,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x2e,0x03,0x00,0x00,
+0x3e,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,0x26,0x03,0x00,0x00,
+0xcd,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0xd0,0x02,0x00,0x00,0x2e,0x03,0x00,0x00,0x60,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xcc,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xd0,0x02,0x00,0x00,
+0xcb,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xcb,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd4,0x02,0x00,0x00,0x2e,0x03,0x00,0x00,0x61,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,
+0xaa,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,0x64,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd8,0x02,0x00,0x00,0xd5,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,
+0x2d,0x03,0x00,0x00,0x48,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,
+0xdc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xdf,0x02,0x00,0x00,0x68,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,
+0xdd,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x30,0x03,0x00,0x00,
+0x3e,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,0x24,0x03,0x00,0x00,
+0xe5,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0xe8,0x02,0x00,0x00,0x30,0x03,0x00,0x00,0xbb,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xe4,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xe8,0x02,0x00,0x00,
+0xe3,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xea,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xea,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x32,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
+0xe3,0x02,0x00,0x00,0x22,0x03,0x00,0x00,0xed,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xf0,0x02,0x00,0x00,
+0x32,0x03,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xec,0x02,0x00,0x00,0xed,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xf0,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,
+0xec,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xeb,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,
+0xd8,0x02,0x00,0x00,0x32,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0xf6,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,
+0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xf8,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf6,0x02,0x00,0x00,
+0xf7,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xfb,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,0x30,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xfc,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,0xfc,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,
+0xfb,0x02,0x00,0x00,0xfd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xf8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf8,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0xff,0x02,0x00,0x00,
+0xf6,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,0xfe,0x02,0x00,0x00,
+0xf7,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x01,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xff,0x02,0x00,0x00,
+0x00,0x03,0x00,0x00,0x01,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x00,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x09,0x03,0x00,0x00,0xe0,0x02,0x00,0x00,0x30,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x0b,0x03,0x00,0x00,
+0x12,0x00,0x00,0x00,0x0a,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x03,0x00,0x00,0x0b,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,
+0x09,0x03,0x00,0x00,0x0c,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x0e,0x03,0x00,0x00,0xc0,0x02,0x00,0x00,
+0x0d,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x10,0x03,0x00,0x00,0x0e,0x03,0x00,0x00,0xd8,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x12,0x03,0x00,0x00,
+0x10,0x03,0x00,0x00,0x32,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x14,0x03,0x00,0x00,0x2d,0x03,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x16,0x03,0x00,0x00,0x14,0x03,0x00,0x00,0x30,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x18,0x03,0x00,0x00,
+0x16,0x03,0x00,0x00,0x17,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x1a,0x03,0x00,0x00,0x2e,0x03,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1b,0x03,0x00,0x00,0x18,0x03,0x00,0x00,0x1a,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1d,0x03,0x00,0x00,
+0x1b,0x03,0x00,0x00,0x32,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0x1e,0x03,0x00,0x00,0xc9,0x00,0x00,0x00,
+0x1d,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0x1f,0x03,0x00,0x00,0x1e,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
+0xdc,0x01,0x00,0x00,0x20,0x03,0x00,0x00,0x05,0x03,0x00,0x00,
+0x34,0x00,0x00,0x00,0x12,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x20,0x03,0x00,0x00,0x1f,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x01,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x01,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xed,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xed,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x22,0x03,0x00,0x00,0x32,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xea,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xec,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xe5,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x24,0x03,0x00,0x00,0x30,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xcd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcd,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x26,0x03,0x00,0x00,
+0x2e,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xca,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xc5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x28,0x03,0x00,0x00,0x2d,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xc2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xc4,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q4_k_f32_len = 12072;
+
+unsigned char matmul_q4_k_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xa8,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x22,0x01,0x00,0x00,0x79,0x01,0x00,0x00,0xc9,0x01,0x00,0x00,
+0xd1,0x01,0x00,0x00,0xde,0x02,0x00,0x00,0x27,0x03,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
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+0xfe,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x01,0x03,0x00,0x00,0x68,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x02,0x03,0x00,0x00,
+0xff,0x02,0x00,0x00,0x01,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x04,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x04,0x03,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x52,0x03,0x00,0x00,
+0x3e,0x00,0x00,0x00,0xed,0x02,0x00,0x00,0x46,0x03,0x00,0x00,
+0x07,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x0a,0x03,0x00,0x00,0x52,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x06,0x03,0x00,0x00,0x07,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x0a,0x03,0x00,0x00,
+0x05,0x03,0x00,0x00,0x06,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x05,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x0c,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x0c,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x54,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x05,0x03,0x00,0x00,0x44,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x12,0x03,0x00,0x00,
+0x54,0x03,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x0e,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x12,0x03,0x00,0x00,0x0d,0x03,0x00,0x00,
+0x0e,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0d,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x15,0x03,0x00,0x00,
+0xfa,0x02,0x00,0x00,0x54,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x18,0x03,0x00,0x00,0x15,0x03,0x00,0x00,
+0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x1a,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x18,0x03,0x00,0x00,
+0x19,0x03,0x00,0x00,0x1a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x19,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1d,0x03,0x00,0x00,0x02,0x03,0x00,0x00,0x52,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x1e,0x03,0x00,0x00,
+0x12,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x1f,0x03,0x00,0x00,0x1e,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x20,0x03,0x00,0x00,
+0x1d,0x03,0x00,0x00,0x1f,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x1a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x1a,0x03,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,0x21,0x03,0x00,0x00,
+0x18,0x03,0x00,0x00,0x0d,0x03,0x00,0x00,0x20,0x03,0x00,0x00,
+0x19,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,0x23,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x21,0x03,0x00,0x00,
+0x22,0x03,0x00,0x00,0x23,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x22,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x03,0x00,0x00,0x02,0x03,0x00,0x00,0x52,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x2d,0x03,0x00,0x00,
+0x12,0x00,0x00,0x00,0x2c,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x2e,0x03,0x00,0x00,0x2d,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2f,0x03,0x00,0x00,
+0x2b,0x03,0x00,0x00,0x2e,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x30,0x03,0x00,0x00,0xe2,0x02,0x00,0x00,
+0x2f,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x32,0x03,0x00,0x00,0x30,0x03,0x00,0x00,0xfa,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x34,0x03,0x00,0x00,
+0x32,0x03,0x00,0x00,0x54,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x36,0x03,0x00,0x00,0x4f,0x03,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x38,0x03,0x00,0x00,0x36,0x03,0x00,0x00,0x52,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3a,0x03,0x00,0x00,
+0x38,0x03,0x00,0x00,0x39,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3c,0x03,0x00,0x00,0x50,0x03,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x3d,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0x3c,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3f,0x03,0x00,0x00,
+0x3d,0x03,0x00,0x00,0x54,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcd,0x00,0x00,0x00,0x40,0x03,0x00,0x00,0xca,0x00,0x00,0x00,
+0x3f,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0x41,0x03,0x00,0x00,0x40,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
+0xd3,0x01,0x00,0x00,0x42,0x03,0x00,0x00,0x27,0x03,0x00,0x00,
+0x34,0x00,0x00,0x00,0x34,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x42,0x03,0x00,0x00,0x41,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x23,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x23,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x0f,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x0f,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x44,0x03,0x00,0x00,0x54,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x0c,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x0e,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x07,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x07,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x46,0x03,0x00,0x00,0x52,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x04,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x06,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xef,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xef,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x48,0x03,0x00,0x00,
+0x50,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xec,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xee,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x4a,0x03,0x00,0x00,0x4f,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe6,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q4_k_f32_aligned_len = 12648;
+
+unsigned char matmul_q4_k_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x7e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x78,0x01,0x00,0x00,
+0xc6,0x01,0x00,0x00,0xcd,0x01,0x00,0x00,0xb4,0x02,0x00,0x00,
+0xfd,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x1c,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x1d,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1e,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1e,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1e,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1f,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x20,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x20,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
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+0xf8,0x00,0x02,0x00,0xf6,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,0x3e,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xf3,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xf5,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xeb,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xd7,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xd7,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9c,0x02,0x00,0x00,0x24,0x03,0x00,0x00,0x6c,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xd4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xd6,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa1,0x02,0x00,0x00,0x55,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
+0x96,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0x59,0x00,0x00,0x00,
+0xb9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xa8,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xad,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,
+0x0f,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0x47,0x00,0x00,0x00,
+0xae,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0xb5,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,
+0xb5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb7,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,
+0xaf,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xba,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xba,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x25,0x03,0x00,0x00,
+0x3e,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x20,0x03,0x00,0x00,
+0xbd,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xc0,0x02,0x00,0x00,0x25,0x03,0x00,0x00,0xbf,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xbc,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xc0,0x02,0x00,0x00,
+0xbb,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xbb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc2,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc2,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x26,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
+0xbb,0x02,0x00,0x00,0x1e,0x03,0x00,0x00,0xc5,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,
+0x26,0x03,0x00,0x00,0x60,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xc4,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xc8,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
+0xc4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcc,0x02,0x00,0x00,
+0x26,0x03,0x00,0x00,0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
+0xcc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcf,0x02,0x00,0x00,0x64,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,
+0xcd,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0x25,0x03,0x00,0x00,
+0x42,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd5,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,
+0x68,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,0xd5,0x02,0x00,0x00,
+0xd7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xda,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xda,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x28,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
+0xc3,0x02,0x00,0x00,0x1c,0x03,0x00,0x00,0xdd,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,
+0x28,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xdc,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xe0,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,
+0xdc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xdb,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe2,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x2a,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,
+0x1a,0x03,0x00,0x00,0xe5,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xe8,0x02,0x00,0x00,0x2a,0x03,0x00,0x00,
+0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xe4,0x02,0x00,0x00,
+0xe5,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xe8,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xeb,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,
+0x2a,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xee,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,0x36,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xf0,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xee,0x02,0x00,0x00,0xef,0x02,0x00,0x00,
+0xf0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xef,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,
+0xd8,0x02,0x00,0x00,0x28,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xf5,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xf6,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,
+0xf5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xf0,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xf0,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0xc2,0x00,0x00,0x00,0xf7,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
+0xe3,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,0xef,0x02,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xf9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xf7,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,
+0xf9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf8,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x01,0x03,0x00,0x00,
+0xd8,0x02,0x00,0x00,0x28,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x03,0x03,0x00,0x00,0x12,0x00,0x00,0x00,
+0x02,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x04,0x03,0x00,0x00,0x03,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x05,0x03,0x00,0x00,0x01,0x03,0x00,0x00,
+0x04,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x06,0x03,0x00,0x00,0xb8,0x02,0x00,0x00,0x05,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x08,0x03,0x00,0x00,
+0x06,0x03,0x00,0x00,0xd0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x0a,0x03,0x00,0x00,0x08,0x03,0x00,0x00,
+0x2a,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x03,0x00,0x00,0x25,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0e,0x03,0x00,0x00,
+0x0c,0x03,0x00,0x00,0x28,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x10,0x03,0x00,0x00,0x0e,0x03,0x00,0x00,
+0x0f,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x12,0x03,0x00,0x00,0x26,0x03,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x13,0x03,0x00,0x00,
+0x10,0x03,0x00,0x00,0x12,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x15,0x03,0x00,0x00,0x13,0x03,0x00,0x00,
+0x2a,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
+0x16,0x03,0x00,0x00,0xca,0x00,0x00,0x00,0x15,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x17,0x03,0x00,0x00,
+0x16,0x03,0x00,0x00,0x41,0x00,0x06,0x00,0xcf,0x01,0x00,0x00,
+0x18,0x03,0x00,0x00,0xfd,0x02,0x00,0x00,0x34,0x00,0x00,0x00,
+0x0a,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x18,0x03,0x00,0x00,
+0x17,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0xf9,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xf9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe5,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1a,0x03,0x00,0x00,
+0x2a,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe4,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xdd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xdd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1c,0x03,0x00,0x00,0x28,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xda,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xdc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x1e,0x03,0x00,0x00,0x26,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc2,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xbd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x20,0x03,0x00,0x00,
+0x25,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xba,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbc,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q4_k_f32_aligned_fp32_len = 11900;
+
+unsigned char matmul_q4_k_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x87,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x21,0x01,0x00,0x00,0x78,0x01,0x00,0x00,
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+0x06,0x00,0x00,0x00,0x05,0x03,0x00,0x00,0x04,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x06,0x03,0x00,0x00,
+0x02,0x03,0x00,0x00,0x05,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x07,0x03,0x00,0x00,0xb9,0x02,0x00,0x00,
+0x06,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x09,0x03,0x00,0x00,0x07,0x03,0x00,0x00,0xd1,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0b,0x03,0x00,0x00,
+0x09,0x03,0x00,0x00,0x2b,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,0x26,0x03,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x0f,0x03,0x00,0x00,0x0d,0x03,0x00,0x00,0x29,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x11,0x03,0x00,0x00,
+0x0f,0x03,0x00,0x00,0x10,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x13,0x03,0x00,0x00,0x27,0x03,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x14,0x03,0x00,0x00,0x11,0x03,0x00,0x00,0x13,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x16,0x03,0x00,0x00,
+0x14,0x03,0x00,0x00,0x2b,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0x17,0x03,0x00,0x00,0xc9,0x00,0x00,0x00,
+0x16,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0x18,0x03,0x00,0x00,0x17,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
+0xda,0x01,0x00,0x00,0x19,0x03,0x00,0x00,0xfe,0x02,0x00,0x00,
+0x34,0x00,0x00,0x00,0x0b,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x19,0x03,0x00,0x00,0x18,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xfa,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xfa,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1b,0x03,0x00,0x00,0x2b,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xde,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xde,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x1d,0x03,0x00,0x00,0x29,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xdb,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xdd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc6,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1f,0x03,0x00,0x00,
+0x27,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xbe,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xbe,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x21,0x03,0x00,0x00,0x26,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xbb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xbd,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q4_k_f32_fp32_len = 11952;
+
+unsigned char matmul_q5_0_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x47,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x50,0x01,0x00,0x00,
+0x83,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x79,0x02,0x00,0x00,
+0xc2,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x05,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x07,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x07,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x07,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x09,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x09,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5d,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x5e,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x8b,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x8c,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x8c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x8c,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8e,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x8e,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x79,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbf,0x02,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0xc0,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xc0,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0xc0,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xc2,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xc2,0x02,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x00,0x10,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
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+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xda,0x02,0x00,0x00,
+0xd8,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
+0xda,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0xdc,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x99,0x01,0x00,0x00,0xdd,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,
+0x34,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xdd,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xbe,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbe,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xaa,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xdf,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe1,0x02,0x00,0x00,0xef,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x8a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8a,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,
+0xed,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x87,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x82,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe5,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x81,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q5_0_f32_len = 11076;
+
+unsigned char matmul_q5_0_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x62,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
+0x86,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x9c,0x02,0x00,0x00,
+0xe5,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x03,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x06,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x06,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x06,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x08,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x08,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x08,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0a,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0a,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5e,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x5f,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x8b,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x8c,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x8c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x8c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x8c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x8c,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8e,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x8e,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x9c,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe2,0x02,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0xe3,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xe3,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0xe3,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xe5,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xe5,0x02,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x00,0x10,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
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+0xd3,0x02,0x00,0x00,0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xd8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xd6,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
+0x12,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0xdc,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,
+0xdc,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xde,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xd8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xd8,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,
+0xdf,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,
+0xde,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xe1,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xdf,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe9,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
+0x12,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0xeb,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xea,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xec,0x02,0x00,0x00,
+0xeb,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xed,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0xec,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xee,0x02,0x00,0x00,
+0xa0,0x02,0x00,0x00,0xed,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf0,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
+0xb8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf2,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0x14,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,
+0x0f,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf6,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,
+0x12,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf8,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,
+0x10,0x03,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xfb,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,
+0xfa,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xfd,0x02,0x00,0x00,0xfb,0x02,0x00,0x00,0x14,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,
+0xca,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc4,0x00,0x00,0x00,0xff,0x02,0x00,0x00,0xfe,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0x90,0x01,0x00,0x00,0x00,0x03,0x00,0x00,
+0xe5,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0xf2,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x00,0x03,0x00,0x00,0xff,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xcd,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xcd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x02,0x03,0x00,0x00,0x14,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xca,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x04,0x03,0x00,0x00,
+0x12,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xad,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x06,0x03,0x00,0x00,0x10,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xac,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x08,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q5_0_f32_aligned_len = 11668;
+
+unsigned char matmul_q5_0_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x38,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,
+0x51,0x01,0x00,0x00,0x84,0x01,0x00,0x00,0x8b,0x01,0x00,0x00,
+0x72,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x06,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x06,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x06,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x07,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x08,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x08,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x08,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0a,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0a,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x5c,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x5d,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x88,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x89,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x89,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x89,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x8b,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x8b,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x72,0x02,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb8,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xb9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xb9,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xb9,0x02,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbb,0x02,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x02,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
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+0xc6,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcc,0x02,0x00,0x00,0xca,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xce,0x02,0x00,0x00,
+0xcc,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd1,0x02,0x00,0x00,0xce,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,
+0xd1,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcd,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0xca,0x00,0x00,0x00,
+0xd3,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0xd5,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x8d,0x01,0x00,0x00,0xd6,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
+0x34,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xd6,0x02,0x00,0x00,0xd5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb7,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd8,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xda,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x98,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x83,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,
+0xe6,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x80,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xde,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7a,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q5_0_f32_aligned_fp32_len = 10920;
+
+unsigned char matmul_q5_0_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x40,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x09,0x01,0x00,0x00,
+0x50,0x01,0x00,0x00,0x81,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,
+0x72,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x06,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x07,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x07,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x07,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x09,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x09,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x5b,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x5c,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x89,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x8a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x8a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x8a,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x8c,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x8c,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x72,0x02,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb8,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xb9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xb9,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xb9,0x02,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbb,0x02,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x02,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x1e,0x00,0x10,0x00,0x10,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
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+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x98,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x83,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,
+0xe6,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x80,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xde,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7a,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q5_0_f32_fp32_len = 10956;
+
+unsigned char matmul_q5_1_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x17,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x4b,0x01,0x00,0x00,
+0x7e,0x01,0x00,0x00,0x89,0x01,0x00,0x00,0x74,0x02,0x00,0x00,
+0xbd,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x03,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x04,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x05,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x58,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x59,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x86,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x87,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x87,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x87,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x89,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x89,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x74,0x02,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xba,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xbb,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xbb,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xbb,0x02,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbd,0x02,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbd,0x02,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x1e,0x00,0x10,0x00,0x10,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x39,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x4f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x61,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x60,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x61,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x61,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
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+0xf8,0x00,0x02,0x00,0x16,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb3,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xb3,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5a,0x02,0x00,0x00,
+0x00,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb0,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xb2,0x01,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xa8,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,
+0x6c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd3,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd5,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,0x55,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x62,0x02,0x00,0x00,0x95,0x00,0x00,0x00,0x61,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x67,0x02,0x00,0x00,
+0x59,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x68,0x02,0x00,0x00,0xa7,0x00,0x00,0x00,
+0x67,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x6d,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0x6c,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,
+0x6d,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6f,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x73,0x02,0x00,0x00,
+0x47,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x0d,0x00,0x00,0x00,0x75,0x02,0x00,0x00,0x74,0x02,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x76,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x77,0x02,0x00,0x00,0x73,0x02,0x00,0x00,
+0x76,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x78,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7a,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xe7,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
+0xe0,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
+0xbe,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x7c,0x02,0x00,0x00,
+0x7d,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x80,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xe8,0x02,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,0xde,0x02,0x00,0x00,
+0x85,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x88,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,0x60,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x84,0x02,0x00,0x00,0x85,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x88,0x02,0x00,0x00,
+0x83,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x83,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8c,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,0x61,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
+0x62,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,0x64,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x90,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
+0xe7,0x02,0x00,0x00,0x00,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,0x68,0x02,0x00,0x00,
+0x94,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x97,0x02,0x00,0x00,0x68,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
+0x95,0x02,0x00,0x00,0x97,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x9a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x83,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
+0x9d,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0xa0,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x9c,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xa0,0x02,0x00,0x00,
+0x9b,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x9b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xec,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x9b,0x02,0x00,0x00,0xda,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
+0xec,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xa4,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xa8,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
+0xa4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xab,0x02,0x00,0x00,
+0x90,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
+0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xb0,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xae,0x02,0x00,0x00,
+0xaf,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xaf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb3,0x02,0x00,0x00,0x98,0x02,0x00,0x00,0xea,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,
+0xb3,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb0,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,
+0xae,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,
+0xaf,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xb9,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xb7,0x02,0x00,0x00,
+0xb8,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc1,0x02,0x00,0x00,0x98,0x02,0x00,0x00,0xea,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,
+0xc1,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc6,0x02,0x00,0x00,0x78,0x02,0x00,0x00,
+0xc5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc8,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,0x90,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,
+0xc8,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xcc,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xce,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,0xea,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,
+0xce,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd3,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,
+0xd3,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
+0xd5,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0xd7,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x94,0x01,0x00,0x00,0xd8,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
+0x34,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xd8,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb9,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xda,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9d,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,0xea,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x85,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x85,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xde,0x02,0x00,0x00,
+0xe8,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x84,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe0,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7c,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q5_1_f32_len = 10956;
+
+unsigned char matmul_q5_1_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x3a,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
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+0x0d,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xbd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbf,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x01,0x03,0x00,0x00,0x0b,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x03,0x03,0x00,0x00,0x0a,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x9d,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q5_1_f32_aligned_len = 11548;
+
+unsigned char matmul_q5_1_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x10,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
+0x4c,0x01,0x00,0x00,0x7f,0x01,0x00,0x00,0x86,0x01,0x00,0x00,
+0x6d,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x04,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x06,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x08,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x57,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x58,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x83,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x84,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x84,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x84,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x86,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x86,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6d,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb3,0x02,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xb4,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xb4,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xb4,0x02,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb6,0x02,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb6,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
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+0x06,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
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+0x5b,0x02,0x00,0x00,0x96,0x00,0x00,0x00,0x5a,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x60,0x02,0x00,0x00,
+0x59,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,0xa8,0x00,0x00,0x00,
+0x60,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x66,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0x65,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x67,0x02,0x00,0x00,
+0x66,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x68,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,0x67,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6c,0x02,0x00,0x00,
+0x47,0x00,0x00,0x00,0x67,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x0d,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x6f,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,
+0x6f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x71,0x02,0x00,0x00,0x68,0x02,0x00,0x00,0x70,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x73,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x73,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xe0,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
+0xd9,0x02,0x00,0x00,0x76,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x79,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,
+0xbf,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x75,0x02,0x00,0x00,
+0x76,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x79,0x02,0x00,0x00,0x74,0x02,0x00,0x00,0x75,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x74,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xe1,0x02,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x74,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,
+0x7e,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x81,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,0x60,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x7d,0x02,0x00,0x00,0x7e,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x81,0x02,0x00,0x00,
+0x7c,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x85,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,0x61,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,
+0x5b,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x64,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x89,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
+0xe0,0x02,0x00,0x00,0xfb,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,0x61,0x02,0x00,0x00,
+0x8d,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x90,0x02,0x00,0x00,0x68,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x91,0x02,0x00,0x00,
+0x8e,0x02,0x00,0x00,0x90,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x93,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x93,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,0xd5,0x02,0x00,0x00,
+0x96,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0x99,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x95,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x99,0x02,0x00,0x00,
+0x94,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x94,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x94,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,
+0xe5,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x9d,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xa1,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
+0x9d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,
+0x89,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,
+0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xa9,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xa7,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xac,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xad,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,
+0xac,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,
+0xa7,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xb2,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xb0,0x02,0x00,0x00,
+0xb1,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xba,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
+0xba,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0x71,0x02,0x00,0x00,
+0xbe,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc1,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,
+0xc1,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc7,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,
+0xc7,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcc,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xce,0x02,0x00,0x00,
+0xcc,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcd,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0xca,0x00,0x00,0x00,
+0xce,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0xd0,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x88,0x01,0x00,0x00,0xd1,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,
+0x34,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xd1,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb2,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x9e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x9e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd3,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x9d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x96,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x96,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x93,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x95,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,
+0xe1,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x76,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd9,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x73,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x75,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q5_1_f32_aligned_fp32_len = 10800;
+
+unsigned char matmul_q5_1_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x10,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x07,0x01,0x00,0x00,
+0x4b,0x01,0x00,0x00,0x7c,0x01,0x00,0x00,0x87,0x01,0x00,0x00,
+0x6d,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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+0x7c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x85,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,0x61,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,
+0x5b,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x64,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x89,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
+0xe0,0x02,0x00,0x00,0xfb,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,0x61,0x02,0x00,0x00,
+0x8d,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x90,0x02,0x00,0x00,0x68,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x91,0x02,0x00,0x00,
+0x8e,0x02,0x00,0x00,0x90,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x93,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x93,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,0xd5,0x02,0x00,0x00,
+0x96,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x99,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x95,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x99,0x02,0x00,0x00,
+0x94,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x94,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x94,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,
+0xe5,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x9d,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xa1,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
+0x9d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,
+0x89,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,
+0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xa9,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xa7,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xac,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xad,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,
+0xac,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,
+0xa7,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xb2,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xb0,0x02,0x00,0x00,
+0xb1,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xba,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
+0xba,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0x71,0x02,0x00,0x00,
+0xbe,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc1,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,
+0xc1,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc7,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,
+0xc7,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xcc,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xce,0x02,0x00,0x00,
+0xcc,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
+0xce,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0xd0,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x92,0x01,0x00,0x00,0xd1,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,
+0x34,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xd1,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xb2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb2,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x9e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x9e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd3,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x9d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x96,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x96,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x93,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x95,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,
+0xe1,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x76,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd9,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x73,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x75,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q5_1_f32_fp32_len = 10836;
+
+unsigned char matmul_q5_k_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xb3,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x2e,0x01,0x00,0x00,0x83,0x01,0x00,0x00,0xeb,0x01,0x00,0x00,
+0xf6,0x01,0x00,0x00,0xe1,0x02,0x00,0x00,0x2a,0x03,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x27,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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+0xf8,0x00,0x02,0x00,0x1d,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,
+0xc1,0x00,0x00,0x00,0x24,0x03,0x00,0x00,0x1b,0x03,0x00,0x00,
+0x10,0x03,0x00,0x00,0x23,0x03,0x00,0x00,0x1c,0x03,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x26,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x24,0x03,0x00,0x00,0x25,0x03,0x00,0x00,
+0x26,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x25,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2e,0x03,0x00,0x00,
+0x05,0x03,0x00,0x00,0x55,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x30,0x03,0x00,0x00,0x12,0x00,0x00,0x00,
+0x2f,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x31,0x03,0x00,0x00,0x30,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x32,0x03,0x00,0x00,0x2e,0x03,0x00,0x00,
+0x31,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x33,0x03,0x00,0x00,0xe5,0x02,0x00,0x00,0x32,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x35,0x03,0x00,0x00,
+0x33,0x03,0x00,0x00,0xfd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x37,0x03,0x00,0x00,0x35,0x03,0x00,0x00,
+0x57,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x39,0x03,0x00,0x00,0x52,0x03,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3b,0x03,0x00,0x00,
+0x39,0x03,0x00,0x00,0x55,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3d,0x03,0x00,0x00,0x3b,0x03,0x00,0x00,
+0x3c,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x3f,0x03,0x00,0x00,0x53,0x03,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x40,0x03,0x00,0x00,
+0x3d,0x03,0x00,0x00,0x3f,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x42,0x03,0x00,0x00,0x40,0x03,0x00,0x00,
+0x57,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,
+0x43,0x03,0x00,0x00,0xc9,0x00,0x00,0x00,0x42,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0x44,0x03,0x00,0x00,
+0x43,0x03,0x00,0x00,0x41,0x00,0x06,0x00,0x01,0x02,0x00,0x00,
+0x45,0x03,0x00,0x00,0x2a,0x03,0x00,0x00,0x34,0x00,0x00,0x00,
+0x37,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x45,0x03,0x00,0x00,
+0x44,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x26,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x26,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x12,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x12,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x47,0x03,0x00,0x00,
+0x57,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0f,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x11,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x0a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x0a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x49,0x03,0x00,0x00,0x55,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x07,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x09,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0xf2,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xf2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x4b,0x03,0x00,0x00,0x53,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xef,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xf1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xea,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xea,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4d,0x03,0x00,0x00,
+0x52,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q5_k_f32_len = 12596;
+
+unsigned char matmul_q5_k_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xcd,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x2f,0x01,0x00,0x00,0x84,0x01,0x00,0x00,0xee,0x01,0x00,0x00,
+0xf6,0x01,0x00,0x00,0x03,0x03,0x00,0x00,0x4c,0x03,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x28,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x29,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x2a,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2b,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2b,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2b,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2b,0x01,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x2c,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0xb0,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x2d,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x2d,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2f,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x2f,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xc6,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc7,0x01,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xf3,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xf4,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xf4,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xf4,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xf4,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xf4,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xf6,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf6,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x03,0x03,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x49,0x03,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4a,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4a,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x4a,0x03,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x03,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4c,0x03,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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+0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x44,0x03,0x00,0x00,0x43,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x45,0x03,0x00,0x00,0x42,0x03,0x00,0x00,
+0x44,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x3f,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x3f,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,
+0xc2,0x00,0x00,0x00,0x46,0x03,0x00,0x00,0x3d,0x03,0x00,0x00,
+0x32,0x03,0x00,0x00,0x45,0x03,0x00,0x00,0x3e,0x03,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x48,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x46,0x03,0x00,0x00,0x47,0x03,0x00,0x00,
+0x48,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x47,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x50,0x03,0x00,0x00,
+0x27,0x03,0x00,0x00,0x77,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x52,0x03,0x00,0x00,0x12,0x00,0x00,0x00,
+0x51,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x53,0x03,0x00,0x00,0x52,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x54,0x03,0x00,0x00,0x50,0x03,0x00,0x00,
+0x53,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x55,0x03,0x00,0x00,0x07,0x03,0x00,0x00,0x54,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x57,0x03,0x00,0x00,
+0x55,0x03,0x00,0x00,0x1f,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x59,0x03,0x00,0x00,0x57,0x03,0x00,0x00,
+0x79,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5b,0x03,0x00,0x00,0x74,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x03,0x00,0x00,
+0x5b,0x03,0x00,0x00,0x77,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5f,0x03,0x00,0x00,0x5d,0x03,0x00,0x00,
+0x5e,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x61,0x03,0x00,0x00,0x75,0x03,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x62,0x03,0x00,0x00,
+0x5f,0x03,0x00,0x00,0x61,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x64,0x03,0x00,0x00,0x62,0x03,0x00,0x00,
+0x79,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
+0x65,0x03,0x00,0x00,0xca,0x00,0x00,0x00,0x64,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x66,0x03,0x00,0x00,
+0x65,0x03,0x00,0x00,0x41,0x00,0x06,0x00,0xf8,0x01,0x00,0x00,
+0x67,0x03,0x00,0x00,0x4c,0x03,0x00,0x00,0x34,0x00,0x00,0x00,
+0x59,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x67,0x03,0x00,0x00,
+0x66,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x48,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x48,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x34,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x34,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x03,0x00,0x00,
+0x79,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x31,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x33,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x2c,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x2c,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6b,0x03,0x00,0x00,0x77,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x29,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x2b,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x14,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x14,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6d,0x03,0x00,0x00,0x75,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x11,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x13,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0c,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0c,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6f,0x03,0x00,0x00,
+0x74,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x09,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0b,0x03,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q5_k_f32_aligned_len = 13172;
+
+unsigned char matmul_q5_k_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xa3,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,0x83,0x01,0x00,0x00,
+0xeb,0x01,0x00,0x00,0xf2,0x01,0x00,0x00,0xd9,0x02,0x00,0x00,
+0x22,0x03,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x28,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x29,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2a,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2b,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2b,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2b,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2b,0x01,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2c,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x2d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x2d,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x2f,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x2f,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc3,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xc4,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xef,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xf0,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xf0,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xf0,0x01,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf2,0x01,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xf2,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xd9,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1f,0x03,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x20,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x20,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x20,0x03,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x22,0x03,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x22,0x03,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
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+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q5_k_f32_aligned_fp32_len = 12424;
+
+unsigned char matmul_q5_k_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xac,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,0x83,0x01,0x00,0x00,
+0xe9,0x01,0x00,0x00,0xf4,0x01,0x00,0x00,0xda,0x02,0x00,0x00,
+0x23,0x03,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
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+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x27,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x29,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xee,0x02,0x00,0x00,
+0x4c,0x03,0x00,0x00,0x60,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xea,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xee,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,
+0xea,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf2,0x02,0x00,0x00,
+0x4c,0x03,0x00,0x00,0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,
+0xf2,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf5,0x02,0x00,0x00,0x64,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf6,0x02,0x00,0x00,
+0xf3,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,0x4b,0x03,0x00,0x00,
+0x68,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xfb,0x02,0x00,0x00,0xce,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,
+0x68,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,0xfb,0x02,0x00,0x00,
+0xfd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x00,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x00,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x4e,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
+0xe9,0x02,0x00,0x00,0x42,0x03,0x00,0x00,0x03,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x06,0x03,0x00,0x00,
+0x4e,0x03,0x00,0x00,0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x02,0x03,0x00,0x00,0x03,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x06,0x03,0x00,0x00,0x01,0x03,0x00,0x00,
+0x02,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x01,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x08,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x08,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x50,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0x01,0x03,0x00,0x00,
+0x40,0x03,0x00,0x00,0x0b,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x0e,0x03,0x00,0x00,0x50,0x03,0x00,0x00,
+0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x0a,0x03,0x00,0x00,
+0x0b,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x0e,0x03,0x00,0x00,0x09,0x03,0x00,0x00,0x0a,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x09,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x11,0x03,0x00,0x00,0xf6,0x02,0x00,0x00,
+0x50,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x14,0x03,0x00,0x00,0x11,0x03,0x00,0x00,0x36,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x16,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x14,0x03,0x00,0x00,0x15,0x03,0x00,0x00,
+0x16,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x15,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x19,0x03,0x00,0x00,
+0xfe,0x02,0x00,0x00,0x4e,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x1a,0x03,0x00,0x00,0x12,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x1b,0x03,0x00,0x00,0x1a,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,0x19,0x03,0x00,0x00,
+0x1b,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x16,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x16,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,
+0xc1,0x00,0x00,0x00,0x1d,0x03,0x00,0x00,0x14,0x03,0x00,0x00,
+0x09,0x03,0x00,0x00,0x1c,0x03,0x00,0x00,0x15,0x03,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x1f,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x1d,0x03,0x00,0x00,0x1e,0x03,0x00,0x00,
+0x1f,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x1e,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x27,0x03,0x00,0x00,
+0xfe,0x02,0x00,0x00,0x4e,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x29,0x03,0x00,0x00,0x12,0x00,0x00,0x00,
+0x28,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x2a,0x03,0x00,0x00,0x29,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x03,0x00,0x00,0x27,0x03,0x00,0x00,
+0x2a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x2c,0x03,0x00,0x00,0xde,0x02,0x00,0x00,0x2b,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2e,0x03,0x00,0x00,
+0x2c,0x03,0x00,0x00,0xf6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x30,0x03,0x00,0x00,0x2e,0x03,0x00,0x00,
+0x50,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x32,0x03,0x00,0x00,0x4b,0x03,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x34,0x03,0x00,0x00,
+0x32,0x03,0x00,0x00,0x4e,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x36,0x03,0x00,0x00,0x34,0x03,0x00,0x00,
+0x35,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x38,0x03,0x00,0x00,0x4c,0x03,0x00,0x00,0x62,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x39,0x03,0x00,0x00,
+0x36,0x03,0x00,0x00,0x38,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3b,0x03,0x00,0x00,0x39,0x03,0x00,0x00,
+0x50,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,
+0x3c,0x03,0x00,0x00,0xc9,0x00,0x00,0x00,0x3b,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0x3d,0x03,0x00,0x00,
+0x3c,0x03,0x00,0x00,0x41,0x00,0x06,0x00,0xff,0x01,0x00,0x00,
+0x3e,0x03,0x00,0x00,0x23,0x03,0x00,0x00,0x34,0x00,0x00,0x00,
+0x30,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x3e,0x03,0x00,0x00,
+0x3d,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x1f,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x1f,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0b,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x40,0x03,0x00,0x00,
+0x50,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x08,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0a,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x03,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x03,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x42,0x03,0x00,0x00,0x4e,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x00,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x02,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0xeb,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xeb,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x44,0x03,0x00,0x00,0x4c,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe8,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xea,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe3,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x46,0x03,0x00,0x00,
+0x4b,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q5_k_f32_fp32_len = 12476;
+
+unsigned char matmul_q6_k_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x44,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x33,0x01,0x00,0x00,0x44,0x01,0x00,0x00,0xaf,0x01,0x00,0x00,
+0xba,0x01,0x00,0x00,0xa5,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,
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+0x45,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x47,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe4,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe4,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8b,0x02,0x00,0x00,0x2d,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe1,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe3,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0xd9,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
+0x13,0x03,0x00,0x00,0x6c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd5,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,
+0x55,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,0x95,0x00,0x00,0x00,
+0x92,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x98,0x02,0x00,0x00,0x59,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,
+0xa7,0x00,0x00,0x00,0x98,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
+0x9d,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x9f,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x9f,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa4,0x02,0x00,0x00,0x47,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,
+0xa5,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
+0xa4,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xab,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xab,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x14,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
+0xd5,0x00,0x00,0x00,0x11,0x03,0x00,0x00,0xae,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,
+0x14,0x03,0x00,0x00,0xbe,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xad,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xb1,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
+0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xac,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb3,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x15,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0xac,0x02,0x00,0x00,
+0x0f,0x03,0x00,0x00,0xb6,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,0x15,0x03,0x00,0x00,
+0x60,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xb5,0x02,0x00,0x00,
+0xb6,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xb9,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,0x15,0x03,0x00,0x00,
+0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbe,0x02,0x00,0x00,0x93,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
+0x64,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
+0xc0,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc5,0x02,0x00,0x00,0x14,0x03,0x00,0x00,0x31,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc6,0x02,0x00,0x00,
+0x99,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,0x68,0x00,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc9,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xcb,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x17,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
+0x0d,0x03,0x00,0x00,0xce,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,0x17,0x03,0x00,0x00,
+0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xcd,0x02,0x00,0x00,
+0xce,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xd1,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd3,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x19,0x03,0x00,0x00,
+0x3e,0x00,0x00,0x00,0xcc,0x02,0x00,0x00,0x0b,0x03,0x00,0x00,
+0xd6,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0xd9,0x02,0x00,0x00,0x19,0x03,0x00,0x00,0x62,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xd5,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xd9,0x02,0x00,0x00,
+0xd4,0x02,0x00,0x00,0xd5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xd4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xdc,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0x19,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
+0xdc,0x02,0x00,0x00,0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xe1,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xdf,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,
+0x17,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0xe5,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xe6,0x02,0x00,0x00,
+0xe5,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0xe7,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe1,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,
+0xe8,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,
+0xe7,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xea,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xe8,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0xea,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf2,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,
+0x17,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0xf4,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,
+0xf4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf6,0x02,0x00,0x00,0xf2,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf7,0x02,0x00,0x00,
+0xa9,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf9,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,
+0xc1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xfb,0x02,0x00,0x00,0xf9,0x02,0x00,0x00,0x19,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,
+0x14,0x03,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xff,0x02,0x00,0x00,0xfd,0x02,0x00,0x00,
+0x17,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x01,0x03,0x00,0x00,0xff,0x02,0x00,0x00,0x00,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x03,0x03,0x00,0x00,
+0x15,0x03,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x04,0x03,0x00,0x00,0x01,0x03,0x00,0x00,
+0x03,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x06,0x03,0x00,0x00,0x04,0x03,0x00,0x00,0x19,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0x07,0x03,0x00,0x00,
+0xc9,0x00,0x00,0x00,0x06,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc3,0x00,0x00,0x00,0x08,0x03,0x00,0x00,0x07,0x03,0x00,0x00,
+0x41,0x00,0x06,0x00,0xc5,0x01,0x00,0x00,0x09,0x03,0x00,0x00,
+0xee,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0xfb,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x09,0x03,0x00,0x00,0x08,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xea,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xea,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xd6,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x0b,0x03,0x00,0x00,0x19,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd3,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xce,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xce,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,
+0x17,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xcb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcd,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x0f,0x03,0x00,0x00,0x15,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xae,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xae,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x11,0x03,0x00,0x00,0x14,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xab,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q6_k_f32_len = 11764;
+
+unsigned char matmul_q6_k_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x65,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
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+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf2,0x02,0x00,0x00,
+0xed,0x02,0x00,0x00,0xee,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xed,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x3a,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
+0xed,0x02,0x00,0x00,0x2c,0x03,0x00,0x00,0xf7,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,
+0x3a,0x03,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xf6,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xfa,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,
+0xf6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf5,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,
+0xe2,0x02,0x00,0x00,0x3a,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0xfd,0x02,0x00,0x00,
+0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x02,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x00,0x03,0x00,0x00,
+0x01,0x03,0x00,0x00,0x02,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x01,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x05,0x03,0x00,0x00,0xea,0x02,0x00,0x00,0x38,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x06,0x03,0x00,0x00,
+0x12,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x07,0x03,0x00,0x00,0x06,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x08,0x03,0x00,0x00,
+0x05,0x03,0x00,0x00,0x07,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x02,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x02,0x03,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,0x09,0x03,0x00,0x00,
+0x00,0x03,0x00,0x00,0xf5,0x02,0x00,0x00,0x08,0x03,0x00,0x00,
+0x01,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,0x0b,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x09,0x03,0x00,0x00,
+0x0a,0x03,0x00,0x00,0x0b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x0a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x13,0x03,0x00,0x00,0xea,0x02,0x00,0x00,0x38,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x15,0x03,0x00,0x00,
+0x12,0x00,0x00,0x00,0x14,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x16,0x03,0x00,0x00,0x15,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x17,0x03,0x00,0x00,
+0x13,0x03,0x00,0x00,0x16,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x18,0x03,0x00,0x00,0xca,0x02,0x00,0x00,
+0x17,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1a,0x03,0x00,0x00,0x18,0x03,0x00,0x00,0xe2,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,
+0x1a,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x1e,0x03,0x00,0x00,0x35,0x03,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x20,0x03,0x00,0x00,0x1e,0x03,0x00,0x00,0x38,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x22,0x03,0x00,0x00,
+0x20,0x03,0x00,0x00,0x21,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x24,0x03,0x00,0x00,0x36,0x03,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x25,0x03,0x00,0x00,0x22,0x03,0x00,0x00,0x24,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x27,0x03,0x00,0x00,
+0x25,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcd,0x00,0x00,0x00,0x28,0x03,0x00,0x00,0xca,0x00,0x00,0x00,
+0x27,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0x29,0x03,0x00,0x00,0x28,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
+0xbb,0x01,0x00,0x00,0x2a,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,
+0x34,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x2a,0x03,0x00,0x00,0x29,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0b,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xf7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x2c,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xef,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xef,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x2e,0x03,0x00,0x00,0x38,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xec,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xee,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd7,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x30,0x03,0x00,0x00,
+0x36,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd6,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xcf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xcf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x32,0x03,0x00,0x00,0x35,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xce,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q6_k_f32_aligned_len = 12324;
+
+unsigned char matmul_q6_k_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x3c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x34,0x01,0x00,0x00,0x45,0x01,0x00,0x00,
+0xaf,0x01,0x00,0x00,0xb6,0x01,0x00,0x00,0x9d,0x02,0x00,0x00,
+0xe6,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x2b,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x2c,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2e,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x30,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x30,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x30,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x30,0x01,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x31,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x32,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x32,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x32,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x34,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x34,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x87,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x88,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb3,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
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+0xa9,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xab,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xab,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,
+0x3e,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,0x07,0x03,0x00,0x00,
+0xae,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xb1,0x02,0x00,0x00,0x0d,0x03,0x00,0x00,0x60,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xad,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xb1,0x02,0x00,0x00,
+0xac,0x02,0x00,0x00,0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xac,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb5,0x02,0x00,0x00,0x0d,0x03,0x00,0x00,0x61,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,
+0x8b,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,0x64,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb9,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,
+0x0c,0x03,0x00,0x00,0x2b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,0x91,0x02,0x00,0x00,
+0xbd,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc0,0x02,0x00,0x00,0x68,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,
+0xbe,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x0f,0x03,0x00,0x00,
+0x3e,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0x05,0x03,0x00,0x00,
+0xc6,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xc9,0x02,0x00,0x00,0x0f,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xc5,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xc9,0x02,0x00,0x00,
+0xc4,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xc4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x11,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
+0xc4,0x02,0x00,0x00,0x03,0x03,0x00,0x00,0xce,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,
+0x11,0x03,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xcd,0x02,0x00,0x00,0xce,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xd1,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,
+0xcd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,
+0xb9,0x02,0x00,0x00,0x11,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,
+0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xd9,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xd7,0x02,0x00,0x00,
+0xd8,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xd8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xdc,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0x0f,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xde,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
+0xdc,0x02,0x00,0x00,0xde,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd9,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,
+0xd7,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,
+0xd8,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xe2,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xe0,0x02,0x00,0x00,
+0xe1,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xea,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0x0f,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xec,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0xeb,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xed,0x02,0x00,0x00,0xec,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xee,0x02,0x00,0x00,
+0xea,0x02,0x00,0x00,0xed,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xef,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
+0xee,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf1,0x02,0x00,0x00,0xef,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,
+0xf1,0x02,0x00,0x00,0x11,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,0x0c,0x03,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf7,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,0x0f,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf9,0x02,0x00,0x00,
+0xf7,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xfb,0x02,0x00,0x00,0x0d,0x03,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xfc,0x02,0x00,0x00,0xf9,0x02,0x00,0x00,0xfb,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,
+0xfc,0x02,0x00,0x00,0x11,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcd,0x00,0x00,0x00,0xff,0x02,0x00,0x00,0xca,0x00,0x00,0x00,
+0xfe,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
+0x00,0x03,0x00,0x00,0xff,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0xb8,0x01,0x00,0x00,0x01,0x03,0x00,0x00,0xe6,0x02,0x00,0x00,
+0x34,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x01,0x03,0x00,0x00,0x00,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xe2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xce,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xce,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x03,0x03,0x00,0x00,0x11,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xcd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc6,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x05,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xae,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xae,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x07,0x03,0x00,0x00,
+0x0d,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xab,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x09,0x03,0x00,0x00,0x0c,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa5,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q6_k_f32_aligned_fp32_len = 11592;
+
+unsigned char matmul_q6_k_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x3d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x33,0x01,0x00,0x00,0x44,0x01,0x00,0x00,
+0xad,0x01,0x00,0x00,0xb8,0x01,0x00,0x00,0x9e,0x02,0x00,0x00,
+0xe7,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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+0xf9,0x00,0x02,0x00,0xdd,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xdf,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0xd5,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,
+0x0c,0x03,0x00,0x00,0x6c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd5,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,
+0x55,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,0x95,0x00,0x00,0x00,
+0x8b,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x91,0x02,0x00,0x00,0x59,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,
+0xa7,0x00,0x00,0x00,0x91,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x97,0x02,0x00,0x00,0x12,0x00,0x00,0x00,
+0x96,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x98,0x02,0x00,0x00,0x97,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x98,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9d,0x02,0x00,0x00,0x47,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
+0x9e,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,
+0x9d,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
+0xa1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,
+0xd5,0x00,0x00,0x00,0x0a,0x03,0x00,0x00,0xa7,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,
+0x0d,0x03,0x00,0x00,0xbe,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xa6,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xaa,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
+0xa6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xac,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xac,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x0e,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
+0x08,0x03,0x00,0x00,0xaf,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,
+0x60,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xae,0x02,0x00,0x00,
+0xaf,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xb2,0x02,0x00,0x00,0xad,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,
+0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb7,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,
+0x64,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,
+0xb9,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbe,0x02,0x00,0x00,0x0d,0x03,0x00,0x00,0x2c,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,
+0x92,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,0x68,0x00,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc2,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xc4,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x10,0x03,0x00,0x00,0x3e,0x00,0x00,0x00,0xad,0x02,0x00,0x00,
+0x06,0x03,0x00,0x00,0xc7,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0x10,0x03,0x00,0x00,
+0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xc6,0x02,0x00,0x00,
+0xc7,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xca,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xcc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x12,0x03,0x00,0x00,
+0x3e,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,0x04,0x03,0x00,0x00,
+0xcf,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0xd2,0x02,0x00,0x00,0x12,0x03,0x00,0x00,0x62,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xce,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xd2,0x02,0x00,0x00,
+0xcd,0x02,0x00,0x00,0xce,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xcd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd5,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0x12,0x03,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,
+0xd5,0x02,0x00,0x00,0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xda,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xd8,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,0xda,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xd9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,
+0x10,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0xde,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
+0xde,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0xe0,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xda,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xda,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,
+0xe1,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,
+0xe0,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xe3,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xe1,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xeb,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,
+0x10,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0xed,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xec,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xee,0x02,0x00,0x00,
+0xed,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xef,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf0,0x02,0x00,0x00,
+0xa2,0x02,0x00,0x00,0xef,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf2,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,
+0xba,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf4,0x02,0x00,0x00,0xf2,0x02,0x00,0x00,0x12,0x03,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf6,0x02,0x00,0x00,
+0x0d,0x03,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf8,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,
+0x10,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xfa,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,0xf9,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfc,0x02,0x00,0x00,
+0x0e,0x03,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,
+0xfc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xff,0x02,0x00,0x00,0xfd,0x02,0x00,0x00,0x12,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0x00,0x03,0x00,0x00,
+0xc9,0x00,0x00,0x00,0xff,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc3,0x00,0x00,0x00,0x01,0x03,0x00,0x00,0x00,0x03,0x00,0x00,
+0x41,0x00,0x06,0x00,0xc3,0x01,0x00,0x00,0x02,0x03,0x00,0x00,
+0xe7,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x02,0x03,0x00,0x00,0x01,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xcf,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xcf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x04,0x03,0x00,0x00,0x12,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xce,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc7,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x06,0x03,0x00,0x00,
+0x10,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc6,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xaf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x08,0x03,0x00,0x00,0x0e,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xac,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xae,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x0a,0x03,0x00,0x00,0x0d,0x03,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q6_k_f32_fp32_len = 11644;
+
+unsigned char matmul_q8_0_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xf2,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x28,0x01,0x00,0x00,
+0x5b,0x01,0x00,0x00,0x66,0x01,0x00,0x00,0x51,0x02,0x00,0x00,
+0x9a,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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+0x74,0x02,0x00,0x00,0x68,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x75,0x02,0x00,0x00,
+0x72,0x02,0x00,0x00,0x74,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x60,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
+0x7a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x7d,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x79,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x7d,0x02,0x00,0x00,
+0x78,0x02,0x00,0x00,0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x78,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x78,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,0x82,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x85,0x02,0x00,0x00,
+0xc7,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x81,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x85,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
+0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,
+0x6d,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
+0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x8d,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x8b,0x02,0x00,0x00,
+0x8c,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x90,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x91,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,0x91,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x93,0x02,0x00,0x00,
+0x90,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x8d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8d,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
+0x8b,0x02,0x00,0x00,0x80,0x02,0x00,0x00,0x93,0x02,0x00,0x00,
+0x8c,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x96,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x94,0x02,0x00,0x00,
+0x95,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x95,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9e,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
+0x9e,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0x55,0x02,0x00,0x00,
+0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa5,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,
+0xa5,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xab,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xad,0x02,0x00,0x00,
+0xab,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb0,0x02,0x00,0x00,0xad,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,
+0xb0,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
+0xb2,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0xb4,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x71,0x01,0x00,0x00,0xb5,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
+0x34,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xb5,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x96,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x96,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x82,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb7,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x81,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x62,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x62,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,
+0xc3,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x61,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xbd,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x59,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q8_0_f32_len = 10608;
+
+unsigned char matmul_q8_0_f32_aligned_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x15,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,0x29,0x01,0x00,0x00,
+0x5e,0x01,0x00,0x00,0x66,0x01,0x00,0x00,0x74,0x02,0x00,0x00,
+0xbd,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3d,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x06,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x07,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x07,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x08,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x09,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x09,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x09,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x36,0x01,0x00,0x00,
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+0xe8,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0x83,0x02,0x00,0x00,
+0xdc,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc2,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,
+0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x9c,0x02,0x00,0x00,
+0x9d,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xa0,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,0xda,0x02,0x00,0x00,
+0xa5,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xa8,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xa4,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xa8,0x02,0x00,0x00,
+0xa3,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xab,0x02,0x00,0x00,0x90,0x02,0x00,0x00,0xea,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xae,0x02,0x00,0x00,
+0xab,0x02,0x00,0x00,0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xb0,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xae,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0x98,0x02,0x00,0x00,
+0xe8,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0xb4,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,
+0xb4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
+0xb6,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb0,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,
+0xb7,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
+0xb6,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xb9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xb7,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,0x98,0x02,0x00,0x00,
+0xe8,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0xc3,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,
+0xc3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc5,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc6,0x02,0x00,0x00,
+0x78,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
+0x90,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xca,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0xea,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcc,0x02,0x00,0x00,
+0xe5,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xce,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,
+0xe8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd0,0x02,0x00,0x00,0xce,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,
+0xe6,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,
+0xd2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd5,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0xea,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
+0xca,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xc4,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,
+0x41,0x00,0x06,0x00,0x68,0x01,0x00,0x00,0xd8,0x02,0x00,0x00,
+0xbd,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0xca,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xd8,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xda,0x02,0x00,0x00,0xea,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x9d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9d,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,
+0xe8,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x9a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x85,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x85,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xde,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x84,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t matmul_q8_0_f32_aligned_len = 11200;
+
+unsigned char matmul_q8_0_f32_aligned_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xeb,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,
+0x29,0x01,0x00,0x00,0x5c,0x01,0x00,0x00,0x63,0x01,0x00,0x00,
+0x4a,0x02,0x00,0x00,0x93,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x07,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x07,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x08,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x09,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x09,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x09,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x34,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x35,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x61,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
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+0xf9,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x73,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x70,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5b,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
+0xbc,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x58,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x53,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb6,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x50,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x52,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q8_0_f32_aligned_fp32_len = 10452;
+
+unsigned char matmul_q8_0_f32_fp32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xeb,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,
+0x28,0x01,0x00,0x00,0x59,0x01,0x00,0x00,0x64,0x01,0x00,0x00,
+0x4a,0x02,0x00,0x00,0x93,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x10,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x10,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x3d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x06,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x06,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x07,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x08,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x08,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x08,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0a,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0a,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x33,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x34,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x62,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x62,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x62,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x64,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x64,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4a,0x02,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x90,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x91,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x91,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x91,0x02,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x93,0x02,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x93,0x02,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x1e,0x00,0x10,0x00,0x10,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x39,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x4f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x61,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x60,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x61,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x61,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
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+0xbe,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x52,0x02,0x00,0x00,
+0x53,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x56,0x02,0x00,0x00,0x51,0x02,0x00,0x00,0x52,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x51,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x58,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x58,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x51,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,
+0x5b,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x5e,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x60,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x5a,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x5e,0x02,0x00,0x00,
+0x59,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x59,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x62,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x61,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x63,0x02,0x00,0x00,
+0x38,0x02,0x00,0x00,0x62,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x65,0x02,0x00,0x00,0x64,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x66,0x02,0x00,0x00,0x63,0x02,0x00,0x00,0x65,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6a,0x02,0x00,0x00,
+0xbb,0x02,0x00,0x00,0xd8,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,0x3e,0x02,0x00,0x00,
+0x6a,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6d,0x02,0x00,0x00,0x68,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,
+0x6b,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x70,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x70,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x59,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,
+0x73,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
+0x76,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x72,0x02,0x00,0x00,0x73,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x76,0x02,0x00,0x00,
+0x71,0x02,0x00,0x00,0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x71,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x71,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x7e,0x02,0x00,0x00,
+0xc0,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x7a,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x7e,0x02,0x00,0x00,0x79,0x02,0x00,0x00,
+0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x81,0x02,0x00,0x00,
+0x66,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
+0xc1,0x00,0x00,0x00,0x84,0x02,0x00,0x00,0x81,0x02,0x00,0x00,
+0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x86,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x84,0x02,0x00,0x00,
+0x85,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x85,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x89,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
+0x89,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x86,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
+0x84,0x02,0x00,0x00,0x79,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,
+0x85,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x8f,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x8d,0x02,0x00,0x00,
+0x8e,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x8e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x97,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x99,0x02,0x00,0x00,
+0x12,0x00,0x00,0x00,0x98,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
+0x97,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,
+0x9b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9e,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0x66,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
+0x9e,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa4,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,
+0xa4,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
+0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa9,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xab,0x02,0x00,0x00,
+0xa9,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
+0xab,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0xad,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x6f,0x01,0x00,0x00,0xae,0x02,0x00,0x00,0x93,0x02,0x00,0x00,
+0x34,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xae,0x02,0x00,0x00,0xad,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x8f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb0,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x73,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x70,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5b,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
+0xbc,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x58,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x53,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb6,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x50,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x52,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t matmul_q8_0_f32_fp32_len = 10488;
+
+unsigned char mul_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x67,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x17,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,
+0x4b,0x01,0x00,0x00,0x56,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x15,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
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+0x80,0x01,0x00,0x00,0x77,0x01,0x00,0x00,0x82,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x86,0x01,0x00,0x00,0x33,0x01,0x00,0x00,
+0x83,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x8b,0x01,0x00,0x00,0x74,0x01,0x00,0x00,0x77,0x01,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x01,0x00,0x00,
+0x86,0x01,0x00,0x00,0x8b,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x90,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,
+0x74,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x93,0x01,0x00,0x00,0x90,0x01,0x00,0x00,0x77,0x01,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x98,0x01,0x00,0x00,
+0x86,0x01,0x00,0x00,0x93,0x01,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x9b,0x01,0x00,0x00,0x98,0x01,0x00,0x00,
+0x77,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa4,0x01,0x00,0x00,0x9b,0x01,0x00,0x00,0x77,0x01,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa5,0x01,0x00,0x00,
+0x98,0x01,0x00,0x00,0xa4,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0xa7,0x01,0x00,0x00,0x17,0x00,0x00,0x00,
+0x15,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xa8,0x01,0x00,0x00,0xa7,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa9,0x01,0x00,0x00,0x79,0x01,0x00,0x00,
+0xa8,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0xab,0x01,0x00,0x00,0x17,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xac,0x01,0x00,0x00,
+0xab,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xad,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,0xac,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xae,0x01,0x00,0x00,
+0xa9,0x01,0x00,0x00,0xad,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0xb0,0x01,0x00,0x00,0x17,0x00,0x00,0x00,
+0x20,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xb1,0x01,0x00,0x00,0xb0,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb2,0x01,0x00,0x00,0x9b,0x01,0x00,0x00,
+0xb1,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb3,0x01,0x00,0x00,0xae,0x01,0x00,0x00,0xb2,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xb5,0x01,0x00,0x00,
+0x17,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xb6,0x01,0x00,0x00,0xb5,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb7,0x01,0x00,0x00,
+0xa5,0x01,0x00,0x00,0xb6,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb8,0x01,0x00,0x00,0xb3,0x01,0x00,0x00,
+0xb7,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x47,0x01,0x00,0x00,0x42,0x01,0x00,0x00,0xb8,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xc2,0x01,0x00,0x00,
+0x17,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xc3,0x01,0x00,0x00,0xc2,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xc4,0x01,0x00,0x00,
+0x17,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xc5,0x01,0x00,0x00,0xc4,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc6,0x01,0x00,0x00,
+0xc3,0x01,0x00,0x00,0xc5,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0xc7,0x01,0x00,0x00,0x17,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xc8,0x01,0x00,0x00,0xc7,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xc9,0x01,0x00,0x00,0xc6,0x01,0x00,0x00,
+0xc8,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xca,0x01,0x00,0x00,0x33,0x01,0x00,0x00,0xc9,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xce,0x01,0x00,0x00,
+0xca,0x01,0x00,0x00,0xc3,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xd1,0x01,0x00,0x00,0xce,0x01,0x00,0x00,
+0xc5,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd4,0x01,0x00,0x00,0xd1,0x01,0x00,0x00,0xc8,0x01,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd7,0x01,0x00,0x00,
+0x33,0x01,0x00,0x00,0xd4,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdc,0x01,0x00,0x00,0xc5,0x01,0x00,0x00,
+0xc8,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xdd,0x01,0x00,0x00,0xd7,0x01,0x00,0x00,0xdc,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe1,0x01,0x00,0x00,
+0xdd,0x01,0x00,0x00,0xc5,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe4,0x01,0x00,0x00,0xe1,0x01,0x00,0x00,
+0xc8,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe9,0x01,0x00,0x00,0xd7,0x01,0x00,0x00,0xe4,0x01,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x01,0x00,0x00,
+0xe9,0x01,0x00,0x00,0xc8,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf5,0x01,0x00,0x00,0xec,0x01,0x00,0x00,
+0xc8,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xf6,0x01,0x00,0x00,0xe9,0x01,0x00,0x00,0xf5,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xf8,0x01,0x00,0x00,
+0x17,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xf9,0x01,0x00,0x00,0xf8,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfa,0x01,0x00,0x00,
+0xca,0x01,0x00,0x00,0xf9,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0xfc,0x01,0x00,0x00,0x17,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xfd,0x01,0x00,0x00,0xfc,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xfe,0x01,0x00,0x00,0xdd,0x01,0x00,0x00,
+0xfd,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xff,0x01,0x00,0x00,0xfa,0x01,0x00,0x00,0xfe,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x01,0x02,0x00,0x00,
+0x17,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x02,0x02,0x00,0x00,0x01,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x03,0x02,0x00,0x00,
+0xec,0x01,0x00,0x00,0x02,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x04,0x02,0x00,0x00,0xff,0x01,0x00,0x00,
+0x03,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x06,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x07,0x02,0x00,0x00,
+0x06,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x08,0x02,0x00,0x00,0xf6,0x01,0x00,0x00,0x07,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x09,0x02,0x00,0x00,
+0x04,0x02,0x00,0x00,0x08,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
+0x50,0x01,0x00,0x00,0x51,0x01,0x00,0x00,0x4b,0x01,0x00,0x00,
+0x34,0x01,0x00,0x00,0x09,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x52,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x49,0x02,0x00,0x00,
+0x17,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,0x49,0x02,0x00,0x00,
+0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4b,0x02,0x00,0x00,
+0xca,0x01,0x00,0x00,0x4a,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x4c,0x02,0x00,0x00,0x17,0x00,0x00,0x00,
+0xb1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x4d,0x02,0x00,0x00,0x4c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x4e,0x02,0x00,0x00,0x4b,0x02,0x00,0x00,
+0x4d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x50,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x51,0x02,0x00,0x00,
+0x50,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x52,0x02,0x00,0x00,0xdd,0x01,0x00,0x00,0x51,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x53,0x02,0x00,0x00,
+0x17,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x54,0x02,0x00,0x00,0x53,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x55,0x02,0x00,0x00,
+0x52,0x02,0x00,0x00,0x54,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x56,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,
+0x55,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x58,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x59,0x02,0x00,0x00,
+0x58,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5a,0x02,0x00,0x00,0xec,0x01,0x00,0x00,0x59,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x5b,0x02,0x00,0x00,
+0x17,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x02,0x00,0x00,
+0x5a,0x02,0x00,0x00,0x5c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5e,0x02,0x00,0x00,0x56,0x02,0x00,0x00,
+0x5d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x60,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,
+0x60,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x62,0x02,0x00,0x00,0xf6,0x01,0x00,0x00,0x61,0x02,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x63,0x02,0x00,0x00,
+0x17,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x63,0x02,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x65,0x02,0x00,0x00,
+0x62,0x02,0x00,0x00,0x64,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x66,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,
+0x65,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x01,0x00,0x00,
+0x5b,0x01,0x00,0x00,0x56,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
+0x66,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x5c,0x01,0x00,0x00,0x5b,0x01,0x00,0x00,0x85,0x00,0x05,0x00,
+0x14,0x00,0x00,0x00,0x5d,0x01,0x00,0x00,0x52,0x01,0x00,0x00,
+0x5c,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x01,0x00,0x00,
+0x5e,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
+0x47,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x5e,0x01,0x00,0x00,
+0x5d,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x62,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x62,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_f32_len = 4276;
+
+unsigned char mul_mat_vec_f16_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xaf,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0b,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
+0x57,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x24,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x24,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x24,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x24,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x54,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x55,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x55,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x96,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x97,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x97,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x97,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x99,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x99,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa2,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa3,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x14,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x1e,0x00,0x05,0x00,0x24,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x25,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x25,0x00,0x00,0x00,
+0x26,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x27,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x27,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x29,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
+0x2d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x54,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x55,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x56,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x55,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x56,0x00,0x00,0x00,
+0x57,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x27,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x60,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x77,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x96,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x97,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x98,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x97,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x98,0x00,0x00,0x00,0x99,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x27,0x00,0x00,0x00,
+0x9a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x33,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,
+0xa2,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x05,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x1e,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x1e,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xab,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,
+0x2e,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x20,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,
+0x1f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x1f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x31,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x31,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x41,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
+0x41,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
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+0x41,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
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+0x58,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
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+0x5e,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x60,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x57,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
+0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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+0x41,0x00,0x06,0x00,0x60,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
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+0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
+0x6d,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
+0xae,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x14,0x00,0x00,0x00,
+0x73,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0xae,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
+0xab,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x1e,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x20,0x00,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x32,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x77,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7a,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7a,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,
+0xac,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
+0xac,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x7c,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x80,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
+0x7c,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x7b,0x00,0x00,0x00,
+0xb0,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,0x83,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x85,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x83,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x85,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x84,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x89,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0xac,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
+0x8a,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x89,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,
+0x8a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x8d,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
+0x14,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,
+0x8b,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,
+0x8e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x85,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x85,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x32,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x77,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7d,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x91,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x7a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7c,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,
+0x93,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x95,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x93,0x00,0x00,0x00,0x94,0x00,0x00,0x00,
+0x95,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x94,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,
+0x26,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
+0x9c,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1b,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0xa0,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x60,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x99,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xa1,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x95,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x95,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_f16_f32_len = 2456;
+
+unsigned char mul_mat_vec_nc_f16_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xb3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x6f,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1b,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1b,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1b,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x6d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x6d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x6d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x6f,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6f,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x78,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x78,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x78,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x7a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x7a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa8,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0xa9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xa9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0xa9,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xab,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xab,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb0,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x1e,0x00,0x09,0x00,0x1b,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x1c,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
+0x1f,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x20,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
+0x29,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x34,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x1c,0x00,0x04,0x00,0x36,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x35,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x37,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x34,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x3b,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
+0x46,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
+0x57,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1e,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x6b,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x6c,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x6d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x6d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,
+0x6f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x71,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x77,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x78,0x00,0x00,0x00,0x77,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x79,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x78,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x79,0x00,0x00,0x00,
+0x7a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x7c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x08,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
+0x89,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0xa8,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0xa9,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0xaa,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0xaa,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,
+0xb0,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x0d,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x0d,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x20,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
+0x1f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x20,0x00,0x00,0x00,
+0x26,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x26,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x20,0x00,0x00,0x00,
+0x2a,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x2a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x31,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
+0x31,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x3b,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x3c,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3e,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x3e,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x85,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
+0xb0,0x00,0x05,0x00,0x46,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
+0xb1,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x40,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x47,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x3f,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
+0xb1,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
+0x46,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x51,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x50,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x40,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x51,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x20,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
+0x57,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x59,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x20,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5f,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x4b,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x66,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
+0x66,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x71,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
+0x25,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x6b,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x34,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
+0x73,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x7c,0x00,0x00,0x00,
+0x7d,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
+0x68,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x34,0x00,0x00,0x00,
+0x7e,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x34,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x34,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
+0x7e,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x3c,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x41,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x41,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x85,0x00,0x00,0x00,
+0xb1,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x3e,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x40,0x00,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8a,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8a,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x1e,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x89,0x00,0x00,0x00,
+0x40,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,
+0xad,0x00,0x05,0x00,0x46,0x00,0x00,0x00,0x90,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x8c,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,
+0x8c,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x8b,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x93,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0x46,0x00,0x00,0x00,
+0x94,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x93,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x96,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x94,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
+0x96,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x95,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x3b,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x9b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x34,0x00,0x00,0x00,
+0x9d,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x34,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x34,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,
+0x9f,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x3c,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x96,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x96,0x00,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x86,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8d,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8d,0x00,0x00,0x00,0xc3,0x00,0x05,0x00,
+0x1e,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
+0x29,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8a,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8c,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,
+0x46,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xa7,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xa5,0x00,0x00,0x00,
+0xa6,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa6,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x3b,0x00,0x00,0x00,
+0xad,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x34,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
+0xad,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x7c,0x00,0x00,0x00,
+0xaf,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
+0x33,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xaf,0x00,0x00,0x00,
+0xae,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa7,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa7,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_nc_f16_f32_len = 2824;
+
+unsigned char mul_mat_vec_p021_f16_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xbc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
+0x66,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x1b,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
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+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
+0xb6,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x3c,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x3e,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x8f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x00,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x1e,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
+0x8e,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
+0x92,0x00,0x00,0x00,0xad,0x00,0x05,0x00,0x44,0x00,0x00,0x00,
+0x95,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x91,0x00,0x00,0x00,0x92,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x95,0x00,0x00,0x00,
+0x90,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x90,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x98,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
+0x44,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x98,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x9b,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x99,0x00,0x00,0x00,
+0x9a,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x9a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa0,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x39,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x32,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x32,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x32,0x00,0x00,0x00,
+0xa5,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x3a,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x9b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x9b,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x92,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x92,0x00,0x00,0x00,
+0xc3,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
+0xb7,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x8f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x91,0x00,0x00,0x00,
+0xaa,0x00,0x05,0x00,0x44,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xac,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xaa,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xab,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x39,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
+0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x32,0x00,0x00,0x00,
+0xb3,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x7b,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
+0x29,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xb4,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xac,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xac,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_p021_f16_f32_len = 2768;
+
+unsigned char mul_mat_vec_q2_K_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xdc,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,
+0xca,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x11,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x20,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x65,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x68,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x68,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x68,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x69,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x6a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x6a,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x6a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x88,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x89,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x89,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x89,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x8b,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8b,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xc7,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xc8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xc8,0x02,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xc8,0x02,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xca,0x02,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xca,0x02,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xd2,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x05,0x00,
+0x11,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x44,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x1c,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x46,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x46,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x44,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x4e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
+0x14,0x00,0x02,0x00,0x59,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x62,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x1c,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
+0x48,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x64,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
+0x65,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x66,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x1e,0x00,0x05,0x00,0x68,0x00,0x00,0x00,
+0x63,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x69,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x6a,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
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+0x85,0x00,0x05,0x00,0x44,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,
+0x7c,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,0x7f,0x00,0x04,0x00,
+0x44,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x44,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
+0xd7,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x44,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,0x4f,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x44,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
+0xa4,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x4f,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x55,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x55,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
+0xd3,0x02,0x00,0x00,0x23,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x52,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x54,0x00,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0xa9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xab,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xab,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0x48,0x00,0x00,0x00,
+0x54,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
+0xac,0x00,0x05,0x00,0x59,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,
+0xd4,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0xad,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xb1,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
+0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xac,0x02,0x00,0x00,
+0xb0,0x00,0x05,0x00,0x59,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
+0x24,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xb6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xb4,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0x24,0x00,0x00,0x00,
+0xd4,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x4e,0x00,0x00,0x00,
+0xbb,0x02,0x00,0x00,0x47,0x00,0x00,0x00,0xba,0x02,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x44,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,
+0xbb,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x4e,0x00,0x00,0x00,
+0xbd,0x02,0x00,0x00,0x47,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x44,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
+0xbd,0x02,0x00,0x00,0x81,0x00,0x05,0x00,0x44,0x00,0x00,0x00,
+0xbf,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xbd,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xb6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xb6,0x02,0x00,0x00,0xe0,0x00,0x04,0x00,0x23,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xae,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xae,0x02,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,
+0xd4,0x02,0x00,0x00,0x8c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xab,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,
+0xaa,0x00,0x05,0x00,0x59,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,
+0x24,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xc6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xc4,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x16,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,0x13,0x00,0x00,0x00,
+0x70,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xcc,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xce,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x4e,0x00,0x00,0x00,
+0xcf,0x02,0x00,0x00,0x47,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x44,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,
+0xcf,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x95,0x00,0x00,0x00,
+0xd1,0x02,0x00,0x00,0xca,0x02,0x00,0x00,0x15,0x00,0x00,0x00,
+0xce,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xd1,0x02,0x00,0x00,
+0xd0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc6,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc6,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q2_K_f32_len = 7612;
+
+unsigned char mul_mat_vec_q3_K_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x1d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x4f,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,
+0x0e,0x03,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x11,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x20,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6f,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x71,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x73,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x73,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x73,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x73,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x74,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x75,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x75,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x75,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x8b,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x8c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x8c,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x8c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x8e,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8e,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x03,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x0c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x0c,0x03,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x0c,0x03,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0e,0x03,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0e,0x03,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x17,0x03,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x05,0x00,
+0x11,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x32,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x3a,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
+0x80,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x4c,0x00,0x00,0x00,
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+0x7f,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,0xe8,0x02,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x57,0x00,0x00,0x00,0xe9,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x60,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x60,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xec,0x02,0x00,0x00,0x18,0x03,0x00,0x00,0x23,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x5d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5f,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x23,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0xed,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xef,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xef,0x02,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x19,0x03,0x00,0x00,
+0x50,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,0x06,0x03,0x00,0x00,
+0xf2,0x02,0x00,0x00,0xac,0x00,0x05,0x00,0x64,0x00,0x00,0x00,
+0xf5,0x02,0x00,0x00,0x19,0x03,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xf1,0x02,0x00,0x00,0xf2,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf5,0x02,0x00,0x00,
+0xf0,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf0,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0x64,0x00,0x00,0x00,
+0xf8,0x02,0x00,0x00,0x24,0x00,0x00,0x00,0x19,0x03,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xfa,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xf8,0x02,0x00,0x00,0xf9,0x02,0x00,0x00,
+0xfa,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf9,0x02,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,
+0x24,0x00,0x00,0x00,0x19,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0x56,0x00,0x00,0x00,0xff,0x02,0x00,0x00,0x4f,0x00,0x00,0x00,
+0xfe,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
+0x00,0x03,0x00,0x00,0xff,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
+0x56,0x00,0x00,0x00,0x01,0x03,0x00,0x00,0x4f,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
+0x02,0x03,0x00,0x00,0x01,0x03,0x00,0x00,0x81,0x00,0x05,0x00,
+0x4c,0x00,0x00,0x00,0x03,0x03,0x00,0x00,0x02,0x03,0x00,0x00,
+0x00,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x01,0x03,0x00,0x00,
+0x03,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0xfa,0x02,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xfa,0x02,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x23,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0xed,0x02,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xf2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf2,0x02,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x06,0x03,0x00,0x00,0x19,0x03,0x00,0x00,0x35,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xef,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf1,0x02,0x00,0x00,0xaa,0x00,0x05,0x00,0x64,0x00,0x00,0x00,
+0x08,0x03,0x00,0x00,0x24,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x0a,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x08,0x03,0x00,0x00,0x09,0x03,0x00,0x00,
+0x0a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x09,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x0f,0x03,0x00,0x00,
+0x13,0x00,0x00,0x00,0x89,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x10,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x12,0x03,0x00,0x00,
+0x10,0x03,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x56,0x00,0x00,0x00,0x13,0x03,0x00,0x00,0x4f,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
+0x14,0x03,0x00,0x00,0x13,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
+0x97,0x00,0x00,0x00,0x15,0x03,0x00,0x00,0x0e,0x03,0x00,0x00,
+0x15,0x00,0x00,0x00,0x12,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x15,0x03,0x00,0x00,0x14,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x0a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0a,0x03,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q3_K_f32_len = 9236;
+
+unsigned char mul_mat_vec_q4_0_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xcb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x26,0x00,0x00,0x00,0x52,0x00,0x00,0x00,
+0x78,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x24,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x24,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x24,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x24,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x50,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x50,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x50,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x52,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x52,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x75,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x76,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x76,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x76,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x78,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x78,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb7,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0xb8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0xb8,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xba,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xba,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xc4,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x1b,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x1e,0x00,0x05,0x00,0x24,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x25,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x25,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x27,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x27,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x29,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x2d,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x4a,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x4b,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0x4b,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x1e,0x00,0x04,0x00,
+0x4e,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x4f,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x50,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x51,0x00,0x00,0x00,
+0x52,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
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+0xbb,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xbd,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
+0xc0,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,
+0xc0,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x80,0x00,0x00,0x00,
+0xc2,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0xbf,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xc2,0x00,0x00,0x00,
+0xc1,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb6,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb6,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q4_0_f32_len = 3184;
+
+unsigned char mul_mat_vec_q4_1_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xce,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x26,0x00,0x00,0x00,0x52,0x00,0x00,0x00,
+0x7e,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x24,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x24,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x24,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x24,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4e,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x50,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x50,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x52,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x7c,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x7c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x7c,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7e,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x7e,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xbd,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xbe,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xbe,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xbe,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc0,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xc0,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xc8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc9,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x1c,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x1e,0x00,0x05,0x00,
+0x24,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x25,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x25,0x00,0x00,0x00,0x26,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x27,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x27,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x29,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x14,0x00,0x02,0x00,0x2d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x4a,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x1c,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x1e,0x00,0x05,0x00,0x4e,0x00,0x00,0x00,
+0x4a,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x4f,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x50,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x51,0x00,0x00,0x00,
+0x52,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x54,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x27,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x27,0x00,0x00,0x00,
+0x60,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x62,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x66,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x6a,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x27,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x7b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x7c,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x7c,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,
+0x7e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x86,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x9e,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0xbd,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0xbe,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0xbf,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0xbf,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xc8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00,
+0x09,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,
+0x8a,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x36,0x00,0x05,0x00,
+0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x1c,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x1e,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x1e,0x00,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,
+0x1f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0x2a,0x00,0x00,0x00,0x26,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x2a,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0xb0,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x20,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x1f,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x31,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x35,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
+0x35,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
+0x35,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
+0x41,0x00,0x07,0x00,0x54,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
+0x52,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,
+0x56,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
+0x41,0x00,0x07,0x00,0x54,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,
+0x52,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x41,0x00,0x08,0x00,0x62,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x52,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x60,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x4b,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x65,0x00,0x00,0x00,
+0x64,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6b,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
+0x6b,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6f,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
+0x6f,0x00,0x00,0x00,0x50,0x00,0x05,0x00,0x66,0x00,0x00,0x00,
+0x71,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
+0x8e,0x00,0x05,0x00,0x66,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
+0x71,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x50,0x00,0x05,0x00,
+0x66,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x66,0x00,0x00,0x00,
+0x77,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
+0x51,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
+0x77,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x80,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x84,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x86,0x00,0x00,0x00,0x87,0x00,0x00,0x00,
+0x7e,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
+0x87,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
+0x8c,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x93,0x00,0x00,0x00,
+0x84,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x86,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x95,0x00,0x00,0x00,0x94,0x00,0x00,0x00,
+0x85,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x96,0x00,0x00,0x00,
+0x8c,0x00,0x00,0x00,0x95,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x14,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
+0x96,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x99,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
+0x14,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x99,0x00,0x00,0x00,
+0x97,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,
+0x9a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9d,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x1e,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x20,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x32,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xa1,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xa1,0x00,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
+0xa0,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
+0xa4,0x00,0x00,0x00,0xac,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,
+0xa7,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xa3,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,
+0xa2,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa2,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,
+0xaa,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xac,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xaa,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
+0xac,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xab,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1b,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0xb0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,
+0xb4,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x1c,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xac,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xac,0x00,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x32,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x9e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa4,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa4,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa1,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa3,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,
+0x2d,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xbc,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
+0xbb,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xbb,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0xc1,0x00,0x00,0x00,0x26,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
+0xc1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc4,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x86,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
+0xc0,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xc7,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xbc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xbc,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t mul_mat_vec_q4_1_f32_len = 3252;
+
+unsigned char mul_mat_vec_q4_K_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x9e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x4d,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0xec,0x01,0x00,0x00,
+0x90,0x03,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x11,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x20,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x70,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x71,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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+0x0c,0x00,0x08,0x00,0x4a,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xef,0x02,0x00,0x00,
+0xc5,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x4a,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,0x07,0x02,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x4a,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,
+0xa6,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x4a,0x00,0x00,0x00,0x05,0x03,0x00,0x00,0x35,0x02,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x4a,0x00,0x00,0x00,0x09,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x05,0x03,0x00,0x00,
+0xb0,0x02,0x00,0x00,0xfe,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x4a,0x00,0x00,0x00,0x10,0x03,0x00,0x00,0x61,0x02,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x4a,0x00,0x00,0x00,0x14,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x10,0x03,0x00,0x00,
+0xba,0x02,0x00,0x00,0x09,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x4a,0x00,0x00,0x00,0x1b,0x03,0x00,0x00,0x8d,0x02,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x4a,0x00,0x00,0x00,0x1f,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x1b,0x03,0x00,0x00,
+0xc5,0x02,0x00,0x00,0x14,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x4a,0x00,0x00,0x00,0x26,0x03,0x00,0x00,0x12,0x02,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x4a,0x00,0x00,0x00,0x2a,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x26,0x03,0x00,0x00,
+0xa6,0x02,0x00,0x00,0x1f,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x4a,0x00,0x00,0x00,0x31,0x03,0x00,0x00,0x41,0x02,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x4a,0x00,0x00,0x00,0x35,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x31,0x03,0x00,0x00,
+0xb0,0x02,0x00,0x00,0x2a,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x4a,0x00,0x00,0x00,0x3c,0x03,0x00,0x00,0x6c,0x02,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x4a,0x00,0x00,0x00,0x40,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x3c,0x03,0x00,0x00,
+0xba,0x02,0x00,0x00,0x35,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x4a,0x00,0x00,0x00,0x47,0x03,0x00,0x00,0x98,0x02,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x4a,0x00,0x00,0x00,0x4b,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x47,0x03,0x00,0x00,
+0xc5,0x02,0x00,0x00,0x40,0x03,0x00,0x00,0x70,0x00,0x04,0x00,
+0x4a,0x00,0x00,0x00,0x53,0x03,0x00,0x00,0x97,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,0x57,0x03,0x00,0x00,
+0xa5,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x4a,0x00,0x00,0x00,
+0x58,0x03,0x00,0x00,0x46,0x02,0x00,0x00,0x57,0x03,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x4a,0x00,0x00,0x00,0x59,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x17,0x02,0x00,0x00,
+0x53,0x03,0x00,0x00,0x58,0x03,0x00,0x00,0x70,0x00,0x04,0x00,
+0x4a,0x00,0x00,0x00,0x5c,0x03,0x00,0x00,0xe0,0x00,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x4a,0x00,0x00,0x00,0x5e,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x71,0x02,0x00,0x00,
+0x5c,0x03,0x00,0x00,0x59,0x03,0x00,0x00,0x70,0x00,0x04,0x00,
+0x4a,0x00,0x00,0x00,0x61,0x03,0x00,0x00,0xfc,0x00,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x4a,0x00,0x00,0x00,0x63,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,
+0x61,0x03,0x00,0x00,0x5e,0x03,0x00,0x00,0x85,0x00,0x05,0x00,
+0x4a,0x00,0x00,0x00,0x67,0x03,0x00,0x00,0x85,0x00,0x00,0x00,
+0x4b,0x03,0x00,0x00,0x7f,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,
+0x9d,0x03,0x00,0x00,0x67,0x03,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x4a,0x00,0x00,0x00,0x68,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x63,0x03,0x00,0x00,
+0x9d,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,
+0x6a,0x03,0x00,0x00,0x55,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
+0x4a,0x00,0x00,0x00,0x6b,0x03,0x00,0x00,0x6a,0x03,0x00,0x00,
+0x68,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x55,0x00,0x00,0x00,
+0x6b,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6e,0x03,0x00,0x00,0x99,0x03,0x00,0x00,0x23,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x58,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x5a,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x23,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x6f,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x71,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x71,0x03,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x9a,0x03,0x00,0x00,
+0x4e,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x88,0x03,0x00,0x00,
+0x74,0x03,0x00,0x00,0xac,0x00,0x05,0x00,0x5f,0x00,0x00,0x00,
+0x77,0x03,0x00,0x00,0x9a,0x03,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x73,0x03,0x00,0x00,0x74,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x77,0x03,0x00,0x00,
+0x72,0x03,0x00,0x00,0x73,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x72,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0x5f,0x00,0x00,0x00,
+0x7a,0x03,0x00,0x00,0x24,0x00,0x00,0x00,0x9a,0x03,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x7c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x7a,0x03,0x00,0x00,0x7b,0x03,0x00,0x00,
+0x7c,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x7b,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x80,0x03,0x00,0x00,
+0x24,0x00,0x00,0x00,0x9a,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0x54,0x00,0x00,0x00,0x81,0x03,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x80,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,
+0x82,0x03,0x00,0x00,0x81,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
+0x54,0x00,0x00,0x00,0x83,0x03,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,
+0x84,0x03,0x00,0x00,0x83,0x03,0x00,0x00,0x81,0x00,0x05,0x00,
+0x4a,0x00,0x00,0x00,0x85,0x03,0x00,0x00,0x84,0x03,0x00,0x00,
+0x82,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x83,0x03,0x00,0x00,
+0x85,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x7c,0x03,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7c,0x03,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x23,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x6f,0x03,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x74,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x74,0x03,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x88,0x03,0x00,0x00,0x9a,0x03,0x00,0x00,0x8b,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x71,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x73,0x03,0x00,0x00,0xaa,0x00,0x05,0x00,0x5f,0x00,0x00,0x00,
+0x8a,0x03,0x00,0x00,0x24,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x8c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x8a,0x03,0x00,0x00,0x8b,0x03,0x00,0x00,
+0x8c,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x8b,0x03,0x00,0x00,
+0x41,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x91,0x03,0x00,0x00,
+0x13,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x92,0x03,0x00,0x00,0x91,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x94,0x03,0x00,0x00,
+0x92,0x03,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x54,0x00,0x00,0x00,0x95,0x03,0x00,0x00,0x4d,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,
+0x96,0x03,0x00,0x00,0x95,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
+0xf1,0x01,0x00,0x00,0x97,0x03,0x00,0x00,0x90,0x03,0x00,0x00,
+0x15,0x00,0x00,0x00,0x94,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x97,0x03,0x00,0x00,0x96,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x8c,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x8c,0x03,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q4_K_f32_len = 9128;
+
+unsigned char mul_mat_vec_q5_0_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xf6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x26,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0xa3,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x24,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x24,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x24,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x24,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x50,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x50,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x52,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x52,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x52,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x54,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa0,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
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+0x67,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
+0x6e,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x70,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x27,0x00,0x00,0x00,0x71,0x00,0x00,0x00,
+0x70,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x75,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
+0x67,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
+0x4e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x27,0x00,0x00,0x00,
+0x78,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
+0x7e,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,
+0x42,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
+0x80,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x87,0x00,0x00,0x00,
+0x81,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0x71,0x00,0x00,0x00,
+0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,
+0x87,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,
+0x81,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
+0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x94,0x00,0x00,0x00,
+0x8f,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x95,0x00,0x00,0x00,0x94,0x00,0x00,0x00,
+0x50,0x00,0x05,0x00,0x82,0x00,0x00,0x00,0x96,0x00,0x00,0x00,
+0x8d,0x00,0x00,0x00,0x95,0x00,0x00,0x00,0x83,0x00,0x05,0x00,
+0x82,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x96,0x00,0x00,0x00,
+0xf5,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,0x82,0x00,0x00,0x00,
+0x9c,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x59,0x00,0x00,0x00,
+0x51,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,
+0x9c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xa5,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa9,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xab,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
+0xa3,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
+0xac,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
+0xb0,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
+0xa9,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0xab,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
+0x85,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
+0xb0,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x14,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
+0xba,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0xbd,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
+0x14,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,
+0xbe,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc1,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x1e,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x20,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x32,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xc5,0x00,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
+0xc4,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,
+0xc8,0x00,0x00,0x00,0xac,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,
+0xcb,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0xc7,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xcb,0x00,0x00,0x00,
+0xc6,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xc6,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,
+0xce,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0xd0,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0xce,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xcf,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x1b,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0xd4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
+0xd8,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x1c,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd0,0x00,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x32,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xc2,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc8,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc8,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc5,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc7,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,
+0x2d,0x00,0x00,0x00,0xde,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xe0,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xde,0x00,0x00,0x00,
+0xdf,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xdf,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
+0xe5,0x00,0x00,0x00,0x26,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
+0xe5,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xe8,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xab,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
+0xe4,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xe0,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t mul_mat_vec_q5_0_f32_len = 3696;
+
+unsigned char mul_mat_vec_q5_1_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xf0,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
+0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
+0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,
+0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
+0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x26,0x00,0x00,0x00,0x52,0x00,0x00,0x00,
+0x9f,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x24,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x24,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x24,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x24,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x4e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4e,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x4e,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x4f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x50,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x50,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x52,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x9c,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,
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+0x7b,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,
+0x7b,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x7d,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x83,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,
+0x82,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x87,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0x83,0x00,0x00,0x00,
+0x87,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x89,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,
+0x6a,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x8f,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,
+0x8f,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x91,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x50,0x00,0x05,0x00,
+0x7e,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x89,0x00,0x00,0x00,
+0x91,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,0x7e,0x00,0x00,0x00,
+0x95,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x57,0x00,0x00,0x00,
+0x50,0x00,0x05,0x00,0x7e,0x00,0x00,0x00,0x97,0x00,0x00,0x00,
+0x5d,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
+0x7e,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
+0x97,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
+0x9b,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,
+0x26,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,
+0xa1,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,
+0x42,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xa7,0x00,0x00,0x00,
+0xa8,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0xa5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0xa9,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x14,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xb3,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xa7,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,
+0x9f,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,
+0xb4,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
+0xb6,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x14,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,
+0xa9,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
+0xb9,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x1c,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x1e,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x20,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x32,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xc1,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xc1,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xef,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0xd8,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xac,0x00,0x05,0x00,
+0x2d,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
+0xc4,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xc7,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xc2,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
+0x2d,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0xef,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xcc,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xca,0x00,0x00,0x00,
+0xcb,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xcb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
+0xd5,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xcc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xcc,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x32,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xc4,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
+0xef,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xc1,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xc3,0x00,0x00,0x00,
+0xaa,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xdc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xda,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xdb,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
+0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xe2,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
+0xe5,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
+0xe5,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xa7,0x00,0x00,0x00,
+0xe7,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0xe4,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xe7,0x00,0x00,0x00,
+0xe6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xdc,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xdc,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q5_1_f32_len = 3592;
+
+unsigned char mul_mat_vec_q5_K_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x8a,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x5b,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xf7,0x01,0x00,0x00,
+0x7c,0x04,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x11,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x20,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7e,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x7f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x80,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x80,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x80,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x80,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x82,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x82,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x82,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x84,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x84,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xf4,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xf5,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xf5,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xf5,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xf7,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf7,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x79,0x04,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x7a,0x04,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7a,0x04,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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+0xe7,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x7e,0x04,0x00,0x00,0x7d,0x04,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x80,0x04,0x00,0x00,0x7e,0x04,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x62,0x00,0x00,0x00,
+0x81,0x04,0x00,0x00,0x5b,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0x82,0x04,0x00,0x00,
+0x81,0x04,0x00,0x00,0x41,0x00,0x06,0x00,0xfc,0x01,0x00,0x00,
+0x83,0x04,0x00,0x00,0x7c,0x04,0x00,0x00,0x15,0x00,0x00,0x00,
+0x80,0x04,0x00,0x00,0x3e,0x00,0x03,0x00,0x83,0x04,0x00,0x00,
+0x82,0x04,0x00,0x00,0xf9,0x00,0x02,0x00,0x78,0x04,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x78,0x04,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q5_K_f32_len = 12016;
+
+unsigned char mul_mat_vec_q6_K_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xc5,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
+0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
+0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,
+0xa5,0x01,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x11,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x20,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x6f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x70,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x72,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x74,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x74,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x80,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x74,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x74,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x75,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x76,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x76,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x76,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x78,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x78,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x8c,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x8d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x8d,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x8d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x8f,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8f,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xa2,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xa3,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xa3,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xa3,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa5,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa5,0x01,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xae,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x05,0x00,
+0x11,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x33,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x4b,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x50,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
+0x51,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x52,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x51,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x59,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
+0x65,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
+0x6f,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
+0x1c,0x00,0x04,0x00,0x70,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x71,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
+0x72,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x73,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x1e,0x00,0x06,0x00,0x74,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
+0x70,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x75,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x76,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x77,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x77,0x00,0x00,0x00,
+0x78,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x7d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x73,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x8a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x8c,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x8d,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x8e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x8e,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x90,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x99,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xa2,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0xb1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0xc9,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x60,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x68,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x84,0x01,0x00,0x00,0x08,0x01,0x00,0x00,
+0x1d,0x00,0x03,0x00,0xa2,0x01,0x00,0x00,0x50,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0xa3,0x01,0x00,0x00,0xa2,0x01,0x00,0x00,
+0x20,0x00,0x04,0x00,0xa4,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,
+0xa3,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0xa4,0x01,0x00,0x00,
+0xa5,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xad,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0xae,0x01,0x00,0x00,
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+0x9f,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x5a,0x03,0x00,0x00,0x59,0x03,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x5b,0x03,0x00,0x00,0x5a,0x03,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x5c,0x03,0x00,0x00,
+0x5b,0x03,0x00,0x00,0x7c,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
+0x14,0x00,0x00,0x00,0x5d,0x03,0x00,0x00,0x5c,0x03,0x00,0x00,
+0x8a,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
+0x5e,0x03,0x00,0x00,0x53,0x03,0x00,0x00,0x5d,0x03,0x00,0x00,
+0x72,0x00,0x04,0x00,0x71,0x00,0x00,0x00,0x5f,0x03,0x00,0x00,
+0x5e,0x03,0x00,0x00,0x72,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x60,0x03,0x00,0x00,0x5f,0x03,0x00,0x00,0x82,0x00,0x05,0x00,
+0x14,0x00,0x00,0x00,0x61,0x03,0x00,0x00,0x60,0x03,0x00,0x00,
+0xc9,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
+0x62,0x03,0x00,0x00,0x61,0x03,0x00,0x00,0x85,0x00,0x05,0x00,
+0x50,0x00,0x00,0x00,0x63,0x03,0x00,0x00,0x4a,0x03,0x00,0x00,
+0x62,0x03,0x00,0x00,0x0c,0x00,0x08,0x00,0x50,0x00,0x00,0x00,
+0x64,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x22,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0x63,0x03,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6a,0x03,0x00,0x00,
+0x94,0x00,0x00,0x00,0xc3,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
+0x99,0x00,0x00,0x00,0x6b,0x03,0x00,0x00,0x8f,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x6a,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x50,0x00,0x00,0x00,0x6c,0x03,0x00,0x00,0x6b,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x71,0x00,0x00,0x00,0x70,0x03,0x00,0x00,
+0x13,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
+0x71,0x03,0x00,0x00,0x70,0x03,0x00,0x00,0x85,0x00,0x05,0x00,
+0x50,0x00,0x00,0x00,0x72,0x03,0x00,0x00,0x6c,0x03,0x00,0x00,
+0x71,0x03,0x00,0x00,0x85,0x00,0x05,0x00,0x50,0x00,0x00,0x00,
+0x73,0x03,0x00,0x00,0x72,0x03,0x00,0x00,0x80,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,0x79,0x03,0x00,0x00,
+0x27,0x03,0x00,0x00,0xc2,0x00,0x05,0x00,0x6e,0x00,0x00,0x00,
+0x7a,0x03,0x00,0x00,0x79,0x03,0x00,0x00,0x8a,0x00,0x00,0x00,
+0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x7b,0x03,0x00,0x00,
+0x7a,0x03,0x00,0x00,0x7c,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x7c,0x03,0x00,0x00,0x7b,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x6e,0x00,0x00,0x00,0x81,0x03,0x00,0x00,0x2f,0x03,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x6e,0x00,0x00,0x00,0x82,0x03,0x00,0x00,
+0x81,0x03,0x00,0x00,0x8a,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x83,0x03,0x00,0x00,0x82,0x03,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x84,0x03,0x00,0x00,
+0x83,0x03,0x00,0x00,0xc7,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
+0x85,0x03,0x00,0x00,0x84,0x03,0x00,0x00,0x7c,0x00,0x00,0x00,
+0xc4,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x86,0x03,0x00,0x00,
+0x85,0x03,0x00,0x00,0x8a,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,
+0x14,0x00,0x00,0x00,0x87,0x03,0x00,0x00,0x7c,0x03,0x00,0x00,
+0x86,0x03,0x00,0x00,0x72,0x00,0x04,0x00,0x71,0x00,0x00,0x00,
+0x88,0x03,0x00,0x00,0x87,0x03,0x00,0x00,0x72,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x89,0x03,0x00,0x00,0x88,0x03,0x00,0x00,
+0x82,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x8a,0x03,0x00,0x00,
+0x89,0x03,0x00,0x00,0xc9,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,
+0x50,0x00,0x00,0x00,0x8b,0x03,0x00,0x00,0x8a,0x03,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x50,0x00,0x00,0x00,0x8d,0x03,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x73,0x03,0x00,0x00,
+0x8b,0x03,0x00,0x00,0x64,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x93,0x03,0x00,0x00,0x94,0x00,0x00,0x00,
+0xc4,0x03,0x00,0x00,0x41,0x00,0x06,0x00,0x99,0x00,0x00,0x00,
+0x94,0x03,0x00,0x00,0x8f,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x93,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
+0x95,0x03,0x00,0x00,0x94,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x71,0x00,0x00,0x00,0x99,0x03,0x00,0x00,0x4c,0x01,0x00,0x00,
+0x6f,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x9a,0x03,0x00,0x00,
+0x99,0x03,0x00,0x00,0x85,0x00,0x05,0x00,0x50,0x00,0x00,0x00,
+0x9b,0x03,0x00,0x00,0x95,0x03,0x00,0x00,0x9a,0x03,0x00,0x00,
+0x85,0x00,0x05,0x00,0x50,0x00,0x00,0x00,0x9c,0x03,0x00,0x00,
+0x9b,0x03,0x00,0x00,0x80,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x6e,0x00,0x00,0x00,0xa2,0x03,0x00,0x00,0x4f,0x03,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x6e,0x00,0x00,0x00,0xa3,0x03,0x00,0x00,
+0xa2,0x03,0x00,0x00,0x8a,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xa4,0x03,0x00,0x00,0xa3,0x03,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0xa5,0x03,0x00,0x00,
+0xa4,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,
+0xaa,0x03,0x00,0x00,0x2f,0x03,0x00,0x00,0xc2,0x00,0x05,0x00,
+0x6e,0x00,0x00,0x00,0xab,0x03,0x00,0x00,0xaa,0x03,0x00,0x00,
+0x68,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xac,0x03,0x00,0x00,0xab,0x03,0x00,0x00,0x7c,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0xad,0x03,0x00,0x00,0xac,0x03,0x00,0x00,
+0xc7,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0xae,0x03,0x00,0x00,
+0xad,0x03,0x00,0x00,0x7c,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
+0x14,0x00,0x00,0x00,0xaf,0x03,0x00,0x00,0xae,0x03,0x00,0x00,
+0x8a,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
+0xb0,0x03,0x00,0x00,0xa5,0x03,0x00,0x00,0xaf,0x03,0x00,0x00,
+0x72,0x00,0x04,0x00,0x71,0x00,0x00,0x00,0xb1,0x03,0x00,0x00,
+0xb0,0x03,0x00,0x00,0x72,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0xb2,0x03,0x00,0x00,0xb1,0x03,0x00,0x00,0x82,0x00,0x05,0x00,
+0x14,0x00,0x00,0x00,0xb3,0x03,0x00,0x00,0xb2,0x03,0x00,0x00,
+0xc9,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
+0xb4,0x03,0x00,0x00,0xb3,0x03,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x50,0x00,0x00,0x00,0xb6,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x9c,0x03,0x00,0x00,0xb4,0x03,0x00,0x00,
+0x8d,0x03,0x00,0x00,0x81,0x00,0x05,0x00,0x50,0x00,0x00,0x00,
+0xb7,0x03,0x00,0x00,0x0b,0x03,0x00,0x00,0xb6,0x03,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x7f,0x01,0x00,0x00,
+0x5b,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x50,0x00,0x00,0x00,
+0x80,0x01,0x00,0x00,0x7f,0x01,0x00,0x00,0xb7,0x03,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x5b,0x00,0x00,0x00,0x80,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x83,0x01,0x00,0x00,
+0xaf,0x01,0x00,0x00,0x23,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x5e,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x60,0x00,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x84,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x86,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x86,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0xb0,0x01,0x00,0x00,0x54,0x00,0x00,0x00,
+0x60,0x00,0x00,0x00,0x9d,0x01,0x00,0x00,0x89,0x01,0x00,0x00,
+0xac,0x00,0x05,0x00,0x65,0x00,0x00,0x00,0x8c,0x01,0x00,0x00,
+0xb0,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x88,0x01,0x00,0x00,0x89,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x8c,0x01,0x00,0x00,0x87,0x01,0x00,0x00,
+0x88,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x87,0x01,0x00,0x00,
+0xb0,0x00,0x05,0x00,0x65,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,
+0x24,0x00,0x00,0x00,0xb0,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x91,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x8f,0x01,0x00,0x00,0x90,0x01,0x00,0x00,0x91,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x90,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,0x24,0x00,0x00,0x00,
+0xb0,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x5a,0x00,0x00,0x00,
+0x96,0x01,0x00,0x00,0x53,0x00,0x00,0x00,0x95,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x97,0x01,0x00,0x00,
+0x96,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x5a,0x00,0x00,0x00,
+0x98,0x01,0x00,0x00,0x53,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x99,0x01,0x00,0x00,
+0x98,0x01,0x00,0x00,0x81,0x00,0x05,0x00,0x50,0x00,0x00,0x00,
+0x9a,0x01,0x00,0x00,0x99,0x01,0x00,0x00,0x97,0x01,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x98,0x01,0x00,0x00,0x9a,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x91,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x91,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,0x23,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x84,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x89,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x89,0x01,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9d,0x01,0x00,0x00,
+0xb0,0x01,0x00,0x00,0x90,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x86,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x88,0x01,0x00,0x00,
+0xaa,0x00,0x05,0x00,0x65,0x00,0x00,0x00,0x9f,0x01,0x00,0x00,
+0x24,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xa1,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x9f,0x01,0x00,0x00,0xa0,0x01,0x00,0x00,0xa1,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa0,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x16,0x00,0x00,0x00,0xa6,0x01,0x00,0x00,0x13,0x00,0x00,0x00,
+0x9f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xa7,0x01,0x00,0x00,0xa6,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xa9,0x01,0x00,0x00,0xa7,0x01,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x5a,0x00,0x00,0x00,
+0xaa,0x01,0x00,0x00,0x53,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0xab,0x01,0x00,0x00,
+0xaa,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x99,0x00,0x00,0x00,
+0xac,0x01,0x00,0x00,0xa5,0x01,0x00,0x00,0x15,0x00,0x00,0x00,
+0xa9,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0xac,0x01,0x00,0x00,
+0xab,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xa1,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xa1,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q6_K_f32_len = 10960;
+
+unsigned char mul_mat_vec_q8_0_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xc4,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
+0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
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+0x56,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
+0x41,0x00,0x08,0x00,0x5e,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
+0x52,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x43,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x4c,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
+0x72,0x00,0x04,0x00,0x27,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
+0x60,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0x43,0x00,0x00,0x00,
+0x42,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x5e,0x00,0x00,0x00,
+0x66,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x65,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
+0x66,0x00,0x00,0x00,0x72,0x00,0x04,0x00,0x27,0x00,0x00,0x00,
+0x68,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
+0x50,0x00,0x05,0x00,0x58,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,
+0x62,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0x8e,0x00,0x05,0x00,
+0x58,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,
+0x57,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
+0x70,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
+0x26,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
+0x43,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x7c,0x00,0x00,0x00,
+0x7d,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x7a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x7e,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
+0x14,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x88,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x7c,0x00,0x00,0x00,0x89,0x00,0x00,0x00,
+0x74,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
+0x89,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
+0x8b,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x14,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
+0x7e,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,
+0x8e,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x1c,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x1e,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x20,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
+0x32,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x93,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x96,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x96,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0xc3,0x00,0x00,0x00,0x95,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0xad,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0xac,0x00,0x05,0x00,
+0x2d,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x98,0x00,0x00,0x00,
+0x99,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x9c,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x97,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
+0x2d,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0xc3,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xa1,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x9f,0x00,0x00,0x00,
+0xa0,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa0,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xa5,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
+0xaa,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xa1,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xa1,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x32,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x99,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x99,0x00,0x00,0x00,
+0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
+0xc3,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x96,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x98,0x00,0x00,0x00,
+0xaa,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0xb1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0xaf,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb0,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x29,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
+0xb6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xb8,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
+0xbb,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x7c,0x00,0x00,0x00,
+0xbd,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0xba,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xbd,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb1,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xb1,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t mul_mat_vec_q8_0_f32_len = 3124;
+
+unsigned char norm_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0xb5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0b,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x29,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x97,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x27,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x27,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x27,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x27,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x27,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x33,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x34,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x36,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x36,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x94,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x95,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x95,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x95,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x97,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x97,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xad,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x1c,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x18,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
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+0x06,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8b,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x8d,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t norm_f32_len = 2572;
+
+unsigned char relu_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x20,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x22,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x22,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x24,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x25,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x25,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x25,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x27,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x27,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x31,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x14,0x00,0x02,0x00,0x1a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x1f,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x20,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x21,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x24,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x25,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x26,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x29,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,
+0x31,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x32,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,
+0x0c,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x33,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0xae,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x1d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x1c,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x32,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x1d,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x29,0x00,0x00,0x00,
+0x2a,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x0c,0x00,0x07,0x00,
+0x11,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x29,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x2e,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x32,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x32,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t relu_f32_len = 1212;
+
+unsigned char rms_norm_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x9e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0b,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x85,0x00,0x00,0x00,
+0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x25,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x25,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x25,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x25,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x25,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x31,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x32,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x32,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x32,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x34,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x34,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x82,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x83,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x83,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x83,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x85,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x85,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
+0x1c,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,
+0x25,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x26,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x28,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x2d,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x31,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x32,0x00,0x00,0x00,0x31,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x33,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x33,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x3b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x49,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x28,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x28,0x00,0x00,0x00,0x65,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
+0x71,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x72,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x82,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x83,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x84,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x83,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x84,0x00,0x00,0x00,
+0x85,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x99,0x00,0x00,0x00,
+0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x05,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x1f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x1f,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x9b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x2a,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
+0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
+0x2d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x2e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x20,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x3a,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x3b,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x3c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x43,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x14,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x43,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,
+0x44,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x47,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x1f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x21,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x48,0x00,0x00,0x00,
+0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x4d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x4d,0x00,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x28,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,
+0x4c,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
+0x50,0x00,0x00,0x00,0xad,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
+0x4e,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x4e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x56,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
+0x2d,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x56,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x59,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x57,0x00,0x00,0x00,
+0x58,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x58,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5e,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
+0x63,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x59,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x59,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x48,0x00,0x00,0x00,
+0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x50,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x50,0x00,0x00,0x00,
+0xc3,0x00,0x05,0x00,0x28,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
+0x9c,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x4d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x4f,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
+0x70,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x88,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
+0x6e,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x72,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
+0x6e,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x0c,0x00,0x06,0x00,
+0x14,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x79,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x79,0x00,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,
+0x13,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
+0x7a,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,
+0x81,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0xf6,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x81,0x00,0x00,0x00,
+0x7a,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x7a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x89,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,
+0x89,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x3b,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
+0x29,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x14,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x93,0x00,0x00,0x00,
+0x85,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x3b,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x85,0x00,0x00,0x00,
+0x29,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x96,0x00,0x00,0x00,0x95,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x7b,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t rms_norm_f32_len = 2344;
+
+unsigned char rope_f16_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x1c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x67,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
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+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
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+0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
+0xe4,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x2f,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x39,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xe8,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0xb7,0x00,0x05,0x00,
+0x3c,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x02,0x01,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xe9,0x00,0x00,0x00,
+0xea,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xea,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x2f,0x00,0x00,0x00,
+0xeb,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
+0x42,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0x2f,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x41,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
+0x86,0x00,0x05,0x00,0x07,0x00,0x00,0x00,0x0e,0x01,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,0x0e,0x01,0x00,0x00,
+0x83,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x10,0x01,0x00,0x00,
+0x0f,0x01,0x00,0x00,0xec,0x00,0x00,0x00,0x83,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x11,0x01,0x00,0x00,0xee,0x00,0x00,0x00,
+0xec,0x00,0x00,0x00,0x0c,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x12,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x11,0x01,0x00,0x00,0x88,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0x10,0x01,0x00,0x00,
+0x12,0x01,0x00,0x00,0x0c,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x15,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0x0c,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x16,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x25,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x15,0x01,0x00,0x00,
+0x83,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x17,0x01,0x00,0x00,
+0x1f,0x00,0x00,0x00,0x16,0x01,0x00,0x00,0x85,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,0x17,0x01,0x00,0x00,
+0xe8,0x00,0x00,0x00,0x83,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x1a,0x01,0x00,0x00,0x16,0x01,0x00,0x00,0x1f,0x00,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,
+0xe8,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
+0xf2,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,
+0xf9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xe5,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
+0x88,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
+0x1f,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x0c,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x06,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
+0x1f,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x01,0x01,0x00,0x00,0xe2,0x00,0x00,0x00,0xff,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x02,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x02,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x19,0x01,0x00,0x00,0xe2,0x00,0x00,0x00,0x77,0x00,0x00,0x00,
+0x01,0x01,0x00,0x00,0xea,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x18,0x01,0x00,0x00,0xe5,0x00,0x00,0x00,
+0x77,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
+0x0c,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x04,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x18,0x01,0x00,0x00,
+0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x06,0x01,0x00,0x00,
+0x04,0x01,0x00,0x00,0x19,0x01,0x00,0x00,0x0c,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0d,0x00,0x00,0x00,0x18,0x01,0x00,0x00,0x85,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x08,0x01,0x00,0x00,
+0x19,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0xb0,0x00,0x00,0x00,
+0xb1,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
+0x7f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xa9,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x07,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
+0x7f,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0xb0,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
+0x42,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0xa9,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,
+0xb8,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xc5,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,
+0x7f,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x1b,0x01,0x00,0x00,
+0xc5,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,
+0xc6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xb3,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x1b,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0xa9,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
+0xc6,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xb0,0x00,0x00,0x00,
+0xc8,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
+0x7f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xc8,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0xd0,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x06,0x01,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,
+0x0a,0x01,0x00,0x00,0xd0,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
+0xa9,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xb0,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,
+0xbd,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xd3,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xd6,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t rope_f16_len = 3156;
+
+unsigned char rope_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x17,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,
+0xac,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x2a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x67,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x88,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x89,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x89,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x89,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8b,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x8b,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xa9,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xaa,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xaa,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xaa,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xac,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xac,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xb7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xb8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xb8,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xba,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x07,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x07,0x00,0x00,0x00,
+0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x6f,0x12,0x83,0x3a,
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+0x06,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
+0xfa,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x14,0x01,0x00,0x00,0xdd,0x00,0x00,0x00,
+0x77,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x13,0x01,0x00,0x00,
+0xe0,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
+0xe5,0x00,0x00,0x00,0x0c,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0xff,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
+0x13,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x01,0x01,0x00,0x00,0xff,0x00,0x00,0x00,0x14,0x01,0x00,0x00,
+0x0c,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x03,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x13,0x01,0x00,0x00,
+0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x05,0x01,0x00,0x00,
+0x03,0x01,0x00,0x00,0x14,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0xaf,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
+0x42,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x07,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,
+0x7f,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0xaf,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
+0x42,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,
+0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
+0xb6,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x7f,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x16,0x01,0x00,0x00,0xc2,0x00,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
+0x01,0x01,0x00,0x00,0x16,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0xaf,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
+0x42,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xc4,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
+0x01,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,
+0xcd,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xb1,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0xcc,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xaf,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
+0xba,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xce,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xd1,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xd1,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t rope_f32_len = 3072;
+
+unsigned char rope_neox_f16_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x5f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
+0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
+0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
+0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x68,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
+0xcf,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x29,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2a,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x2a,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x68,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x95,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x96,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x96,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x96,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x98,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x98,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x9b,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0x9c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x9c,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x9c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x9e,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9e,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xcc,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x04,0x00,0xcd,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0xcd,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xcf,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xcf,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x16,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
+0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x07,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x07,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x6f,0x12,0x83,0x3a,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x00,0x00,0x80,0x3f,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x07,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
+0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x1e,0x00,0x0c,0x00,0x2a,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x2a,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
+0x2d,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x2f,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x2d,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x39,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x3c,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
+0x42,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x2d,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0xcd,0xcc,0xcc,0x3d,0x17,0x00,0x04,0x00,0x66,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x67,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x07,0x00,0x00,0x00,
+0x69,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x6a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x07,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x73,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x94,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x95,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x96,0x00,0x00,0x00,0x95,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x97,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x96,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x97,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x9b,0x00,0x00,0x00,
+0x94,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x9c,0x00,0x00,0x00,
+0x9b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x9d,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x94,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
+0xbc,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x2d,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
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+0x0c,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x3e,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,0x40,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
+0x3e,0x01,0x00,0x00,0x1f,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x23,0x01,0x00,0x00,
+0x40,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x43,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x43,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x06,0x00,0x00,0x00,0x5a,0x01,0x00,0x00,0x23,0x01,0x00,0x00,
+0x87,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x2b,0x01,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x59,0x01,0x00,0x00,
+0x26,0x01,0x00,0x00,0x87,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,
+0x2b,0x01,0x00,0x00,0x0c,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x45,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
+0x59,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x47,0x01,0x00,0x00,0x45,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,
+0x0c,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x49,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x59,0x01,0x00,0x00,
+0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4b,0x01,0x00,0x00,
+0x49,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0xa1,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
+0x42,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x94,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
+0xf1,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x07,0x00,0x00,0x00,
+0xf7,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x07,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
+0xb9,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0xa1,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
+0x42,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x94,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
+0x73,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
+0xfa,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x03,0x01,0x00,0x00,0xfb,0x00,0x00,0x00,0x4b,0x01,0x00,0x00,
+0x7f,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x5e,0x01,0x00,0x00,
+0x03,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,
+0x04,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xf2,0x00,0x00,0x00,0x47,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,
+0x73,0x00,0x04,0x00,0x94,0x00,0x00,0x00,0x05,0x01,0x00,0x00,
+0x04,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0xa1,0x00,0x00,0x00,
+0x06,0x01,0x00,0x00,0x98,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
+0xb9,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x06,0x01,0x00,0x00,
+0x05,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x11,0x01,0x00,0x00,0xfb,0x00,0x00,0x00,0x47,0x01,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,0x12,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
+0x4b,0x01,0x00,0x00,0x11,0x01,0x00,0x00,0x73,0x00,0x04,0x00,
+0x94,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0x12,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0xa1,0x00,0x00,0x00,0x14,0x01,0x00,0x00,
+0x98,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x14,0x01,0x00,0x00,0x13,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x17,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x17,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t rope_neox_f16_len = 3876;
+
+unsigned char rope_neox_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x5a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x97,0x00,0x00,0x00,
+0x9d,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2a,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x2a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x68,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x94,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x95,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x95,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x95,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x97,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x97,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x9a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x9b,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x9b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x9b,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x9d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xcb,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xcc,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xcc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xcc,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xce,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xce,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x11,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x16,0x00,0x03,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x07,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x07,0x00,0x00,0x00,
+0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x6f,0x12,0x83,0x3a,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
+0x00,0x00,0x80,0x3f,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x07,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x1c,0x00,0x04,0x00,0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x1e,0x00,0x0c,0x00,0x2a,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x2b,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
+0x2e,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x2f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
+0x39,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
+0x3c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
+0x41,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x2d,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0xcd,0xcc,0xcc,0x3d,0x17,0x00,0x04,0x00,
+0x66,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x66,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x67,0x00,0x00,0x00,
+0x68,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x07,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x6a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x07,0x00,0x00,0x00,
+0x6f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x73,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x1d,0x00,0x03,0x00,0x94,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x00,0x03,0x00,0x95,0x00,0x00,0x00,0x94,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x96,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
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+0x2e,0x01,0x00,0x00,0x53,0x01,0x00,0x00,0x24,0x01,0x00,0x00,
+0x83,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x58,0x01,0x00,0x00,
+0x52,0x01,0x00,0x00,0x1f,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,
+0x06,0x00,0x00,0x00,0x31,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x32,0x00,0x00,0x00,0x58,0x01,0x00,0x00,0x24,0x01,0x00,0x00,
+0x1f,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x34,0x01,0x00,0x00,0xe1,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,0x35,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x21,0x01,0x00,0x00,
+0x31,0x01,0x00,0x00,0x34,0x01,0x00,0x00,0x88,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x38,0x01,0x00,0x00,0x1f,0x00,0x00,0x00,
+0xd7,0x00,0x00,0x00,0x0c,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x39,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x38,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,
+0x3b,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x53,0x00,0x00,0x00,0x39,0x01,0x00,0x00,0x1f,0x00,0x00,0x00,
+0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,
+0x1e,0x01,0x00,0x00,0x3b,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x3e,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x3e,0x01,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x55,0x01,0x00,0x00,
+0x1e,0x01,0x00,0x00,0x87,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,
+0x26,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x54,0x01,0x00,0x00,0x21,0x01,0x00,0x00,0x87,0x00,0x00,0x00,
+0x35,0x01,0x00,0x00,0x26,0x01,0x00,0x00,0x0c,0x00,0x06,0x00,
+0x06,0x00,0x00,0x00,0x40,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x54,0x01,0x00,0x00,0x85,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x40,0x01,0x00,0x00,
+0x55,0x01,0x00,0x00,0x0c,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
+0x44,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x54,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x46,0x01,0x00,0x00,0x44,0x01,0x00,0x00,0x55,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0xa0,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0x9d,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
+0xef,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x07,0x00,0x00,0x00,
+0xf5,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x07,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,
+0xb8,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0xa0,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,
+0x42,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
+0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
+0xf8,0x00,0x00,0x00,0x46,0x01,0x00,0x00,0x7f,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x59,0x01,0x00,0x00,0x00,0x01,0x00,0x00,
+0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,0x01,0x01,0x00,0x00,
+0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
+0x42,0x01,0x00,0x00,0x59,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0xa0,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x97,0x00,0x00,0x00,
+0x42,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x02,0x01,0x00,0x00,0x01,0x01,0x00,0x00,0x85,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,0xf8,0x00,0x00,0x00,
+0x42,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,
+0x0e,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xf0,0x00,0x00,0x00,0x46,0x01,0x00,0x00,0x0d,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0xa0,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,
+0x97,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x0f,0x01,0x00,0x00,0x0e,0x01,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x12,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x12,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t rope_neox_f32_len = 3792;
+
+unsigned char scale_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x9c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
+0xe3,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xc7,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xd4,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xd5,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xd5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xd5,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd7,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xd7,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xe0,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xe1,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xe1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xe1,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe3,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xe3,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xf3,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x1e,0x00,0x16,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x6f,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
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+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x93,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x94,0x01,0x00,0x00,0x93,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,
+0x7e,0x01,0x00,0x00,0x94,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x96,0x01,0x00,0x00,0x91,0x01,0x00,0x00,
+0x95,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x98,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x99,0x01,0x00,0x00,
+0x98,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9a,0x01,0x00,0x00,0x88,0x01,0x00,0x00,0x99,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x01,0x00,0x00,
+0x96,0x01,0x00,0x00,0x9a,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0xe8,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0x9b,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0xec,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0xea,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
+0xe8,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0xf0,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xf4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t scale_f32_len = 3320;
+
+unsigned char silu_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x3b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x22,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x24,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x24,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x29,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
+0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x2a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x2c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
+0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x16,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x14,0x00,0x02,0x00,0x1a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x21,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x22,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x26,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
+0x29,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
+0x2a,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x2b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x2f,0x00,0x00,0x00,0x00,0x00,0x80,0x3f,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
+0x37,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
+0x39,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,
+0x0c,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0xae,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x1d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x1c,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x39,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x1d,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x26,0x00,0x00,0x00,
+0x27,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
+0x28,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x7f,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x0c,0x00,0x06,0x00,0x11,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x31,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
+0x2f,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x88,0x00,0x05,0x00,
+0x11,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
+0x33,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x26,0x00,0x00,0x00,
+0x35,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x35,0x00,0x00,0x00,
+0x34,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x39,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x39,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
+0x38,0x00,0x01,0x00,
+};
+const uint64_t silu_f32_len = 1264;
+
+unsigned char soft_max_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x5c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
+0x5c,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,
+0xa3,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
+0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
+0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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+0xf5,0x00,0x07,0x00,0x16,0x00,0x00,0x00,0x4e,0x01,0x00,0x00,
+0xf9,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
+0xfa,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x16,0x00,0x00,0x00,
+0xfc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
+0xe8,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x4e,0x01,0x00,0x00,
+0x83,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
+0xfc,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x0c,0x00,0x06,0x00,
+0x16,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x61,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x04,0x01,0x00,0x00,
+0x03,0x01,0x00,0x00,0xff,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x61,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0x7c,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,0x09,0x01,0x00,0x00,
+0x6a,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x0c,0x01,0x00,0x00,0xff,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd8,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd8,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0e,0x01,0x00,0x00,
+0x4a,0x01,0x00,0x00,0x59,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0xd5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd7,0x00,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0xaf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x10,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x10,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,
+0x1a,0x00,0x00,0x00,0x4b,0x01,0x00,0x00,0xb2,0x00,0x00,0x00,
+0xd7,0x00,0x00,0x00,0x29,0x01,0x00,0x00,0x13,0x01,0x00,0x00,
+0xad,0x00,0x05,0x00,0x27,0x00,0x00,0x00,0x16,0x01,0x00,0x00,
+0x4b,0x01,0x00,0x00,0x6a,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
+0x12,0x01,0x00,0x00,0x13,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x16,0x01,0x00,0x00,0x11,0x01,0x00,0x00,
+0x12,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x11,0x01,0x00,0x00,
+0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x19,0x01,0x00,0x00,
+0x4b,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,0x27,0x00,0x00,0x00,
+0x1a,0x01,0x00,0x00,0x0f,0x00,0x00,0x00,0x19,0x01,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x1c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfa,0x00,0x04,0x00,0x1a,0x01,0x00,0x00,0x1b,0x01,0x00,0x00,
+0x1c,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x1b,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x21,0x01,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x19,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x60,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x5c,0x00,0x00,0x00,
+0x21,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
+0x23,0x01,0x00,0x00,0x22,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x25,0x01,0x00,0x00,0x61,0x00,0x00,0x00,
+0x81,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x26,0x01,0x00,0x00,
+0x25,0x01,0x00,0x00,0x23,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
+0x61,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x1c,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x1c,0x01,0x00,0x00,
+0xe0,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
+0xaf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x13,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x13,0x01,0x00,0x00,0xc3,0x00,0x05,0x00,
+0x1a,0x00,0x00,0x00,0x29,0x01,0x00,0x00,0x4b,0x01,0x00,0x00,
+0x1b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x10,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x12,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,0xcf,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x2f,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x2f,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x4c,0x01,0x00,0x00,0x0f,0x00,0x00,0x00,0x12,0x01,0x00,0x00,
+0x44,0x01,0x00,0x00,0x30,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,
+0x27,0x00,0x00,0x00,0x37,0x01,0x00,0x00,0x4c,0x01,0x00,0x00,
+0x6c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x31,0x01,0x00,0x00,
+0x30,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x37,0x01,0x00,0x00,0x30,0x01,0x00,0x00,0x31,0x01,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x30,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x3b,0x01,0x00,0x00,0x13,0x00,0x00,0x00,
+0x6c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x3d,0x01,0x00,0x00,0x3b,0x01,0x00,0x00,0x4c,0x01,0x00,0x00,
+0x41,0x00,0x06,0x00,0x7c,0x00,0x00,0x00,0x3f,0x01,0x00,0x00,
+0x09,0x01,0x00,0x00,0x6a,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x40,0x01,0x00,0x00,
+0x3f,0x01,0x00,0x00,0x88,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
+0x41,0x01,0x00,0x00,0x40,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x3f,0x01,0x00,0x00,0x41,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x44,0x01,0x00,0x00,
+0x4c,0x01,0x00,0x00,0x59,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x2f,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x31,0x01,0x00,0x00,
+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+};
+const uint64_t soft_max_f32_len = 4916;
+
+unsigned char split_k_reduce_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x50,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x41,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
+0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x11,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x47,0x00,0x03,0x00,0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x2e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x2e,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x30,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x30,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x3f,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x3f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x3f,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x41,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0x41,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0x47,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x1e,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x16,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x14,0x00,0x02,0x00,0x19,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
+0x1e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x1e,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x2d,0x00,0x00,0x00,
+0x1e,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x2e,0x00,0x00,0x00,
+0x2d,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x2f,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x2f,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x1e,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x3e,0x00,0x00,0x00,
+0x1e,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x3f,0x00,0x00,0x00,
+0x3e,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x40,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0x40,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
+0x00,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x46,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,
+0x09,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
+0x46,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x36,0x00,0x05,0x00,
+0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,
+0xf7,0x00,0x03,0x00,0x48,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x49,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x16,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x15,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
+0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x1b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x48,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x1c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x23,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x23,0x00,0x00,0x00,
+0xf5,0x00,0x07,0x00,0x1e,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
+0x21,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
+0x4e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x3d,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
+0x16,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
+0x19,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x2b,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x25,0x00,0x00,0x00,
+0x24,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
+0x2c,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
+0xf8,0x00,0x02,0x00,0x24,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
+0x18,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x36,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x37,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
+0x30,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0x39,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
+0x3b,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x39,0x00,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
+0x4e,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
+0x23,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x25,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0x37,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
+0x41,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0x44,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0x48,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0x48,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t split_k_reduce_len = 1416;
+
+unsigned char sqr_f32_data[] = {
+0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
+0x9c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
+0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
+0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
+0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
+0xe4,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
+0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0x12,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
+0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
+0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
+0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
+0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xc7,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xd6,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xd7,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xd7,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xd7,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd9,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xd9,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xe1,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xe2,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
+0xe2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xe2,0x00,0x00,0x00,
+0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe4,0x00,0x00,0x00,
+0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
+0xe4,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x47,0x00,0x04,0x00,0xf3,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
+0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
+0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
+0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x1e,0x00,0x16,0x00,0x12,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
+0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
+0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
+0x08,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
+0x05,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0x6f,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
+0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0xad,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x15,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
+0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
+0x0e,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0xbe,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
+0xc5,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0xc6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0xc5,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0xc6,0x00,0x00,0x00,
+0xc7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0xc9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
+0xcf,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xd6,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xd7,0x00,0x00,0x00,
+0xd6,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xd8,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
+0xd8,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x20,0x00,0x04,0x00,0xde,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xe1,0x00,0x00,0x00,
+0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xe2,0x00,0x00,0x00,
+0xe1,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xe3,0x00,0x00,0x00,
+0x0c,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
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+0xcb,0x00,0x00,0x00,0x15,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
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+0x1e,0x01,0x00,0x00,0x06,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x25,0x01,0x00,0x00,0x22,0x01,0x00,0x00,
+0x09,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x2a,0x01,0x00,0x00,0x18,0x01,0x00,0x00,0x25,0x01,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2d,0x01,0x00,0x00,
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+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x39,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
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+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3b,0x01,0x00,0x00,
+0x0b,0x01,0x00,0x00,0x3a,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0x5a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x3e,0x01,0x00,0x00,0x3d,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
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+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x42,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x43,0x01,0x00,0x00,0x42,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x44,0x01,0x00,0x00,
+0x2d,0x01,0x00,0x00,0x43,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x45,0x01,0x00,0x00,0x40,0x01,0x00,0x00,
+0x44,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x47,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x48,0x01,0x00,0x00,
+0x47,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x49,0x01,0x00,0x00,0x37,0x01,0x00,0x00,0x48,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,
+0x45,0x01,0x00,0x00,0x49,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
+0xde,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
+0xcc,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x11,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
+0x14,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x54,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x55,0x01,0x00,0x00,0x54,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x56,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x57,0x01,0x00,0x00,0x56,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x58,0x01,0x00,0x00,
+0x55,0x01,0x00,0x00,0x57,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x59,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0x76,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x5a,0x01,0x00,0x00,0x59,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x5b,0x01,0x00,0x00,0x58,0x01,0x00,0x00,
+0x5a,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x5c,0x01,0x00,0x00,0xcb,0x00,0x00,0x00,0x5b,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x60,0x01,0x00,0x00,
+0x5c,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x63,0x01,0x00,0x00,0x60,0x01,0x00,0x00,
+0x57,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x66,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,
+0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x01,0x00,0x00,
+0xcb,0x00,0x00,0x00,0x66,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x6e,0x01,0x00,0x00,0x57,0x01,0x00,0x00,
+0x5a,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x6f,0x01,0x00,0x00,0x69,0x01,0x00,0x00,0x6e,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x73,0x01,0x00,0x00,
+0x6f,0x01,0x00,0x00,0x57,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x76,0x01,0x00,0x00,0x73,0x01,0x00,0x00,
+0x5a,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x7b,0x01,0x00,0x00,0x69,0x01,0x00,0x00,0x76,0x01,0x00,0x00,
+0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x01,0x00,0x00,
+0x7b,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x87,0x01,0x00,0x00,0x7e,0x01,0x00,0x00,
+0x5a,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x88,0x01,0x00,0x00,0x7b,0x01,0x00,0x00,0x87,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x8a,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x01,0x00,0x00,
+0x5c,0x01,0x00,0x00,0x8b,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
+0x17,0x00,0x00,0x00,0x8e,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
+0xb2,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
+0x8f,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x90,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,
+0x8f,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x91,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,0x90,0x01,0x00,0x00,
+0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x93,0x01,0x00,0x00,
+0x14,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
+0x06,0x00,0x00,0x00,0x94,0x01,0x00,0x00,0x93,0x01,0x00,0x00,
+0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,
+0x7e,0x01,0x00,0x00,0x94,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0x96,0x01,0x00,0x00,0x91,0x01,0x00,0x00,
+0x95,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
+0x98,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
+0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x99,0x01,0x00,0x00,
+0x98,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
+0x9a,0x01,0x00,0x00,0x88,0x01,0x00,0x00,0x99,0x01,0x00,0x00,
+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x01,0x00,0x00,
+0x96,0x01,0x00,0x00,0x9a,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
+0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
+0x9b,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
+0xef,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
+0x41,0x00,0x06,0x00,0xde,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
+0xe4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
+0x3e,0x00,0x03,0x00,0xf0,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
+0xf9,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
+0xf4,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
+
+};
+const uint64_t sqr_f32_len = 3252;
+
diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp
new file mode 100644
index 000000000..1736ab736
--- /dev/null
+++ b/ggml-vulkan.cpp
@@ -0,0 +1,6442 @@
+#include "ggml-vulkan.h"
+
+#ifdef GGML_VULKAN_RUN_TESTS
+#include <chrono>
+#endif
+
+#include <vulkan/vulkan.hpp>
+
+#include <algorithm>
+#include <cmath>
+#include <iostream>
+#include <limits>
+#include <tuple>
+#include <vector>
+#include <sstream>
+#include <utility>
+#include <memory>
+
+#include "ggml.h"
+#include "ggml-backend-impl.h"
+
+#include "ggml-vulkan-shaders.hpp"
+
+#define VK_API_VERSION VK_API_VERSION_1_2
+
+#define CEIL_DIV(M, N) (((M) + (N)-1) / (N))
+
+#define VK_VENDOR_ID_AMD 0x1002
+#define VK_VENDOR_ID_APPLE 0x106b
+#define VK_VENDOR_ID_INTEL 0x8086
+#define VK_VENDOR_ID_NVIDIA 0x10de
+
+#define VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN 0
+#define VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI 1
+#define VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE 2
+
+#define VK_NUM_TYPES 16
+
+#define GGML_VK_MAX_NODES 8192
+
+#define MAX_VK_BUFFERS 256
+
+#ifndef K_QUANTS_PER_ITERATION
+#define K_QUANTS_PER_ITERATION 1
+#else
+static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
+#endif
+
+#define VK_CHECK(err, msg)                                          \
+    do {                                                            \
+        vk::Result err_ = (err);                                    \
+        if (err_ != vk::Result::eSuccess) {                         \
+            fprintf(stderr, "ggml_vulkan: %s error %s at %s:%d\n",  \
+                #err, to_string(err_).c_str(), __FILE__, __LINE__); \
+            exit(1);                                                \
+        }                                                           \
+    } while (0)
+
+struct ggml_backend_vk_context;
+
+struct vk_queue {
+    uint32_t queue_family_index;
+    vk::Queue queue;
+    vk::CommandPool pool;
+    uint32_t cmd_buffer_idx;
+    std::vector<vk::CommandBuffer> cmd_buffers;
+
+    vk::PipelineStageFlags stage_flags;
+};
+
+struct vk_pipeline_struct {
+    std::string name;
+    vk::ShaderModule shader_module;
+    vk::DescriptorSetLayout dsl;
+    std::vector<vk::DescriptorPool> descriptor_pools;
+    std::vector<vk::DescriptorSet> descriptor_sets;
+    uint32_t descriptor_set_idx;
+    vk::PipelineLayout layout;
+    vk::Pipeline pipeline;
+    uint32_t push_constant_size;
+    uint32_t parameter_count;
+    std::array<uint32_t, 3> wg_denoms;
+    uint32_t align;
+};
+
+typedef std::shared_ptr<vk_pipeline_struct> vk_pipeline;
+typedef std::weak_ptr<vk_pipeline_struct> vk_pipeline_ref;
+
+static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline);
+
+struct vk_matmul_pipeline_struct {
+    vk_pipeline l, m, s;
+    vk_pipeline a_l, a_m, a_s;
+};
+
+typedef std::shared_ptr<vk_matmul_pipeline_struct> vk_matmul_pipeline;
+
+struct vk_device {
+    vk::PhysicalDevice physical_device;
+    vk::PhysicalDeviceProperties properties;
+    std::string name;
+    uint64_t max_memory_allocation_size;
+    bool fp16;
+    vk::Device device;
+    uint32_t vendor_id;
+    vk_queue compute_queue;
+    vk_queue transfer_queue;
+    bool single_queue;
+    uint32_t descriptor_set_mode;
+    uint32_t subgroup_size;
+    bool uma;
+
+    bool initialized;
+    size_t idx;
+
+    vk_matmul_pipeline pipeline_matmul_f32;
+    vk_matmul_pipeline pipeline_matmul_f16;
+    vk_matmul_pipeline pipeline_matmul_f16_f32;
+    vk_pipeline pipeline_matmul_split_k_reduce;
+
+    vk_matmul_pipeline pipeline_dequant_mul_mat_mat[VK_NUM_TYPES];
+
+    vk_pipeline pipeline_dequant[VK_NUM_TYPES];
+    vk_pipeline pipeline_dequant_mul_mat_vec_f32[VK_NUM_TYPES];
+
+    vk_pipeline pipeline_mul_mat_vec_p021_f16_f32;
+    vk_pipeline pipeline_mul_mat_vec_nc_f16_f32;
+    vk_pipeline pipeline_get_rows[VK_NUM_TYPES];
+    vk_pipeline pipeline_get_rows_f32[VK_NUM_TYPES];
+    vk_pipeline pipeline_mul_f32;
+    vk_pipeline pipeline_add_f32;
+    vk_pipeline pipeline_scale_f32;
+    vk_pipeline pipeline_sqr_f32;
+    vk_pipeline pipeline_clamp_f32;
+    vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16;
+    vk_pipeline pipeline_norm_f32;
+    vk_pipeline pipeline_rms_norm_f32;
+    vk_pipeline pipeline_gelu_f32;
+    vk_pipeline pipeline_silu_f32;
+    vk_pipeline pipeline_relu_f32;
+    vk_pipeline pipeline_diag_mask_inf_f32;
+    vk_pipeline pipeline_soft_max_f32;
+    vk_pipeline pipeline_rope_f32, pipeline_rope_f16;
+    vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16;
+    vk_pipeline pipeline_argsort_f32;
+
+    std::vector<vk_pipeline_ref> pipelines;
+
+    ~vk_device() {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "destroy device " << name << std::endl;
+#endif
+        device.destroyCommandPool(compute_queue.pool);
+        if (!single_queue) {
+            device.destroyCommandPool(transfer_queue.pool);
+        }
+
+        for (auto& pipeline : pipelines) {
+            if (pipeline.expired()) {
+                continue;
+            }
+
+            vk_pipeline pl = pipeline.lock();
+            ggml_vk_destroy_pipeline(device, pl);
+        }
+        pipelines.clear();
+
+        device.destroy();
+    }
+};
+
+struct vk_buffer_struct {
+    vk::Buffer buffer;
+    vk::DeviceMemory device_memory;
+    vk::MemoryPropertyFlags memory_property_flags;
+    void * ptr;
+    size_t size = 0;
+
+    ggml_backend_vk_context * ctx;
+
+    std::shared_ptr<vk_device> device;
+
+    ~vk_buffer_struct() {
+        if (size == 0) {
+            return;
+        }
+#ifdef GGML_VULKAN_DEBUG
+        std::cerr << "~vk_buffer_struct(" << buffer << ", " << size << ")" << std::endl;
+#endif
+
+        device->device.freeMemory(device_memory);
+        device->device.destroyBuffer(buffer);
+    }
+};
+
+typedef std::shared_ptr<vk_buffer_struct> vk_buffer;
+typedef std::weak_ptr<vk_buffer_struct> vk_buffer_ref;
+
+struct vk_subbuffer {
+    vk_buffer buffer;
+    uint64_t offset;
+    uint64_t size;
+};
+
+struct vk_semaphore {
+    vk::Semaphore s;
+    uint64_t value;
+};
+
+struct vk_submission {
+    vk::CommandBuffer buffer;
+    std::vector<vk_semaphore> wait_semaphores;
+    std::vector<vk_semaphore> signal_semaphores;
+};
+
+typedef std::vector<vk_submission> vk_sequence;
+
+struct vk_op_push_constants {
+    uint32_t KX;
+    uint32_t KY;
+    float param1;
+    float param2;
+};
+
+struct vk_op_unary_push_constants {
+    uint32_t ne;
+    uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
+    uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
+    uint32_t d_offset;
+    float param1; float param2;
+};
+
+struct vk_op_binary_push_constants {
+    uint32_t ne;
+    uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
+    uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
+    uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23; uint32_t nb20; uint32_t nb21; uint32_t nb22; uint32_t nb23;
+    uint32_t d_offset;
+    float param1; float param2;
+};
+
+struct vk_op_diag_mask_push_constants {
+    uint32_t ncols;
+    uint32_t rows_per_channel;
+    int32_t n_past;
+};
+
+struct vk_op_rope_push_constants {
+    uint32_t ncols;
+    float freq_scale;
+    uint32_t p_delta_rows;
+    float freq_base;
+    float ext_factor;
+    float attn_factor;
+    float corr_dims[4];
+};
+
+struct vk_op_rope_neox_push_constants {
+    uint32_t ncols;
+    uint32_t ndims;
+    float freq_scale;
+    uint32_t p_delta_rows;
+    float freq_base;
+    float ext_factor;
+    float attn_factor;
+    float corr_dims[4];
+    float theta_scale;
+    float inv_ndims;
+};
+
+struct vk_op_soft_max_push_constants {
+    uint32_t KX;
+    uint32_t KY;
+    uint32_t KZ;
+    float scale;
+    float max_bias;
+    float m0;
+    float m1;
+    uint32_t n_head_log2;
+};
+
+struct vk_op_argsort_push_constants {
+    uint32_t ncols;
+    bool ascending;
+};
+
+// Allow pre-recording command buffers
+struct vk_staging_memcpy {
+    vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {}
+
+    void * dst;
+    const void * src;
+    size_t n;
+};
+
+struct vk_context {
+    size_t idx;
+
+    vk_submission * s;
+    std::vector<vk_sequence> seqs;
+
+    ggml_tensor * exit_tensor;
+
+    std::vector<vk_staging_memcpy> in_memcpys;
+    std::vector<vk_staging_memcpy> out_memcpys;
+
+    vk_queue * q;
+};
+
+struct ggml_tensor_extra_gpu {
+    bool ready;
+
+    size_t ctx_idx;
+
+    vk_buffer_ref buffer_gpu;
+    uint64_t offset;
+
+    void reset() {
+        ready = false;
+        ctx_idx = 0;
+        buffer_gpu.reset();
+        offset = 0;
+    }
+};
+
+struct ggml_vk_garbage_collector {
+    std::vector<vk_semaphore> tl_semaphores;
+    std::vector<vk_semaphore> semaphores;
+    std::vector<vk::Event> events;
+    std::vector<vk_buffer> temp_buffers;
+    std::vector<vk_context> contexts;
+};
+
+struct ggml_backend_vk_context {
+    std::string name;
+
+    std::shared_ptr<vk_device> device;
+
+    size_t semaphore_idx, event_idx;
+    ggml_vk_garbage_collector gc;
+    std::vector<std::tuple<void*, size_t, vk_buffer>> pinned_memory;
+    size_t prealloc_size_x, prealloc_size_y, prealloc_size_split_k;
+    vk_buffer prealloc_x, prealloc_y, prealloc_split_k;
+    vk::Fence fence;
+    vk_buffer staging;
+    size_t staging_size;
+    size_t staging_offset;
+    vk_buffer sync_staging;
+
+    vk_buffer buffer_pool[MAX_VK_BUFFERS];
+
+    vk_context * compute_ctx;
+    vk_context * transfer_ctx;
+
+    bool disable;
+    bool initialized;
+
+    size_t idx;
+};
+
+struct vk_instance {
+    vk::Instance instance;
+
+    std::vector<size_t> device_indices;
+
+    ggml_backend_t backends[GGML_VK_MAX_DEVICES];
+    ggml_backend_vk_context contexts[GGML_VK_MAX_DEVICES];
+    ggml_backend_buffer_type buffer_types[GGML_VK_MAX_DEVICES];
+    bool initialized[GGML_VK_MAX_DEVICES];
+};
+
+static std::shared_ptr<vk_device> ggml_vk_get_device(size_t idx) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_get_device(" << idx << ")" << std::endl;
+#endif
+    static std::weak_ptr<vk_device> devices[GGML_VK_MAX_DEVICES];
+
+    if (devices[idx].expired()) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "Initializing new vk_device" << std::endl;
+#endif
+        std::shared_ptr<vk_device> device = std::make_shared<vk_device>();
+        device->initialized = false;
+        devices[idx] = device;
+        return device;
+    }
+
+    return devices[idx].lock();
+}
+
+#ifdef GGML_VULKAN_CHECK_RESULTS
+static size_t vk_skip_checks;
+static size_t vk_output_tensor;
+
+static void ggml_vk_print_tensor(ggml_backend * ctx, const ggml_tensor * tensor, const char * name);
+static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor);
+static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor);
+#endif
+
+typedef void (*ggml_vk_func_t)(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
+
+static bool vk_instance_initialized = false;
+static vk_instance vk_instance;
+
+GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend);
+
+static void ggml_vk_create_pipeline(ggml_backend_vk_context * ctx, vk_pipeline& pipeline, const std::string& name, size_t spv_size, const void* spv_data, const std::string& entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, std::vector<uint32_t>&& specialization_constants, uint32_t align) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_create_pipeline(" << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")" << std::endl;
+#endif
+    GGML_ASSERT(parameter_count > 0);
+    GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT
+
+    pipeline = std::make_shared<vk_pipeline_struct>();
+    pipeline->name = name;
+    pipeline->parameter_count = parameter_count;
+    pipeline->push_constant_size = push_constant_size;
+    pipeline->wg_denoms = wg_denoms;
+    pipeline->align = align;
+
+    vk::ShaderModuleCreateInfo shader_module_create_info({}, spv_size, reinterpret_cast<const uint32_t *>(spv_data));
+    pipeline->shader_module = ctx->device->device.createShaderModule(shader_module_create_info);
+
+    std::vector<vk::DescriptorSetLayoutBinding> dsl_binding;
+    std::vector<vk::DescriptorBindingFlags> dsl_binding_flags;
+    for (uint32_t i = 0; i < parameter_count; i++) {
+        dsl_binding.push_back({i, vk::DescriptorType::eStorageBuffer, 1, vk::ShaderStageFlagBits::eCompute});
+        dsl_binding_flags.push_back({});
+    }
+
+    vk::DescriptorSetLayoutBindingFlagsCreateInfo dslbfci = { dsl_binding_flags };
+
+    vk::PushConstantRange pcr(
+        vk::ShaderStageFlagBits::eCompute,
+        0,
+        pipeline->push_constant_size
+    );
+
+    vk::DescriptorSetLayoutCreateInfo descriptor_set_layout_create_info(
+        {},
+        dsl_binding);
+    descriptor_set_layout_create_info.setPNext(&dslbfci);
+    pipeline->dsl = ctx->device->device.createDescriptorSetLayout(descriptor_set_layout_create_info);
+
+    // Check if device supports multiple descriptors per pool
+    if (ctx->device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN) {
+        const uint32_t alloc_count = 2;
+
+        // Try allocating multiple sets from one pool
+        // This fails on AMD for some reason, so add a fall back to allocating one pool per set
+        vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count);
+        vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, alloc_count, descriptor_pool_size);
+        vk::DescriptorPool pool = ctx->device->device.createDescriptorPool(descriptor_pool_create_info);
+
+        std::vector<vk::DescriptorSetLayout> layouts(alloc_count);
+        for (uint32_t i = 0; i < alloc_count; i++) {
+            layouts[i] = pipeline->dsl;
+        }
+        try {
+            vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pool, alloc_count, layouts.data());
+            std::vector<vk::DescriptorSet> sets = ctx->device->device.allocateDescriptorSets(descriptor_set_alloc_info);
+        } catch(vk::OutOfPoolMemoryError const&) {
+            ctx->device->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE;
+        }
+
+        ctx->device->device.destroyDescriptorPool(pool);
+    }
+
+    if (ctx->device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) {
+        vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count);
+        vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 128, descriptor_pool_size);
+        pipeline->descriptor_pools.push_back(ctx->device->device.createDescriptorPool(descriptor_pool_create_info));
+    }
+
+    pipeline->descriptor_set_idx = 0;
+
+    vk::PipelineLayoutCreateInfo pipeline_layout_create_info(vk::PipelineLayoutCreateFlags(), pipeline->dsl, pcr);
+    pipeline->layout = ctx->device->device.createPipelineLayout(pipeline_layout_create_info);
+
+    std::vector<vk::SpecializationMapEntry> specialization_entries(specialization_constants.size());
+
+    for (size_t i = 0; i < specialization_constants.size(); i++) {
+        specialization_entries[i].constantID = i;
+        specialization_entries[i].offset = i * sizeof(uint32_t);
+        specialization_entries[i].size = sizeof(uint32_t);
+    }
+
+    vk::SpecializationInfo specialization_info(
+        specialization_entries.size(),
+        specialization_entries.data(),
+        specialization_constants.size() * sizeof(uint32_t),
+        specialization_constants.data()
+    );
+
+    vk::PipelineShaderStageCreateInfo pipeline_shader_create_info(
+            vk::PipelineShaderStageCreateFlags(),
+            vk::ShaderStageFlagBits::eCompute,
+            pipeline->shader_module,
+            entrypoint.c_str(),
+            &specialization_info);
+    vk::ComputePipelineCreateInfo compute_pipeline_create_info(
+        vk::PipelineCreateFlags(),
+        pipeline_shader_create_info,
+        pipeline->layout);
+    pipeline->pipeline = ctx->device->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value;
+
+    ctx->device->pipelines.push_back(pipeline);
+}
+
+static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_pipeline_destroy_pipeline(" << pipeline->name << ")" << std::endl;
+#endif
+    for (auto& pool : pipeline->descriptor_pools) {
+        device.destroyDescriptorPool(pool);
+    }
+    pipeline->descriptor_pools.clear();
+    pipeline->descriptor_sets.clear();
+    pipeline->descriptor_set_idx = 0;
+
+    device.destroyDescriptorSetLayout(pipeline->dsl);
+
+    device.destroyPipelineLayout(pipeline->layout);
+
+    device.destroyShaderModule(pipeline->shader_module);
+
+    device.destroyPipeline(pipeline->pipeline);
+}
+
+static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx, vk_pipeline& pipeline, uint32_t n) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_pipeline_allocate_descriptor_sets(" << pipeline->name << ", " << n << ")" << std::endl;
+#endif
+    if (pipeline->descriptor_sets.size() >= pipeline->descriptor_set_idx + n) {
+        // Enough descriptors are available
+        return;
+    }
+
+    if (ctx->device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) {
+        const uint32_t alloc_count = pipeline->descriptor_set_idx + n - pipeline->descriptor_sets.size();
+
+        std::vector<vk::DescriptorSetLayout> layouts(alloc_count);
+        for (uint32_t i = 0; i < alloc_count; i++) {
+            layouts[i] = pipeline->dsl;
+        }
+        vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline->descriptor_pools[0], alloc_count, layouts.data());
+        std::vector<vk::DescriptorSet> sets = ctx->device->device.allocateDescriptorSets(descriptor_set_alloc_info);
+        pipeline->descriptor_sets.insert(pipeline->descriptor_sets.end(), sets.begin(), sets.end());
+    } else {
+        for (uint32_t i = pipeline->descriptor_sets.size(); i < pipeline->descriptor_set_idx + n; i++) {
+            vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count);
+            vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 1, descriptor_pool_size);
+            pipeline->descriptor_pools.push_back(ctx->device->device.createDescriptorPool(descriptor_pool_create_info));
+
+            vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline->descriptor_pools[i], 1, &pipeline->dsl);
+            std::vector<vk::DescriptorSet> sets = ctx->device->device.allocateDescriptorSets(descriptor_set_alloc_info);
+            pipeline->descriptor_sets.push_back(sets[0]);
+        }
+    }
+}
+
+static void ggml_pipeline_cleanup(vk_pipeline& pipeline) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_pipeline_cleanup(" << pipeline->name << ")" << std::endl;
+#endif
+    pipeline->descriptor_set_idx = 0;
+}
+
+static vk::CommandBuffer ggml_vk_create_cmd_buffer(ggml_backend_vk_context * ctx, vk_queue& q) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_create_cmd_buffer()" << std::endl;
+#endif
+    if (q.cmd_buffers.size() > q.cmd_buffer_idx) {
+        // Reuse command buffer
+        return q.cmd_buffers[q.cmd_buffer_idx++];
+    }
+
+    vk::CommandBufferAllocateInfo command_buffer_alloc_info(
+        q.pool,
+        vk::CommandBufferLevel::ePrimary,
+        1);
+    const std::vector<vk::CommandBuffer> cmd_buffers = ctx->device->device.allocateCommandBuffers(command_buffer_alloc_info);
+    auto buf = cmd_buffers.front();
+
+    q.cmd_buffers.push_back(buf);
+    q.cmd_buffer_idx++;
+
+    return buf;
+}
+
+static vk_submission ggml_vk_create_submission(ggml_backend_vk_context * ctx, vk_queue& q, std::vector<vk_semaphore> wait_semaphores, std::vector<vk_semaphore> signal_semaphores) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_create_submission()" << std::endl;
+#endif
+    vk_submission s;
+    s.buffer = ggml_vk_create_cmd_buffer(ctx, q);
+    s.wait_semaphores = std::move(wait_semaphores);
+    s.signal_semaphores = std::move(signal_semaphores);
+    return s;
+}
+
+static void ggml_vk_submit(vk_context * ctx, vk::Fence fence) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_submit(" << ctx->seqs.size() << ", " << fence << ")" << std::endl;
+#endif
+    if (ctx->seqs.empty()) {
+        return;
+    }
+
+    std::vector<std::vector<uint64_t>> tl_wait_vals;
+    std::vector<std::vector<uint64_t>> tl_signal_vals;
+    std::vector<std::vector<vk::Semaphore>> tl_wait_semaphores;
+    std::vector<std::vector<vk::Semaphore>> tl_signal_semaphores;
+    std::vector<vk::TimelineSemaphoreSubmitInfo> tl_submit_infos;
+    std::vector<vk::SubmitInfo> submit_infos;
+    int idx = -1;
+    std::vector<std::vector<vk::PipelineStageFlags>> stage_flags;
+
+    size_t reserve = 0;
+
+    for (const auto& sequence : ctx->seqs) {
+        reserve += sequence.size();
+    }
+
+    // Pre-reserve vectors to prevent reallocation, which invalidates pointers
+    tl_wait_semaphores.reserve(reserve);
+    tl_wait_vals.reserve(reserve);
+    tl_signal_semaphores.reserve(reserve);
+    tl_signal_vals.reserve(reserve);
+    tl_submit_infos.reserve(reserve);
+    submit_infos.reserve(reserve);
+    stage_flags.reserve(reserve);
+
+    for (const auto& sequence : ctx->seqs) {
+        for (const auto& submission : sequence) {
+            stage_flags.push_back({});
+            idx++;
+            tl_wait_vals.push_back({});
+            tl_wait_semaphores.push_back({});
+            tl_signal_vals.push_back({});
+            tl_signal_semaphores.push_back({});
+            for (size_t i = 0; i < submission.wait_semaphores.size(); i++) {
+                stage_flags[idx].push_back(ctx->q->stage_flags);
+                tl_wait_vals[idx].push_back(submission.wait_semaphores[i].value);
+                tl_wait_semaphores[idx].push_back(submission.wait_semaphores[i].s);
+            }
+            for (size_t i = 0; i < submission.signal_semaphores.size(); i++) {
+                tl_signal_vals[idx].push_back(submission.signal_semaphores[i].value);
+                tl_signal_semaphores[idx].push_back(submission.signal_semaphores[i].s);
+            }
+            tl_submit_infos.push_back({
+                (uint32_t) submission.wait_semaphores.size(),
+                tl_wait_vals[idx].data(),
+                (uint32_t) submission.signal_semaphores.size(),
+                tl_signal_vals[idx].data(),
+            });
+            tl_submit_infos[idx].sType = vk::StructureType::eTimelineSemaphoreSubmitInfo;
+            tl_submit_infos[idx].pNext = nullptr;
+            vk::SubmitInfo si{
+                (uint32_t) submission.wait_semaphores.size(),
+                tl_wait_semaphores[idx].data(),
+                stage_flags[idx].data(),
+                1,
+                &submission.buffer,
+                (uint32_t) submission.signal_semaphores.size(),
+                tl_signal_semaphores[idx].data(),
+            };
+            si.setPNext(&tl_submit_infos[idx]);
+            submit_infos.push_back(si);
+        }
+    }
+
+    ctx->q->queue.submit(submit_infos, fence);
+
+    ctx->seqs.clear();
+}
+
+static uint32_t ggml_vk_find_queue_family_index(std::vector<vk::QueueFamilyProperties>& queue_family_props, const vk::QueueFlags& required, const vk::QueueFlags& avoid, int32_t compute_index, uint32_t min_num_queues) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_find_queue_family_index()" << std::endl;
+#endif
+    const uint32_t qfsize = queue_family_props.size();
+
+    // Try with avoid preferences first
+    for (uint32_t i = 0; i < qfsize; i++) {
+        if (queue_family_props[i].queueCount >= min_num_queues && (compute_index < 0 || i != (uint32_t) compute_index) && queue_family_props[i].queueFlags & required && !(queue_family_props[i].queueFlags & avoid)) {
+            return i;
+        }
+    }
+
+    // Fall back to only required
+    for (size_t i = 0; i < qfsize; i++) {
+        if (queue_family_props[i].queueCount >= min_num_queues && (compute_index < 0 || i != (uint32_t) compute_index) && queue_family_props[i].queueFlags & required) {
+            return i;
+        }
+    }
+
+    // Fall back to reusing compute queue
+    for (size_t i = 0; i < qfsize; i++) {
+        if (queue_family_props[i].queueCount >= min_num_queues && queue_family_props[i].queueFlags & required) {
+            return i;
+        }
+    }
+
+    // Fall back to ignoring min_num_queries
+    for (size_t i = 0; i < qfsize; i++) {
+        if (queue_family_props[i].queueFlags & required) {
+            return i;
+        }
+    }
+
+    // All commands that are allowed on a queue that supports transfer operations are also allowed on a queue that supports either graphics or compute operations.
+    // Thus, if the capabilities of a queue family include VK_QUEUE_GRAPHICS_BIT or VK_QUEUE_COMPUTE_BIT, then reporting the VK_QUEUE_TRANSFER_BIT capability separately for that queue family is optional.
+    if (compute_index >= 0) {
+        return compute_index;
+    }
+
+    std::cerr << "ggml_vulkan: No suitable queue family index found." << std::endl;
+
+    for(auto &q_family : queue_family_props) {
+        std::cerr << "Queue number: "  + std::to_string(q_family.queueCount) << " flags: " + to_string(q_family.queueFlags) << std::endl;
+    }
+    abort();
+}
+
+static void ggml_vk_create_queue(ggml_backend_vk_context * ctx, vk_queue& q, uint32_t queue_family_index, uint32_t queue_index, vk::PipelineStageFlags&& stage_flags) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_create_queue()" << std::endl;
+#endif
+    q.queue_family_index = queue_family_index;
+
+    vk::CommandPoolCreateInfo command_pool_create_info_compute(vk::CommandPoolCreateFlags(VK_COMMAND_POOL_CREATE_TRANSIENT_BIT), queue_family_index);
+    q.pool = ctx->device->device.createCommandPool(command_pool_create_info_compute);
+
+    q.cmd_buffer_idx = 0;
+
+    q.queue = ctx->device->device.getQueue(queue_family_index, queue_index);
+
+    q.stage_flags = stage_flags;
+}
+
+static vk_context * ggml_vk_create_context(ggml_backend_vk_context * ctx, vk_queue& q) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_create_context()" << std::endl;
+#endif
+    ctx->gc.contexts.emplace_back();
+    vk_context * result = &ctx->gc.contexts[ctx->gc.contexts.size() - 1];
+    memset((void *) result, 0, sizeof(vk_context));
+    result->idx = ctx->gc.contexts.size() - 1;
+    result->q = &q;
+    return result;
+}
+
+static vk_semaphore * ggml_vk_create_binary_semaphore(ggml_backend_vk_context * ctx) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_create_timeline_semaphore()" << std::endl;
+#endif
+    vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eBinary, 0 };
+    vk::SemaphoreCreateInfo ci{};
+    ci.setPNext(&tci);
+    vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci);
+    ctx->gc.semaphores.push_back({ semaphore, 0 });
+    return &ctx->gc.semaphores[ctx->gc.semaphores.size() - 1];
+}
+
+static vk_semaphore * ggml_vk_create_timeline_semaphore(ggml_backend_vk_context * ctx) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_create_timeline_semaphore()" << std::endl;
+#endif
+    if (ctx->semaphore_idx >= ctx->gc.tl_semaphores.size()) {
+        vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 };
+        vk::SemaphoreCreateInfo ci{};
+        ci.setPNext(&tci);
+        vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci);
+        ctx->gc.tl_semaphores.push_back({ semaphore, 0 });
+    }
+    return &ctx->gc.tl_semaphores[ctx->semaphore_idx++];
+}
+
+static vk::Event ggml_vk_create_event(ggml_backend_vk_context * ctx) {
+    if (ctx->event_idx >= ctx->gc.events.size()) {
+        ctx->gc.events.push_back(ctx->device->device.createEvent({}));
+    }
+    return ctx->gc.events[ctx->event_idx++];
+}
+
+static void ggml_vk_queue_cleanup(ggml_backend_vk_context * ctx, vk_queue& q) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_queue_cleanup()" << std::endl;
+#endif
+    // Requires command buffers to be done
+
+    ctx->device->device.resetCommandPool(q.pool);
+    q.cmd_buffer_idx = 0;
+}
+
+static uint32_t find_properties(const vk::PhysicalDeviceMemoryProperties* mem_props, vk::MemoryRequirements* mem_req, vk::MemoryPropertyFlags flags) {
+    for (uint32_t i = 0; i < mem_props->memoryTypeCount; ++i) {
+        vk::MemoryType memory_type = mem_props->memoryTypes[i];
+        if ((mem_req->memoryTypeBits & ((uint64_t)1 << i)) &&
+            (flags & memory_type.propertyFlags) == flags &&
+            mem_props->memoryHeaps[memory_type.heapIndex].size >= mem_req->size) {
+            return static_cast<int32_t>(i);
+        }
+    }
+    return UINT32_MAX;
+}
+
+static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_create_buffer(device " << ctx->idx << ", " << size << ", " << to_string(req_flags) << ", " << to_string(fallback_flags) << ")" << std::endl;
+#endif
+    vk_buffer buf = std::make_shared<vk_buffer_struct>();
+
+    if (size == 0) {
+        buf->size = 0;
+        return buf;
+    }
+
+    buf->size = size;
+    vk::BufferCreateInfo buffer_create_info{
+        vk::BufferCreateFlags(),
+        size,
+        vk::BufferUsageFlagBits::eStorageBuffer | vk::BufferUsageFlagBits::eTransferSrc | vk::BufferUsageFlagBits::eTransferDst,
+        vk::SharingMode::eExclusive,
+        0,
+        nullptr,
+    };
+
+    buf->buffer = ctx->device->device.createBuffer(buffer_create_info);
+
+    vk::MemoryRequirements mem_req = ctx->device->device.getBufferMemoryRequirements(buf->buffer);
+
+    vk::PhysicalDeviceMemoryProperties mem_props = ctx->device->physical_device.getMemoryProperties();
+
+    uint32_t memory_type_index = UINT32_MAX;
+
+    memory_type_index = find_properties(&mem_props, &mem_req, req_flags);
+    buf->memory_property_flags = req_flags;
+
+    if (memory_type_index == UINT32_MAX && fallback_flags) {
+        memory_type_index = find_properties(&mem_props, &mem_req, fallback_flags);
+        buf->memory_property_flags = fallback_flags;
+    }
+
+    if (memory_type_index == UINT32_MAX) {
+        ctx->device->device.destroyBuffer(buf->buffer);
+        buf->size = 0;
+        throw vk::OutOfDeviceMemoryError("No suitable memory type found");
+    }
+
+    try {
+        buf->device_memory = ctx->device->device.allocateMemory({ mem_req.size, memory_type_index });
+    } catch (const vk::SystemError& e) {
+        // Out of Host/Device memory, clean up buffer
+        ctx->device->device.destroyBuffer(buf->buffer);
+        buf->size = 0;
+        throw e;
+    }
+    buf->ptr = nullptr;
+
+    if (buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
+        buf->ptr = ctx->device->device.mapMemory(buf->device_memory, 0, VK_WHOLE_SIZE);
+    }
+
+    ctx->device->device.bindBufferMemory(buf->buffer, buf->device_memory, 0);
+
+    buf->ctx = ctx;
+
+    buf->device = ctx->device;
+
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "Created buffer " << buf->buffer << std::endl;
+#endif
+
+    return buf;
+}
+
+static vk_buffer ggml_vk_create_buffer_check(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) {
+    try {
+        return ggml_vk_create_buffer(ctx, size, req_flags, fallback_flags);
+    } catch (const vk::SystemError& e) {
+        std::cerr << "ggml_vulkan: Memory allocation of size " << size << " failed." << std::endl;
+        std::cerr << "ggml_vulkan: " << e.what() << std::endl;
+        throw e;
+    }
+}
+
+static vk_buffer ggml_vk_create_buffer_device(ggml_backend_vk_context * ctx, size_t size) {
+    vk_buffer buf;
+    try {
+        if (ctx->device->uma) {
+            // Fall back to host memory type
+            buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
+        } else {
+            buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal);
+        }
+    } catch (const vk::SystemError& e) {
+        std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl;
+        std::cerr << "ggml_vulkan: " << e.what() << std::endl;
+        throw e;
+    }
+
+    return buf;
+}
+
+static void ggml_vk_destroy_buffer(vk_buffer& buf) {
+    buf.reset();
+}
+
+static vk_subbuffer ggml_vk_subbuffer(vk_buffer& buf) {
+    return { buf, 0, VK_WHOLE_SIZE };
+}
+
+static void ggml_vk_sync_buffers(vk_context * ctx) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_sync_buffers()" << std::endl;
+#endif
+    const std::vector<vk::MemoryBarrier> mem_barriers{ { { vk::AccessFlagBits::eMemoryRead | vk::AccessFlagBits::eMemoryWrite }, { vk::AccessFlagBits::eMemoryRead | vk::AccessFlagBits::eMemoryWrite } } };
+
+    ctx->s->buffer.pipelineBarrier(
+        ctx->q->stage_flags,
+        ctx->q->stage_flags,
+        {},
+        mem_barriers,
+        {},
+        {}
+    );
+}
+
+static void ggml_vk_wait_events(vk_context * ctx, std::vector<vk::Event>&& events) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_wait_events()" << std::endl;
+#endif
+    if (events.empty()) {
+        return;
+    }
+
+    ctx->s->buffer.waitEvents(
+        events,
+        ctx->q->stage_flags,
+        ctx->q->stage_flags,
+        {},
+        {},
+        {}
+    );
+}
+
+static bool ggml_vk_build_shader(ggml_type type) {
+    switch(type) {
+    case GGML_TYPE_F16:
+    case GGML_TYPE_Q4_0:
+    case GGML_TYPE_Q4_1:
+    case GGML_TYPE_Q5_0:
+    case GGML_TYPE_Q5_1:
+    case GGML_TYPE_Q8_0:
+    case GGML_TYPE_Q2_K:
+    case GGML_TYPE_Q3_K:
+    case GGML_TYPE_Q4_K:
+    case GGML_TYPE_Q5_K:
+    case GGML_TYPE_Q6_K:
+        return true;
+    default:
+        return false;
+    }
+}
+
+static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_load_shaders(" << ctx->name << ")" << std::endl;
+#endif
+
+    const std::shared_ptr<vk_device> device = ctx->device;
+
+    // mulmat
+    std::initializer_list<uint32_t> warptile_l = { 128, 128, 128, 16, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size };
+    std::initializer_list<uint32_t> warptile_m = { 128,  64,  64, 16, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size };
+    std::initializer_list<uint32_t> warptile_s = { device->subgroup_size,  32,  32, 16, 32, 32, 2, 2, 2, device->subgroup_size };
+
+    std::initializer_list<uint32_t> warptile_mmq_l = { 128, 128, 128, 32, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size };
+    std::initializer_list<uint32_t> warptile_mmq_m = { 128,  64,  64, 32, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size };
+    std::initializer_list<uint32_t> warptile_mmq_s = { device->subgroup_size,  32,  32, 32, 32, 32, 2, 2, 2, device->subgroup_size };
+
+    std::array<uint32_t, 3> l_wg_denoms = {128, 128, 1 };
+    std::array<uint32_t, 3> m_wg_denoms = { 64,  64, 1 };
+    std::array<uint32_t, 3> s_wg_denoms = { 32,  32, 1 };
+
+    uint32_t l_align = 128;
+    uint32_t m_align =  64;
+    uint32_t s_align =  32;
+
+    ctx->device->pipeline_matmul_f32 = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_matmul_f16_f32 = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_matmul_f16 = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K] = std::make_shared<vk_matmul_pipeline_struct>();
+
+    if (device->fp16) {
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_len, matmul_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_len, matmul_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_len, matmul_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_len, matmul_f16_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_len, matmul_f16_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_len, matmul_f16_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_0_f32_l", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_0_f32_m", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_0_f32_s", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->l, "matmul_q2_k_f32_l", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->m, "matmul_q2_k_f32_m", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->s, "matmul_q2_k_f32_s", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_l, "matmul_q2_k_f32_aligned_l", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_m, "matmul_q2_k_f32_aligned_m", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_s, "matmul_q2_k_f32_aligned_s", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->l, "matmul_q3_k_f32_l", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->m, "matmul_q3_k_f32_m", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->s, "matmul_q3_k_f32_s", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_l, "matmul_q3_k_f32_aligned_l", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_m, "matmul_q3_k_f32_aligned_m", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_s, "matmul_q3_k_f32_aligned_s", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->l, "matmul_q4_k_f32_l", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->m, "matmul_q4_k_f32_m", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->s, "matmul_q4_k_f32_s", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_l, "matmul_q4_k_f32_aligned_l", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_m, "matmul_q4_k_f32_aligned_m", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_s, "matmul_q4_k_f32_aligned_s", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->l, "matmul_q5_k_f32_l", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->m, "matmul_q5_k_f32_m", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->s, "matmul_q5_k_f32_s", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_l, "matmul_q5_k_f32_aligned_l", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_m, "matmul_q5_k_f32_aligned_m", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_s, "matmul_q5_k_f32_aligned_s", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->l, "matmul_q6_k_f32_l", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->m, "matmul_q6_k_f32_m", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->s, "matmul_q6_k_f32_s", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_l, "matmul_q6_k_f32_aligned_l", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+    } else {
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_1_f32_l", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_1_f32_m", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_1_f32_s", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_1_f32_aligned_l", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_1_f32_aligned_m", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_1_f32_aligned_s", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->l, "matmul_q2_k_f32_l", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->m, "matmul_q2_k_f32_m", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->s, "matmul_q2_k_f32_s", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_l, "matmul_q2_k_f32_aligned_l", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_m, "matmul_q2_k_f32_aligned_m", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_s, "matmul_q2_k_f32_aligned_s", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->l, "matmul_q3_k_f32_l", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->m, "matmul_q3_k_f32_m", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->s, "matmul_q3_k_f32_s", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_l, "matmul_q3_k_f32_aligned_l", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_m, "matmul_q3_k_f32_aligned_m", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_s, "matmul_q3_k_f32_aligned_s", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->l, "matmul_q4_k_f32_l", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->m, "matmul_q4_k_f32_m", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->s, "matmul_q4_k_f32_s", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_l, "matmul_q4_k_f32_aligned_l", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_m, "matmul_q4_k_f32_aligned_m", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_s, "matmul_q4_k_f32_aligned_s", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->l, "matmul_q5_k_f32_l", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->m, "matmul_q5_k_f32_m", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->s, "matmul_q5_k_f32_s", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_l, "matmul_q5_k_f32_aligned_l", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_m, "matmul_q5_k_f32_aligned_m", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_s, "matmul_q5_k_f32_aligned_s", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->l, "matmul_q6_k_f32_l", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->m, "matmul_q6_k_f32_m", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->s, "matmul_q6_k_f32_s", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_l, "matmul_q6_k_f32_aligned_l", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align);
+    }
+
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32",  mul_mat_vec_f16_f32_len,  mul_mat_vec_f16_f32_data,  "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32", mul_mat_vec_q4_0_f32_len, mul_mat_vec_q4_0_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32", mul_mat_vec_q4_1_f32_len, mul_mat_vec_q4_1_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32", mul_mat_vec_q5_0_f32_len, mul_mat_vec_q5_0_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32", mul_mat_vec_q5_1_f32_len, mul_mat_vec_q5_1_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32", mul_mat_vec_q8_0_f32_len, mul_mat_vec_q8_0_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_K_f32", mul_mat_vec_q2_K_f32_len, mul_mat_vec_q2_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_K_f32", mul_mat_vec_q3_K_f32_len, mul_mat_vec_q3_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_K_f32", mul_mat_vec_q4_K_f32_len, mul_mat_vec_q4_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_K_f32", mul_mat_vec_q5_K_f32_len, mul_mat_vec_q5_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_K_f32", mul_mat_vec_q6_K_f32_len, mul_mat_vec_q6_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1);
+
+    // dequant shaders
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16",   dequant_f32_len,  dequant_f32_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_0], "dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_1], "dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_0], "dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_1], "dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q8_0], "dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q2_K], "dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q3_K], "dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
+
+    // get_rows
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_F32 ], "get_rows_f32",  get_rows_f32_len,  get_rows_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_F16 ], "get_rows_f16",  get_rows_f16_len,  get_rows_f16_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q4_0], "get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q4_1], "get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q5_1], "get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_Q8_0], "get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f32_f32",  get_rows_f32_f32_len,  get_rows_f32_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_F16 ], "get_rows_f16_f32",  get_rows_f16_f32_len,  get_rows_f16_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q4_0], "get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q4_1], "get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q5_1], "get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, "mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_cpy_f32_f32, "cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_gelu_f32, "gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_silu_f32, "silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_relu_f32, "relu_f32", relu_f32_len, relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f32, "rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f16, "rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1);
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1);
+
+    ggml_vk_create_pipeline(ctx, ctx->device->pipeline_argsort_f32, "argsort_f32", argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1024, 1, 1}, {}, 1);
+}
+
+static void ggml_vk_print_gpu_info(size_t idx) {
+    GGML_ASSERT(idx < vk_instance.device_indices.size());
+    size_t dev_num = vk_instance.device_indices[idx];
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_print_gpu_info(" << dev_num << ")" << std::endl;
+#endif
+    GGML_ASSERT(vk_instance.initialized);
+
+    std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();
+
+    if (dev_num >= devices.size()) {
+        std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl;
+        throw std::runtime_error("Device not found");
+    }
+
+    vk::PhysicalDevice physical_device = devices[dev_num];
+    std::vector<vk::ExtensionProperties> ext_props = physical_device.enumerateDeviceExtensionProperties();
+
+    vk::PhysicalDeviceProperties2 props2;
+    vk::PhysicalDeviceMaintenance3Properties props3;
+    vk::PhysicalDeviceSubgroupProperties subgroup_props;
+    props2.pNext = &props3;
+    props3.pNext = &subgroup_props;
+    physical_device.getProperties2(&props2);
+
+    const size_t subgroup_size = subgroup_props.subgroupSize;
+    const bool uma = props2.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu;
+
+    bool fp16_storage = false;
+    bool fp16_compute = false;
+
+    for (auto properties : ext_props) {
+        if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) {
+            fp16_storage = true;
+        } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) {
+            fp16_compute = true;
+        }
+    }
+
+    const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16");
+    bool force_disable_f16 = GGML_VK_DISABLE_F16 != nullptr;
+
+    bool fp16 = !force_disable_f16 && fp16_storage && fp16_compute;
+
+    vk::PhysicalDeviceFeatures device_features = physical_device.getFeatures();
+
+    VkPhysicalDeviceFeatures2 device_features2;
+    device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2;
+    device_features2.pNext = nullptr;
+    device_features2.features = (VkPhysicalDeviceFeatures)device_features;
+
+    VkPhysicalDeviceVulkan11Features vk11_features;
+    vk11_features.pNext = nullptr;
+    vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES;
+    device_features2.pNext = &vk11_features;
+
+    VkPhysicalDeviceVulkan12Features vk12_features;
+    vk12_features.pNext = nullptr;
+    vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES;
+    vk11_features.pNext = &vk12_features;
+
+    vkGetPhysicalDeviceFeatures2(physical_device, &device_features2);
+
+    fp16 = fp16 && vk12_features.shaderFloat16;
+
+    std::string device_name = props2.properties.deviceName.data();
+    std::cerr << GGML_VK_NAME << idx << ": " << device_name << " | uma: " << uma << " | fp16: " << fp16 << " | warp size: " << subgroup_size << std::endl;
+
+    if (props2.properties.deviceType == vk::PhysicalDeviceType::eCpu) {
+        std::cerr << "ggml_vulkan: Warning: Device type is CPU. This is probably not the device you want." << std::endl;
+    }
+}
+
+static bool ggml_vk_instance_validation_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
+static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
+
+void ggml_vk_instance_init() {
+    if (vk_instance_initialized) {
+        return;
+    }
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_instance_init()" << std::endl;
+#endif
+
+    vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, VK_API_VERSION };
+
+    const std::vector<vk::ExtensionProperties> instance_extensions = vk::enumerateInstanceExtensionProperties();
+    const bool validation_ext = ggml_vk_instance_validation_ext_available(instance_extensions);
+#ifdef __APPLE__
+    const bool portability_enumeration_ext = ggml_vk_instance_portability_enumeration_ext_available(instance_extensions);
+#endif
+
+    std::vector<const char*> layers;
+
+    if (validation_ext) {
+        layers.push_back("VK_LAYER_KHRONOS_validation");
+    }
+    std::vector<const char*> extensions;
+    if (validation_ext) {
+        extensions.push_back("VK_EXT_validation_features");
+    }
+#ifdef __APPLE__
+    if (portability_enumeration_ext) {
+        extensions.push_back("VK_KHR_portability_enumeration");
+    }
+#endif
+    vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags{}, &app_info, layers, extensions);
+#ifdef __APPLE__
+    if (portability_enumeration_ext) {
+        instance_create_info.flags |= vk::InstanceCreateFlagBits::eEnumeratePortabilityKHR;
+    }
+#endif
+
+    std::vector<vk::ValidationFeatureEnableEXT> features_enable;
+    vk::ValidationFeaturesEXT validation_features;
+
+    if (validation_ext) {
+        features_enable = { vk::ValidationFeatureEnableEXT::eBestPractices };
+        validation_features = {
+            features_enable,
+            {},
+        };
+        validation_features.setPNext(nullptr);
+        instance_create_info.setPNext(&validation_features);
+
+        std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl;
+    }
+    vk_instance.instance = vk::createInstance(instance_create_info);
+
+    memset(vk_instance.initialized, 0, sizeof(bool) * GGML_VK_MAX_DEVICES);
+
+    size_t num_available_devices = vk_instance.instance.enumeratePhysicalDevices().size();
+
+    // Emulate behavior of CUDA_VISIBLE_DEVICES for Vulkan
+    char * devices_env = getenv("GGML_VK_VISIBLE_DEVICES");
+    if (devices_env != nullptr) {
+        std::string devices(devices_env);
+        std::replace(devices.begin(), devices.end(), ',', ' ');
+
+        std::stringstream ss(devices);
+        size_t tmp;
+        while (ss >> tmp) {
+            if(tmp >= num_available_devices) {
+                std::cerr << "ggml_vulkan: Invalid device index " << tmp << " in GGML_VK_VISIBLE_DEVICES." << std::endl;
+                throw std::runtime_error("Invalid Vulkan device index");
+            }
+            vk_instance.device_indices.push_back(tmp);
+        }
+    } else {
+        std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();
+
+        // Make sure at least one device exists
+        if (devices.empty()) {
+            std::cerr << "ggml_vulkan: Error: No devices found." << std::endl;
+            GGML_ASSERT(false);
+        }
+
+        // Default to using all dedicated GPUs
+        for (size_t i = 0; i < devices.size(); i++) {
+            vk::PhysicalDeviceProperties props = devices[i].getProperties();
+
+            if (props.deviceType == vk::PhysicalDeviceType::eDiscreteGpu) {
+                vk_instance.device_indices.push_back(i);
+            }
+        }
+
+        // If no dedicated GPUs found, fall back to GPU 0
+        if (vk_instance.device_indices.empty()) {
+            vk_instance.device_indices.push_back(0);
+        }
+    }
+
+    std::cerr << "ggml_vulkan: Found " << vk_instance.device_indices.size() << " Vulkan devices:" << std::endl;
+
+    for (size_t i = 0; i < vk_instance.device_indices.size(); i++) {
+        ggml_vk_print_gpu_info(i);
+    }
+
+    vk_instance_initialized = true;
+}
+
+static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) {
+    GGML_ASSERT(idx < vk_instance.device_indices.size());
+    size_t dev_num = vk_instance.device_indices[idx];
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_init(" << ctx->name << ", " << dev_num << ")" << std::endl;
+#endif
+    ggml_vk_instance_init();
+
+    std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();
+
+    if (dev_num >= devices.size()) {
+        std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl;
+        throw std::runtime_error("Device not found");
+    }
+
+    ctx->device = ggml_vk_get_device(idx);
+    if (!ctx->device->initialized) {
+        ctx->device->physical_device = devices[dev_num];
+        const std::vector<vk::ExtensionProperties> ext_props = ctx->device->physical_device.enumerateDeviceExtensionProperties();
+
+        bool maintenance4_support = false;
+
+        // Check if maintenance4 is supported
+        for (const auto& properties : ext_props) {
+            if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) {
+                maintenance4_support = true;
+            }
+        }
+
+        vk::PhysicalDeviceProperties2 props2;
+        vk::PhysicalDeviceMaintenance3Properties props3;
+        vk::PhysicalDeviceMaintenance4Properties props4;
+        vk::PhysicalDeviceSubgroupProperties subgroup_props;
+        props2.pNext = &props3;
+        props3.pNext = &subgroup_props;
+        if (maintenance4_support) {
+            subgroup_props.pNext = &props4;
+        }
+        ctx->device->physical_device.getProperties2(&props2);
+        ctx->device->properties = props2.properties;
+
+        const char* GGML_VK_FORCE_MAX_ALLOCATION_SIZE = getenv("GGML_VK_FORCE_MAX_ALLOCATION_SIZE");
+
+        if (GGML_VK_FORCE_MAX_ALLOCATION_SIZE != nullptr) {
+            ctx->device->max_memory_allocation_size = std::stoi(GGML_VK_FORCE_MAX_ALLOCATION_SIZE);
+        } else if (maintenance4_support) {
+            ctx->device->max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize);
+        } else {
+            ctx->device->max_memory_allocation_size = props3.maxMemoryAllocationSize;
+        }
+
+        ctx->device->vendor_id = ctx->device->properties.vendorID;
+        ctx->device->subgroup_size = subgroup_props.subgroupSize;
+        ctx->device->uma = ctx->device->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu;
+
+        bool fp16_storage = false;
+        bool fp16_compute = false;
+
+        for (const auto& properties : ext_props) {
+            if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) {
+                fp16_storage = true;
+            } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) {
+                fp16_compute = true;
+            }
+        }
+
+        const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16");
+        const bool force_disable_f16 = GGML_VK_DISABLE_F16 != nullptr;
+
+        ctx->device->fp16 = !force_disable_f16 && fp16_storage && fp16_compute;
+
+        std::vector<vk::QueueFamilyProperties> queue_family_props = ctx->device->physical_device.getQueueFamilyProperties();
+
+        // Try to find a non-graphics compute queue and transfer-focused queues
+        const uint32_t compute_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eCompute, vk::QueueFlagBits::eGraphics, -1, 1);
+        const uint32_t transfer_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eTransfer, vk::QueueFlagBits::eCompute | vk::QueueFlagBits::eGraphics, compute_queue_family_index, 1);
+
+        const float priorities[] = { 1.0f, 1.0f };
+        ctx->device->single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1;
+
+        std::vector<vk::DeviceQueueCreateInfo> device_queue_create_infos;
+        if (compute_queue_family_index != transfer_queue_family_index) {
+            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities});
+            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), transfer_queue_family_index, 1, priorities + 1});
+        } else if(!ctx->device->single_queue) {
+            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 2, priorities});
+        } else {
+            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities});
+        }
+        vk::DeviceCreateInfo device_create_info;
+        std::vector<const char *> device_extensions;
+        vk::PhysicalDeviceFeatures device_features = ctx->device->physical_device.getFeatures();
+
+        VkPhysicalDeviceFeatures2 device_features2;
+        device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2;
+        device_features2.pNext = nullptr;
+        device_features2.features = (VkPhysicalDeviceFeatures)device_features;
+
+        VkPhysicalDeviceVulkan11Features vk11_features;
+        vk11_features.pNext = nullptr;
+        vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES;
+        device_features2.pNext = &vk11_features;
+
+        VkPhysicalDeviceVulkan12Features vk12_features;
+        vk12_features.pNext = nullptr;
+        vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES;
+        vk11_features.pNext = &vk12_features;
+
+        vkGetPhysicalDeviceFeatures2(ctx->device->physical_device, &device_features2);
+
+        ctx->device->fp16 = ctx->device->fp16 && vk12_features.shaderFloat16;
+
+        if (!vk11_features.storageBuffer16BitAccess) {
+            std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl;
+            throw std::runtime_error("Unsupported device");
+        }
+
+        device_extensions.push_back("VK_KHR_16bit_storage");
+
+#ifdef GGML_VULKAN_VALIDATE
+        device_extensions.push_back("VK_KHR_shader_non_semantic_info");
+#endif
+
+        if (ctx->device->fp16) {
+            device_extensions.push_back("VK_KHR_shader_float16_int8");
+        }
+        ctx->device->name = ctx->device->properties.deviceName.data();
+
+        device_create_info = {
+            vk::DeviceCreateFlags(),
+            device_queue_create_infos,
+            {},
+            device_extensions
+        };
+        device_create_info.setPNext(&device_features2);
+        ctx->device->device = ctx->device->physical_device.createDevice(device_create_info);
+
+        ctx->device->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN;
+
+        // Queues
+        ggml_vk_create_queue(ctx, ctx->device->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer });
+
+        // Shaders
+        ggml_vk_load_shaders(ctx);
+
+        if (!ctx->device->single_queue) {
+            const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0;
+            ggml_vk_create_queue(ctx, ctx->device->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer });
+        } else {
+            // TODO: Use pointer or reference to avoid copy
+            ctx->device->transfer_queue = ctx->device->compute_queue;
+        }
+
+        ctx->device->idx = dev_num;
+        ctx->device->initialized = true;
+    } else if (ctx->device->idx != dev_num) {
+        std::cerr << "ggml_vulkan: Device " << ctx->device->name << " already initialized with index " << ctx->device->idx << ", but trying to reinitialize with index " << dev_num << std::endl;
+        throw std::runtime_error("Device already initialized");
+    }
+
+    ctx->fence = ctx->device->device.createFence({});
+
+    ctx->compute_ctx = nullptr;
+    ctx->transfer_ctx = nullptr;
+
+    ctx->disable = false;
+    ctx->initialized = true;
+
+    ctx->idx = idx;
+
+#ifdef GGML_VULKAN_CHECK_RESULTS
+    const char* skip_checks = getenv("GGML_VULKAN_SKIP_CHECKS");
+    vk_skip_checks = (skip_checks == NULL ? 0 : atoi(skip_checks));
+    const char* output_tensor = getenv("GGML_VULKAN_OUTPUT_TENSOR");
+    vk_output_tensor = (output_tensor == NULL ? 0 : atoi(output_tensor));
+#endif
+}
+
+static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type type) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_get_to_fp16()" << std::endl;
+#endif
+    switch (type) {
+        case GGML_TYPE_F32:
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+            break;
+        default:
+            return nullptr;
+    }
+
+    return ctx->device->pipeline_dequant[type];
+}
+
+static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_get_mul_mat_mat_pipeline()" << std::endl;
+#endif
+    if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
+        return ctx->device->pipeline_matmul_f32;
+    }
+    if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
+        return ctx->device->pipeline_matmul_f16_f32;
+    }
+    if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
+        return ctx->device->pipeline_matmul_f16;
+    }
+
+    GGML_ASSERT(src1_type == GGML_TYPE_F32);
+
+    switch (src0_type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+            break;
+        default:
+            return nullptr;
+    }
+
+    return ctx->device->pipeline_dequant_mul_mat_mat[src0_type];
+}
+
+static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type type) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_get_dequantize_mul_mat_vec()" << std::endl;
+#endif
+    switch (type) {
+        case GGML_TYPE_F16:
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_Q2_K:
+        case GGML_TYPE_Q3_K:
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+        case GGML_TYPE_Q6_K:
+            break;
+        default:
+            return nullptr;
+    }
+
+    return ctx->device->pipeline_dequant_mul_mat_vec_f32[type];
+}
+
+static vk_buffer ggml_vk_pool_malloc(ggml_backend_vk_context * ctx, size_t size) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_pool_malloc(" << size << ")" << std::endl;
+#endif
+    int best_i = -1;
+    size_t best_size = std::numeric_limits<size_t>::max(); //smallest unused buffer that fits our needs
+    int worst_i = -1;
+    size_t worst_size = 0; //largest unused buffer seen so far
+    for (int i = 0; i < MAX_VK_BUFFERS; ++i) {
+        vk_buffer &b = ctx->buffer_pool[i];
+        if (b != nullptr && b->size >= size && b->size < best_size) {
+            best_i = i;
+            best_size = b->size;
+        }
+        if (b != nullptr && b->size > worst_size) {
+            worst_i = i;
+            worst_size = b->size;
+        }
+    }
+    if(best_i != -1) {
+        //found the smallest buffer that fits our needs
+        vk_buffer b = ctx->buffer_pool[best_i];
+        ctx->buffer_pool[best_i].reset();
+        return b;
+    }
+    if(worst_i != -1) {
+        //no buffer that fits our needs, resize largest one to save memory
+        vk_buffer& b = ctx->buffer_pool[worst_i];
+        ggml_vk_destroy_buffer(b);
+    }
+
+    return ggml_vk_create_buffer_check(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal);
+}
+
+static void ggml_vk_pool_free(ggml_backend_vk_context * ctx, vk_buffer& buffer) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_pool_free(" << buffer->size << ")" << std::endl;
+#endif
+    for (int i = 0; i < MAX_VK_BUFFERS; ++i) {
+        vk_buffer& b = ctx->buffer_pool[i];
+        if (b == nullptr) {
+            b = buffer;
+            return;
+        }
+    }
+    std::cerr << "ggml_vulkan: WARNING: vk buffer pool full, increase MAX_VK_BUFFERS" << std::endl;
+    ggml_vk_destroy_buffer(buffer);
+}
+
+// Returns an available temporary buffer that may only be used temporarily, it will be reused
+static vk_buffer ggml_vk_create_buffer_temp(ggml_backend_vk_context * ctx, size_t size) {
+    // Try to find existing temp buffer with enough capacity
+    for (auto& buffer : ctx->gc.temp_buffers) {
+        if (buffer->size >= size) {
+            return buffer;
+        }
+    }
+
+    // Otherwise create new buffer
+    vk_buffer buf = ggml_vk_pool_malloc(ctx, size);
+    ctx->gc.temp_buffers.push_back(buf);
+
+    return buf;
+}
+
+static void * ggml_vk_host_malloc(ggml_backend_vk_context * ctx, size_t size) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_host_malloc(" << size << ")" << std::endl;
+#endif
+    vk_buffer buf = ggml_vk_create_buffer(ctx, size,
+        vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
+        vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
+
+    if(!(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) {
+        fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory\n",
+            size/1024.0/1024.0);
+        ctx->device->device.freeMemory(buf->device_memory);
+        ctx->device->device.destroyBuffer(buf->buffer);
+        return nullptr;
+    }
+
+    ctx->pinned_memory.push_back(std::make_tuple(buf->ptr, size, buf));
+
+    return buf->ptr;
+}
+
+static void ggml_vk_host_free(ggml_backend_vk_context * ctx, void* ptr) {
+    if (ptr == nullptr) {
+        return;
+    }
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_host_free(" << ptr << ")" << std::endl;
+#endif
+    vk_buffer buf;
+    size_t index;
+    for (size_t i = 0; i < ctx->pinned_memory.size(); i++) {
+        const uint8_t* addr = (const uint8_t*) std::get<0>(ctx->pinned_memory[i]);
+        const uint8_t* endr = addr + std::get<1>(ctx->pinned_memory[i]);
+        if (ptr >= addr && ptr < endr) {
+            buf = std::get<2>(ctx->pinned_memory[i]);
+            index = i;
+            break;
+        }
+    }
+    if (buf == nullptr) {
+        fprintf(stderr, "WARNING: failed to free pinned memory: memory not in map\n");
+        return;
+    }
+
+    ggml_vk_destroy_buffer(buf);
+
+    ctx->pinned_memory.erase(ctx->pinned_memory.begin() + index);
+}
+
+static void ggml_vk_host_get(ggml_backend_vk_context * ctx, const void * ptr, vk_buffer& buf, size_t& buf_offset) {
+    buf = nullptr;
+    buf_offset = 0;
+    for (size_t i = 0; i < ctx->pinned_memory.size(); i++) {
+        const uint8_t* addr = (const uint8_t*) std::get<0>(ctx->pinned_memory[i]);
+        const uint8_t* endr = addr + std::get<1>(ctx->pinned_memory[i]);
+        if (ptr >= addr && ptr < endr) {
+            buf = std::get<2>(ctx->pinned_memory[i]);
+            buf_offset = ((const uint8_t *)ptr) - addr;
+            break;
+        }
+    }
+}
+
+static vk_submission ggml_vk_begin_submission(ggml_backend_vk_context * ctx, vk_queue& q, bool one_time = true) {
+    vk_submission s;
+    s.buffer = ggml_vk_create_cmd_buffer(ctx, q);
+    if (one_time) {
+        s.buffer.begin({ vk::CommandBufferUsageFlagBits::eOneTimeSubmit });
+    } else {
+        s.buffer.begin({ vk::CommandBufferUsageFlags{} });
+    }
+
+    return s;
+}
+
+static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, std::vector<vk_subbuffer>&& buffers, size_t push_constant_size, const void* push_constants, std::array<uint32_t, 3> elements) {
+    const uint32_t wg0 = CEIL_DIV(elements[0], pipeline->wg_denoms[0]);
+    const uint32_t wg1 = CEIL_DIV(elements[1], pipeline->wg_denoms[1]);
+    const uint32_t wg2 = CEIL_DIV(elements[2], pipeline->wg_denoms[2]);
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_dispatch_pipeline(" << pipeline->name << ", (" << wg0 << "," << wg1 << "," << wg2 << "))" << std::endl;
+#endif
+    std::vector<vk::DescriptorBufferInfo> descriptor_buffer_infos;
+    std::vector<vk::WriteDescriptorSet> write_descriptor_sets;
+    GGML_ASSERT(pipeline->descriptor_set_idx < pipeline->descriptor_sets.size());
+    GGML_ASSERT(buffers.size() == pipeline->parameter_count);
+    vk::DescriptorSet& descriptor_set = pipeline->descriptor_sets[pipeline->descriptor_set_idx++];
+    for (uint32_t i = 0; i < pipeline->parameter_count; i++) {
+        descriptor_buffer_infos.push_back({buffers[i].buffer->buffer, buffers[i].offset, buffers[i].size});
+    }
+    for (uint32_t i = 0; i < pipeline->parameter_count; i++) {
+        write_descriptor_sets.push_back({descriptor_set, i, 0, 1, vk::DescriptorType::eStorageBuffer, nullptr, &descriptor_buffer_infos[i]});
+    }
+
+    ctx->device->device.updateDescriptorSets(write_descriptor_sets, {});
+
+    subctx->s->buffer.pushConstants(pipeline->layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size, push_constants);
+    subctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline->pipeline);
+    subctx->s->buffer.bindDescriptorSets(vk::PipelineBindPoint::eCompute,
+                                pipeline->layout,
+                                0,
+                                { descriptor_set },
+                                {});
+    subctx->s->buffer.dispatch(wg0, wg1, wg2);
+}
+
+static void ggml_vk_end_submission(vk_submission& s, std::vector<vk_semaphore> wait_semaphores, std::vector<vk_semaphore> signal_semaphores) {
+    s.buffer.end();
+
+    s.wait_semaphores = std::move(wait_semaphores);
+    s.signal_semaphores = std::move(signal_semaphores);
+}
+
+static void ggml_vk_ctx_end(vk_context * ctx) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_ctx_end(" << ctx << ", " << ctx->seqs.size() << ")" << std::endl;
+#endif
+    if (ctx->s == nullptr) {
+        return;
+    }
+
+    ctx->s->buffer.end();
+    ctx->s = nullptr;
+}
+
+static void ggml_vk_ctx_begin(ggml_backend_vk_context * ctx, vk_context * subctx) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_ctx_begin(" << ctx << ")" << std::endl;
+#endif
+    if (subctx->s != nullptr) {
+        ggml_vk_ctx_end(subctx);
+    }
+
+    subctx->seqs.push_back({ ggml_vk_begin_submission(ctx, *subctx->q) });
+    subctx->s = subctx->seqs[subctx->seqs.size() - 1].data();
+}
+
+static size_t ggml_vk_align_size(size_t width, size_t align) {
+    return CEIL_DIV(width, align) * align;
+}
+
+static void deferred_memcpy(void * dst, const void * src, size_t size, std::vector<vk_staging_memcpy>* memcpys = nullptr) {
+    if (memcpys == nullptr) {
+        memcpy(dst, src, size);
+    } else {
+        memcpys->emplace_back(dst, src, size);
+    }
+}
+
+static void ggml_vk_ensure_sync_staging_buffer(ggml_backend_vk_context * ctx, size_t size) {
+    if (ctx->sync_staging == nullptr || ctx->sync_staging->size < size) {
+        ggml_vk_destroy_buffer(ctx->sync_staging);
+        ctx->sync_staging = ggml_vk_create_buffer_check(ctx, size,
+            vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
+            vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
+    }
+}
+
+static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_buffer_write_nc_async(" << tensor << ")" << std::endl;
+#endif
+    GGML_ASSERT(!ggml_is_contiguous(tensor));
+    // Buffer is already mapped
+    if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
+        std::cerr << "ggml_vulkan: buffer_write_nc_async dst buffer is host_visible. Use synchronous write." << std::endl;
+        GGML_ASSERT(false);
+    }
+    // Check if src is pinned memory
+    vk_buffer buf;
+    size_t buf_offset;
+    ggml_vk_host_get(ctx, tensor->data, buf, buf_offset);
+
+    const uint64_t ne0 = tensor->ne[0];
+    const uint64_t ne1 = tensor->ne[1];
+    const uint64_t ne2 = tensor->ne[2];
+    const uint64_t ne3 = tensor->ne[3];
+    const uint64_t nb0 = tensor->nb[0];
+    const uint64_t nb1 = tensor->nb[1];
+    const uint64_t nb2 = tensor->nb[2];
+    const uint64_t nb3 = tensor->nb[3];
+    const ggml_type type = tensor->type;
+    const uint64_t ts = ggml_type_size(type);
+    const uint64_t bs = ggml_blck_size(type);
+
+    const uint64_t dstnb0 = ts;
+    const uint64_t dstnb1 = dstnb0*(ne0/bs);
+    const uint64_t dstnb2 = dstnb1*ne1;
+    const uint64_t dstnb3 = dstnb2*ne2;
+
+    const uint64_t ne = ggml_nelements(tensor);
+
+    if (buf != nullptr) {
+        // Memory is pinned, use as staging buffer
+        std::vector<vk::BufferCopy> slices;
+
+        for (uint64_t i3 = 0; i3 < ne3; i3++) {
+            for (uint64_t i2 = 0; i2 < ne2; i2++) {
+                // Find longest contiguous slice
+                if (ne1*nb1 == dstnb2) {
+                    slices.push_back({ buf_offset + i3*nb3 + i2*nb2, offset + i3*dstnb3 + i2*dstnb2, dstnb2 });
+                } else {
+                    for (uint64_t i1 = 0; i1 < ne1; i1++) {
+                        if (ne0*nb0/bs == dstnb1) {
+                            slices.push_back({ buf_offset + i3*nb3 + i2*nb2 + i1*nb1, offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, dstnb1 });
+                        } else {
+                            const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1;
+                            const uint64_t d_off = offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1;
+                            for (uint64_t i0 = 0; i0 < ne0; i0++) {
+                                slices.push_back({ s_off + i1*nb0, d_off + i0*dstnb0, dstnb0 });
+                            }
+                        }
+                    }
+                }
+            }
+        }
+
+        ggml_vk_sync_buffers(subctx);
+        subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices);
+        return;
+    }
+
+    // Staging buffer required
+    vk_buffer staging = ctx->staging;
+    size_t staging_offset = ctx->staging_offset;
+    const size_t copy_size = ts*ne/bs;
+    if (ctx->staging->size < ctx->staging_offset + copy_size) {
+        if (sync_staging) {
+            // Create temporary larger buffer
+            ggml_vk_ensure_sync_staging_buffer(ctx, copy_size);
+
+            staging = ctx->sync_staging;
+            staging_offset = 0;
+        } else {
+            GGML_ASSERT(false);
+        }
+    }
+
+    VkBufferCopy buf_copy{ staging_offset, offset, copy_size };
+
+    ggml_vk_sync_buffers(subctx);
+    vkCmdCopyBuffer(subctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy);
+
+    for (uint64_t i3 = 0; i3 < ne3; i3++) {
+        for (uint64_t i2 = 0; i2 < ne2; i2++) {
+            // Find longest contiguous slice
+            if (ne1*nb1 == dstnb2) {
+                deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2, dstnb2, &subctx->in_memcpys);
+            } else {
+                for (uint64_t i1 = 0; i1 < ne1; i1++) {
+                    if (ne0*nb0/bs == dstnb1) {
+                        deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2 + i1*nb1, dstnb1, &subctx->in_memcpys);
+                    } else {
+                        const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1;
+                        const uint64_t d_off = staging_offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1;
+                        for (uint64_t i0 = 0; i0 < ne0; i0++) {
+                            deferred_memcpy((uint8_t *)staging->ptr + d_off + i0*dstnb0, (const uint8_t *) tensor->data + s_off + i0*nb0, dstnb0, &subctx->in_memcpys);
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+static void ggml_vk_buffer_write_2d_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height, bool sync_staging = false) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_buffer_write_2d_async(" << width << ", " << height << ")" << std::endl;
+#endif
+    // Make sure ctx owns the buffer
+    GGML_ASSERT(dst->ctx == ctx);
+
+    // Buffer is already mapped
+    if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
+        std::cerr << "ggml_vulkan: buffer_write_async dst buffer is host_visible. Use synchronous write." << std::endl;
+        GGML_ASSERT(false);
+    }
+    // Check if src is pinned memory
+    vk_buffer buf = nullptr;
+    size_t buf_offset;
+    ggml_vk_host_get(ctx, src, buf, buf_offset);
+
+    if (buf != nullptr) {
+        // Memory is pinned, use as staging buffer
+        std::vector<vk::BufferCopy> slices(1);
+        if (width == spitch) {
+            // Only do single write if stride is equal
+            slices[0].srcOffset = buf_offset;
+            slices[0].dstOffset = offset;
+            slices[0].size = width * height;
+        } else {
+            slices.resize(height);
+            for (size_t i = 0; i < height; i++) {
+                slices[i].srcOffset = buf_offset + i * spitch;
+                slices[i].dstOffset = offset + i * width;
+                slices[i].size = width;
+            }
+        }
+
+        ggml_vk_sync_buffers(subctx);
+        subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices);
+        return;
+    }
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "STAGING" << std::endl;
+#endif
+
+    // Staging buffer required
+    vk_buffer staging = ctx->staging;
+    size_t staging_offset = ctx->staging_offset;
+    const size_t copy_size = width*height;
+    if (ctx->staging == nullptr || ctx->staging->size < ctx->staging_offset + copy_size) {
+        if (sync_staging) {
+            ggml_vk_ensure_sync_staging_buffer(ctx, copy_size);
+
+            staging = ctx->sync_staging;
+            staging_offset = 0;
+        } else {
+            GGML_ASSERT(false);
+        }
+    }
+
+    VkBufferCopy buf_copy = {
+        staging_offset,
+        offset,
+        copy_size};
+
+    ggml_vk_sync_buffers(subctx);
+    vkCmdCopyBuffer(subctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy);
+
+    if (width == spitch) {
+        deferred_memcpy((uint8_t *)staging->ptr + staging_offset, src, width * height, &subctx->in_memcpys);
+    } else {
+        for (size_t i = 0; i < height; i++) {
+            deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i * width, (const uint8_t *) src + i * spitch, width, &subctx->in_memcpys);
+        }
+    }
+}
+
+static void ggml_vk_buffer_write_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const void * src, size_t size, bool sync_staging = false) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_buffer_write_async(" << size << ")" << std::endl;
+#endif
+    return ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, src, size, size, 1, sync_staging);
+}
+
+static void ggml_vk_buffer_write_2d(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_buffer_write_2d(" << width << ", " << height << ")" << std::endl;
+#endif
+    // Buffer is already mapped
+    if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
+        GGML_ASSERT(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);
+
+        for (size_t i = 0; i < height; i++) {
+            memcpy((uint8_t *)dst->ptr + offset + i * width, (const uint8_t *) src + i * spitch, width);
+        }
+    } else {
+        vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
+        ggml_vk_ctx_begin(ctx, subctx);
+        ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, src, spitch, width, height, true);
+        ggml_vk_ctx_end(subctx);
+
+        for (auto& cpy : subctx->in_memcpys) {
+            memcpy(cpy.dst, cpy.src, cpy.n);
+        }
+
+        ggml_vk_submit(subctx, ctx->fence);
+        VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences");
+        ctx->device->device.resetFences({ ctx->fence });
+    }
+}
+
+static void ggml_vk_buffer_write(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, const void * src, size_t size) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_buffer_write(" << size << ")" << std::endl;
+#endif
+    ggml_vk_buffer_write_2d(ctx, dst, offset, src, 0, size, 1);
+}
+
+static void ggml_vk_buffer_read_2d_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_buffer_read_2d_async(offset=" << offset << ", width=" << width << ", height=" << height << ")" << std::endl;
+#endif
+    GGML_ASSERT(width > 0);
+    GGML_ASSERT(height > 0);
+    GGML_ASSERT(src != nullptr);
+    // Make sure ctx owns the buffer
+    GGML_ASSERT(src->ctx == ctx);
+
+    // Check if dst is pinned memory
+    vk_buffer buf = nullptr;
+    size_t buf_offset;
+    ggml_vk_host_get(ctx, dst, buf, buf_offset);
+
+    std::vector<vk::BufferCopy> slices(1);
+    if (width == spitch && width == dpitch) {
+        // Only do single write if stride is equal
+        slices[0].srcOffset = offset;
+        slices[0].dstOffset = buf_offset;
+        slices[0].size = width * height;
+    } else {
+        slices.resize(height);
+        for (size_t i = 0; i < height; i++) {
+            slices[i].srcOffset = offset + i * spitch;
+            slices[i].dstOffset = buf_offset + i * dpitch;
+            slices[i].size = width;
+        }
+    }
+
+    if (buf != nullptr) {
+        // Memory is pinned, use as staging buffer
+        ggml_vk_sync_buffers(subctx);
+        subctx->s->buffer.copyBuffer(src->buffer, buf->buffer, slices);
+
+        return;
+    }
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "STAGING" << std::endl;
+#endif
+
+    // Fall back to staging buffer
+    vk_buffer staging = ctx->staging;
+    const size_t copy_size = dpitch * height;
+    if (ctx->staging == nullptr || ctx->staging->size < ctx->staging_offset + copy_size) {
+        if (sync_staging) {
+            // Create temporary larger buffer
+            ggml_vk_ensure_sync_staging_buffer(ctx, copy_size);
+
+            staging = ctx->sync_staging;
+        } else {
+            GGML_ASSERT(false);
+        }
+    }
+
+    ggml_vk_sync_buffers(subctx);
+    subctx->s->buffer.copyBuffer(src->buffer, staging->buffer, slices);
+
+    deferred_memcpy(dst, staging->ptr, copy_size, &subctx->out_memcpys);
+}
+
+static void ggml_vk_buffer_read_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, void * dst, size_t size, bool sync_staging = false) {
+    return ggml_vk_buffer_read_2d_async(ctx, subctx, src, offset, dst, size, size, size, 1, sync_staging);
+}
+
+static void ggml_vk_buffer_read(ggml_backend_vk_context * ctx, vk_buffer& src, size_t offset, void * dst, size_t size) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_buffer_read(" << offset << ", " << size << ")" << std::endl;
+#endif
+    if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
+        GGML_ASSERT(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);
+
+        memcpy(dst, (uint8_t *) src->ptr + offset, size);
+    } else {
+        vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
+        ggml_vk_ctx_begin(ctx, subctx);
+        ggml_vk_buffer_read_async(ctx, subctx, src, offset, dst, size, true);
+        ggml_vk_ctx_end(subctx);
+
+        ggml_vk_submit(subctx, ctx->fence);
+        VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_read waitForFences");
+        ctx->device->device.resetFences({ ctx->fence });
+
+        for (auto& cpy : subctx->out_memcpys) {
+            memcpy(cpy.dst, cpy.src, cpy.n);
+        }
+    }
+}
+
+static void ggml_vk_buffer_copy_async(vk_context * ctx, vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_buffer_copy_async(" << size << ")" << std::endl;
+#endif
+    // Make sure both buffers are on same ctx
+    GGML_ASSERT(src->ctx == dst->ctx);
+
+    VkBufferCopy bc{ src_offset, dst_offset, size };
+
+    vkCmdCopyBuffer(ctx->s->buffer, src->buffer, dst->buffer, 1, &bc);
+}
+
+static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) {
+    if (src->ctx == dst->ctx) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_buffer_copy(SINGLE_DEVICE, " << size << ")" << std::endl;
+#endif
+        // Copy within the device
+        ggml_backend_vk_context * ctx = src->ctx;
+
+        vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
+        ggml_vk_ctx_begin(ctx, subctx);
+        ggml_vk_buffer_copy_async(subctx, dst, dst_offset, src, src_offset, size);
+        ggml_vk_ctx_end(subctx);
+        ggml_vk_submit(subctx, ctx->fence);
+        VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences");
+        ctx->device->device.resetFences({ ctx->fence });
+    } else {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_buffer_copy(MULTI_DEVICE, " << size << ")" << std::endl;
+#endif
+        // Copy device to device
+        ggml_backend_vk_context * src_ctx = src->ctx;
+        ggml_backend_vk_context * dst_ctx = dst->ctx;
+
+        ggml_vk_ensure_sync_staging_buffer(src_ctx, size);
+        ggml_vk_ensure_sync_staging_buffer(dst_ctx, size);
+
+        // Copy to src staging buffer
+        ggml_vk_buffer_copy(src_ctx->sync_staging, 0, src, src_offset, size);
+        // memcpy to dst staging buffer
+        memcpy(dst_ctx->sync_staging->ptr, src_ctx->sync_staging->ptr, size);
+        // Copy to dst buffer
+        ggml_vk_buffer_copy(dst, dst_offset, dst_ctx->sync_staging, 0, size);
+    }
+}
+
+static void ggml_vk_buffer_memset(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, uint32_t c, size_t size) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_buffer_memset(" << offset << ", " << c << ", " << size << ")" << std::endl;
+#endif
+    // Make sure ctx owns the buffer
+    GGML_ASSERT(dst->ctx == ctx);
+
+    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
+    ggml_vk_ctx_begin(ctx, subctx);
+    subctx->s->buffer.fillBuffer(dst->buffer, offset, size, c);
+    ggml_vk_ctx_end(subctx);
+
+    ggml_vk_submit(subctx, ctx->fence);
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_memset waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
+}
+
+static void ggml_vk_h2d_tensor_2d(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const ggml_tensor * src, uint64_t i3, uint64_t i2, uint64_t i1) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_h2d_tensor_2d(dst=" << dst << ", offset=" << offset << ", src=" << src << ", i3=" << i3 << ", i2=" << i2 << ", i1=" << i1 << ")" << std::endl;
+#endif
+    const uint64_t ne0 = src->ne[0];
+    const uint64_t ne1 = src->ne[1];
+    const uint64_t nb0 = src->nb[0];
+    const uint64_t nb1 = src->nb[1];
+    const uint64_t nb2 = src->nb[2];
+    const uint64_t nb3 = src->nb[3];
+    const enum ggml_type type = src->type;
+    const size_t ts = ggml_type_size(type);
+    const size_t bs = ggml_blck_size(type);
+    const size_t row_length = ts*ne0/bs;
+
+    const void * x = (const void *) ((const char *) src->data + i2*nb2 + i3*nb3);
+    if (nb0 == ts && nb1 == row_length) {
+        return ggml_vk_buffer_write_async(ctx, subctx, dst, offset, x, i1*nb1);
+    }
+    if (nb0 == ts && (i1 == ne1 || !ggml_is_permuted(src))) {
+        return ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, x, nb1, row_length, i1);
+    }
+
+    GGML_ASSERT(i3 == 0);
+    GGML_ASSERT(i2 == 0);
+    GGML_ASSERT(i1 == (uint64_t) ggml_nrows(src));
+
+    return ggml_vk_buffer_write_nc_async(ctx, subctx, dst, offset, src);
+}
+
+static void ggml_vk_d2h_tensor_2d(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, const ggml_tensor * dst) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_d2h_tensor_2d()" << std::endl;
+#endif
+    const uint64_t ne0 = dst->ne[0];
+    const uint64_t ne1 = dst->ne[1];
+    const uint64_t ne2 = dst->ne[2];
+    const uint64_t ne3 = dst->ne[3];
+    const uint64_t nb0 = dst->nb[0];
+    const uint64_t nb1 = dst->nb[1];
+    // const uint64_t nb2 = dst->nb[2];
+    // const uint64_t nb3 = dst->nb[3];
+    const enum ggml_type type = dst->type;
+    const size_t ts = ggml_type_size(type);
+    const size_t bs = ggml_blck_size(type);
+    const size_t row_length = ts*ne0/bs;
+
+    if (ggml_is_contiguous(dst)) {
+        return ggml_vk_buffer_read_async(ctx, subctx, src, offset, dst->data, ne1*nb1*ne2*ne3);
+    }
+    if (nb0 == ts) {
+        return ggml_vk_buffer_read_2d_async(ctx, subctx, src, offset, dst->data, nb1, nb1, row_length, ne1*ne2*ne3);
+    }
+    GGML_ASSERT(false);
+}
+
+static uint32_t ggml_vk_guess_split_k(int m, int n, int k) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")" << std::endl;
+#endif
+    if (k > 128 && (m < 128 || n < 128) && m > 2 && n > 2) {
+        return 4;
+    }
+
+    return 1;
+}
+
+static vk_pipeline ggml_vk_guess_matmul_pipeline_amd(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) {
+    if (m <= 32 || n <= 32) {
+        return aligned ? mmp->a_s : mmp->s;
+    }
+    return aligned ? mmp->a_m : mmp->m;
+
+    GGML_UNUSED(ctx);
+}
+
+static vk_pipeline ggml_vk_guess_matmul_pipeline_apple(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, bool aligned) {
+    return aligned ? mmp->a_m : mmp->m;
+
+    GGML_UNUSED(ctx);
+}
+
+static vk_pipeline ggml_vk_guess_matmul_pipeline_intel(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, bool aligned) {
+    return aligned ? mmp->a_s : mmp->s;
+
+    GGML_UNUSED(ctx);
+}
+
+static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ")" << std::endl;
+#endif
+    switch (ctx->device->vendor_id) {
+    case VK_VENDOR_ID_AMD:
+        return ggml_vk_guess_matmul_pipeline_amd(ctx, mmp, m, n, aligned);
+    case VK_VENDOR_ID_APPLE:
+        return ggml_vk_guess_matmul_pipeline_apple(ctx, mmp, aligned);
+    case VK_VENDOR_ID_INTEL:
+        return ggml_vk_guess_matmul_pipeline_intel(ctx, mmp, aligned);
+    default:
+        break;
+    }
+
+    if (m <= 32 || n <= 32) {
+        return aligned ? mmp->a_s : mmp->s;
+    }
+    if (m <= 64 || n <= 64) {
+        return aligned ? mmp->a_m : mmp->m;
+    }
+    return aligned ? mmp->a_l : mmp->l;
+}
+
+static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")" << std::endl;
+#endif
+    return ggml_vk_guess_matmul_pipeline(ctx, mmp, m, n, false)->align;
+}
+
+static void ggml_vk_matmul(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer->buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl;
+#endif
+    ggml_vk_sync_buffers(subctx);
+    if (split_k == 1) {
+        const std::array<uint32_t, 14> pc = { m, n, k, stride_a, stride_b, stride_d, k, ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d };
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d }, pc.size() * sizeof(uint32_t), pc.data(), { m, n, batch });
+        return;
+    }
+
+    GGML_ASSERT(batch_stride_d == m * n);
+
+    const std::array<uint32_t, 14> pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d };
+    // Make sure enough workgroups get assigned for split k to work
+    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, pc1.size() * sizeof(uint32_t), pc1.data(), { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, batch });
+    ggml_vk_sync_buffers(subctx);
+    const std::array<uint32_t, 2> pc2 = { (uint32_t)(m * n * batch), split_k };
+    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 });
+}
+
+static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) {
+    return
+        tensor->nb[0] == ggml_type_size(tensor->type) &&
+        tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/ggml_blck_size(tensor->type) &&
+        tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
+}
+
+static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, ggml_type from, ggml_type to) {
+    if (from == GGML_TYPE_F32 && to == GGML_TYPE_F32) {
+        return ctx->device->pipeline_cpy_f32_f32;
+    }
+    if (from == GGML_TYPE_F32 && to == GGML_TYPE_F16) {
+        return ctx->device->pipeline_cpy_f32_f16;
+    }
+    if (from == GGML_TYPE_F16 && to == GGML_TYPE_F16) {
+        return ctx->device->pipeline_cpy_f16_f16;
+    }
+
+    std::cerr << "Missing CPY op for types: " << ggml_type_name(from) << " " << ggml_type_name(to) << std::endl;
+    GGML_ASSERT(false);
+}
+
+static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), ";
+    std::cerr << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")" << std::endl;
+#endif
+    const int tensor_type_size = ggml_type_size(tensor->type);
+
+    const uint32_t ne = ggml_nelements(tensor);
+
+    const vk_op_unary_push_constants pc = {
+        (uint32_t)ne,
+        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], (uint32_t)tensor->nb[0] / tensor_type_size, (uint32_t)tensor->nb[1] / tensor_type_size, (uint32_t)tensor->nb[2] / tensor_type_size, (uint32_t)tensor->nb[3] / tensor_type_size,
+        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3],                       1                   , (uint32_t)tensor->ne[0]                   , (uint32_t)(tensor->ne[0] * tensor->ne[1]) , (uint32_t)(tensor->ne[0] * tensor->ne[1] * tensor->ne[2]),
+        0,
+        0.0f, 0.0f,
+    };
+    ggml_vk_sync_buffers(subctx);
+    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, sizeof(vk_op_unary_push_constants), &pc, { ne, 1, 1 });
+}
+
+static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
+    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
+    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl;
+#endif
+    GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);  // NOLINT
+    GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);  // NOLINT
+
+    const uint64_t ne00 = src0->ne[0];
+    const uint64_t ne01 = src0->ne[1];
+    const uint64_t ne02 = src0->ne[2];
+    const uint64_t ne03 = src0->ne[3];
+
+    const uint64_t ne10 = src1->ne[0];
+    const uint64_t ne11 = src1->ne[1];
+    const uint64_t ne12 = src1->ne[2];
+    const uint64_t ne13 = src1->ne[3];
+
+    const uint64_t ne20 = dst->ne[0];
+    const uint64_t ne21 = dst->ne[1];
+
+    const uint64_t r2 = ne12 / ne02;
+    const uint64_t r3 = ne13 / ne03;
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
+    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
+
+    vk_buffer d_Qx;
+    size_t qx_buf_offset = 0;
+    vk_buffer d_Qy;
+    size_t qy_buf_offset = 0;
+
+    bool src0_uma = false;
+    bool src1_uma = false;
+
+    if (ctx->device->uma) {
+        ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset);
+        ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
+        src0_uma = d_Qx != nullptr;
+        src1_uma = d_Qy != nullptr;
+    }
+
+    const bool x_non_contig = !ggml_vk_dim01_contiguous(src0);
+    const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);
+
+    const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig;
+
+    vk_matmul_pipeline mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type);
+
+    const bool qx_needs_dequant = mmp == nullptr || x_non_contig;
+    const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig;
+
+    if (mmp == nullptr) {
+        // Fall back to dequant + f16 mulmat
+        mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, GGML_TYPE_F16, y_f32_kernel ? GGML_TYPE_F32 : GGML_TYPE_F16);
+    }
+
+    // Not implemented
+    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
+
+    const int x_ne = ne01 * ne00;
+    const int y_ne = ne11 * ne10;
+    const int d_ne = ne11 * ne01;
+
+    const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11));
+    const bool aligned = ne10 == kpad;
+
+    const uint32_t split_k = ggml_vk_guess_split_k(ne01, ne11, ne10);
+
+    vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned);
+
+    const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type);
+    const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
+    const uint64_t x_sz = !qx_needs_dequant ? qx_sz : sizeof(ggml_fp16_t) * x_ne;
+    const uint64_t y_sz = y_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne;
+    const uint64_t d_sz = sizeof(float) * d_ne;
+
+    vk_buffer d_D = extra->buffer_gpu.lock();
+    const uint64_t d_buf_offset = extra->offset;
+    GGML_ASSERT(d_D != nullptr);
+    GGML_ASSERT(d_D->size >= d_buf_offset + d_sz * ne02 * ne03);
+    vk_buffer d_X;
+    uint64_t x_buf_offset = 0;
+    vk_buffer d_Y;
+    uint64_t y_buf_offset = 0;
+    if (!src0_uma) {
+        d_Qx = extra_src0->buffer_gpu.lock();
+        qx_buf_offset = extra_src0->offset;
+        GGML_ASSERT(d_Qx != nullptr);
+    }
+    if (!src1_uma) {
+        d_Qy = extra_src1->buffer_gpu.lock();
+        qy_buf_offset = extra_src1->offset;
+        GGML_ASSERT(d_Qy != nullptr);
+    }
+    if (qx_needs_dequant) {
+        d_X = ctx->prealloc_x;
+        GGML_ASSERT(d_X->size >= x_sz * ne02 * ne03);
+    } else {
+        d_X = d_Qx;
+        x_buf_offset = qx_buf_offset;
+        GGML_ASSERT(qx_sz == x_sz);
+    }
+    if (qy_needs_dequant) {
+        d_Y = ctx->prealloc_y;
+        GGML_ASSERT(d_Y->size >= y_sz * ne02 * ne03);
+    } else {
+        d_Y = d_Qy;
+        y_buf_offset = qy_buf_offset;
+        GGML_ASSERT(qy_sz == y_sz);
+    }
+
+    vk_pipeline to_fp16_vk_0 = nullptr;
+    vk_pipeline to_fp16_vk_1 = nullptr;
+
+    if (x_non_contig) {
+        to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16);
+    } else {
+        to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type);
+    }
+    if (y_non_contig) {
+        to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, GGML_TYPE_F16);
+    } else {
+        to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
+    }
+    GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
+    GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
+
+    // Allocate descriptor sets
+    ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, 1);
+    if (qx_needs_dequant) {
+        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_0, 1);
+    }
+    if (qy_needs_dequant) {
+        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_1, 1);
+    }
+    if (split_k > 1) {
+        ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, 1);
+    }
+
+    if (x_non_contig) {
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
+    } else if (qx_needs_dequant) {
+        const std::vector<uint32_t> pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) };
+        ggml_vk_sync_buffers(subctx);
+        ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, { { d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1});
+    }
+    if (y_non_contig) {
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
+    }
+
+    uint32_t stride_batch_x = ne00*ne01;
+    uint32_t stride_batch_y = ne10*ne11;
+
+    if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) {
+        stride_batch_x = src0->nb[0] / ggml_type_size(src0->type);
+    }
+
+    if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) {
+        stride_batch_y = src1->nb[0] / ggml_type_size(src1->type);
+    }
+
+    // compute
+    ggml_vk_matmul(ctx, subctx, pipeline, { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21);  // NOLINT
+
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
+        // copy dst to host
+        float * d = (float *) ((char *) dst->data);
+        ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, sizeof(float) * d_ne * ne12 * ne13);
+    }
+}
+
+static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ",  backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
+    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ",  backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
+    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ",  backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl;
+#endif
+    GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);  // NOLINT
+    GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);  // NOLINT
+
+    const uint64_t ne00 = src0->ne[0];
+    const uint64_t ne01 = src0->ne[1];
+    const uint64_t ne02 = src0->ne[2];
+    const uint64_t ne03 = src0->ne[3];
+
+    const uint64_t ne10 = src1->ne[0];
+    const uint64_t ne11 = src1->ne[1];
+    const uint64_t ne12 = src1->ne[2];
+    const uint64_t ne13 = src1->ne[3];
+
+    GGML_ASSERT(ne11 == 1);
+
+    const uint64_t nb2  = dst->nb[2];
+    const uint64_t nb3  = dst->nb[3];
+
+    const uint64_t r2 = ne12 / ne02;
+    const uint64_t r3 = ne13 / ne03;
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
+    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
+
+    vk_buffer d_Qx;
+    size_t qx_buf_offset = 0;
+    vk_buffer d_Qy;
+    size_t qy_buf_offset = 0;
+
+    bool src0_uma = false;
+    bool src1_uma = false;
+
+    if (ctx->device->uma) {
+        ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset);
+        ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
+        src0_uma = d_Qx != nullptr;
+        src1_uma = d_Qy != nullptr;
+    }
+
+    const bool x_non_contig = !ggml_vk_dim01_contiguous(src0);
+    const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);
+
+    const bool f16_f32_kernel = src1->type == GGML_TYPE_F32;
+
+    const bool qx_needs_dequant = x_non_contig;
+    const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig;
+
+    const uint64_t x_ne = ne01 * ne00;
+    const uint64_t y_ne = ne11 * ne10;
+    const uint64_t d_ne = ne11 * ne01;
+
+    const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment);
+    const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
+    const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz;
+    const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne;
+    const uint64_t d_sz = sizeof(float) * d_ne;
+
+    vk_buffer d_D = extra->buffer_gpu.lock();
+    const uint64_t d_buf_offset = extra->offset;
+    GGML_ASSERT(d_D != nullptr);
+    vk_buffer d_X;
+    uint64_t x_buf_offset = 0;
+    vk_buffer d_Y;
+    uint64_t y_buf_offset = 0;
+    if(!src0_uma) {
+        d_Qx = extra_src0->buffer_gpu.lock();
+        qx_buf_offset = extra_src0->offset;
+        GGML_ASSERT(d_Qx != nullptr);
+    }
+    if(!src1_uma) {
+        d_Qy = extra_src1->buffer_gpu.lock();
+        qy_buf_offset = extra_src1->offset;
+        GGML_ASSERT(d_Qy != nullptr);
+    }
+    if (qx_needs_dequant) {
+        d_X = ctx->prealloc_x;
+    } else {
+        d_X = d_Qx;
+        x_buf_offset = qx_buf_offset;
+        GGML_ASSERT(qx_sz == x_sz);
+    }
+    if (qy_needs_dequant) {
+        d_Y = ctx->prealloc_y;
+    } else {
+        d_Y = d_Qy;
+        y_buf_offset = qy_buf_offset;
+        GGML_ASSERT(qy_sz == y_sz);
+    }
+
+    vk_pipeline to_fp16_vk_0 = nullptr;
+    vk_pipeline to_fp16_vk_1 = nullptr;
+    if (x_non_contig) {
+        to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type);
+    }
+    if (y_non_contig) {
+        to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, src1->type);
+    } else {
+        to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
+    }
+    vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type);
+    GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
+    GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
+    GGML_ASSERT(dmmv != nullptr);
+
+    // Allocate descriptor sets
+    if (qx_needs_dequant) {
+        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_0, 1);
+    }
+    if (qy_needs_dequant) {
+        ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13);
+    }
+    ggml_pipeline_allocate_descriptor_sets(ctx, dmmv, ne12 * ne13);
+
+    if (x_non_contig) {
+        GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment));
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
+    }
+    if (y_non_contig) {
+        GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne);
+        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
+    }
+
+    for (uint64_t i13 = 0; i13 < ne13; i13++) {
+        const uint64_t i03 = i13 / r3;
+        for (uint64_t i12 = 0; i12 < ne12; i12++) {
+            const uint64_t i02 = i12 / r2;
+
+            const uint64_t it_idx0 = (i03 * ne02 + i02);
+            const uint64_t it_idx1 = (i13 * ne12 + i12);
+            const uint64_t x_offset = x_buf_offset + x_sz * it_idx0;
+            const uint64_t qy_offset = qy_buf_offset + qy_sz * it_idx1;
+            const uint64_t y_offset = y_buf_offset + y_sz * it_idx1;
+            const uint64_t d_offset = d_buf_offset + d_sz * it_idx1;
+
+            const uint64_t y_buffer_offset = (y_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
+            const uint64_t y_shader_offset = y_offset - y_buffer_offset;
+
+            const uint64_t d_buffer_offset = (d_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
+            const uint64_t d_shader_offset = d_offset - d_buffer_offset;
+
+            if (!y_non_contig && qy_needs_dequant) {
+                const std::vector<uint32_t> pc = { (uint32_t)ne11, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(y_ne / 32) };
+                ggml_vk_sync_buffers(subctx);
+                ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_1, { { d_Qy, qy_offset, qy_sz }, { d_Y, y_offset, y_sz } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)y_ne, 1, 1});
+            }
+
+            // compute
+            const std::array<uint32_t, 3> pc = { (uint32_t)ne00, (uint32_t)(y_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type))};
+            ggml_vk_sync_buffers(subctx);
+            ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, { { d_X, x_offset, x_sz }, { d_Y, y_buffer_offset, y_sz + y_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 3 * sizeof(int), &pc, { (uint32_t)ne01, 1, 1});
+
+            if (dst->backend == GGML_BACKEND_TYPE_CPU) {
+                // copy dst to host
+                float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
+                ggml_vk_sync_buffers(subctx);
+                ggml_vk_buffer_read_async(ctx, subctx, d_D, d_offset, d, sizeof(float) * d_ne);
+            }
+        }
+    }
+}
+
+static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_mul_mat_p021_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ",  backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
+    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ",  backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
+    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ",  backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl;
+#endif
+    GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
+    GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
+    GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]);  // NOLINT
+    GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]);  // NOLINT
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    const uint64_t ne00 = src0->ne[0];
+    const uint64_t ne01 = src0->ne[1];
+    const uint64_t ne02 = src0->ne[2];
+    // const uint64_t ne03 = src0->ne[3];
+
+    const uint64_t ne10 = src1->ne[0];
+    const uint64_t ne11 = src1->ne[1];
+    const uint64_t ne12 = src1->ne[2];
+    // const uint64_t ne13 = src1->ne[3];
+
+    GGML_ASSERT(ne11 == 1);
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
+    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
+
+    vk_buffer d_Qy;
+    size_t qy_buf_offset = 0;
+
+    bool src1_uma = false;
+
+    if (ctx->device->uma) {
+        ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
+        src1_uma = d_Qy != nullptr;
+    }
+
+    const uint64_t x_ne = ne00 * ne01 * ne02;
+    const uint64_t y_ne = ne10 * ne11 * ne12;
+    const uint64_t d_ne = ne01 * ne11 * ne12;
+
+    const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment);
+    const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
+    const uint64_t d_sz = sizeof(float) * d_ne;
+
+    vk_buffer d_D = extra->buffer_gpu.lock();
+    const uint64_t d_buf_offset = extra->offset;
+    GGML_ASSERT(d_D != nullptr);
+    vk_buffer d_Qx = extra_src0->buffer_gpu.lock();
+    const uint64_t qx_buf_offset = extra_src0->offset;
+    GGML_ASSERT(d_Qx != nullptr);
+    if (!src1_uma) {
+        d_Qy = extra_src1->buffer_gpu.lock();
+        qy_buf_offset = extra_src1->offset;
+        GGML_ASSERT(d_Qx != nullptr);
+    }
+
+    // Allocate descriptor sets
+    ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, 1);
+
+    const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
+    const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset;
+
+    const uint64_t d_buffer_offset = (d_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
+    const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset;
+
+    // compute
+    const std::array<uint32_t, 6> pc = { (uint32_t)ne00, (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) };
+    ggml_vk_sync_buffers(subctx);
+    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 });
+
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
+        // copy dst to host
+        float * d = (float *) dst->data;
+        ggml_vk_sync_buffers(subctx);
+        ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset, d, sizeof(float) * d_ne);
+    }
+}
+
+static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ",  backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
+    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ",  backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
+    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ",  backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl;
+#endif
+    GGML_ASSERT(!ggml_is_transposed(src0));
+    GGML_ASSERT(!ggml_is_transposed(src1));
+    GGML_ASSERT(!ggml_is_permuted(src0));
+    GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    const uint64_t ne00 = src0->ne[0];
+    const uint64_t ne01 = src0->ne[1];
+    const uint64_t ne02 = src0->ne[2];
+    // const uint64_t ne03 = src0->ne[3];
+
+    const uint64_t nb01 = src0->nb[1];
+    const uint64_t nb02 = src0->nb[2];
+
+    // const uint64_t ne10 = src1->ne[0];
+    const uint64_t ne11 = src1->ne[1];
+    const uint64_t ne12 = src1->ne[2];
+    // const uint64_t ne13 = src1->ne[3];
+
+    GGML_ASSERT(ne11 == 1);
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
+    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
+
+    vk_buffer d_Qy = nullptr;
+    size_t qy_buf_offset = 0;
+
+    bool src1_uma = false;
+
+    if (ctx->device->uma) {
+        ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset);
+        src1_uma = d_Qy != nullptr;
+    }
+
+    const uint64_t d_ne = ne01 * ne11 * ne12;
+
+    const uint32_t row_stride_x = nb01 / sizeof(ggml_fp16_t);
+    const uint32_t channel_stride_x = nb02 / sizeof(ggml_fp16_t);
+
+    const uint64_t qx_sz = ggml_nbytes(src0);
+    const uint64_t qy_sz = ggml_nbytes(src1);
+    const uint64_t d_sz = sizeof(float) * d_ne;
+
+    vk_buffer d_D = extra->buffer_gpu.lock();
+    const uint64_t d_buf_offset = extra->offset;
+    GGML_ASSERT(d_D != nullptr);
+    vk_buffer d_Qx = extra_src0->buffer_gpu.lock();
+    const uint64_t qx_buf_offset = extra_src0->offset;
+    GGML_ASSERT(d_Qx != nullptr);
+    if (!src1_uma) {
+        d_Qy = extra_src1->buffer_gpu.lock();
+        qy_buf_offset = extra_src1->offset;
+        GGML_ASSERT(d_Qx != nullptr);
+    }
+
+    // Allocate descriptor sets
+    ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, 1);
+
+    const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
+    const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset;
+
+    const uint64_t d_buffer_offset = (d_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
+    const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset;
+
+    // compute
+    const std::array<uint32_t, 7> pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, (uint32_t)(ne12 / ne02), (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) };
+    ggml_vk_sync_buffers(subctx);
+    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 });
+
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
+        // copy dst to host
+        float * d = (float *) dst->data;
+        ggml_vk_sync_buffers(subctx);
+        ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset, d, sizeof(float) * d_ne);
+    }
+}
+
+static bool ggml_vk_can_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * dst) {
+    const uint64_t ne10 = src1->ne[0];
+
+    const uint64_t ne0 = dst->ne[0];
+    const uint64_t ne1 = dst->ne[1];
+
+    // TODO: find the optimal values for these
+    return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
+           (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16 || ggml_is_quantized(src1->type)) &&
+           dst->type == GGML_TYPE_F32 &&
+           ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_TYPE_GPU);
+}
+
+static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")" << std::endl;
+#endif
+    if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
+        ggml_vk_mul_mat_vec_p021_f16_f32(ctx, subctx, src0, src1, dst);
+    } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
+        ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, src0, src1, dst);
+    } else if (src1->ne[1] == 1 && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) {
+        ggml_vk_mul_mat_vec_q_f16(ctx, subctx, src0, src1, dst);
+    } else {
+        ggml_vk_mul_mat_q_f16(ctx, subctx, src0, src1, dst);
+    }
+}
+
+// static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context * subctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
+//
+// }
+
+static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    // guaranteed to be an integer due to the check in ggml_can_repeat
+    const uint64_t ne0 = dst->ne[0];
+    const uint64_t ne1 = dst->ne[1];
+    const uint64_t ne2 = dst->ne[2];
+    const uint64_t ne3 = dst->ne[3];
+
+    const uint64_t ne00 = src0->ne[0];
+    const uint64_t ne01 = src0->ne[1];
+    const uint64_t ne02 = src0->ne[2];
+    const uint64_t ne03 = src0->ne[3];
+
+    const uint64_t nb0 = dst->nb[0];
+    const uint64_t nb1 = dst->nb[1];
+    const uint64_t nb2 = dst->nb[2];
+    const uint64_t nb3 = dst->nb[3];
+
+    const uint64_t nb00 = src0->nb[0];
+    const uint64_t nb01 = src0->nb[1];
+    const uint64_t nb02 = src0->nb[2];
+    const uint64_t nb03 = src0->nb[3];
+
+    const uint64_t nr0 = ne0/ne00;
+    const uint64_t nr1 = ne1/ne01;
+    const uint64_t nr2 = ne2/ne02;
+    const uint64_t nr3 = ne3/ne03;
+
+    // TODO: support for transposed / permuted tensors
+    GGML_ASSERT(nb0  == sizeof(float));
+    GGML_ASSERT(nb00 == sizeof(float));
+    GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
+    GGML_ASSERT(dst->backend == GGML_BACKEND_TYPE_GPU);
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
+    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
+
+    const vk_buffer src_buf = extra_src0->buffer_gpu.lock();
+    const uint64_t src_offset = extra_src0->offset;
+    vk_buffer dst_buf = extra->buffer_gpu.lock();
+    const uint64_t dst_offset = extra->offset;
+
+    std::vector<vk::BufferCopy> copies;
+
+    for                         (uint64_t i3 = 0; i3 < nr3;  i3++) {
+        for                     (uint64_t k3 = 0; k3 < ne03; k3++) {
+            for                 (uint64_t i2 = 0; i2 < nr2;  i2++) {
+                for             (uint64_t k2 = 0; k2 < ne02; k2++) {
+                    for         (uint64_t i1 = 0; i1 < nr1;  i1++) {
+                        for     (uint64_t k1 = 0; k1 < ne01; k1++) {
+                            for (uint64_t i0 = 0; i0 < nr0;  i0++) {
+                                copies.push_back({
+                                    src_offset + (i3*ne03 + k3)*nb3  + (i2*ne02 + k2)*nb2  + (i1*ne01 + k1)*nb1  + (i0*ne00)*nb0,
+                                    dst_offset + (          k3)*nb03 + (          k2)*nb02 + (          k1)*nb01,
+                                    ne00*nb0,
+                                });
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+
+    ggml_vk_sync_buffers(subctx);
+    subctx->s->buffer.copyBuffer(src_buf->buffer, dst_buf->buffer, copies);
+
+    GGML_UNUSED(ctx);
+    GGML_UNUSED(src1);
+}
+
+
+static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op) {
+    switch (op) {
+    case GGML_OP_ADD:
+        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_add_f32;
+        }
+        return nullptr;
+    case GGML_OP_GET_ROWS:
+        GGML_ASSERT(src1->type == GGML_TYPE_I32);
+        if (dst->type == GGML_TYPE_F16) {
+            return ctx->device->pipeline_get_rows[src0->type];
+        }
+        if (dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_get_rows_f32[src0->type];
+        }
+        return nullptr;
+    case GGML_OP_MUL:
+        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_mul_f32;
+        }
+        return nullptr;
+    case GGML_OP_SCALE:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_scale_f32;
+        }
+        return nullptr;
+    case GGML_OP_SQR:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_sqr_f32;
+        }
+        return nullptr;
+    case GGML_OP_CLAMP:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_clamp_f32;
+        }
+        return nullptr;
+    case GGML_OP_CPY:
+    case GGML_OP_CONT:
+    case GGML_OP_DUP:
+        return ggml_vk_get_cpy_pipeline(ctx, src0->type, dst->type);
+    case GGML_OP_NORM:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_norm_f32;
+        }
+        return nullptr;
+    case GGML_OP_RMS_NORM:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_rms_norm_f32;
+        }
+        return nullptr;
+    case GGML_OP_UNARY:
+        switch (ggml_get_unary_op(dst)) {
+            case GGML_UNARY_OP_SILU:
+                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+                    return ctx->device->pipeline_silu_f32;
+                }
+                break;
+            case GGML_UNARY_OP_GELU:
+                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+                    return ctx->device->pipeline_gelu_f32;
+                }
+                break;
+            case GGML_UNARY_OP_RELU:
+                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+                    return ctx->device->pipeline_relu_f32;
+                }
+                break;
+            default:
+                break;
+        }
+        return nullptr;
+    case GGML_OP_DIAG_MASK_INF:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_diag_mask_inf_f32;
+        }
+        return nullptr;
+    case GGML_OP_SOFT_MAX:
+        if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && (src2 == nullptr || src2->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_soft_max_f32;
+        }
+        return nullptr;
+    case GGML_OP_ROPE:
+        {
+            const int mode = ((const int32_t *) dst->op_params)[2];
+            const bool is_neox = mode & 2;
+            const bool is_glm  = mode & 4;
+
+            if (is_glm) {
+                return nullptr;
+            }
+
+            if (is_neox) {
+                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+                    return ctx->device->pipeline_rope_neox_f32;
+                }
+                if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
+                    return ctx->device->pipeline_rope_neox_f16;
+                }
+            } else {
+                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+                    return ctx->device->pipeline_rope_f32;
+                }
+                if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
+                    return ctx->device->pipeline_rope_f16;
+                }
+            }
+            return nullptr;
+        }
+    case GGML_OP_ARGSORT:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_I32) {
+            return ctx->device->pipeline_argsort_f32;
+        }
+        return nullptr;
+    default:
+        return nullptr;
+    }
+}
+
+static ggml_vk_func_t ggml_vk_op_get_func(ggml_op op) {
+    switch(op) {
+    case GGML_OP_REPEAT:
+        return ggml_vk_op_repeat;
+    default:
+        return nullptr;
+    }
+}
+
+template<typename PC>
+static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op, const PC&& pc) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
+    if (src1 != nullptr) {
+        std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
+    }
+    if (src2 != nullptr) {
+        std::cerr << "), (" << src2 << ", name=" << src2->name << ", type=" << src2->type << ", backend=" << src2->backend << ", ne0=" << src2->ne[0] << ", ne1=" << src2->ne[1] << ", ne2=" << src2->ne[2] << ", ne3=" << src2->ne[3] << ", nb0=" << src2->nb[0] << ", nb1=" << src2->nb[1] << ", nb2=" << src2->nb[2] << ", nb3=" << src2->nb[3];
+    }
+    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "), " << ggml_op_name(op) << ")" << std::endl;
+#endif
+    GGML_ASSERT(op == GGML_OP_GET_ROWS || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type))));  // NOLINT
+    GGML_ASSERT(op == GGML_OP_CPY || ggml_vk_dim01_contiguous(src0));  // NOLINT
+    GGML_ASSERT(dst->extra != nullptr);
+    const uint64_t ne00 = src0->ne[0];
+    const uint64_t ne01 = src0->ne[1];
+    const uint64_t ne02 = src0->ne[2];
+    const uint64_t ne03 = src0->ne[3];
+    const uint64_t ne0 = ne00 * ne01;
+    const bool use_src1 = src1 != nullptr;
+    const uint64_t ne10 = use_src1 ? src1->ne[0] : 0;
+    const uint64_t ne11 = use_src1 ? src1->ne[1] : 0;
+    const uint64_t ne12 = use_src1 ? src1->ne[2] : 0;
+    const uint64_t ne13 = use_src1 ? src1->ne[3] : 0;
+    const uint64_t ne1 = ne10 * ne11;
+    // const uint64_t nb10 = use_src1 ? src1->nb[0] : 0;
+    const uint64_t nb2  = dst->nb[2];
+    const uint64_t nb3  = dst->nb[3];
+
+    const bool use_src2 = src2 != nullptr;
+    const uint64_t ne2 = use_src2 ? src2->ne[0] * src2->ne[1] : 0;
+
+    vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, op);
+    ggml_vk_func_t op_func;
+
+    if (pipeline == nullptr) {
+        op_func = ggml_vk_op_get_func(op);
+        if (op_func == nullptr) {
+            std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(op) << " for " << ggml_type_name(src0->type);
+            if (src1 != nullptr) {
+                std::cerr << " and " << ggml_type_name(src1->type);
+            }
+            std::cerr << " to " << ggml_type_name(dst->type) << std::endl;
+            GGML_ASSERT(false);
+        }
+
+        op_func(ctx, subctx, src0, src1, dst);
+        return;
+    }
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
+    ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
+    ggml_tensor_extra_gpu * extra_src1 = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
+    ggml_tensor_extra_gpu * extra_src2 = use_src2 ? (ggml_tensor_extra_gpu *) src2->extra : nullptr;
+
+    vk_buffer d_X = nullptr;
+    size_t x_buf_offset = 0;
+    vk_buffer d_Y = nullptr;
+    size_t y_buf_offset = 0;
+    vk_buffer d_Z = nullptr;
+    size_t z_buf_offset = 0;
+
+    bool src0_uma = false;
+    bool src1_uma = false;
+    bool src2_uma = false;
+
+    if (ctx->device->uma) {
+        ggml_vk_host_get(ctx, src0->data, d_X, x_buf_offset);
+        src0_uma = d_X != nullptr;
+        if (use_src1) {
+            ggml_vk_host_get(ctx, src1->data, d_Y, y_buf_offset);
+            src1_uma = d_Y != nullptr;
+        }
+        if (use_src2) {
+            ggml_vk_host_get(ctx, src1->data, d_Z, z_buf_offset);
+            src2_uma = d_Z != nullptr;
+        }
+    }
+
+    uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type)/ggml_blck_size(src0->type) * ne0, ctx->device->properties.limits.minStorageBufferOffsetAlignment);
+    uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0;
+    uint64_t z_sz = use_src2 ? ggml_vk_align_size(ggml_type_size(src2->type) * ne2, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0;
+    uint64_t d_sz = ggml_type_size(dst->type) * ne0;
+
+    vk_buffer d_D = extra->buffer_gpu.lock();
+
+    // Workaround for tiny tensor inputs on ROPE
+    if (use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU && y_sz > d_D->size) {
+        y_sz = VK_WHOLE_SIZE;
+    }
+
+    GGML_ASSERT(d_D != nullptr);
+    uint64_t d_buf_offset = (extra->offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
+    GGML_ASSERT(d_buf_offset == extra->offset || op == GGML_OP_CPY);  // NOLINT
+    if(!src0_uma) {
+        d_X = extra_src0->buffer_gpu.lock();
+        x_buf_offset = extra_src0->offset;
+        GGML_ASSERT(d_X != nullptr);
+    }
+    if (use_src1 && !src1_uma) {
+        d_Y = extra_src1->buffer_gpu.lock();
+        y_buf_offset = extra_src1->offset;
+        GGML_ASSERT(d_Y != nullptr);
+    }
+
+    if (use_src2 && !src2_uma) {
+        d_Z = extra_src2->buffer_gpu.lock();
+        z_buf_offset = extra_src2->offset;
+        GGML_ASSERT(d_Z != nullptr);
+    }
+
+    if (op == GGML_OP_CPY || op == GGML_OP_GET_ROWS) {
+        x_sz = ggml_nbytes(src0);
+        y_sz = use_src1 ? ggml_nbytes(src1) : 0;
+        d_sz = ggml_nbytes(dst);
+
+        if (x_buf_offset + x_sz >= d_X->size) {
+            x_sz = VK_WHOLE_SIZE;
+        }
+        if (use_src1 && y_buf_offset + y_sz >= d_Y->size) {
+            y_sz = VK_WHOLE_SIZE;
+        }
+        if (d_buf_offset + d_sz >= d_D->size) {
+            d_sz = VK_WHOLE_SIZE;
+        }
+    }
+
+    std::array<uint32_t, 3> elements;
+
+    // Single call if dimension 2 is contiguous
+    if (op == GGML_OP_CPY || op == GGML_OP_GET_ROWS || (ggml_is_contiguous(src0) && (src1 == nullptr || ggml_is_contiguous(src1)))) {
+        ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, 1);
+
+        switch (dst->op) {
+        case GGML_OP_NORM:
+        case GGML_OP_RMS_NORM:
+        case GGML_OP_SOFT_MAX:
+            elements = { (uint32_t)ggml_nrows(src0), 1, 1 };
+            break;
+        case GGML_OP_DIAG_MASK_INF:
+        case GGML_OP_ROPE:
+            elements = { (uint32_t)ggml_nrows(src0), (uint32_t)ne00, 1 };
+            break;
+        case GGML_OP_GET_ROWS:
+            elements = {  (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) };
+            break;
+        default:
+            elements = { (uint32_t)ggml_nelements(src0), 1, 1 };
+            break;
+        }
+
+        if (op != GGML_OP_CPY && op != GGML_OP_GET_ROWS) {
+            if (x_sz != VK_WHOLE_SIZE) {
+                x_sz *= ne02 * ne03;
+            }
+            if (use_src1 && y_sz != VK_WHOLE_SIZE) {
+                y_sz *= ne12 * ne13;
+            }
+            if (d_sz != VK_WHOLE_SIZE) {
+                d_sz *= ne02 * ne03;
+            }
+        }
+
+        if (op == GGML_OP_SOFT_MAX) {
+            // Empty src1 and src2 are possible on soft_max, but the shader needs buffers
+            vk_subbuffer subbuf_y;
+            if (use_src1) {
+                subbuf_y = { d_Y, y_buf_offset, y_sz };
+            } else {
+                subbuf_y = { ctx->prealloc_y, 0, ctx->prealloc_y->size };
+            }
+
+            vk_subbuffer subbuf_z;
+            if (use_src2) {
+                subbuf_z = { d_Z, z_buf_offset, z_sz };
+            } else {
+                subbuf_z = { ctx->prealloc_y, 0, ctx->prealloc_y->size };
+            }
+
+            ggml_vk_sync_buffers(subctx);
+            ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, subbuf_y, subbuf_z, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
+        } else if (use_src1) {
+            ggml_vk_sync_buffers(subctx);
+            ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
+        } else {
+            ggml_vk_sync_buffers(subctx);
+            ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
+        }
+        if (dst->backend == GGML_BACKEND_TYPE_CPU && op == GGML_OP_CPY) {
+            ggml_vk_d2h_tensor_2d(ctx, subctx, d_D, 0, dst);
+        } else if(dst->backend == GGML_BACKEND_TYPE_CPU) {
+            // copy dst to host
+            float * d = (float *) dst->data;
+            ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, d_sz);
+        }
+    } else {
+        GGML_ASSERT(op != GGML_OP_SOFT_MAX);
+
+        ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, ne02 * ne03);
+
+        switch (dst->op) {
+        case GGML_OP_NORM:
+        case GGML_OP_RMS_NORM:
+        case GGML_OP_SOFT_MAX:
+            elements = { (uint32_t)ne01, 1, 1 };
+            break;
+        case GGML_OP_DIAG_MASK_INF:
+        case GGML_OP_ROPE:
+            elements = { (uint32_t)ne01, (uint32_t)ne00, 1 };
+            break;
+        case GGML_OP_GET_ROWS:
+            elements = {  (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) };
+            break;
+        default:
+            elements = { (uint32_t)ne0, 1, 1 };
+            break;
+        }
+
+        for (uint64_t i03 = 0; i03 < ne03; i03++) {
+            for (uint64_t i02 = 0; i02 < ne02; i02++) {
+                const uint32_t it_idx0 = (i03 * ne02 + i02);
+                const uint32_t it_idx1 = use_src1 ? ((i03 % ne13) * ne12 + (i02 % ne12)) : 0;
+                const uint32_t x_offset = x_sz * it_idx0;
+                const uint32_t y_offset = y_sz * it_idx1;
+                const uint32_t d_offset = d_sz * it_idx0;
+
+                if (use_src1) {
+                    ggml_vk_sync_buffers(subctx);
+                    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_Y, y_buf_offset + y_offset, y_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements);
+                } else {
+                    ggml_vk_sync_buffers(subctx);
+                    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements);
+                }
+                if (dst->backend == GGML_BACKEND_TYPE_CPU) {
+                    // copy dst to host
+                    ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset + d_offset, (char *) dst->data + i02*nb2 + i03*nb3, d_sz);
+                }
+            }
+        }
+    }
+}
+
+static void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f });
+}
+
+static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t src1_type_size = ggml_type_size(src1->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_GET_ROWS, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        0.0f, 0.0f,
+    });
+}
+
+static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t src1_type_size = ggml_type_size(src1->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ADD, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        0.0f, 0.0f,
+    });
+}
+
+static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t src1_type_size = ggml_type_size(src1->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_MUL, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        0.0f, 0.0f,
+    });
+}
+
+static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
+    float * op_params = (float *)dst->op_params;
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SCALE, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        op_params[0], 0.0f
+    });
+}
+
+static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SQR, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        0.0f, 0.0f,
+    });
+}
+
+static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
+    float * op_params = (float *)dst->op_params;
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CLAMP, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        op_params[0], op_params[1],
+    });
+}
+
+static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+    const uint32_t d_offset = (extra->offset % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size;
+
+    ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CPY, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        d_offset,
+        0.0f, 0.0f,
+    });
+}
+
+static void ggml_vk_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
+    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], 0.0f, 0.0f });
+}
+
+static void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
+    float * op_params = (float *)dst->op_params;
+    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f });
+}
+
+static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
+    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f });
+}
+
+static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
+    int32_t * op_params = (int32_t *)dst->op_params;
+    ggml_vk_op_f32<vk_op_diag_mask_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] });
+}
+
+static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) {
+    float * op_params = (float *)dst->op_params;
+
+    float scale = op_params[0];
+    float max_bias = op_params[1];
+
+    const uint32_t ncols =   (uint32_t)src0->ne[0];
+    const uint32_t nrows_x = (uint32_t)ggml_nrows(src0);
+    const uint32_t nrows_y = (uint32_t)src0->ne[1];
+
+    const uint32_t n_head_kv   = nrows_x/nrows_y;
+    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
+
+    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+    ggml_vk_op_f32<vk_op_soft_max_push_constants>(ctx, subctx, src0, src1, src2, dst, GGML_OP_SOFT_MAX, {
+        ncols,
+        nrows_y,
+        src2 != nullptr ? (uint32_t)1 : (uint32_t)0,
+        scale, max_bias,
+        m0, m1,
+        n_head_log2,
+    });
+}
+
+static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    const int n_dims        = ((int32_t *) dst->op_params)[1];
+    const int mode          = ((int32_t *) dst->op_params)[2];
+    // const int n_ctx         = ((int32_t *) dst->op_params)[3];
+    const int n_orig_ctx    = ((int32_t *) dst->op_params)[4];
+    const float freq_base   = ((float *)   dst->op_params)[5];
+    const float freq_scale  = ((float *)   dst->op_params)[6];
+    const float ext_factor  = ((float *)   dst->op_params)[7];
+    const float attn_factor = ((float *)   dst->op_params)[8];
+    const float beta_fast   = ((float *)   dst->op_params)[9];
+    const float beta_slow   = ((float *)   dst->op_params)[10];
+
+    const bool is_neox = mode & 2;
+    const bool is_glm  = mode & 4;
+
+    GGML_ASSERT(!is_glm);
+
+    float corr_dims[2];
+    ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims);
+
+    if (is_neox) {
+        const float theta_scale = powf(freq_base, -2.0f/n_dims);
+        const float inv_ndims = -1.0f / n_dims;
+        ggml_vk_op_f32<vk_op_rope_neox_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f}, theta_scale, inv_ndims });
+    } else {
+        ggml_vk_op_f32<vk_op_rope_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f} });
+    }
+}
+
+static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
+    int32_t * op_params = (int32_t *)dst->op_params;
+    ggml_vk_op_f32<vk_op_argsort_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_ARGSORT, { (uint32_t)src0->ne[0], ((ggml_sort_order) op_params[0]) == GGML_SORT_ORDER_ASC });
+}
+
+#ifdef GGML_VULKAN_RUN_TESTS
+static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0, int ne1, int i0, int i1, int i2) {
+    if (type != GGML_TYPE_F32 && type != GGML_TYPE_F16) {
+        return;
+    }
+    i0 = std::max(i0, 5);
+    i1 = std::max(i1, 5);
+    i2 = std::max(i2, 0);
+    fprintf(stderr, "         ");
+    for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
+        fprintf(stderr, "%7d ", idx1);
+    }
+    fprintf(stderr, "\n");
+    for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) {
+        fprintf(stderr, "%7d: ", idx0);
+        for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
+            if (idx0 >= 0 && idx0 < ne0 && idx1 >= 0 && idx1 < ne1) {
+                float val;
+                if (type == GGML_TYPE_F32) {
+                    val = *((const float *) data + i2*ne1*ne0 + idx1*ne0 + idx0);
+                } else if (type == GGML_TYPE_F16) {
+                    val = ggml_fp16_to_fp32(*((const ggml_fp16_t *) data + i2*ne1*ne0 + idx1*ne0 + idx0));
+                } else {
+                    GGML_ASSERT(false);
+                }
+                fprintf(stderr, "% 7.2f ", val);
+            } else {
+                fprintf(stderr, "        ");
+            }
+        }
+        fprintf(stderr, "\n");
+    }
+}
+
+template <typename X_TYPE, typename Y_TYPE>
+static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, int split_k, int shader_size) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_test_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << shader_size << ")" << std::endl;
+#endif
+    const size_t x_ne = m * k * batch;
+    const size_t y_ne = k * n * batch;
+    const size_t d_ne = m * n * batch;
+
+    vk_pipeline p;
+    std::string shname;
+    if (shader_size == 0) {
+        if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f32->a_s;
+            shname = "F32_ALIGNED_S";
+        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f16_f32->a_s;
+            shname = "F16_F32_ALIGNED_S";
+        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f16->a_s;
+            shname = "F16_ALIGNED_S";
+        } else {
+            GGML_ASSERT(false);
+        }
+    } else if (shader_size == 1) {
+        if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f32->a_m;
+            shname = "F32_ALIGNED_M";
+        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f16_f32->a_m;
+            shname = "F16_F32_ALIGNED_M";
+        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f16->a_m;
+            shname = "F16_ALIGNED_M";
+        } else {
+            GGML_ASSERT(false);
+        }
+    } else if (shader_size == 2) {
+        if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f32->a_l;
+            shname = "F32_ALIGNED_L";
+        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f16_f32->a_l;
+            shname = "F16_F32_ALIGNED_L";
+        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
+            p = ctx->device->pipeline_matmul_f16->a_l;
+            shname = "F16_ALIGNED_L";
+        } else {
+            GGML_ASSERT(false);
+        }
+    } else {
+        GGML_ASSERT(0);
+    }
+
+    const size_t kpad = ggml_vk_align_size(k, p->align);
+
+    if (k != kpad) {
+        if (shader_size == 0) {
+            if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f32->s;
+                shname = "F32_S";
+            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f16_f32->s;
+                shname = "F16_F32_S";
+            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f16->s;
+                shname = "F16_S";
+            }
+        } else if (shader_size == 1) {
+            if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f32->m;
+                shname = "F32_M";
+            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f16_f32->m;
+                shname = "F16_F32_M";
+            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f16->m;
+                shname = "F16_M";
+            }
+        } else if (shader_size == 2) {
+            if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f32->l;
+                shname = "F32_L";
+            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f16_f32->l;
+                shname = "F16_F32_L";
+            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
+                p = ctx->device->pipeline_matmul_f16->l;
+                shname = "F16_L";
+            }
+        }
+    }
+
+    ggml_pipeline_allocate_descriptor_sets(ctx, p, num_it);
+    if (split_k > 1) {
+        ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it);
+
+        if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) {
+            // Resize buffer
+            if (ctx->prealloc_split_k != nullptr) {
+                ggml_vk_destroy_buffer(ctx->prealloc_split_k);
+            }
+            ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal);
+        }
+    }
+
+    vk_buffer d_X = ggml_vk_create_buffer_check(ctx, sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer d_Y = ggml_vk_create_buffer_check(ctx, sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer d_D = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
+
+    X_TYPE* x = (X_TYPE *) malloc(sizeof(X_TYPE) * x_ne);
+    Y_TYPE* y = (Y_TYPE *) malloc(sizeof(Y_TYPE) * y_ne);
+    float* d = (float *) malloc(sizeof(float) * d_ne);
+
+    for (size_t i = 0; i < x_ne; i++) {
+        if (std::is_same<float, X_TYPE>()) {
+            x[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
+        } else if (std::is_same<ggml_fp16_t, X_TYPE>()) {
+            x[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f);
+        } else {
+            GGML_ASSERT(false);
+        }
+    }
+    for (size_t i = 0; i < y_ne; i++) {
+        if (std::is_same<float, Y_TYPE>()) {
+            // y[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
+            y[i] = (i % k == i / k) ? 1.0f : 0.0f;
+        } else if (std::is_same<ggml_fp16_t, Y_TYPE>()) {
+            // y[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f);
+            y[i] = ggml_fp32_to_fp16((i % k == i / k) ? 1.0f : 0.0f);
+        } else {
+            GGML_ASSERT(false);
+        }
+    }
+
+    ggml_vk_buffer_write(ctx, d_X, 0, x, sizeof(X_TYPE) * k * m * batch);
+    ggml_vk_buffer_write(ctx, d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch);
+
+    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+    for (size_t i = 0; i < num_it; i++) {
+        ggml_vk_ctx_begin(ctx, subctx);
+        ggml_vk_matmul(ctx, subctx, p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n);
+        ggml_vk_ctx_end(subctx);
+    }
+
+    auto begin = std::chrono::high_resolution_clock::now();
+    ggml_vk_submit(subctx, ctx->fence);
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
+
+    auto end = std::chrono::high_resolution_clock::now();
+    double time = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
+
+    // copy dst to host
+    ggml_vk_buffer_read(ctx, d_D, 0, d, sizeof(float) * d_ne);
+
+    float * d_chk = (float *) malloc(sizeof(float) * d_ne);
+
+    ggml_init_params iparams = {
+        /*.mem_size   =*/ 1024*1024*1024,
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ true,
+    };
+
+    ggml_context * ggml_ctx = ggml_init(iparams);
+
+    ggml_type src0_type;
+    ggml_type src1_type;
+
+    if (std::is_same<float, X_TYPE>()) {
+        src0_type = GGML_TYPE_F32;
+    } else if (std::is_same<ggml_fp16_t, X_TYPE>()) {
+        src0_type = GGML_TYPE_F16;
+    } else {
+        GGML_ASSERT(false);
+    }
+    if (std::is_same<float, Y_TYPE>()) {
+        src1_type = GGML_TYPE_F32;
+    } else if (std::is_same<ggml_fp16_t, Y_TYPE>()) {
+        src1_type = GGML_TYPE_F16;
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    ggml_tensor * src0_ggml = ggml_new_tensor_3d(ggml_ctx, src0_type, k, m, batch);
+    ggml_tensor * src1_ggml = ggml_new_tensor_3d(ggml_ctx, src1_type, k, n, batch);
+    ggml_tensor * tensor_ggml = ggml_mul_mat(ggml_ctx, src0_ggml, src1_ggml);
+
+    src0_ggml->data = x;
+    src1_ggml->data = y;
+    tensor_ggml->data = d_chk;
+
+    ctx->disable = true;
+
+    ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx);
+    ggml_build_forward_expand(cgraph, tensor_ggml);
+
+    ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1);
+
+    ctx->disable = false;
+
+    ggml_free(ggml_ctx);
+
+    double avg_err = 0.0;
+    int first_err_n = -1;
+    int first_err_m = -1;
+    int first_err_b = -1;
+
+    for (size_t i = 0; i < m*n*batch; i++) {
+        double err = std::fabs(d[i] - d_chk[i]);
+        avg_err += err;
+
+        if (err > 0.05f && first_err_n == -1) {
+            first_err_b = i / (m * n);
+            first_err_n = (i % (m * n)) / m;
+            first_err_m = (i % (m * n)) % m;
+        }
+    }
+
+    avg_err /= m * n;
+
+    std::cerr << "TEST " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time / num_it << "ms avg_err=" << avg_err << std::endl;
+
+    if (avg_err > 0.1) {
+        std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl;
+        std::cerr << "Actual result: " << std::endl << std::endl;
+        ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+        std::cerr << std::endl;
+        ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n + 15, first_err_b);
+        std::cerr << "Expected result: " << std::endl << std::endl;
+        ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+        if (split_k > 1) {
+            float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k);
+            ggml_vk_buffer_read(ctx, ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k);
+
+            std::cerr << "d_buf0: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            std::cerr << "d_buf1: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf + d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            std::cerr << "d_buf2: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf + 2 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            std::cerr << "d_buf3: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf + 3 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            free(split_k_buf);
+        }
+    }
+
+    free(d_chk);
+
+    ggml_vk_queue_cleanup(ctx, ctx->device->transfer_queue);
+    ggml_vk_queue_cleanup(ctx, ctx->device->compute_queue);
+
+    ggml_vk_destroy_buffer(d_X);
+    ggml_vk_destroy_buffer(d_Y);
+    ggml_vk_destroy_buffer(d_D);
+
+    ggml_pipeline_cleanup(p);
+    ggml_pipeline_cleanup(ctx->device->pipeline_matmul_split_k_reduce);
+
+    free(x);
+    free(y);
+    free(d);
+}
+
+static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, int i0, int i1, int i2, int i3) {
+    if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) {
+        return;
+    }
+    i0 = std::max(i0, 5);
+    i1 = std::max(i1, 5);
+    i2 = std::max(i2, 0);
+    i3 = std::max(i3, 0);
+    fprintf(stderr, "         ");
+    for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
+        fprintf(stderr, "%7d ", idx1);
+    }
+    fprintf(stderr, "\n");
+    for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) {
+        fprintf(stderr, "%7d: ", idx0);
+        for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
+            if (idx0 >= 0 && idx0 < tensor->ne[0] && idx1 >= 0 && idx1 < tensor->ne[1] && i2 >= 0 && i2 < tensor->ne[2] && i3 >= 0 && i3 < tensor->ne[3]) {
+                float val;
+                if (tensor->type == GGML_TYPE_F32) {
+                    val = *(float *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]);
+                } else if (tensor->type == GGML_TYPE_F16) {
+                    val = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]));
+                } else {
+                    GGML_ASSERT(false);
+                }
+                fprintf(stderr, "% 7.2f ", val);
+            } else {
+                fprintf(stderr, "        ");
+            }
+        }
+        fprintf(stderr, "\n");
+    }
+}
+
+static void ggml_vk_test_h2d_nc(ggml_backend_vk_context * ctx, size_t ne0, size_t ne1, size_t ne2, size_t ne3) {
+    const size_t ne = ne0 * ne1 * ne2 * ne3;
+
+    ggml_init_params iparams = {
+        /*.mem_size   =*/ 1024*1024*1024,
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ true,
+    };
+
+    ggml_context * ggml_ctx = ggml_init(iparams);
+
+    ggml_tensor * tensor = ggml_new_tensor_4d(ggml_ctx, GGML_TYPE_F32, ne0, ne2, ne1, ne3);  // NOLINT
+    ggml_tensor * result_tensor = ggml_new_tensor_4d(ggml_ctx, GGML_TYPE_F32, ne0, ne1, ne2, ne3);
+
+    float * data = (float *) ggml_vk_host_malloc(ctx, ggml_nbytes(tensor));
+    tensor->data = data;
+
+    float * result_data = (float *) malloc(ggml_nbytes(tensor));
+    result_tensor->data = result_data;
+
+    // Permute
+    {
+        size_t tmp = tensor->nb[2];
+        tensor->nb[2] = tensor->nb[1];
+        tensor->nb[1] = tmp;
+
+        tensor->ne[2] = ne2;
+        tensor->ne[1] = ne1;
+    }
+
+    for (size_t i = 0; i < ne; i++) {
+        data[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
+    }
+
+    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+    ggml_vk_ctx_begin(ctx, subctx);
+
+    vk_buffer buffer = ggml_vk_create_buffer_check(ctx, ggml_nbytes(tensor), vk::MemoryPropertyFlagBits::eDeviceLocal);
+
+    ggml_vk_h2d_tensor_2d(ctx, subctx, buffer, 0, tensor, 0, 0, ggml_nrows(tensor));
+
+    ggml_vk_ctx_end(subctx);
+    ggml_vk_submit(subctx, ctx->fence);
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_h2d_nc waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
+
+    ggml_vk_buffer_read(ctx, buffer, 0, result_data, ggml_nbytes(tensor));
+
+    double avg_err = 0.0;
+    int first_err_i0 = -1;
+    int first_err_i1 = -1;
+    int first_err_i2 = -1;
+    int first_err_i3 = -1;
+
+    for (size_t i3 = 0; i3 < ne3; i3++) {
+        for (size_t i2 = 0; i2 < ne2; i2++) {
+            for (size_t i1 = 0; i1 < ne1; i1++) {
+                for (size_t i0 = 0; i0 < ne0; i0++) {
+                    float correct = *(float *) ((char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]);
+                    float result = *(float *) ((char *) result_data + i3*ne2*ne1*ne0*sizeof(float) + i2*ne1*ne0*sizeof(float) + i1*ne0*sizeof(float) + i0*sizeof(float));
+                    double err = std::fabs(result - correct);
+
+                    avg_err += err;
+
+                    if (err > 0.05f && first_err_i0 == -1) {
+                        first_err_i0 = i0;
+                        first_err_i1 = i1;
+                        first_err_i2 = i2;
+                        first_err_i3 = i3;
+                    }
+                }
+            }
+        }
+    }
+
+    avg_err /= ne;
+
+    std::cerr << "TEST nc copy ne0=" << ne0 << " ne1=" << ne1 << " ne2=" << ne2 << " ne3=" << ne3 << " avg_err=" << avg_err << std::endl;
+
+    if (avg_err > 0.1) {
+        std::cerr << "i0 = " << first_err_i0 << " i1 = " << first_err_i1 << " i2 = " << first_err_i2 << " i3 = " << first_err_i3 << std::endl;
+        std::cerr << "Actual result: " << std::endl << std::endl;
+        ggml_vk_print_tensor_area(result_tensor, first_err_i0, first_err_i1, first_err_i2, first_err_i3);
+        std::cerr << "Expected result: " << std::endl << std::endl;
+        ggml_vk_print_tensor_area(tensor, first_err_i0, first_err_i1, first_err_i2, first_err_i3);
+    }
+
+    ggml_free(ggml_ctx);
+
+    ggml_vk_destroy_buffer(buffer);
+
+    ggml_vk_host_free(ctx, data);
+    free(result_data);
+}
+
+static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool pinned) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_test_transfer(" << ne << ")" << std::endl;
+#endif
+    // Check transfers are correct
+    vk_buffer buffer = ggml_vk_create_buffer_check(ctx, sizeof(float) * ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
+
+    float * x;
+    float * y;
+    if (pinned) {
+        x = (float *) ggml_vk_host_malloc(ctx, sizeof(float) * ne);
+        y = (float *) ggml_vk_host_malloc(ctx, sizeof(float) * ne);
+    } else {
+        x = (float *) malloc(sizeof(float) * ne);
+        y = (float *) malloc(sizeof(float) * ne);
+    }
+
+    for (size_t i = 0; i < ne; i++) {
+        x[i] = rand() / (float)RAND_MAX;
+    }
+
+    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+    ggml_vk_ctx_begin(ctx, subctx);
+
+    auto begin = std::chrono::high_resolution_clock::now();
+
+    ggml_vk_buffer_write_async(ctx, subctx, buffer, 0, x, sizeof(float) * ne);
+
+    for (auto& cpy : subctx->in_memcpys) {
+        memcpy(cpy.dst, cpy.src, cpy.n);
+    }
+    subctx->in_memcpys.clear();
+
+    ggml_vk_ctx_end(subctx);
+    ggml_vk_submit(subctx, ctx->fence);
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
+
+    auto end = std::chrono::high_resolution_clock::now();
+
+    double ms_to_gpu = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
+
+    ggml_vk_ctx_begin(ctx, subctx);
+
+    begin = std::chrono::high_resolution_clock::now();
+
+    ggml_vk_buffer_read_async(ctx, subctx, buffer, 0, y, sizeof(float) * ne);
+
+    ggml_vk_ctx_end(subctx);
+    ggml_vk_submit(subctx, ctx->fence);
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
+
+    for (auto& cpy : subctx->out_memcpys) {
+        memcpy(cpy.dst, cpy.src, cpy.n);
+    }
+    subctx->out_memcpys.clear();
+
+    end = std::chrono::high_resolution_clock::now();
+
+    double ms_from_gpu = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
+
+    double avg_err = 0.0;
+    for (size_t i = 0; i < ne; i++) {
+        avg_err += std::fabs(x[i] - y[i]);
+    }
+
+    double kb = ne * sizeof(float) / 1024.0;
+
+    std::cerr << "TEST TRANSFER " << kb << " KB to_gpu " << ms_to_gpu << "ms (" << kb / ms_to_gpu * 1000.0 / 1024.0 << " MB/s) from_gpu " << ms_from_gpu << "ms (" << kb / ms_from_gpu * 1000.0 / 1024.0 << " MB/s) avg_err=" << avg_err / ne << std::endl;
+
+    ggml_vk_destroy_buffer(buffer);
+
+    if (pinned) {
+        ggml_vk_host_free(ctx, x);
+        ggml_vk_host_free(ctx, y);
+    } else {
+        free(x);
+        free(y);
+    }
+}
+
+static void ggml_vk_quantize_data(const float * from, void * to, size_t ne, ggml_type quant) {
+    ggml_quantize_chunk(quant, from, to, 0, 1, ne, nullptr);
+}
+
+static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_test_dequant(" << ne << ")" << std::endl;
+#endif
+    const size_t x_sz = sizeof(float) * ne;
+    const size_t x_sz_f16 = sizeof(ggml_fp16_t) * ne;
+    const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant);
+    float * x = (float *) malloc(x_sz);
+    void * qx = malloc(qx_sz);
+    vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer x_buf = ggml_vk_create_buffer_check(ctx, x_sz_f16, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    ggml_fp16_t * x_chk = (ggml_fp16_t *) malloc(x_sz_f16);
+
+    for (size_t i = 0; i < ne; i++) {
+        x[i] = rand() / (float)RAND_MAX;
+    }
+
+    vk_pipeline p = ctx->device->pipeline_dequant[quant];
+
+    ggml_vk_quantize_data(x, qx, ne, quant);
+
+    ggml_pipeline_allocate_descriptor_sets(ctx, p, 1);
+
+    ggml_vk_buffer_write(ctx, qx_buf, 0, qx, qx_sz);
+
+    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+    ggml_vk_ctx_begin(ctx, subctx);
+    const std::vector<uint32_t> pc = { 1, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne };
+    ggml_vk_dispatch_pipeline(ctx, subctx, p, { { qx_buf, 0, qx_sz }, { x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1});
+    ggml_vk_ctx_end(subctx);
+
+    auto begin = std::chrono::high_resolution_clock::now();
+
+    ggml_vk_submit(subctx, ctx->fence);
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
+
+    auto end = std::chrono::high_resolution_clock::now();
+
+    double ms_dequant = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
+    ggml_vk_buffer_read(ctx, x_buf, 0, x_chk, x_sz_f16);
+
+    int first_err = -1;
+
+    double avg_err = 0.0;
+    for (size_t i = 0; i < ne; i++) {
+        double error = std::fabs(x[i] - ggml_fp16_to_fp32(x_chk[i]));
+        avg_err += error;
+
+        if (first_err < 0 && error > 0.05) {
+            first_err = i;
+        }
+    }
+
+    avg_err /= ne;
+
+    std::cerr << "TEST DEQUANT " << ggml_type_name(quant) << " time=" << ms_dequant << "ms avg_err=" << avg_err << std::endl;
+
+    if (avg_err > 0.1) {
+        std::cerr << "first_error = " << first_err << std::endl;
+        std::cerr << "Actual result: " << std::endl << std::endl;
+        for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) {
+            std::cerr << ggml_fp16_to_fp32(x_chk[i]) << ", ";
+        }
+        std::cerr << std::endl << "Expected result: " << std::endl << std::endl;
+        for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) {
+            std::cerr << x[i] << ", ";
+        }
+        std::cerr << std::endl;
+    }
+
+    ggml_vk_destroy_buffer(x_buf);
+    ggml_vk_destroy_buffer(qx_buf);
+
+    free(x);
+    free(qx);
+    free(x_chk);
+}
+
+static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, size_t split_k, size_t shader_size, ggml_type quant) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_test_dequant_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << ggml_type_name(quant) << ")" << std::endl;
+#endif
+    const size_t x_ne = m * k * batch;
+    const size_t y_ne = k * n * batch;
+    const size_t d_ne = m * n * batch;
+
+    vk_pipeline p;
+    std::string shname;
+    if (shader_size == 0) {
+        p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_s;
+        shname = std::string(ggml_type_name(quant)) + "_ALIGNED_S";
+    } else if (shader_size == 1) {
+        p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_m;
+        shname = std::string(ggml_type_name(quant)) + "_ALIGNED_M";
+    } else if (shader_size == 2) {
+        p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_l;
+        shname = std::string(ggml_type_name(quant)) + "_ALIGNED_L";
+    } else {
+        GGML_ASSERT(0);
+    }
+
+    const size_t kpad = ggml_vk_align_size(k, p->align);
+
+    if (k != kpad) {
+        if (shader_size == 0) {
+            p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->s;
+            shname = std::string(ggml_type_name(quant)) + "_S";
+        } else if (shader_size == 1) {
+            p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->m;
+            shname = std::string(ggml_type_name(quant)) + "_M";
+        } else if (shader_size == 2) {
+            p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->l;
+            shname = std::string(ggml_type_name(quant)) + "_L";
+        } else {
+            GGML_ASSERT(0);
+        }
+    }
+
+    const size_t x_sz = sizeof(float) * x_ne;
+    const size_t y_sz = sizeof(float) * y_ne;
+    const size_t qx_sz = x_ne * ggml_type_size(quant)/ggml_blck_size(quant);
+    const size_t d_sz = sizeof(float) * d_ne;
+    float * x = (float *) malloc(x_sz);
+    float * y = (float *) malloc(y_sz);
+    void * qx = malloc(qx_sz);
+    vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer y_buf = ggml_vk_create_buffer_check(ctx, y_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer d_buf = ggml_vk_create_buffer_check(ctx, d_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    float * d = (float *) malloc(d_sz);
+    float * d_chk = (float *) malloc(d_sz);
+
+    for (size_t i = 0; i < x_ne; i++) {
+        x[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
+    }
+
+    ggml_vk_quantize_data(x, qx, x_ne, quant);
+
+    for (size_t i = 0; i < y_ne; i++) {
+        // y[i] = rand() / (float)RAND_MAX;
+        y[i] = (i % k == i / k) ? 1.0f : 0.0f;
+    }
+
+    ggml_pipeline_allocate_descriptor_sets(ctx, p, num_it);
+    if (split_k > 1) {
+        ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it);
+
+        if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) {
+            // Resize buffer
+            if (ctx->prealloc_split_k != nullptr) {
+                ggml_vk_destroy_buffer(ctx->prealloc_split_k);
+            }
+            ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal);
+        }
+    }
+
+    ggml_vk_buffer_write(ctx, qx_buf, 0, qx, qx_sz);
+    ggml_vk_buffer_write(ctx, y_buf, 0, y, y_sz);
+
+    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+    for (size_t i = 0; i < num_it; i++) {
+        ggml_vk_ctx_begin(ctx, subctx);
+        ggml_vk_matmul(ctx, subctx, p, ggml_vk_subbuffer(qx_buf), ggml_vk_subbuffer(y_buf), ggml_vk_subbuffer(d_buf), ggml_vk_subbuffer(ctx->prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n);
+        ggml_vk_ctx_end(subctx);
+    }
+
+    auto begin = std::chrono::high_resolution_clock::now();
+
+    ggml_vk_submit(subctx, ctx->fence);
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
+
+    auto end = std::chrono::high_resolution_clock::now();
+
+    double time_ms = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
+    ggml_vk_buffer_read(ctx, d_buf, 0, d, d_sz);
+
+    ggml_init_params iparams = {
+        /*.mem_size   =*/ 1024*1024*1024,
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ true,
+    };
+
+    ggml_context * ggml_ctx = ggml_init(iparams);
+
+    ggml_tensor * src0_ggml = ggml_new_tensor_3d(ggml_ctx, quant, k, m, batch);
+    ggml_tensor * src1_ggml = ggml_new_tensor_3d(ggml_ctx, GGML_TYPE_F32, k, n, batch);
+    ggml_tensor * tensor_ggml = ggml_mul_mat(ggml_ctx, src0_ggml, src1_ggml);
+
+    src0_ggml->data = qx;
+    src1_ggml->data = y;
+    tensor_ggml->data = d_chk;
+
+    ctx->disable = true;
+
+    ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx);
+    ggml_build_forward_expand(cgraph, tensor_ggml);
+
+    ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1);
+
+    ctx->disable = false;
+
+    ggml_free(ggml_ctx);
+
+    double avg_err = 0.0;
+    int first_err_n = -1;
+    int first_err_m = -1;
+    int first_err_b = -1;
+
+    for (size_t i = 0; i < m*n*batch; i++) {
+        double err = std::fabs(d[i] - d_chk[i]);
+        avg_err += err;
+
+        if ((err > 0.05f || std::isnan(err)) && first_err_n == -1) {
+            first_err_b = i / (m * n);
+            first_err_n = (i % (m * n)) / m;
+            first_err_m = (i % (m * n)) % m;
+        }
+    }
+
+    avg_err /= m * n;
+
+    std::cerr << "TEST MMQ " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time_ms / num_it << "ms avg_err=" << avg_err << std::endl;
+
+    if (avg_err > 0.01 || std::isnan(avg_err)) {
+        std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl;
+        std::cerr << "Actual result: " << std::endl << std::endl;
+        ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+        std::cerr << std::endl;
+        std::cerr << "Expected result: " << std::endl << std::endl;
+        ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+        if (split_k > 1) {
+            float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k);
+            ggml_vk_buffer_read(ctx, ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k);
+
+            std::cerr << "d_buf0: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            std::cerr << "d_buf1: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf + d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            std::cerr << "d_buf2: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf + 2 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            std::cerr << "d_buf3: " << std::endl << std::endl;
+            ggml_vk_print_matrix_area(split_k_buf + 3 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
+
+            free(split_k_buf);
+        }
+    }
+
+    ggml_vk_destroy_buffer(qx_buf);
+    ggml_vk_destroy_buffer(y_buf);
+    ggml_vk_destroy_buffer(d_buf);
+
+    free(x);
+    free(qx);
+    free(y);
+    free(d);
+    free(d_chk);
+}
+#endif
+
+static ggml_tensor_extra_gpu * ggml_vk_tensor_create_extra(ggml_tensor * tensor) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_create_extra(" << tensor << " (" << tensor->name << ", " << ggml_op_name(tensor->op) << "))" << std::endl;
+#endif
+    ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu;
+    extra->reset();
+    tensor->extra = extra;
+    return extra;
+}
+
+static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggml_tensor * node){
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl;
+#endif
+    if (ctx->disable || node->backend != GGML_BACKEND_TYPE_GPU) {
+        return;
+    }
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra;
+
+    ggml_tensor * src0 = node->src[0];
+    ggml_tensor * src1 = node->src[1];
+
+    const bool use_src0 = src0 != nullptr;
+    const int64_t ne00 = use_src0 ? src0->ne[0] : 0;
+    const int64_t ne01 = use_src0 ? src0->ne[1] : 0;
+    const int64_t ne02 = use_src0 ? src0->ne[2] : 0;
+    const int64_t ne03 = use_src0 ? src0->ne[3] : 0;
+    const bool use_src1 = src1 != nullptr && node->op != GGML_OP_CPY && node->op != GGML_OP_CONT && node->op != GGML_OP_DUP;
+    const int64_t ne10 = use_src1 ? src1->ne[0] : 0;
+    const int64_t ne11 = use_src1 ? src1->ne[1] : 0;
+    const int64_t ne12 = use_src1 ? src1->ne[2] : 0;
+    const int64_t ne13 = use_src1 ? src1->ne[3] : 0;
+    const int64_t ne20 = node->ne[0];
+    const int64_t ne21 = node->ne[1];
+    const int64_t ne22 = node->ne[2];
+    const int64_t ne23 = node->ne[3];
+
+    const ggml_type src0_type = (use_src0 && src0->type == GGML_TYPE_F32) ? src0->type : GGML_TYPE_F16;
+    const ggml_type src1_type = (use_src1 && src1->type == GGML_TYPE_F32) ? src1->type : GGML_TYPE_F16;
+
+    const bool x_non_contig = use_src0 && !ggml_vk_dim01_contiguous(src0);
+    const bool y_non_contig = use_src1 && !ggml_vk_dim01_contiguous(src1);
+
+    const bool y_f32_kernel = use_src1 && src1->type == GGML_TYPE_F32 && !y_non_contig;
+
+    bool mmp = (use_src0 && use_src1 && src1_type == GGML_TYPE_F32) ? ggml_vk_get_mul_mat_mat_pipeline(ctx, src0_type, y_non_contig ? GGML_TYPE_F16 : src1->type) != nullptr : false;
+
+    const bool qx_needs_dequant = use_src0 && (mmp || x_non_contig);
+    const bool qy_needs_dequant = use_src1 && ((src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig);
+
+    int split_k;
+    if (node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID) {
+        split_k = ggml_vk_guess_split_k(ne01, ne11, ne10);
+    } else {
+        split_k = 1;
+    }
+    const uint32_t x_ne = ne00 * ne01;
+    const uint32_t y_ne = ne10 * ne11;
+    const uint32_t d_ne = ne20 * ne21;
+
+    const uint64_t x_sz = (use_src0 && qx_needs_dequant) ? ggml_vk_align_size(sizeof(src0_type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0;
+    const uint64_t y_sz = (use_src1 && qy_needs_dequant) ? ggml_vk_align_size(sizeof(src1_type) * y_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0;
+    uint64_t d_sz = ggml_vk_align_size(ggml_type_size(node->type) * d_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne22 * ne23;
+    const uint64_t split_k_size = split_k > 1 ? d_sz * 4 : 0;
+
+    if (extra->buffer_gpu.expired()) {
+        // Workaround for CPU backend BLAS matmul calls
+        extra->buffer_gpu = ggml_vk_create_buffer_temp(ctx, d_sz);
+    }
+
+    switch (node->op) {
+    case GGML_OP_REPEAT:
+    case GGML_OP_GET_ROWS:
+    case GGML_OP_RESHAPE:
+    case GGML_OP_VIEW:
+    case GGML_OP_PERMUTE:
+    case GGML_OP_TRANSPOSE:
+    case GGML_OP_ADD:
+    case GGML_OP_SCALE:
+    case GGML_OP_SQR:
+    case GGML_OP_CLAMP:
+    case GGML_OP_CPY:
+    case GGML_OP_CONT:
+    case GGML_OP_DUP:
+    case GGML_OP_MUL:
+    case GGML_OP_NORM:
+    case GGML_OP_RMS_NORM:
+    case GGML_OP_DIAG_MASK_INF:
+    case GGML_OP_SOFT_MAX:
+    case GGML_OP_ROPE:
+    case GGML_OP_ARGSORT:
+        break;
+    case GGML_OP_UNARY:
+        switch (ggml_get_unary_op(node)) {
+        case GGML_UNARY_OP_SILU:
+        case GGML_UNARY_OP_GELU:
+        case GGML_UNARY_OP_RELU:
+            break;
+        default:
+            return;
+        }
+        break;
+    case GGML_OP_MUL_MAT:
+    case GGML_OP_MUL_MAT_ID:
+        if (ctx->prealloc_size_x < x_sz) {
+            ctx->prealloc_size_x = x_sz;
+        }
+        if (ctx->prealloc_size_y < y_sz) {
+            ctx->prealloc_size_y = y_sz;
+        }
+        if (ctx->prealloc_size_split_k < split_k_size) {
+            ctx->prealloc_size_split_k = split_k_size;
+        }
+        if (ctx->staging_size < x_sz + y_sz) {
+            ctx->staging_size = x_sz + y_sz;
+        }
+        break;
+    default:
+        return;
+    }
+}
+
+static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) {
+    if (ctx->disable) {
+        return;
+    }
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_preallocate_buffers(x_size: " << ctx->prealloc_size_x << " y_size: " << ctx->prealloc_size_y << " split_k_size: " << ctx->prealloc_size_split_k << ")" << std::endl;
+#endif
+#if defined(GGML_VULKAN_RUN_TESTS)
+    ctx->staging = ggml_vk_create_buffer_check(ctx, 100ul * 1024ul * 1024ul,
+        vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
+        vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
+    ggml_vk_test_transfer(ctx, 8192 * 1000, false);
+    ggml_vk_test_transfer(ctx, 8192 * 1000, true);
+
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_F32);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_0);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_1);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_0);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_1);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q8_0);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q2_K);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q3_K);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_K);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_K);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q6_K);
+
+    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 0);
+    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 1);
+    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 2);
+    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 0);
+    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 1);
+    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 2);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_0);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_1);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_0);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_1);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_1);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q8_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q8_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q8_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q8_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q8_0);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q8_0);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q2_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q2_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q2_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q2_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q2_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q2_K);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q3_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q3_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q3_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q3_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q3_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q3_K);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_K);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_K);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q6_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q6_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q6_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q6_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q6_K);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q6_K);
+
+    std::cerr << std::endl;
+
+    const std::vector<size_t> vals {
+        8, 8, 8,
+        100, 46, 576,
+        623, 111, 128,
+        100, 46, 558,
+        512, 1, 256,
+        128, 110, 622,
+        511, 511, 127,
+        511, 511, 7,
+        511, 511, 17,
+        49, 49, 128,
+        128, 49, 49,
+        4096, 49, 4096,
+        11008, 49, 4096,
+        4096, 49, 11008,
+        32000, 49, 4096,
+        512, 512, 128,
+        128, 512, 512,
+        4096, 512, 4096,
+        11008, 512, 4096,
+        4096, 512, 11008,
+        32000, 512, 4096,
+    };
+    const size_t num_it = 1;
+    for (size_t i = 0; i < vals.size(); i += 3) {
+        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0);
+        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1);
+        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 2);
+        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0);
+        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1);
+        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2);
+        std::cerr << std::endl;
+    }
+
+    GGML_ASSERT(false);
+#endif
+
+    if (ctx->prealloc_x == nullptr || (ctx->prealloc_size_x > 0 && ctx->prealloc_x->size < ctx->prealloc_size_x)) {
+        // Resize buffer
+        if (ctx->prealloc_x != nullptr) {
+            ggml_vk_destroy_buffer(ctx->prealloc_x);
+        }
+        ctx->prealloc_x = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_x);
+    }
+    if (ctx->prealloc_y == nullptr || (ctx->prealloc_size_y > 0 && ctx->prealloc_y->size < ctx->prealloc_size_y)) {
+        // Resize buffer
+        if (ctx->prealloc_y != nullptr) {
+            ggml_vk_destroy_buffer(ctx->prealloc_y);
+        }
+        ctx->prealloc_y = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_y);
+    }
+    if (ctx->prealloc_split_k == nullptr || (ctx->prealloc_size_split_k > 0 && ctx->prealloc_split_k->size < ctx->prealloc_size_split_k)) {
+        // Resize buffer
+        if (ctx->prealloc_split_k != nullptr) {
+            ggml_vk_destroy_buffer(ctx->prealloc_split_k);
+        }
+        ctx->prealloc_split_k = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_split_k);
+    }
+    if (ctx->staging == nullptr || (ctx->staging_size > 0 && ctx->staging->size < ctx->staging_size)) {
+        // Resize buffer
+        if (ctx->staging != nullptr) {
+            ggml_vk_destroy_buffer(ctx->staging);
+        }
+        ctx->staging = ggml_vk_create_buffer_check(ctx, ctx->staging_size,
+            vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
+            vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
+    }
+}
+
+static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, bool last_node){
+    if (ctx->disable || node->backend != GGML_BACKEND_TYPE_GPU) {
+        return;
+    }
+
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_build_graph(" << node << ", " << ggml_op_name(node->op) << ")" << std::endl;
+#endif
+    ctx->semaphore_idx = 0;
+    ctx->staging_offset = 0;
+
+    const ggml_tensor * src0 = node->src[0];
+    const ggml_tensor * src1 = node->src[1];
+    const ggml_tensor * src2 = node->src[2];
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra;
+
+    switch (node->op) {
+    case GGML_OP_UNARY:
+        switch (ggml_get_unary_op(node)) {
+        case GGML_UNARY_OP_SILU:
+        case GGML_UNARY_OP_GELU:
+        case GGML_UNARY_OP_RELU:
+            break;
+        default:
+            return;
+        }
+        break;
+    case GGML_OP_REPEAT:
+    case GGML_OP_GET_ROWS:
+    case GGML_OP_ADD:
+    case GGML_OP_MUL:
+    case GGML_OP_SCALE:
+    case GGML_OP_SQR:
+    case GGML_OP_CLAMP:
+    case GGML_OP_CPY:
+    case GGML_OP_CONT:
+    case GGML_OP_DUP:
+    case GGML_OP_RESHAPE:
+    case GGML_OP_VIEW:
+    case GGML_OP_PERMUTE:
+    case GGML_OP_TRANSPOSE:
+    case GGML_OP_NORM:
+    case GGML_OP_RMS_NORM:
+    case GGML_OP_DIAG_MASK_INF:
+    case GGML_OP_SOFT_MAX:
+    case GGML_OP_ROPE:
+    case GGML_OP_MUL_MAT:
+    case GGML_OP_MUL_MAT_ID:
+    case GGML_OP_NONE:
+    case GGML_OP_ARGSORT:
+        break;
+    default:
+        std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(node->op) << std::endl;
+        GGML_ASSERT(false);
+        return;
+    }
+
+    if (ctx->compute_ctx == nullptr) {
+        ctx->compute_ctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+        ggml_vk_ctx_begin(ctx, ctx->compute_ctx);
+    }
+
+    switch (node->op) {
+    case GGML_OP_REPEAT:
+        ggml_vk_repeat(ctx, ctx->compute_ctx, src0, src1, node);
+
+        break;
+    case GGML_OP_GET_ROWS:
+        ggml_vk_get_rows(ctx, ctx->compute_ctx, src0, src1, node);
+
+        break;
+    case GGML_OP_ADD:
+        ggml_vk_add(ctx, ctx->compute_ctx, src0, src1, node);
+
+        break;
+    case GGML_OP_MUL:
+        ggml_vk_mul(ctx, ctx->compute_ctx, src0, src1, node);
+
+        break;
+    case GGML_OP_SCALE:
+        ggml_vk_scale(ctx, ctx->compute_ctx, src0, node);
+
+        break;
+    case GGML_OP_SQR:
+        ggml_vk_sqr(ctx, ctx->compute_ctx, src0, node);
+
+        break;
+    case GGML_OP_CLAMP:
+        ggml_vk_clamp(ctx, ctx->compute_ctx, src0, node);
+
+        break;
+    case GGML_OP_CPY:
+    case GGML_OP_CONT:
+    case GGML_OP_DUP:
+        ggml_vk_cpy(ctx, ctx->compute_ctx, src0, node);
+
+        break;
+    case GGML_OP_RESHAPE:
+    case GGML_OP_VIEW:
+    case GGML_OP_PERMUTE:
+    case GGML_OP_TRANSPOSE:
+    case GGML_OP_NONE:
+        break;
+    case GGML_OP_NORM:
+        ggml_vk_norm(ctx, ctx->compute_ctx, src0, node);
+
+        break;
+    case GGML_OP_RMS_NORM:
+        ggml_vk_rms_norm(ctx, ctx->compute_ctx, src0, node);
+
+        break;
+    case GGML_OP_UNARY:
+        switch (ggml_get_unary_op(node)) {
+        case GGML_UNARY_OP_SILU:
+        case GGML_UNARY_OP_GELU:
+        case GGML_UNARY_OP_RELU:
+            ggml_vk_unary(ctx, ctx->compute_ctx, src0, node);
+            break;
+        default:
+            return;
+        }
+        break;
+    case GGML_OP_DIAG_MASK_INF:
+        ggml_vk_diag_mask_inf(ctx, ctx->compute_ctx, src0, node);
+
+        break;
+    case GGML_OP_SOFT_MAX:
+        ggml_vk_soft_max(ctx, ctx->compute_ctx, src0, src1, src2, node);
+
+        break;
+    case GGML_OP_ROPE:
+        ggml_vk_rope(ctx, ctx->compute_ctx, src0, src1, node);
+
+        break;
+    case GGML_OP_ARGSORT:
+        ggml_vk_argsort(ctx, ctx->compute_ctx, src0, node);
+        break;
+    case GGML_OP_MUL_MAT:
+        ggml_vk_mul_mat(ctx, ctx->compute_ctx, src0, src1, node);
+
+        break;
+    case GGML_OP_MUL_MAT_ID:
+        //ggml_vk_mul_mat_id(ctx, ctx->compute_ctx, src0, src1, node);
+        std::cerr << "ggml_vulkan: GGML_OP_MUL_MAT_ID not implemented yet." << std::endl;
+        GGML_ASSERT(false);
+
+        break;
+    default:
+        return;
+    }
+
+    extra->ready = true;
+    extra->ctx_idx = ctx->compute_ctx->idx;
+
+#ifdef GGML_VULKAN_CHECK_RESULTS
+    // Force context reset on each node so that each tensor ends up in its own context
+    // and can be run and compared to its CPU equivalent separately
+    last_node = true;
+#endif
+
+    if (node->backend == GGML_BACKEND_TYPE_CPU || last_node) {
+        ggml_vk_ctx_end(ctx->compute_ctx);
+        ctx->compute_ctx->exit_tensor = node;
+        ctx->compute_ctx = nullptr;
+    }
+}
+
+static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor){
+    if (ctx->disable) {
+        return false;
+    }
+
+    ggml_tensor_extra_gpu * extra = nullptr;
+
+    switch (tensor->op) {
+    case GGML_OP_ADD:
+    case GGML_OP_GET_ROWS:
+    case GGML_OP_MUL:
+    case GGML_OP_SCALE:
+    case GGML_OP_SQR:
+    case GGML_OP_CLAMP:
+    case GGML_OP_CPY:
+    case GGML_OP_CONT:
+    case GGML_OP_DUP:
+    case GGML_OP_NORM:
+    case GGML_OP_RMS_NORM:
+    case GGML_OP_DIAG_MASK_INF:
+    case GGML_OP_SOFT_MAX:
+    case GGML_OP_ROPE:
+    case GGML_OP_RESHAPE:
+    case GGML_OP_VIEW:
+    case GGML_OP_PERMUTE:
+    case GGML_OP_TRANSPOSE:
+    case GGML_OP_NONE:
+    case GGML_OP_ARGSORT:
+        extra = (ggml_tensor_extra_gpu *) tensor->extra;
+
+        break;
+    case GGML_OP_UNARY:
+        switch (ggml_get_unary_op(tensor)) {
+        case GGML_UNARY_OP_SILU:
+        case GGML_UNARY_OP_GELU:
+        case GGML_UNARY_OP_RELU:
+            extra = (ggml_tensor_extra_gpu *) tensor->extra;
+            break;
+        default:
+            return false;
+        }
+        break;
+    case GGML_OP_MUL_MAT:
+    case GGML_OP_MUL_MAT_ID:
+        extra = (ggml_tensor_extra_gpu *) tensor->extra;
+
+        break;
+    default:
+        return false;
+    }
+
+    if (extra == nullptr) {
+        return false;
+    }
+
+    if (params->ith != 0) {
+        return true;
+    }
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return true;
+    }
+
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")" << std::endl;
+#endif
+
+#ifdef GGML_VULKAN_CHECK_RESULTS
+    ggml_vk_check_results_0(ctx, params, tensor);
+#endif
+
+    GGML_ASSERT(extra->ready);
+
+    vk_context& subctx = ctx->gc.contexts[extra->ctx_idx];
+
+    // Only run if ctx hasn't been submitted yet
+    if (!subctx.seqs.empty()) {
+        // Do staging buffer copies
+        for (auto& cpy : subctx.in_memcpys) {
+            memcpy(cpy.dst, cpy.src, cpy.n);
+        }
+
+        ggml_vk_submit(&subctx, ctx->fence);
+    }
+
+    if (tensor == subctx.exit_tensor) {
+        VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences");
+        ctx->device->device.resetFences({ ctx->fence });
+
+        // Do staging buffer copies
+        for (auto& cpy : subctx.out_memcpys) {
+            memcpy(cpy.dst, cpy.src, cpy.n);
+        }
+        subctx.in_memcpys.clear();
+        subctx.out_memcpys.clear();
+    }
+
+    extra->ready = false;
+
+    return true;
+}
+
+// Clean up after graph processing is done
+static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) {
+    if (ctx->disable) {
+        return;
+    }
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_graph_cleanup()" << std::endl;
+#endif
+    for (auto& buffer : ctx->gc.temp_buffers) {
+        ggml_vk_pool_free(ctx, buffer);
+    }
+    ctx->gc.temp_buffers.clear();
+
+    for (auto& pipeline : ctx->device->pipelines) {
+        if (pipeline.expired()) {
+            continue;
+        }
+
+        vk_pipeline pl = pipeline.lock();
+        ggml_pipeline_cleanup(pl);
+    }
+
+    ggml_vk_queue_cleanup(ctx, ctx->device->compute_queue);
+    ggml_vk_queue_cleanup(ctx, ctx->device->transfer_queue);
+
+    for (size_t i = 0; i < ctx->gc.semaphores.size(); i++) {
+        ctx->device->device.destroySemaphore({ ctx->gc.semaphores[i].s });
+    }
+    ctx->gc.semaphores.clear();
+
+    for (size_t i = 0; i < ctx->gc.tl_semaphores.size(); i++) {
+        ctx->device->device.destroySemaphore({ ctx->gc.tl_semaphores[i].s });
+    }
+    ctx->gc.tl_semaphores.clear();
+    ctx->semaphore_idx = 0;
+
+    ctx->event_idx = 0;
+
+    for (auto& event : ctx->gc.events) {
+        ctx->device->device.resetEvent(event);
+    }
+
+    ctx->staging_offset = 0;
+
+    ctx->compute_ctx = nullptr;
+    ctx->transfer_ctx = nullptr;
+    ctx->gc.contexts.clear();
+}
+
+// Clean up on backend free
+static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_cleanup(" << ctx->idx << ")" << std::endl;
+#endif
+    ggml_vk_graph_cleanup(ctx);
+
+    ggml_vk_destroy_buffer(ctx->prealloc_x);
+    ggml_vk_destroy_buffer(ctx->prealloc_y);
+    ggml_vk_destroy_buffer(ctx->prealloc_split_k);
+    ggml_vk_destroy_buffer(ctx->staging);
+    ggml_vk_destroy_buffer(ctx->sync_staging);
+
+    for (auto& buffer : ctx->buffer_pool) {
+        ggml_vk_destroy_buffer(buffer);
+    }
+
+    ctx->prealloc_size_x = 0;
+    ctx->prealloc_size_y = 0;
+    ctx->prealloc_size_split_k = 0;
+    ctx->staging_size = 0;
+
+    for (auto& event : ctx->gc.events) {
+        ctx->device->device.destroyEvent(event);
+    }
+    ctx->gc.events.clear();
+
+    ctx->device->device.destroyFence(ctx->fence);
+}
+
+GGML_CALL static int ggml_vk_get_device_count() {
+    ggml_vk_instance_init();
+
+    return vk_instance.device_indices.size();
+}
+
+GGML_CALL static void ggml_vk_get_device_description(int device, char * description, size_t description_size) {
+    ggml_vk_instance_init();
+
+    std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();
+
+    vk::PhysicalDeviceProperties props;
+    devices[device].getProperties(&props);
+
+    snprintf(description, description_size, "%s", props.deviceName.data());
+}
+
+// backend interface
+
+#define UNUSED GGML_UNUSED
+
+// device backend
+
+static void * const vk_ptr_base = (void *)(uintptr_t) 0x1000;  // NOLINT
+
+struct ggml_backend_vk_buffer_context {
+    ggml_backend_vk_context * ctx;
+    vk_buffer dev_buffer;
+    ggml_tensor_extra_gpu * temp_tensor_extras = nullptr;
+    size_t temp_tensor_extra_index = 0;
+    std::string name;
+
+    ggml_backend_vk_buffer_context(ggml_backend_vk_context * ctx, vk_buffer&& dev_buffer, std::string& name) :
+        ctx(ctx),
+        dev_buffer(dev_buffer),
+        name(name) {
+    }
+
+    ~ggml_backend_vk_buffer_context() {
+        ggml_vk_destroy_buffer(dev_buffer);
+        delete[] temp_tensor_extras;
+    }
+
+    ggml_tensor_extra_gpu * ggml_vk_alloc_temp_tensor_extra() {
+        if (temp_tensor_extras == nullptr) {
+            temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_VK_MAX_NODES];
+        }
+
+        size_t alloc_index = temp_tensor_extra_index;
+        temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_VK_MAX_NODES;
+        ggml_tensor_extra_gpu * extra = &temp_tensor_extras[alloc_index];
+        extra->reset();
+
+        return extra;
+    }
+};
+
+GGML_CALL static const char * ggml_backend_vk_buffer_get_name(ggml_backend_buffer_t buffer) {
+    ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
+    return ctx->name.c_str();
+}
+
+GGML_CALL static bool ggml_backend_buffer_is_vk(ggml_backend_buffer_t buffer) {
+    return buffer->iface.get_name == ggml_backend_vk_buffer_get_name;
+}
+
+GGML_CALL static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_backend_vk_buffer_free_buffer()" << std::endl;
+#endif
+    ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
+    ggml_vk_destroy_buffer(ctx->dev_buffer);
+    delete ctx;
+}
+
+GGML_CALL static void * ggml_backend_vk_buffer_get_base(ggml_backend_buffer_t buffer) {
+    return vk_ptr_base;
+
+    UNUSED(buffer);
+}
+
+GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_backend_vk_buffer_init_tensor(" << buffer << " (" << buffer->context << "), " << tensor << ")" << std::endl;
+#endif
+    ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
+
+    ggml_tensor_extra_gpu * extra = ctx->ggml_vk_alloc_temp_tensor_extra();
+    if (tensor->view_src != nullptr && tensor->view_src->extra != nullptr) {
+        GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft);
+        ggml_tensor_extra_gpu * extra_view = (ggml_tensor_extra_gpu *) tensor->view_src->extra;
+        extra->buffer_gpu = extra_view->buffer_gpu;
+        extra->offset = extra_view->offset + tensor->view_offs;
+    } else {
+        extra->buffer_gpu = ctx->dev_buffer;
+        extra->offset = (uint8_t *) tensor->data - (uint8_t *) vk_ptr_base;
+    }
+
+    tensor->backend = GGML_BACKEND_TYPE_GPU;
+    tensor->extra = extra;
+}
+
+GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_backend_vk_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl;
+#endif
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
+
+    ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
+
+    vk_buffer buf = extra->buffer_gpu.lock();
+
+    ggml_vk_buffer_write(ctx->ctx, buf, extra->offset + offset, data, size);
+}
+
+GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl;
+#endif
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
+
+    ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
+
+    vk_buffer buf = extra->buffer_gpu.lock();
+
+    ggml_vk_buffer_read(ctx->ctx, buf, extra->offset + offset, data, size);
+}
+
+GGML_CALL static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
+    if (ggml_backend_buffer_is_vk(src->buffer)) {
+        ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra;
+        ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
+
+        vk_buffer src_buf = src_extra->buffer_gpu.lock();
+        vk_buffer dst_buf = dst_extra->buffer_gpu.lock();
+
+        ggml_vk_buffer_copy(dst_buf, dst_extra->offset, src_buf, src_extra->offset, ggml_nbytes(src));
+
+        return true;
+    }
+    return false;
+
+    UNUSED(buffer);
+}
+
+GGML_CALL static void ggml_backend_vk_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
+
+    ggml_vk_buffer_memset(ctx->ctx, ctx->dev_buffer, 0, value, buffer->size);
+}
+
+static ggml_backend_buffer_i ggml_backend_vk_buffer_interface = {
+    /* .get_name        = */ ggml_backend_vk_buffer_get_name,
+    /* .free_buffer     = */ ggml_backend_vk_buffer_free_buffer,
+    /* .get_base        = */ ggml_backend_vk_buffer_get_base,
+    /* .init_tensor     = */ ggml_backend_vk_buffer_init_tensor,
+    /* .set_tensor      = */ ggml_backend_vk_buffer_set_tensor,
+    /* .get_tensor      = */ ggml_backend_vk_buffer_get_tensor,
+    /* .cpy_tensor      = */ ggml_backend_vk_buffer_cpy_tensor,
+    /* .clear           = */ ggml_backend_vk_buffer_clear,
+    /* .reset           = */ NULL,
+};
+
+// vk buffer type
+struct ggml_backend_vk_buffer_type_context {
+    std::string name;
+    ggml_backend_vk_context * ctx;
+};
+
+GGML_CALL static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *)buft->context;
+
+    return ctx->name.c_str();
+}
+
+GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_backend_vk_buffer_type_alloc_buffer(" << size << ")" << std::endl;
+#endif
+    ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
+    vk_buffer dev_buffer = ggml_vk_create_buffer_device(ctx->ctx, size);
+
+    ggml_backend_vk_buffer_context * bufctx = new ggml_backend_vk_buffer_context(ctx->ctx, std::move(dev_buffer), ctx->name);
+
+    return ggml_backend_buffer_init(buft, ggml_backend_vk_buffer_interface, bufctx, size);
+}
+
+GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+    ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
+    return ctx->ctx->device->properties.limits.minStorageBufferOffsetAlignment;
+}
+
+GGML_CALL static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
+    ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
+    return ctx->ctx->device->max_memory_allocation_size;
+}
+
+GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
+    return ggml_nbytes(tensor);
+
+    UNUSED(buft);
+}
+
+GGML_CALL static bool ggml_backend_vk_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
+    if (!ggml_backend_is_vk(backend)) {
+        return false;
+    }
+
+    ggml_backend_vk_buffer_type_context * buft_ctx = (ggml_backend_vk_buffer_type_context *)buft->context;
+    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
+
+    return buft_ctx->ctx->idx == ctx->idx;
+}
+
+static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = {
+    /* .get_name         = */ ggml_backend_vk_buffer_type_name,
+    /* .alloc_buffer     = */ ggml_backend_vk_buffer_type_alloc_buffer,
+    /* .get_alignment    = */ ggml_backend_vk_buffer_type_get_alignment,
+    /* .get_max_size     = */ ggml_backend_vk_buffer_type_get_max_size,
+    /* .get_alloc_size   = */ ggml_backend_vk_buffer_type_get_alloc_size,
+    /* .supports_backend = */ ggml_backend_vk_buffer_type_supports_backend,
+    /* .is_host          = */ NULL,
+};
+
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_backend_vk_buffer_type(" << dev_num << ")" << std::endl;
+#endif
+
+    GGML_ASSERT(dev_num < vk_instance.device_indices.size());
+
+    ggml_backend_vk_init(dev_num);
+
+    return &vk_instance.buffer_types[dev_num];
+}
+
+// host buffer type
+
+GGML_CALL static const char * ggml_backend_vk_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    return GGML_VK_NAME "_Host";
+
+    UNUSED(buft);
+}
+
+GGML_CALL static const char * ggml_backend_vk_host_buffer_name(ggml_backend_buffer_t buffer) {
+    return GGML_VK_NAME "_Host";
+
+    UNUSED(buffer);
+}
+
+GGML_CALL static void ggml_backend_vk_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_backend_vk_host_buffer_free_buffer()" << std::endl;
+#endif
+    ggml_vk_host_free(&vk_instance.contexts[0], buffer->context);
+}
+
+GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_backend_vk_host_buffer_type_alloc_buffer(" << size << ")" << std::endl;
+#endif
+    void * ptr = nullptr;
+    try {
+        ptr = ggml_vk_host_malloc(&vk_instance.contexts[0], size);
+    } catch (vk::SystemError& e) {
+        std::cerr << "ggml_vulkan: Failed to allocate pinned memory." << std::endl;
+        std::cerr << "ggml_vulkan: " << e.what() << std::endl;
+        // fallback to cpu buffer
+        return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size);
+    }
+
+    ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
+    buffer->buft = buft;
+    buffer->iface.get_name = ggml_backend_vk_host_buffer_name;
+    buffer->iface.free_buffer = ggml_backend_vk_host_buffer_free_buffer;
+
+    return buffer;
+}
+
+GGML_CALL static size_t ggml_backend_vk_host_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+    return vk_instance.contexts[0].device->properties.limits.minMemoryMapAlignment;
+
+    UNUSED(buft);
+}
+
+GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type() {
+    static struct ggml_backend_buffer_type ggml_backend_vk_buffer_type_host = {
+        /* .iface    = */ {
+            /* .get_name         = */ ggml_backend_vk_host_buffer_type_name,
+            /* .alloc_buffer     = */ ggml_backend_vk_host_buffer_type_alloc_buffer,
+            /* .get_alignment    = */ ggml_backend_vk_host_buffer_type_get_alignment,
+            /* .get_max_size     = */ NULL, // defaults to SIZE_MAX
+            /* .get_alloc_size   = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
+            /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend,
+            /* .is_host          = */ ggml_backend_cpu_buffer_type()->iface.is_host,
+        },
+        /* .context  = */ nullptr,
+    };
+
+    if (!vk_instance.contexts[0].initialized) {
+        // Fall back to CPU
+        return ggml_backend_cpu_buffer_type();
+    }
+
+    return &ggml_backend_vk_buffer_type_host;
+}
+
+// backend
+
+GGML_CALL static const char * ggml_backend_vk_name(ggml_backend_t backend) {
+    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
+
+    return ctx->name.c_str();
+}
+
+GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend) {
+    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_backend_vk_free(" << ctx->name << ")" << std::endl;
+#endif
+
+    size_t idx = ctx->idx;
+
+    ggml_vk_cleanup(ctx);
+
+    ctx->device.reset();
+    ctx->initialized = false;
+
+    vk_instance.initialized[idx] = false;
+    vk_instance.backends[idx] = nullptr;
+    memset(&vk_instance.buffer_types[idx], 0, sizeof(ggml_backend_buffer_type));
+    delete backend;
+}
+
+GGML_CALL static ggml_backend_buffer_type_t ggml_backend_vk_get_default_buffer_type(ggml_backend_t backend) {
+    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
+
+    GGML_ASSERT(ctx->initialized);
+
+    return ggml_backend_vk_buffer_type(ctx->idx);
+}
+
+GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_backend_vk_set_tensor_async(" << size << ")" << std::endl;
+#endif
+    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
+    GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
+
+    if (ctx->transfer_ctx == nullptr) {
+        // Initialize new transfer context
+        ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
+        ggml_vk_ctx_begin(ctx, ctx->transfer_ctx);
+    }
+
+    vk_buffer buf = extra->buffer_gpu.lock();
+
+    ggml_vk_buffer_write_async(ctx, ctx->transfer_ctx, buf, extra->offset + offset, data, size);
+}
+
+GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_backend_vk_get_tensor_async(" << size << ")" << std::endl;
+#endif
+    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
+    GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
+
+    ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
+
+    if (ctx->transfer_ctx == nullptr) {
+        // Initialize new transfer context
+        ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
+        ggml_vk_ctx_begin(ctx, ctx->transfer_ctx);
+    }
+
+    vk_buffer buf = extra->buffer_gpu.lock();
+
+    ggml_vk_buffer_read_async(ctx, ctx->transfer_ctx, buf, extra->offset + offset, data, size);
+}
+
+GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_backend_vk_cpy_tensor_async()" << std::endl;
+#endif
+    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
+    if ((dst->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || dst->buffer->buft == ggml_backend_vk_host_buffer_type()) && ggml_backend_buffer_is_vk(src->buffer)) {
+        ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra;
+        ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
+
+        if (ctx->transfer_ctx == nullptr) {
+            // Initialize new transfer context
+            ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue);
+            ggml_vk_ctx_begin(ctx, ctx->transfer_ctx);
+        }
+
+        vk_buffer src_buf = src_extra->buffer_gpu.lock();
+        vk_buffer dst_buf = dst_extra->buffer_gpu.lock();
+
+        ggml_vk_buffer_copy_async(ctx->transfer_ctx, dst_buf, dst_extra->offset, src_buf, src_extra->offset, ggml_nbytes(src));
+        return true;
+    }
+
+    return false;
+}
+
+GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_backend_vk_synchronize()" << std::endl;
+#endif
+    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
+    if(ctx->transfer_ctx == nullptr) {
+        return;
+    }
+
+    ggml_vk_ctx_end(ctx->transfer_ctx);
+
+    for (auto& cpy : ctx->transfer_ctx->in_memcpys) {
+        memcpy(cpy.dst, cpy.src, cpy.n);
+    }
+
+    ggml_vk_submit(ctx->transfer_ctx, ctx->fence);
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences");
+    ctx->device->device.resetFences({ ctx->fence });
+
+    for (auto& cpy : ctx->transfer_ctx->out_memcpys) {
+        memcpy(cpy.dst, cpy.src, cpy.n);
+    }
+
+    ctx->transfer_ctx = nullptr;
+}
+
+GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_backend_vk_graph_compute(" << cgraph->n_nodes << " nodes)" << std::endl;
+#endif
+    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
+
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        ggml_vk_preallocate_buffers_graph(ctx, cgraph->nodes[i]);
+    }
+    ggml_vk_preallocate_buffers(ctx);
+
+    int last_node = cgraph->n_nodes - 1;
+
+    // If the last op in the cgraph isn't backend GPU, the command buffer doesn't get closed properly
+    while (last_node > 0 && cgraph->nodes[last_node]->backend != GGML_BACKEND_TYPE_GPU) {
+        last_node -= 1;
+    }
+
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        ggml_vk_build_graph(ctx,cgraph->nodes[i], i == last_node);
+    }
+
+    ggml_compute_params params = {};
+    params.type = GGML_TASK_TYPE_COMPUTE;
+    params.ith = 0;
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        ggml_tensor * node = cgraph->nodes[i];
+
+        if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
+            continue;
+        }
+
+        bool ok = ggml_vk_compute_forward(ctx, &params, node);
+        if (!ok) {
+            fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
+        }
+#ifdef GGML_VULKAN_CHECK_RESULTS
+        else {
+            ggml_vk_check_results_1(ctx, &params, node);
+        }
+#endif
+        GGML_ASSERT(ok);
+    }
+
+    ggml_vk_graph_cleanup(ctx);
+
+    return GGML_STATUS_SUCCESS;
+
+    UNUSED(backend);
+}
+
+GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
+    switch (op->op) {
+        case GGML_OP_UNARY:
+            switch (ggml_get_unary_op(op)) {
+                case GGML_UNARY_OP_GELU:
+                case GGML_UNARY_OP_SILU:
+                case GGML_UNARY_OP_RELU:
+                    return true;
+                default:
+                    return false;
+            }
+            break;
+        case GGML_OP_MUL_MAT:
+        // case GGML_OP_MUL_MAT_ID:
+            {
+                switch (op->src[0]->type) {
+                    case GGML_TYPE_F32:
+                    case GGML_TYPE_F16:
+                    case GGML_TYPE_Q4_0:
+                    case GGML_TYPE_Q4_1:
+                    case GGML_TYPE_Q5_0:
+                    case GGML_TYPE_Q5_1:
+                    case GGML_TYPE_Q8_0:
+                    case GGML_TYPE_Q2_K:
+                    case GGML_TYPE_Q3_K:
+                    case GGML_TYPE_Q4_K:
+                    case GGML_TYPE_Q5_K:
+                    case GGML_TYPE_Q6_K:
+                        break;
+                    default:
+                        return false;
+                }
+                struct ggml_tensor * a;
+                struct ggml_tensor * b;
+                if (op->op == GGML_OP_MUL_MAT) {
+                    a = op->src[0];
+                    b = op->src[1];
+                } else {
+                    a = op->src[2];
+                    b = op->src[1];
+                }
+                if (a->ne[3] != b->ne[3]) {
+                    return false;
+                }
+                return true;
+            } break;
+        case GGML_OP_GET_ROWS:
+            {
+                switch (op->src[0]->type) {
+                    case GGML_TYPE_F32:
+                    case GGML_TYPE_F16:
+                    case GGML_TYPE_Q4_0:
+                    case GGML_TYPE_Q4_1:
+                    case GGML_TYPE_Q5_0:
+                    case GGML_TYPE_Q5_1:
+                    case GGML_TYPE_Q8_0:
+                        return true;
+                    default:
+                        return false;
+                }
+            } break;
+        case GGML_OP_CPY:
+        case GGML_OP_DUP:
+            {
+                ggml_type src0_type = op->src[0]->type;
+                ggml_type src1_type = op->src[1] != nullptr ? op->src[1]->type : src0_type;
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) {
+                    return true;
+                }
+                if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
+                    return true;
+                }
+                return false;
+            } break;
+        // case GGML_OP_REPEAT:
+        //     {
+        //         ggml_type src0_type = op->src[0]->type;
+        //         return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
+        //     } break;
+        case GGML_OP_ROPE:
+            {
+                const int mode = ((const int32_t *) op->op_params)[2];
+                const bool is_glm  = mode & 4;
+
+                return !is_glm;
+            } break;
+        case GGML_OP_NONE:
+        case GGML_OP_RESHAPE:
+        case GGML_OP_VIEW:
+        case GGML_OP_PERMUTE:
+        case GGML_OP_TRANSPOSE:
+        case GGML_OP_NORM:
+        case GGML_OP_ADD:
+        case GGML_OP_MUL:
+        case GGML_OP_RMS_NORM:
+        case GGML_OP_SCALE:
+        case GGML_OP_SQR:
+        case GGML_OP_CLAMP:
+        case GGML_OP_CONT:
+        case GGML_OP_DIAG_MASK_INF:
+        case GGML_OP_SOFT_MAX:
+        case GGML_OP_ARGSORT:
+            return true;
+        default:
+            return false;
+    }
+
+    UNUSED(backend);
+}
+
+GGML_CALL static bool ggml_backend_vk_offload_op(ggml_backend_t backend, const ggml_tensor * op) {
+    const ggml_tensor * dst = op;
+
+    const int min_batch_size = 32;
+
+    if (dst->ne[1] > min_batch_size && dst->op != GGML_OP_GET_ROWS) {
+        return true;
+    }
+
+    return false;
+
+    UNUSED(backend);
+}
+
+// TODO: enable async and synchronize
+static ggml_backend_i ggml_backend_vk_interface = {
+    /* .get_name                = */ ggml_backend_vk_name,
+    /* .free                    = */ ggml_backend_vk_free,
+    /* .get_default_buffer_type = */ ggml_backend_vk_get_default_buffer_type,
+    /* .set_tensor_async        = */ NULL,  // ggml_backend_vk_set_tensor_async,
+    /* .get_tensor_async        = */ NULL,  // ggml_backend_vk_get_tensor_async,
+    /* .cpy_tensor_async        = */ NULL,  // ggml_backend_vk_cpy_tensor_async,
+    /* .synchronize             = */ NULL,  // ggml_backend_vk_synchronize,
+    /* .graph_plan_create       = */ NULL,
+    /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_compute      = */ NULL,
+    /* .graph_compute           = */ ggml_backend_vk_graph_compute,
+    /* .supports_op             = */ ggml_backend_vk_supports_op,
+    /* .offload_op              = */ ggml_backend_vk_offload_op,
+    /* .event_new               = */ NULL,
+    /* .event_free              = */ NULL,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .event_synchronize       = */ NULL,
+};
+
+static ggml_guid_t ggml_backend_vk_guid() {
+    static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x40, 0x3c, 0xe1, 0x02, 0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b };
+    return &guid;
+}
+
+GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t dev_num) {
+    if (vk_instance.initialized[dev_num]) {
+        return vk_instance.backends[dev_num];
+    }
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_backend_vk_init(" << dev_num << ")" << std::endl;
+#endif
+
+    ggml_backend_vk_context * ctx = &vk_instance.contexts[dev_num];
+    ggml_vk_init(ctx, dev_num);
+    ctx->name = GGML_VK_NAME + std::to_string(dev_num);
+    vk_instance.buffer_types[dev_num] = {
+        /* .iface    = */ ggml_backend_vk_buffer_type_interface,
+        /* .context  = */ new ggml_backend_vk_buffer_type_context{ ctx->name, ctx },
+    };
+    vk_instance.initialized[dev_num] = true;
+
+    ggml_backend_t vk_backend = new ggml_backend {
+        /* .guid      = */ ggml_backend_vk_guid(),
+        /* .interface = */ ggml_backend_vk_interface,
+        /* .context   = */ &vk_instance.contexts[ctx->idx],
+    };
+
+    vk_instance.backends[dev_num] = vk_backend;
+
+    return vk_backend;
+}
+
+GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend) {
+    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_vk_guid());
+}
+
+GGML_CALL int ggml_backend_vk_get_device_count() {
+    return ggml_vk_get_device_count();
+}
+
+GGML_CALL void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size) {
+    ggml_vk_get_device_description(device, description, description_size);
+}
+
+GGML_CALL void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) {
+    GGML_ASSERT(device < (int) vk_instance.device_indices.size());
+
+    vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]];
+
+    vk::PhysicalDeviceMemoryProperties memprops = vkdev.getMemoryProperties();
+
+    for (const vk::MemoryHeap& heap : memprops.memoryHeaps) {
+        if (heap.flags & vk::MemoryHeapFlagBits::eDeviceLocal) {
+            *total = heap.size;
+            *free = heap.size;
+            break;
+        }
+    }
+}
+
+// backend registry
+GGML_CALL static ggml_backend_t ggml_backend_reg_vk_init(const char * params, void * user_data) {
+    ggml_backend_t vk_backend = ggml_backend_vk_init((int) (intptr_t) user_data);
+    return vk_backend;
+
+    UNUSED(params);
+}
+
+extern "C" GGML_CALL int ggml_backend_vk_reg_devices();
+
+GGML_CALL int ggml_backend_vk_reg_devices() {
+    ggml_vk_instance_init();
+
+    for (size_t i = 0; i < vk_instance.device_indices.size(); i++) {
+        char name[128];
+        snprintf(name, sizeof(name), "%s%ld", GGML_VK_NAME, i);
+        ggml_backend_register(name, ggml_backend_reg_vk_init, ggml_backend_vk_buffer_type(i), (void *) (intptr_t) i);  // NOLINT
+    }
+    return vk_instance.device_indices.size();
+}
+
+// Extension availability
+static bool ggml_vk_instance_validation_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions) {
+#ifdef GGML_VULKAN_VALIDATE
+    bool portability_enumeration_ext = false;
+    // Check for portability enumeration extension for MoltenVK support
+    for (const auto& properties : instance_extensions) {
+        if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) {
+            return true;
+        }
+    }
+    if (!portability_enumeration_ext) {
+        std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl;
+    }
+#endif
+    return false;
+
+    UNUSED(instance_extensions);
+}
+static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions) {
+#ifdef __APPLE__
+    bool portability_enumeration_ext = false;
+    // Check for portability enumeration extension for MoltenVK support
+    for (const auto& properties : instance_extensions) {
+        if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) {
+            return true;
+        }
+    }
+    if (!portability_enumeration_ext) {
+        std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl;
+    }
+#endif
+    return false;
+
+    UNUSED(instance_extensions);
+}
+
+// checks
+
+#ifdef GGML_VULKAN_CHECK_RESULTS
+static void ggml_vk_print_graph_origin(const ggml_tensor * tensor, std::vector<const ggml_tensor *>& done, int level = 0) {
+    if (std::find(done.begin(), done.end(), tensor) != done.end() || level > 10) {
+        return;
+    }
+    for (int j = 0; j < level; j++) {
+        std::cerr << " ";
+    }
+    std::cerr << ggml_op_name(tensor->op) << " gpu=" << (tensor->extra != nullptr) << " backend=" << tensor->backend << std::endl;
+
+    done.push_back(tensor);
+
+    for (int i = 0; i < GGML_MAX_SRC; i++) {
+        if (tensor->src[i] != nullptr) {
+            ggml_vk_print_graph_origin(tensor->src[i], done, level + 1);
+        }
+    }
+}
+
+static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * data, int i0, int i1, int i2, int i3) {
+    if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) {
+        return;
+    }
+    i0 = std::max(i0, 5);
+    i1 = std::max(i1, 5);
+    i2 = std::max(i2, 0);
+    i3 = std::max(i3, 0);
+    fprintf(stderr, "         ");
+    for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
+        fprintf(stderr, "%7d ", idx1);
+    }
+    fprintf(stderr, "\n");
+    for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) {
+        fprintf(stderr, "%7d: ", idx0);
+        for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
+            if (idx0 >= 0 && idx0 < tensor->ne[0] && idx1 >= 0 && idx1 < tensor->ne[1] && i2 >= 0 && i2 < tensor->ne[2] && i3 >= 0 && i3 < tensor->ne[3]) {
+                float val;
+                if (tensor->type == GGML_TYPE_F32) {
+                    val = *(const float *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]);
+                } else if (tensor->type == GGML_TYPE_F16) {
+                    val = ggml_fp16_to_fp32(*(const ggml_fp16_t *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]));
+                } else {
+                    GGML_ASSERT(false);
+                }
+                fprintf(stderr, "% 7.2f ", val);
+            } else {
+                fprintf(stderr, "        ");
+            }
+        }
+        fprintf(stderr, "\n");
+    }
+}
+
+static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tensor * tensor, const char * name) {
+    void * tensor_data = tensor->data;
+
+    if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
+        const size_t tensor_size = ggml_nbytes(tensor);
+        tensor_data = malloc(tensor_size);
+
+        ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
+
+        vk_buffer buffer_gpu = extra->buffer_gpu.lock();
+        ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size);
+    }
+
+    std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl;
+    std::cerr << "tensor=" << tensor << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << std::endl;
+    if (tensor->src[0] != nullptr) {
+        std::cerr << "tensor->src[0]=" << tensor->src[0] << " name=" << tensor->src[0]->name << " op=" << ggml_op_name(tensor->src[0]->op) << " type=" << ggml_type_name(tensor->src[0]->type) << " backend=" << tensor->src[0]->backend << " ne0=" << tensor->src[0]->ne[0] << " nb0=" << tensor->src[0]->nb[0] << " ne1=" << tensor->src[0]->ne[1] << " nb1=" << tensor->src[0]->nb[1] << " ne2=" << tensor->src[0]->ne[2] << " nb2=" << tensor->src[0]->nb[2] << " ne3=" << tensor->src[0]->ne[3] << " nb3=" << tensor->src[0]->nb[3] << std::endl;
+    }
+    if (tensor->src[1] != nullptr) {
+        std::cerr << "tensor->src[1]=" << tensor->src[1] << " name=" << tensor->src[1]->name << " op=" << ggml_op_name(tensor->src[1]->op) << " type=" << ggml_type_name(tensor->src[1]->type) << " backend=" << tensor->src[1]->backend << " ne0=" << tensor->src[1]->ne[0] << " nb0=" << tensor->src[1]->nb[0] << " ne1=" << tensor->src[1]->ne[1] << " nb1=" << tensor->src[1]->nb[1] << " ne2=" << tensor->src[1]->ne[2] << " nb2=" << tensor->src[1]->nb[2] << " ne3=" << tensor->src[1]->ne[3] << " nb3=" << tensor->src[1]->nb[3] << std::endl;
+    }
+    std::cerr << std::endl << "Result:" << std::endl;
+    ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0);
+    std::cerr << std::endl;
+    std::cerr << std::endl << "Result:" << std::endl;
+    ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 1, 0);
+    std::cerr << std::endl;
+    std::vector<const ggml_tensor *> done;
+    ggml_vk_print_graph_origin(tensor, done);
+
+    if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
+        free(tensor_data);
+    }
+}
+
+static void ggml_vk_check_tensor(const std::string& name, const ggml_tensor * tensor) {
+    return;
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_CPU);
+    if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) {
+        return;
+    }
+    for (int i3 = 0; i3 < tensor->ne[3]; i3++) {
+        for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
+            for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
+                for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
+                    float val = 0.0f;
+                    if (tensor->type == GGML_TYPE_F32) {
+                        val = *(float *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]);
+                    } else if (tensor->type == GGML_TYPE_F16) {
+                        val = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]));
+                    }
+                    if (std::isnan(val)) {
+                        std::cerr << "ERROR: TENSOR CHECK " << name << ": Invalid value in " << ggml_op_name(tensor->op) << " i3=" << i3 << " i2=" << i2 << " i1=" << i1 << " i0=" << i0 << " val=" << val << std::endl;
+                        std::cerr << "tensor=" << tensor << " tensor->type=" << ggml_type_name(tensor->type) << " tensor->backend: " << tensor->backend << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << std::endl;
+                        std::cerr << std::endl;
+                        ggml_vk_print_tensor_area(tensor, tensor->data, i0, i1, i2, i3);
+                        std::cerr << std::endl;
+                        std::vector<const ggml_tensor *> done;
+                        ggml_vk_print_graph_origin(tensor, done);
+                        GGML_ASSERT(false);
+                    }
+                }
+            }
+        }
+    }
+}
+
+void * comp_result;
+size_t comp_size;
+size_t comp_nb[GGML_MAX_DIMS];
+size_t check_counter = 0;
+static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor) {
+    if (params->ith != 0) {
+        return;
+    }
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
+        return;
+    }
+
+    check_counter++;
+    if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) {
+        return;
+    }
+
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_check_results_0(" << tensor->name << ")" << std::endl;
+#endif
+
+    ggml_tensor * src0 = tensor->src[0];
+    ggml_tensor * src1 = tensor->src[1];
+    ggml_tensor * src2 = tensor->src[2];
+
+    struct ggml_init_params iparams = {
+        /*.mem_size   =*/ 1024*1024*1024,
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ false,
+    };
+
+    struct ggml_context * ggml_ctx = ggml_init(iparams);
+
+    struct ggml_tensor * src0_clone = nullptr;
+    struct ggml_tensor * src1_clone = nullptr;
+    struct ggml_tensor * src2_clone = nullptr;
+    struct ggml_tensor * tensor_clone = nullptr;
+
+    size_t src0_size;
+    size_t src1_size;
+    size_t src2_size;
+
+    void * src0_buffer;
+    void * src1_buffer;
+    void * src2_buffer;
+
+    if (src0 != nullptr) {
+        src0_clone = ggml_dup_tensor(ggml_ctx, src0);
+
+        src0_size = ggml_nbytes(src0);
+
+        src0_buffer = malloc(src0_size);
+        src0_clone->data = src0_buffer;
+        if (src0->backend == GGML_BACKEND_TYPE_CPU) {
+            memcpy(src0_clone->data, src0->data, src0_size);
+            memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS);
+        } else if (src0->backend == GGML_BACKEND_TYPE_GPU) {
+            ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src0->extra;
+            vk_buffer buffer_gpu = extra->buffer_gpu.lock();
+            uint64_t offset = extra->offset;
+            if (!ggml_is_contiguous(src0) && ggml_vk_dim01_contiguous(src0)) {
+                for (int i3 = 0; i3 < src0->ne[3]; i3++) {
+                    for (int i2 = 0; i2 < src0->ne[2]; i2++) {
+                        const int idx = i3*src0->ne[2] + i2;
+                        ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]);
+                    }
+                }
+
+                src0_clone->nb[0] = src0->nb[0];
+                src0_clone->nb[1] = src0->nb[1];
+                for (int i = 2; i < GGML_MAX_DIMS; i++) {
+                    src0_clone->nb[i] = src0_clone->nb[i - 1]*src0_clone->ne[i - 1];
+                }
+            } else {
+                if (offset + src0_size >= buffer_gpu->size) {
+                    src0_size = buffer_gpu->size - offset;
+                }
+                ggml_vk_buffer_read(ctx, buffer_gpu, offset, src0_clone->data, src0_size);
+                memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS);
+            }
+        } else {
+            GGML_ASSERT(false);
+        }
+
+        if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
+            ggml_vk_print_tensor(ctx, src0, "src0");
+        }
+
+        ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src0", src0_clone);
+    }
+    if (src1 != nullptr) {
+        src1_clone = ggml_dup_tensor(ggml_ctx, src1);
+
+        src1_size = ggml_nbytes(src1);
+
+        src1_buffer = malloc(src1_size);
+        src1_clone->data = src1_buffer;
+        if (src1->backend == GGML_BACKEND_TYPE_CPU) {
+            memcpy(src1_clone->data, src1->data, src1_size);
+            memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS);
+        } else if (src1->backend == GGML_BACKEND_TYPE_GPU) {
+            ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src1->extra;
+            vk_buffer buffer_gpu = extra->buffer_gpu.lock();
+            uint64_t offset = extra->offset;
+            if (!ggml_is_contiguous(src1) && ggml_vk_dim01_contiguous(src1)) {
+                for (int i3 = 0; i3 < src1->ne[3]; i3++) {
+                    for (int i2 = 0; i2 < src1->ne[2]; i2++) {
+                        const int idx = i3*src1->ne[2] + i2;
+                        ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]);
+                    }
+                }
+
+                src1_clone->nb[0] = src1->nb[0];
+                src1_clone->nb[1] = src1->nb[1];
+                for (int i = 2; i < GGML_MAX_DIMS; i++) {
+                    src1_clone->nb[i] = src1_clone->nb[i - 1]*src1_clone->ne[i - 1];
+                }
+            } else {
+                if (offset + src1_size >= buffer_gpu->size) {
+                    src1_size = buffer_gpu->size - offset;
+                }
+                ggml_vk_buffer_read(ctx, buffer_gpu, offset, src1_clone->data, src1_size);
+                memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS);
+            }
+        } else {
+            GGML_ASSERT(false);
+        }
+
+        if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
+            ggml_vk_print_tensor(ctx, src1, "src1");
+            std::cerr << "TENSOR CHECK: " << ggml_op_name(src1_clone->op) << " (check " << check_counter << ")" << std::endl;
+            std::cerr << "src1_clone=" << tensor << " src1_clone->backend: " << src1_clone->backend << " src1_clone->type: " << ggml_type_name(src1_clone->type) << " ne0=" << src1_clone->ne[0] << " nb0=" << src1_clone->nb[0] << " ne1=" << src1_clone->ne[1] << " nb1=" << src1_clone->nb[1] << " ne2=" << src1_clone->ne[2] << " nb2=" << src1_clone->nb[2] << " ne3=" << src1_clone->ne[3] << " nb3=" << src1_clone->nb[3] << std::endl;
+            if (src1->src[0] != nullptr) {
+                std::cerr << "src1->src[0]=" << src1->src[0] << " op=" << ggml_op_name(src1->src[0]->op) << " type=" << ggml_type_name(src1->src[0]->type) << " backend=" << src1->src[0]->backend << " ne0=" << src1->src[0]->ne[0] << " nb0=" << src1->src[0]->nb[0] << " ne1=" << src1->src[0]->ne[1] << " nb1=" << src1->src[0]->nb[1] << " ne2=" << src1->src[0]->ne[2] << " nb2=" << src1->src[0]->nb[2] << " ne3=" << src1->src[0]->ne[3] << " nb3=" << src1->src[0]->nb[3] << std::endl;
+            }
+            if (src1->src[1] != nullptr) {
+                std::cerr << "src1->src[1]=" << src1->src[1] << " op=" << ggml_op_name(src1->src[1]->op) << " type=" << ggml_type_name(src1->src[1]->type) << " backend=" << src1->src[1]->backend << " ne0=" << src1->src[1]->ne[0] << " nb0=" << src1->src[1]->nb[0] << " ne1=" << src1->src[1]->ne[1] << " nb1=" << src1->src[1]->nb[1] << " ne2=" << src1->src[1]->ne[2] << " nb2=" << src1->src[1]->nb[2] << " ne3=" << src1->src[1]->ne[3] << " nb3=" << src1->src[1]->nb[3] << std::endl;
+            }
+            std::cerr << std::endl << "Result:" << std::endl;
+            ggml_vk_print_tensor_area(src1_clone, src1_clone->data, 5, 5, 0, 0);
+            std::cerr << std::endl;
+            std::cerr << std::endl << "Result:" << std::endl;
+            ggml_vk_print_tensor_area(src1_clone, src1_clone->data, 5, 5, 1, 0);
+            std::cerr << std::endl;
+            std::vector<const ggml_tensor *> done;
+            ggml_vk_print_graph_origin(src1_clone, done);
+        }
+
+        ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src1", src1_clone);
+    }
+    if (src2 != nullptr) {
+        src2_clone = ggml_dup_tensor(ggml_ctx, src2);
+
+        src2_size = ggml_nbytes(src2);
+
+        src2_buffer = malloc(src2_size);
+        src2_clone->data = src2_buffer;
+        if (src2->backend == GGML_BACKEND_TYPE_CPU) {
+            memcpy(src2_clone->data, src2->data, src2_size);
+            memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS);
+        } else if (src2->backend == GGML_BACKEND_TYPE_GPU) {
+            ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src2->extra;
+            vk_buffer buf = extra->buffer_gpu.lock();
+            uint64_t offset = extra->offset;
+            if (!ggml_is_contiguous(src2) && ggml_vk_dim01_contiguous(src2)) {
+                for (int i3 = 0; i3 < src2->ne[3]; i3++) {
+                    for (int i2 = 0; i2 < src2->ne[2]; i2++) {
+                        const int idx = i3*src2->ne[2] + i2;
+                        ggml_vk_buffer_read(ctx, buf, offset + idx * src2->nb[2], ((char *)src2_clone->data + idx * src2_clone->nb[2]), src2->ne[1] * src2->nb[1]);
+                    }
+                }
+
+                src2_clone->nb[0] = src2->nb[0];
+                src2_clone->nb[1] = src2->nb[1];
+                for (int i = 2; i < GGML_MAX_DIMS; i++) {
+                    src2_clone->nb[i] = src2_clone->nb[i - 1]*src2_clone->ne[i - 1];
+                }
+            } else {
+                if (offset + src2_size >= buf->size) {
+                    src2_size = buf->size - offset;
+                }
+                ggml_vk_buffer_read(ctx, buf, offset, src2_clone->data, src2_size);
+                memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS);
+            }
+        } else {
+            GGML_ASSERT(false);
+        }
+
+        if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
+            ggml_vk_print_tensor(ctx, src2, "src2");
+            std::cerr << "TENSOR CHECK: " << ggml_op_name(src2_clone->op) << " (check " << check_counter << ")" << std::endl;
+            std::cerr << "src2_clone=" << tensor << " src2_clone->backend: " << src2_clone->backend << " src2_clone->type: " << ggml_type_name(src2_clone->type) << " ne0=" << src2_clone->ne[0] << " nb0=" << src2_clone->nb[0] << " ne1=" << src2_clone->ne[1] << " nb1=" << src2_clone->nb[1] << " ne2=" << src2_clone->ne[2] << " nb2=" << src2_clone->nb[2] << " ne3=" << src2_clone->ne[3] << " nb3=" << src2_clone->nb[3] << std::endl;
+            if (src2->src[0] != nullptr) {
+                std::cerr << "src2->src[0]=" << src2->src[0] << " op=" << ggml_op_name(src2->src[0]->op) << " type=" << ggml_type_name(src2->src[0]->type) << " backend=" << src2->src[0]->backend << " ne0=" << src2->src[0]->ne[0] << " nb0=" << src2->src[0]->nb[0] << " ne1=" << src2->src[0]->ne[1] << " nb1=" << src2->src[0]->nb[1] << " ne2=" << src2->src[0]->ne[2] << " nb2=" << src2->src[0]->nb[2] << " ne3=" << src2->src[0]->ne[3] << " nb3=" << src2->src[0]->nb[3] << std::endl;
+            }
+            if (src2->src[1] != nullptr) {
+                std::cerr << "src2->src[1]=" << src2->src[1] << " op=" << ggml_op_name(src2->src[1]->op) << " type=" << ggml_type_name(src2->src[1]->type) << " backend=" << src2->src[1]->backend << " ne0=" << src2->src[1]->ne[0] << " nb0=" << src2->src[1]->nb[0] << " ne1=" << src2->src[1]->ne[1] << " nb1=" << src2->src[1]->nb[1] << " ne2=" << src2->src[1]->ne[2] << " nb2=" << src2->src[1]->nb[2] << " ne3=" << src2->src[1]->ne[3] << " nb3=" << src2->src[1]->nb[3] << std::endl;
+            }
+            std::cerr << std::endl << "Result:" << std::endl;
+            ggml_vk_print_tensor_area(src2_clone, src2_clone->data, 5, 5, 0, 0);
+            std::cerr << std::endl;
+            std::cerr << std::endl << "Result:" << std::endl;
+            ggml_vk_print_tensor_area(src2_clone, src2_clone->data, 5, 5, 1, 0);
+            std::cerr << std::endl;
+            std::vector<const ggml_tensor *> done;
+            ggml_vk_print_graph_origin(src2_clone, done);
+        }
+
+        ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src2", src2_clone);
+    }
+
+    if (tensor->op == GGML_OP_MUL_MAT) {
+        tensor_clone = ggml_mul_mat(ggml_ctx, src0_clone, src1_clone);
+    } else if (tensor->op == GGML_OP_MUL) {
+        tensor_clone = ggml_mul(ggml_ctx, src0_clone, src1_clone);
+    } else if (tensor->op == GGML_OP_SCALE) {
+        tensor_clone = ggml_scale(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0]);
+    } else if (tensor->op == GGML_OP_SQR) {
+        tensor_clone = ggml_sqr(ggml_ctx, src0_clone);
+    } else if (tensor->op == GGML_OP_CLAMP) {
+        tensor_clone = ggml_clamp(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]);
+    } else if (tensor->op == GGML_OP_ADD) {
+        tensor_clone = ggml_add(ggml_ctx, src0_clone, src1_clone);
+    } else if (tensor->op == GGML_OP_NORM) {
+        tensor_clone = ggml_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params);
+    } else if (tensor->op == GGML_OP_RMS_NORM) {
+        tensor_clone = ggml_rms_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params);
+    } else if (tensor->op == GGML_OP_SOFT_MAX) {
+        if (src1 != nullptr) {
+            tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, src2_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]);
+        } else {
+            tensor_clone = ggml_soft_max(ggml_ctx, src0_clone);
+        }
+    } else if (tensor->op == GGML_OP_DIAG_MASK_INF) {
+        tensor_clone = ggml_diag_mask_inf(ggml_ctx, src0_clone, *(float *)tensor->op_params);
+    } else if (tensor->op == GGML_OP_ROPE) {
+        const int n_dims      = ((int32_t *) tensor->op_params)[1];
+        const int mode        = ((int32_t *) tensor->op_params)[2];
+        const int n_ggml_ctx       = ((int32_t *) tensor->op_params)[3];
+        const int n_orig_ggml_ctx  = ((int32_t *) tensor->op_params)[4];
+        float freq_base       = ((float *)   tensor->op_params)[5];
+        float freq_scale      = ((float *)   tensor->op_params)[6];
+        float ext_factor      = ((float *)   tensor->op_params)[7];
+        float attn_factor     = ((float *)   tensor->op_params)[8];
+        float beta_fast       = ((float *)   tensor->op_params)[9];
+        float beta_slow       = ((float *)   tensor->op_params)[10];
+        tensor_clone = ggml_rope_custom(ggml_ctx, src0_clone, src1_clone, n_dims, mode, n_ggml_ctx, n_orig_ggml_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
+    } else if (tensor->op == GGML_OP_UNARY) {
+        switch (ggml_get_unary_op(tensor)) {
+        case GGML_UNARY_OP_SILU:
+            tensor_clone = ggml_silu(ggml_ctx, src0_clone);
+            break;
+        case GGML_UNARY_OP_GELU:
+            tensor_clone = ggml_gelu(ggml_ctx, src0_clone);
+            break;
+        case GGML_UNARY_OP_RELU:
+            tensor_clone = ggml_relu(ggml_ctx, src0_clone);
+            break;
+        default:
+            std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl;
+            GGML_ASSERT(false);
+        }
+    } else if (tensor->op == GGML_OP_CPY || tensor->op == GGML_OP_DUP) {
+        if (src1 == nullptr) {
+            tensor_clone = ggml_dup(ggml_ctx, src0_clone);
+            tensor_clone->type = tensor->type;
+        } else {
+            tensor_clone = ggml_cpy(ggml_ctx, src0_clone, src1_clone);
+        }
+    } else if (tensor->op == GGML_OP_CONT) {
+        tensor_clone = ggml_cont_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
+    } else if (tensor->op == GGML_OP_RESHAPE) {
+        tensor_clone = ggml_reshape_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
+    } else if (tensor->op == GGML_OP_VIEW) {
+        tensor_clone = ggml_view_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], tensor->nb[1], tensor->nb[2], tensor->nb[3], ((int32_t *) tensor->op_params)[0]);
+    } else if (tensor->op == GGML_OP_PERMUTE) {
+        int32_t * params = (int32_t *)tensor->op_params;
+        tensor_clone = ggml_permute(ggml_ctx, src0_clone, params[0], params[1], params[2], params[3]);
+    } else if (tensor->op == GGML_OP_TRANSPOSE) {
+        tensor_clone = ggml_transpose(ggml_ctx, src0_clone);
+    } else if (tensor->op == GGML_OP_GET_ROWS) {
+        tensor_clone = ggml_get_rows(ggml_ctx, src0_clone, src1_clone);
+    } else {
+        std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl;
+        GGML_ASSERT(false);
+    }
+
+    // Disable vulkan here to avoid the hooks in ggml.c
+    ctx->disable = true;
+
+    ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx);
+    ggml_build_forward_expand(cgraph, tensor_clone);
+
+    ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 8);
+
+    ctx->disable = false;
+
+    ggml_vk_check_tensor(ggml_op_name(tensor->op), tensor_clone);
+    if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
+        ggml_vk_print_tensor(ctx, tensor_clone, "tensor_clone");
+    }
+
+    comp_size = ggml_nbytes(tensor_clone);
+
+    comp_result = malloc(comp_size);
+    memcpy(comp_result, tensor_clone->data, comp_size);
+    memcpy(comp_nb, tensor_clone->nb, sizeof(size_t) * GGML_MAX_DIMS);
+
+    if (src0 != nullptr) {
+        free(src0_buffer);
+    }
+    if (src1 != nullptr) {
+        free(src1_buffer);
+    }
+    if (src2 != nullptr) {
+        free(src1_buffer);
+    }
+
+    ggml_free(ggml_ctx);
+}
+
+static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor) {
+    if (params->ith != 0) {
+        return;
+    }
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
+        return;
+    }
+    if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) {
+        return;
+    }
+
+#ifdef GGML_VULKAN_DEBUG
+    std::cerr << "ggml_vk_check_results_1(" << tensor->name << ")" << std::endl;
+#endif
+
+    ggml_tensor * src0 = tensor->src[0];
+    ggml_tensor * src1 = tensor->src[1];
+
+    void * tensor_data = tensor->data;
+
+    if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
+        size_t tensor_size = ggml_nbytes(tensor);
+        tensor_data = malloc(tensor_size);
+
+        ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
+
+        vk_buffer buffer_gpu = extra->buffer_gpu.lock();
+        if (extra->offset + tensor_size >= buffer_gpu->size) {
+            tensor_size = buffer_gpu->size - (extra->offset);
+        }
+
+        ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size);
+    }
+
+    float first_error_result = -1.0f;
+    float first_error_correct = -1.0f;
+    std::array<int, 4> first_error = { -1, -1, -1, -1 };
+    double avg_err = 0.0;
+    size_t counter = 0;
+
+    for (int i3 = 0; i3 < tensor->ne[3]; i3++) {
+        for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
+            for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
+                for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
+                    const bool buffer_size_fit = i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0] < comp_size;
+                    float correct = 0.0f;
+                    float result = 0.0f;
+
+                    if (buffer_size_fit) {
+                        if (tensor->type == GGML_TYPE_F32) {
+                            correct = *(float *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]);
+                            result  = *(float *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]);
+                        } else if (tensor->type == GGML_TYPE_F16) {
+                            correct = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]));
+                            result  = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]));
+                        } else {
+                            std::cerr << "comp_size=" << comp_size << " but required is " << (i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]) << std::endl;
+                        }
+                    } else {
+                        std::cerr << "Missing debug code for type " << ggml_type_name(tensor->type) << std::endl;
+                        GGML_ASSERT(false);
+                    }
+
+                    if ((std::isnan(correct) != std::isnan(result)) || (std::isinf(correct) != std::isinf(result)) || !buffer_size_fit) {
+                        std::cerr << "ERROR: Invalid value in " << ggml_op_name(tensor->op) << " i3=" << i3 << " i2=" << i2 << " i1=" << i1 << " i0=" << i0 << " result=" << result << " correct=" << correct << " avg_err=" << (avg_err / counter) << std::endl;
+                        std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl;
+                        if (src0 != nullptr) {
+                            std::cerr << "src0=" << src0 << " src0->name=" << src0->name << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " backend=" << src0->backend << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl;
+                        }
+                        if (src1 != nullptr) {
+                            std::cerr << "src1=" << src1 << " src1->name=" << src1->name << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " backend=" << src1->backend << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl;
+                        }
+                        std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct  << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl;
+                        std::cerr << std::endl << "Result:" << std::endl;
+                        ggml_vk_print_tensor_area(tensor, tensor_data, i0, i1, i2, i3);
+                        std::cerr << std::endl << "Correct:" << std::endl;
+                        ggml_vk_print_tensor_area(tensor, comp_result, i0, i1, i2, i3);
+                        std::cerr << std::endl;
+                        std::vector<const ggml_tensor *> done;
+                        ggml_vk_print_graph_origin(tensor, done);
+                        GGML_ASSERT(false);
+                    }
+                    if (first_error[0] == -1 && std::fabs(correct - result) > 0.1f) {
+                        first_error[0] = i0;
+                        first_error[1] = i1;
+                        first_error[2] = i2;
+                        first_error[3] = i3;
+                        first_error_result = result;
+                        first_error_correct = correct;
+                    }
+
+                    // Special case, value is infinite, avoid NaN result in avg_err
+                    // NaN also appears in results, if both are nan error is 0
+                    if (!std::isinf(correct) && !std::isinf(result) && !std::isnan(correct) && !std::isnan(result)) {
+                        avg_err += std::fabs(correct - result);
+                    }
+                    counter++;
+                }
+            }
+        }
+    }
+
+    avg_err /= counter;
+
+    if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
+        std::cerr << "TENSOR CHECK: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl;
+        std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl;
+        if (src0 != nullptr) {
+            std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " backend=" << src0->backend << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl;
+        }
+        if (src1 != nullptr) {
+            std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " backend=" << src1->backend << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl;
+        }
+        std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct  << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl;
+        std::cerr << std::endl << "Result:" << std::endl;
+        ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0);
+        std::cerr << std::endl << "Correct:" << std::endl;
+        ggml_vk_print_tensor_area(tensor, comp_result, 5, 5, 0, 0);
+        std::cerr << std::endl;
+        std::cerr << std::endl << "Result:" << std::endl;
+        ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 1, 0);
+        std::cerr << std::endl << "Correct:" << std::endl;
+        ggml_vk_print_tensor_area(tensor, comp_result, 5, 5, 1, 0);
+        std::cerr << std::endl;
+        std::vector<const ggml_tensor *> done;
+        ggml_vk_print_graph_origin(tensor, done);
+    }
+
+    if (avg_err > 0.05 || std::isnan(avg_err)) {
+        std::cerr << "ERROR: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl;
+        std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl;
+        if (src0 != nullptr) {
+            std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " backend=" << src0->backend << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl;
+        }
+        if (src1 != nullptr) {
+            std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " backend=" << src1->backend << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl;
+        }
+        std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct  << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl;
+        std::cerr << std::endl << "Result:" << std::endl;
+        ggml_vk_print_tensor_area(tensor, tensor_data, first_error[0], first_error[1], first_error[2], first_error[3]);
+        std::cerr << std::endl << "Correct:" << std::endl;
+        ggml_vk_print_tensor_area(tensor, comp_result, first_error[0], first_error[1], first_error[2], first_error[3]);
+        std::cerr << std::endl;
+        std::vector<const ggml_tensor *> done;
+        ggml_vk_print_graph_origin(tensor, done);
+        GGML_ASSERT(false);
+    } else {
+        std::cerr << check_counter << " " << tensor->name << " op=" << ggml_op_name(tensor->op) << " backend=" << tensor->backend << " avg_err=" << avg_err << std::endl;
+    }
+
+    free(comp_result);
+    comp_result = nullptr;
+    comp_size = 0;
+
+    if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
+        free(tensor_data);
+    }
+}
+#endif
diff --git a/ggml-vulkan.h b/ggml-vulkan.h
new file mode 100644
index 000000000..af661c2d7
--- /dev/null
+++ b/ggml-vulkan.h
@@ -0,0 +1,29 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+#define GGML_VK_NAME "Vulkan"
+#define GGML_VK_MAX_DEVICES 16
+
+GGML_API void ggml_vk_instance_init(void);
+
+// backend API
+GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t dev_num);
+
+GGML_API GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend);
+GGML_API GGML_CALL int  ggml_backend_vk_get_device_count(void);
+GGML_API GGML_CALL void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size);
+GGML_API GGML_CALL void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total);
+
+GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num);
+// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
+GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type(void);
+
+#ifdef  __cplusplus
+}
+#endif
diff --git a/ggml.c b/ggml.c
index adb387100..c9b0a6a0e 100644
--- a/ggml.c
+++ b/ggml.c
@@ -3,6 +3,7 @@
 
 #include "ggml-impl.h"
 #include "ggml-quants.h"
+#include "ggml.h"
 
 #if defined(_MSC_VER) || defined(__MINGW32__)
 #include <malloc.h> // using malloc.h with MSC/MINGW
@@ -23,6 +24,9 @@
 #include <limits.h>
 #include <stdarg.h>
 #include <signal.h>
+#if defined(__gnu_linux__)
+#include <syscall.h>
+#endif
 
 #ifdef GGML_USE_METAL
 #include <unistd.h>
@@ -40,6 +44,10 @@
 
 #if defined(_WIN32)
 
+#define WIN32_LEAN_AND_MEAN
+#ifndef NOMINMAX
+    #define NOMINMAX
+#endif
 #include <windows.h>
 
 typedef volatile LONG atomic_int;
@@ -132,7 +140,7 @@ void ggml_print_backtrace(void) {
             "-ex", "bt -frame-info source-and-location",
             "-ex", "detach",
             "-ex", "quit",
-            NULL);
+            (char *) NULL);
     } else {
         waitpid(pid, NULL, 0);
     }
@@ -218,6 +226,7 @@ inline static void * ggml_aligned_malloc(size_t size) {
                 break;
         }
         GGML_PRINT("%s: %s (attempted to allocate %6.2f MB)\n", __func__, error_desc, size/(1024.0*1024.0));
+        GGML_ASSERT(false);
         return NULL;
     }
     return aligned_memory;
@@ -230,6 +239,38 @@ inline static void * ggml_aligned_malloc(size_t size) {
 #endif
 #endif
 
+inline static void * ggml_malloc(size_t size) {
+    if (size == 0) {
+        GGML_PRINT("WARNING: Behavior may be unexpected when allocating 0 bytes for ggml_malloc!\n");
+        return NULL;
+    }
+    void * result = malloc(size);
+    if (result == NULL) {
+        GGML_PRINT("%s: failed to allocate %6.2f MB\n", __func__, size/(1024.0*1024.0));
+        GGML_ASSERT(false);
+    }
+    return result;
+}
+
+// calloc
+inline static void * ggml_calloc(size_t num, size_t size) {
+    if (num == 0 || size == 0) {
+        GGML_PRINT("WARNING: Behavior may be unexpected when allocating 0 bytes for ggml_calloc!\n");
+        return NULL;
+    }
+    void * result = calloc(num, size);
+    if (result == NULL) {
+        GGML_PRINT("%s: failed to allocate %6.2f MB\n", __func__, size/(1024.0*1024.0));
+        GGML_ASSERT(false);
+    }
+    return result;
+}
+
+#define GGML_MALLOC(size)      ggml_malloc(size)
+#define GGML_CALLOC(num, size) ggml_calloc(num, size)
+
+#define GGML_FREE(ptr) free(ptr)
+
 #define UNUSED GGML_UNUSED
 #define SWAP(x, y, T) do { T SWAP = x; x = y; y = SWAP; } while (0)
 
@@ -244,8 +285,6 @@ inline static void * ggml_aligned_malloc(size_t size) {
 #else
 #include <cblas.h>
 #endif
-#elif defined(GGML_USE_CUBLAS)
-#include "ggml-cuda.h"
 #elif defined(GGML_USE_CLBLAST)
 #include "ggml-opencl.h"
 #endif
@@ -278,10 +317,21 @@ static ggml_fp16_t ggml_table_exp_f16[1 << 16];
 // precomputed f32 table for f16 (256 KB) (ggml-impl.h)
 float ggml_table_f32_f16[1 << 16];
 
+const char * ggml_status_to_string(enum ggml_status status) {
+    switch (status) {
+        case GGML_STATUS_ALLOC_FAILED: return "GGML status: error (failed to allocate memory)";
+        case GGML_STATUS_FAILED:       return "GGML status: error (operation failed)";
+        case GGML_STATUS_SUCCESS:      return "GGML status: success";
+        case GGML_STATUS_ABORTED:      return "GGML status: warning (operation aborted)";
+    }
+
+    return "GGML status: unknown";
+}
+
 // note: do not use these inside ggml.c
 // these are meant to be used via the ggml.h API
 float ggml_fp16_to_fp32(ggml_fp16_t x) {
-    return (float) GGML_FP16_TO_FP32(x);
+    return GGML_FP16_TO_FP32(x);
 }
 
 ggml_fp16_t ggml_fp32_to_fp16(float x) {
@@ -313,6 +363,10 @@ void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n) {
     }
 }
 
+bool ggml_guid_matches(ggml_guid_t guid_a, ggml_guid_t guid_b) {
+    return memcmp(guid_a, guid_b, sizeof(ggml_guid)) == 0;
+}
+
 //
 // timing
 //
@@ -375,6 +429,57 @@ int64_t ggml_cycles_per_ms(void) {
 #define ggml_perf_cycles_per_ms() 0
 #endif
 
+//
+// cross-platform UTF-8 file paths
+//
+
+#ifdef _WIN32
+static wchar_t * ggml_mbstowcs(const char * mbs) {
+    int wlen = MultiByteToWideChar(CP_UTF8, 0, mbs, -1, NULL, 0);
+    if (!wlen) {
+        errno = EINVAL;
+        return NULL;
+    }
+
+    wchar_t * wbuf = GGML_MALLOC(wlen * sizeof(wchar_t));
+    wlen = MultiByteToWideChar(CP_UTF8, 0, mbs, -1, wbuf, wlen);
+    if (!wlen) {
+        GGML_FREE(wbuf);
+        errno = EINVAL;
+        return NULL;
+    }
+
+    return wbuf;
+}
+#endif
+
+FILE * ggml_fopen(const char * fname, const char * mode) {
+#ifdef _WIN32
+    FILE * file = NULL;
+
+    // convert fname (UTF-8)
+    wchar_t * wfname = ggml_mbstowcs(fname);
+    if (wfname) {
+        // convert mode (ANSI)
+        wchar_t * wmode = GGML_MALLOC((strlen(mode) + 1) * sizeof(wchar_t));
+        wchar_t * wmode_p = wmode;
+        do {
+            *wmode_p++ = (wchar_t)*mode;
+        } while (*mode++);
+
+        // open file
+        file = _wfopen(wfname, wmode);
+
+        GGML_FREE(wfname);
+        GGML_FREE(wmode);
+    }
+
+    return file;
+#else
+    return fopen(fname, mode);
+#endif
+}
+
 //
 // cache line
 //
@@ -391,8 +496,8 @@ int64_t ggml_cycles_per_ms(void) {
 
 static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float);
 
-static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y);
-static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y);
+static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * restrict x, size_t bx, const float * restrict y, size_t by, int nrc);
+static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc);
 
 static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
     [GGML_TYPE_I8] = {
@@ -413,6 +518,19 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .type_size                = sizeof(int32_t),
         .is_quantized             = false,
     },
+    [GGML_TYPE_I64] = {
+        .type_name                = "i64",
+        .blck_size                = 1,
+        .type_size                = sizeof(int64_t),
+        .is_quantized             = false,
+    },
+    [GGML_TYPE_F64] = {
+        .type_name                = "f64",
+        .blck_size                = 1,
+        .type_size                = sizeof(double),
+        .is_quantized             = false,
+        .nrows                    = 1,
+    },
     [GGML_TYPE_F32] = {
         .type_name                = "f32",
         .blck_size                = 1,
@@ -420,6 +538,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = false,
         .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_f32,
         .vec_dot_type             = GGML_TYPE_F32,
+        .nrows                    = 1,
     },
     [GGML_TYPE_F16] = {
         .type_name                = "f16",
@@ -431,6 +550,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .from_float_reference     = (ggml_from_float_t) ggml_fp32_to_fp16_row,
         .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_f16,
         .vec_dot_type             = GGML_TYPE_F16,
+        .nrows                    = 1,
     },
     [GGML_TYPE_Q4_0] = {
         .type_name                = "q4_0",
@@ -442,6 +562,11 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .from_float_reference     = (ggml_from_float_t) quantize_row_q4_0_reference,
         .vec_dot                  = ggml_vec_dot_q4_0_q8_0,
         .vec_dot_type             = GGML_TYPE_Q8_0,
+#if defined (__ARM_FEATURE_MATMUL_INT8)
+        .nrows                    = 2,
+#else
+        .nrows                    = 1,
+#endif
     },
     [GGML_TYPE_Q4_1] = {
         .type_name                = "q4_1",
@@ -453,6 +578,11 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .from_float_reference     = (ggml_from_float_t) quantize_row_q4_1_reference,
         .vec_dot                  = ggml_vec_dot_q4_1_q8_1,
         .vec_dot_type             = GGML_TYPE_Q8_1,
+#if defined (__ARM_FEATURE_MATMUL_INT8)
+        .nrows                    = 2,
+#else
+        .nrows                    = 1,
+#endif
     },
     [4] = { // GGML_TYPE_Q4_2
         .type_name                = "DEPRECATED",
@@ -464,6 +594,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .from_float_reference     = NULL,
         .vec_dot                  = NULL,
         .vec_dot_type             = GGML_TYPE_COUNT,
+        .nrows                    = 1,
     },
     [5] = { // GGML_TYPE_Q4_3
         .type_name                = "DEPRECATED",
@@ -475,6 +606,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .from_float_reference     = NULL,
         .vec_dot                  = NULL,
         .vec_dot_type             = GGML_TYPE_COUNT,
+        .nrows                    = 1,
     },
     [GGML_TYPE_Q5_0] = {
         .type_name                = "q5_0",
@@ -486,6 +618,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .from_float_reference     = (ggml_from_float_t) quantize_row_q5_0_reference,
         .vec_dot                  = ggml_vec_dot_q5_0_q8_0,
         .vec_dot_type             = GGML_TYPE_Q8_0,
+        .nrows                    = 1,
     },
     [GGML_TYPE_Q5_1] = {
         .type_name                = "q5_1",
@@ -497,6 +630,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .from_float_reference     = (ggml_from_float_t) quantize_row_q5_1_reference,
         .vec_dot                  = ggml_vec_dot_q5_1_q8_1,
         .vec_dot_type             = GGML_TYPE_Q8_1,
+        .nrows                    = 1,
     },
     [GGML_TYPE_Q8_0] = {
         .type_name                = "q8_0",
@@ -508,6 +642,11 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .from_float_reference     = (ggml_from_float_t) quantize_row_q8_0_reference,
         .vec_dot                  = ggml_vec_dot_q8_0_q8_0,
         .vec_dot_type             = GGML_TYPE_Q8_0,
+#if defined (__ARM_FEATURE_MATMUL_INT8)
+        .nrows                    = 2,
+#else
+        .nrows                    = 1,
+#endif
     },
     [GGML_TYPE_Q8_1] = {
         .type_name                = "q8_1",
@@ -517,6 +656,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .from_float               = quantize_row_q8_1,
         .from_float_reference     = (ggml_from_float_t) quantize_row_q8_1_reference,
         .vec_dot_type             = GGML_TYPE_Q8_1,
+        .nrows                    = 1,
     },
     [GGML_TYPE_Q2_K] = {
         .type_name                = "q2_K",
@@ -528,6 +668,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .from_float_reference     = (ggml_from_float_t) quantize_row_q2_K_reference,
         .vec_dot                  = ggml_vec_dot_q2_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
+        .nrows                    = 1,
     },
     [GGML_TYPE_Q3_K] = {
         .type_name                = "q3_K",
@@ -539,6 +680,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .from_float_reference     = (ggml_from_float_t) quantize_row_q3_K_reference,
         .vec_dot                  = ggml_vec_dot_q3_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
+        .nrows                    = 1,
     },
     [GGML_TYPE_Q4_K] = {
         .type_name                = "q4_K",
@@ -550,6 +692,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .from_float_reference     = (ggml_from_float_t) quantize_row_q4_K_reference,
         .vec_dot                  = ggml_vec_dot_q4_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
+        .nrows                    = 1,
     },
     [GGML_TYPE_Q5_K] = {
         .type_name                = "q5_K",
@@ -561,6 +704,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .from_float_reference     = (ggml_from_float_t) quantize_row_q5_K_reference,
         .vec_dot                  = ggml_vec_dot_q5_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
+        .nrows                    = 1,
     },
     [GGML_TYPE_Q6_K] = {
         .type_name                = "q6_K",
@@ -572,6 +716,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .from_float_reference     = (ggml_from_float_t) quantize_row_q6_K_reference,
         .vec_dot                  = ggml_vec_dot_q6_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
+        .nrows                    = 1,
     },
     [GGML_TYPE_IQ2_XXS] = {
         .type_name                = "iq2_xxs",
@@ -579,10 +724,115 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .type_size                = sizeof(block_iq2_xxs),
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq2_xxs,
-        .from_float               = quantize_row_iq2_xxs,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_iq2_xxs_reference,
+        .from_float               = NULL,
+        .from_float_reference     = NULL,
         .vec_dot                  = ggml_vec_dot_iq2_xxs_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
+        .nrows                    = 1,
+    },
+    [GGML_TYPE_IQ2_XS] = {
+        .type_name                = "iq2_xs",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_iq2_xs),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_iq2_xs,
+        .from_float               = NULL,
+        .from_float_reference     = NULL,
+        .vec_dot                  = ggml_vec_dot_iq2_xs_q8_K,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+        .nrows                    = 1,
+    },
+    [GGML_TYPE_IQ3_XXS] = {
+        .type_name                = "iq3_xxs",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_iq3_xxs),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_iq3_xxs,
+        .from_float               = quantize_row_iq3_xxs,
+        .from_float_reference     = (ggml_from_float_t)quantize_row_iq3_xxs_reference,
+        .vec_dot                  = ggml_vec_dot_iq3_xxs_q8_K,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+        .nrows                    = 1,
+    },
+    [GGML_TYPE_IQ3_S] = {
+        .type_name                = "iq3_s",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_iq3_s),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_iq3_s,
+        .from_float               = quantize_row_iq3_s,
+        .from_float_reference     = (ggml_from_float_t)quantize_row_iq3_s_reference,
+        .vec_dot                  = ggml_vec_dot_iq3_s_q8_K,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+        .nrows                    = 1,
+    },
+    [GGML_TYPE_IQ2_S] = {
+        .type_name                = "iq2_s",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_iq2_s),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_iq2_s,
+        .from_float               = quantize_row_iq2_s,
+        .from_float_reference     = (ggml_from_float_t)quantize_row_iq2_s_reference,
+        .vec_dot                  = ggml_vec_dot_iq2_s_q8_K,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+        .nrows                    = 1,
+    },
+    [GGML_TYPE_IQ1_S] = {
+        .type_name                = "iq1_s",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_iq1_s),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_iq1_s,
+        .from_float               = NULL,
+        .from_float_reference     = NULL,
+        .vec_dot                  = ggml_vec_dot_iq1_s_q8_K,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+        .nrows                    = 1,
+    },
+    [GGML_TYPE_IQ1_M] = {
+        .type_name                = "iq1_m",
+        .blck_size                = QK_K,
+        .type_size                = sizeof(block_iq1_m),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_iq1_m,
+        .from_float               = NULL,
+        .from_float_reference     = NULL,
+        .vec_dot                  = ggml_vec_dot_iq1_m_q8_K,
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+        .nrows                    = 1,
+    },
+    [GGML_TYPE_IQ4_NL] = {
+        .type_name                = "iq4_nl",
+        .blck_size                = QK4_NL,
+        .type_size                = sizeof(block_iq4_nl),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_iq4_nl,
+        .from_float               = quantize_row_iq4_nl,
+        .from_float_reference     = (ggml_from_float_t)quantize_row_iq4_nl_reference,
+        .vec_dot                  = ggml_vec_dot_iq4_nl_q8_0,
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+        .nrows                    = 1,
+    },
+    [GGML_TYPE_IQ4_XS] = {
+        .type_name                = "iq4_xs",
+#if QK_K == 64
+        .blck_size                = QK4_NL,
+#else
+        .blck_size                = QK_K,
+#endif
+        .type_size                = sizeof(block_iq4_xs),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_iq4_xs,
+        .from_float               = quantize_row_iq4_xs,
+        .from_float_reference     = (ggml_from_float_t)quantize_row_iq4_xs_reference,
+        .vec_dot                  = ggml_vec_dot_iq4_xs_q8_K,
+#if QK_K == 64
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+#else
+        .vec_dot_type             = GGML_TYPE_Q8_K,
+#endif
+        .nrows                    = 1,
     },
     [GGML_TYPE_Q8_K] = {
         .type_name                = "q8_K",
@@ -680,7 +930,7 @@ inline static float vaddvq_f32(float32x4_t v) {
     #define GGML_F16x8              float16x8_t
     #define GGML_F16x8_ZERO         vdupq_n_f16(0.0f)
     #define GGML_F16x8_SET1(x)      vdupq_n_f16(x)
-    #define GGML_F16x8_LOAD         vld1q_f16
+    #define GGML_F16x8_LOAD(x)      vld1q_f16((const ggml_fp16_internal_t *)(x))
     #define GGML_F16x8_STORE        vst1q_f16
     #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c)
     #define GGML_F16x8_ADD          vaddq_f16
@@ -723,7 +973,7 @@ inline static float vaddvq_f32(float32x4_t v) {
     #define GGML_F32Cx4              float32x4_t
     #define GGML_F32Cx4_ZERO         vdupq_n_f32(0.0f)
     #define GGML_F32Cx4_SET1(x)      vdupq_n_f32(x)
-    #define GGML_F32Cx4_LOAD(x)      vcvt_f32_f16(vld1_f16(x))
+    #define GGML_F32Cx4_LOAD(x)      vcvt_f32_f16(vld1_f16((const ggml_fp16_internal_t *)(x)))
     #define GGML_F32Cx4_STORE(x, y)  vst1_f16(x, vcvt_f16_f32(y))
     #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c)
     #define GGML_F32Cx4_ADD          vaddq_f32
@@ -741,6 +991,101 @@ inline static float vaddvq_f32(float32x4_t v) {
     #define GGML_F16_VEC_REDUCE         GGML_F32Cx4_REDUCE
 #endif
 
+#elif defined(__AVX512F__)
+
+#define GGML_SIMD
+
+// F32 AVX512
+
+#define GGML_F32_STEP 64
+#define GGML_F32_EPR  16
+
+#define GGML_F32x16         __m512
+#define GGML_F32x16_ZERO    _mm512_setzero_ps()
+#define GGML_F32x16_SET1(x) _mm512_set1_ps(x)
+#define GGML_F32x16_LOAD    _mm512_loadu_ps
+#define GGML_F32x16_STORE   _mm512_storeu_ps
+// _mm512_fmadd_ps is defined in AVX512F so no guard is required
+#define GGML_F32x16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a)
+#define GGML_F32x16_ADD     _mm512_add_ps
+#define GGML_F32x16_MUL     _mm512_mul_ps
+#define GGML_F32x16_REDUCE(res, x)                                    \
+do {                                                                  \
+    int offset = GGML_F32_ARR >> 1;                                   \
+    for (int i = 0; i < offset; ++i) {                                \
+        x[i] = _mm512_add_ps(x[i], x[offset+i]);                      \
+    }                                                                 \
+    offset >>= 1;                                                     \
+    for (int i = 0; i < offset; ++i) {                                \
+        x[i] = _mm512_add_ps(x[i], x[offset+i]);                      \
+    }                                                                 \
+    offset >>= 1;                                                     \
+    for (int i = 0; i < offset; ++i) {                                \
+        x[i] = _mm512_add_ps(x[i], x[offset+i]);                      \
+    }                                                                 \
+    res = _mm512_reduce_add_ps(x[0]);                                 \
+} while (0)
+
+// TODO: is this optimal ?
+
+#define GGML_F32_VEC        GGML_F32x16
+#define GGML_F32_VEC_ZERO   GGML_F32x16_ZERO
+#define GGML_F32_VEC_SET1   GGML_F32x16_SET1
+#define GGML_F32_VEC_LOAD   GGML_F32x16_LOAD
+#define GGML_F32_VEC_STORE  GGML_F32x16_STORE
+#define GGML_F32_VEC_FMA    GGML_F32x16_FMA
+#define GGML_F32_VEC_ADD    GGML_F32x16_ADD
+#define GGML_F32_VEC_MUL    GGML_F32x16_MUL
+#define GGML_F32_VEC_REDUCE GGML_F32x16_REDUCE
+
+// F16 AVX512
+
+// F16 AVX
+
+#define GGML_F16_STEP 64
+#define GGML_F16_EPR  16
+
+// AVX512 has FP16 extension (AVX512_FP16) but I don't have it on my machine so I use FP32 instead
+
+#define GGML_F32Cx16             __m512
+#define GGML_F32Cx16_ZERO        _mm512_setzero_ps()
+#define GGML_F32Cx16_SET1(x)     _mm512_set1_ps(x)
+
+// unlike  _mm256_cvt intrinsics that require F16C, _mm512_cvt is defined in AVX512F
+// so F16C guard isn't required
+#define GGML_F32Cx16_LOAD(x)     _mm512_cvtph_ps(_mm256_loadu_si256((__m256i *)(x)))
+#define GGML_F32Cx16_STORE(x, y) _mm256_storeu_si256((__m256i *)(x), _mm512_cvtps_ph(y, 0))
+
+#define GGML_F32Cx16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a)
+#define GGML_F32Cx16_ADD         _mm512_add_ps
+#define GGML_F32Cx16_MUL         _mm512_mul_ps
+#define GGML_F32Cx16_REDUCE(res, x)                               \
+do {                                                              \
+    int offset = GGML_F32_ARR >> 1;                               \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm512_add_ps(x[i], x[offset+i]);                  \
+    }                                                             \
+    offset >>= 1;                                                 \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm512_add_ps(x[i], x[offset+i]);                  \
+    }                                                             \
+    offset >>= 1;                                                 \
+    for (int i = 0; i < offset; ++i) {                            \
+        x[i] = _mm512_add_ps(x[i], x[offset+i]);                  \
+    }                                                             \
+    res = _mm512_reduce_add_ps(x[0]);                             \
+} while (0)
+
+#define GGML_F16_VEC                GGML_F32Cx16
+#define GGML_F16_VEC_ZERO           GGML_F32Cx16_ZERO
+#define GGML_F16_VEC_SET1           GGML_F32Cx16_SET1
+#define GGML_F16_VEC_LOAD(p, i)     GGML_F32Cx16_LOAD(p)
+#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx16_STORE(p, r[i])
+#define GGML_F16_VEC_FMA            GGML_F32Cx16_FMA
+#define GGML_F16_VEC_ADD            GGML_F32Cx16_ADD
+#define GGML_F16_VEC_MUL            GGML_F32Cx16_MUL
+#define GGML_F16_VEC_REDUCE         GGML_F32Cx16_REDUCE
+
 #elif defined(__AVX__)
 
 #define GGML_SIMD
@@ -779,7 +1124,7 @@ do {                                                              \
     const __m128 t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]),    \
                                  _mm256_extractf128_ps(x[0], 1)); \
     const __m128 t1 = _mm_hadd_ps(t0, t0);                        \
-    res = _mm_cvtss_f32(_mm_hadd_ps(t1, t1));                     \
+    res = (ggml_float) _mm_cvtss_f32(_mm_hadd_ps(t1, t1));        \
 } while (0)
 // TODO: is this optimal ?
 
@@ -1060,7 +1405,7 @@ inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) {
         x[i] = _mm_add_ps(x[i], x[offset+i]);                     \
     }                                                             \
     const __m128 t0 = _mm_hadd_ps(x[0], x[0]);                    \
-    res = _mm_cvtss_f32(_mm_hadd_ps(t0, t0));                     \
+    res = (ggml_float) _mm_cvtss_f32(_mm_hadd_ps(t0, t0));        \
 }
 // TODO: is this optimal ?
 
@@ -1153,7 +1498,13 @@ inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x)
 inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i]*y[i];   }
 inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i]/y[i];   }
 
-static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) {
+static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * restrict x, size_t bx, const float * restrict y, size_t by, int nrc) {
+   assert(nrc == 1);
+   UNUSED(nrc);
+   UNUSED(bx);
+   UNUSED(by);
+   UNUSED(bs);
+
 #ifdef GGML_SIMD
     float sumf = 0.0f;
     const int np = (n & ~(GGML_F32_STEP - 1));
@@ -1190,7 +1541,13 @@ static void ggml_vec_dot_f32(const int n, float * restrict s, const float * rest
     *s = sumf;
 }
 
-static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) {
+static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc) {
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+
     ggml_float sumf = 0.0;
 
 #if defined(GGML_SIMD)
@@ -1396,7 +1753,7 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float   v) {
 #endif
 }
 
-inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, x, x); *s = sqrtf(*s);   }
+inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, 0, x, 0, x, 0, 1); *s = sqrtf(*s);   }
 inline static void ggml_vec_sqr_f32  (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i];   }
 inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); }
 inline static void ggml_vec_log_f32  (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = logf(x[i]);   }
@@ -1407,6 +1764,9 @@ inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) {
 inline static void ggml_vec_elu_f32  (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expf(x[i])-1; }
 inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; }
 inline static void ggml_vec_leaky_relu_f32 (const int n, float * y, const float * x, const float ns) { for (int i = 0; i < n; ++i) y[i] = ((x[i] > 0.f) ? x[i] : 0.f) + ns * ((x[i] < 0.0f) ? x[i] : 0.f); }
+// TODO: optimize performance
+inline static void ggml_vec_hardswish_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); }
+inline static void ggml_vec_hardsigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); }
 
 static const float GELU_COEF_A     = 0.044715f;
 static const float GELU_QUICK_COEF = -1.702f;
@@ -1427,9 +1787,15 @@ inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp
 inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) {
     uint16_t t;
     for (int i = 0; i < n; ++i) {
-        ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]);
-        memcpy(&t, &fp16, sizeof(uint16_t));
-        y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]);
+        if (x[i] <= -10.0f) {
+            y[i] = 0.0f;
+        } else if (x[i] >= 10.0f) {
+            y[i] = x[i];
+        } else {
+            ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]);
+            memcpy(&t, &fp16, sizeof(uint16_t));
+            y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]);
+        }
     }
 }
 #else
@@ -1635,12 +2001,16 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "POOL_2D",
     "UPSCALE",
     "PAD",
+    "ARANGE",
+    "TIMESTEP_EMBEDDING",
     "ARGSORT",
     "LEAKY_RELU",
 
     "FLASH_ATTN",
     "FLASH_FF",
     "FLASH_ATTN_BACK",
+    "SSM_CONV",
+    "SSM_SCAN",
     "WIN_PART",
     "WIN_UNPART",
     "GET_REL_POS",
@@ -1663,7 +2033,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "CROSS_ENTROPY_LOSS_BACK",
 };
 
-static_assert(GGML_OP_COUNT == 72, "GGML_OP_COUNT != 72");
+static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76");
 
 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "none",
@@ -1721,12 +2091,16 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "pool_2d(x)",
     "upscale(x)",
     "pad(x)",
+    "arange(start, stop, step)",
+    "timestep_embedding(timesteps, dim, max_period)",
     "argsort(x)",
     "leaky_relu(x)",
 
     "flash_attn(x)",
     "flash_ff(x)",
     "flash_attn_back(x)",
+    "ssm_conv(x)",
+    "ssm_scan(x)",
     "win_part(x)",
     "win_unpart(x)",
     "get_rel_pos(x)",
@@ -1749,7 +2123,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "cross_entropy_loss_back(x,y)",
 };
 
-static_assert(GGML_OP_COUNT == 72, "GGML_OP_COUNT != 72");
+static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76");
 
 static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
 
@@ -1765,9 +2139,11 @@ static const char * GGML_UNARY_OP_NAME[GGML_UNARY_OP_COUNT] = {
     "GELU",
     "GELU_QUICK",
     "SILU",
+    "HARDSWISH",
+    "HARDSIGMOID",
 };
 
-static_assert(GGML_UNARY_OP_COUNT == 10, "GGML_UNARY_OP_COUNT != 10");
+static_assert(GGML_UNARY_OP_COUNT == 12, "GGML_UNARY_OP_COUNT != 12");
 
 
 static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
@@ -1848,9 +2224,16 @@ struct ggml_numa_node {
 };
 
 struct ggml_numa_nodes {
+    enum ggml_numa_strategy numa_strategy;
     struct ggml_numa_node nodes[GGML_NUMA_MAX_NODES];
     uint32_t n_nodes;
     uint32_t total_cpus; // hardware threads on system
+    uint32_t current_node; // node on which main process is execting
+#if defined(__gnu_linux__)
+    cpu_set_t cpuset; // cpuset from numactl
+#else
+    uint32_t cpuset; // no NUMA support outside of Linux at this time. Use a portable datatype
+#endif
 };
 
 //
@@ -1884,18 +2267,40 @@ inline static void ggml_critical_section_end(void) {
     atomic_fetch_sub(&g_state_barrier, 1);
 }
 
-void ggml_numa_init(void) {
+#if defined(__gnu_linux__)
+static cpu_set_t ggml_get_numa_affinity(void) {
+    cpu_set_t cpuset;
+    pthread_t thread;
+    thread = pthread_self();
+    CPU_ZERO(&cpuset);
+    pthread_getaffinity_np(thread, sizeof(cpu_set_t), &cpuset);
+    return cpuset;
+}
+#else
+static uint32_t ggml_get_numa_affinity(void) {
+    return 0; // no NUMA support
+}
+#endif
+
+void ggml_numa_init(enum ggml_numa_strategy numa_flag) {
     if (g_state.numa.n_nodes > 0) {
         fprintf(stderr, "ggml_numa_init: NUMA already initialized\n");
 
         return;
     }
 
-#ifdef __linux__
+#if defined(__gnu_linux__)
     struct stat st;
     char path[256];
     int rv;
 
+    // set numa scheme
+    g_state.numa.numa_strategy = numa_flag;
+
+    GGML_PRINT_DEBUG("numa strategy %u\n",g_state.numa.numa_strategy);
+
+    g_state.numa.cpuset = ggml_get_numa_affinity();
+
     // enumerate nodes
     while (g_state.numa.n_nodes < GGML_NUMA_MAX_NODES) {
         rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u", g_state.numa.n_nodes);
@@ -1914,11 +2319,26 @@ void ggml_numa_init(void) {
 
     GGML_PRINT_DEBUG("found %u numa nodes, %u CPUs\n", g_state.numa.n_nodes, g_state.numa.total_cpus);
 
-    if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1) {
+    // figure out which node we're on
+    uint current_cpu;
+    int getcpu_ret = 0;
+#if __GLIBC__ > 2 || (__GLIBC__ == 2 && __GLIBC_MINOR__ > 28)
+    getcpu_ret = getcpu(&current_cpu, &g_state.numa.current_node);
+#else
+    // old glibc doesn't have a wrapper for this call. Fall back on direct syscall
+#   if !defined(SYS_getcpu) && defined(SYS_get_cpu)
+#       define SYS_getcpu SYS_get_cpu // some older glibc versions use this name
+#   endif
+    getcpu_ret = syscall(SYS_getcpu, &current_cpu, &g_state.numa.current_node);
+#endif
+
+    if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1 || getcpu_ret != 0) {
         g_state.numa.n_nodes = 0;
         return;
     }
 
+    GGML_PRINT_DEBUG("found our process on numa node %u, CPU %u\n", g_state.numa.current_node, current_cpu);
+
     for (uint32_t n = 0; n < g_state.numa.n_nodes; ++n) {
         struct ggml_numa_node * node = &g_state.numa.nodes[n];
         GGML_PRINT_DEBUG("CPUs on node %u:", n);
@@ -1945,6 +2365,7 @@ void ggml_numa_init(void) {
         }
     }
 #else
+    GGML_UNUSED(numa_flag);
     // TODO
 #endif
 }
@@ -1973,19 +2394,19 @@ void ggml_print_objects(const struct ggml_context * ctx) {
     GGML_PRINT("%s: --- end ---\n", __func__);
 }
 
-int64_t ggml_nelements(const struct ggml_tensor * tensor) {
+GGML_CALL int64_t ggml_nelements(const struct ggml_tensor * tensor) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return tensor->ne[0]*tensor->ne[1]*tensor->ne[2]*tensor->ne[3];
 }
 
-int64_t ggml_nrows(const struct ggml_tensor * tensor) {
+GGML_CALL int64_t ggml_nrows(const struct ggml_tensor * tensor) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return tensor->ne[1]*tensor->ne[2]*tensor->ne[3];
 }
 
-size_t ggml_nbytes(const struct ggml_tensor * tensor) {
+GGML_CALL size_t ggml_nbytes(const struct ggml_tensor * tensor) {
     size_t nbytes;
     size_t blck_size = ggml_blck_size(tensor->type);
     if (blck_size == 1) {
@@ -2008,15 +2429,15 @@ size_t ggml_nbytes_pad(const struct ggml_tensor * tensor) {
     return GGML_PAD(ggml_nbytes(tensor), GGML_MEM_ALIGN);
 }
 
-int ggml_blck_size(enum ggml_type type) {
+GGML_CALL int ggml_blck_size(enum ggml_type type) {
     return type_traits[type].blck_size;
 }
 
-size_t ggml_type_size(enum ggml_type type) {
+GGML_CALL size_t ggml_type_size(enum ggml_type type) {
     return type_traits[type].type_size;
 }
 
-size_t ggml_row_size(enum ggml_type type, int64_t ne) {
+GGML_CALL size_t ggml_row_size(enum ggml_type type, int64_t ne) {
     assert(ne % ggml_blck_size(type) == 0);
     return ggml_type_size(type)*ne/ggml_blck_size(type);
 }
@@ -2025,15 +2446,15 @@ double ggml_type_sizef(enum ggml_type type) {
     return ((double)(type_traits[type].type_size))/type_traits[type].blck_size;
 }
 
-const char * ggml_type_name(enum ggml_type type) {
+GGML_CALL const char * ggml_type_name(enum ggml_type type) {
     return type_traits[type].type_name;
 }
 
-bool ggml_is_quantized(enum ggml_type type) {
+GGML_CALL bool ggml_is_quantized(enum ggml_type type) {
     return type_traits[type].is_quantized;
 }
 
-const char * ggml_op_name(enum ggml_op op) {
+GGML_CALL const char * ggml_op_name(enum ggml_op op) {
     return GGML_OP_NAME[op];
 }
 
@@ -2045,7 +2466,7 @@ const char * ggml_unary_op_name(enum ggml_unary_op op) {
     return GGML_UNARY_OP_NAME[op];
 }
 
-const char * ggml_op_desc(const struct ggml_tensor * t) {
+GGML_CALL const char * ggml_op_desc(const struct ggml_tensor * t) {
     if (t->op == GGML_OP_UNARY) {
         enum ggml_unary_op uop = ggml_get_unary_op(t);
         return ggml_unary_op_name(uop);
@@ -2055,7 +2476,7 @@ const char * ggml_op_desc(const struct ggml_tensor * t) {
     }
 }
 
-size_t ggml_element_size(const struct ggml_tensor * tensor) {
+GGML_CALL size_t ggml_element_size(const struct ggml_tensor * tensor) {
     return ggml_type_size(tensor->type);
 }
 
@@ -2123,6 +2544,14 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
         case GGML_FTYPE_MOSTLY_Q5_K:          wtype = GGML_TYPE_Q5_K;  break;
         case GGML_FTYPE_MOSTLY_Q6_K:          wtype = GGML_TYPE_Q6_K;  break;
         case GGML_FTYPE_MOSTLY_IQ2_XXS:       wtype = GGML_TYPE_IQ2_XXS;  break;
+        case GGML_FTYPE_MOSTLY_IQ2_XS:        wtype = GGML_TYPE_IQ2_XS;   break;
+        case GGML_FTYPE_MOSTLY_IQ3_XXS:       wtype = GGML_TYPE_IQ3_XXS;  break;
+        case GGML_FTYPE_MOSTLY_IQ1_S:         wtype = GGML_TYPE_IQ1_S;    break;
+        case GGML_FTYPE_MOSTLY_IQ1_M:         wtype = GGML_TYPE_IQ1_M;    break;
+        case GGML_FTYPE_MOSTLY_IQ4_NL:        wtype = GGML_TYPE_IQ4_NL;   break;
+        case GGML_FTYPE_MOSTLY_IQ4_XS:        wtype = GGML_TYPE_IQ4_XS;   break;
+        case GGML_FTYPE_MOSTLY_IQ3_S:         wtype = GGML_TYPE_IQ3_S;    break;
+        case GGML_FTYPE_MOSTLY_IQ2_S:         wtype = GGML_TYPE_IQ2_S;    break;
         case GGML_FTYPE_UNKNOWN:              wtype = GGML_TYPE_COUNT; break;
         case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break;
     }
@@ -2136,11 +2565,11 @@ size_t ggml_tensor_overhead(void) {
     return GGML_OBJECT_SIZE + GGML_TENSOR_SIZE;
 }
 
-bool ggml_is_transposed(const struct ggml_tensor * tensor) {
+GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor) {
     return tensor->nb[0] > tensor->nb[1];
 }
 
-bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
+GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return
@@ -2159,7 +2588,7 @@ static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * te
         tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
 }
 
-bool ggml_is_permuted(const struct ggml_tensor * tensor) {
+GGML_CALL bool ggml_is_permuted(const struct ggml_tensor * tensor) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return tensor->nb[0] > tensor->nb[1] || tensor->nb[1] > tensor->nb[2] || tensor->nb[2] > tensor->nb[3];
@@ -2174,6 +2603,16 @@ static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) {
         tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
 }
 
+GGML_CALL bool ggml_is_empty(const struct ggml_tensor * tensor) {
+    for (int i = 0; i < GGML_MAX_DIMS; ++i) {
+        if (tensor->ne[i] == 0) {
+            // empty if any dimension has no elements
+            return true;
+        }
+    }
+    return false;
+}
+
 bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
@@ -2188,7 +2627,7 @@ bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor
 static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
-    return
+    return ggml_is_empty(t0) ? ggml_is_empty(t1) :
         (t1->ne[0]%t0->ne[0] == 0) &&
         (t1->ne[1]%t0->ne[1] == 0) &&
         (t1->ne[2]%t0->ne[2] == 0) &&
@@ -2272,9 +2711,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
             GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f);
         }
 
-#if defined(GGML_USE_CUBLAS)
-        ggml_init_cublas();
-#elif defined(GGML_USE_CLBLAST)
+#if defined(GGML_USE_CLBLAST)
         ggml_cl_init();
 #endif
 
@@ -2336,6 +2773,10 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
 }
 
 void ggml_free(struct ggml_context * ctx) {
+    if (ctx == NULL) {
+        return;
+    }
+
     // make this function thread safe
     ggml_critical_section_start();
 
@@ -2396,7 +2837,8 @@ size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) {
     size_t max_size = 0;
 
     for (struct ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor != NULL; tensor = ggml_get_next_tensor(ctx, tensor)) {
-        max_size = MAX(max_size, ggml_nbytes(tensor));
+        size_t bytes = ggml_nbytes(tensor);
+        max_size = MAX(max_size, bytes);
     }
 
     return max_size;
@@ -2490,7 +2932,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
         data_size *= ne[i];
     }
 
-    GGML_ASSERT(view_src == NULL || data_size + view_offs <= ggml_nbytes(view_src));
+    GGML_ASSERT(view_src == NULL || data_size == 0 || data_size + view_offs <= ggml_nbytes(view_src));
 
     void * data = view_src != NULL ? view_src->data : NULL;
     if (data != NULL) {
@@ -2518,7 +2960,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
         }
     }
 
-    struct ggml_object * const obj_new = ggml_new_object(ctx, GGML_OBJECT_TENSOR, GGML_TENSOR_SIZE + obj_alloc_size);
+    struct ggml_object * const obj_new = ggml_new_object(ctx, GGML_OBJECT_TYPE_TENSOR, GGML_TENSOR_SIZE + obj_alloc_size);
 
     // TODO: for recoverable errors, we would need to free the data allocated from the scratch buffer here
 
@@ -2526,13 +2968,13 @@ static struct ggml_tensor * ggml_new_tensor_impl(
 
     *result = (struct ggml_tensor) {
         /*.type         =*/ type,
-        /*.backend      =*/ GGML_BACKEND_CPU,
+        /*.backend      =*/ GGML_BACKEND_TYPE_CPU,
         /*.buffer       =*/ NULL,
         /*.ne           =*/ { 1, 1, 1, 1 },
         /*.nb           =*/ { 0, 0, 0, 0 },
         /*.op           =*/ GGML_OP_NONE,
         /*.op_params    =*/ { 0 },
-        /*.is_param     =*/ false,
+        /*.flags        =*/ 0,
         /*.grad         =*/ NULL,
         /*.src          =*/ { NULL },
         /*.perf_runs    =*/ 0,
@@ -2648,11 +3090,21 @@ static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_
     return ((const int32_t *)(tensor->op_params))[i];
 }
 
+static float ggml_get_op_params_f32(const struct ggml_tensor * tensor, uint32_t i) {
+    assert(i < GGML_MAX_OP_PARAMS / sizeof(float));
+    return ((const float *)(tensor->op_params))[i];
+}
+
 static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) {
     assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t));
     ((int32_t *)(tensor->op_params))[i] = value;
 }
 
+static void ggml_set_op_params_f32(struct ggml_tensor * tensor, uint32_t i, float value) {
+    assert(i < GGML_MAX_OP_PARAMS / sizeof(float));
+    ((float *)(tensor->op_params))[i] = value;
+}
+
 struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) {
     memset(tensor->data, 0, ggml_nbytes(tensor));
     return tensor;
@@ -3057,7 +3509,7 @@ float * ggml_get_data_f32(const struct ggml_tensor * tensor) {
     return (float *)(tensor->data);
 }
 
-enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) {
+GGML_CALL enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) {
     GGML_ASSERT(tensor->op == GGML_OP_UNARY);
     return (enum ggml_unary_op) ggml_get_op_params_i32(tensor, 0);
 }
@@ -3067,7 +3519,7 @@ const char * ggml_get_name(const struct ggml_tensor * tensor) {
 }
 
 struct ggml_tensor * ggml_set_name(struct ggml_tensor * tensor, const char * name) {
-    strncpy(tensor->name, name, sizeof(tensor->name));
+    strncpy(tensor->name, name, sizeof(tensor->name) - 1);
     tensor->name[sizeof(tensor->name) - 1] = '\0';
     return tensor;
 }
@@ -3099,7 +3551,7 @@ struct ggml_tensor * ggml_get_first_tensor(const struct ggml_context * ctx) {
     char * const mem_buffer = ctx->mem_buffer;
 
     while (obj != NULL) {
-        if (obj->type == GGML_OBJECT_TENSOR) {
+        if (obj->type == GGML_OBJECT_TYPE_TENSOR) {
             return (struct ggml_tensor *)(mem_buffer + obj->offs);
         }
 
@@ -3116,7 +3568,7 @@ struct ggml_tensor * ggml_get_next_tensor(const struct ggml_context * ctx, struc
     char * const mem_buffer = ctx->mem_buffer;
 
     while (obj != NULL) {
-        if (obj->type == GGML_OBJECT_TENSOR) {
+        if (obj->type == GGML_OBJECT_TYPE_TENSOR) {
             return (struct ggml_tensor *)(mem_buffer + obj->offs);
         }
 
@@ -3132,7 +3584,7 @@ struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * nam
     char * const mem_buffer = ctx->mem_buffer;
 
     while (obj != NULL) {
-        if (obj->type == GGML_OBJECT_TENSOR) {
+        if (obj->type == GGML_OBJECT_TYPE_TENSOR) {
             struct ggml_tensor * cur = (struct ggml_tensor *)(mem_buffer + obj->offs);
             if (strcmp(cur->name, name) == 0) {
                 return cur;
@@ -3929,6 +4381,20 @@ struct ggml_tensor * ggml_silu_back(
     return result;
 }
 
+// ggml hardswish
+struct ggml_tensor * ggml_hardswish(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary(ctx, a, GGML_UNARY_OP_HARDSWISH);
+}
+
+// ggml hardsigmoid
+struct ggml_tensor * ggml_hardsigmoid(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary(ctx, a, GGML_UNARY_OP_HARDSIGMOID);
+}
+
 // ggml_norm
 
 static struct ggml_tensor * ggml_norm_impl(
@@ -4107,45 +4573,38 @@ void ggml_mul_mat_set_prec(
 
 // ggml_mul_mat_id
 
+// NOTE: id will be removed in the future and instead all the experts listed in ids will be computed
+//       this will allow computing all the used experts in a single matrix multiplication
 struct ggml_tensor * ggml_mul_mat_id(
         struct ggml_context * ctx,
-        struct ggml_tensor  * const as[],
-        int                   n_as,
+        struct ggml_tensor  * as,
         struct ggml_tensor  * ids,
         int                   id,
         struct ggml_tensor  * b) {
 
     GGML_ASSERT(ids->type == GGML_TYPE_I32);
-    GGML_ASSERT(ids->ne[2] == 1 && ids->ne[3] == 1);
-    GGML_ASSERT(ids->ne[1] == b->ne[1]);
+    GGML_ASSERT(ids->ne[2] == 1 && ids->ne[3] == 1); // ids is 2d
+    GGML_ASSERT(ids->ne[1] == b->ne[1]); // must have an expert per b row
     GGML_ASSERT(ids->ne[2] == b->ne[2] && ids->ne[3] == b->ne[3]);
-    GGML_ASSERT(n_as > 0 && n_as <= GGML_MAX_SRC - 2);
-    GGML_ASSERT(id >= 0 && id < ids->ne[0]);
+    GGML_ASSERT(id >= 0 && id < ids->ne[0]); // valid id
+    GGML_ASSERT(as->ne[0] == b->ne[0]); // can_mul_mat
 
     bool is_node = false;
 
-    if (as[0]->grad || b->grad) {
+    if (as->grad || b->grad) {
         is_node = true;
     }
 
-    const int64_t ne[4] = { as[0]->ne[1], b->ne[1], b->ne[2], b->ne[3] };
+    const int64_t ne[4] = { as->ne[1], b->ne[1], b->ne[2], b->ne[3] };
     struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
 
     ggml_set_op_params_i32(result, 0, id);
-    ggml_set_op_params_i32(result, 1, n_as);
 
     result->op   = GGML_OP_MUL_MAT_ID;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
-    result->src[0] = ids;
+    result->src[0] = as;
     result->src[1] = b;
-
-    for (int i = 0; i < n_as; i++) {
-        struct ggml_tensor * a = as[i];
-        GGML_ASSERT(ggml_are_same_shape(as[0], a));
-        GGML_ASSERT(ggml_can_mul_mat(a, b));
-        GGML_ASSERT(!ggml_is_transposed(a));
-        result->src[i + 2] = a;
-    }
+    result->src[2] = ids;
 
     return result;
 }
@@ -4311,13 +4770,13 @@ struct ggml_tensor * ggml_set_2d_inplace(
 static struct ggml_tensor * ggml_cpy_impl(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
-        struct ggml_tensor  * b,
-        bool inplace) {
+        struct ggml_tensor  * b) {
     GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b));
 
     bool is_node = false;
 
-    if (!inplace && (a->grad || b->grad)) {
+    if (a->grad || b->grad) {
+        // inplace is false and either one have a grad
         is_node = true;
     }
 
@@ -4341,29 +4800,38 @@ struct ggml_tensor * ggml_cpy(
         struct ggml_context * ctx,
         struct ggml_tensor * a,
         struct ggml_tensor * b) {
-    return ggml_cpy_impl(ctx, a, b, false);
+    return ggml_cpy_impl(ctx, a, b);
 }
 
-struct ggml_tensor * ggml_cpy_inplace(
+struct ggml_tensor * ggml_cast(
         struct ggml_context * ctx,
-        struct ggml_tensor * a,
-        struct ggml_tensor * b) {
-    return ggml_cpy_impl(ctx, a, b, true);
+        struct ggml_tensor  * a,
+        enum   ggml_type      type) {
+    bool is_node = false;
+
+    struct ggml_tensor * result = ggml_new_tensor(ctx, type, GGML_MAX_DIMS, a->ne);
+    ggml_format_name(result, "%s (copy)", a->name);
+
+    result->op   = GGML_OP_CPY;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = a;
+    result->src[1] = result;
+
+    return result;
 }
 
 // ggml_cont
 
 static struct ggml_tensor * ggml_cont_impl(
         struct ggml_context * ctx,
-        struct ggml_tensor  * a,
-        bool inplace) {
+        struct ggml_tensor  * a) {
     bool is_node = false;
 
-    if (!inplace && a->grad) {
+    if (a->grad) {
         is_node = true;
     }
 
-    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+    struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
     ggml_format_name(result, "%s (cont)", a->name);
 
     result->op   = GGML_OP_CONT;
@@ -4376,13 +4844,7 @@ static struct ggml_tensor * ggml_cont_impl(
 struct ggml_tensor * ggml_cont(
         struct ggml_context * ctx,
         struct ggml_tensor * a) {
-    return ggml_cont_impl(ctx, a, false);
-}
-
-struct ggml_tensor * ggml_cont_inplace(
-        struct ggml_context * ctx,
-        struct ggml_tensor * a) {
-    return ggml_cont_impl(ctx, a, true);
+    return ggml_cont_impl(ctx, a);
 }
 
 // make contiguous, with new shape
@@ -4926,16 +5388,28 @@ static struct ggml_tensor * ggml_soft_max_impl(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
         struct ggml_tensor  * mask,
+        struct ggml_tensor  * pos,
         float                 scale,
+        float                 max_bias,
         bool                  inplace) {
     GGML_ASSERT(ggml_is_contiguous(a));
+
     if (mask) {
         GGML_ASSERT(ggml_is_contiguous(mask));
-        GGML_ASSERT(mask->ne[2] == 1);
-        GGML_ASSERT(mask->ne[3] == 1);
+        GGML_ASSERT(ggml_is_matrix(mask));
         GGML_ASSERT(ggml_can_repeat_rows(mask, a));
     }
 
+    if (pos) {
+        GGML_ASSERT(ggml_is_vector(pos));
+        GGML_ASSERT(pos->type == GGML_TYPE_F32);
+        GGML_ASSERT(pos->ne[0] == a->ne[0]);
+    }
+
+    if (max_bias > 0.0f) {
+        GGML_ASSERT(pos);
+    }
+
     bool is_node = false;
 
     if (a->grad) {
@@ -4944,13 +5418,14 @@ static struct ggml_tensor * ggml_soft_max_impl(
 
     struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
 
-    float params[] = { scale };
+    float params[] = { scale, max_bias };
     ggml_set_op_params(result, params, sizeof(params));
 
     result->op   = GGML_OP_SOFT_MAX;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
     result->src[0] = a;
     result->src[1] = mask;
+    result->src[2] = pos;
 
     return result;
 }
@@ -4958,21 +5433,23 @@ static struct ggml_tensor * ggml_soft_max_impl(
 struct ggml_tensor * ggml_soft_max(
         struct ggml_context * ctx,
         struct ggml_tensor  * a) {
-    return ggml_soft_max_impl(ctx, a, NULL, 1.0f, false);
+    return ggml_soft_max_impl(ctx, a, NULL, NULL, 1.0f, 0.0f, false);
 }
 
 struct ggml_tensor * ggml_soft_max_inplace(
         struct ggml_context * ctx,
         struct ggml_tensor  * a) {
-    return ggml_soft_max_impl(ctx, a, NULL, 1.0f, true);
+    return ggml_soft_max_impl(ctx, a, NULL, NULL, 1.0f, 0.0f, true);
 }
 
 struct ggml_tensor * ggml_soft_max_ext(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
         struct ggml_tensor  * mask,
-        float                 scale) {
-    return ggml_soft_max_impl(ctx, a, mask, scale, false);
+        struct ggml_tensor  * pos,
+        float                 scale,
+        float                 max_bias) {
+    return ggml_soft_max_impl(ctx, a, mask, pos, scale, max_bias, false);
 }
 
 // ggml_soft_max_back
@@ -5258,7 +5735,7 @@ GGML_API struct ggml_tensor * ggml_conv_1d(
         int                   s0,
         int                   p0,
         int                   d0) {
-    struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false); // [N, OL, IC * K]
+    struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16); // [N, OL, IC * K]
 
     struct ggml_tensor * result =
         ggml_mul_mat(ctx,
@@ -5325,6 +5802,30 @@ GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
     return result;
 }
 
+// ggml_conv_depthwise
+struct ggml_tensor * ggml_conv_depthwise_2d(
+    struct ggml_context * ctx,
+    struct ggml_tensor * a,
+    struct ggml_tensor * b,
+    int                  s0,
+    int                  s1,
+    int                  p0,
+    int                  p1,
+    int                  d0,
+    int                  d1) {
+
+    struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], a->ne[1], 1, a->ne[2] * a->ne[3]);
+    struct ggml_tensor * im2col = ggml_im2col(ctx, new_a,
+                                        ggml_reshape_4d(ctx, b, b->ne[0], b->ne[1], 1, b->ne[2] * b->ne[3]),
+                                        s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N * IC, OH, OW, KH * KW]
+    struct ggml_tensor * new_b = ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3]); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW]
+
+    new_a = ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2],  new_a->ne[3], 1);                       // [OC，1, KH, KW] => [1, OC, 1, KH * KW]
+    struct ggml_tensor * result = ggml_mul_mat(ctx, new_a, new_b);
+    result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], b->ne[2], b->ne[3]); // [N, OC, OH, OW]
+
+    return result;
+}
 // ggml_conv_2d
 
 // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW]
@@ -5341,7 +5842,8 @@ struct ggml_tensor * ggml_im2col(
     int                  p1,
     int                  d0,
     int                  d1,
-    bool                 is_2D) {
+    bool                 is_2D,
+    enum ggml_type       dst_type) {
 
     if(is_2D) {
         GGML_ASSERT(a->ne[2] == b->ne[2]);
@@ -5365,7 +5867,7 @@ struct ggml_tensor * ggml_im2col(
         is_2D ?      b->ne[3] : 1,
     };
 
-    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 4, ne);
+    struct ggml_tensor * result = ggml_new_tensor(ctx, dst_type, 4, ne);
     int32_t params[] = { s0, s1, p0, p1, d0, d1, (is_2D ? 1 : 0) };
     ggml_set_op_params(result, params, sizeof(params));
 
@@ -5390,14 +5892,16 @@ struct ggml_tensor * ggml_conv_2d(
         int                  p1,
         int                  d0,
         int                  d1) {
-    struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, s1, p0, p1, d0, d1, true); // [N, OH, OW, IC * KH * KW]
+    struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N, OH, OW, IC * KH * KW]
 
     struct ggml_tensor * result =
         ggml_mul_mat(ctx,
                 ggml_reshape_2d(ctx, im2col, im2col->ne[0],  im2col->ne[3] * im2col->ne[2] * im2col->ne[1]), // [N, OH, OW, IC * KH * KW] => [N*OH*OW, IC * KH * KW]
                 ggml_reshape_2d(ctx, a, (a->ne[0] * a->ne[1] * a->ne[2]),  a->ne[3]));                       // [OC，IC, KH, KW] => [OC, IC * KH * KW]
 
-    result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], a->ne[3], im2col->ne[3]); // [N, OC, OH, OW]
+    result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], im2col->ne[3], a->ne[3]); // [OC, N, OH, OW]
+    result = ggml_cont(ctx, ggml_permute(ctx, result, 0, 1, 3, 2)); // [N, OC, OH, OW]
+
 
     return result;
 }
@@ -5480,11 +5984,13 @@ struct ggml_tensor * ggml_pool_1d(
         is_node = true;
     }
 
-    const int64_t ne[2] = {
+    const int64_t ne[4] = {
         ggml_calc_pool_output_size(a->ne[0], k0, s0, p0),
         a->ne[1],
+        a->ne[2],
+        a->ne[3],
     };
-    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
 
     int32_t params[] = { op, k0, s0, p0 };
     ggml_set_op_params(result, params, sizeof(params));
@@ -5516,12 +6022,13 @@ struct ggml_tensor * ggml_pool_2d(
         is_node = true;
     }
 
+    struct ggml_tensor * result;
     const int64_t ne[3] = {
         ggml_calc_pool_output_size(a->ne[0], k0, s0, p0),
         ggml_calc_pool_output_size(a->ne[1], k1, s1, p1),
         a->ne[2],
     };
-    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne);
+    result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne);
 
     int32_t params[] = { op, k0, k1, s0, s1, p0, p1 };
     ggml_set_op_params(result, params, sizeof(params));
@@ -5529,7 +6036,6 @@ struct ggml_tensor * ggml_pool_2d(
     result->op = GGML_OP_POOL_2D;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
     result->src[0] = a;
-
     return result;
 }
 
@@ -5590,6 +6096,55 @@ struct ggml_tensor * ggml_upscale(
     return ggml_upscale_impl(ctx, a, scale_factor);
 }
 
+struct ggml_tensor * ggml_arange(
+    struct ggml_context * ctx,
+    float start,
+    float stop,
+    float step) {
+
+    GGML_ASSERT(stop > start);
+
+    const int64_t steps = (int64_t) ceilf((stop - start) / step);
+
+    struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, steps);
+
+    result->op = GGML_OP_ARANGE;
+    ggml_set_op_params_f32(result, 0, start);
+    ggml_set_op_params_f32(result, 1, stop);
+    ggml_set_op_params_f32(result, 2, step);
+
+    return result;
+}
+
+struct ggml_tensor * ggml_timestep_embedding(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * timesteps,
+            int                   dim,
+            int                   max_period) {
+    bool is_node = false;
+
+    if (timesteps->grad) {
+        GGML_ASSERT(false); // TODO: implement backward
+        is_node = true;
+    }
+
+    int actual_dim = dim;
+    if (dim % 2 != 0) {
+        actual_dim = dim + 1;
+    }
+
+    struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, actual_dim, timesteps->ne[0]);
+
+    result->op = GGML_OP_TIMESTEP_EMBEDDING;
+    ggml_set_op_params_i32(result, 0, dim);
+    ggml_set_op_params_i32(result, 1, max_period);
+
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = timesteps;
+
+    return result;
+}
+
 // ggml_argsort
 
 struct ggml_tensor * ggml_argsort(
@@ -5617,7 +6172,7 @@ struct ggml_tensor * ggml_top_k(
         int                   k) {
     GGML_ASSERT(a->ne[0] >= k);
 
-    struct ggml_tensor * result = ggml_argsort(ctx, a, GGML_SORT_DESC);
+    struct ggml_tensor * result = ggml_argsort(ctx, a, GGML_SORT_ORDER_DESC);
 
     result = ggml_view_4d(ctx, result,
                 k, result->ne[1], result->ne[2], result->ne[3],
@@ -5769,6 +6324,108 @@ struct ggml_tensor * ggml_flash_attn_back(
     return result;
 }
 
+// ggml_ssm_conv
+
+struct ggml_tensor * ggml_ssm_conv(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * s,
+        struct ggml_tensor  * x,
+        struct ggml_tensor  * c,
+        struct ggml_tensor  * sq) {
+    GGML_ASSERT(ggml_is_3d(s));
+    GGML_ASSERT(ggml_is_matrix(x));
+    GGML_ASSERT(ggml_is_matrix(c));
+    GGML_ASSERT(ggml_is_matrix(sq));
+    GGML_ASSERT(sq->type == GGML_TYPE_I32);
+
+    const int64_t d_conv   = c->ne[0];
+    const int64_t d_inner  = c->ne[1];
+    const int64_t n_tokens = x->ne[1];
+    const int64_t n_kv     = s->ne[2];
+
+    GGML_ASSERT( s->ne[0] == d_conv - 1);
+    GGML_ASSERT( s->ne[1] == d_inner);
+    GGML_ASSERT( x->ne[0] == d_inner);
+    GGML_ASSERT(sq->ne[0] == n_kv);
+    GGML_ASSERT(sq->ne[1] == n_tokens);
+
+    bool is_node = false;
+
+    if (s->grad || x->grad || c->grad || sq->grad) {
+        GGML_ASSERT(false); // TODO: implement
+        is_node = true;
+    }
+
+    // 2-in-1 concatenated x and conv_states, {d_inner, n_tokens} with {d_conv, d_inner, n_kv}
+    struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, (d_inner*n_tokens) + (d_conv*d_inner*n_kv));
+
+    result->op   = GGML_OP_SSM_CONV;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = s;
+    result->src[1] = x;
+    result->src[2] = c;
+    result->src[3] = sq;
+
+    return result;
+}
+
+// ggml_ssm_scan
+
+struct ggml_tensor * ggml_ssm_scan(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * s,
+        struct ggml_tensor  * x,
+        struct ggml_tensor  * dt,
+        struct ggml_tensor  * A,
+        struct ggml_tensor  * B,
+        struct ggml_tensor  * C,
+        struct ggml_tensor  * sq) {
+    GGML_ASSERT(ggml_is_contiguous(s));
+    GGML_ASSERT(ggml_is_contiguous(x));
+    GGML_ASSERT(ggml_is_contiguous(dt));
+    GGML_ASSERT(ggml_is_contiguous(A));
+    GGML_ASSERT(sq->type == GGML_TYPE_I32);
+    GGML_ASSERT(B->nb[0] == ggml_type_size(B->type));
+    GGML_ASSERT(C->nb[0] == ggml_type_size(C->type));
+    GGML_ASSERT(ggml_are_same_shape(x, dt));
+
+    {
+        const int64_t d_state  = s->ne[0];
+        const int64_t d_inner  = s->ne[1];
+        const int64_t n_tokens = x->ne[1];
+
+        GGML_ASSERT(x->ne[0] == d_inner);
+        GGML_ASSERT(A->ne[0] == d_state);
+        GGML_ASSERT(A->ne[1] == d_inner);
+        GGML_ASSERT(B->ne[0] == d_state);
+        GGML_ASSERT(B->ne[1] == n_tokens);
+        GGML_ASSERT(C->ne[0] == d_state);
+        GGML_ASSERT(C->ne[1] == n_tokens);
+    }
+
+    bool is_node = false;
+
+    if (s->grad || x->grad || dt->grad || A->grad || B->grad || C->grad || sq->grad) {
+        GGML_ASSERT(false); // TODO: implement
+        is_node = true;
+    }
+
+    // 2-in-1 concatenated y and ssm_states, {d_inner, n_tokens} with {d_state, d_inner, n_kv}
+    struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, ggml_nelements(x) + ggml_nelements(s));
+
+    result->op   = GGML_OP_SSM_SCAN;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = s;
+    result->src[1] = x;
+    result->src[2] = dt;
+    result->src[3] = A;
+    result->src[4] = B;
+    result->src[5] = C;
+    result->src[6] = sq;
+
+    return result;
+}
+
 // ggml_win_part
 
 struct ggml_tensor * ggml_win_part(
@@ -6392,7 +7049,7 @@ struct ggml_tensor * ggml_cross_entropy_loss_back(
 void ggml_set_param(
         struct ggml_context * ctx,
         struct ggml_tensor * tensor) {
-    tensor->is_param = true;
+    tensor->flags |= GGML_TENSOR_FLAG_PARAM;
 
     GGML_ASSERT(tensor->grad == NULL);
     tensor->grad = ggml_dup_tensor(ctx, tensor);
@@ -6403,13 +7060,15 @@ void ggml_set_param(
 
 static void ggml_compute_forward_dup_same_cont(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
     GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));
     GGML_ASSERT(src0->type == dst->type);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -6435,11 +7094,13 @@ static void ggml_compute_forward_dup_same_cont(
 }
 static void ggml_compute_forward_dup_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -6449,7 +7110,7 @@ static void ggml_compute_forward_dup_f16(
     const int nth = params->nth; // number of threads
 
     if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
-        ggml_compute_forward_dup_same_cont(params, src0, dst);
+        ggml_compute_forward_dup_same_cont(params, dst);
         return;
     }
 
@@ -6706,11 +7367,13 @@ static void ggml_compute_forward_dup_f16(
 
 static void ggml_compute_forward_dup_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -6720,7 +7383,7 @@ static void ggml_compute_forward_dup_f32(
     const int nth = params->nth; // number of threads
 
     if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
-        ggml_compute_forward_dup_same_cont(params, src0, dst);
+        ggml_compute_forward_dup_same_cont(params, dst);
         return;
     }
 
@@ -6956,17 +7619,19 @@ static void ggml_compute_forward_dup_f32(
 // A simplified version of ggml_compute_forward_dup that doesn't do float upcasting, and just plain old memcpy.
 static void ggml_compute_forward_dup_bytes(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
     GGML_ASSERT(src0->type == dst->type);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
     if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) {
-        ggml_compute_forward_dup_same_cont(params, src0, dst);
+        ggml_compute_forward_dup_same_cont(params, dst);
         return;
     }
 
@@ -7105,21 +7770,23 @@ static void ggml_compute_forward_dup_bytes(
 
 static void ggml_compute_forward_dup(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     if (src0->type == dst->type) {
-        ggml_compute_forward_dup_bytes(params, src0, dst);
+        ggml_compute_forward_dup_bytes(params, dst);
         return;
     }
 
     switch (src0->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_dup_f16(params, src0, dst);
+                ggml_compute_forward_dup_f16(params, dst);
             } break;
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_dup_f32(params, src0, dst);
+                ggml_compute_forward_dup_f32(params, dst);
             } break;
         default:
             {
@@ -7132,18 +7799,31 @@ static void ggml_compute_forward_dup(
 
 static void ggml_compute_forward_add_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
     const int ith = params->ith;
     const int nth = params->nth;
 
+#ifdef GGML_USE_CLBLAST
+    if (src1->backend == GGML_BACKEND_TYPE_GPU) {
+        // TODO: OpenCL kernel support full broadcast
+        GGML_ASSERT(ggml_can_repeat_rows(src1, src0));
+        if (ith == 0) {
+            ggml_cl_add(src0, src1, dst);
+        }
+        return;
+    }
+#endif
+
     const int nr  = ggml_nrows(src0);
 
     GGML_TENSOR_BINARY_OP_LOCALS
@@ -7209,12 +7889,14 @@ static void ggml_compute_forward_add_f32(
 
 static void ggml_compute_forward_add_f16_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -7286,12 +7968,14 @@ static void ggml_compute_forward_add_f16_f32(
 
 static void ggml_compute_forward_add_f16_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -7340,12 +8024,14 @@ static void ggml_compute_forward_add_f16_f16(
 
 static void ggml_compute_forward_add_q_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -7418,21 +8104,28 @@ static void ggml_compute_forward_add_q_f32(
 
 static void ggml_compute_forward_add(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_add_f32(params, src0, src1, dst);
+                if (src1->type == GGML_TYPE_F32) {
+                    ggml_compute_forward_add_f32(params, dst);
+                }
+                else {
+                    GGML_ASSERT(false);
+                }
             } break;
         case GGML_TYPE_F16:
             {
                 if (src1->type == GGML_TYPE_F16) {
-                    ggml_compute_forward_add_f16_f16(params, src0, src1, dst);
+                    ggml_compute_forward_add_f16_f16(params, dst);
                 }
                 else if (src1->type == GGML_TYPE_F32) {
-                    ggml_compute_forward_add_f16_f32(params, src0, src1, dst);
+                    ggml_compute_forward_add_f16_f32(params, dst);
                 }
                 else {
                     GGML_ASSERT(false);
@@ -7449,8 +8142,16 @@ static void ggml_compute_forward_add(
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
         case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ2_S:
             {
-                ggml_compute_forward_add_q_f32(params, src0, src1, dst);
+                ggml_compute_forward_add_q_f32(params, dst);
             } break;
         default:
             {
@@ -7463,13 +8164,15 @@ static void ggml_compute_forward_add(
 
 static void ggml_compute_forward_add1_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_is_scalar(src1));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -7515,13 +8218,15 @@ static void ggml_compute_forward_add1_f32(
 
 static void ggml_compute_forward_add1_f16_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_is_scalar(src1));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -7565,13 +8270,15 @@ static void ggml_compute_forward_add1_f16_f32(
 
 static void ggml_compute_forward_add1_f16_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_is_scalar(src1));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -7615,13 +8322,15 @@ static void ggml_compute_forward_add1_f16_f16(
 
 static void ggml_compute_forward_add1_q_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_is_scalar(src1));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -7682,21 +8391,23 @@ static void ggml_compute_forward_add1_q_f32(
 
 static void ggml_compute_forward_add1(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_add1_f32(params, src0, src1, dst);
+                ggml_compute_forward_add1_f32(params, dst);
             } break;
         case GGML_TYPE_F16:
             {
                 if (src1->type == GGML_TYPE_F16) {
-                    ggml_compute_forward_add1_f16_f16(params, src0, src1, dst);
+                    ggml_compute_forward_add1_f16_f16(params, dst);
                 }
                 else if (src1->type == GGML_TYPE_F32) {
-                    ggml_compute_forward_add1_f16_f32(params, src0, src1, dst);
+                    ggml_compute_forward_add1_f16_f32(params, dst);
                 }
                 else {
                     GGML_ASSERT(false);
@@ -7714,8 +8425,16 @@ static void ggml_compute_forward_add1(
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
         case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ2_S:
             {
-                ggml_compute_forward_add1_q_f32(params, src0, src1, dst);
+                ggml_compute_forward_add1_q_f32(params, dst);
             } break;
         default:
             {
@@ -7728,9 +8447,11 @@ static void ggml_compute_forward_add1(
 
 static void ggml_compute_forward_acc_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));
 
@@ -7742,7 +8463,10 @@ static void ggml_compute_forward_acc_f32(
     size_t offset  = ((int32_t *) dst->op_params)[3];
     bool   inplace = (bool) ((int32_t *) dst->op_params)[4];
 
-    if (!inplace && (params->type == GGML_TASK_INIT)) {
+    if (!inplace && (params->type == GGML_TASK_TYPE_INIT)) {
+        if (params->ith != 0) {
+            return;
+        }
         // memcpy needs to be synchronized across threads to avoid race conditions.
         // => do it in INIT phase
         memcpy(
@@ -7751,7 +8475,7 @@ static void ggml_compute_forward_acc_f32(
             ggml_nbytes(dst));
     }
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -7807,14 +8531,14 @@ static void ggml_compute_forward_acc_f32(
 
 static void ggml_compute_forward_acc(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
 
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_acc_f32(params, src0, src1, dst);
+                ggml_compute_forward_acc_f32(params, dst);
             } break;
         case GGML_TYPE_F16:
         case GGML_TYPE_Q4_0:
@@ -7829,6 +8553,14 @@ static void ggml_compute_forward_acc(
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
         case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ2_S:
         default:
             {
                 GGML_ASSERT(false);
@@ -7840,13 +8572,15 @@ static void ggml_compute_forward_acc(
 
 static void ggml_compute_forward_sub_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -7900,13 +8634,14 @@ static void ggml_compute_forward_sub_f32(
 
 static void ggml_compute_forward_sub(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_sub_f32(params, src0, src1, dst);
+                ggml_compute_forward_sub_f32(params, dst);
             } break;
         default:
             {
@@ -7919,19 +8654,21 @@ static void ggml_compute_forward_sub(
 
 static void ggml_compute_forward_mul_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
     const int ith = params->ith;
     const int nth = params->nth;
 
-#ifdef GGML_USE_CLBLAST
-    if (src1->backend == GGML_BACKEND_GPU) {
+#if defined(GGML_USE_CLBLAST)
+    if (src1->backend == GGML_BACKEND_TYPE_GPU) {
         // TODO: OpenCL kernel support full broadcast
         GGML_ASSERT(ggml_can_repeat_rows(src1, src0));
         if (ith == 0) {
@@ -8002,15 +8739,17 @@ static void ggml_compute_forward_mul_f32(
 
 static void ggml_compute_forward_mul(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(src1->type == GGML_TYPE_F32 && "only f32 src1 supported for now");
 
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_mul_f32(params, src0, src1, dst);
+                ggml_compute_forward_mul_f32(params, dst);
             } break;
         default:
             {
@@ -8023,12 +8762,14 @@ static void ggml_compute_forward_mul(
 
 static void ggml_compute_forward_div_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8096,13 +8837,14 @@ static void ggml_compute_forward_div_f32(
 
 static void ggml_compute_forward_div(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_div_f32(params, src0, src1, dst);
+                ggml_compute_forward_div_f32(params, dst);
             } break;
         default:
             {
@@ -8115,12 +8857,14 @@ static void ggml_compute_forward_div(
 
 static void ggml_compute_forward_sqr_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8139,12 +8883,14 @@ static void ggml_compute_forward_sqr_f32(
 
 static void ggml_compute_forward_sqr(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_sqr_f32(params, src0, dst);
+                ggml_compute_forward_sqr_f32(params, dst);
             } break;
         default:
             {
@@ -8157,12 +8903,14 @@ static void ggml_compute_forward_sqr(
 
 static void ggml_compute_forward_sqrt_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8181,12 +8929,14 @@ static void ggml_compute_forward_sqrt_f32(
 
 static void ggml_compute_forward_sqrt(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_sqrt_f32(params, src0, dst);
+                ggml_compute_forward_sqrt_f32(params, dst);
             } break;
         default:
             {
@@ -8199,12 +8949,14 @@ static void ggml_compute_forward_sqrt(
 
 static void ggml_compute_forward_log_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(params->ith == 0);
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8223,12 +8975,14 @@ static void ggml_compute_forward_log_f32(
 
 static void ggml_compute_forward_log(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_log_f32(params, src0, dst);
+                ggml_compute_forward_log_f32(params, dst);
             } break;
         default:
             {
@@ -8241,12 +8995,14 @@ static void ggml_compute_forward_log(
 
 static void ggml_compute_forward_sum_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_is_scalar(dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8274,12 +9030,14 @@ static void ggml_compute_forward_sum_f32(
 
 static void ggml_compute_forward_sum_f16(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
           struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_is_scalar(dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8306,16 +9064,18 @@ static void ggml_compute_forward_sum_f16(
 
 static void ggml_compute_forward_sum(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_sum_f32(params, src0, dst);
+                ggml_compute_forward_sum_f32(params, dst);
             } break;
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_sum_f16(params, src0, dst);
+                ggml_compute_forward_sum_f16(params, dst);
             } break;
         default:
             {
@@ -8328,11 +9088,13 @@ static void ggml_compute_forward_sum(
 
 static void ggml_compute_forward_sum_rows_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8361,12 +9123,14 @@ static void ggml_compute_forward_sum_rows_f32(
 
 static void ggml_compute_forward_sum_rows(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_sum_rows_f32(params, src0, dst);
+                ggml_compute_forward_sum_rows_f32(params, dst);
             } break;
         default:
             {
@@ -8379,11 +9143,13 @@ static void ggml_compute_forward_sum_rows(
 
 static void ggml_compute_forward_mean_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8416,12 +9182,14 @@ static void ggml_compute_forward_mean_f32(
 
 static void ggml_compute_forward_mean(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_mean_f32(params, src0, dst);
+                ggml_compute_forward_mean_f32(params, dst);
             } break;
         default:
             {
@@ -8434,11 +9202,13 @@ static void ggml_compute_forward_mean(
 
 static void ggml_compute_forward_argmax_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8462,12 +9232,14 @@ static void ggml_compute_forward_argmax_f32(
 
 static void ggml_compute_forward_argmax(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_argmax_f32(params, src0, dst);
+                ggml_compute_forward_argmax_f32(params, dst);
             } break;
         default:
             {
@@ -8480,12 +9252,14 @@ static void ggml_compute_forward_argmax(
 
 static void ggml_compute_forward_repeat_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(params->ith == 0);
     GGML_ASSERT(ggml_can_repeat(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8523,12 +9297,14 @@ static void ggml_compute_forward_repeat_f32(
 
 static void ggml_compute_forward_repeat_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(params->ith == 0);
     GGML_ASSERT(ggml_can_repeat(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8569,18 +9345,20 @@ static void ggml_compute_forward_repeat_f16(
 
 static void ggml_compute_forward_repeat(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F16:
         case GGML_TYPE_I16:
             {
-                ggml_compute_forward_repeat_f16(params, src0, dst);
+                ggml_compute_forward_repeat_f16(params, dst);
             } break;
         case GGML_TYPE_F32:
         case GGML_TYPE_I32:
             {
-                ggml_compute_forward_repeat_f32(params, src0, dst);
+                ggml_compute_forward_repeat_f32(params, dst);
             } break;
         default:
             {
@@ -8593,12 +9371,14 @@ static void ggml_compute_forward_repeat(
 
 static void ggml_compute_forward_repeat_back_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(params->ith == 0);
     GGML_ASSERT(ggml_can_repeat(dst, src0));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8650,12 +9430,14 @@ static void ggml_compute_forward_repeat_back_f32(
 
 static void ggml_compute_forward_repeat_back(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_repeat_back_f32(params, src0, dst);
+                ggml_compute_forward_repeat_back_f32(params, dst);
             } break;
         default:
             {
@@ -8668,11 +9450,12 @@ static void ggml_compute_forward_repeat_back(
 
 static void ggml_compute_forward_concat_f32(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
-    const struct ggml_tensor * src1,
     struct ggml_tensor * dst) {
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8716,14 +9499,15 @@ static void ggml_compute_forward_concat_f32(
 
 static void ggml_compute_forward_concat(
     const struct ggml_compute_params* params,
-    const struct ggml_tensor* src0,
-    const struct ggml_tensor* src1,
     struct ggml_tensor* dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
         case GGML_TYPE_I32:
             {
-                ggml_compute_forward_concat_f32(params, src0, src1, dst);
+                ggml_compute_forward_concat_f32(params, dst);
             } break;
         default:
             {
@@ -8736,12 +9520,14 @@ static void ggml_compute_forward_concat(
 
 static void ggml_compute_forward_abs_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8760,12 +9546,14 @@ static void ggml_compute_forward_abs_f32(
 
 static void ggml_compute_forward_abs(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_abs_f32(params, src0, dst);
+                ggml_compute_forward_abs_f32(params, dst);
             } break;
         default:
             {
@@ -8778,12 +9566,14 @@ static void ggml_compute_forward_abs(
 
 static void ggml_compute_forward_sgn_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8802,12 +9592,14 @@ static void ggml_compute_forward_sgn_f32(
 
 static void ggml_compute_forward_sgn(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_sgn_f32(params, src0, dst);
+                ggml_compute_forward_sgn_f32(params, dst);
             } break;
         default:
             {
@@ -8820,12 +9612,14 @@ static void ggml_compute_forward_sgn(
 
 static void ggml_compute_forward_neg_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8844,12 +9638,14 @@ static void ggml_compute_forward_neg_f32(
 
 static void ggml_compute_forward_neg(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_neg_f32(params, src0, dst);
+                ggml_compute_forward_neg_f32(params, dst);
             } break;
         default:
             {
@@ -8862,12 +9658,14 @@ static void ggml_compute_forward_neg(
 
 static void ggml_compute_forward_step_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8886,12 +9684,14 @@ static void ggml_compute_forward_step_f32(
 
 static void ggml_compute_forward_step(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_step_f32(params, src0, dst);
+                ggml_compute_forward_step_f32(params, dst);
             } break;
         default:
             {
@@ -8904,12 +9704,14 @@ static void ggml_compute_forward_step(
 
 static void ggml_compute_forward_tanh_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8928,12 +9730,14 @@ static void ggml_compute_forward_tanh_f32(
 
 static void ggml_compute_forward_tanh(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_tanh_f32(params, src0, dst);
+                ggml_compute_forward_tanh_f32(params, dst);
             } break;
         default:
             {
@@ -8946,12 +9750,14 @@ static void ggml_compute_forward_tanh(
 
 static void ggml_compute_forward_elu_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -8970,12 +9776,14 @@ static void ggml_compute_forward_elu_f32(
 
 static void ggml_compute_forward_elu(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_elu_f32(params, src0, dst);
+                ggml_compute_forward_elu_f32(params, dst);
             } break;
         default:
             {
@@ -8988,12 +9796,14 @@ static void ggml_compute_forward_elu(
 
 static void ggml_compute_forward_relu_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9012,12 +9822,14 @@ static void ggml_compute_forward_relu_f32(
 
 static void ggml_compute_forward_relu(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_relu_f32(params, src0, dst);
+                ggml_compute_forward_relu_f32(params, dst);
             } break;
         default:
             {
@@ -9030,13 +9842,15 @@ static void ggml_compute_forward_relu(
 
 static void ggml_compute_forward_gelu_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
     GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9071,12 +9885,14 @@ static void ggml_compute_forward_gelu_f32(
 
 static void ggml_compute_forward_gelu(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_gelu_f32(params, src0, dst);
+                ggml_compute_forward_gelu_f32(params, dst);
             } break;
         default:
             {
@@ -9089,13 +9905,15 @@ static void ggml_compute_forward_gelu(
 
 static void ggml_compute_forward_gelu_quick_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
     GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9130,12 +9948,14 @@ static void ggml_compute_forward_gelu_quick_f32(
 
 static void ggml_compute_forward_gelu_quick(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_gelu_quick_f32(params, src0, dst);
+                ggml_compute_forward_gelu_quick_f32(params, dst);
             } break;
         default:
             {
@@ -9148,13 +9968,15 @@ static void ggml_compute_forward_gelu_quick(
 
 static void ggml_compute_forward_silu_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
     GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9189,12 +10011,14 @@ static void ggml_compute_forward_silu_f32(
 
 static void ggml_compute_forward_silu(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_silu_f32(params, src0, dst);
+                ggml_compute_forward_silu_f32(params, dst);
             } break;
         default:
             {
@@ -9206,12 +10030,14 @@ static void ggml_compute_forward_silu(
 
 static void ggml_compute_forward_leaky_relu_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9233,12 +10059,14 @@ static void ggml_compute_forward_leaky_relu_f32(
 
 static void ggml_compute_forward_leaky_relu(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_leaky_relu_f32(params, src0, dst);
+                ggml_compute_forward_leaky_relu_f32(params, dst);
             } break;
         default:
             {
@@ -9251,16 +10079,18 @@ static void ggml_compute_forward_leaky_relu(
 
 static void ggml_compute_forward_silu_back_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * grad,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * grad = dst->src[1];
+
     GGML_ASSERT(ggml_is_contiguous_except_dim_1(grad));
     GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
     GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_are_same_shape(src0, grad));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9296,13 +10126,14 @@ static void ggml_compute_forward_silu_back_f32(
 
 static void ggml_compute_forward_silu_back(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * grad,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_silu_back_f32(params, src0, grad, dst);
+                ggml_compute_forward_silu_back_f32(params, dst);
             } break;
         default:
             {
@@ -9311,15 +10142,106 @@ static void ggml_compute_forward_silu_back(
     }
 }
 
+
+static void ggml_compute_forward_hardswish_f32(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    assert(dst->nb[0]  == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        ggml_vec_hardswish_f32(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])));
+    }
+}
+static void ggml_compute_forward_hardswish(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_hardswish_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+static void ggml_compute_forward_hardsigmoid_f32(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    assert(dst->nb[0]  == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        ggml_vec_hardsigmoid_f32(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])));
+    }
+}
+
+static void ggml_compute_forward_hardsigmoid(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_hardsigmoid_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+
 // ggml_compute_forward_norm
 
 static void ggml_compute_forward_norm_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9368,12 +10290,14 @@ static void ggml_compute_forward_norm_f32(
 
 static void ggml_compute_forward_norm(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_norm_f32(params, src0, dst);
+                ggml_compute_forward_norm_f32(params, dst);
             } break;
         default:
             {
@@ -9386,11 +10310,13 @@ static void ggml_compute_forward_norm(
 
 static void ggml_compute_forward_rms_norm_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9436,12 +10362,14 @@ static void ggml_compute_forward_rms_norm_f32(
 
 static void ggml_compute_forward_rms_norm(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_rms_norm_f32(params, src0, dst);
+                ggml_compute_forward_rms_norm_f32(params, dst);
             } break;
         default:
             {
@@ -9452,12 +10380,14 @@ static void ggml_compute_forward_rms_norm(
 
 static void ggml_compute_forward_rms_norm_back_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_are_same_shape(src0, src1));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9609,13 +10539,14 @@ static void ggml_compute_forward_rms_norm_back_f32(
 
 static void ggml_compute_forward_rms_norm_back(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_rms_norm_back_f32(params, src0, src1, dst);
+                ggml_compute_forward_rms_norm_back_f32(params, dst);
             } break;
         default:
             {
@@ -9628,11 +10559,13 @@ static void ggml_compute_forward_rms_norm_back(
 
 static void ggml_compute_forward_group_norm_f32(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
     struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9650,7 +10583,7 @@ static void ggml_compute_forward_group_norm_f32(
     int n_channels = src0->ne[2];
     int n_groups = dst->op_params[0];
     int n_channels_per_group = (n_channels + n_groups - 1) / n_groups;
-    for (int i = ith; i < n_groups; i+=nth) {
+    for (int i = ith; i < n_groups; i += nth) {
         int start = i * n_channels_per_group;
         int end = start + n_channels_per_group;
         if (end > n_channels) {
@@ -9664,28 +10597,32 @@ static void ggml_compute_forward_group_norm_f32(
                 for (int64_t i01 = 0; i01 < ne01; i01++) {
                     const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03);
 
+                    ggml_float sumr = 0.0;
                     for (int64_t i00 = 0; i00 < ne00; i00++) {
-                        sum += (ggml_float)x[i00];
+                        sumr += (ggml_float)x[i00];
                     }
+                    sum += sumr;
                 }
             }
-            float mean = sum / (ne00 * ne01 * step);
-            ggml_float sum2 = 0.0;
+            const float mean = sum / (ne00 * ne01 * step);
 
+            ggml_float sum2 = 0.0;
             for (int64_t i02 = start; i02 < end; i02++) {
                 for (int64_t i01 = 0; i01 < ne01; i01++) {
                     const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03);
 
                     float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3);
 
+                    ggml_float sumr = 0.0;
                     for (int64_t i00 = 0; i00 < ne00; i00++) {
                         float v = x[i00] - mean;
                         y[i00] = v;
-                        sum2 += (ggml_float)(v * v);
+                        sumr += (ggml_float)(v * v);
                     }
+                    sum2 += sumr;
                 }
             }
-            float variance = sum2 / (ne00 * ne01 * step);
+            const float variance = sum2 / (ne00 * ne01 * step);
             const float scale = 1.0f / sqrtf(variance + eps);
 
             for (int64_t i02 = start; i02 < end; i02++) {
@@ -9700,12 +10637,14 @@ static void ggml_compute_forward_group_norm_f32(
 
 static void ggml_compute_forward_group_norm(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
     struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_group_norm_f32(params, src0, dst);
+                ggml_compute_forward_group_norm_f32(params, dst);
             } break;
         default:
             {
@@ -9751,9 +10690,11 @@ static bool ggml_compute_forward_mul_mat_use_blas(struct ggml_tensor * dst) {
 
 static void ggml_compute_forward_mul_mat(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
@@ -9769,6 +10710,7 @@ static void ggml_compute_forward_mul_mat(
     ggml_vec_dot_t    const vec_dot               = type_traits[type].vec_dot;
     enum ggml_type    const vec_dot_type          = type_traits[type].vec_dot_type;
     ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
+    int64_t           const vec_dot_num_rows      = type_traits[type].nrows;
 
     GGML_ASSERT(ne0 == ne01);
     GGML_ASSERT(ne1 == ne11);
@@ -9794,7 +10736,7 @@ static void ggml_compute_forward_mul_mat(
 
 #if defined(GGML_USE_CLBLAST)
     if (ggml_cl_can_mul_mat(src0, src1, dst)) {
-        if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
+        if (params->ith == 0 && params->type == GGML_TASK_TYPE_COMPUTE) {
             ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
         }
         return;
@@ -9803,21 +10745,45 @@ static void ggml_compute_forward_mul_mat(
 
 #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
     if (ggml_compute_forward_mul_mat_use_blas(dst)) {
-        if (params->ith != 0) {
+        const int64_t ne_plane      = ne01*ne00;
+        const size_t  desired_wsize = ne13*ne12*ne_plane*sizeof(float);
+        UNUSED(desired_wsize);
+
+        if (params->type == GGML_TASK_TYPE_INIT) {
+            if (type != GGML_TYPE_F32) {
+                assert(params->wsize >= desired_wsize);
+                // parallelize by src0 rows
+                for (int64_t i13 = 0; i13 < ne13; i13++) {
+                    for (int64_t i12 = 0; i12 < ne12; i12++) {
+                        // broadcast src0 into src1 across 2nd,3rd dimension
+                        const int64_t i03 = i13/r3;
+                        const int64_t i02 = i12/r2;
+
+                        const void           *       x        = (char *)  src0->data    + i02*nb02          + i03*nb03;
+                              float          * const wdata    = (float *) params->wdata + i13*ne12*ne_plane + i12*ne_plane;
+                              ggml_to_float_t  const to_float = type_traits[type].to_float;
+
+                        for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
+                            to_float((const char *) x + i01*nb01, wdata + i01*ne00, ne00);
+                        }
+                    }
+                }
+            }
             return;
         }
 
-        if (params->type == GGML_TASK_INIT) {
+        if (params->type == GGML_TASK_TYPE_FINALIZE) {
             return;
         }
 
-        if (params->type == GGML_TASK_FINALIZE) {
+        // perform sgemm, parallelization controlled by blas lib
+        if (ith != 0) {
             return;
         }
 
+        //const int64_t tgemm0 = ggml_perf_time_us();
         for (int64_t i13 = 0; i13 < ne13; i13++) {
             for (int64_t i12 = 0; i12 < ne12; i12++) {
-                // broadcast src0 into src1 across 2nd,3rd dimension
                 const int64_t i03 = i13/r3;
                 const int64_t i02 = i12/r2;
 
@@ -9826,17 +10792,7 @@ static void ggml_compute_forward_mul_mat(
                       float * d = (float *) ((char *)  dst->data + i12*nb2  + i13*nb3);
 
                 if (type != GGML_TYPE_F32) {
-                            float * const wdata    = params->wdata;
-                    ggml_to_float_t const to_float = type_traits[type].to_float;
-
-                    size_t id = 0;
-                    for (int64_t i01 = 0; i01 < ne01; ++i01) {
-                        to_float((const char *) x + i01*nb01, wdata + id, ne00);
-                        id += ne00;
-                    }
-
-                    assert(id*sizeof(float) <= params->wsize);
-                    x = wdata;
+                    x = (float *) params->wdata + i13*ne12*ne_plane + i12*ne_plane;
                 }
 
                 cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
@@ -9846,6 +10802,7 @@ static void ggml_compute_forward_mul_mat(
                          0.0f,    d, ne01);
             }
         }
+        //printf("cblas_sgemm = %.3f ms, %lld flops\n", (ggml_perf_time_us() - tgemm0)/1000.0, ne13*ne12*ne1*ne01*ne10*2);
 
         //printf("CBLAS = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);
 
@@ -9853,7 +10810,10 @@ static void ggml_compute_forward_mul_mat(
     }
 #endif
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
+        if (ith != 0) {
+            return;
+        }
         if (src1->type != vec_dot_type) {
             char * wdata = params->wdata;
             const size_t row_size = ggml_row_size(vec_dot_type, ne10);
@@ -9874,7 +10834,7 @@ static void ggml_compute_forward_mul_mat(
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -9918,12 +10878,23 @@ static void ggml_compute_forward_mul_mat(
     const int64_t blck_0 = 16;
     const int64_t blck_1 = 16;
 
+    // dot kernels can handle 1 row and col at a time, but mmla kernels can process 2 rows and cols
+    int64_t nrc = vec_dot_num_rows;
+    // TODO: currently the mmla kernels support only even numbered rows/cols.
+    // this check can be removed once they are extended to support odd numbered rows/cols too
+    if ((nr0 % 2 != 0) || (ne11 % 2 != 0)) {
+        nrc = 1;
+    }
+
+    const size_t src1_col_stride = src1_cont || src1->type != vec_dot_type ? row_size : nb11;
+
     // attempt to reduce false-sharing (does not seem to make a difference)
-    float tmp[16];
+    // 16 * 2, accounting for mmla kernels
+    float tmp[32];
 
     for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) {
         for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) {
-            for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) {
+            for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ir1 += nrc) {
                 const int64_t i13 = (ir1/(ne12*ne1));
                 const int64_t i12 = (ir1 - i13*ne12*ne1)/ne1;
                 const int64_t i11 = (ir1 - i13*ne12*ne1 - i12*ne1);
@@ -9946,17 +10917,19 @@ static void ggml_compute_forward_mul_mat(
                     (src1_cont || src1->type != vec_dot_type
                      ? (i11      + i12*ne11 + i13*ne12*ne11)*row_size
                      : (i11*nb11 + i12*nb12 + i13*nb13));
-
                 float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3));
 
                 //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
                 //    vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col);
                 //}
 
-                for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
-                    vec_dot(ne00, &tmp[ir0 - iir0], src0_row + ir0*nb01, src1_col);
+                for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ir0 += nrc) {
+                    vec_dot(ne00, &tmp[ir0 - iir0], (nrc>1 ? 16 : 0), src0_row + ir0*nb01, (nrc>1 ? nb01 : 0), src1_col, (nrc>1 ? src1_col_stride : 0), nrc);
+                }
+
+                for (int cn = 0; cn < nrc; ++cn) {
+                    memcpy(&dst_col[iir0 + cn*nb1/nb0], tmp + (cn*16), (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float));
                 }
-                memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float));
             }
         }
     }
@@ -9966,11 +10939,11 @@ static void ggml_compute_forward_mul_mat(
 
 static void ggml_compute_forward_mul_mat_id(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * ids,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
 
-    const struct ggml_tensor * src0 = dst->src[2]; // only for GGML_TENSOR_BINARY_OP_LOCALS
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+    const struct ggml_tensor * ids = dst->src[2];
 
     GGML_TENSOR_BINARY_OP_LOCALS
 
@@ -10000,13 +10973,13 @@ static void ggml_compute_forward_mul_mat_id(
     GGML_ASSERT(nb1 <= nb2);
     GGML_ASSERT(nb2 <= nb3);
 
-    // broadcast factors
-    const int64_t r2 = ne12/ne02;
-    const int64_t r3 = ne13/ne03;
+    // broadcast is not supported with mmid
+    assert(ne12 == 1);
+    assert(ne13 == 1);
 
     // row groups
     const int id   = ggml_get_op_params_i32(dst, 0);
-    const int n_as = ggml_get_op_params_i32(dst, 1);
+    const int n_as = src0->ne[2];
 
     char * wdata_src1_end = (src1->type == vec_dot_type) ?
             (char *) params->wdata :
@@ -10017,7 +10990,10 @@ static void ggml_compute_forward_mul_mat_id(
 
     #define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne11 + (i1)]
 
-   if (params->type == GGML_TASK_INIT) {
+   if (params->type == GGML_TASK_TYPE_INIT) {
+        if (ith != 0) {
+            return;
+        }
         char * wdata = params->wdata;
         if (src1->type != vec_dot_type) {
             const size_t row_size = ggml_row_size(vec_dot_type, ne10);
@@ -10051,7 +11027,7 @@ static void ggml_compute_forward_mul_mat_id(
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -10063,7 +11039,7 @@ static void ggml_compute_forward_mul_mat_id(
             continue;
         }
 
-        const struct ggml_tensor * src0_cur = dst->src[cur_a + 2];
+        size_t src0_offset = cur_a*src0->nb[2];
 
         const void * wdata    = (src1->type == vec_dot_type) ? src1->data : params->wdata;
         const size_t row_size = ggml_row_size(vec_dot_type, ne10);
@@ -10098,9 +11074,6 @@ static void ggml_compute_forward_mul_mat_id(
             continue;
         }
 
-        assert(ne12 % ne02 == 0);
-        assert(ne13 % ne03 == 0);
-
         // block-tiling attempt
         const int64_t blck_0 = 16;
         const int64_t blck_1 = 16;
@@ -10117,14 +11090,14 @@ static void ggml_compute_forward_mul_mat_id(
                     const int64_t  i11 = MMID_MATRIX_ROW(cur_a, _i11);
 
                     // broadcast src0 into src1
-                    const int64_t i03 = i13/r3;
-                    const int64_t i02 = i12/r2;
+                    //const int64_t i03 = i13/r3;
+                    //const int64_t i02 = i12/r2;
 
                     const int64_t i1 = i11;
                     const int64_t i2 = i12;
                     const int64_t i3 = i13;
 
-                    const char * src0_row = (const char *) src0_cur->data + (0 + i02*nb02 + i03*nb03);
+                    const char * src0_row = (const char *) src0->data + src0_offset;
 
                     // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
                     //       if it is, then we have either copied the data to params->wdata and made it contiguous or we are using
@@ -10142,7 +11115,7 @@ static void ggml_compute_forward_mul_mat_id(
                     //}
 
                     for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
-                        vec_dot(ne00, &tmp[ir0 - iir0], src0_row + ir0*nb01, src1_col);
+                        vec_dot(ne00, &tmp[ir0 - iir0], 0, src0_row + ir0*nb01, 0, src1_col, 0, 1);
                     }
                     memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float));
                 }
@@ -10157,9 +11130,11 @@ static void ggml_compute_forward_mul_mat_id(
 
 static void ggml_compute_forward_out_prod_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     // int64_t t0 = ggml_perf_time_us();
     // UNUSED(t0);
 
@@ -10187,7 +11162,6 @@ static void ggml_compute_forward_out_prod_f32(
     // nb01 >= nb00 - src0 is not transposed
     //   compute by src0 rows
 
-    // TODO: #if defined(GGML_USE_CUBLAS) ggml_cuda_out_prod
     // TODO: #if defined(GGML_USE_CLBLAST)
 
 #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
@@ -10197,17 +11171,20 @@ static void ggml_compute_forward_out_prod_f32(
         (ggml_is_contiguous(src1) || ggml_is_transposed(src1));
 #endif
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
 #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) // gemm beta will zero dst
         if (use_blas) {
             return;
         }
 #endif
+        if (ith != 0) {
+            return;
+        }
         ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0);
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -10346,9 +11323,11 @@ static void ggml_compute_forward_out_prod_f32(
 
 static void ggml_compute_forward_out_prod_q_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     // int64_t t0 = ggml_perf_time_us();
     // UNUSED(t0);
 
@@ -10382,15 +11361,17 @@ static void ggml_compute_forward_out_prod_q_f32(
     // nb01 >= nb00 - src0 is not transposed
     //   compute by src0 rows
 
-    // TODO: #if defined(GGML_USE_CUBLAS) ggml_cuda_out_prod
     // TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
+        if (ith != 0) {
+            return;
+        }
         ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0);
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -10456,9 +11437,10 @@ static void ggml_compute_forward_out_prod_q_f32(
 
 static void ggml_compute_forward_out_prod(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
@@ -10471,17 +11453,25 @@ static void ggml_compute_forward_out_prod(
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
         case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ2_S:
             {
-                ggml_compute_forward_out_prod_q_f32(params, src0, src1, dst);
+                ggml_compute_forward_out_prod_q_f32(params, dst);
             } break;
         case GGML_TYPE_F16:
             {
                 GGML_ASSERT(false); // todo
-                // ggml_compute_forward_out_prod_f16_f32(params, src0, src1, dst);
+                // ggml_compute_forward_out_prod_f16_f32(params, dst);
             } break;
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_out_prod_f32(params, src0, src1, dst);
+                ggml_compute_forward_out_prod_f32(params, dst);
             } break;
         default:
             {
@@ -10494,13 +11484,15 @@ static void ggml_compute_forward_out_prod(
 
 static void ggml_compute_forward_scale_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_is_contiguous(src0));
     GGML_ASSERT(ggml_is_contiguous(dst));
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -10536,12 +11528,14 @@ static void ggml_compute_forward_scale_f32(
 
 static void ggml_compute_forward_scale(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_scale_f32(params, src0, dst);
+                ggml_compute_forward_scale_f32(params, dst);
             } break;
         default:
             {
@@ -10554,9 +11548,11 @@ static void ggml_compute_forward_scale(
 
 static void ggml_compute_forward_set_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));
 
@@ -10568,7 +11564,10 @@ static void ggml_compute_forward_set_f32(
     size_t offset  = ((int32_t *) dst->op_params)[3];
     bool   inplace = (bool) ((int32_t *) dst->op_params)[4];
 
-    if (!inplace && (params->type == GGML_TASK_INIT)) {
+    if (!inplace && (params->type == GGML_TASK_TYPE_INIT)) {
+        if (params->ith != 0) {
+            return;
+        }
         // memcpy needs to be synchronized across threads to avoid race conditions.
         // => do it in INIT phase
         memcpy(
@@ -10577,7 +11576,7 @@ static void ggml_compute_forward_set_f32(
             ggml_nbytes(dst));
     }
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -10624,14 +11623,14 @@ static void ggml_compute_forward_set_f32(
 
 static void ggml_compute_forward_set(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
 
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_set_f32(params, src0, src1, dst);
+                ggml_compute_forward_set_f32(params, dst);
             } break;
         case GGML_TYPE_F16:
         case GGML_TYPE_Q4_0:
@@ -10646,6 +11645,14 @@ static void ggml_compute_forward_set(
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
         case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ2_S:
         default:
             {
                 GGML_ASSERT(false);
@@ -10657,29 +11664,25 @@ static void ggml_compute_forward_set(
 
 static void ggml_compute_forward_cpy(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
-    ggml_compute_forward_dup(params, src0, dst);
+    ggml_compute_forward_dup(params, dst);
 }
 
 // ggml_compute_forward_cont
 
 static void ggml_compute_forward_cont(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
-    ggml_compute_forward_dup(params, src0, dst);
+    ggml_compute_forward_dup(params, dst);
 }
 
 // ggml_compute_forward_reshape
 
 static void ggml_compute_forward_reshape(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
     // NOP
     UNUSED(params);
-    UNUSED(src0);
     UNUSED(dst);
 }
 
@@ -10687,49 +11690,49 @@ static void ggml_compute_forward_reshape(
 
 static void ggml_compute_forward_view(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0) {
+        const struct ggml_tensor * dst) {
     // NOP
     UNUSED(params);
-    UNUSED(src0);
+    UNUSED(dst);
 }
 
 // ggml_compute_forward_permute
 
 static void ggml_compute_forward_permute(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0) {
+        const struct ggml_tensor * dst) {
     // NOP
     UNUSED(params);
-    UNUSED(src0);
+    UNUSED(dst);
 }
 
 // ggml_compute_forward_transpose
 
 static void ggml_compute_forward_transpose(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0) {
+        const struct ggml_tensor * dst) {
     // NOP
     UNUSED(params);
-    UNUSED(src0);
+    UNUSED(dst);
 }
 
 // ggml_compute_forward_get_rows
 
 static void ggml_compute_forward_get_rows_q(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
-    assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int64_t nc = ne00;
-    const int64_t nr = ggml_nelements(src1); GGML_UNUSED(nr);
+    const int64_t nr = ggml_nelements(src1);
 
     const enum ggml_type type = src0->type;
     ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
@@ -10739,95 +11742,120 @@ static void ggml_compute_forward_get_rows_q(
     assert(nb00 == ggml_type_size(type));
     assert(ggml_nrows(dst) == nr);
 
-    // TODO: multi-thread
-    for (int64_t i12 = 0; i12 < ne12; ++i12) {
-        for (int64_t i11 = 0; i11 < ne11; ++i11) {
-            for (int64_t i10 = 0; i10 < ne10; ++i10) {
-                const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
+    const int ith = params->ith;
+    const int nth = params->nth;
 
-                dequantize_row_q(
-                        (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
-                             (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3), nc);
-            }
-        }
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int64_t i = ir0; i < ir1; ++i) {
+        const int64_t i12 = i/(ne11*ne10);
+        const int64_t i11 = (i - i12*ne11*ne10)/ne10;
+        const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10);
+        const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
+
+        dequantize_row_q(
+                (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
+                     (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3), nc);
     }
 }
 
 static void ggml_compute_forward_get_rows_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
-    assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int64_t nc = ne00;
-    const int64_t nr = ggml_nelements(src1); GGML_UNUSED(nr);
+    const int64_t nr = ggml_nelements(src1);
 
     assert(ne0  == nc);
     assert(ne02 == ne11);
     assert(nb00 == sizeof(ggml_fp16_t));
     assert(ggml_nrows(dst) == nr);
 
-    // TODO: multi-thread
-    for (int64_t i12 = 0; i12 < ne12; ++i12) {
-        for (int64_t i11 = 0; i11 < ne11; ++i11) {
-            for (int64_t i10 = 0; i10 < ne10; ++i10) {
-                const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
+    const int ith = params->ith;
+    const int nth = params->nth;
 
-                ggml_fp16_to_fp32_row(
-                        (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
-                             (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3), nc);
-            }
-        }
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int64_t i = ir0; i < ir1; ++i) {
+        const int64_t i12 = i/(ne11*ne10);
+        const int64_t i11 = (i - i12*ne11*ne10)/ne10;
+        const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10);
+        const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
+
+        ggml_fp16_to_fp32_row(
+                (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
+                     (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3), nc);
     }
 }
 
 static void ggml_compute_forward_get_rows_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
-    assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int64_t nc = ne00;
-    const int64_t nr = ggml_nelements(src1); GGML_UNUSED(nr);
+    const int64_t nr = ggml_nelements(src1);
 
     assert(ne0  == nc);
     assert(ne02 == ne11);
     assert(nb00 == sizeof(float));
     assert(ggml_nrows(dst) == nr);
 
-    // TODO: multi-thread
-    for (int64_t i12 = 0; i12 < ne12; ++i12) {
-        for (int64_t i11 = 0; i11 < ne11; ++i11) {
-            for (int64_t i10 = 0; i10 < ne10; ++i10) {
-                const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
+    const int ith = params->ith;
+    const int nth = params->nth;
 
-                ggml_vec_cpy_f32(nc,
-                        (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3),
-                        (float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03));
-            }
-        }
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int64_t i = ir0; i < ir1; ++i) {
+        const int64_t i12 = i/(ne11*ne10);
+        const int64_t i11 = (i - i12*ne11*ne10)/ne10;
+        const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10);
+        const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
+
+        ggml_vec_cpy_f32(nc,
+                (float *) ((char *)  dst->data + i10*nb1  + i11*nb2  + i12*nb3),
+                (float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03));
     }
 }
 
 static void ggml_compute_forward_get_rows(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
@@ -10841,17 +11869,25 @@ static void ggml_compute_forward_get_rows(
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
         case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ2_S:
             {
-                ggml_compute_forward_get_rows_q(params, src0, src1, dst);
+                ggml_compute_forward_get_rows_q(params, dst);
             } break;
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_get_rows_f16(params, src0, src1, dst);
+                ggml_compute_forward_get_rows_f16(params, dst);
             } break;
         case GGML_TYPE_F32:
         case GGML_TYPE_I32:
             {
-                ggml_compute_forward_get_rows_f32(params, src0, src1, dst);
+                ggml_compute_forward_get_rows_f32(params, dst);
             } break;
         default:
             {
@@ -10882,19 +11918,24 @@ static void ggml_compute_forward_get_rows(
 
 static void ggml_compute_forward_get_rows_back_f32_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(params->ith == 0);
     GGML_ASSERT(ggml_is_contiguous(dst));
 
     // ggml_compute_forward_dup_same_cont(params, opt0, dst);
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
+        if (params->ith != 0) {
+            return;
+        }
         memset(dst->data, 0, ggml_nbytes(dst));
     }
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -10916,19 +11957,24 @@ static void ggml_compute_forward_get_rows_back_f32_f16(
 
 static void ggml_compute_forward_get_rows_back_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(params->ith == 0);
     GGML_ASSERT(ggml_is_contiguous(dst));
 
     // ggml_compute_forward_dup_same_cont(params, opt0, dst);
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
+        if (params->ith != 0) {
+            return;
+        }
         memset(dst->data, 0, ggml_nbytes(dst));
     }
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -10950,17 +11996,18 @@ static void ggml_compute_forward_get_rows_back_f32(
 
 static void ggml_compute_forward_get_rows_back(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_get_rows_back_f32_f16(params, src0, src1, dst);
+                ggml_compute_forward_get_rows_back_f32_f16(params, dst);
             } break;
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_get_rows_back_f32(params, src0, src1, dst);
+                ggml_compute_forward_get_rows_back_f32(params, dst);
             } break;
         default:
             {
@@ -10991,11 +12038,13 @@ static void ggml_compute_forward_get_rows_back(
 
 static void ggml_compute_forward_diag_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -11031,12 +12080,14 @@ static void ggml_compute_forward_diag_f32(
 
 static void ggml_compute_forward_diag(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_diag_f32(params, src0, dst);
+                ggml_compute_forward_diag_f32(params, dst);
             } break;
         default:
             {
@@ -11049,10 +12100,11 @@ static void ggml_compute_forward_diag(
 
 static void ggml_compute_forward_diag_mask_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst,
         const float value) {
 
+    const struct ggml_tensor * src0 = dst->src[0];
+
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -11061,7 +12113,10 @@ static void ggml_compute_forward_diag_mask_f32(
 
     GGML_ASSERT(n_past >= 0);
 
-    if (!inplace && (params->type == GGML_TASK_INIT)) {
+    if (!inplace && (params->type == GGML_TASK_TYPE_INIT)) {
+        if (ith != 0) {
+            return;
+        }
         // memcpy needs to be synchronized across threads to avoid race conditions.
         // => do it in INIT phase
         GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
@@ -11072,7 +12127,7 @@ static void ggml_compute_forward_diag_mask_f32(
             ggml_nbytes(dst));
     }
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -11099,12 +12154,14 @@ static void ggml_compute_forward_diag_mask_f32(
 
 static void ggml_compute_forward_diag_mask_inf(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_diag_mask_f32(params, src0, dst, -INFINITY);
+                ggml_compute_forward_diag_mask_f32(params, dst, -INFINITY);
             } break;
         default:
             {
@@ -11115,12 +12172,14 @@ static void ggml_compute_forward_diag_mask_inf(
 
 static void ggml_compute_forward_diag_mask_zero(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_diag_mask_f32(params, src0, dst, 0);
+                ggml_compute_forward_diag_mask_f32(params, dst, 0);
             } break;
         default:
             {
@@ -11133,26 +12192,42 @@ static void ggml_compute_forward_diag_mask_zero(
 
 static void ggml_compute_forward_soft_max_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+    const struct ggml_tensor * src2 = dst->src[2];
+
     assert(ggml_is_contiguous(dst));
     assert(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
-    float scale = 1.0f;
-    memcpy(&scale, (float *) dst->op_params + 0, sizeof(float));
+    float scale    = 1.0f;
+    float max_bias = 0.0f;
+
+    memcpy(&scale,    (float *) dst->op_params + 0, sizeof(float));
+    memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float));
 
     // TODO: handle transposed/permuted matrices
 
     const int ith = params->ith;
     const int nth = params->nth;
 
+    GGML_TENSOR_UNARY_OP_LOCALS
+
     const int64_t ne11 = src1 ? src1->ne[1] : 1;
 
+    // TODO: is this supposed to be ceil instead of floor?
+    //       https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L370
+    const uint32_t n_head_kv   = ne02;
+    const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head_kv));
+
+    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
     const int nc = src0->ne[0];
     const int nr = ggml_nrows(src0);
 
@@ -11165,6 +12240,9 @@ static void ggml_compute_forward_soft_max_f32(
 
     float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith;
 
+    // when max_bias <= 0.0f, src2 is not used and we default it to src0 to avoid branching
+    float * pos = src2 ? (float *) src2->data : src0->data;
+
     for (int i1 = ir0; i1 < ir1; i1++) {
         float * sp = (float *)((char *) src0->data + i1*src0->nb[1]);
         float * dp = (float *)((char *)  dst->data +  i1*dst->nb[1]);
@@ -11178,6 +12256,16 @@ static void ggml_compute_forward_soft_max_f32(
             ggml_vec_acc_f32(nc, wp, mp);
         }
 
+        // ALiBi bias
+        if (max_bias > 0.0f) {
+            const uint32_t h  = (i1/ne01)%ne02; // head
+            const float slope = h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1);
+
+            for (int i = 0; i < nc; i++) {
+                wp[i] = wp[i] + slope*pos[i];
+            }
+        }
+
 #ifndef NDEBUG
         for (int i = 0; i < nc; ++i) {
             //printf("p[%d] = %f\n", i, p[i]);
@@ -11220,13 +12308,14 @@ static void ggml_compute_forward_soft_max_f32(
 
 static void ggml_compute_forward_soft_max(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_soft_max_f32(params, src0, src1, dst);
+                ggml_compute_forward_soft_max_f32(params, dst);
             } break;
         default:
             {
@@ -11239,16 +12328,18 @@ static void ggml_compute_forward_soft_max(
 
 static void ggml_compute_forward_soft_max_back_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_is_contiguous(src0));
     GGML_ASSERT(ggml_is_contiguous(src1));
     GGML_ASSERT(ggml_is_contiguous(dst));
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
     GGML_ASSERT(ggml_are_same_shape(src1, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -11300,7 +12391,7 @@ static void ggml_compute_forward_soft_max_back_f32(
 
         // linear runtime, no additional memory
         float dot_y_dy = 0;
-        ggml_vec_dot_f32 (nc, &dot_y_dy, y, dy);
+        ggml_vec_dot_f32 (nc, &dot_y_dy, 0, y, 0, dy, 0, 1);
         ggml_vec_cpy_f32 (nc, dx, dy);
         ggml_vec_acc1_f32(nc, dx, -dot_y_dy);
         ggml_vec_mul_f32 (nc, dx, dx, y);
@@ -11316,13 +12407,14 @@ static void ggml_compute_forward_soft_max_back_f32(
 
 static void ggml_compute_forward_soft_max_back(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_soft_max_back_f32(params, src0, src1, dst);
+                ggml_compute_forward_soft_max_back_f32(params, dst);
             } break;
         default:
             {
@@ -11335,11 +12427,13 @@ static void ggml_compute_forward_soft_max_back(
 
 static void ggml_compute_forward_alibi_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -11370,22 +12464,20 @@ static void ggml_compute_forward_alibi_f32(
     const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
     const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
 
-    for (int64_t i = 0; i < ne0; i++) {
-        for (int64_t j = 0; j < ne1; j++) {
-            for (int64_t k = 0; k < ne2_ne3; k++) {
+    for (int64_t k = 0; k < ne2_ne3; k++) {
+        // TODO: k*nb2 or k*nb3
+        float m_k;
+
+        if (k < n_heads_log2_floor) {
+            m_k = powf(m0, k + 1);
+        } else {
+            m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
+        }
+
+        for (int64_t i = 0; i < ne0; i++) {
+            for (int64_t j = 0; j < ne1; j++) {
                 float * const src = (float *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2);
                 float *      pdst = (float *)((char *)  dst->data + i*nb0 + j*nb1 + k*nb2);
-
-                // TODO: k*nb2 or k*nb3
-
-                float m_k;
-
-                if (k < n_heads_log2_floor) {
-                    m_k = powf(m0, k + 1);
-                } else {
-                    m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
-                }
-
                 pdst[0] = i * m_k + src[0];
             }
         }
@@ -11394,11 +12486,13 @@ static void ggml_compute_forward_alibi_f32(
 
 static void ggml_compute_forward_alibi_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -11430,21 +12524,20 @@ static void ggml_compute_forward_alibi_f16(
     const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
     const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
 
-    for (int i = 0; i < ne0; i++) {
-        for (int j = 0; j < ne1; j++) {
-            for (int k = 0; k < ne2_ne3; k++) {
+    for (int k = 0; k < ne2_ne3; k++) {
+        // TODO: k*nb2 or k*nb3
+        float m_k;
+
+        if (k < n_heads_log2_floor) {
+            m_k = powf(m0, k + 1);
+        } else {
+            m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
+        }
+
+        for (int i = 0; i < ne0; i++) {
+            for (int j = 0; j < ne1; j++) {
                 ggml_fp16_t * const src  = (ggml_fp16_t *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2);
-                      float *      pdst  =       (float *)((char *)  dst->data + i*nb0 + j*nb1 + k*nb2);
-
-                // TODO: k*nb2 or k*nb3
-
-                float m_k;
-
-                if (k < n_heads_log2_floor) {
-                    m_k = powf(m0, k + 1);
-                } else {
-                    m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
-                }
+                float       *      pdst  =       (float *)((char *)  dst->data + i*nb0 + j*nb1 + k*nb2);
 
                 // we return F32
                 pdst[0] = i * m_k + GGML_FP16_TO_FP32(src[0]);
@@ -11455,16 +12548,18 @@ static void ggml_compute_forward_alibi_f16(
 
 static void ggml_compute_forward_alibi(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_alibi_f16(params, src0, dst);
+                ggml_compute_forward_alibi_f16(params, dst);
             } break;
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_alibi_f32(params, src0, dst);
+                ggml_compute_forward_alibi_f32(params, dst);
             } break;
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
@@ -11478,10 +12573,20 @@ static void ggml_compute_forward_alibi(
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
         case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ2_S:
         case GGML_TYPE_Q8_K:
         case GGML_TYPE_I8:
         case GGML_TYPE_I16:
         case GGML_TYPE_I32:
+        case GGML_TYPE_I64:
+        case GGML_TYPE_F64:
         case GGML_TYPE_COUNT:
             {
                 GGML_ASSERT(false);
@@ -11493,11 +12598,13 @@ static void ggml_compute_forward_alibi(
 
 static void ggml_compute_forward_clamp_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -11533,12 +12640,14 @@ static void ggml_compute_forward_clamp_f32(
 
 static void ggml_compute_forward_clamp(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_clamp_f32(params, src0, dst);
+                ggml_compute_forward_clamp_f32(params, dst);
             } break;
         case GGML_TYPE_F16:
         case GGML_TYPE_Q4_0:
@@ -11553,10 +12662,20 @@ static void ggml_compute_forward_clamp(
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
         case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ2_S:
         case GGML_TYPE_Q8_K:
         case GGML_TYPE_I8:
         case GGML_TYPE_I16:
         case GGML_TYPE_I32:
+        case GGML_TYPE_I64:
+        case GGML_TYPE_F64:
         case GGML_TYPE_COUNT:
             {
                 GGML_ASSERT(false);
@@ -11597,21 +12716,40 @@ static float ggml_rope_yarn_corr_dim(int n_dims, int n_orig_ctx, float n_rot, fl
     return n_dims * logf(n_orig_ctx / (n_rot * 2 * (float)M_PI)) / (2 * logf(base));
 }
 
-void ggml_rope_yarn_corr_dims(
+static void ggml_rope_cache_init(
+     float theta_base, float freq_scale, float corr_dims[2], int64_t ne0, float ext_factor, float mscale,
+     float * cache, float sin_sign, float theta_scale
+) {
+    float theta = theta_base;
+    for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
+        rope_yarn(
+            theta, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1]
+        );
+        cache[i0 + 1] *= sin_sign;
+
+        theta *= theta_scale;
+    }
+}
+
+GGML_CALL void ggml_rope_yarn_corr_dims(
     int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]
 ) {
     // start and end correction dims
-    dims[0] = MAX(0,         floorf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_fast, freq_base)));
-    dims[1] = MIN(n_dims - 1, ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_slow, freq_base)));
+    float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_fast, freq_base));
+    float end   =  ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_slow, freq_base));
+    dims[0] = MAX(0, start);
+    dims[1] = MIN(n_dims - 1, end);
 }
 
 static void ggml_compute_forward_rope_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst,
         const bool forward) {
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -11679,6 +12817,12 @@ static void ggml_compute_forward_rope_f32(
     for (int64_t i3 = 0; i3 < ne3; i3++) {
         for (int64_t i2 = 0; i2 < ne2; i2++) {
             const int64_t p = pos[i2];
+
+            float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith;
+            if (!is_glm && !is_neox) { // TODO: cache sin/cos for glm, neox
+                ggml_rope_cache_init(p, freq_scale, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
+            }
+
             for (int64_t i1 = 0; i1 < ne1; i1++) {
                 if (ir++ < ir0) continue;
                 if (ir   > ir1) break;
@@ -11712,18 +12856,13 @@ static void ggml_compute_forward_rope_f32(
                     }
                 } else if (!is_neox) {
                     for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
-                        float cos_theta, sin_theta;
-                        rope_yarn(
-                            theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta
-                        );
-                        sin_theta *= sin_sign;
+                        const float cos_theta = cache[i0 + 0];
+                        const float sin_theta = cache[i0 + 1];
 
                         // zeta scaling for xPos only:
                         float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f;
                         if (xpos_down) zeta = 1.0f / zeta;
 
-                        theta_base *= theta_scale;
-
                         const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
                               float * dst_data  = (float *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
 
@@ -11782,11 +12921,13 @@ static void ggml_compute_forward_rope_f32(
 
 static void ggml_compute_forward_rope_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst,
         const bool forward) {
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -11847,6 +12988,12 @@ static void ggml_compute_forward_rope_f16(
     for (int64_t i3 = 0; i3 < ne3; i3++) {
         for (int64_t i2 = 0; i2 < ne2; i2++) {
             const int64_t p = pos[i2];
+
+            float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith;
+            if (!is_glm && !is_neox) { // TODO: cache sin/cos for glm, neox
+                ggml_rope_cache_init(p, freq_scale, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
+            }
+
             for (int64_t i1 = 0; i1 < ne1; i1++) {
                 if (ir++ < ir0) continue;
                 if (ir   > ir1) break;
@@ -11880,13 +13027,8 @@ static void ggml_compute_forward_rope_f16(
                     }
                 } else if (!is_neox) {
                     for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
-                        float cos_theta, sin_theta;
-                        rope_yarn(
-                            theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta
-                        );
-                        sin_theta *= sin_sign;
-
-                        theta_base *= theta_scale;
+                        const float cos_theta = cache[i0 + 0];
+                        const float sin_theta = cache[i0 + 1];
 
                         const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
                               ggml_fp16_t * dst_data  = (ggml_fp16_t *)((char *)  dst->data + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
@@ -11946,17 +13088,18 @@ static void ggml_compute_forward_rope_f16(
 
 static void ggml_compute_forward_rope(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_rope_f16(params, src0, src1, dst, true);
+                ggml_compute_forward_rope_f16(params, dst, true);
             } break;
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_rope_f32(params, src0, src1, dst, true);
+                ggml_compute_forward_rope_f32(params, dst, true);
             } break;
         default:
             {
@@ -11969,17 +13112,18 @@ static void ggml_compute_forward_rope(
 
 static void ggml_compute_forward_rope_back(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_rope_f16(params, src0, src1, dst, false);
+                ggml_compute_forward_rope_f16(params, dst, false);
             } break;
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_rope_f32(params, src0, src1, dst, false);
+                ggml_compute_forward_rope_f32(params, dst, false);
             } break;
         default:
             {
@@ -11992,9 +13136,11 @@ static void ggml_compute_forward_rope_back(
 
 static void ggml_compute_forward_conv_transpose_1d_f16_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
@@ -12012,7 +13158,10 @@ static void ggml_compute_forward_conv_transpose_1d_f16_f32(
     GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
     GGML_ASSERT(nb10 == sizeof(float));
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
+        if (ith != 0) {
+            return;
+        }
         memset(params->wdata, 0, params->wsize);
 
         // permute kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout)
@@ -12049,7 +13198,7 @@ static void ggml_compute_forward_conv_transpose_1d_f16_f32(
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -12075,9 +13224,9 @@ static void ggml_compute_forward_conv_transpose_1d_f16_f32(
             const int i1n = i10*ne11;
             for (int i00 = 0; i00 < ne00; i00++) {
                 float v = 0;
-                ggml_vec_dot_f16(ne02, &v,
-                        (ggml_fp16_t *)    wdata_src + i1n,
-                        (ggml_fp16_t *) wdata_kernel + i00*ne02);
+                ggml_vec_dot_f16(ne02, &v, 0,
+                        (ggml_fp16_t *)    wdata_src + i1n, 0,
+                        (ggml_fp16_t *) wdata_kernel + i00*ne02, 0, 1);
                 dst_data[i10*s0 + i00] += v;
             }
         }
@@ -12086,9 +13235,11 @@ static void ggml_compute_forward_conv_transpose_1d_f16_f32(
 
 static void ggml_compute_forward_conv_transpose_1d_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
@@ -12106,7 +13257,10 @@ static void ggml_compute_forward_conv_transpose_1d_f32(
     GGML_ASSERT(nb00 == sizeof(float));
     GGML_ASSERT(nb10 == sizeof(float));
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
+        if (ith != 0) {
+            return;
+        }
         memset(params->wdata, 0, params->wsize);
 
         // prepare kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout)
@@ -12143,7 +13297,7 @@ static void ggml_compute_forward_conv_transpose_1d_f32(
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -12169,9 +13323,9 @@ static void ggml_compute_forward_conv_transpose_1d_f32(
             const int i1n = i10*ne11;
             for (int i00 = 0; i00 < ne00; i00++) {
                 float v = 0;
-                ggml_vec_dot_f32(ne02, &v,
-                        wdata_src + i1n,
-                        wdata_kernel + i00*ne02);
+                ggml_vec_dot_f32(ne02, &v, 0,
+                        wdata_src + i1n, 0,
+                        wdata_kernel + i00*ne02, 0, 1);
                 dst_data[i10*s0 + i00] += v;
             }
         }
@@ -12180,17 +13334,18 @@ static void ggml_compute_forward_conv_transpose_1d_f32(
 
 static void ggml_compute_forward_conv_transpose_1d(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_conv_transpose_1d_f16_f32(params, src0, src1, dst);
+                ggml_compute_forward_conv_transpose_1d_f16_f32(params, dst);
             } break;
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_conv_transpose_1d_f32(params, src0, src1, dst);
+                ggml_compute_forward_conv_transpose_1d_f32(params, dst);
             } break;
         default:
             {
@@ -12199,14 +13354,104 @@ static void ggml_compute_forward_conv_transpose_1d(
     }
 }
 
+// src0: kernel [OC, IC, KH, KW]
+// src1: image [N, IC, IH, IW]
+// dst:  result [N, OH, OW, IC*KH*KW]
+static void ggml_compute_forward_im2col_f32(
+        const struct ggml_compute_params * params,
+              struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    int64_t t0 = ggml_perf_time_us();
+    UNUSED(t0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS;
+
+    const int32_t s0 = ((const int32_t *)(dst->op_params))[0];
+    const int32_t s1 = ((const int32_t *)(dst->op_params))[1];
+    const int32_t p0 = ((const int32_t *)(dst->op_params))[2];
+    const int32_t p1 = ((const int32_t *)(dst->op_params))[3];
+    const int32_t d0 = ((const int32_t *)(dst->op_params))[4];
+    const int32_t d1 = ((const int32_t *)(dst->op_params))[5];
+    const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1;
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int64_t N  = is_2D ? ne13 : ne12;
+    const int64_t IC = is_2D ? ne12 : ne11;
+    const int64_t IH = is_2D ? ne11 : 1;
+    const int64_t IW = ne10;
+
+    const int64_t KH = is_2D ? ne01 : 1;
+    const int64_t KW = ne00;
+
+    const int64_t OH = is_2D ? ne2 : 1;
+    const int64_t OW = ne1;
+
+    int ofs0 = is_2D ? nb13 : nb12;
+    int ofs1 = is_2D ? nb12 : nb11;
+
+    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    if (params->type == GGML_TASK_TYPE_INIT) {
+        return;
+    }
+
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW]
+    {
+        float * const wdata = (float *) dst->data;
+
+        for (int64_t in = 0; in < N; in++) {
+            for (int64_t ioh = 0; ioh < OH; ioh++) { // 1
+                for (int64_t iow = 0; iow < OW; iow++) {
+                    for (int64_t iic = ith; iic < IC; iic += nth) {
+
+                        // micro kernel
+                        float * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW]
+                        const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW]
+
+                        for (int64_t ikh = 0; ikh < KH; ikh++) {  // 1
+                            for (int64_t ikw = 0; ikw < KW; ikw++) {
+                                const int64_t iiw = iow*s0 + ikw*d0 - p0;
+                                const int64_t iih = ioh*s1 + ikh*d1 - p1;
+
+                                if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
+                                    dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0;
+                                } else {
+                                    dst_data[iic*(KH*KW) + ikh*KW + ikw] = (src_data[iih*IW + iiw]);
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+
+
 // src0: kernel [OC, IC, KH, KW]
 // src1: image [N, IC, IH, IW]
 // dst:  result [N, OH, OW, IC*KH*KW]
 static void ggml_compute_forward_im2col_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F16);
@@ -12244,11 +13489,11 @@ static void ggml_compute_forward_im2col_f16(
     GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
     GGML_ASSERT(nb10 == sizeof(float));
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -12286,17 +13531,15 @@ static void ggml_compute_forward_im2col_f16(
 
 static void ggml_compute_forward_im2col(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
-    switch (src0->type) {
+    switch (dst->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_im2col_f16(params, src0, src1, dst);
+                ggml_compute_forward_im2col_f16(params, dst);
             } break;
         case GGML_TYPE_F32:
             {
-                GGML_ASSERT(false);
+                ggml_compute_forward_im2col_f32(params, dst);
             } break;
         default:
             {
@@ -12305,13 +13548,16 @@ static void ggml_compute_forward_im2col(
     }
 }
 
+
 // ggml_compute_forward_conv_transpose_2d
 
 static void ggml_compute_forward_conv_transpose_2d(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
@@ -12329,7 +13575,10 @@ static void ggml_compute_forward_conv_transpose_2d(
     GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
     GGML_ASSERT(nb10 == sizeof(float));
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
+        if (ith != 0) {
+            return;
+        }
         memset(params->wdata, 0, params->wsize);
 
         // permute kernel data (src0) from (Kw x Kh x Cout x Cin) to (Cin x Kw x Kh x Cout)
@@ -12368,7 +13617,7 @@ static void ggml_compute_forward_conv_transpose_2d(
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -12396,9 +13645,9 @@ static void ggml_compute_forward_conv_transpose_2d(
                 for (int i01 = 0; i01 < ne01; i01++) {
                     for (int i00 = 0; i00 < ne00; i00++) {
                         float v = 0;
-                        ggml_vec_dot_f16(ne03, &v,
-                                wdata_src + i1n,
-                                wdata_kernel + i01*ne00*ne03 + i00*ne03);
+                        ggml_vec_dot_f16(ne03, &v, 0,
+                                wdata_src + i1n, 0,
+                                wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1);
                         dst_data[(i11*stride + i01)*ne0 + i10*stride + i00] += v;
                     }
                 }
@@ -12412,13 +13661,15 @@ static void ggml_compute_forward_conv_transpose_2d(
 static void ggml_compute_forward_pool_1d_sk_p0(
         const struct ggml_compute_params * params,
         const enum ggml_op_pool op,
-        const struct ggml_tensor * src,
         const int k,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src = dst->src[0];
+
     assert(src->type == GGML_TYPE_F32);
     assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -12463,7 +13714,6 @@ static void ggml_compute_forward_pool_1d_sk_p0(
 
 static void ggml_compute_forward_pool_1d(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
               struct ggml_tensor * dst) {
 
     const int32_t * opts = (const int32_t *)dst->op_params;
@@ -12474,19 +13724,21 @@ static void ggml_compute_forward_pool_1d(
     GGML_ASSERT(p0 == 0); // padding not supported
     GGML_ASSERT(k0 == s0); // only s = k supported
 
-    ggml_compute_forward_pool_1d_sk_p0(params, op, src0, k0, dst);
+    ggml_compute_forward_pool_1d_sk_p0(params, op, k0, dst);
 }
 
 // ggml_compute_forward_pool_2d
 
 static void ggml_compute_forward_pool_2d(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src,
         struct ggml_tensor * dst) {
-    assert(src->type == GGML_TYPE_F32);
-    assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    const struct ggml_tensor * src = dst->src[0];
+
+    GGML_ASSERT(src->type == GGML_TYPE_F32);
+    GGML_ASSERT(params->ith == 0);
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -12555,10 +13807,11 @@ static void ggml_compute_forward_pool_2d(
 
 static void ggml_compute_forward_upscale_f32(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
     struct ggml_tensor * dst) {
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -12594,12 +13847,14 @@ static void ggml_compute_forward_upscale_f32(
 
 static void ggml_compute_forward_upscale(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
     struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_upscale_f32(params, src0, dst);
+                ggml_compute_forward_upscale_f32(params, dst);
             } break;
         default:
             {
@@ -12612,10 +13867,11 @@ static void ggml_compute_forward_upscale(
 
 static void ggml_compute_forward_pad_f32(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
           struct ggml_tensor * dst) {
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -12652,12 +13908,114 @@ static void ggml_compute_forward_pad_f32(
 
 static void ggml_compute_forward_pad(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
     struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_pad_f32(params, src0, dst);
+                ggml_compute_forward_pad_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+
+// ggml_compute_forward_arange
+
+static void ggml_compute_forward_arange_f32(
+    const struct ggml_compute_params * params,
+    struct ggml_tensor * dst) {
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    GGML_ASSERT(dst->nb[0] == sizeof(float));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const float start = ggml_get_op_params_f32(dst, 0);
+    const float stop  = ggml_get_op_params_f32(dst, 1);
+    const float step  = ggml_get_op_params_f32(dst, 2);
+
+    const int64_t steps = (int64_t) ceilf((stop - start) / step);
+
+    GGML_ASSERT(ggml_nelements(dst) == steps);
+
+    for (int64_t i = ith; i < steps; i+= nth) {
+        float value = start + step * i;
+        ((float *)dst->data)[i] = value;
+    }
+}
+
+static void ggml_compute_forward_arange(
+    const struct ggml_compute_params * params,
+    struct ggml_tensor * dst) {
+    switch (dst->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_arange_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+static void ggml_compute_forward_timestep_embedding_f32(
+    const struct ggml_compute_params * params,
+    struct ggml_tensor * dst) {
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    const int dim = ggml_get_op_params_i32(dst, 0);
+    const int max_period = ggml_get_op_params_i32(dst, 1);
+
+    int half = dim / 2;
+
+    for (int64_t i = 0; i < ne00; i++) {
+        float * embed_data = (float *)((char *)  dst->data +  i*nb1);
+        for (int64_t j = ith; j < half; j += nth) {
+            float timestep = ((float *)src0->data)[i];
+            float freq = (float)expf(-logf(max_period) * j / half);
+            float arg = timestep * freq;
+            embed_data[j] = cosf(arg);
+            embed_data[j + half] = sinf(arg);
+        }
+        if (dim % 2 != 0 && ith == 0) {
+            embed_data[dim] = 0.f;
+        }
+    }
+}
+
+static void ggml_compute_forward_timestep_embedding(
+    const struct ggml_compute_params * params,
+    struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_timestep_embedding_f32(params, dst);
             } break;
         default:
             {
@@ -12670,10 +14028,11 @@ static void ggml_compute_forward_pad(
 
 static void ggml_compute_forward_argsort_f32(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
     struct ggml_tensor * dst) {
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -12699,8 +14058,8 @@ static void ggml_compute_forward_argsort_f32(
         // C doesn't have a functional sort, so we do a bubble sort instead
         for (int64_t j = 0; j < ne0; j++) {
             for (int64_t k = j + 1; k < ne0; k++) {
-                if ((order == GGML_SORT_ASC && src_data[dst_data[j]] > src_data[dst_data[k]]) ||
-                    (order == GGML_SORT_DESC && src_data[dst_data[j]] < src_data[dst_data[k]])) {
+                if ((order == GGML_SORT_ORDER_ASC  && src_data[dst_data[j]] > src_data[dst_data[k]]) ||
+                    (order == GGML_SORT_ORDER_DESC && src_data[dst_data[j]] < src_data[dst_data[k]])) {
                     int32_t tmp = dst_data[j];
                     dst_data[j] = dst_data[k];
                     dst_data[k] = tmp;
@@ -12712,13 +14071,14 @@ static void ggml_compute_forward_argsort_f32(
 
 static void ggml_compute_forward_argsort(
     const struct ggml_compute_params * params,
-    const struct ggml_tensor * src0,
     struct ggml_tensor * dst) {
 
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_argsort_f32(params, src0, dst);
+                ggml_compute_forward_argsort_f32(params, dst);
             } break;
         default:
             {
@@ -12731,11 +14091,13 @@ static void ggml_compute_forward_argsort(
 
 static void ggml_compute_forward_flash_attn_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * q,
-        const struct ggml_tensor * k,
-        const struct ggml_tensor * v,
         const bool masked,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * q = dst->src[0];
+    const struct ggml_tensor * k = dst->src[1];
+    const struct ggml_tensor * v = dst->src[2];
+
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
@@ -12780,11 +14142,11 @@ static void ggml_compute_forward_flash_attn_f32(
     GGML_ASSERT(nb1 <= nb2);
     GGML_ASSERT(nb2 <= nb3);
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -12827,9 +14189,9 @@ static void ggml_compute_forward_flash_attn_f32(
             const int i1 = ik1;
 
             ggml_vec_dot_f32(neq0,
-                    S + i1,
-                    (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)),
-                    (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)));
+                    S + i1, 0,
+                    (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0,
+                    (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1);
         }
 
         // scale
@@ -12912,20 +14274,22 @@ static void ggml_compute_forward_flash_attn_f32(
             const int iv3 = iq3;
 
             ggml_vec_dot_f32(masked_begin,
-                    (float *) ((char *) dst->data + (ic*nb0 + i1*nb1  + i2*nb2   + i3*nb3)),
-                    (float *) ((char *) v->data   + (         ic*nbv1 + iv2*nbv2 + iv3*nbv3)),
-                    S);
+                    (float *) ((char *) dst->data + (ic*nb0 + i1*nb1  + i2*nb2   + i3*nb3)), 0,
+                    (float *) ((char *) v->data   + (         ic*nbv1 + iv2*nbv2 + iv3*nbv3)), 0,
+                    S, 0, 1);
         }
     }
 }
 
 static void ggml_compute_forward_flash_attn_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * q,
-        const struct ggml_tensor * k,
-        const struct ggml_tensor * v,
         const bool masked,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * q = dst->src[0];
+    const struct ggml_tensor * k = dst->src[1];
+    const struct ggml_tensor * v = dst->src[2];
+
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
@@ -12970,11 +14334,11 @@ static void ggml_compute_forward_flash_attn_f16(
     GGML_ASSERT(nb1 <= nb2);
     GGML_ASSERT(nb2 <= nb3);
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -13017,9 +14381,9 @@ static void ggml_compute_forward_flash_attn_f16(
                 const int i1 = ik1;
 
                 ggml_vec_dot_f16(neq0,
-                        S + i1,
-                        (ggml_fp16_t *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)),
-                        (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)));
+                        S + i1, 0,
+                        (ggml_fp16_t *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0,
+                        (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1);
             }
         } else {
             for (int64_t ic = 0; ic < nek1; ic += GGML_VEC_DOT_UNROLL) {
@@ -13121,9 +14485,9 @@ static void ggml_compute_forward_flash_attn_f16(
                 const int iv3 = iq3;
 
                 ggml_vec_dot_f16(nev0,
-                        (float *)       ((char *) dst->data + (ic*nb0 + i1*nb1  + i2*nb2   + i3*nb3)),
-                        (ggml_fp16_t *) ((char *) v->data   + (         ic*nbv1 + iv2*nbv2 + iv3*nbv3)),
-                        S16);
+                        (float *)       ((char *) dst->data + (ic*nb0 + i1*nb1  + i2*nb2   + i3*nb3)), 0,
+                        (ggml_fp16_t *) ((char *) v->data   + (         ic*nbv1 + iv2*nbv2 + iv3*nbv3)), 0,
+                        S16, 0, 1);
             }
         } else {
             for (int64_t ic = 0; ic < nev1; ic += GGML_VEC_DOT_UNROLL) {
@@ -13147,19 +14511,19 @@ static void ggml_compute_forward_flash_attn_f16(
 
 static void ggml_compute_forward_flash_attn(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * q,
-        const struct ggml_tensor * k,
-        const struct ggml_tensor * v,
         const bool masked,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * q = dst->src[0];
+
     switch (q->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_flash_attn_f16(params, q, k, v, masked, dst);
+                ggml_compute_forward_flash_attn_f16(params, masked, dst);
             } break;
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_flash_attn_f32(params, q, k, v, masked, dst);
+                ggml_compute_forward_flash_attn_f32(params, masked, dst);
             } break;
         default:
             {
@@ -13172,12 +14536,14 @@ static void ggml_compute_forward_flash_attn(
 
 static void ggml_compute_forward_flash_ff_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * a,  // F16
-        const struct ggml_tensor * b0, // F16 fc_w
-        const struct ggml_tensor * b1, // F32 fc_b
-        const struct ggml_tensor * c0, // F16 proj_w
-        const struct ggml_tensor * c1, // F32 proj_b
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * a = dst->src[0];  // F16
+    const struct ggml_tensor * b0 = dst->src[1]; // F16 fc_w
+    const struct ggml_tensor * b1 = dst->src[2]; // F32 fc_b
+    const struct ggml_tensor * c0 = dst->src[3]; // F16 proj_w
+    const struct ggml_tensor * c1 = dst->src[4]; // F32 proj_b
+
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
@@ -13227,11 +14593,11 @@ static void ggml_compute_forward_flash_ff_f16(
     GGML_ASSERT(nb1 <= nb2);
     GGML_ASSERT(nb2 <= nb3);
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -13265,9 +14631,9 @@ static void ggml_compute_forward_flash_ff_f16(
             const int i1 = ib01;
 
             ggml_vec_dot_f16(nea0,
-                    S + i1,
-                    (ggml_fp16_t *) ((char *) b0->data + (ib01*nbb01 + ib02*nbb02 + ib03*nbb03)),
-                    (ggml_fp16_t *) ((char *)  a->data + ( ia1*nba1  +  ia2*nba2  +  ia3*nba3)));
+                    S + i1, 0,
+                    (ggml_fp16_t *) ((char *) b0->data + (ib01*nbb01 + ib02*nbb02 + ib03*nbb03)), 0,
+                    (ggml_fp16_t *) ((char *)  a->data + ( ia1*nba1  +  ia2*nba2  +  ia3*nba3)), 0, 1);
         }
 
         ggml_vec_add_f32(neb01, S, S, (float *) b1->data);
@@ -13290,9 +14656,9 @@ static void ggml_compute_forward_flash_ff_f16(
             for (int64_t ic = 0; ic < nec01; ++ic) {
 
                 ggml_vec_dot_f16(neb01,
-                        (float *)       ((char *) dst->data + (ic*nb0 + i1*nb1   + i2*nb2   + i3*nb3)),
-                        (ggml_fp16_t *) ((char *) c0->data  + (         ic*nbc01 + i2*nbc02 + i3*nbc03)),
-                        S16);
+                        (float *)       ((char *) dst->data + (ic*nb0 + i1*nb1   + i2*nb2   + i3*nb3)), 0,
+                        (ggml_fp16_t *) ((char *) c0->data  + (         ic*nbc01 + i2*nbc02 + i3*nbc03)), 0,
+                        S16, 0, 1);
             }
 
             ggml_vec_add_f32(nec01,
@@ -13305,16 +14671,14 @@ static void ggml_compute_forward_flash_ff_f16(
 
 static void ggml_compute_forward_flash_ff(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * a,
-        const struct ggml_tensor * b0,
-        const struct ggml_tensor * b1,
-        const struct ggml_tensor * c0,
-        const struct ggml_tensor * c1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * b0 = dst->src[1];
+
     switch (b0->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_flash_ff_f16(params, a, b0, b1, c0, c1, dst);
+                ggml_compute_forward_flash_ff_f16(params, dst);
             } break;
         case GGML_TYPE_F32:
             {
@@ -13331,12 +14695,14 @@ static void ggml_compute_forward_flash_ff(
 
 static void ggml_compute_forward_flash_attn_back_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * q,
-        const struct ggml_tensor * k,
-        const struct ggml_tensor * v,
-        const struct ggml_tensor * d,
         const bool masked,
               struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * q = dst->src[0];
+    const struct ggml_tensor * k = dst->src[1];
+    const struct ggml_tensor * v = dst->src[2];
+    const struct ggml_tensor * d = dst->src[3];
+
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
@@ -13386,14 +14752,14 @@ static void ggml_compute_forward_flash_attn_back_f32(
     GGML_ASSERT(nb1 <= nb2);
     GGML_ASSERT(nb2 <= nb3);
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
         if (ith == 0) {
             memset(dst->data, 0, nb0*ne0*ne1*ne2*ne3);
         }
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -13479,9 +14845,9 @@ static void ggml_compute_forward_flash_attn_back_f32(
                     const int i1 = ik1;
 
                     ggml_vec_dot_f32(neq0,
-                            S + i1,
-                            (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)),
-                            (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)));
+                            S + i1, 0,
+                            (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0,
+                            (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1);
                 }
 
                 // scale
@@ -13626,7 +14992,7 @@ static void ggml_compute_forward_flash_attn_back_f32(
 
                 // S = SM * (S - dot(SM, S))
                 float dot_SM_gradSM = 0;
-                ggml_vec_dot_f32 (masked_begin, &dot_SM_gradSM, SM, S);
+                ggml_vec_dot_f32 (masked_begin, &dot_SM_gradSM, 0, SM, 0, S, 0, 1);
                 ggml_vec_acc1_f32(M, S, -dot_SM_gradSM);
                 ggml_vec_mul_f32 (masked_begin, S, S, SM);
 
@@ -13684,16 +15050,266 @@ static void ggml_compute_forward_flash_attn_back_f32(
 
 static void ggml_compute_forward_flash_attn_back(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * q,
-        const struct ggml_tensor * k,
-        const struct ggml_tensor * v,
-        const struct ggml_tensor * d,
         const bool masked,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * q = dst->src[0];
+
     switch (q->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_flash_attn_back_f32(params, q, k, v, d, masked, dst);
+                ggml_compute_forward_flash_attn_back_f32(params, masked, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_ssm_conv
+
+static void ggml_compute_forward_ssm_conv_f32(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    const struct ggml_tensor * src0 = dst->src[0]; // conv_state
+    const struct ggml_tensor * src1 = dst->src[1]; // x
+    const struct ggml_tensor * src2 = dst->src[2]; // conv1d.weight
+    const struct ggml_tensor * src3 = dst->src[3]; // state_seq
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nc   = src2->ne[0]; // d_conv
+    const int nr   = src0->ne[1]; // d_inner
+    const int n_t  = src1->ne[1]; // n_tokens
+    const int n_kv = src0->ne[2]; // max number of sequences in the batch
+
+    GGML_ASSERT((nr*n_t) + (nc*nr*n_kv) == ggml_nelements(dst));
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+    GGML_ASSERT(src1->nb[0] == sizeof(float));
+    GGML_ASSERT(src2->nb[0] == sizeof(float));
+    GGML_ASSERT(src3->nb[0] == sizeof(int32_t));
+    GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float));
+    // for use with the destination state offset between sequences
+    GGML_ASSERT(src2->nb[2] == src2->ne[1]*src2->ne[0]*sizeof(float));
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+    const int ir  = ir1 - ir0;
+
+    if (n_kv > 1) {
+        // multiple sequences means it's hard to know when it's the first time a state is read,
+        // so copy them all over to the destination, just to be sure.
+        for (int i3 = 0; i3 < n_kv; ++i3) {
+            float * s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]));
+            float * s  = (float *) ((char *)  dst->data + ir0*(src2->nb[1]) + i3*(src2->nb[2]) + nr*n_t*sizeof(float));
+            // can't use memcpy because of d_conv vs d_conv - 1
+            for (int i1 = 0; i1 < ir; ++i1) {
+                for (int i0 = 0; i0 < nc - 1; ++i0) {
+                    // copy s0 to last (d_conv - 1) columns of s
+                    s[1 + i0 + i1*nc] = s0[i0 + i1*(nc - 1)];
+                }
+            }
+        }
+    }
+
+    for (int i2 = 0; i2 < n_t; ++i2) {
+        int32_t * sq = (int32_t *) ((char *) src3->data +  i2*(src3->nb[1])); // {n_kv, n_tokens}
+        float *   x  = (float *)   ((char *)  dst->data + ir0*sizeof(float) + i2*(nr*sizeof(float))); // {d_inner, n_tokens}
+        float *   s  = (float *)   ((char *)  dst->data + ir0*(src2->nb[1]) + sq[0]*(src2->nb[2]) + nr*n_t*sizeof(float)); // {d_conv, d_inner, n_kv}
+        float *   s0; // {d_conv - 1, d_inner, n_kv}
+        float *   x0 = (float *)   ((char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens}
+        float *   c  = (float *)   ((char *) src2->data + ir0*(src2->nb[1])); // {d_conv, d_inner}
+        int ne0s0;
+
+        GGML_ASSERT(0 <= sq[0] && sq[0] < n_kv);
+
+        // avoid needing to copy the state for the first token
+        if (i2 == 0) {
+            s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2])); // {d_conv - 1, d_inner, n_kv}
+            ne0s0 = src0->ne[0];
+        } else {
+            // the source is the last (d_conv - 1) columns of the destination
+            s0 = s + 1;
+            ne0s0 = nc;
+        }
+
+        // d_inner
+        for (int i1 = 0; i1 < ir; ++i1) {
+            // shift state left
+            for (int i0 = 0; i0 < nc - 1; ++i0) {
+                s[i0 + i1*nc] = s0[i0 + i1*ne0s0];
+            }
+            // insert x on the last column
+            s[(nc - 1) + i1*nc] = x0[i1];
+        }
+
+        // handle copies when there are multiple output states
+        for (int i3 = 1; i3 < n_kv; ++i3) {
+            int32_t seq = sq[i3];
+            if (0 <= seq && seq < n_kv) {
+                float * s1 = s + (seq - sq[0])*nc*nr;
+                memcpy(s1, s, nc*ir*sizeof(float));
+            } else {
+                // stop at negative or too big seq_ids
+                break;
+            }
+        }
+
+        // it seems a little faster when this is separate from the state shift
+        for (int i1 = 0; i1 < ir; ++i1) {
+            // rowwise dot product
+            float sumf = 0.0f;
+            for (int i0 = 0; i0 < nc; ++i0) {
+                int i = i0 + i1*nc;
+                sumf += s[i] * c[i];
+            }
+            x[i1] = sumf;
+        }
+    }
+}
+
+static void ggml_compute_forward_ssm_conv(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+    switch (dst->src[0]->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_ssm_conv_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_ssm_scan
+
+static void ggml_compute_forward_ssm_scan_f32(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    const struct ggml_tensor * src0 = dst->src[0]; // s
+    const struct ggml_tensor * src1 = dst->src[1]; // x
+    const struct ggml_tensor * src2 = dst->src[2]; // dt
+    const struct ggml_tensor * src3 = dst->src[3]; // A
+    const struct ggml_tensor * src4 = dst->src[4]; // B
+    const struct ggml_tensor * src5 = dst->src[5]; // C
+    const struct ggml_tensor * src6 = dst->src[6]; // sq
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int64_t nc   = src0->ne[0]; // d_state
+    const int64_t nr   = src0->ne[1]; // d_inner
+    const int64_t n_t  = src1->ne[1]; // number of tokens in the batch
+    const int64_t n_kv = src0->ne[2]; // max number of sequences in the batch
+
+    GGML_ASSERT(ggml_nelements(src1) + ggml_nelements(src0) == ggml_nelements(dst));
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+    GGML_ASSERT(src1->nb[0] == sizeof(float));
+    GGML_ASSERT(src2->nb[0] == sizeof(float));
+    GGML_ASSERT(src3->nb[0] == sizeof(float));
+    GGML_ASSERT(src4->nb[0] == sizeof(float));
+    GGML_ASSERT(src5->nb[0] == sizeof(float));
+    // required for the dot product between s and C, and when copying the states
+    GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float));
+    // required for per-sequence offsets for states
+    GGML_ASSERT(src0->nb[2] == src0->ne[0]*src0->ne[1]*sizeof(float));
+    // required to get correct offset for state destination (i.e. src1->nb[2])
+    GGML_ASSERT(src1->nb[2] == src1->ne[0]*src1->ne[1]*sizeof(float));
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+    const int ir  = ir1 - ir0;
+
+    if (n_kv > 1) {
+        // it's hard to know if the source states have already been copied
+        // when there are multiple, so copy them already.
+        for (int i3 = 0; i3 < n_kv; ++i3) {
+            float * s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]));
+            float * s  = (float *) ((char *)  dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[2]);
+            memcpy(s, s0, nc*ir*sizeof(float));
+        }
+    }
+
+    for (int i2 = 0; i2 < n_t; ++i2) {
+        int32_t * sq = (int32_t *) ((char *) src6->data +  i2*(src6->nb[1])); // {n_kv, n_tokens}
+        float *   y  = (float *)   ((char *)  dst->data + ir0*(src1->nb[0]) +    i2*(src1->nb[1])); // {d_inner, n_tokens}
+        float *   s  = (float *)   ((char *)  dst->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2]) + src1->nb[2]); // {d_state, d_inner, n_kv}
+        float *   s0;
+        float *   x  = (float *)   ((char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens}
+        float *   dt = (float *)   ((char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1])); // {d_inner, n_tokens}
+        float *   A  = (float *)   ((char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner}
+        float *   B  = (float *)   ((char *) src4->data +  i2*(src4->nb[1])); // {d_state, n_tokens}
+        float *   C  = (float *)   ((char *) src5->data +  i2*(src5->nb[1])); // {d_state, n_tokens}
+
+        GGML_ASSERT(0 <= sq[0] && sq[0] < n_kv);
+
+        // avoid needing to copy the state for the first token
+        if (i2 == 0) {
+            s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2])); // {d_state, d_inner, n_kv}
+        } else {
+            // otherwise the source is the same as the destination
+            s0 = s;
+        }
+
+        // d_inner
+        for (int i1 = 0; i1 < ir; ++i1) {
+            // ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78
+            float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1];
+            float x_dt = x[i1] * dt_soft_plus;
+            float sumf = 0.0f;
+            // d_state
+            for (int i0 = 0; i0 < nc; ++i0) {
+                int i = i0 + i1*nc;
+                // state = prev_state * dA + dB * x
+                float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt);
+                // y = rowwise_dotprod(state, C)
+                sumf += state * C[i0];
+                s[i] = state;
+            }
+            y[i1] = sumf;
+        }
+
+        // handle copies when there are multiple output states
+        for (int i3 = 1; i3 < n_kv; ++i3) {
+            int32_t seq = sq[i3];
+            if (0 <= seq && seq < n_kv) {
+                float * s1 = s + (seq - sq[0])*nc*nr;
+                memcpy(s1, s, nc*ir*sizeof(float));
+            } else {
+                // stop at negative or too big seq_ids
+                break;
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_ssm_scan(
+        const struct ggml_compute_params * params,
+        struct ggml_tensor * dst) {
+    switch (dst->src[0]->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_ssm_scan_f32(params, dst);
             } break;
         default:
             {
@@ -13706,9 +15322,11 @@ static void ggml_compute_forward_flash_attn_back(
 
 static void ggml_compute_forward_win_part_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -13750,12 +15368,14 @@ static void ggml_compute_forward_win_part_f32(
 
 static void ggml_compute_forward_win_part(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_win_part_f32(params, src0, dst);
+                ggml_compute_forward_win_part_f32(params, dst);
             } break;
         default:
             {
@@ -13768,9 +15388,11 @@ static void ggml_compute_forward_win_part(
 
 static void ggml_compute_forward_win_unpart_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -13810,12 +15432,14 @@ static void ggml_compute_forward_win_unpart_f32(
 
 static void ggml_compute_forward_win_unpart(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_win_unpart_f32(params, src0, dst);
+                ggml_compute_forward_win_unpart_f32(params, dst);
             } break;
         default:
             {
@@ -13828,50 +15452,58 @@ static void ggml_compute_forward_win_unpart(
 
 static void ggml_compute_forward_unary(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
     const enum ggml_unary_op op = ggml_get_unary_op(dst);
 
     switch (op) {
         case GGML_UNARY_OP_ABS:
             {
-                ggml_compute_forward_abs(params, src0, dst);
+                ggml_compute_forward_abs(params, dst);
             } break;
         case GGML_UNARY_OP_SGN:
             {
-                ggml_compute_forward_sgn(params, src0, dst);
+                ggml_compute_forward_sgn(params, dst);
             } break;
         case GGML_UNARY_OP_NEG:
             {
-                ggml_compute_forward_neg(params, src0, dst);
+                ggml_compute_forward_neg(params, dst);
             } break;
         case GGML_UNARY_OP_STEP:
             {
-                ggml_compute_forward_step(params, src0, dst);
+                ggml_compute_forward_step(params, dst);
             } break;
         case GGML_UNARY_OP_TANH:
             {
-                ggml_compute_forward_tanh(params, src0, dst);
+                ggml_compute_forward_tanh(params, dst);
             } break;
         case GGML_UNARY_OP_ELU:
             {
-                ggml_compute_forward_elu(params, src0, dst);
+                ggml_compute_forward_elu(params, dst);
             } break;
         case GGML_UNARY_OP_RELU:
             {
-                ggml_compute_forward_relu(params, src0, dst);
+                ggml_compute_forward_relu(params, dst);
             } break;
         case GGML_UNARY_OP_GELU:
             {
-                ggml_compute_forward_gelu(params, src0, dst);
+                ggml_compute_forward_gelu(params, dst);
             } break;
         case GGML_UNARY_OP_GELU_QUICK:
             {
-                ggml_compute_forward_gelu_quick(params, src0, dst);
+                ggml_compute_forward_gelu_quick(params, dst);
             } break;
         case GGML_UNARY_OP_SILU:
             {
-                ggml_compute_forward_silu(params, src0, dst);
+                ggml_compute_forward_silu(params, dst);
+            } break;
+        case GGML_UNARY_OP_HARDSWISH:
+            {
+                ggml_compute_forward_hardswish(params, dst);
+            } break;
+        case GGML_UNARY_OP_HARDSIGMOID:
+            {
+                ggml_compute_forward_hardsigmoid(params, dst);
             } break;
         default:
             {
@@ -13884,9 +15516,11 @@ static void ggml_compute_forward_unary(
 
 static void ggml_compute_forward_get_rel_pos_f16(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -13911,12 +15545,14 @@ static void ggml_compute_forward_get_rel_pos_f16(
 
 static void ggml_compute_forward_get_rel_pos(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F16:
             {
-                ggml_compute_forward_get_rel_pos_f16(params, src0, dst);
+                ggml_compute_forward_get_rel_pos_f16(params, dst);
             } break;
         default:
             {
@@ -13929,17 +15565,21 @@ static void ggml_compute_forward_get_rel_pos(
 
 static void ggml_compute_forward_add_rel_pos_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-        const struct ggml_tensor * src2,
         struct ggml_tensor * dst) {
 
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+    const struct ggml_tensor * src2 = dst->src[2];
+
     const bool inplace = (bool) ((int32_t *) dst->op_params)[0];
-    if (!inplace && params->type == GGML_TASK_INIT) {
+    if (!inplace && params->type == GGML_TASK_TYPE_INIT) {
+        if (params->ith != 0) {
+            return;
+        }
         memcpy((char *) dst->data, (char *) src0->data, ggml_nbytes(dst));
         return;
     }
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -13994,14 +15634,14 @@ static void ggml_compute_forward_add_rel_pos_f32(
 
 static void ggml_compute_forward_add_rel_pos(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-        const struct ggml_tensor * src2,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_add_rel_pos_f32(params, src0, src1, src2, dst);
+                ggml_compute_forward_add_rel_pos_f32(params, dst);
             } break;
         default:
             {
@@ -14014,12 +15654,14 @@ static void ggml_compute_forward_add_rel_pos(
 
 static void ggml_compute_forward_map_unary_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst,
         const ggml_unary_op_f32_t fun) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -14038,13 +15680,15 @@ static void ggml_compute_forward_map_unary_f32(
 
 static void ggml_compute_forward_map_unary(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
         struct ggml_tensor * dst,
         const ggml_unary_op_f32_t fun) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_map_unary_f32(params, src0, dst, fun);
+                ggml_compute_forward_map_unary_f32(params, dst, fun);
             } break;
         default:
             {
@@ -14057,14 +15701,16 @@ static void ggml_compute_forward_map_unary(
 
 static void ggml_compute_forward_map_binary_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst,
         const ggml_binary_op_f32_t fun) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     assert(params->ith == 0);
     assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -14085,14 +15731,15 @@ static void ggml_compute_forward_map_binary_f32(
 
 static void ggml_compute_forward_map_binary(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst,
         const ggml_binary_op_f32_t fun) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_map_binary_f32(params, src0, src1, dst, fun);
+                ggml_compute_forward_map_binary_f32(params, dst, fun);
             } break;
         default:
             {
@@ -14105,12 +15752,14 @@ static void ggml_compute_forward_map_binary(
 
 static void ggml_compute_forward_map_custom1_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * a,
         struct ggml_tensor * dst,
         const ggml_custom1_op_f32_t fun) {
+
+    const struct ggml_tensor * a = dst->src[0];
+
     assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -14121,13 +15770,15 @@ static void ggml_compute_forward_map_custom1_f32(
 
 static void ggml_compute_forward_map_custom2_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * a,
-        const struct ggml_tensor * b,
         struct ggml_tensor * dst,
         const ggml_custom2_op_f32_t fun) {
+
+    const struct ggml_tensor * a = dst->src[0];
+    const struct ggml_tensor * b = dst->src[1];
+
     assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -14138,14 +15789,16 @@ static void ggml_compute_forward_map_custom2_f32(
 
 static void ggml_compute_forward_map_custom3_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * a,
-        const struct ggml_tensor * b,
-        const struct ggml_tensor * c,
         struct ggml_tensor * dst,
         const ggml_custom3_op_f32_t fun) {
+
+    const struct ggml_tensor * a = dst->src[0];
+    const struct ggml_tensor * b = dst->src[1];
+    const struct ggml_tensor * c = dst->src[1];
+
     assert(params->ith == 0);
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -14156,57 +15809,68 @@ static void ggml_compute_forward_map_custom3_f32(
 
 static void ggml_compute_forward_map_custom1(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * a,
               struct ggml_tensor * dst) {
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+
+    const struct ggml_tensor * a = dst->src[0];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
-    struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) dst->op_params;
+    struct ggml_map_custom1_op_params p;
+    memcpy(&p, dst->op_params, sizeof(p));
 
-    p->fun(dst, a, params->ith, params->nth, p->userdata);
+    p.fun(dst, a, params->ith, params->nth, p.userdata);
 }
 
 // ggml_compute_forward_map_custom2
 
 static void ggml_compute_forward_map_custom2(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * a,
-        const struct ggml_tensor * b,
               struct ggml_tensor * dst) {
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+
+    const struct ggml_tensor * a = dst->src[0];
+    const struct ggml_tensor * b = dst->src[1];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
-    struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) dst->op_params;
+    struct ggml_map_custom2_op_params p;
+    memcpy(&p, dst->op_params, sizeof(p));
 
-    p->fun(dst, a, b, params->ith, params->nth, p->userdata);
+    p.fun(dst, a, b, params->ith, params->nth, p.userdata);
 }
 
 // ggml_compute_forward_map_custom3
 
 static void ggml_compute_forward_map_custom3(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * a,
-        const struct ggml_tensor * b,
-        const struct ggml_tensor * c,
               struct ggml_tensor * dst) {
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+
+    const struct ggml_tensor * a = dst->src[0];
+    const struct ggml_tensor * b = dst->src[1];
+    const struct ggml_tensor * c = dst->src[2];
+
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
-    struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) dst->op_params;
+    struct ggml_map_custom3_op_params p;
+    memcpy(&p, dst->op_params, sizeof(p));
 
-    p->fun(dst, a, b, c, params->ith, params->nth, p->userdata);
+    p.fun(dst, a, b, c, params->ith, params->nth, p.userdata);
 }
 
 // ggml_compute_forward_cross_entropy_loss
 
 static void ggml_compute_forward_cross_entropy_loss_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
     GGML_ASSERT(ggml_is_contiguous(src0));
     GGML_ASSERT(ggml_is_contiguous(src1));
     GGML_ASSERT(ggml_is_scalar(dst));
@@ -14223,14 +15887,14 @@ static void ggml_compute_forward_cross_entropy_loss_f32(
 
     GGML_ASSERT(params->wsize >= sizeof(float) * (nth + nth * nc));
 
-    if (params->type == GGML_TASK_INIT) {
+    if (params->type == GGML_TASK_TYPE_INIT) {
         if (ith == 0) {
             memset(sums, 0, sizeof(float) * (nth + nth * nc));
         }
         return;
     }
 
-    if (params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
         if (ith == 0) {
             float * dp = (float *) dst->data;
             ggml_vec_sum_f32(nth, dp, sums);
@@ -14310,13 +15974,14 @@ static void ggml_compute_forward_cross_entropy_loss_f32(
 
 static void ggml_compute_forward_cross_entropy_loss(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_cross_entropy_loss_f32(params, src0, src1, dst);
+                ggml_compute_forward_cross_entropy_loss_f32(params, dst);
             } break;
         default:
             {
@@ -14329,10 +15994,12 @@ static void ggml_compute_forward_cross_entropy_loss(
 
 static void ggml_compute_forward_cross_entropy_loss_back_f32(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-        const struct ggml_tensor * opt0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+    const struct ggml_tensor * opt0 = dst->src[2];
+
     GGML_ASSERT(ggml_is_contiguous(dst));
     GGML_ASSERT(ggml_is_contiguous(src0));
     GGML_ASSERT(ggml_is_contiguous(src1));
@@ -14342,7 +16009,7 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
     const int64_t ith = params->ith;
     const int64_t nth = params->nth;
 
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return;
     }
 
@@ -14419,14 +16086,14 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
 
 static void ggml_compute_forward_cross_entropy_loss_back(
         const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-        const struct ggml_tensor * opt0,
         struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_cross_entropy_loss_back_f32(params, src0, src1, opt0, dst);
+                ggml_compute_forward_cross_entropy_loss_back_f32(params, dst);
             } break;
         default:
             {
@@ -14440,328 +16107,335 @@ static void ggml_compute_forward_cross_entropy_loss_back(
 static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
     GGML_ASSERT(params);
 
-    if (tensor->op == GGML_OP_NONE) {
+    if (tensor->op == GGML_OP_NONE || ggml_is_empty(tensor)) {
         return;
     }
 
-#ifdef GGML_USE_CUBLAS
-    bool skip_cpu = ggml_cuda_compute_forward(params, tensor);
-    if (skip_cpu) {
-        return;
-    }
-    GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_CPU);
-    GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_CPU);
-#endif // GGML_USE_CUBLAS
-
     switch (tensor->op) {
         case GGML_OP_DUP:
             {
-                ggml_compute_forward_dup(params, tensor->src[0], tensor);
+                ggml_compute_forward_dup(params, tensor);
             } break;
         case GGML_OP_ADD:
             {
-                ggml_compute_forward_add(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_add(params, tensor);
             } break;
         case GGML_OP_ADD1:
             {
-                ggml_compute_forward_add1(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_add1(params, tensor);
             } break;
         case GGML_OP_ACC:
             {
-                ggml_compute_forward_acc(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_acc(params, tensor);
             } break;
         case GGML_OP_SUB:
             {
-                ggml_compute_forward_sub(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_sub(params, tensor);
             } break;
         case GGML_OP_MUL:
             {
-                ggml_compute_forward_mul(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_mul(params, tensor);
             } break;
         case GGML_OP_DIV:
             {
-                ggml_compute_forward_div(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_div(params, tensor);
             } break;
         case GGML_OP_SQR:
             {
-                ggml_compute_forward_sqr(params, tensor->src[0], tensor);
+                ggml_compute_forward_sqr(params, tensor);
             } break;
         case GGML_OP_SQRT:
             {
-                ggml_compute_forward_sqrt(params, tensor->src[0], tensor);
+                ggml_compute_forward_sqrt(params, tensor);
             } break;
         case GGML_OP_LOG:
             {
-                ggml_compute_forward_log(params, tensor->src[0], tensor);
+                ggml_compute_forward_log(params, tensor);
             } break;
         case GGML_OP_SUM:
             {
-                ggml_compute_forward_sum(params, tensor->src[0], tensor);
+                ggml_compute_forward_sum(params, tensor);
             } break;
         case GGML_OP_SUM_ROWS:
             {
-                ggml_compute_forward_sum_rows(params, tensor->src[0], tensor);
+                ggml_compute_forward_sum_rows(params, tensor);
             } break;
         case GGML_OP_MEAN:
             {
-                ggml_compute_forward_mean(params, tensor->src[0], tensor);
+                ggml_compute_forward_mean(params, tensor);
             } break;
         case GGML_OP_ARGMAX:
             {
-                ggml_compute_forward_argmax(params, tensor->src[0], tensor);
+                ggml_compute_forward_argmax(params, tensor);
             } break;
         case GGML_OP_REPEAT:
             {
-                ggml_compute_forward_repeat(params, tensor->src[0], tensor);
+                ggml_compute_forward_repeat(params, tensor);
             } break;
         case GGML_OP_REPEAT_BACK:
             {
-                ggml_compute_forward_repeat_back(params, tensor->src[0], tensor);
+                ggml_compute_forward_repeat_back(params, tensor);
             } break;
         case GGML_OP_CONCAT:
             {
-                ggml_compute_forward_concat(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_concat(params, tensor);
             } break;
         case GGML_OP_SILU_BACK:
             {
-                ggml_compute_forward_silu_back(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_silu_back(params, tensor);
             } break;
         case GGML_OP_NORM:
             {
-                ggml_compute_forward_norm(params, tensor->src[0], tensor);
+                ggml_compute_forward_norm(params, tensor);
             } break;
         case GGML_OP_RMS_NORM:
             {
-                ggml_compute_forward_rms_norm(params, tensor->src[0], tensor);
+                ggml_compute_forward_rms_norm(params, tensor);
             } break;
         case GGML_OP_RMS_NORM_BACK:
             {
-                ggml_compute_forward_rms_norm_back(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_rms_norm_back(params, tensor);
             } break;
         case GGML_OP_GROUP_NORM:
             {
-                ggml_compute_forward_group_norm(params, tensor->src[0], tensor);
+                ggml_compute_forward_group_norm(params, tensor);
             } break;
         case GGML_OP_MUL_MAT:
             {
-                ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_mul_mat(params, tensor);
             } break;
         case GGML_OP_MUL_MAT_ID:
             {
-                ggml_compute_forward_mul_mat_id(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_mul_mat_id(params, tensor);
             } break;
         case GGML_OP_OUT_PROD:
             {
-                ggml_compute_forward_out_prod(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_out_prod(params, tensor);
             } break;
         case GGML_OP_SCALE:
             {
-                ggml_compute_forward_scale(params, tensor->src[0], tensor);
+                ggml_compute_forward_scale(params, tensor);
             } break;
         case GGML_OP_SET:
             {
-                ggml_compute_forward_set(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_set(params, tensor);
             } break;
         case GGML_OP_CPY:
             {
-                ggml_compute_forward_cpy(params, tensor->src[0], tensor);
+                ggml_compute_forward_cpy(params, tensor);
             } break;
         case GGML_OP_CONT:
             {
-                ggml_compute_forward_cont(params, tensor->src[0], tensor);
+                ggml_compute_forward_cont(params, tensor);
             } break;
         case GGML_OP_RESHAPE:
             {
-                ggml_compute_forward_reshape(params, tensor->src[0], tensor);
+                ggml_compute_forward_reshape(params, tensor);
             } break;
         case GGML_OP_VIEW:
             {
-                ggml_compute_forward_view(params, tensor->src[0]);
+                ggml_compute_forward_view(params, tensor);
             } break;
         case GGML_OP_PERMUTE:
             {
-                ggml_compute_forward_permute(params, tensor->src[0]);
+                ggml_compute_forward_permute(params, tensor);
             } break;
         case GGML_OP_TRANSPOSE:
             {
-                ggml_compute_forward_transpose(params, tensor->src[0]);
+                ggml_compute_forward_transpose(params, tensor);
             } break;
         case GGML_OP_GET_ROWS:
             {
-                ggml_compute_forward_get_rows(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_get_rows(params, tensor);
             } break;
         case GGML_OP_GET_ROWS_BACK:
             {
-                ggml_compute_forward_get_rows_back(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_get_rows_back(params, tensor);
             } break;
         case GGML_OP_DIAG:
             {
-                ggml_compute_forward_diag(params, tensor->src[0], tensor);
+                ggml_compute_forward_diag(params, tensor);
             } break;
         case GGML_OP_DIAG_MASK_INF:
             {
-                ggml_compute_forward_diag_mask_inf(params, tensor->src[0], tensor);
+                ggml_compute_forward_diag_mask_inf(params, tensor);
             } break;
         case GGML_OP_DIAG_MASK_ZERO:
             {
-                ggml_compute_forward_diag_mask_zero(params, tensor->src[0], tensor);
+                ggml_compute_forward_diag_mask_zero(params, tensor);
             } break;
         case GGML_OP_SOFT_MAX:
             {
-                ggml_compute_forward_soft_max(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_soft_max(params, tensor);
             } break;
         case GGML_OP_SOFT_MAX_BACK:
             {
-                ggml_compute_forward_soft_max_back(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_soft_max_back(params, tensor);
             } break;
         case GGML_OP_ROPE:
             {
-                ggml_compute_forward_rope(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_rope(params, tensor);
             } break;
         case GGML_OP_ROPE_BACK:
             {
-                ggml_compute_forward_rope_back(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_rope_back(params, tensor);
             } break;
         case GGML_OP_ALIBI:
             {
-                ggml_compute_forward_alibi(params, tensor->src[0], tensor);
+                ggml_compute_forward_alibi(params, tensor);
             } break;
         case GGML_OP_CLAMP:
             {
-                ggml_compute_forward_clamp(params, tensor->src[0], tensor);
+                ggml_compute_forward_clamp(params, tensor);
             } break;
         case GGML_OP_CONV_TRANSPOSE_1D:
             {
-                ggml_compute_forward_conv_transpose_1d(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_conv_transpose_1d(params, tensor);
             } break;
         case GGML_OP_IM2COL:
             {
-                ggml_compute_forward_im2col(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_im2col(params, tensor);
             } break;
         case GGML_OP_CONV_TRANSPOSE_2D:
             {
-                ggml_compute_forward_conv_transpose_2d(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_conv_transpose_2d(params, tensor);
             } break;
         case GGML_OP_POOL_1D:
             {
-                ggml_compute_forward_pool_1d(params, tensor->src[0], tensor);
+                ggml_compute_forward_pool_1d(params, tensor);
             } break;
         case GGML_OP_POOL_2D:
             {
-                ggml_compute_forward_pool_2d(params, tensor->src[0], tensor);
+                ggml_compute_forward_pool_2d(params, tensor);
             } break;
         case GGML_OP_UPSCALE:
             {
-                ggml_compute_forward_upscale(params, tensor->src[0], tensor);
+                ggml_compute_forward_upscale(params, tensor);
             } break;
         case GGML_OP_PAD:
             {
-                ggml_compute_forward_pad(params, tensor->src[0], tensor);
+                ggml_compute_forward_pad(params, tensor);
+            } break;
+        case GGML_OP_ARANGE:
+            {
+                ggml_compute_forward_arange(params, tensor);
+            } break;
+        case GGML_OP_TIMESTEP_EMBEDDING:
+            {
+                ggml_compute_forward_timestep_embedding(params, tensor);
             } break;
         case GGML_OP_ARGSORT:
             {
-                ggml_compute_forward_argsort(params, tensor->src[0], tensor);
+                ggml_compute_forward_argsort(params, tensor);
             } break;
         case GGML_OP_LEAKY_RELU:
             {
-                ggml_compute_forward_leaky_relu(params, tensor->src[0], tensor);
+                ggml_compute_forward_leaky_relu(params, tensor);
             } break;
         case GGML_OP_FLASH_ATTN:
             {
                 const int32_t t = ggml_get_op_params_i32(tensor, 0);
                 GGML_ASSERT(t == 0 || t == 1);
                 const bool masked = t != 0;
-                ggml_compute_forward_flash_attn(params, tensor->src[0], tensor->src[1], tensor->src[2], masked, tensor);
+                ggml_compute_forward_flash_attn(params, masked, tensor);
             } break;
         case GGML_OP_FLASH_FF:
             {
-                ggml_compute_forward_flash_ff(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor->src[4], tensor);
+                ggml_compute_forward_flash_ff(params, tensor);
             } break;
         case GGML_OP_FLASH_ATTN_BACK:
             {
                 int32_t t = ggml_get_op_params_i32(tensor, 0);
                 GGML_ASSERT(t == 0 || t == 1);
                 bool masked = t != 0;
-                ggml_compute_forward_flash_attn_back(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], masked, tensor);
+                ggml_compute_forward_flash_attn_back(params, masked, tensor);
+            } break;
+        case GGML_OP_SSM_CONV:
+            {
+                ggml_compute_forward_ssm_conv(params, tensor);
+            } break;
+        case GGML_OP_SSM_SCAN:
+            {
+                ggml_compute_forward_ssm_scan(params, tensor);
             } break;
         case GGML_OP_WIN_PART:
             {
-                ggml_compute_forward_win_part(params, tensor->src[0], tensor);
+                ggml_compute_forward_win_part(params, tensor);
             } break;
         case GGML_OP_WIN_UNPART:
             {
-                ggml_compute_forward_win_unpart(params, tensor->src[0], tensor);
+                ggml_compute_forward_win_unpart(params, tensor);
             } break;
         case GGML_OP_UNARY:
             {
-                ggml_compute_forward_unary(params, tensor->src[0], tensor);
+                ggml_compute_forward_unary(params, tensor);
             } break;
         case GGML_OP_GET_REL_POS:
             {
-                ggml_compute_forward_get_rel_pos(params, tensor->src[0], tensor);
+                ggml_compute_forward_get_rel_pos(params, tensor);
             } break;
         case GGML_OP_ADD_REL_POS:
             {
-                ggml_compute_forward_add_rel_pos(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
+                ggml_compute_forward_add_rel_pos(params, tensor);
             } break;
         case GGML_OP_MAP_UNARY:
             {
                 ggml_unary_op_f32_t fun;
                 memcpy(&fun, tensor->op_params, sizeof(fun));
-                ggml_compute_forward_map_unary(params, tensor->src[0], tensor, fun);
+                ggml_compute_forward_map_unary(params, tensor, fun);
             }
             break;
         case GGML_OP_MAP_BINARY:
             {
                 ggml_binary_op_f32_t fun;
                 memcpy(&fun, tensor->op_params, sizeof(fun));
-                ggml_compute_forward_map_binary(params, tensor->src[0], tensor->src[1], tensor, fun);
+                ggml_compute_forward_map_binary(params, tensor, fun);
             }
             break;
         case GGML_OP_MAP_CUSTOM1_F32:
             {
                 ggml_custom1_op_f32_t fun;
                 memcpy(&fun, tensor->op_params, sizeof(fun));
-                ggml_compute_forward_map_custom1_f32(params, tensor->src[0], tensor, fun);
+                ggml_compute_forward_map_custom1_f32(params, tensor, fun);
             }
             break;
         case GGML_OP_MAP_CUSTOM2_F32:
             {
                 ggml_custom2_op_f32_t fun;
                 memcpy(&fun, tensor->op_params, sizeof(fun));
-                ggml_compute_forward_map_custom2_f32(params, tensor->src[0], tensor->src[1], tensor, fun);
+                ggml_compute_forward_map_custom2_f32(params, tensor, fun);
             }
             break;
         case GGML_OP_MAP_CUSTOM3_F32:
             {
                 ggml_custom3_op_f32_t fun;
                 memcpy(&fun, tensor->op_params, sizeof(fun));
-                ggml_compute_forward_map_custom3_f32(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor, fun);
+                ggml_compute_forward_map_custom3_f32(params, tensor, fun);
             }
             break;
         case GGML_OP_MAP_CUSTOM1:
             {
-                ggml_compute_forward_map_custom1(params, tensor->src[0], tensor);
+                ggml_compute_forward_map_custom1(params, tensor);
             }
             break;
         case GGML_OP_MAP_CUSTOM2:
             {
-                ggml_compute_forward_map_custom2(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_map_custom2(params, tensor);
             }
             break;
         case GGML_OP_MAP_CUSTOM3:
             {
-                ggml_compute_forward_map_custom3(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
+                ggml_compute_forward_map_custom3(params, tensor);
             }
             break;
         case GGML_OP_CROSS_ENTROPY_LOSS:
             {
-                ggml_compute_forward_cross_entropy_loss(params, tensor->src[0], tensor->src[1], tensor);
+                ggml_compute_forward_cross_entropy_loss(params, tensor);
             }
             break;
         case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
             {
-                ggml_compute_forward_cross_entropy_loss_back(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
+                ggml_compute_forward_cross_entropy_loss_back(params, tensor);
             }
             break;
         case GGML_OP_NONE:
@@ -14851,17 +16525,17 @@ size_t ggml_hash_find_or_insert(struct ggml_hash_set hash_set, struct ggml_tenso
     return i;
 }
 
-static struct ggml_hash_set ggml_hash_set_new(size_t size) {
+struct ggml_hash_set ggml_hash_set_new(size_t size) {
     size = ggml_hash_size(size);
     struct ggml_hash_set result;
     result.size = size;
-    result.keys = malloc(sizeof(struct ggml_tensor *) * size);
+    result.keys = GGML_MALLOC(sizeof(struct ggml_tensor *) * size);
     memset(result.keys, 0, sizeof(struct ggml_tensor *) * size);
     return result;
 }
 
 static void ggml_hash_set_free(struct ggml_hash_set hash_set) {
-    free(hash_set.keys);
+    GGML_FREE(hash_set.keys);
 }
 
 struct hash_map {
@@ -14870,17 +16544,17 @@ struct hash_map {
 };
 
 static struct hash_map * ggml_new_hash_map(size_t size) {
-    struct hash_map * result = malloc(sizeof(struct hash_map));
+    struct hash_map * result = GGML_MALLOC(sizeof(struct hash_map));
     result->set = ggml_hash_set_new(size);
-    result->vals = malloc(sizeof(struct ggml_tensor *) * result->set.size);
+    result->vals = GGML_MALLOC(sizeof(struct ggml_tensor *) * result->set.size);
     memset(result->vals, 0, sizeof(struct ggml_tensor *) * result->set.size);
     return result;
 }
 
 static void ggml_hash_map_free(struct hash_map * map) {
     ggml_hash_set_free(map->set);
-    free(map->vals);
-    free(map);
+    GGML_FREE(map->vals);
+    GGML_FREE(map);
 }
 
 // gradient checkpointing
@@ -14895,7 +16569,7 @@ static struct ggml_tensor * ggml_recompute_graph_node(
         return NULL;
     }
 
-    if (node->is_param) {
+    if (node->flags & GGML_TENSOR_FLAG_PARAM) {
         return node;
     }
 
@@ -14929,7 +16603,7 @@ static struct ggml_tensor * ggml_recompute_graph_node(
 
     clone->op       = node->op;
     clone->grad     = node->grad;
-    clone->is_param = node->is_param;
+    clone->flags    = node->flags;
     clone->extra    = node->extra;
     for (int k = 0; k < GGML_MAX_DIMS; ++k) {
         clone->nb[k] = node->nb[k];
@@ -15660,6 +17334,14 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             {
                 GGML_ASSERT(false); // TODO: not implemented
             } break;
+        case GGML_OP_ARANGE:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_TIMESTEP_EMBEDDING:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
         case GGML_OP_ARGSORT:
             {
                 GGML_ASSERT(false); // TODO: not implemented
@@ -15730,6 +17412,11 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             {
                 GGML_ASSERT(false); // not supported
             } break;
+        case GGML_OP_SSM_CONV:
+        case GGML_OP_SSM_SCAN:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
         case GGML_OP_WIN_PART:
         case GGML_OP_WIN_UNPART:
         case GGML_OP_UNARY:
@@ -15961,7 +17648,7 @@ void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph *
     for (int i = 0; i < gf->n_nodes; i++) {
         struct ggml_tensor * node = gf->nodes[i];
 
-        if (node->is_param) {
+        if (node->flags & GGML_TENSOR_FLAG_PARAM) {
             GGML_PRINT_DEBUG("%s: found root node %p\n", __func__, (void *) node);
             ggml_build_forward_expand(gb, node->grad);
         }
@@ -15990,7 +17677,7 @@ size_t ggml_graph_overhead(void) {
 
 struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t size, bool grads) {
     const size_t obj_size = ggml_graph_nbytes(size, grads);
-    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_GRAPH, obj_size);
+    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_GRAPH, obj_size);
     struct ggml_cgraph * cgraph = (struct ggml_cgraph *) ((char *) ctx->mem_buffer + obj->offs);
 
     struct ggml_tensor ** data_start = (struct ggml_tensor **) (cgraph + 1);
@@ -16165,27 +17852,47 @@ typedef pthread_t ggml_thread_t;
 #endif
 
 // Android's libc implementation "bionic" does not support setting affinity
-#if defined(__linux__) && !defined(__BIONIC__)
-static void set_numa_thread_affinity(int thread_n, int n_threads) {
+#if defined(__gnu_linux__)
+static void set_numa_thread_affinity(int thread_n) {
     if (!ggml_is_numa()) {
         return;
     }
 
-    // run thread on node_num thread_n / (threads per node)
-    const int node_num = thread_n / ((n_threads + g_state.numa.n_nodes - 1) / g_state.numa.n_nodes);
-    struct ggml_numa_node * node = &g_state.numa.nodes[node_num];
+    int node_num;
+    int rv;
     size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus);
 
+    switch(g_state.numa.numa_strategy) {
+        case GGML_NUMA_STRATEGY_DISTRIBUTE:
+            // run thread on node_num thread_n / (threads per node)
+            node_num = thread_n % g_state.numa.n_nodes;
+            break;
+        case GGML_NUMA_STRATEGY_ISOLATE:
+            // run thread on current_node
+            node_num = g_state.numa.current_node;
+            break;
+        case GGML_NUMA_STRATEGY_NUMACTL:
+            // use the cpuset that numactl gave us
+            rv = pthread_setaffinity_np(pthread_self(), setsize, &g_state.numa.cpuset);
+            if (rv) {
+                fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n",strerror(rv));
+            }
+            return;
+        default:
+            return;
+    }
+
+    struct ggml_numa_node * node = &g_state.numa.nodes[node_num];
+
     cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus);
     CPU_ZERO_S(setsize, cpus);
     for (size_t i = 0; i < node->n_cpus; ++i) {
         CPU_SET_S(node->cpus[i], setsize, cpus);
     }
 
-    int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus);
+    rv = pthread_setaffinity_np(pthread_self(), setsize, cpus);
     if (rv) {
-            fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n",
-                    strerror(rv));
+            fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv));
     }
 
     CPU_FREE(cpus);
@@ -16206,8 +17913,7 @@ static void clear_numa_thread_affinity(void) {
 
     int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus);
     if (rv) {
-        fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n",
-            strerror(rv));
+        fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv));
     }
 
     CPU_FREE(cpus);
@@ -16215,7 +17921,7 @@ static void clear_numa_thread_affinity(void) {
 #else
 // TODO: Windows etc.
 // (the linux implementation may also work on BSD, someone should test)
-static void set_numa_thread_affinity(int thread_n, int n_threads) { UNUSED(thread_n); UNUSED(n_threads);  }
+static void set_numa_thread_affinity(int thread_n) { UNUSED(thread_n);  }
 static void clear_numa_thread_affinity(void) {}
 #endif
 
@@ -16229,10 +17935,11 @@ struct ggml_compute_state_shared {
     const int n_threads;
 
     // synchronization primitives
-    atomic_int n_active; // num active threads
-    atomic_int node_n;   // active graph node
+    atomic_int n_active;  // num active threads
+    atomic_int node_n;    // active graph node
+    atomic_int node_task; // active graph node task phase
 
-    bool (*abort_callback)(void * data); // abort ggml_graph_compute when true
+    ggml_abort_callback abort_callback; // abort ggml_graph_compute when true
     void * abort_callback_data;
 };
 
@@ -16240,6 +17947,7 @@ struct ggml_compute_state {
     ggml_thread_t thrd;
     int ith;
     struct ggml_compute_state_shared * shared;
+    enum ggml_status ec;
 };
 
 static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const struct ggml_compute_state_shared * st) {
@@ -16251,9 +17959,15 @@ static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const
     node->perf_time_us += time_us_cur;
 }
 
-static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
+static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_threads) {
     int n_tasks = 0;
 
+    if (ggml_is_empty(node)) {
+        // no need to multi-thread a no-op
+        n_tasks = 1;
+        return n_tasks;
+    }
+
     switch (node->op) {
         case GGML_OP_CPY:
         case GGML_OP_DUP:
@@ -16286,6 +18000,8 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
                 case GGML_UNARY_OP_TANH:
                 case GGML_UNARY_OP_ELU:
                 case GGML_UNARY_OP_RELU:
+                case GGML_UNARY_OP_HARDSWISH: // to opt for multiple threads
+                case GGML_UNARY_OP_HARDSIGMOID: // to opt for multiple threads
                     {
                         n_tasks = 1;
                     } break;
@@ -16330,6 +18046,12 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
             {
                 n_tasks = n_threads;
             } break;
+        case GGML_OP_GET_ROWS:
+            {
+                // FIXME: the cost of launching additional threads decreases performance with GPU offloading
+                //n_tasks = MIN(n_threads, ggml_nelements(node->src[1]));
+                n_tasks = MIN(n_cur_threads, ggml_nelements(node->src[1]));
+            } break;
         case GGML_OP_SCALE:
         case GGML_OP_SET:
         case GGML_OP_CONT:
@@ -16337,7 +18059,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
         case GGML_OP_TRANSPOSE:
-        case GGML_OP_GET_ROWS:
         case GGML_OP_GET_ROWS_BACK:
         case GGML_OP_DIAG:
             {
@@ -16362,7 +18083,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
             } break;
         case GGML_OP_SOFT_MAX:
             {
-                n_tasks = MIN(MIN(4, n_threads), ggml_nrows(node->src[0]));
+                n_tasks = MIN(n_threads, ggml_nrows(node->src[0]));
             } break;
         case GGML_OP_CONV_TRANSPOSE_1D:
             {
@@ -16389,6 +18110,14 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
             {
                 n_tasks = n_threads;
             } break;
+        case GGML_OP_ARANGE:
+            {
+                n_tasks = n_threads;
+            } break;
+        case GGML_OP_TIMESTEP_EMBEDDING:
+            {
+                n_tasks = n_threads;
+            } break;
         case GGML_OP_ARGSORT:
             {
                 n_tasks = n_threads;
@@ -16405,6 +18134,11 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
             {
                 n_tasks = n_threads;
             } break;
+        case GGML_OP_SSM_CONV:
+        case GGML_OP_SSM_SCAN:
+            {
+                n_tasks = n_threads;
+            } break;
         case GGML_OP_WIN_PART:
         case GGML_OP_WIN_UNPART:
         case GGML_OP_GET_REL_POS:
@@ -16418,29 +18152,32 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
             } break;
         case GGML_OP_MAP_CUSTOM1:
             {
-                struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) node->op_params;
-                if (p->n_tasks == GGML_N_TASKS_MAX) {
+                struct ggml_map_custom1_op_params p;
+                memcpy(&p, node->op_params, sizeof(p));
+                if (p.n_tasks == GGML_N_TASKS_MAX) {
                     n_tasks = n_threads;
                 } else {
-                    n_tasks = MIN(p->n_tasks, n_threads);
+                    n_tasks = MIN(p.n_tasks, n_threads);
                 }
             } break;
         case GGML_OP_MAP_CUSTOM2:
             {
-                struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) node->op_params;
-                if (p->n_tasks == GGML_N_TASKS_MAX) {
+                struct ggml_map_custom2_op_params p;
+                memcpy(&p, node->op_params, sizeof(p));
+                if (p.n_tasks == GGML_N_TASKS_MAX) {
                     n_tasks = n_threads;
                 } else {
-                    n_tasks = MIN(p->n_tasks, n_threads);
+                    n_tasks = MIN(p.n_tasks, n_threads);
                 }
             } break;
         case GGML_OP_MAP_CUSTOM3:
             {
-                struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) node->op_params;
-                if (p->n_tasks == GGML_N_TASKS_MAX) {
+                struct ggml_map_custom3_op_params p;
+                memcpy(&p, node->op_params, sizeof(p));
+                if (p.n_tasks == GGML_N_TASKS_MAX) {
                     n_tasks = n_threads;
                 } else {
-                    n_tasks = MIN(p->n_tasks, n_threads);
+                    n_tasks = MIN(p.n_tasks, n_threads);
                 }
             } break;
         case GGML_OP_CROSS_ENTROPY_LOSS:
@@ -16476,6 +18213,34 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
     return n_tasks;
 }
 
+static void ggml_graph_compute_thread_sync_node(int * node_n, struct ggml_compute_state * state, const bool do_yield) {
+    // wait for other threads to finish
+    const int last_node_n = * node_n;
+
+    while (true) {
+        if (do_yield) {
+            sched_yield();
+        }
+
+        * node_n = atomic_load(&state->shared->node_n);
+        if (* node_n != last_node_n) break;
+    }
+}
+
+static void ggml_graph_compute_thread_sync_task(int * task_phase, struct ggml_compute_state * state, const bool do_yield) {
+    // wait for other threads to finish
+    const int last_task_phase = * task_phase;
+
+    while (true) {
+        if (do_yield) {
+            sched_yield();
+        }
+
+        * task_phase = atomic_load(&state->shared->node_task);
+        if (* task_phase != last_task_phase) break;
+    }
+}
+
 static thread_ret_t ggml_graph_compute_thread(void * data) {
     struct ggml_compute_state * state = (struct ggml_compute_state *) data;
 
@@ -16484,21 +18249,23 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
 
     const int   n_threads   = state->shared->n_threads;
 
-    set_numa_thread_affinity(state->ith, n_threads);
+    set_numa_thread_affinity(state->ith);
 
-    int node_n = -1;
+    int node_n     = -1;
+    int task_phase = GGML_TASK_TYPE_FINALIZE;
 
     while (true) {
         if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) {
             state->shared->node_n += 1;
-            return (thread_ret_t) GGML_EXIT_ABORTED;
+            state->ec = GGML_STATUS_ABORTED;
+            return 0;
         }
 
         if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
             // all other threads are finished and spinning
             // do finalize and init here so we don't have synchronize again
             struct ggml_compute_params params = {
-                /*.type  =*/ GGML_TASK_FINALIZE,
+                /*.type  =*/ GGML_TASK_TYPE_FINALIZE,
                 /*.ith   =*/ 0,
                 /*.nth   =*/ 0,
                 /*.wsize =*/ cplan->work_size,
@@ -16509,7 +18276,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
                 /* FINALIZE */
                 struct ggml_tensor * node = cgraph->nodes[node_n];
                 if (GGML_OP_HAS_FINALIZE[node->op]) {
-                    params.nth = ggml_get_n_tasks(node, n_threads);
+                    params.nth = ggml_get_n_tasks(node, n_threads, state->shared->n_threads);
                     ggml_compute_forward(&params, node);
                 }
                 ggml_graph_compute_perf_stats_node(node, state->shared);
@@ -16518,29 +18285,28 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
             // distribute new work or execute it direct if 1T
             while (++node_n < cgraph->n_nodes) {
                 GGML_PRINT_DEBUG_5("%s: %d/%d\n", __func__, node_n, cgraph->n_nodes);
-
                 struct ggml_tensor * node = cgraph->nodes[node_n];
-                const int n_tasks = ggml_get_n_tasks(node, n_threads);
+                const int n_tasks = ggml_get_n_tasks(node, n_threads, state->shared->n_threads);
 
                 state->shared->perf_node_start_cycles  = ggml_perf_cycles();
                 state->shared->perf_node_start_time_us = ggml_perf_time_us();
 
                 params.nth = n_tasks;
 
-                /* INIT */
-                if (GGML_OP_HAS_INIT[node->op]) {
-                    params.type = GGML_TASK_INIT;
-                    ggml_compute_forward(&params, node);
-                }
-
                 if (n_tasks == 1) {
+                    /* INIT */
+                    if (GGML_OP_HAS_INIT[node->op]) {
+                        params.type = GGML_TASK_TYPE_INIT;
+                        ggml_compute_forward(&params, node);
+                    }
+
                     // TODO: maybe push node_n to the atomic but if other threads see n_tasks is 1,
                     // they do something more efficient than spinning (?)
-                    params.type = GGML_TASK_COMPUTE;
+                    params.type = GGML_TASK_TYPE_COMPUTE;
                     ggml_compute_forward(&params, node);
 
                     if (GGML_OP_HAS_FINALIZE[node->op]) {
-                        params.type = GGML_TASK_FINALIZE;
+                        params.type = GGML_TASK_TYPE_FINALIZE;
                         ggml_compute_forward(&params, node);
                     }
 
@@ -16554,38 +18320,24 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
                 }
             }
 
-            atomic_store(&state->shared->n_active, n_threads);
-            atomic_store(&state->shared->node_n,   node_n);
+            task_phase = GGML_TASK_TYPE_INIT;
+            atomic_store(&state->shared->n_active,  n_threads);
+            atomic_store(&state->shared->node_n,    node_n);
+            atomic_store(&state->shared->node_task, task_phase);
         } else {
-            // wait for other threads to finish
-            const int last = node_n;
-
-            const bool do_yield = last < 0 || cgraph->nodes[last]->op == GGML_OP_MUL_MAT;
-
-            while (true) {
-                // TODO: this sched_yield can have significant impact on the performance - either positive or negative
-                //       depending on the workload and the operating system.
-                //       since it is not clear what is the best approach, it should potentially become user-configurable
-                //       ref: https://github.com/ggerganov/ggml/issues/291
-                // UPD:  adding the do_yield flag seems to resolve the issue universally
-                if (do_yield) {
-                    sched_yield();
-                }
-
-                node_n = atomic_load(&state->shared->node_n);
-                if (node_n != last) break;
-            };
+            ggml_graph_compute_thread_sync_node(&node_n,     state, false);
+            ggml_graph_compute_thread_sync_task(&task_phase, state, false);
         }
 
         // check if we should stop
         if (node_n >= cgraph->n_nodes) break;
 
-        /* COMPUTE */
+        /* INIT & COMPUTE */
         struct ggml_tensor * node = cgraph->nodes[node_n];
-        const int n_tasks = ggml_get_n_tasks(node, n_threads);
+        const int n_tasks = ggml_get_n_tasks(node, n_threads, state->shared->n_threads);
 
         struct ggml_compute_params params = {
-            /*.type  =*/ GGML_TASK_COMPUTE,
+            /*.type  =*/ GGML_TASK_TYPE_INIT,
             /*.ith   =*/ state->ith,
             /*.nth   =*/ n_tasks,
             /*.wsize =*/ cplan->work_size,
@@ -16593,14 +18345,45 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
         };
 
         if (state->ith < n_tasks) {
+            if (GGML_OP_HAS_INIT[node->op]) {
+                ggml_compute_forward(&params, node);
+            }
+        }
+
+        if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
+            task_phase = GGML_TASK_TYPE_COMPUTE;
+            atomic_store(&state->shared->n_active,  n_threads);
+            atomic_store(&state->shared->node_task, task_phase);
+        }
+        else {
+            // TODO: this sched_yield can have significant impact on the performance - either positive or negative
+            //       depending on the workload and the operating system.
+            //       since it is not clear what is the best approach, it should potentially become user-configurable
+            //       ref: https://github.com/ggerganov/ggml/issues/291
+            // UPD:  adding the do_yield flag seems to resolve the issue universally
+            const bool do_yield = node_n < 0 || cgraph->nodes[node_n]->op == GGML_OP_MUL_MAT;
+            ggml_graph_compute_thread_sync_task(&task_phase, state, do_yield);
+        }
+
+        if (state->ith < n_tasks) {
+            params.type = GGML_TASK_TYPE_COMPUTE;
             ggml_compute_forward(&params, node);
         }
+
+        if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
+            task_phase = GGML_TASK_TYPE_FINALIZE;
+            atomic_store(&state->shared->n_active,  n_threads);
+            atomic_store(&state->shared->node_task, task_phase);
+        }
+        else {
+            ggml_graph_compute_thread_sync_task(&task_phase, state, false);
+        }
     }
 
-    return GGML_EXIT_SUCCESS;
+    return 0;
 }
 
-struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
+struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threads) {
     if (n_threads <= 0) {
         n_threads = GGML_DEFAULT_N_THREADS;
     }
@@ -16610,11 +18393,15 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
     struct ggml_cplan cplan;
     memset(&cplan, 0, sizeof(struct ggml_cplan));
 
+    int max_tasks = 1;
+
     // thread scheduling for the different operations + work buffer size estimation
     for (int i = 0; i < cgraph->n_nodes; i++) {
         struct ggml_tensor * node = cgraph->nodes[i];
 
-        const int n_tasks = ggml_get_n_tasks(node, n_threads);
+        const int n_tasks = ggml_get_n_tasks(node, n_threads, 1);
+
+        max_tasks = MAX(max_tasks, n_tasks);
 
         size_t cur = 0;
 
@@ -16651,8 +18438,11 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
 #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
                     if (ggml_compute_forward_mul_mat_use_blas(node)) {
                         if (node->src[0]->type != GGML_TYPE_F32) {
-                            // here we need memory just for single 2D matrix from src0
-                            cur = ggml_type_size(GGML_TYPE_F32)*(node->src[0]->ne[0]*node->src[0]->ne[1]);
+                            // here we need memory for fully dequantized matrix from src0
+                            // take into account that src0 can be broadcasted into src1[2,3]
+                            cur = ggml_type_size(GGML_TYPE_F32)
+                                * node->src[0]->ne[0]*node->src[0]->ne[1]
+                                * node->src[1]->ne[2]*node->src[1]->ne[3];
                         }
                     } else
 #endif
@@ -16662,14 +18452,15 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
                 } break;
             case GGML_OP_MUL_MAT_ID:
                 {
-                    const struct ggml_tensor * src0 = node->src[2];
+                    cur = 0;
+                    const struct ggml_tensor * src0 = node->src[0];
                     const struct ggml_tensor * src1 = node->src[1];
                     const enum ggml_type vec_dot_type = type_traits[src0->type].vec_dot_type;
                     if (src1->type != vec_dot_type) {
-                        cur = ggml_row_size(vec_dot_type, ggml_nelements(src1));
+                        cur += ggml_row_size(vec_dot_type, ggml_nelements(src1));
                     }
-                    const int n_as = ggml_get_op_params_i32(node, 1);
-                    cur = GGML_PAD(cur, sizeof(int64_t));        // align
+                    const int n_as = src0->ne[2];
+                    cur += GGML_PAD(cur, sizeof(int64_t));       // align
                     cur += n_as * sizeof(int64_t);               // matrix_row_counts
                     cur += n_as * src1->ne[1] * sizeof(int64_t); // matrix_rows
                 } break;
@@ -16680,6 +18471,7 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
                     }
                 } break;
             case GGML_OP_SOFT_MAX:
+            case GGML_OP_ROPE:
                 {
                     cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
                 } break;
@@ -16777,14 +18569,14 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
         work_size += CACHE_LINE_SIZE*(n_threads - 1);
     }
 
-    cplan.n_threads = n_threads;
+    cplan.n_threads = MIN(max_tasks, n_threads);
     cplan.work_size = work_size;
     cplan.work_data = NULL;
 
     return cplan;
 }
 
-int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
+enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
     {
         GGML_ASSERT(cplan);
         GGML_ASSERT(cplan->n_threads > 0);
@@ -16804,6 +18596,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
         /*.n_threads               =*/ n_threads,
         /*.n_active                =*/ n_threads,
         /*.node_n                  =*/ -1,
+        /*.node_task               =*/ GGML_TASK_TYPE_FINALIZE,
         /*.abort_callback          =*/ NULL,
         /*.abort_callback_data     =*/ NULL,
     };
@@ -16816,6 +18609,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
                 .thrd   = 0,
                 .ith = j,
                 .shared = &state_shared,
+                .ec = GGML_STATUS_SUCCESS,
             };
 
             const int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]);
@@ -16826,12 +18620,14 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
 
     workers[0].ith = 0;
     workers[0].shared = &state_shared;
+    workers[0].ec = GGML_STATUS_SUCCESS;
 
     const int64_t perf_start_cycles  = ggml_perf_cycles();
     const int64_t perf_start_time_us = ggml_perf_time_us();
 
     // this is a work thread too
-    int compute_status = (size_t) ggml_graph_compute_thread(&workers[0]);
+    ggml_graph_compute_thread(&workers[0]);
+    enum ggml_status compute_status = workers[0].ec;
 
     // don't leave affinity set on the main thread
     clear_numa_thread_affinity();
@@ -16841,6 +18637,8 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
         for (int j = 1; j < n_threads; j++) {
             const int rc = ggml_thread_join(workers[j].thrd, NULL);
             GGML_ASSERT(rc == 0);
+            if (workers[j].ec != GGML_STATUS_SUCCESS)
+                compute_status = workers[j].ec;
         }
     }
 
@@ -16864,14 +18662,14 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
     return compute_status;
 }
 
-void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) {
+enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) {
     struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads);
 
-    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size);
+    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size);
 
     cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
 
-    ggml_graph_compute(cgraph, &cplan);
+    return ggml_graph_compute(cgraph, &cplan);
 }
 
 struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name) {
@@ -16978,7 +18776,7 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
 
     // write binary data
     {
-        FILE * fout = fopen(fname, "wb");
+        FILE * fout = ggml_fopen(fname, "wb");
 
         if (!fout) {
             fprintf(stderr, "%s: failed to open %s\n", __func__, fname);
@@ -17116,7 +18914,7 @@ struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context *
 
     // read file into data
     {
-        FILE * fin = fopen(fname, "rb");
+        FILE * fin = ggml_fopen(fname, "rb");
         if (!fin) {
             fprintf(stderr, "%s: failed to open %s\n", __func__, fname);
             return result;
@@ -17248,7 +19046,7 @@ struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context *
 
                 ptr += ggml_nbytes(tensor);
 
-                fprintf(stderr, "%s: loaded leaf %d: '%16s', %9zu bytes\n", __func__, i, tensor->name, ggml_nbytes(tensor));
+                fprintf(stderr, "%s: loaded leaf %u: '%16s', %9zu bytes\n", __func__, i, tensor->name, ggml_nbytes(tensor));
             }
         }
 
@@ -17351,7 +19149,7 @@ struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context *
 
                 result->nodes[i] = tensor;
 
-                fprintf(stderr, "%s: loaded node %d: '%16s', %9zu bytes\n", __func__, i, tensor->name, ggml_nbytes(tensor));
+                fprintf(stderr, "%s: loaded node %u: '%16s', %9zu bytes\n", __func__, i, tensor->name, ggml_nbytes(tensor));
             }
         }
     }
@@ -17373,7 +19171,7 @@ void ggml_graph_print(const struct ggml_cgraph * cgraph) {
         GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 ", %5" PRId64 "] %16s %s (%3d) cpu = %7.3f / %7.3f ms, wall = %7.3f / %7.3f ms\n",
                 i,
                 node->ne[0], node->ne[1], node->ne[2],
-                ggml_op_name(node->op), node->is_param ? "x" : node->grad ? "g" : " ", node->perf_runs,
+                ggml_op_name(node->op), (node->flags & GGML_TENSOR_FLAG_PARAM) ? "x" : node->grad ? "g" : " ", node->perf_runs,
                 (double) node->perf_cycles  / (double) ggml_cycles_per_ms(),
                 (double) node->perf_cycles  / (double) ggml_cycles_per_ms() / (double) node->perf_runs,
                 (double) node->perf_time_us / 1000.0,
@@ -17452,7 +19250,7 @@ static void ggml_graph_dump_dot_leaf_edge(FILE * fp, struct ggml_tensor * node,
 void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename) {
     char color[16];
 
-    FILE * fp = fopen(filename, "w");
+    FILE * fp = ggml_fopen(filename, "w");
     GGML_ASSERT(fp);
 
     fprintf(fp, "digraph G {\n");
@@ -17466,7 +19264,7 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph
             continue;
         }
 
-        if (node->is_param) {
+        if (node->flags & GGML_TENSOR_FLAG_PARAM) {
             snprintf(color, sizeof(color), "yellow");
         } else if (node->grad) {
             if (ggml_graph_find(gf, node)) {
@@ -17640,7 +19438,7 @@ static enum ggml_opt_result ggml_opt_adam(
     int np = 0;
     int64_t nx = 0;
     for (int i = 0; i < gf->n_nodes; ++i) {
-        if (gf->nodes[i]->is_param) {
+        if (gf->nodes[i]->flags & GGML_TENSOR_FLAG_PARAM) {
             GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op);
 
             GGML_ASSERT(np < GGML_MAX_PARAMS);
@@ -17675,7 +19473,7 @@ static enum ggml_opt_result ggml_opt_adam(
     float * pf = params.past > 0 ? opt->adam.pf->data : NULL; // past function values
 
     struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads);
-    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size);
+    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size);
     cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
 
     bool cancel = false;
@@ -17687,7 +19485,7 @@ static enum ggml_opt_result ggml_opt_adam(
         if (callback) {
             callback(callback_data, accum_step, &sched, &cancel);
             if (cancel) {
-                return GGML_OPT_CANCEL;
+                return GGML_OPT_RESULT_CANCEL;
             }
         }
         // ggml_graph_reset  (gf);
@@ -17778,7 +19576,7 @@ static enum ggml_opt_result ggml_opt_adam(
             if (callback) {
                 callback(callback_data, accum_step, &sched, &cancel);
                 if (cancel) {
-                    return GGML_OPT_CANCEL;;
+                    return GGML_OPT_RESULT_CANCEL;;
                 }
             }
             // ggml_graph_reset  (gf);
@@ -17795,7 +19593,7 @@ static enum ggml_opt_result ggml_opt_adam(
         if (fabsf(fx - fx_prev[0])/fx < params.adam.eps_f) {
             GGML_PRINT_DEBUG("converged\n");
 
-            return GGML_OPT_OK;
+            return GGML_OPT_RESULT_OK;
         }
 
         // delta-based convergence test
@@ -17805,7 +19603,7 @@ static enum ggml_opt_result ggml_opt_adam(
                 const float rate = (pf[(iter0 + t)%params.past] - fx)/fx;
 
                 if (fabsf(rate) < params.delta) {
-                    return GGML_OPT_OK;
+                    return GGML_OPT_RESULT_OK;
                 }
             }
 
@@ -17821,7 +19619,7 @@ static enum ggml_opt_result ggml_opt_adam(
                 ++n_no_improvement[0];
 
                 if (n_no_improvement[0] >= params.max_no_improvement) {
-                    return GGML_OPT_OK;
+                    return GGML_OPT_RESULT_OK;
                 }
             }
         }
@@ -17839,7 +19637,7 @@ static enum ggml_opt_result ggml_opt_adam(
         }
     }
 
-    return GGML_OPT_DID_NOT_CONVERGE;
+    return GGML_OPT_RESULT_DID_NOT_CONVERGE;
 }
 
 //
@@ -17893,7 +19691,7 @@ static enum ggml_opt_result linesearch_backtracking(
     }
 
     // compute the initial gradient in the search direction
-    ggml_vec_dot_f32(nx, &dginit, g, d);
+    ggml_vec_dot_f32(nx, &dginit, 0, g, 0, d, 0, 1);
 
     // make sure that d points to a descent direction
     if (0 < dginit) {
@@ -17920,7 +19718,7 @@ static enum ggml_opt_result linesearch_backtracking(
                     float sched = 0;
                     callback(callback_data, accum_step, &sched, cancel);
                     if (*cancel) {
-                        return GGML_OPT_CANCEL;
+                        return GGML_OPT_RESULT_CANCEL;
                     }
                 }
                 // ggml_graph_reset  (gf);
@@ -17943,7 +19741,7 @@ static enum ggml_opt_result linesearch_backtracking(
                 return count;
             }
 
-            ggml_vec_dot_f32(nx, &dg, g, d);
+            ggml_vec_dot_f32(nx, &dg, 0, g, 0, d, 0, 1);
 
             // check the Wolfe condition
             if (dg < params->lbfgs.wolfe * dginit) {
@@ -17976,7 +19774,9 @@ static enum ggml_opt_result linesearch_backtracking(
         (*step) *= width;
     }
 
-    GGML_UNREACHABLE();
+    GGML_ASSERT(false && "line search failed");
+
+    return GGML_LINESEARCH_FAIL;
 }
 
 static enum ggml_opt_result ggml_opt_lbfgs(
@@ -17991,7 +19791,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
     if (params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE ||
         params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) {
         if (params.lbfgs.wolfe <= params.lbfgs.ftol || 1.f <= params.lbfgs.wolfe) {
-            return GGML_OPT_INVALID_WOLFE;
+            return GGML_OPT_RESULT_INVALID_WOLFE;
         }
     }
 
@@ -18003,7 +19803,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
     int np = 0;
     int nx = 0;
     for (int i = 0; i < gf->n_nodes; ++i) {
-        if (gf->nodes[i]->is_param) {
+        if (gf->nodes[i]->flags & GGML_TENSOR_FLAG_PARAM) {
             GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op);
 
             GGML_ASSERT(np < GGML_MAX_PARAMS);
@@ -18020,7 +19820,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
     }
 
     struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads);
-    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size);
+    struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size);
     cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
 
     float * x  = opt->lbfgs.x->data;  // current parameters
@@ -18061,7 +19861,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
                 float sched = 0;
                 callback(callback_data, accum_step, &sched, &cancel);
                 if (cancel) {
-                    return GGML_OPT_CANCEL;
+                    return GGML_OPT_RESULT_CANCEL;
                 }
             }
             // ggml_graph_reset  (gf);
@@ -18089,7 +19889,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
 
     // already optimized
     if (gnorm/xnorm <= params.lbfgs.eps) {
-        return GGML_OPT_OK;
+        return GGML_OPT_RESULT_OK;
     }
 
     if (opt->just_initialized) {
@@ -18134,7 +19934,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
         //       way to test and don't want to break something with so many changes lined up
         ls = linesearch_backtracking(&params, nx, x, &fx, g, d, step, xp, f, gb, &cplan, np, ps, &cancel, callback, callback_data);
         if (cancel) {
-            return GGML_OPT_CANCEL;
+            return GGML_OPT_RESULT_CANCEL;
         }
 
         if (ls < 0) {
@@ -18157,7 +19957,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
         }
         if (gnorm/xnorm <= params.lbfgs.eps) {
             // converged
-            return GGML_OPT_OK;
+            return GGML_OPT_RESULT_OK;
         }
 
         // delta-based convergence test
@@ -18167,7 +19967,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
                 const float rate = (pf[k[0]%params.past] - fx)/fx;
 
                 if (fabsf(rate) < params.delta) {
-                    return GGML_OPT_OK;
+                    return GGML_OPT_RESULT_OK;
                 }
             }
 
@@ -18183,14 +19983,14 @@ static enum ggml_opt_result ggml_opt_lbfgs(
                 n_no_improvement[0]++;
 
                 if (n_no_improvement[0] >= params.max_no_improvement) {
-                    return GGML_OPT_OK;
+                    return GGML_OPT_RESULT_OK;
                 }
             }
         }
 
         if (params.lbfgs.n_iter != 0 && params.lbfgs.n_iter < it + 1) {
             // reached the maximum number of iterations
-            return GGML_OPT_DID_NOT_CONVERGE;
+            return GGML_OPT_RESULT_DID_NOT_CONVERGE;
         }
 
         // update vectors s and y:
@@ -18204,8 +20004,8 @@ static enum ggml_opt_result ggml_opt_lbfgs(
         //     ys = y^t \cdot s    -> 1 / \rho.
         //     yy = y^t \cdot y.
         //
-        ggml_vec_dot_f32(nx, &ys, &lm_y[end[0]*nx], &lm_s[end[0]*nx]);
-        ggml_vec_dot_f32(nx, &yy, &lm_y[end[0]*nx], &lm_y[end[0]*nx]);
+        ggml_vec_dot_f32(nx, &ys, 0, &lm_y[end[0]*nx], 0, &lm_s[end[0]*nx], 0, 1);
+        ggml_vec_dot_f32(nx, &yy, 0, &lm_y[end[0]*nx], 0, &lm_y[end[0]*nx], 0, 1);
 
         lm_ys[end[0]] = ys;
 
@@ -18224,7 +20024,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
         for (int i = 0; i < bound; ++i) {
             j[0] = (j[0] + m - 1) % m;
             // \alpha_{j} = \rho_{j} s^{t}_{j} \cdot q_{k+1}
-            ggml_vec_dot_f32(nx, &lm_alpha[j[0]], &lm_s[j[0]*nx], d);
+            ggml_vec_dot_f32(nx, &lm_alpha[j[0]], 0, &lm_s[j[0]*nx], 0, d, 0, 1);
             lm_alpha[j[0]] /= lm_ys[j[0]];
             // q_{i} = q_{i+1} - \alpha_{i} y_{i}
             ggml_vec_mad_f32(nx, d, &lm_y[j[0]*nx], -lm_alpha[j[0]]);
@@ -18234,7 +20034,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
 
         for (int i = 0; i < bound; ++i) {
             // \beta_{j} = \rho_{j} y^t_{j} \cdot \gamma_{i}
-            ggml_vec_dot_f32(nx, &beta, &lm_y[j[0]*nx], d);
+            ggml_vec_dot_f32(nx, &beta, 0, &lm_y[j[0]*nx], 0, d, 0, 1);
             beta /= lm_ys[j[0]];
             // \gamma_{i+1} = \gamma_{i} + (\alpha_{j} - \beta_{j}) s_{j}
             ggml_vec_mad_f32(nx, d, &lm_s[j[0]*nx], lm_alpha[j[0]] - beta);
@@ -18244,17 +20044,19 @@ static enum ggml_opt_result ggml_opt_lbfgs(
         step[0] = 1.0;
     }
 
-    GGML_UNREACHABLE();
+    GGML_ASSERT(false && "lbfgs failed");
+
+    return GGML_OPT_RESULT_DID_NOT_CONVERGE;
 }
 
 struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) {
     struct ggml_opt_params result;
 
     switch (type) {
-        case GGML_OPT_ADAM:
+        case GGML_OPT_TYPE_ADAM:
             {
                 result = (struct ggml_opt_params) {
-                    .type       = GGML_OPT_ADAM,
+                    .type       = GGML_OPT_TYPE_ADAM,
                     .graph_size = GGML_DEFAULT_GRAPH_SIZE,
                     .n_threads  = 1, // FIXME: GGML_DEFAULT_N_THREADS ?
                     .past       = 0,
@@ -18282,10 +20084,10 @@ struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) {
                     },
                 };
             } break;
-        case GGML_OPT_LBFGS:
+        case GGML_OPT_TYPE_LBFGS:
             {
                 result = (struct ggml_opt_params) {
-                    .type       = GGML_OPT_LBFGS,
+                    .type       = GGML_OPT_TYPE_LBFGS,
                     .graph_size = GGML_DEFAULT_GRAPH_SIZE,
                     .n_threads  = 1,
                     .past       = 0,
@@ -18330,12 +20132,12 @@ GGML_API void ggml_opt_init(
     opt->just_initialized = true;
     if (opt->ctx == NULL) {
         struct ggml_init_params ctx_opt_params;
-        if (opt->params.type == GGML_OPT_ADAM) {
+        if (opt->params.type == GGML_OPT_TYPE_ADAM) {
             ctx_opt_params.mem_size = GGML_MEM_ALIGN*3 + ggml_tensor_overhead()*3 + ggml_type_size(GGML_TYPE_F32)*nx*3;
             if (opt->params.past > 0) {
                 ctx_opt_params.mem_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_size(GGML_TYPE_F32)*opt->params.past;
             }
-        } else if (opt->params.type == GGML_OPT_LBFGS) {
+        } else if (opt->params.type == GGML_OPT_TYPE_LBFGS) {
             ctx_opt_params.mem_size = GGML_MEM_ALIGN*9 + ggml_tensor_overhead()*9 + ggml_type_size(GGML_TYPE_F32)*(nx*5 + opt->params.lbfgs.m*2 + nx*opt->params.lbfgs.m*2);
             if (opt->params.past > 0) {
                 ctx_opt_params.mem_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_size(GGML_TYPE_F32)*opt->params.past;
@@ -18347,7 +20149,7 @@ GGML_API void ggml_opt_init(
         opt->ctx = ggml_init(ctx_opt_params);
     }
     switch (opt->params.type) {
-        case GGML_OPT_ADAM:
+        case GGML_OPT_TYPE_ADAM:
             {
                 opt->adam.g  = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx);
                 opt->adam.m  = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx);
@@ -18361,7 +20163,7 @@ GGML_API void ggml_opt_init(
                     ggml_set_zero(opt->adam.pf);
                 }
             } break;
-        case GGML_OPT_LBFGS:
+        case GGML_OPT_TYPE_LBFGS:
             {
                 opt->lbfgs.x  = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx);
                 opt->lbfgs.xp = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx);
@@ -18405,13 +20207,13 @@ enum ggml_opt_result ggml_opt(
 
         ctx = ggml_init(params_ctx);
         if (ctx == NULL) {
-            return GGML_OPT_NO_CONTEXT;
+            return GGML_OPT_RESULT_NO_CONTEXT;
         }
 
         free_ctx = true;
     }
 
-    enum ggml_opt_result result = GGML_OPT_OK;
+    enum ggml_opt_result result = GGML_OPT_RESULT_OK;
 
     struct ggml_opt_context * opt = (struct ggml_opt_context *) alloca(sizeof(struct ggml_opt_context));
 
@@ -18450,14 +20252,14 @@ enum ggml_opt_result ggml_opt_resume_g(
         void * callback_data) {
 
     // build forward + backward compute graphs
-    enum ggml_opt_result result = GGML_OPT_OK;
+    enum ggml_opt_result result = GGML_OPT_RESULT_OK;
 
     switch (opt->params.type) {
-        case GGML_OPT_ADAM:
+        case GGML_OPT_TYPE_ADAM:
             {
                 result = ggml_opt_adam(ctx, opt, opt->params, f, gf, gb, callback, callback_data);
             } break;
-        case GGML_OPT_LBFGS:
+        case GGML_OPT_TYPE_LBFGS:
             {
                 result = ggml_opt_lbfgs(ctx, opt, opt->params, f, gf, gb, callback, callback_data);
             } break;
@@ -18478,217 +20280,119 @@ enum ggml_opt_result ggml_opt_resume_g(
 
 ////////////////////////////////////////////////////////////////////////////////
 
-size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist) {
-    assert(k % QK4_0 == 0);
-    const int nb = k / QK4_0;
-
-    for (int b = 0; b < n; b += k) {
-        block_q4_0 * restrict y = (block_q4_0 *) dst + b/QK4_0;
-
-        quantize_row_q4_0_reference(src + b, y, k);
-
-        for (int i = 0; i < nb; i++) {
-            for (int j = 0; j < QK4_0; j += 2) {
-                const uint8_t vi0 = y[i].qs[j/2] & 0x0F;
-                const uint8_t vi1 = y[i].qs[j/2] >> 4;
-
-                hist[vi0]++;
-                hist[vi1]++;
-            }
-        }
-    }
-
-    return (n/QK4_0*sizeof(block_q4_0));
+void ggml_set_input(struct ggml_tensor * tensor) {
+    tensor->flags |= GGML_TENSOR_FLAG_INPUT;
 }
 
-size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist) {
-    assert(k % QK4_1 == 0);
-    const int nb = k / QK4_1;
-
-    for (int b = 0; b < n; b += k) {
-        block_q4_1 * restrict y = (block_q4_1 *) dst + b/QK4_1;
-
-        quantize_row_q4_1_reference(src + b, y, k);
-
-        for (int i = 0; i < nb; i++) {
-            for (int j = 0; j < QK4_1; j += 2) {
-                const uint8_t vi0 = y[i].qs[j/2] & 0x0F;
-                const uint8_t vi1 = y[i].qs[j/2] >> 4;
-
-                hist[vi0]++;
-                hist[vi1]++;
-            }
-        }
-    }
-
-    return (n/QK4_1*sizeof(block_q4_1));
+void ggml_set_output(struct ggml_tensor * tensor) {
+    tensor->flags |= GGML_TENSOR_FLAG_OUTPUT;
 }
 
-size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist) {
-    assert(k % QK5_0 == 0);
-    const int nb = k / QK5_0;
+////////////////////////////////////////////////////////////////////////////////
 
-    for (int b = 0; b < n; b += k) {
-        block_q5_0 * restrict y = (block_q5_0 *)dst + b/QK5_0;
+void ggml_quantize_init(enum ggml_type type) {
+    ggml_critical_section_start();
 
-        quantize_row_q5_0_reference(src + b, y, k);
-
-        for (int i = 0; i < nb; i++) {
-            uint32_t qh;
-            memcpy(&qh, &y[i].qh, sizeof(qh));
-
-            for (int j = 0; j < QK5_0; j += 2) {
-                const uint8_t vh0 = ((qh & (1u << (j/2 + 0 ))) >> (j/2 + 0 )) << 4;
-                const uint8_t vh1 = ((qh & (1u << (j/2 + 16))) >> (j/2 + 12));
-
-                // cast to 16 bins
-                const uint8_t vi0 = ((y[i].qs[j/2] & 0x0F) | vh0) / 2;
-                const uint8_t vi1 = ((y[i].qs[j/2] >>   4) | vh1) / 2;
-
-                hist[vi0]++;
-                hist[vi1]++;
-            }
-        }
-    }
-
-    return (n/QK5_0*sizeof(block_q5_0));
-}
-
-size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist) {
-    assert(k % QK5_1 == 0);
-    const int nb = k / QK5_1;
-
-    for (int b = 0; b < n; b += k) {
-        block_q5_1 * restrict y = (block_q5_1 *)dst + b/QK5_1;
-
-        quantize_row_q5_1_reference(src + b, y, k);
-
-        for (int i = 0; i < nb; i++) {
-            uint32_t qh;
-            memcpy(&qh, &y[i].qh, sizeof(qh));
-
-            for (int j = 0; j < QK5_1; j += 2) {
-                const uint8_t vh0 = ((qh & (1u << (j/2 + 0 ))) >> (j/2 + 0 )) << 4;
-                const uint8_t vh1 = ((qh & (1u << (j/2 + 16))) >> (j/2 + 12));
-
-                // cast to 16 bins
-                const uint8_t vi0 = ((y[i].qs[j/2] & 0x0F) | vh0) / 2;
-                const uint8_t vi1 = ((y[i].qs[j/2] >>   4) | vh1) / 2;
-
-                hist[vi0]++;
-                hist[vi1]++;
-            }
-        }
-    }
-
-    return (n/QK5_1*sizeof(block_q5_1));
-}
-
-size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist) {
-    assert(k % QK8_0 == 0);
-    const int nb = k / QK8_0;
-
-    for (int b = 0; b < n; b += k) {
-        block_q8_0 * restrict y = (block_q8_0 *)dst + b/QK8_0;
-
-        quantize_row_q8_0_reference(src + b, y, k);
-
-        for (int i = 0; i < nb; i++) {
-            for (int j = 0; j < QK8_0; ++j) {
-                const int8_t vi = y[i].qs[j];
-
-                hist[vi/16 + 8]++;
-            }
-        }
-    }
-
-    return (n/QK8_0*sizeof(block_q8_0));
-}
-
-size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist) {
-    size_t result = 0;
     switch (type) {
-        case GGML_TYPE_Q4_0:
-            {
-                GGML_ASSERT(start % QK4_0 == 0);
-                block_q4_0 * block = (block_q4_0*)dst + start / QK4_0;
-                result = ggml_quantize_q4_0(src + start, block, n, n, hist);
-            } break;
-        case GGML_TYPE_Q4_1:
-            {
-                GGML_ASSERT(start % QK4_1 == 0);
-                block_q4_1 * block = (block_q4_1*)dst + start / QK4_1;
-                result = ggml_quantize_q4_1(src + start, block, n, n, hist);
-            } break;
-        case GGML_TYPE_Q5_0:
-            {
-                GGML_ASSERT(start % QK5_0 == 0);
-                block_q5_0 * block = (block_q5_0*)dst + start / QK5_0;
-                result = ggml_quantize_q5_0(src + start, block, n, n, hist);
-            } break;
-        case GGML_TYPE_Q5_1:
-            {
-                GGML_ASSERT(start % QK5_1 == 0);
-                block_q5_1 * block = (block_q5_1*)dst + start / QK5_1;
-                result = ggml_quantize_q5_1(src + start, block, n, n, hist);
-            } break;
-        case GGML_TYPE_Q8_0:
-            {
-                GGML_ASSERT(start % QK8_0 == 0);
-                block_q8_0 * block = (block_q8_0*)dst + start / QK8_0;
-                result = ggml_quantize_q8_0(src + start, block, n, n, hist);
-            } break;
-        case GGML_TYPE_Q2_K:
-            {
-                GGML_ASSERT(start % QK_K == 0);
-                block_q2_K * block = (block_q2_K*)dst + start / QK_K;
-                result = ggml_quantize_q2_K(src + start, block, n, n, hist);
-            } break;
-        case GGML_TYPE_Q3_K:
-            {
-                GGML_ASSERT(start % QK_K == 0);
-                block_q3_K * block = (block_q3_K*)dst + start / QK_K;
-                result = ggml_quantize_q3_K(src + start, block, n, n, hist);
-            } break;
-        case GGML_TYPE_Q4_K:
-            {
-                GGML_ASSERT(start % QK_K == 0);
-                block_q4_K * block = (block_q4_K*)dst + start / QK_K;
-                result = ggml_quantize_q4_K(src + start, block, n, n, hist);
-            } break;
-        case GGML_TYPE_Q5_K:
-            {
-                GGML_ASSERT(start % QK_K == 0);
-                block_q5_K * block = (block_q5_K*)dst + start / QK_K;
-                result = ggml_quantize_q5_K(src + start, block, n, n, hist);
-            } break;
-        case GGML_TYPE_Q6_K:
-            {
-                GGML_ASSERT(start % QK_K == 0);
-                block_q6_K * block = (block_q6_K*)dst + start / QK_K;
-                result = ggml_quantize_q6_K(src + start, block, n, n, hist);
-            } break;
         case GGML_TYPE_IQ2_XXS:
-            {
-                GGML_ASSERT(start % QK_K == 0);
-                block_iq2_xxs * block = (block_iq2_xxs*)dst + start / QK_K;
-                result = ggml_quantize_iq2_xxs(src + start, block, n, n, hist);
-            } break;
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ1_S:
+        case GGML_TYPE_IQ1_M:   iq2xs_init_impl(type); break;
+        case GGML_TYPE_IQ3_XXS: iq3xs_init_impl(256); break;
+        case GGML_TYPE_IQ3_S:   iq3xs_init_impl(512); break;
+        default: // nothing
+            break;
+    }
+
+    ggml_critical_section_end();
+}
+
+void ggml_quantize_free(void) {
+    ggml_critical_section_start();
+
+    iq2xs_free_impl(GGML_TYPE_IQ2_XXS);
+    iq2xs_free_impl(GGML_TYPE_IQ2_XS);
+    iq2xs_free_impl(GGML_TYPE_IQ1_S);
+    iq3xs_free_impl(256);
+
+    ggml_critical_section_end();
+}
+
+bool ggml_quantize_requires_imatrix(enum ggml_type type) {
+    return
+        type == GGML_TYPE_IQ2_XXS ||
+        type == GGML_TYPE_IQ2_XS  ||
+        type == GGML_TYPE_IQ1_S;//   ||
+        //type == GGML_TYPE_IQ1_M;
+}
+
+size_t ggml_quantize_chunk(
+        enum ggml_type   type,
+           const float * src,
+                  void * dst,
+                   int   start,
+                   int   nrows,
+                   int   n_per_row,
+           const float * imatrix) {
+    const int n = nrows * n_per_row;
+
+    if (ggml_quantize_requires_imatrix(type)) {
+        GGML_ASSERT(imatrix != NULL);
+    }
+
+    GGML_ASSERT(start % type_traits[type].blck_size == 0);
+    GGML_ASSERT(start % n_per_row == 0);
+
+    ggml_quantize_init(type); // this is noop if already initialized
+
+    const size_t start_row = start / n_per_row;
+    const size_t row_size  = ggml_row_size(type, n_per_row);
+
+    size_t result = 0;
+
+    switch (type) {
+        case GGML_TYPE_Q4_0:    result = quantize_q4_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q4_1:    result = quantize_q4_1(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q5_0:    result = quantize_q5_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q5_1:    result = quantize_q5_1(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q8_0:    result = quantize_q8_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q2_K:    result = quantize_q2_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q3_K:    result = quantize_q3_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q4_K:    result = quantize_q4_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q5_K:    result = quantize_q5_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q6_K:    result = quantize_q6_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_IQ2_XXS: result = quantize_iq2_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_IQ2_XS:  result = quantize_iq2_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_IQ3_XXS: result = quantize_iq3_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_IQ3_S:   result = quantize_iq3_s  (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_IQ2_S:   result = quantize_iq2_s  (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_IQ1_S:   result = quantize_iq1_s  (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_IQ1_M:   result = quantize_iq1_m  (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_IQ4_NL:  result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+#if QK_K == 64
+        case GGML_TYPE_IQ4_XS:  result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+#else
+        case GGML_TYPE_IQ4_XS:  result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+#endif
         case GGML_TYPE_F16:
             {
-                int elemsize = sizeof(ggml_fp16_t);
+                size_t elemsize = sizeof(ggml_fp16_t);
                 ggml_fp32_to_fp16_row(src + start, (ggml_fp16_t *)dst + start, n);
                 result = n * elemsize;
             } break;
         case GGML_TYPE_F32:
             {
-                int elemsize = sizeof(float);
+                size_t elemsize = sizeof(float);
                 result = n * elemsize;
                 memcpy((uint8_t *)dst + start * elemsize, src + start, result);
             } break;
         default:
             assert(false);
     }
+
+    GGML_ASSERT(result == nrows * row_size);
+
     return result;
 }
 
@@ -18800,6 +20504,25 @@ struct gguf_context {
     void * data;
 };
 
+static size_t gguf_type_size(enum gguf_type type) {
+    GGML_ASSERT(0 <= type && type < GGUF_TYPE_COUNT);
+    return GGUF_TYPE_SIZE[type];
+}
+
+static void gguf_tensor_info_sanitize(struct gguf_tensor_info * info) {
+    GGML_ASSERT(info->n_dims <= GGML_MAX_DIMS);
+    GGML_ASSERT(0 <= info->type && info->type < GGML_TYPE_COUNT);
+
+    for (uint32_t i = 0; i < info->n_dims; ++i) {
+        GGML_ASSERT(info->ne[i] > 0);
+    }
+
+    // prevent overflow for total number of elements
+    GGML_ASSERT(INT64_MAX/info->ne[1] > info->ne[0]);
+    GGML_ASSERT(INT64_MAX/info->ne[2] > info->ne[0]*info->ne[1]);
+    GGML_ASSERT(INT64_MAX/info->ne[3] > info->ne[0]*info->ne[1]*info->ne[2]);
+}
+
 static bool gguf_fread_el(FILE * file, void * dst, size_t size, size_t * offset) {
     const size_t n = fread(dst, 1, size, file);
     *offset += n;
@@ -18812,8 +20535,17 @@ static bool gguf_fread_str(FILE * file, struct gguf_str * p, size_t * offset) {
 
     bool ok = true;
 
-    ok = ok && gguf_fread_el(file, &p->n,    sizeof(p->n), offset); p->data = calloc(p->n + 1, 1);
-    ok = ok && gguf_fread_el(file,  p->data, p->n,         offset);
+    ok = ok && gguf_fread_el(file, &p->n, sizeof(p->n), offset);
+
+    // early exit if string length is invalid, prevents from integer overflow
+    if (p->n == SIZE_MAX) {
+        fprintf(stderr, "%s: invalid string length (%" PRIu64 ")\n", __func__, p->n);
+        return false;
+    }
+
+    p->data = GGML_CALLOC(p->n + 1, 1);
+
+    ok = ok && gguf_fread_el(file,  p->data, p->n, offset);
 
     return ok;
 }
@@ -18839,7 +20571,7 @@ struct gguf_context * gguf_init_empty(void) {
 }
 
 struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) {
-    FILE * file = fopen(fname, "rb");
+    FILE * file = ggml_fopen(fname, "rb");
     if (!file) {
         return NULL;
     }
@@ -18885,6 +20617,12 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
             return NULL;
         }
 
+        // sanity-checks to prevent from integer/buffer overflows
+
+        ok = ok && (ctx->header.n_tensors < (SIZE_MAX/2)/sizeof(struct gguf_tensor_info));
+        ok = ok && (ctx->header.n_tensors < (SIZE_MAX/2)/ggml_tensor_overhead());
+        ok = ok && (ctx->header.n_kv      < (SIZE_MAX/2)/sizeof(struct gguf_kv));
+
         if (!ok) {
             fprintf(stderr, "%s: failed to read header\n", __func__);
             fclose(file);
@@ -18895,7 +20633,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
     // read the kv pairs
     {
-        ctx->kv = malloc(ctx->header.n_kv * sizeof(struct gguf_kv));
+        ctx->kv = GGML_MALLOC(ctx->header.n_kv * sizeof(struct gguf_kv));
 
         for (uint64_t i = 0; i < ctx->header.n_kv; ++i) {
             struct gguf_kv * kv = &ctx->kv[i];
@@ -18923,7 +20661,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                 case GGUF_TYPE_ARRAY:
                     {
                         ok = ok && gguf_fread_el(file, &kv->value.arr.type, sizeof(kv->value.arr.type), &offset);
-                        ok = ok && gguf_fread_el(file, &kv->value.arr.n,    sizeof(kv->value.arr.n), &offset);
+                        ok = ok && gguf_fread_el(file, &kv->value.arr.n,    sizeof(kv->value.arr.n),    &offset);
 
                         switch (kv->value.arr.type) {
                             case GGUF_TYPE_UINT8:
@@ -18938,21 +20676,39 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                             case GGUF_TYPE_FLOAT64:
                             case GGUF_TYPE_BOOL:
                                 {
-                                    kv->value.arr.data = malloc(kv->value.arr.n * GGUF_TYPE_SIZE[kv->value.arr.type]);
-                                    ok = ok && gguf_fread_el(file, kv->value.arr.data, kv->value.arr.n * GGUF_TYPE_SIZE[kv->value.arr.type], &offset);
+                                    // prevent from integer overflow in the malloc below
+                                    if (kv->value.arr.n >= SIZE_MAX/gguf_type_size(kv->value.arr.type)) {
+                                        fprintf(stderr, "%s: array size is too large (%" PRIu64 ")\n", __func__, kv->value.arr.n);
+                                        fclose(file);
+                                        gguf_free(ctx);
+                                        return NULL;
+                                    }
+
+                                    kv->value.arr.data = GGML_MALLOC(kv->value.arr.n * gguf_type_size(kv->value.arr.type));
+
+                                    ok = ok && gguf_fread_el(file, kv->value.arr.data, kv->value.arr.n * gguf_type_size(kv->value.arr.type), &offset);
                                 } break;
                             case GGUF_TYPE_STRING:
                                 {
-                                    kv->value.arr.data = malloc(kv->value.arr.n * sizeof(struct gguf_str));
+                                    // prevent from integer overflow in the malloc below
+                                    if (kv->value.arr.n >= SIZE_MAX/sizeof(struct gguf_str)) {
+                                        fprintf(stderr, "%s: array size is too large (%" PRIu64 ")\n", __func__, kv->value.arr.n);
+                                        fclose(file);
+                                        gguf_free(ctx);
+                                        return NULL;
+                                    }
+
+                                    kv->value.arr.data = GGML_MALLOC(kv->value.arr.n * sizeof(struct gguf_str));
+
                                     for (uint64_t j = 0; j < kv->value.arr.n; ++j) {
                                         ok = ok && gguf_fread_str(file, &((struct gguf_str *) kv->value.arr.data)[j], &offset);
                                     }
                                 } break;
                             case GGUF_TYPE_ARRAY:
-                            case GGUF_TYPE_COUNT: GGML_ASSERT(false && "invalid type"); break;
+                            default: GGML_ASSERT(false && "invalid type"); break;
                         }
                     } break;
-                case GGUF_TYPE_COUNT: GGML_ASSERT(false && "invalid type");
+                default: GGML_ASSERT(false && "invalid type");
             }
 
             if (!ok) {
@@ -18970,7 +20726,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
     // read the tensor infos
     {
-        ctx->infos = malloc(ctx->header.n_tensors * sizeof(struct gguf_tensor_info));
+        ctx->infos = GGML_MALLOC(ctx->header.n_tensors * sizeof(struct gguf_tensor_info));
 
         for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) {
             struct gguf_tensor_info * info = &ctx->infos[i];
@@ -18981,12 +20737,18 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
             ok = ok && gguf_fread_str(file, &info->name,                          &offset);
             ok = ok && gguf_fread_el (file, &info->n_dims, sizeof(info->n_dims),  &offset);
+
+            ok = ok && (info->n_dims <= GGML_MAX_DIMS);
+
             for (uint32_t j = 0; j < info->n_dims; ++j) {
                 ok = ok && gguf_fread_el(file, &info->ne[j], sizeof(info->ne[j]), &offset);
             }
+
             ok = ok && gguf_fread_el (file, &info->type,   sizeof(info->type),    &offset);
             ok = ok && gguf_fread_el (file, &info->offset, sizeof(info->offset),  &offset);
 
+            gguf_tensor_info_sanitize(info);
+
             if (!ok) {
                 fprintf(stderr, "%s: failed to read tensor info\n", __func__);
                 fclose(file);
@@ -19029,8 +20791,8 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                 (int64_t) info->ne[3];
 
             if (ne % ggml_blck_size(info->type) != 0) {
-                fprintf(stderr, "%s: tensor '%s' number of elements (%" PRId64 ") is not a multiple of block size (%d)\n",
-                        __func__, info->name.data, ne, ggml_blck_size(info->type));
+                fprintf(stderr, "%s: tensor '%s' of type %d (%s) number of elements (%" PRId64 ") is not a multiple of block size (%d)\n",
+                        __func__, info->name.data, (int)info->type, ggml_type_name(info->type), ne, ggml_blck_size(info->type));
                 fclose(file);
                 gguf_free(ctx);
                 return NULL;
@@ -19136,47 +20898,47 @@ void gguf_free(struct gguf_context * ctx) {
 
     if (ctx->kv) {
         // free string memory - not great..
-        for (uint32_t i = 0; i < ctx->header.n_kv; ++i) {
+        for (uint64_t i = 0; i < ctx->header.n_kv; ++i) {
             struct gguf_kv * kv = &ctx->kv[i];
 
             if (kv->key.data) {
-                free(kv->key.data);
+                GGML_FREE(kv->key.data);
             }
 
             if (kv->type == GGUF_TYPE_STRING) {
                 if (kv->value.str.data) {
-                    free(kv->value.str.data);
+                    GGML_FREE(kv->value.str.data);
                 }
             }
 
             if (kv->type == GGUF_TYPE_ARRAY) {
                 if (kv->value.arr.data) {
                     if (kv->value.arr.type == GGUF_TYPE_STRING) {
-                        for (uint32_t j = 0; j < kv->value.arr.n; ++j) {
+                        for (uint64_t j = 0; j < kv->value.arr.n; ++j) {
                             struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[j];
                             if (str->data) {
-                                free(str->data);
+                                GGML_FREE(str->data);
                             }
                         }
                     }
-                    free(kv->value.arr.data);
+                    GGML_FREE(kv->value.arr.data);
                 }
             }
         }
 
-        free(ctx->kv);
+        GGML_FREE(ctx->kv);
     }
 
     if (ctx->infos) {
-        for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) {
+        for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) {
             struct gguf_tensor_info * info = &ctx->infos[i];
 
             if (info->name.data) {
-                free(info->name.data);
+                GGML_FREE(info->name.data);
             }
         }
 
-        free(ctx->infos);
+        GGML_FREE(ctx->infos);
     }
 
     GGML_ALIGNED_FREE(ctx);
@@ -19477,8 +21239,8 @@ void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_ty
     ctx->kv[idx].type           = GGUF_TYPE_ARRAY;
     ctx->kv[idx].value.arr.type = type;
     ctx->kv[idx].value.arr.n    = n;
-    ctx->kv[idx].value.arr.data = malloc(n*GGUF_TYPE_SIZE[type]);
-    memcpy(ctx->kv[idx].value.arr.data, data, n*GGUF_TYPE_SIZE[type]);
+    ctx->kv[idx].value.arr.data = GGML_MALLOC(n*gguf_type_size(type));
+    memcpy(ctx->kv[idx].value.arr.data, data, n*gguf_type_size(type));
 }
 
 void gguf_set_arr_str(struct gguf_context * ctx, const char * key, const char ** data, int n) {
@@ -19487,7 +21249,7 @@ void gguf_set_arr_str(struct gguf_context * ctx, const char * key, const char **
     ctx->kv[idx].type           = GGUF_TYPE_ARRAY;
     ctx->kv[idx].value.arr.type = GGUF_TYPE_STRING;
     ctx->kv[idx].value.arr.n    = n;
-    ctx->kv[idx].value.arr.data = malloc(n*sizeof(struct gguf_str));
+    ctx->kv[idx].value.arr.data = GGML_MALLOC(n*sizeof(struct gguf_str));
     for (int i = 0; i < n; i++) {
         struct gguf_str * str = &((struct gguf_str *)ctx->kv[idx].value.arr.data)[i];
         str->n    = strlen(data[i]);
@@ -19514,19 +21276,19 @@ void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) {
             case GGUF_TYPE_ARRAY:
                 {
                     if (src->kv[i].value.arr.type == GGUF_TYPE_STRING) {
-                        const char ** data = malloc(src->kv[i].value.arr.n*sizeof(char *));
+                        const char ** data = GGML_MALLOC(src->kv[i].value.arr.n*sizeof(char *));
                         for (uint32_t j = 0; j < src->kv[i].value.arr.n; j++) {
                             data[j] = ((struct gguf_str *)src->kv[i].value.arr.data)[j].data;
                         }
                         gguf_set_arr_str(ctx, src->kv[i].key.data, data, src->kv[i].value.arr.n);
-                        free((void *)data);
+                        GGML_FREE((void *)data);
                     } else if (src->kv[i].value.arr.type == GGUF_TYPE_ARRAY) {
                         GGML_ASSERT(false && "nested arrays not supported");
                     } else {
                         gguf_set_arr_data(ctx, src->kv[i].key.data, src->kv[i].value.arr.type, src->kv[i].value.arr.data, src->kv[i].value.arr.n);
                     }
                 } break;
-            case GGUF_TYPE_COUNT:  GGML_ASSERT(false && "invalid type"); break;
+            default: GGML_ASSERT(false && "invalid type"); break;
         }
     }
 }
@@ -19602,7 +21364,7 @@ struct gguf_buf {
 
 static struct gguf_buf gguf_buf_init(size_t size) {
     struct gguf_buf buf = {
-        /*buf.data   =*/ size == 0 ? NULL : malloc(size),
+        /*buf.data   =*/ size == 0 ? NULL : GGML_MALLOC(size),
         /*buf.size   =*/ size,
         /*buf.offset =*/ 0,
     };
@@ -19612,7 +21374,7 @@ static struct gguf_buf gguf_buf_init(size_t size) {
 
 static void gguf_buf_free(struct gguf_buf buf) {
     if (buf.data) {
-        free(buf.data);
+        GGML_FREE(buf.data);
     }
 }
 
@@ -19693,7 +21455,7 @@ static void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf *
                         case GGUF_TYPE_FLOAT64:
                         case GGUF_TYPE_BOOL:
                             {
-                                gguf_bwrite_el(buf, kv->value.arr.data, kv->value.arr.n * GGUF_TYPE_SIZE[kv->value.arr.type]);
+                                gguf_bwrite_el(buf, kv->value.arr.data, kv->value.arr.n * gguf_type_size(kv->value.arr.type));
                             } break;
                         case GGUF_TYPE_STRING:
                             {
@@ -19702,10 +21464,10 @@ static void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf *
                                 }
                             } break;
                         case GGUF_TYPE_ARRAY:
-                        case GGUF_TYPE_COUNT: GGML_ASSERT(false && "invalid type"); break;
+                        default: GGML_ASSERT(false && "invalid type"); break;
                     }
                 } break;
-            case GGUF_TYPE_COUNT: GGML_ASSERT(false && "invalid type");
+            default: GGML_ASSERT(false && "invalid type");
         }
     }
 
@@ -19764,7 +21526,7 @@ static void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf *
 }
 
 void gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta) {
-    FILE * file = fopen(fname, "wb");
+    FILE * file = ggml_fopen(fname, "wb");
     if (!file) {
         GGML_ASSERT(false && "failed to open file for writing");
     }
@@ -19906,15 +21668,15 @@ int ggml_cpu_has_wasm_simd(void) {
 }
 
 int ggml_cpu_has_blas(void) {
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
+#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_SYCL)
     return 1;
 #else
     return 0;
 #endif
 }
 
-int ggml_cpu_has_cublas(void) {
-#if defined(GGML_USE_CUBLAS)
+int ggml_cpu_has_cuda(void) {
+#if defined(GGML_USE_CUDA)
     return 1;
 #else
     return 0;
@@ -19929,8 +21691,33 @@ int ggml_cpu_has_clblast(void) {
 #endif
 }
 
+int ggml_cpu_has_vulkan(void) {
+#if defined(GGML_USE_VULKAN)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_kompute(void) {
+#if defined(GGML_USE_KOMPUTE)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
+int ggml_cpu_has_sycl(void) {
+#if defined(GGML_USE_SYCL)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
 int ggml_cpu_has_gpublas(void) {
-    return ggml_cpu_has_cublas() || ggml_cpu_has_clblast();
+    return ggml_cpu_has_cuda() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() ||
+           ggml_cpu_has_sycl();
 }
 
 int ggml_cpu_has_sse3(void) {
@@ -19957,4 +21744,12 @@ int ggml_cpu_has_vsx(void) {
 #endif
 }
 
+int ggml_cpu_has_matmul_int8(void) {
+#if defined(__ARM_FEATURE_MATMUL_INT8)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
 ////////////////////////////////////////////////////////////////////////////////
diff --git a/ggml.h b/ggml.h
index c55e598b4..5cef45c0b 100644
--- a/ggml.h
+++ b/ggml.h
@@ -187,6 +187,16 @@
 #    define GGML_API
 #endif
 
+#ifdef GGML_MULTIPLATFORM
+#    if defined(_WIN32)
+#        define GGML_CALL
+#    else
+#        define GGML_CALL __attribute__((__ms_abi__))
+#    endif
+#else
+#    define GGML_CALL
+#endif
+
 // TODO: support for clang
 #ifdef __GNUC__
 #    define GGML_DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
@@ -204,9 +214,10 @@
 #    define GGML_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
 #endif
 
-#include <stdint.h>
-#include <stddef.h>
 #include <stdbool.h>
+#include <stddef.h>
+#include <stdint.h>
+#include <stdio.h>
 
 #define GGML_FILE_MAGIC   0x67676d6c // "ggml"
 #define GGML_FILE_VERSION 1
@@ -218,7 +229,9 @@
 #define GGML_MAX_PARAMS         2048
 #define GGML_MAX_CONTEXTS       64
 #define GGML_MAX_SRC            10
+#ifndef GGML_MAX_NAME
 #define GGML_MAX_NAME           64
+#endif
 #define GGML_MAX_OP_PARAMS      64
 #define GGML_DEFAULT_N_THREADS  4
 #define GGML_DEFAULT_GRAPH_SIZE 2048
@@ -303,13 +316,17 @@
 extern "C" {
 #endif
 
-#if defined(__ARM_NEON) && defined(__CUDACC__)
-    typedef half ggml_fp16_t;
-#elif defined(__ARM_NEON) && !defined(_MSC_VER)
-    typedef __fp16 ggml_fp16_t;
-#else
+    enum ggml_status {
+        GGML_STATUS_ALLOC_FAILED = -2,
+        GGML_STATUS_FAILED = -1,
+        GGML_STATUS_SUCCESS = 0,
+        GGML_STATUS_ABORTED = 1,
+    };
+
+    // get ggml_status name string
+    GGML_API GGML_CALL const char * ggml_status_to_string(enum ggml_status status);
+
     typedef uint16_t ggml_fp16_t;
-#endif
 
     // convert FP16 <-> FP32
     GGML_API float       ggml_fp16_to_fp32(ggml_fp16_t x);
@@ -321,28 +338,38 @@ extern "C" {
     struct ggml_object;
     struct ggml_context;
 
+    // NOTE: always add types at the end of the enum to keep backward compatibility
     enum ggml_type {
-        GGML_TYPE_F32  = 0,
-        GGML_TYPE_F16  = 1,
-        GGML_TYPE_Q4_0 = 2,
-        GGML_TYPE_Q4_1 = 3,
+        GGML_TYPE_F32     = 0,
+        GGML_TYPE_F16     = 1,
+        GGML_TYPE_Q4_0    = 2,
+        GGML_TYPE_Q4_1    = 3,
         // GGML_TYPE_Q4_2 = 4, support has been removed
-        // GGML_TYPE_Q4_3 (5) support has been removed
-        GGML_TYPE_Q5_0 = 6,
-        GGML_TYPE_Q5_1 = 7,
-        GGML_TYPE_Q8_0 = 8,
-        GGML_TYPE_Q8_1 = 9,
-        // k-quantizations
-        GGML_TYPE_Q2_K = 10,
-        GGML_TYPE_Q3_K = 11,
-        GGML_TYPE_Q4_K = 12,
-        GGML_TYPE_Q5_K = 13,
-        GGML_TYPE_Q6_K = 14,
-        GGML_TYPE_Q8_K = 15,
+        // GGML_TYPE_Q4_3 = 5, support has been removed
+        GGML_TYPE_Q5_0    = 6,
+        GGML_TYPE_Q5_1    = 7,
+        GGML_TYPE_Q8_0    = 8,
+        GGML_TYPE_Q8_1    = 9,
+        GGML_TYPE_Q2_K    = 10,
+        GGML_TYPE_Q3_K    = 11,
+        GGML_TYPE_Q4_K    = 12,
+        GGML_TYPE_Q5_K    = 13,
+        GGML_TYPE_Q6_K    = 14,
+        GGML_TYPE_Q8_K    = 15,
         GGML_TYPE_IQ2_XXS = 16,
-        GGML_TYPE_I8,
-        GGML_TYPE_I16,
-        GGML_TYPE_I32,
+        GGML_TYPE_IQ2_XS  = 17,
+        GGML_TYPE_IQ3_XXS = 18,
+        GGML_TYPE_IQ1_S   = 19,
+        GGML_TYPE_IQ4_NL  = 20,
+        GGML_TYPE_IQ3_S   = 21,
+        GGML_TYPE_IQ2_S   = 22,
+        GGML_TYPE_IQ4_XS  = 23,
+        GGML_TYPE_I8      = 24,
+        GGML_TYPE_I16     = 25,
+        GGML_TYPE_I32     = 26,
+        GGML_TYPE_I64     = 27,
+        GGML_TYPE_F64     = 28,
+        GGML_TYPE_IQ1_M   = 29,
         GGML_TYPE_COUNT,
     };
 
@@ -353,28 +380,36 @@ extern "C" {
     };
 
     enum ggml_backend_type {
-        GGML_BACKEND_CPU = 0,
-        GGML_BACKEND_GPU = 10,
-        GGML_BACKEND_GPU_SPLIT = 20,
+        GGML_BACKEND_TYPE_CPU = 0,
+        GGML_BACKEND_TYPE_GPU = 10,
+        GGML_BACKEND_TYPE_GPU_SPLIT = 20,
     };
 
     // model file types
     enum ggml_ftype {
-        GGML_FTYPE_UNKNOWN     = -1,
-        GGML_FTYPE_ALL_F32     = 0,
-        GGML_FTYPE_MOSTLY_F16  = 1,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q4_0 = 2,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q4_1 = 3,  // except 1d tensors
+        GGML_FTYPE_UNKNOWN        = -1,
+        GGML_FTYPE_ALL_F32        = 0,
+        GGML_FTYPE_MOSTLY_F16     = 1,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_0    = 2,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_1    = 3,  // except 1d tensors
         GGML_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
-        GGML_FTYPE_MOSTLY_Q8_0 = 7,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q5_0 = 8,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q5_1 = 9,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q3_K = 11, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q4_K = 12, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q5_K = 13, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q6_K = 14, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q8_0    = 7,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q5_0    = 8,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q5_1    = 9,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q2_K    = 10, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q3_K    = 11, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_K    = 12, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q5_K    = 13, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q6_K    = 14, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ2_XXS = 15, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ2_XS  = 16, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ3_XXS = 17, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ1_S   = 18, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ4_NL  = 19, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ3_S   = 20, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ2_S   = 21, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ4_XS  = 22, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ1_M   = 23, // except 1d tensors
     };
 
     // available tensor operations:
@@ -434,12 +469,16 @@ extern "C" {
         GGML_OP_POOL_2D,
         GGML_OP_UPSCALE, // nearest interpolate
         GGML_OP_PAD,
+        GGML_OP_ARANGE,
+        GGML_OP_TIMESTEP_EMBEDDING,
         GGML_OP_ARGSORT,
         GGML_OP_LEAKY_RELU,
 
         GGML_OP_FLASH_ATTN,
         GGML_OP_FLASH_FF,
         GGML_OP_FLASH_ATTN_BACK,
+        GGML_OP_SSM_CONV,
+        GGML_OP_SSM_SCAN,
         GGML_OP_WIN_PART,
         GGML_OP_WIN_UNPART,
         GGML_OP_GET_REL_POS,
@@ -475,23 +514,31 @@ extern "C" {
         GGML_UNARY_OP_GELU,
         GGML_UNARY_OP_GELU_QUICK,
         GGML_UNARY_OP_SILU,
+        GGML_UNARY_OP_HARDSWISH,
+        GGML_UNARY_OP_HARDSIGMOID,
 
         GGML_UNARY_OP_COUNT,
     };
 
     enum ggml_object_type {
-        GGML_OBJECT_TENSOR,
-        GGML_OBJECT_GRAPH,
-        GGML_OBJECT_WORK_BUFFER
+        GGML_OBJECT_TYPE_TENSOR,
+        GGML_OBJECT_TYPE_GRAPH,
+        GGML_OBJECT_TYPE_WORK_BUFFER
     };
 
     enum ggml_log_level {
         GGML_LOG_LEVEL_ERROR = 2,
-        GGML_LOG_LEVEL_WARN = 3,
-        GGML_LOG_LEVEL_INFO = 4,
+        GGML_LOG_LEVEL_WARN  = 3,
+        GGML_LOG_LEVEL_INFO  = 4,
         GGML_LOG_LEVEL_DEBUG = 5
     };
 
+    enum ggml_tensor_flag {
+        GGML_TENSOR_FLAG_INPUT  = 1,
+        GGML_TENSOR_FLAG_OUTPUT = 2,
+        GGML_TENSOR_FLAG_PARAM  = 4,
+    };
+
     // ggml object
     struct ggml_object {
         size_t offs;
@@ -525,7 +572,7 @@ extern "C" {
         // op params - allocated as int32_t for alignment
         int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)];
 
-        bool is_param;
+        int32_t flags;
 
         struct ggml_tensor * grad;
         struct ggml_tensor * src[GGML_MAX_SRC];
@@ -549,6 +596,11 @@ extern "C" {
 
     static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor);
 
+    // Abort callback
+    // If not NULL, called before ggml computation
+    // If it returns true, the computation is aborted
+    typedef bool (*ggml_abort_callback)(void * data);
+
     // the compute plan that needs to be prepared for ggml_graph_compute()
     // since https://github.com/ggerganov/ggml/issues/287
     struct ggml_cplan {
@@ -558,8 +610,8 @@ extern "C" {
         int n_threads;
 
         // abort ggml_graph_compute when true
-        bool (*abort_callback)(void * data);
-        void * abort_callback_data;
+        ggml_abort_callback abort_callback;
+        void *              abort_callback_data;
     };
 
     enum ggml_cgraph_eval_order {
@@ -613,9 +665,9 @@ extern "C" {
     // NOTE: the INIT or FINALIZE pass is not scheduled unless explicitly enabled.
     // This behavior was changed since https://github.com/ggerganov/llama.cpp/pull/1995.
     enum ggml_task_type {
-        GGML_TASK_INIT = 0,
-        GGML_TASK_COMPUTE,
-        GGML_TASK_FINALIZE,
+        GGML_TASK_TYPE_INIT = 0,
+        GGML_TASK_TYPE_COMPUTE,
+        GGML_TASK_TYPE_FINALIZE,
     };
 
     struct ggml_compute_params {
@@ -629,6 +681,26 @@ extern "C" {
         void * wdata;
     };
 
+    // numa strategies
+    enum ggml_numa_strategy {
+        GGML_NUMA_STRATEGY_DISABLED   = 0,
+        GGML_NUMA_STRATEGY_DISTRIBUTE = 1,
+        GGML_NUMA_STRATEGY_ISOLATE    = 2,
+        GGML_NUMA_STRATEGY_NUMACTL    = 3,
+        GGML_NUMA_STRATEGY_MIRROR     = 4,
+        GGML_NUMA_STRATEGY_COUNT
+    };
+
+    //
+    // GUID
+    //
+
+    // GUID types
+    typedef uint8_t ggml_guid[16];
+    typedef ggml_guid * ggml_guid_t;
+
+    GGML_API bool ggml_guid_matches(ggml_guid_t guid_a, ggml_guid_t guid_b);
+
     // misc
 
     GGML_API void    ggml_time_init(void); // call this once at the beginning of the program
@@ -639,47 +711,51 @@ extern "C" {
 
     GGML_API void    ggml_print_backtrace(void);
 
-    GGML_API void    ggml_numa_init(void); // call once for better performance on NUMA systems
+    // accepts a UTF-8 path, even on Windows
+    GGML_API FILE *  ggml_fopen(const char * fname, const char * mode);
+
+    GGML_API void    ggml_numa_init(enum ggml_numa_strategy numa); // call once for better performance on NUMA systems
     GGML_API bool    ggml_is_numa(void); // true if init detected that system has >1 NUMA node
 
     GGML_API void    ggml_print_object (const struct ggml_object * obj);
     GGML_API void    ggml_print_objects(const struct ggml_context * ctx);
 
-    GGML_API int64_t ggml_nelements   (const struct ggml_tensor * tensor);
-    GGML_API int64_t ggml_nrows       (const struct ggml_tensor * tensor);
-    GGML_API size_t  ggml_nbytes      (const struct ggml_tensor * tensor);
-    GGML_API size_t  ggml_nbytes_pad  (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN
+    GGML_API GGML_CALL int64_t ggml_nelements   (const struct ggml_tensor * tensor);
+    GGML_API GGML_CALL int64_t ggml_nrows       (const struct ggml_tensor * tensor);
+    GGML_API GGML_CALL size_t  ggml_nbytes      (const struct ggml_tensor * tensor);
+    GGML_API           size_t  ggml_nbytes_pad  (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN
 
-    GGML_API int    ggml_blck_size(enum ggml_type type);
-    GGML_API size_t ggml_type_size(enum ggml_type type);             // size in bytes for all elements in a block
-    GGML_API size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row
+    GGML_API GGML_CALL int    ggml_blck_size(enum ggml_type type);
+    GGML_API GGML_CALL size_t ggml_type_size(enum ggml_type type);             // size in bytes for all elements in a block
+    GGML_API GGML_CALL size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row
 
     GGML_DEPRECATED(
     GGML_API double ggml_type_sizef(enum ggml_type type), // ggml_type_size()/ggml_blck_size() as float
     "use ggml_row_size() instead");
 
-    GGML_API const char * ggml_type_name(enum ggml_type type);
-    GGML_API const char * ggml_op_name  (enum ggml_op   op);
-    GGML_API const char * ggml_op_symbol(enum ggml_op   op);
+    GGML_API GGML_CALL const char * ggml_type_name(enum ggml_type type);
+    GGML_API GGML_CALL const char * ggml_op_name  (enum ggml_op   op);
+    GGML_API           const char * ggml_op_symbol(enum ggml_op   op);
 
-    GGML_API const char * ggml_unary_op_name(enum ggml_unary_op op);
-    GGML_API const char * ggml_op_desc(const struct ggml_tensor * t); // unary or op name
+    GGML_API           const char * ggml_unary_op_name(enum ggml_unary_op op);
+    GGML_API GGML_CALL const char * ggml_op_desc(const struct ggml_tensor * t); // unary or op name
 
-    GGML_API size_t  ggml_element_size(const struct ggml_tensor * tensor);
+    GGML_API GGML_CALL size_t  ggml_element_size(const struct ggml_tensor * tensor);
 
-    GGML_API bool    ggml_is_quantized(enum ggml_type type);
+    GGML_API GGML_CALL bool    ggml_is_quantized(enum ggml_type type);
 
     // TODO: temporary until model loading of ggml examples is refactored
     GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype);
 
-    GGML_API bool ggml_is_transposed(const struct ggml_tensor * tensor);
-    GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor);
-    GGML_API bool ggml_is_permuted  (const struct ggml_tensor * tensor);
-    GGML_API bool ggml_is_scalar    (const struct ggml_tensor * tensor);
-    GGML_API bool ggml_is_vector    (const struct ggml_tensor * tensor);
-    GGML_API bool ggml_is_matrix    (const struct ggml_tensor * tensor);
-    GGML_API bool ggml_is_3d        (const struct ggml_tensor * tensor);
-    GGML_API int  ggml_n_dims       (const struct ggml_tensor * tensor); // returns 1 for scalars
+    GGML_API GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor);
+    GGML_API GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor);
+    GGML_API GGML_CALL bool ggml_is_permuted  (const struct ggml_tensor * tensor);
+    GGML_API GGML_CALL bool ggml_is_empty     (const struct ggml_tensor * tensor);
+    GGML_API           bool ggml_is_scalar    (const struct ggml_tensor * tensor);
+    GGML_API           bool ggml_is_vector    (const struct ggml_tensor * tensor);
+    GGML_API           bool ggml_is_matrix    (const struct ggml_tensor * tensor);
+    GGML_API           bool ggml_is_3d        (const struct ggml_tensor * tensor);
+    GGML_API           int  ggml_n_dims       (const struct ggml_tensor * tensor); // returns 1 for scalars
 
     GGML_API bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1);
 
@@ -766,7 +842,7 @@ extern "C" {
     GGML_API void *  ggml_get_data    (const struct ggml_tensor * tensor);
     GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor);
 
-    GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor);
+    GGML_API GGML_CALL enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor);
 
     GGML_API const char *         ggml_get_name   (const struct ggml_tensor * tensor);
     GGML_API struct ggml_tensor * ggml_set_name   (      struct ggml_tensor * tensor, const char * name);
@@ -1018,6 +1094,16 @@ extern "C" {
             struct ggml_tensor  * a,
             struct ggml_tensor  * b);
 
+    // hardswish(x) = x * relu6(x + 3) / 6
+    GGML_API struct ggml_tensor * ggml_hardswish(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // hardsigmoid(x) = relu6(x + 3) / 6
+    GGML_API struct ggml_tensor * ggml_hardsigmoid(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
     // normalize along rows
     GGML_API struct ggml_tensor * ggml_norm(
             struct ggml_context * ctx,
@@ -1078,8 +1164,7 @@ extern "C" {
     //  ggml_mul_mat_id(ctx, as, ids, id, b) ~= ggml_mul_mat(as[ids[id]], b)
     GGML_API struct ggml_tensor * ggml_mul_mat_id(
             struct ggml_context * ctx,
-            struct ggml_tensor  * const as[],
-            int                   n_as,
+            struct ggml_tensor  * as,
             struct ggml_tensor  * ids,
             int                   id,
             struct ggml_tensor  * b);
@@ -1161,22 +1246,16 @@ extern "C" {
             struct ggml_tensor  * a,
             struct ggml_tensor  * b);
 
-    // a -> b, in-place, return view(b)
-    GGML_API struct ggml_tensor * ggml_cpy_inplace(
+    GGML_API struct ggml_tensor * ggml_cast(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
-            struct ggml_tensor  * b);
+            enum   ggml_type      type);
 
     // make contiguous
     GGML_API struct ggml_tensor * ggml_cont(
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
 
-    // make contiguous, in-place
-    GGML_API struct ggml_tensor * ggml_cont_inplace(
-            struct ggml_context * ctx,
-            struct ggml_tensor  * a);
-
     // make contiguous, with new shape
     GGML_API struct ggml_tensor * ggml_cont_1d(
             struct ggml_context * ctx,
@@ -1340,13 +1419,17 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
 
-    // fused soft_max(a*scale + mask)
+    // fused soft_max(a*scale + mask + pos[i]*(ALiBi slope))
     // mask is optional
+    // pos is required when max_bias > 0.0f
+    // max_bias = 0.0f for no ALiBi
     GGML_API struct ggml_tensor * ggml_soft_max_ext(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
             struct ggml_tensor  * mask,
-            float                 scale);
+            struct ggml_tensor  * pos,
+            float                 scale,
+            float                 max_bias);
 
     GGML_API struct ggml_tensor * ggml_soft_max_back(
             struct ggml_context * ctx,
@@ -1415,7 +1498,7 @@ extern "C" {
             float                 beta_slow);
 
     // compute correction dims for YaRN RoPE scaling
-    void ggml_rope_yarn_corr_dims(
+    GGML_CALL void ggml_rope_yarn_corr_dims(
         int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]);
 
     // xPos RoPE, in-place, returns view(a)
@@ -1448,12 +1531,13 @@ extern "C" {
 
     // alibi position embedding
     // in-place, returns view(a)
-    GGML_API struct ggml_tensor * ggml_alibi(
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_alibi(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
             int                   n_past,
             int                   n_head,
-            float                 bias_max);
+            float                 bias_max),
+        "use ggml_soft_max_ext instead (will be removed in Mar 2024)");
 
     // clamp
     // in-place, returns view(a)
@@ -1473,7 +1557,19 @@ extern "C" {
             int                  p1,
             int                  d0,
             int                  d1,
-            bool                 is_2D);
+            bool                 is_2D,
+            enum ggml_type       dst_type);
+
+    GGML_API struct ggml_tensor * ggml_conv_depthwise_2d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                  s0,
+            int                  s1,
+            int                  p0,
+            int                  p1,
+            int                  d0,
+            int                  d1);
 
     GGML_API struct ggml_tensor * ggml_conv_1d(
             struct ggml_context * ctx,
@@ -1587,10 +1683,19 @@ extern "C" {
             int                  p2,
             int                  p3);
 
+    // Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151
+    // timesteps: [N,]
+    // return: [N, dim]
+    GGML_API struct ggml_tensor * ggml_timestep_embedding(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * timesteps,
+            int                   dim,
+            int                   max_period);
+
     // sort rows
     enum ggml_sort_order {
-        GGML_SORT_ASC,
-        GGML_SORT_DESC,
+        GGML_SORT_ORDER_ASC,
+        GGML_SORT_ORDER_DESC,
     };
 
     GGML_API struct ggml_tensor * ggml_argsort(
@@ -1598,6 +1703,12 @@ extern "C" {
             struct ggml_tensor  * a,
             enum ggml_sort_order  order);
 
+    GGML_API struct ggml_tensor * ggml_arange(
+            struct ggml_context * ctx,
+            float                 start,
+            float                 stop,
+            float                 step);
+
     // top k elements per row
     GGML_API struct ggml_tensor * ggml_top_k(
             struct ggml_context * ctx,
@@ -1627,6 +1738,23 @@ extern "C" {
             struct ggml_tensor  * c0,
             struct ggml_tensor  * c1);
 
+    GGML_API struct ggml_tensor * ggml_ssm_conv(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * s,
+            struct ggml_tensor  * x,
+            struct ggml_tensor  * c,
+            struct ggml_tensor  * sq);
+
+    GGML_API struct ggml_tensor * ggml_ssm_scan(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * s,
+            struct ggml_tensor  * x,
+            struct ggml_tensor  * dt,
+            struct ggml_tensor  * A,
+            struct ggml_tensor  * B,
+            struct ggml_tensor  * C,
+            struct ggml_tensor  * sq);
+
     // partition into non-overlapping windows with padding if needed
     // example:
     // a:   768   64   64    1
@@ -1849,12 +1977,11 @@ extern "C" {
 
     // ggml_graph_plan() has to be called before ggml_graph_compute()
     // when plan.work_size > 0, caller must allocate memory for plan.work_data
-    GGML_API struct ggml_cplan ggml_graph_plan   (struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
-    GGML_API int               ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
-
+    GGML_API struct ggml_cplan ggml_graph_plan            (const struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
+    GGML_API enum ggml_status  ggml_graph_compute         (      struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
     // same as ggml_graph_compute() but the work data is allocated as a part of the context
     // note: the drawback of this API is that you must have ensured that the context has enough memory for the work data
-    GGML_API void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads);
+    GGML_API enum ggml_status  ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads);
 
     GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name);
 
@@ -1883,8 +2010,8 @@ extern "C" {
 
     // optimization methods
     enum ggml_opt_type {
-        GGML_OPT_ADAM,
-        GGML_OPT_LBFGS,
+        GGML_OPT_TYPE_ADAM,
+        GGML_OPT_TYPE_LBFGS,
     };
 
     // linesearch methods
@@ -1898,12 +2025,12 @@ extern "C" {
 
     // optimization return values
     enum ggml_opt_result {
-        GGML_OPT_OK = 0,
-        GGML_OPT_DID_NOT_CONVERGE,
-        GGML_OPT_NO_CONTEXT,
-        GGML_OPT_INVALID_WOLFE,
-        GGML_OPT_FAIL,
-        GGML_OPT_CANCEL,
+        GGML_OPT_RESULT_OK = 0,
+        GGML_OPT_RESULT_DID_NOT_CONVERGE,
+        GGML_OPT_RESULT_NO_CONTEXT,
+        GGML_OPT_RESULT_INVALID_WOLFE,
+        GGML_OPT_RESULT_FAIL,
+        GGML_OPT_RESULT_CANCEL,
 
         GGML_LINESEARCH_FAIL = -128,
         GGML_LINESEARCH_MINIMUM_STEP,
@@ -2053,25 +2180,40 @@ extern "C" {
             ggml_opt_callback callback,
             void * callback_data);
 
+    //
+    // tensor flags
+    //
+    GGML_API void ggml_set_input(struct ggml_tensor * tensor);
+    GGML_API void ggml_set_output(struct ggml_tensor * tensor);
+
     //
     // quantization
     //
 
-    // TODO: these would probably get removed in favor of the more general ggml_quantize_chunk
-    GGML_API size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist);
-    GGML_API size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);
-    GGML_API size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist);
-    GGML_API size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist);
-    GGML_API size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist);
+    // - ggml_quantize_init can be called multiple times with the same type
+    //   it will only initialize the quantization tables for the first call or after ggml_quantize_free
+    //   automatically called by ggml_quantize_chunk for convenience
+    //
+    // - ggml_quantize_free will free any memory allocated by ggml_quantize_init
+    //   call this at the end of the program to avoid memory leaks
+    //
+    // note: these are thread-safe
+    //
+    GGML_API void ggml_quantize_init(enum ggml_type type);
+    GGML_API void ggml_quantize_free(void);
 
-    GGML_API size_t ggml_quantize_q2_K(const float * src, void * dst, int n, int k, int64_t * hist);
-    GGML_API size_t ggml_quantize_q3_K(const float * src, void * dst, int n, int k, int64_t * hist);
-    GGML_API size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist);
-    GGML_API size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist);
-    GGML_API size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist);
-    GGML_API size_t ggml_quantize_iq2_xxs(const float * src, void * dst, int n, int k, int64_t * hist);
+    // some quantization type cannot be used without an importance matrix
+    GGML_API bool ggml_quantize_requires_imatrix(enum ggml_type type);
 
-    GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist);
+    // calls ggml_quantize_init internally (i.e. can allocate memory)
+    GGML_API size_t ggml_quantize_chunk(
+            enum ggml_type   type,
+               const float * src,
+                      void * dst,
+                       int   start,
+                       int   nrows,
+                       int   n_per_row,
+               const float * imatrix);
 
     //
     // gguf
@@ -2214,12 +2356,16 @@ extern "C" {
     GGML_API int ggml_cpu_has_fp16_va    (void);
     GGML_API int ggml_cpu_has_wasm_simd  (void);
     GGML_API int ggml_cpu_has_blas       (void);
-    GGML_API int ggml_cpu_has_cublas     (void);
+    GGML_API int ggml_cpu_has_cuda       (void);
     GGML_API int ggml_cpu_has_clblast    (void);
+    GGML_API int ggml_cpu_has_vulkan     (void);
+    GGML_API int ggml_cpu_has_kompute    (void);
     GGML_API int ggml_cpu_has_gpublas    (void);
     GGML_API int ggml_cpu_has_sse3       (void);
     GGML_API int ggml_cpu_has_ssse3      (void);
+    GGML_API int ggml_cpu_has_sycl       (void);
     GGML_API int ggml_cpu_has_vsx        (void);
+    GGML_API int ggml_cpu_has_matmul_int8(void);
 
     //
     // Internal types and functions exposed for tests and benchmarks
@@ -2233,7 +2379,8 @@ extern "C" {
 #endif
     typedef void (*ggml_to_float_t)  (const void  * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
     typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void  * GGML_RESTRICT y, int k);
-    typedef void (*ggml_vec_dot_t)   (const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y);
+    typedef void (*ggml_vec_dot_t)   (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx,
+                                      const void * GGML_RESTRICT y, size_t by, int nrc);
 
     typedef struct {
         const char      * type_name;
@@ -2245,6 +2392,7 @@ extern "C" {
         ggml_from_float_t from_float_reference;
         ggml_vec_dot_t    vec_dot;
         enum ggml_type    vec_dot_type;
+        int64_t           nrows; // number of rows to process simultaneously;
     } ggml_type_traits_t;
 
     GGML_API ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type);
diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py
new file mode 100644
index 000000000..5dd700963
--- /dev/null
+++ b/ggml_vk_generate_shaders.py
@@ -0,0 +1,2777 @@
+#!/usr/bin/env python
+
+import argparse
+import asyncio
+import os
+import sys
+from tempfile import gettempdir, NamedTemporaryFile
+
+shader_f32 = """
+#define FLOAT_TYPE float
+"""
+shader_f16 = """
+#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
+#define FLOAT_TYPE float16_t
+"""
+shader_int8_ext = """
+#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require
+"""
+
+# Type-specific defines
+shader_f32_defines = """
+#define QUANT_K 1
+#define QUANT_R 1
+
+#define A_TYPE float
+"""
+shader_f16_defines = """
+#define QUANT_K 1
+#define QUANT_R 1
+
+#define A_TYPE float16_t
+"""
+shader_q4_0_defines = """
+#define QUANT_K 32
+#define QUANT_R 2
+
+struct block_q4_0
+{
+    float16_t d;
+    uint8_t qs[16];
+};
+
+#define A_TYPE block_q4_0
+"""
+shader_q4_1_defines = """
+#define QUANT_K 32
+#define QUANT_R 2
+
+struct block_q4_1
+{
+    float16_t d;
+    float16_t m;
+    uint8_t qs[16];
+};
+
+#define A_TYPE block_q4_1
+"""
+shader_q5_0_defines = """
+#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require
+#define QUANT_K 32
+#define QUANT_R 2
+
+struct block_q5_0
+{
+    float16_t d;
+    uint16_t qh[2];
+    uint8_t qs[16];
+};
+
+#define A_TYPE block_q5_0
+"""
+shader_q5_1_defines = """
+#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require
+#define QUANT_K 32
+#define QUANT_R 2
+
+struct block_q5_1
+{
+    float16_t d;
+    float16_t m;
+    uint qh;
+    uint8_t qs[16];
+};
+
+#define A_TYPE block_q5_1
+"""
+shader_q8_0_defines = """
+#define QUANT_K 32
+#define QUANT_R 1
+
+struct block_q8_0
+{
+    float16_t d;
+    int8_t qs[32];
+};
+
+#define A_TYPE block_q8_0
+"""
+
+# K-quants
+shader_q2_K_defines = """
+#define QUANT_K 256
+
+struct block_q2_K
+{
+    uint8_t scales[QUANT_K/16];
+    uint8_t qs[QUANT_K/4];
+    f16vec2 d;
+};
+
+#define A_TYPE block_q2_K
+"""
+shader_q3_K_defines = """
+#define QUANT_K 256
+
+struct block_q3_K
+{
+    uint8_t hmask[QUANT_K/8];
+    uint8_t qs[QUANT_K/4];
+    uint8_t scales[12];
+    float16_t d;
+};
+
+#define A_TYPE block_q3_K
+"""
+shader_q4_K_defines = """
+#define QUANT_K 256
+
+struct block_q4_K
+{
+    f16vec2 d;
+    uint8_t scales[3*QUANT_K/64];
+    uint8_t qs[QUANT_K/2];
+};
+
+#define A_TYPE block_q4_K
+"""
+shader_q5_K_defines = """
+#define QUANT_K 256
+
+struct block_q5_K
+{
+    f16vec2 d;
+    uint8_t scales[12];
+    uint8_t qh[QUANT_K/8];
+    uint8_t qs[QUANT_K/2];
+};
+
+#define A_TYPE block_q5_K
+"""
+shader_q6_K_defines = """
+#define QUANT_K 256
+
+struct block_q6_K
+{
+    uint8_t ql[QUANT_K/2];
+    uint8_t qh[QUANT_K/4];
+    int8_t scales[QUANT_K/16];
+    float16_t d;
+};
+
+#define A_TYPE block_q6_K
+"""
+
+# Dequant functions
+shader_float_dequant_func = """
+#define DEQUANT_FUNC vec2 v = vec2(ib, ib);  // data_a[ib], data_a[ib + 1]);
+"""
+
+shader_q4_0_dequant_func = """
+#define DEQUANT_FUNC const float d = float(data_a[ib].d); \
+const uint vui = uint(data_a[ib].qs[iqs]); \
+vec2 v = vec2(vui & 0xF, vui >> 4); \
+v = (v - 8.0f)*d;
+"""
+
+shader_q4_1_dequant_func = """
+#define DEQUANT_FUNC const float d = float(data_a[ib].d); \
+const float m = float(data_a[ib].m); \
+const uint vui = uint(data_a[ib].qs[iqs]); \
+vec2 v = vec2(vui & 0xF, vui >> 4); \
+v = v*d + m;
+"""
+
+shader_q5_0_dequant_func = """
+#define DEQUANT_FUNC const float d = float(data_a[ib].d); \
+const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; \
+const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); \
+const uint vui = uint(data_a[ib].qs[iqs]); \
+vec2 v = vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \
+v = (v - 16.0f) * d;
+"""
+
+shader_q5_1_dequant_func = """
+#define DEQUANT_FUNC const float d = float(data_a[ib].d); \
+const float m = float(data_a[ib].m); \
+const uint uint_qh = data_a[ib].qh; \
+const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); \
+const uint vui = uint(data_a[ib].qs[iqs]); \
+vec2 v = vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \
+v = v*d + m;
+"""
+
+shader_q8_0_dequant_func = """
+#define DEQUANT_FUNC const float d = float(data_a[ib].d); \
+vec2 v = vec2(int(data_a[ib].qs[iqs]), int(data_a[ib].qs[iqs + 1])); \
+v = v * d;
+"""
+
+# MULMAT
+
+mulmat_head = """#version 450
+
+#extension GL_EXT_control_flow_attributes : enable
+#extension GL_EXT_shader_16bit_storage : require
+
+#ifndef LOAD_VEC_A
+#define LOAD_VEC_A 1
+#endif
+#ifndef LOAD_VEC_B
+#define LOAD_VEC_B 1
+#endif
+"""
+
+mulmat_body1 = """
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
+
+layout (push_constant) uniform parameter
+{
+    uint M;
+    uint N;
+    uint K;
+    uint stride_a;
+    uint stride_b;
+    uint stride_d;
+    uint k_split;
+
+    uint ne02;
+    uint ne12;
+    uint broadcast2;
+    uint broadcast3;
+
+    uint batch_stride_a;
+    uint batch_stride_b;
+    uint batch_stride_d;
+} p;
+
+layout (constant_id = 1) const uint BM = 64;
+layout (constant_id = 2) const uint BN = 64;
+layout (constant_id = 3) const uint BK = 16;  // Assumed to be 32 if working with a quant
+layout (constant_id = 4) const uint WM = 32;
+layout (constant_id = 5) const uint WN = 32;
+layout (constant_id = 6) const uint WMITER = 2;
+layout (constant_id = 7) const uint TM = 4;
+layout (constant_id = 8) const uint TN = 2;
+layout (constant_id = 9) const uint WARP = 32;
+
+shared FLOAT_TYPE buf_a[BM * (BK+1)];
+shared FLOAT_TYPE buf_b[BN * (BK+1)];
+
+void main() {
+    const uint i13 = gl_GlobalInvocationID.z / p.ne12;
+    const uint i12 = gl_GlobalInvocationID.z % p.ne12;
+
+    const uint i03 = i13 / p.broadcast3;
+    const uint i02 = i12 / p.broadcast2;
+
+    const uint batch_idx_a = i03 * p.ne02 + i02;
+
+    const uint blocks_m = (p.M + BM - 1) / BM;
+    const uint ir = gl_WorkGroupID.x % blocks_m;
+    const uint ik = gl_WorkGroupID.x / blocks_m;
+    const uint ic = gl_WorkGroupID.y;
+
+    const uint warp_i = gl_LocalInvocationID.x / WARP;
+    const uint warp_r = warp_i % (BM / WM);
+    const uint warp_c = warp_i / (BM / WM);
+
+    const uint WNITER = (WM * WN) / (WARP * TM * TN * WMITER);
+    const uint WSUBM = WM / WMITER;
+    const uint WSUBN = WN / WNITER;
+
+    const uint tiw = gl_LocalInvocationID.x % WARP;
+    const uint tiwr = tiw % (WSUBM / TM);
+    const uint tiwc = tiw / (WSUBM / TM);
+
+    const uint loadr_a = gl_LocalInvocationID.x % (BK / LOAD_VEC_A);
+    const uint loadc_a = gl_LocalInvocationID.x / (BK / LOAD_VEC_A);
+    const uint loadr_b = gl_LocalInvocationID.x % (BK / LOAD_VEC_B);
+    const uint loadc_b = gl_LocalInvocationID.x / (BK / LOAD_VEC_B);
+
+    const uint loadstride_a = gl_WorkGroupSize.x * LOAD_VEC_A / BK;
+    const uint loadstride_b = gl_WorkGroupSize.x * LOAD_VEC_B / BK;
+
+    const uint start_k = ik * p.k_split;
+    const uint end_k = min(p.K, (ik + 1) * p.k_split);
+
+    uint pos_a = (batch_idx_a * p.batch_stride_a + ir * BM * p.stride_a + start_k) / LOAD_VEC_A;
+    uint pos_b = (gl_GlobalInvocationID.z * p.batch_stride_b + ic * BN * p.stride_b + start_k) / LOAD_VEC_B;
+
+    float sums[WMITER * TM * WNITER * TN];
+    FLOAT_TYPE cache_a[WMITER * TM];
+    FLOAT_TYPE cache_b[WNITER * TN];
+
+    [[unroll]] for (uint i = 0; i < WMITER*TM*WNITER*TN; i++) {
+        sums[i] = 0.0f;
+    }
+
+    [[unroll]] for (uint block = start_k; block < end_k; block += BK) {
+        [[unroll]] for (uint l = 0; l < BM; l += loadstride_a) {"""
+
+mulmat_load_scalar = """
+#if LOAD_VEC_A == 8
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+            buf_a[buf_idx    ] = FLOAT_TYPE(data_a[idx][0].x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(data_a[idx][0].y);
+            buf_a[buf_idx + 2] = FLOAT_TYPE(data_a[idx][0].z);
+            buf_a[buf_idx + 3] = FLOAT_TYPE(data_a[idx][0].w);
+            buf_a[buf_idx + 4] = FLOAT_TYPE(data_a[idx][1].x);
+            buf_a[buf_idx + 5] = FLOAT_TYPE(data_a[idx][1].y);
+            buf_a[buf_idx + 6] = FLOAT_TYPE(data_a[idx][1].z);
+            buf_a[buf_idx + 7] = FLOAT_TYPE(data_a[idx][1].w);
+#elif LOAD_VEC_A == 4
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+            buf_a[buf_idx    ] = FLOAT_TYPE(data_a[idx].x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(data_a[idx].y);
+            buf_a[buf_idx + 2] = FLOAT_TYPE(data_a[idx].z);
+            buf_a[buf_idx + 3] = FLOAT_TYPE(data_a[idx].w);
+#else
+            if (ir * BM + loadc_a + l < p.M && block + loadr_a < end_k) {
+                buf_a[(loadc_a + l) * (BK+1) + loadr_a] = FLOAT_TYPE(data_a[pos_a + (loadc_a + l) * p.stride_a + loadr_a]);
+            } else {
+                buf_a[(loadc_a + l) * (BK+1) + loadr_a] = FLOAT_TYPE(0.0f);
+            }
+#endif
+"""
+
+mulmat_load_q4_0 = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
+
+            const uint ib = idx / 16;
+            const uint iqs = idx & 0xF;
+
+            const float d = float(data_a[ib].d);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const vec2 v = (vec2(vui & 0xF, vui >> 4) - 8.0f) * d;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);"""
+
+mulmat_load_q4_1 = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
+
+            const uint ib = idx / 16;
+            const uint iqs = idx & 0xF;
+
+            const float d = float(data_a[ib].d);
+            const float m = float(data_a[ib].m);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const vec2 v = vec2(vui & 0xF, vui >> 4) * d + m;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);"""
+
+mulmat_load_q5_0 = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
+
+            const uint ib = idx / 16;
+            const uint iqs = idx & 0xF;
+
+            const float d = float(data_a[ib].d);
+            const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0];
+            const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const vec2 v = (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);"""
+
+mulmat_load_q5_1 = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
+
+            const uint ib = idx / 16;
+            const uint iqs = idx & 0xF;
+
+            const float d = float(data_a[ib].d);
+            const float m = float(data_a[ib].m);
+            const uint uint_qh = data_a[ib].qh;
+            const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const vec2 v = vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);"""
+
+mulmat_load_q8_0 = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 16;
+            const uint iqs = (idx & 0xF) * 2;
+
+            const float d = float(data_a[ib].d);
+            const vec2 v = vec2(int(data_a[ib].qs[iqs]), int(data_a[ib].qs[iqs + 1])) * d;
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);"""
+
+
+mulmat_load_q2_K = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                         // 2 values per idx
+            const uint iqs = idx % 128;                        // 0..127
+
+            const uint qsi = (iqs / 64) * 32 + (iqs % 16) * 2; // 0,2,4..30
+            const uint scalesi = iqs / 8;                      // 0..15
+            const uint qsshift = ((iqs % 64) / 16) * 2;        // 0,2,4,6
+
+            const uvec2 qs = uvec2(data_a[ib].qs[qsi], data_a[ib].qs[qsi + 1]);
+            const uint scales = data_a[ib].scales[scalesi];
+            const vec2 d = vec2(data_a[ib].d);
+
+            const vec2 v = d.x * float(scales & 0xF) * vec2((qs >> qsshift) & 3) - d.y * float(scales >> 4);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);"""
+
+mulmat_load_q3_K = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                   // 2 values per idx
+            const uint iqs = idx % 128;                  // 0..127
+
+            const uint n = iqs / 64;                     // 0,1
+            const uint qsi = n * 32 + (iqs % 16) * 2;    // 0,2,4..62
+            const uint hmi =          (iqs % 16) * 2;    // 0,2,4..30
+            const uint j = (iqs % 64) / 4;               // 0..3
+            const uint is = iqs / 8;                     // 0..15
+            const uint halfsplit = ((iqs % 64) / 16);    // 0,1,2,3
+            const uint qsshift = halfsplit * 2;          // 0,2,4,6
+            const uint m = 1 << (4 * n + halfsplit);     // 1,2,4,8,16,32,64,128
+
+            const int8_t us = int8_t(is <  4 ? (data_a[ib].scales[is-0] & 0xF) | (((data_a[ib].scales[is+8] >> 0) & 3) << 4) :
+                                    is <  8 ? (data_a[ib].scales[is-0] & 0xF) | (((data_a[ib].scales[is+4] >> 2) & 3) << 4) :
+                                    is < 12 ? (data_a[ib].scales[is-8] >>  4) | (((data_a[ib].scales[is+0] >> 4) & 3) << 4) :
+                                            (data_a[ib].scales[is-8] >>  4) | (((data_a[ib].scales[is-4] >> 6) & 3) << 4));
+            const float dl = float(data_a[ib].d) * float(us - 32);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(dl * float(int8_t((data_a[ib].qs[qsi    ] >> qsshift) & 3) - (((data_a[ib].hmask[hmi    ] & m) != 0) ? 0 : 4)));
+            buf_a[buf_idx + 1] = FLOAT_TYPE(dl * float(int8_t((data_a[ib].qs[qsi + 1] >> qsshift) & 3) - (((data_a[ib].hmask[hmi + 1] & m) != 0) ? 0 : 4)));"""
+
+mulmat_load_q4_K = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                 // 2 values per idx
+            const uint iqs = idx % 128;                // 0..127
+
+            const uint n = iqs / 32;                   // 0,1,2,3
+            const uint b = (iqs % 32) / 16;            // 0,1
+            const uint is = 2 * n + b;                 // 0..7
+            const uint qsi = n * 32 + (iqs % 16) * 2;  // 0,2,4..126
+
+            const vec2 loadd = vec2(data_a[ib].d);
+
+            uint8_t sc;
+            uint8_t mbyte;
+            if (is < 4) {
+                sc    = uint8_t(data_a[ib].scales[is    ] & 63);
+                mbyte = uint8_t(data_a[ib].scales[is + 4] & 63);
+            } else {
+                sc    = uint8_t((data_a[ib].scales[is + 4] & 0xF) | ((data_a[ib].scales[is - 4] >> 6) << 4));
+                mbyte = uint8_t((data_a[ib].scales[is + 4] >>  4) | ((data_a[ib].scales[is    ] >> 6) << 4));
+            }
+            const float d = loadd.x * sc;
+            const float m = loadd.y * mbyte;
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(d * float((data_a[ib].qs[qsi    ] >> (b * 4)) & 0xF) - m);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(d * float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF) - m);"""
+
+mulmat_load_q5_K = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                 // 2 values per idx
+            const uint iqs = idx % 128;                // 0..127
+
+            const uint n = iqs / 32;                   // 0,1,2,3
+            const uint b = (iqs % 32) / 16;            // 0,1
+            const uint is = 2 * n + b;                 // 0..7
+            const uint qsi = n * 32 + (iqs % 16) * 2;  // 0,2,4..126
+            const uint qhi = (iqs % 16) * 2;           // 0,2,4..30
+
+            const uint8_t hm = uint8_t(1 << (iqs / 16));
+
+            const vec2 loadd = vec2(data_a[ib].d);
+
+            uint8_t sc;
+            uint8_t mbyte;
+            if (is < 4) {
+                sc    = uint8_t(data_a[ib].scales[is    ] & 63);
+                mbyte = uint8_t(data_a[ib].scales[is + 4] & 63);
+            } else {
+                sc    = uint8_t((data_a[ib].scales[is + 4] & 0xF) | ((data_a[ib].scales[is - 4] >> 6) << 4));
+                mbyte = uint8_t((data_a[ib].scales[is + 4] >>  4) | ((data_a[ib].scales[is    ] >> 6) << 4));
+            }
+            const float d = loadd.x * sc;
+            const float m = loadd.y * mbyte;
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(d * (float((data_a[ib].qs[qsi    ] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi    ] & hm) != 0 ? 16 : 0)) - m);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(d * (float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi + 1] & hm) != 0 ? 16 : 0)) - m);"""
+
+mulmat_load_q6_K = """
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                  // 2 values per idx
+            const uint iqs = idx % 128;                 // 0..127
+
+            const uint n = iqs / 64;                    // 0,1
+            const uint b = (iqs % 64) / 32;             // 0,1
+            const uint is_b = (iqs % 16) / 8;           // 0,1
+            const uint qhshift = ((iqs % 64) / 16) * 2; // 0,2,4,6
+            const uint is = 8 * n + qhshift + is_b;     // 0..15
+            const uint qsi = n * 64 + (iqs % 32) * 2;   // 0,2,4..126
+            const uint qhi = n * 32 + (iqs % 16) * 2;   // 0,2,4..62
+
+            const float dscale = float(data_a[ib].d) * float(data_a[ib].scales[is]);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi    ] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi    ] >> qhshift) & 3) << 4)) - 32));
+            buf_a[buf_idx + 1] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi + 1] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi + 1] >> qhshift) & 3) << 4)) - 32));"""
+
+mulmat_body2 = """
+        }
+        [[unroll]] for (uint l = 0; l < BN; l += loadstride_b) {
+#if LOAD_VEC_B == 8
+            const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b;
+            const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B;
+            buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx][0].x);
+            buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx][0].y);
+            buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx][0].z);
+            buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx][0].w);
+            buf_b[buf_idx + 4] = FLOAT_TYPE(data_b[idx][1].x);
+            buf_b[buf_idx + 5] = FLOAT_TYPE(data_b[idx][1].y);
+            buf_b[buf_idx + 6] = FLOAT_TYPE(data_b[idx][1].z);
+            buf_b[buf_idx + 7] = FLOAT_TYPE(data_b[idx][1].w);
+#elif LOAD_VEC_B == 4
+            const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b;
+            const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B;
+            buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx].x);
+            buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx].y);
+            buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx].z);
+            buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx].w);
+#else
+            if (ic * BN + loadc_b + l < p.N && block + loadr_b < end_k) {
+                buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(data_b[pos_b + (loadc_b + l) * p.stride_b + loadr_b]);
+            } else {
+                buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(0.0f);
+            }
+#endif
+        }
+
+        barrier();
+
+        pos_a += BK / LOAD_VEC_A;
+        pos_b += BK / LOAD_VEC_B;
+
+        for (uint i = 0; i < BK; i++) {
+            // Load from shared into cache
+            [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) {
+                [[unroll]] for (uint j = 0; j < TM; j++) {
+                    cache_a[wsir * TM + j] = buf_a[(warp_r * WM + wsir * WSUBM + tiwr * TM + j) * (BK+1) + i];
+                }
+            }
+            [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) {
+                [[unroll]] for (uint j = 0; j < TN; j++) {
+                    cache_b[wsic * TN + j] = buf_b[(warp_c * WN + wsic * WSUBN + tiwc * TN + j) * (BK+1) + i];
+                }
+            }
+
+            [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) {
+                [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) {
+                    [[unroll]] for (uint cc = 0; cc < TN; cc++) {
+                        [[unroll]] for (uint cr = 0; cr < TM; cr++) {
+                            sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr] += float(cache_a[wsir * TM + cr]) * float(cache_b[wsic * TN + cc]);
+                        }
+                    }
+                }
+            }
+        }
+
+        barrier();
+    }
+
+    const uint dr = ir * BM + warp_r * WM;
+    const uint dc = ic * BN + warp_c * WN;
+
+    const uint offsets = gl_GlobalInvocationID.z * p.batch_stride_d + ik * p.batch_stride_d * gl_NumWorkGroups.z;
+
+    [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) {
+        [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) {
+
+            const uint dr_warp = dr + wsir * WSUBM + tiwr * TM;
+            const uint dc_warp = dc + wsic * WSUBN + tiwc * TN;
+            [[unroll]] for (uint cc = 0; cc < TN; cc++) {
+                [[unroll]] for (uint cr = 0; cr < TM; cr++) {
+                    if (dr_warp + cr < p.M && dc_warp + cc < p.N) {
+                        data_d[offsets + (dc_warp + cc) * p.stride_d + dr_warp + cr] = D_TYPE(sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr]);
+                    }
+                }
+            }
+        }
+    }
+}
+"""
+
+mulmat_split_k_reduce_src = """#version 450
+
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {float data_a[];};
+layout (binding = 1) writeonly buffer D {float data_d[];};
+
+layout (push_constant) uniform parameter {
+    uint ne;
+    uint k_num;
+} p;
+
+void main() {
+    const uint idx = gl_GlobalInvocationID.x;
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    float result = 0.0f;
+
+    [[unroll]] for (uint i = 0; i < p.k_num; i++) {
+        result += data_a[i * p.ne + idx];
+    }
+
+    data_d[idx] = result;
+}
+"""
+
+# DEQUANT SHADER
+dequant_head = """#version 450
+
+#extension GL_EXT_control_flow_attributes : require
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint M;
+    uint K;
+    uint stride_a;
+    uint stride_b;
+    uint nel;
+} p;
+"""
+
+dequant_f32_body = """
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {float data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.x * 16;
+
+    if (i >= p.nel) {
+        return;
+    }
+
+    [[unroll]] for (uint l = 0; l < 16; l++) {
+        data_b[i + l] = D_TYPE(data_a[i + l]);
+    }
+}
+"""
+
+dequant_q4_0_body = """
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_q4_0 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint b_idx = 1024*i + 32*ir + 8*il;
+
+    const float d = float(data_a[ib].d);
+    const float dm = -8.0f * d;
+
+    const uint q_idx = 8*il;
+
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        data_b[b_idx + l +  0] = D_TYPE(d * (data_a[ib].qs[q_idx + l] & 0xF) + dm);
+        data_b[b_idx + l + 16] = D_TYPE(d * (data_a[ib].qs[q_idx + l] >>  4) + dm);
+    }
+}
+"""
+
+dequant_q4_1_body = """
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_q4_1 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint b_idx = 1024*i + 32*ir + 8*il;
+
+    const float d = float(data_a[ib].d);
+    const float m = float(data_a[ib].m);
+
+    const uint q_idx = 8*il;
+
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        data_b[b_idx + l +  0] = D_TYPE(d * (data_a[ib].qs[q_idx + l] & 0xF) + m);
+        data_b[b_idx + l + 16] = D_TYPE(d * (data_a[ib].qs[q_idx + l] >>  4) + m);
+    }
+}
+"""
+
+dequant_q5_0_body = """
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_q5_0 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint b_idx = 1024*i + 32*ir + 8*il;
+
+    const float d = float(data_a[ib].d);
+    const uint qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0];
+
+    const uint q_idx = 8*il;
+
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        const uint iqs = q_idx + l;
+        const uint vui = uint(data_a[ib].qs[iqs]);
+        data_b[b_idx + l +  0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10)) - 16.0f));
+        data_b[b_idx + l + 16] = D_TYPE(d * (((vui >>  4) | ((qh >> (iqs + 12)) & 0x10)) - 16.0f));
+    }
+}
+"""
+
+dequant_q5_1_body = """
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_q5_1 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint b_idx = 1024*i + 32*ir + 8*il;
+
+    const float d = float(data_a[ib].d);
+    const float m = float(data_a[ib].m);
+    const uint qh = data_a[ib].qh;
+
+    const uint q_idx = 8*il;
+
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        const uint iqs = q_idx + l;
+        const uint vui = uint(data_a[ib].qs[iqs]);
+        data_b[b_idx + l +  0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10))) + m);
+        data_b[b_idx + l + 16] = D_TYPE(d * (((vui >>  4) | ((qh >> (iqs + 12)) & 0x10))) + m);
+    }
+}
+"""
+
+dequant_q8_0_body = """
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_q8_0 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint b_idx = 1024*i + 32*ir + 16*il;
+
+    const float d = float(data_a[ib].d);
+
+    const uint q_idx = 16*il;
+
+    [[unroll]] for (uint l = 0; l < 16; l += 2) {
+        data_b[b_idx + l    ] = D_TYPE(d * data_a[ib].qs[q_idx + l    ]);
+        data_b[b_idx + l + 1] = D_TYPE(d * data_a[ib].qs[q_idx + l + 1]);
+    }
+}
+"""
+
+# K-quants
+dequant_q2_K_body = """
+layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = gl_WorkGroupID.x * 256 + wgy;
+        if (i >= p.M * p.K / QUANT_K) {
+            return;
+        }
+
+        const uint tid = gl_LocalInvocationID.x;
+        const uint ip = tid / 32;
+        const uint il = tid - 32 * ip;
+        const uint is = 8 * ip + il / 16;
+
+        const uint y_idx = i * QUANT_K + 128 * ip + il;
+
+        const uint ql_idx = 32 * ip + il;
+        const uint8_t qs = data_a[i].qs[32 * ip + il];
+
+        FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x);
+        FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y);
+        data_b[y_idx +  0] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+0] & 0xF) * ((qs >> 0) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+0] >> 4));
+        data_b[y_idx + 32] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+2] & 0xF) * ((qs >> 2) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+2] >> 4));
+        data_b[y_idx + 64] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+4] & 0xF) * ((qs >> 4) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+4] >> 4));
+        data_b[y_idx + 96] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+6] & 0xF) * ((qs >> 6) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+6] >> 4));
+    }
+}
+"""
+dequant_q3_K_body = """
+layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = uint(gl_WorkGroupID.x * 256 + wgy);
+        if (i >= p.M * p.K / QUANT_K) {
+            return;
+        }
+
+        const uint r = gl_LocalInvocationID.x / 4;
+        const uint tid = r / 2;
+        const uint is0 = r % 2;
+        const uint l0 = 16 * is0 + 4 * (gl_LocalInvocationID.x % 4);
+        const uint n = tid / 4;
+        const uint j = tid - 4*n;
+
+        const uint8_t m = uint8_t(1 << (4*n + j));
+        const uint is = 8*n + 2*j + is0;
+        const uint shift = 2*j;
+
+        const int8_t us = int8_t(is <  4 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+8] >> 0) & 3) << 4) :
+                                 is <  8 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+4] >> 2) & 3) << 4) :
+                                 is < 12 ? (data_a[i].scales[is-8] >>  4) | (((data_a[i].scales[is+0] >> 4) & 3) << 4) :
+                                           (data_a[i].scales[is-8] >>  4) | (((data_a[i].scales[is-4] >> 6) & 3) << 4));
+        const FLOAT_TYPE d_all = FLOAT_TYPE(data_a[i].d);
+        const FLOAT_TYPE dl    = d_all * FLOAT_TYPE(us - 32);
+
+        const uint y_idx = i * QUANT_K + 128 * n + 32 * j;
+        const uint qs_idx = 32*n;
+
+        for (uint l = l0; l < l0 + 4; ++l) {
+            data_b[y_idx + l] = D_TYPE(dl * FLOAT_TYPE(int8_t((data_a[i].qs[qs_idx + l] >> shift) & 3) - (((data_a[i].hmask[l] & m) != 0) ? 0 : 4)));
+        }
+    }
+}
+"""
+dequant_q4_K_body = """
+layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = gl_WorkGroupID.x * 256 + wgy;
+        if (i >= p.M * p.K / QUANT_K) {
+            return;
+        }
+
+        const uint tid = gl_LocalInvocationID.x;
+        const uint il = tid / 8;
+        const uint ir = tid % 8;
+        const uint is = 2 * il;
+        const uint n = 4;
+
+        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y);
+
+        const uint y_idx = i * QUANT_K + 64 * il + n * ir;
+        const uint qs_idx = 32*il + n * ir;
+
+        uint8_t sc;
+        uint8_t m;
+        if (is < 4) {
+            sc = uint8_t(data_a[i].scales[is] & 63);
+            m  = uint8_t(data_a[i].scales[is + 4] & 63);
+        } else {
+            sc = uint8_t((data_a[i].scales[is + 4] & 0xF) | ((data_a[i].scales[is - 4] >> 6) << 4));
+            m  = uint8_t((data_a[i].scales[is + 4] >>  4) | ((data_a[i].scales[is    ] >> 6) << 4));
+        }
+        const FLOAT_TYPE d1 = dall * sc;
+        const FLOAT_TYPE m1 = dmin * m;
+
+        if (is < 4) {
+            sc = uint8_t(data_a[i].scales[is + 1] & 63);
+            m  = uint8_t(data_a[i].scales[is + 5] & 63);
+        } else {
+            sc = uint8_t((data_a[i].scales[is + 5] & 0xF) | ((data_a[i].scales[is - 3] >> 6) << 4));
+            m  = uint8_t((data_a[i].scales[is + 5] >>  4) | ((data_a[i].scales[is + 1] >> 6) << 4));
+        }
+        const FLOAT_TYPE d2 = dall * sc;
+        const FLOAT_TYPE m2 = dmin * m;
+
+        [[unroll]] for (uint l = 0; l < n; ++l) {
+            data_b[y_idx + l     ] = D_TYPE(d1 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] & 0xF) - m1);
+            data_b[y_idx + l + 32] = D_TYPE(d2 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] >>  4) - m2);
+        }
+    }
+}
+"""
+dequant_q5_K_body = """
+layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = gl_WorkGroupID.x * 256 + wgy;
+        if (i >= p.M * p.K / QUANT_K) {
+            return;
+        }
+
+        const uint tid = gl_LocalInvocationID.x;
+        const uint il = tid / 16;
+        const uint ir = tid % 16;
+        const uint is = 2 * il;
+
+        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y);
+
+        const uint y_idx = i * QUANT_K + 64 * il + 2 * ir;
+        const uint qs_idx = 32*il + 2 * ir;
+        const uint qh_idx = 2 * ir;
+
+        uint8_t sc;
+        uint8_t m;
+        if (is < 4) {
+            sc = uint8_t(data_a[i].scales[is] & 63);
+            m  = uint8_t(data_a[i].scales[is + 4] & 63);
+        } else {
+            sc = uint8_t((data_a[i].scales[is + 4] & 0xF) | ((data_a[i].scales[is - 4] >> 6) << 4));
+            m  = uint8_t((data_a[i].scales[is + 4] >>  4) | ((data_a[i].scales[is    ] >> 6) << 4));
+        }
+        const FLOAT_TYPE d1 = dall * sc;
+        const FLOAT_TYPE m1 = dmin * m;
+
+        if (is < 4) {
+            sc = uint8_t(data_a[i].scales[is + 1] & 63);
+            m  = uint8_t(data_a[i].scales[is + 5] & 63);
+        } else {
+            sc = uint8_t((data_a[i].scales[is + 5] & 0xF) | ((data_a[i].scales[is - 3] >> 6) << 4));
+            m  = uint8_t((data_a[i].scales[is + 5] >>  4) | ((data_a[i].scales[is + 1] >> 6) << 4));
+        }
+        const FLOAT_TYPE d2 = dall * sc;
+        const FLOAT_TYPE m2 = dmin * m;
+
+        const uint8_t hm1 = uint8_t(1 << (2 * il    ));
+        const uint8_t hm2 = uint8_t(1 << (2 * il + 1));
+        data_b[y_idx     ] = D_TYPE(d1 * FLOAT_TYPE((data_a[i].qs[qs_idx    ] & 0xF) + (((data_a[i].qh[qh_idx    ] & hm1) != 0) ? 16 : 0)) - m1);
+        data_b[y_idx +  1] = D_TYPE(d1 * FLOAT_TYPE((data_a[i].qs[qs_idx + 1] & 0xF) + (((data_a[i].qh[qh_idx + 1] & hm1) != 0) ? 16 : 0)) - m1);
+        data_b[y_idx + 32] = D_TYPE(d2 * FLOAT_TYPE((data_a[i].qs[qs_idx    ]  >> 4) + (((data_a[i].qh[qh_idx    ] & hm2) != 0) ? 16 : 0)) - m2);
+        data_b[y_idx + 33] = D_TYPE(d2 * FLOAT_TYPE((data_a[i].qs[qs_idx + 1]  >> 4) + (((data_a[i].qh[qh_idx + 1] & hm2) != 0) ? 16 : 0)) - m2);
+    }
+}
+"""
+dequant_q6_K_body = """
+layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = gl_WorkGroupID.x * 256 + wgy;
+        if (i >= p.M * p.K / QUANT_K) {
+            return;
+        }
+        const uint tid = gl_LocalInvocationID.x;
+        const uint ip = tid / 32;
+        const uint il = tid - 32 * ip;
+        const uint is = 8 * ip + il / 16;
+
+        const uint y_idx = i * QUANT_K + 128 * ip + il;
+
+        const uint ql_idx = 64 * ip + il;
+        const uint8_t qh = data_a[i].qh[32 * ip + il];
+
+        const FLOAT_TYPE d = FLOAT_TYPE(data_a[i].d);
+
+        data_b[y_idx +  0] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 0] * (int8_t((data_a[i].ql[ql_idx +  0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32)));
+        data_b[y_idx + 32] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 2] * (int8_t((data_a[i].ql[ql_idx + 32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32)));
+        data_b[y_idx + 64] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 4] * (int8_t((data_a[i].ql[ql_idx +  0] >>  4) | (((qh >> 4) & 3) << 4)) - 32)));
+        data_b[y_idx + 96] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 6] * (int8_t((data_a[i].ql[ql_idx + 32] >>  4) | (((qh >> 6) & 3) << 4)) - 32)));
+    }
+}
+"""
+
+# Mul Mat Vec
+mul_mat_vec_head = """#version 450
+
+#extension GL_EXT_control_flow_attributes : enable
+#extension GL_EXT_shader_16bit_storage : require
+#extension GL_EXT_shader_8bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint ncols;
+    uint b_offset;
+    uint d_offset;
+} p;
+"""
+
+mul_mat_vec_body = """
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
+
+layout (constant_id = 0) const uint BLOCK_SIZE = 32;
+
+shared FLOAT_TYPE tmp[BLOCK_SIZE];
+
+void main() {
+    const uint row = gl_WorkGroupID.x;
+    const uint tid = gl_LocalInvocationID.x;
+
+    const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2;
+
+    tmp[tid] = FLOAT_TYPE(0.0f);
+
+    [[unroll]] for (uint i = 0; i < p.ncols/BLOCK_SIZE; i += 2) {
+        const uint col = i*BLOCK_SIZE + 2*tid;
+        const uint ib = (row*p.ncols + col)/QUANT_K; // block index
+        const uint iqs = (col%QUANT_K)/QUANT_R; // quant index
+        const uint iybs = col - col%QUANT_K; // y block start index
+
+        DEQUANT_FUNC
+
+        // matrix multiplication
+        tmp[tid] += FLOAT_TYPE(v.x) * FLOAT_TYPE(data_b[p.b_offset + iybs + iqs + 0]) +
+                    FLOAT_TYPE(v.y) * FLOAT_TYPE(data_b[p.b_offset + iybs + iqs + y_offset]);
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+    if (tid == 0) {
+        dst[p.d_offset + row] = D_TYPE(tmp[0]);
+    }
+}
+"""
+
+# K-quants
+mul_mat_vec_q2_K_body = """
+layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
+
+shared FLOAT_TYPE tmp[32];
+
+void main() {
+    const uint row = gl_WorkGroupID.x;
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const uint step = 16/K_QUANTS_PER_ITERATION;            // 16 or 8
+
+    const uint v_im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const uint v_in = tid - step*v_im;                      // 0...15 or 0...7
+
+    const uint l0 = K_QUANTS_PER_ITERATION*v_in;            // 0...15
+    const uint q_offset = 32*v_im + l0;
+    const uint s_offset = 8*v_im;
+    const uint y_offset = 128*v_im + l0;
+
+    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
+
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+        const uint y_idx = i * QUANT_K + y_offset;
+
+        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y);
+
+        FLOAT_TYPE sum1 = FLOAT_TYPE(0.0);
+        FLOAT_TYPE sum2 = FLOAT_TYPE(0.0);
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            sum1 += FLOAT_TYPE(data_b[p.b_offset + y_idx + l +  0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 0) & 3)
+                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 0) & 3)
+                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 2) & 3)
+                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 2) & 3)
+                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 4) & 3)
+                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 4) & 3)
+                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 6) & 3)
+                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 6) & 3);
+            sum2 += FLOAT_TYPE(data_b[p.b_offset + y_idx + l +  0]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 0] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 16]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 1] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 32]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 2] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 48]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 3] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 64]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 4] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 80]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 5] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 96]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 6] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[p.b_offset + y_idx + l +112]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 7] >> 4) & 0xF);
+        }
+        tmp[16 * ix + tid] += dall * sum1 - dmin * sum2;
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+    if (tid == 0) {
+        dst[p.d_offset + row] = D_TYPE(tmp[0]);
+    }
+}
+"""
+mul_mat_vec_q3_K_body = """
+layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
+
+shared FLOAT_TYPE tmp[32];
+
+void main() {
+    const uint row = gl_WorkGroupID.x;
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const uint step = 16/K_QUANTS_PER_ITERATION;            // 16 or 8
+
+    const uint v_im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const uint v_in = tid - step*v_im;                      // 0...15 or 0...7
+
+    const uint8_t m = uint8_t(1 << (4 * v_im));
+
+    const uint l0 = K_QUANTS_PER_ITERATION*v_in;            // 0...15
+    const uint q_offset = 32*v_im + l0;
+    const uint y_offset = 128*v_im + l0;
+
+    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
+
+    const uint s_shift = 4 * v_im;
+
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+        const uint y_idx = i * QUANT_K + y_offset;
+
+        const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
+
+        FLOAT_TYPE sum = FLOAT_TYPE(0.0);
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            sum += FLOAT_TYPE(data_b[p.b_offset + y_idx + l +  0]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[0] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 0)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 32]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[2] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 1)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 64]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[4] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 2)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 96]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[6] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 3)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 16]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[1] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 48]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[3] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 80]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[5] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l +112]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[7] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4));
+        }
+        tmp[16 * ix + tid] += d * sum;
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+    if (tid == 0) {
+        dst[p.d_offset + row] = D_TYPE(tmp[0]);
+    }
+}
+"""
+mul_mat_vec_q4_K_body = """
+layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
+
+shared FLOAT_TYPE tmp[32];
+
+void main() {
+    const uint row = gl_WorkGroupID.x;
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const uint step = 8/K_QUANTS_PER_ITERATION;             // 8 or 4
+
+    const uint il = tid/step;                               // 0...3
+    const uint ir = tid - step*il;                          // 0...7 or 0...3
+    const uint n =  2 * K_QUANTS_PER_ITERATION;             // 2 or 4
+
+    const uint v_im = il / 2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const uint v_in = il % 2;
+
+    const uint l0 = n * (2 * ir + v_in);            // 0...15
+    const uint q_offset = 32*v_im + l0;
+    const uint y_offset = 64*v_im + l0;
+
+    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
+
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+        const uint y1_idx = i * QUANT_K + y_offset;
+        const uint y2_idx = y1_idx + 128;
+
+        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y);
+
+        const uint8_t sc0 = uint8_t(  data_a[ib0 + i].scales[v_im * 2    ]       & 0x3f);
+        const uint8_t sc1 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 1]       & 0x3f);
+        const uint8_t sc2 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 4]       & 0x3f);
+        const uint8_t sc3 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 5]       & 0x3f);
+        const uint8_t sc4 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 8]       & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2    ] & 0xc0) >> 2));
+        const uint8_t sc5 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 9]       & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 1] & 0xc0) >> 2));
+        const uint8_t sc6 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 8] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 4] & 0xc0) >> 2));
+        const uint8_t sc7 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 9] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 5] & 0xc0) >> 2));
+
+#if K_QUANTS_PER_ITERATION == 2
+        const uint8_t q4_0  = uint8_t(data_a[ib0 + i].qs[q_offset     ] & 0xf);
+        const uint8_t q4_1  = uint8_t(data_a[ib0 + i].qs[q_offset +  1] & 0xf);
+        const uint8_t q4_2  = uint8_t(data_a[ib0 + i].qs[q_offset +  2] & 0xf);
+        const uint8_t q4_3  = uint8_t(data_a[ib0 + i].qs[q_offset +  3] & 0xf);
+        const uint8_t q4_4  = uint8_t(data_a[ib0 + i].qs[q_offset     ]  >> 4);
+        const uint8_t q4_5  = uint8_t(data_a[ib0 + i].qs[q_offset +  1]  >> 4);
+        const uint8_t q4_6  = uint8_t(data_a[ib0 + i].qs[q_offset +  2]  >> 4);
+        const uint8_t q4_7  = uint8_t(data_a[ib0 + i].qs[q_offset +  3]  >> 4);
+        const uint8_t q4_8  = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf);
+        const uint8_t q4_9  = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf);
+        const uint8_t q4_10 = uint8_t(data_a[ib0 + i].qs[q_offset + 66] & 0xf);
+        const uint8_t q4_11 = uint8_t(data_a[ib0 + i].qs[q_offset + 67] & 0xf);
+        const uint8_t q4_12 = uint8_t(data_a[ib0 + i].qs[q_offset + 64]  >> 4);
+        const uint8_t q4_13 = uint8_t(data_a[ib0 + i].qs[q_offset + 65]  >> 4);
+        const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 66]  >> 4);
+        const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 67]  >> 4);
+
+        const FLOAT_TYPE sx = FLOAT_TYPE(data_b[p.b_offset + y1_idx] * q4_0 + data_b[p.b_offset + y1_idx + 1] * q4_1 + data_b[p.b_offset + y1_idx + 2] * q4_2 + data_b[p.b_offset + y1_idx + 3] * q4_3);
+        const FLOAT_TYPE sy = FLOAT_TYPE(data_b[p.b_offset + y1_idx + 32] * q4_4 + data_b[p.b_offset + y1_idx + 33] * q4_5 + data_b[p.b_offset + y1_idx + 34] * q4_6 + data_b[p.b_offset + y1_idx + 35] * q4_7);
+        const FLOAT_TYPE sz = FLOAT_TYPE(data_b[p.b_offset + y2_idx] * q4_8 + data_b[p.b_offset + y2_idx + 1] * q4_9 + data_b[p.b_offset + y2_idx + 2] * q4_10 + data_b[p.b_offset + y2_idx + 3] * q4_11);
+        const FLOAT_TYPE sw = FLOAT_TYPE(data_b[p.b_offset + y2_idx + 32] * q4_12 + data_b[p.b_offset + y2_idx + 33] * q4_13 + data_b[p.b_offset + y2_idx + 34] * q4_14 + data_b[p.b_offset + y2_idx + 35] * q4_15);
+        const FLOAT_TYPE smin = FLOAT_TYPE(
+            data_b[p.b_offset + y1_idx    ] * sc2 + data_b[p.b_offset + y1_idx + 32] * sc3 + data_b[p.b_offset + y2_idx    ] * sc6 + data_b[p.b_offset + y2_idx + 32] * sc7
+          + data_b[p.b_offset + y1_idx + 1] * sc2 + data_b[p.b_offset + y1_idx + 33] * sc3 + data_b[p.b_offset + y2_idx + 1] * sc6 + data_b[p.b_offset + y2_idx + 33] * sc7
+          + data_b[p.b_offset + y1_idx + 2] * sc2 + data_b[p.b_offset + y1_idx + 34] * sc3 + data_b[p.b_offset + y2_idx + 2] * sc6 + data_b[p.b_offset + y2_idx + 34] * sc7
+          + data_b[p.b_offset + y1_idx + 3] * sc2 + data_b[p.b_offset + y1_idx + 35] * sc3 + data_b[p.b_offset + y2_idx + 3] * sc6 + data_b[p.b_offset + y2_idx + 35] * sc7
+        );
+        tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * sc0 + sy * sc1 + sz * sc4 + sw * sc5) - dmin * smin);
+#else
+        const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset     ] & 0xf);
+        const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset +  1] & 0xf);
+        const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset     ]  >> 4);
+        const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset +  1]  >> 4);
+        const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf);
+        const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf);
+        const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 64]  >> 4);
+        const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 65]  >> 4);
+
+        const FLOAT_TYPE sx = FLOAT_TYPE(data_b[p.b_offset + y1_idx     ] * q4_0  + data_b[p.b_offset + y1_idx +  1] * q4_1);
+        const FLOAT_TYPE sy = FLOAT_TYPE(data_b[p.b_offset + y1_idx + 32] * q4_2  + data_b[p.b_offset + y1_idx + 33] * q4_3);
+        const FLOAT_TYPE sz = FLOAT_TYPE(data_b[p.b_offset + y2_idx     ] * q4_4  + data_b[p.b_offset + y2_idx +  1] * q4_5);
+        const FLOAT_TYPE sw = FLOAT_TYPE(data_b[p.b_offset + y2_idx + 32] * q4_6 + data_b[p.b_offset + y2_idx + 33] * q4_7);
+        const FLOAT_TYPE smin = FLOAT_TYPE(
+            data_b[p.b_offset + y1_idx] * sc2 + data_b[p.b_offset + y1_idx + 32] * sc3 + data_b[p.b_offset + y2_idx] * sc6 + data_b[p.b_offset + y2_idx + 32] * sc7
+          + data_b[p.b_offset + y1_idx + 1] * sc2 + data_b[p.b_offset + y1_idx + 33] * sc3 + data_b[p.b_offset + y2_idx + 1] * sc6 + data_b[p.b_offset + y2_idx + 33] * sc7
+        );
+
+        tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * FLOAT_TYPE(data_a[ib0 + i].scales[v_im] & 0x3f) + sy * FLOAT_TYPE(data_a[ib0 + i].scales[v_im + 1] & 0x3f) + sz * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 4] & 0x0f) | ((data_a[ib0 + i].scales[v_im] & 0xc0) >> 2)) + sw * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 5] & 0x0f) | ((data_a[ib0 + i].scales[v_im + 1] & 0xc0) >> 2))) - dmin * smin);
+#endif
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+    if (tid == 0) {
+        dst[p.d_offset + row] = D_TYPE(tmp[0]);
+    }
+}
+"""
+mul_mat_vec_q5_K_body = """
+layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
+
+shared FLOAT_TYPE tmp[32];
+
+void main() {
+    const uint row = gl_WorkGroupID.x;
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/2;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%2;  // 0 or 0, 1
+
+    const uint il = tid/4;                           // 0...3
+    const uint ir = tid - 4*il;                      // 0...7 or 0...3
+
+    const uint v_im = il / 2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const uint v_in = il % 2;
+
+    const uint l0 = 4*ir + 2*v_in;                   // 0...15
+    const uint q_offset = 32*v_im + l0;
+    const uint y_offset = 64*v_im + l0;
+
+    const uint8_t hm1 = uint8_t(1 << (2*v_im));
+    const uint8_t hm2 = uint8_t(hm1 << 4);
+
+    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
+
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += 2) {
+        const uint y1_idx = i * QUANT_K + y_offset;
+        const uint y2_idx = y1_idx + 128;
+
+        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y);
+
+        const uint8_t sc0 = uint8_t(  data_a[ib0 + i].scales[v_im * 2    ]       & 0x3f);
+        const uint8_t sc1 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 1]       & 0x3f);
+        const uint8_t sc2 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 4]       & 0x3f);
+        const uint8_t sc3 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 5]       & 0x3f);
+        const uint8_t sc4 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 8]       & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2    ] & 0xc0) >> 2));
+        const uint8_t sc5 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 9]       & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 1] & 0xc0) >> 2));
+        const uint8_t sc6 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 8] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 4] & 0xc0) >> 2));
+        const uint8_t sc7 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 9] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 5] & 0xc0) >> 2));
+
+        const uint8_t q4_0  = uint8_t(data_a[ib0 + i].qs[q_offset     ] & 0xf);
+        const uint8_t q4_1  = uint8_t(data_a[ib0 + i].qs[q_offset +  1] & 0xf);
+        const uint8_t q4_2  = uint8_t(data_a[ib0 + i].qs[q_offset + 16] & 0xf);
+        const uint8_t q4_3  = uint8_t(data_a[ib0 + i].qs[q_offset + 17] & 0xf);
+        const uint8_t q4_4  = uint8_t(data_a[ib0 + i].qs[q_offset     ]  >> 4);
+        const uint8_t q4_5  = uint8_t(data_a[ib0 + i].qs[q_offset +  1]  >> 4);
+        const uint8_t q4_6  = uint8_t(data_a[ib0 + i].qs[q_offset + 16]  >> 4);
+        const uint8_t q4_7  = uint8_t(data_a[ib0 + i].qs[q_offset + 17]  >> 4);
+        const uint8_t q4_8  = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf);
+        const uint8_t q4_9  = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf);
+        const uint8_t q4_10 = uint8_t(data_a[ib0 + i].qs[q_offset + 80] & 0xf);
+        const uint8_t q4_11 = uint8_t(data_a[ib0 + i].qs[q_offset + 81] & 0xf);
+        const uint8_t q4_12 = uint8_t(data_a[ib0 + i].qs[q_offset + 64]  >> 4);
+        const uint8_t q4_13 = uint8_t(data_a[ib0 + i].qs[q_offset + 65]  >> 4);
+        const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 80]  >> 4);
+        const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 81]  >> 4);
+
+        const FLOAT_TYPE sx = FLOAT_TYPE(
+            data_b[p.b_offset + y1_idx     ] * (q4_0 + (((data_a[ib0 + i].qh[l0     ] & hm1) != 0) ? 16 : 0))
+          + data_b[p.b_offset + y1_idx +  1] * (q4_1 + (((data_a[ib0 + i].qh[l0 +  1] & hm1) != 0) ? 16 : 0))
+          + data_b[p.b_offset + y1_idx + 16] * (q4_2 + (((data_a[ib0 + i].qh[l0 + 16] & hm1) != 0) ? 16 : 0))
+          + data_b[p.b_offset + y1_idx + 17] * (q4_3 + (((data_a[ib0 + i].qh[l0 + 17] & hm1) != 0) ? 16 : 0))
+        );
+        const FLOAT_TYPE sy = FLOAT_TYPE(
+            data_b[p.b_offset + y1_idx + 32] * (q4_4 + (((data_a[ib0 + i].qh[l0     ] & (hm1 << 1)) != 0) ? 16 : 0))
+          + data_b[p.b_offset + y1_idx + 33] * (q4_5 + (((data_a[ib0 + i].qh[l0 +  1] & (hm1 << 1)) != 0) ? 16 : 0))
+          + data_b[p.b_offset + y1_idx + 48] * (q4_6 + (((data_a[ib0 + i].qh[l0 + 16] & (hm1 << 1)) != 0) ? 16 : 0))
+          + data_b[p.b_offset + y1_idx + 49] * (q4_7 + (((data_a[ib0 + i].qh[l0 + 17] & (hm1 << 1)) != 0) ? 16 : 0))
+        );
+        const FLOAT_TYPE sz = FLOAT_TYPE(
+            data_b[p.b_offset + y2_idx     ] * (q4_8  + (((data_a[ib0 + i].qh[l0     ] & hm2) != 0) ? 16 : 0))
+          + data_b[p.b_offset + y2_idx +  1] * (q4_9  + (((data_a[ib0 + i].qh[l0 +  1] & hm2) != 0) ? 16 : 0))
+          + data_b[p.b_offset + y2_idx + 16] * (q4_10 + (((data_a[ib0 + i].qh[l0 + 16] & hm2) != 0) ? 16 : 0))
+          + data_b[p.b_offset + y2_idx + 17] * (q4_11 + (((data_a[ib0 + i].qh[l0 + 17] & hm2) != 0) ? 16 : 0))
+        );
+        const FLOAT_TYPE sw = FLOAT_TYPE(
+            data_b[p.b_offset + y2_idx + 32] * (q4_12 + (((data_a[ib0 + i].qh[l0     ] & (hm2 << 1)) != 0) ? 16 : 0))
+          + data_b[p.b_offset + y2_idx + 33] * (q4_13 + (((data_a[ib0 + i].qh[l0 +  1] & (hm2 << 1)) != 0) ? 16 : 0))
+          + data_b[p.b_offset + y2_idx + 48] * (q4_14 + (((data_a[ib0 + i].qh[l0 + 16] & (hm2 << 1)) != 0) ? 16 : 0))
+          + data_b[p.b_offset + y2_idx + 49] * (q4_15 + (((data_a[ib0 + i].qh[l0 + 17] & (hm2 << 1)) != 0) ? 16 : 0))
+        );
+        const FLOAT_TYPE smin = FLOAT_TYPE(
+            (data_b[p.b_offset + y1_idx] + data_b[p.b_offset + y1_idx + 1] + data_b[p.b_offset + y1_idx + 16] + data_b[p.b_offset + y1_idx + 17]) * sc2 + (data_b[p.b_offset + y1_idx + 32] + data_b[p.b_offset + y1_idx + 33] + data_b[p.b_offset + y1_idx + 48] + data_b[p.b_offset + y1_idx + 49]) * sc3
+          + (data_b[p.b_offset + y2_idx] + data_b[p.b_offset + y2_idx + 1] + data_b[p.b_offset + y2_idx + 16] + data_b[p.b_offset + y2_idx + 17]) * sc6 + (data_b[p.b_offset + y2_idx + 32] + data_b[p.b_offset + y2_idx + 33] + data_b[p.b_offset + y2_idx + 48] + data_b[p.b_offset + y2_idx + 49]) * sc7
+        );
+        tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * sc0 + sy * sc1 + sz * sc4 + sw * sc5) - dmin * smin);
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+    if (tid == 0) {
+        dst[p.d_offset + row] = D_TYPE(tmp[0]);
+    }
+}
+"""
+mul_mat_vec_q6_K_body = """
+layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
+
+shared FLOAT_TYPE tmp[32];
+
+void main() {
+    const uint block_size = gl_WorkGroupSize.x;
+    const uint row = gl_WorkGroupID.x;
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const uint step = 16/K_QUANTS_PER_ITERATION;            // 16 or 8
+
+    const uint v_im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const uint v_in = tid - step*v_im;                      // 0...15 or 0...7
+
+#if K_QUANTS_PER_ITERATION == 1
+    const uint l0 = v_in;                                   // 0...15
+    const uint is = 0;
+#else
+    const uint l0 = 4 * v_in;                               // 0, 4, 8, ..., 28
+    const uint is = v_in / 4;
+#endif
+
+    const uint ql_offset = 64*v_im + l0;
+    const uint qh_offset = 32*v_im + l0;
+    const uint s_offset  =  8*v_im + is;
+    const uint y_offset = 128*v_im + l0;
+
+    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
+
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+        const uint y_idx   = i * QUANT_K + y_offset;
+
+        const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
+
+#if K_QUANTS_PER_ITERATION == 1
+        FLOAT_TYPE sum = FLOAT_TYPE(data_b[p.b_offset + y_idx +  0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset +  0] & 0xF) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0x03) << 4)) - 32)
+                       + FLOAT_TYPE(data_b[p.b_offset + y_idx + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x03) << 4)) - 32)
+                       + FLOAT_TYPE(data_b[p.b_offset + y_idx + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] & 0xF) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0x0c) << 2)) - 32)
+                       + FLOAT_TYPE(data_b[p.b_offset + y_idx + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x0c) << 2)) - 32)
+                       + FLOAT_TYPE(data_b[p.b_offset + y_idx + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset +  0]  >> 4) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0x30) >> 0)) - 32)
+                       + FLOAT_TYPE(data_b[p.b_offset + y_idx + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16]  >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x30) >> 0)) - 32)
+                       + FLOAT_TYPE(data_b[p.b_offset + y_idx + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32]  >> 4) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0xc0) >> 2)) - 32)
+                       + FLOAT_TYPE(data_b[p.b_offset + y_idx +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48]  >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0xc0) >> 2)) - 32);
+        tmp[16 * ix + tid] += sum;
+#else
+        FLOAT_TYPE sum = FLOAT_TYPE(0.0);
+        [[unroll]] for (int l = 0; l < 4; ++l) {
+            sum += FLOAT_TYPE(data_b[p.b_offset + y_idx + l+ 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 0) & 3) << 4)) - 32)
+                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l+32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 2) & 3) << 4)) - 32)
+                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l+64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0]  >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 4) & 3) << 4)) - 32)
+                 + FLOAT_TYPE(data_b[p.b_offset + y_idx + l+96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32]  >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 6) & 3) << 4)) - 32);
+        }
+        tmp[16 * ix + tid] += sum;
+#endif
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+       }
+        barrier();
+    }
+    if (tid == 0) {
+        dst[p.d_offset + row] = D_TYPE(tmp[0]);
+    }
+}
+"""
+
+mul_mat_p021_src = """#version 450
+
+#extension GL_EXT_control_flow_attributes : enable
+#extension GL_EXT_shader_16bit_storage : require
+
+#define BLOCK_SIZE 32
+#define FLOAT_TYPE float
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
+
+layout (push_constant) uniform parameter
+{
+    uint ncols_x;
+    uint nrows_x;
+    uint nchannels_x;
+    uint nchannels_y;
+    uint b_offset;
+    uint d_offset;
+} p;
+
+shared FLOAT_TYPE tmp[BLOCK_SIZE];
+
+void main() {
+    const uint tid = gl_LocalInvocationID.x;
+    const uint row_x = gl_GlobalInvocationID.y;
+    const uint channel = gl_GlobalInvocationID.z;
+    const uint channel_x = channel / (p.nchannels_y / p.nchannels_x);
+
+    const uint nrows_y = p.ncols_x;
+    const uint nrows_dst = p.nrows_x;
+    const uint row_dst = row_x;
+
+    tmp[tid] = FLOAT_TYPE(0.0f);
+
+    for (uint col_x0 = 0; col_x0 < p.ncols_x; col_x0 += BLOCK_SIZE) {
+        const uint col_x = col_x0 + tid;
+
+        if (col_x >= p.ncols_x) {
+            break;
+        }
+
+        // x is transposed and permuted
+        const uint ix = row_x*p.nchannels_x*p.ncols_x + channel_x*p.ncols_x + col_x;
+        const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]);
+
+        const uint row_y = col_x;
+
+        // y is not transposed but permuted
+        const uint iy = channel*nrows_y + row_y;
+
+        tmp[tid] += xi * FLOAT_TYPE(data_b[iy]);
+    }
+
+    // dst is not transposed and not permuted
+    const uint idst = channel*nrows_dst + row_dst;
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+
+    if (tid == 0) {
+        dst[idst] = tmp[0];
+    }
+}
+"""
+
+
+mul_mat_nc_src = """#version 450
+
+#extension GL_EXT_control_flow_attributes : enable
+#extension GL_EXT_shader_16bit_storage : require
+
+#define BLOCK_SIZE 32
+#define FLOAT_TYPE float
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
+
+layout (push_constant) uniform parameter
+{
+    uint ncols_x;
+    uint nrows_x;
+    uint row_stride_x;
+    uint channel_stride_x;
+    uint channel_x_divisor;
+    uint b_offset;
+    uint d_offset;
+} p;
+
+shared FLOAT_TYPE tmp[BLOCK_SIZE];
+
+void main() {
+    const uint tid       = gl_LocalInvocationID.x;
+    const uint row_x     = gl_GlobalInvocationID.y;
+    const uint channel   = gl_GlobalInvocationID.z;
+    const uint channel_x = channel / p.channel_x_divisor;
+
+    const uint nrows_y   = p.ncols_x;
+    const uint nrows_dst = p.nrows_x;
+    const uint row_dst   = row_x;
+
+    const uint idst = channel*nrows_dst + row_dst;
+
+    tmp[tid] = 0.0f;
+
+    for (uint col_x0 = 0; col_x0 < p.ncols_x; col_x0 += BLOCK_SIZE) {
+        const uint col_x = col_x0 + tid;
+
+        if (col_x >= p.ncols_x) {
+            break;
+        }
+
+        const uint row_y = col_x;
+
+        const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
+        const uint iy = channel*nrows_y + row_y;
+
+        const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]);
+
+        tmp[tid] += xi * FLOAT_TYPE(data_b[iy]);
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+
+    if (tid == 0) {
+        dst[idst] = tmp[0];
+    }
+}
+"""
+
+generic_head = """
+#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint KX;
+    uint KY;
+    float param1;
+    float param2;
+} p;
+"""
+
+generic_unary_op_head = """#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint ne;
+    uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03;
+    uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13;
+    uint d_offset;
+    float param1; float param2;
+} p;"""
+
+generic_unary_op_funcs = """
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+uint src0_idx(uint idx) {
+    const uint i03 = idx / (p.ne02*p.ne01*p.ne00);
+    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
+    const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00);
+    const uint i02_offset = i02*p.ne01*p.ne00;
+    const uint i01 = (idx - i03_offset - i02_offset) / p.ne00;
+    const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
+    return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00;
+}
+
+uint dst_idx(uint idx) {
+    const uint i13 = idx / (p.ne12*p.ne11*p.ne10);
+    const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10;
+    const uint i12 = (idx - i13_offset) / (p.ne11*p.ne10);
+    const uint i12_offset = i12*p.ne11*p.ne10;
+    const uint i11 = (idx - i13_offset - i12_offset) / p.ne10;
+    const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10;
+    return i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + i10*p.nb10;
+}"""
+
+generic_unary_op_main = """
+void main() {
+    if (gl_GlobalInvocationID.x >= p.ne) {
+        return;
+    }
+"""
+
+generic_unary_op_combined = f"{generic_unary_op_head}\n{generic_unary_op_funcs}\n{generic_unary_op_main}"
+
+generic_binary_op_head = """#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint ne;
+    uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03;
+    uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13;
+    uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23;
+    uint d_offset;
+    float param1; float param2;
+} p;"""
+
+generic_binary_op_funcs = """
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
+
+uint src0_idx(uint idx) {
+    const uint i03 = idx / (p.ne02*p.ne01*p.ne00);
+    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
+    const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00);
+    const uint i02_offset = i02*p.ne01*p.ne00;
+    const uint i01 = (idx - i03_offset - i02_offset) / p.ne00;
+    const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
+    return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00;
+}
+
+uint src1_idx(uint idx) {
+    const uint i03 = idx / (p.ne02*p.ne01*p.ne00);
+    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
+    const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00);
+    const uint i02_offset = i02*p.ne01*p.ne00;
+    const uint i01 = (idx - i03_offset - i02_offset) / p.ne00;
+    const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
+
+    return (i03 % p.ne13)*p.nb13 + (i02 % p.ne12)*p.nb12 + (i01 % p.ne11)*p.nb11 + (i00 % p.ne10)*p.nb10;
+}
+
+uint dst_idx(uint idx) {
+    const uint i23 = idx / (p.ne22*p.ne21*p.ne20);
+    const uint i23_offset = i23 * p.ne22*p.ne21*p.ne20;
+    const uint i22 = (idx - i23_offset) / (p.ne21*p.ne20);
+    const uint i22_offset = i22*p.ne21*p.ne20;
+    const uint i21 = (idx - i23_offset - i22_offset) / p.ne20;
+    const uint i20 = idx - i23_offset - i22_offset - i21*p.ne20;
+    return i23*p.nb23 + i22*p.nb22 + i21*p.nb21 + i20*p.nb20;
+}"""
+
+generic_binary_op_main = """
+void main() {
+    if (gl_GlobalInvocationID.x >= p.ne) {
+        return;
+    }
+"""
+
+generic_binary_op_combined = f"{generic_binary_op_head}\n{generic_binary_op_funcs}\n{generic_binary_op_main}"
+
+# MUL F32
+mul_body = """
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) * FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)]));
+}
+"""
+
+# ADD
+add_body = """
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) + FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)]));
+}
+"""
+
+# SCALE
+scale_body = """
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) + FLOAT_TYPE(p.param1));
+}
+"""
+
+# SQR
+sqr_body = """
+    const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]);
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(val * val);
+}
+"""
+
+# CLAMP
+clamp_body = """
+    const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]);
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val));
+}
+"""
+
+# CPY
+cpy_end = """
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]);
+}
+"""
+# Causes an optimization error otherwise
+cpy_f16_f16_end = """
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = data_a[src0_idx(gl_GlobalInvocationID.x)];
+}
+"""
+
+# GET_ROWS
+get_rows_float_body = """
+void main() {
+    const uint i00 = gl_GlobalInvocationID.x;
+    const uint i10 = gl_GlobalInvocationID.y;
+    const uint i11 = (gl_GlobalInvocationID.z)/p.ne12;
+    const uint i12 = (gl_GlobalInvocationID.z)%p.ne12;
+
+    if (i00 >= p.ne00) {
+        return;
+    }
+
+    const uint i01 = data_b[i10*p.nb10 + i11*p.nb11 + i12*p.nb12];
+
+    const uint a_offset = i01*p.nb01 + i11*p.nb02 + i12*p.nb03;
+    const uint d_offset = i10*p.nb21 + i11*p.nb22 + i12*p.nb23;
+
+#ifndef OPTIMIZATION_ERROR_WORKAROUND
+    data_d[d_offset + i00] = D_TYPE(data_a[a_offset + i00]);
+#else
+    data_d[d_offset + i00] = data_a[a_offset + i00];
+#endif
+}
+"""
+
+get_rows_body = """
+void main() {
+    const uint i00 = (gl_GlobalInvocationID.x)*2;
+    const uint i10 = gl_GlobalInvocationID.y;
+    const uint i11 = (gl_GlobalInvocationID.z)/p.ne12;
+    const uint i12 = (gl_GlobalInvocationID.z)%p.ne12;
+
+    if (i00 >= p.ne00) {
+        return;
+    }
+
+    const uint i01 = data_b[i10*p.nb10 + i11*p.nb11 + i12*p.nb12];
+
+    const uint a_offset = i01*p.nb01 + i11*p.nb02 + i12*p.nb03;
+    const uint d_offset = i10*p.nb21 + i11*p.nb22 + i12*p.nb23;
+
+    const uint ib = a_offset + i00/QUANT_K; // block index
+    const uint iqs = (i00%QUANT_K)/QUANT_R; // quant index
+    const uint iybs = i00 - i00%QUANT_K; // dst block start index
+    const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2;
+
+    DEQUANT_FUNC
+
+    data_d[d_offset + iybs + iqs           ] = D_TYPE(v.x);
+    data_d[d_offset + iybs + iqs + y_offset] = D_TYPE(v.y);
+}
+"""
+
+# UNARY
+gelu_body = """
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const float GELU_COEF_A    = 0.044715f;
+    const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
+    const uint i = gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+
+    const float xi = float(data_a[i]);
+    const float val = SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi);
+    data_d[i] = D_TYPE(0.5f*xi*(2.0f - 2.0f / (exp(2 * val) + 1)));
+}
+"""
+
+silu_body = """
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+
+    const float xi = float(data_a[i]);
+    data_d[i] = D_TYPE(xi / (1.0f + exp(-xi)));
+}
+"""
+
+relu_body = """
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+
+    data_d[i] = max(float(data_a[i]), 0);
+}
+"""
+
+# DIAG_MASK_INF
+diag_mask_inf_head = """#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint ncols;
+    uint rows_per_channel;
+    uint n_past;
+} p;
+"""
+diag_mask_inf_body = """
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint col = gl_GlobalInvocationID.y;
+    const uint row = gl_GlobalInvocationID.x;
+
+    if (col >= p.ncols) {
+        return;
+    }
+
+    const uint i = row*p.ncols + col;
+    data_d[i] = D_TYPE(data_a[i] - float(uint(col > p.n_past + row % p.rows_per_channel) * 0xFFFFFFFF));
+}
+"""
+
+# NORMS
+norm_body = """
+#extension GL_EXT_control_flow_attributes : enable
+#define BLOCK_SIZE 512
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+shared vec2 sum[BLOCK_SIZE];
+
+void main() {
+    const uint row = gl_WorkGroupID.x;
+    const uint tid = gl_LocalInvocationID.x;
+
+    const float eps = 1e-5f;
+
+    sum[tid] = vec2(0.0f, 0.0f);
+
+    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
+        const float xi = float(data_a[row*p.KX + col]);
+        sum[tid].x += xi;
+        sum[tid].y += xi * xi;
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            sum[tid] += sum[tid + s];
+        }
+        barrier();
+    }
+
+    const float mean = sum[0].x / p.KX;
+    const float var = sum[0].y / p.KX - mean * mean;
+    const float inv_std = inversesqrt(var + 1e-5f);
+
+    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
+        data_d[row*p.KX + col] = D_TYPE((float(data_a[row*p.KX + col]) - mean) * inv_std);
+    }
+}
+"""
+
+rms_norm_body = """
+#extension GL_EXT_control_flow_attributes : enable
+#define BLOCK_SIZE 512
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+shared FLOAT_TYPE sum[BLOCK_SIZE];
+
+void main() {
+    const uint row = gl_WorkGroupID.x;
+    const uint tid = gl_LocalInvocationID.x;
+
+    sum[tid] = FLOAT_TYPE(0.0f); // partial sum for thread in warp
+
+    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
+        const FLOAT_TYPE xi = FLOAT_TYPE(data_a[row*p.KX + col]);
+        sum[tid] += xi * xi;
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            sum[tid] += sum[tid + s];
+        }
+        barrier();
+    }
+
+    const FLOAT_TYPE mean = sum[0] / FLOAT_TYPE(p.KX);
+    const FLOAT_TYPE scale = inversesqrt(mean + FLOAT_TYPE(p.param1));
+
+    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
+        data_d[row*p.KX + col] = D_TYPE(scale * FLOAT_TYPE(data_a[row*p.KX + col]));
+    }
+}
+"""
+
+# SOFT_MAX
+soft_max_head = """
+#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint KX;
+    uint KY;
+    uint KZ;
+    float scale;
+    float max_bias;
+    float m0;
+    float m1;
+    uint n_head_log2;
+} p;
+"""
+
+soft_max_body = """
+#extension GL_EXT_control_flow_attributes : enable
+#define BLOCK_SIZE 512
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer Y {B_TYPE data_b[];};
+layout (binding = 2) readonly buffer Z {C_TYPE data_c[];};
+layout (binding = 3) buffer D {D_TYPE data_d[];};
+
+shared FLOAT_TYPE vals[BLOCK_SIZE];
+
+void main() {
+    const uint tid = gl_LocalInvocationID.x;
+    const uint rowx = gl_WorkGroupID.x;
+    const uint rowy = rowx % p.KY;
+
+    float slope = 0.0f;
+
+    // ALiBi
+    if (p.max_bias > 0.0f) {
+        const uint h = rowx/p.KY; // head index
+
+        const float base = h < p.n_head_log2 ? p.m0 : p.m1;
+        const uint   exp  = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1;
+
+        slope = pow(base, exp);
+    }
+
+    // Find max
+    vals[tid] = uintBitsToFloat(0xFF800000);
+
+    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
+        vals[tid] = max(vals[tid], FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) + (p.KZ > 0 ? slope * data_c[col] : 0.0f));
+    }
+
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            vals[tid] = max(vals[tid], vals[tid + s]);
+        }
+        barrier();
+    }
+
+    const FLOAT_TYPE max_val = vals[0];
+    barrier();
+
+    // Sum up values
+    vals[tid] = FLOAT_TYPE(0.0f);
+
+    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
+        const uint i = rowx * p.KX + col;
+        const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val);
+        vals[tid] += val;
+        data_d[i] = D_TYPE(val);
+    }
+
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            vals[tid] += vals[tid + s];
+        }
+        barrier();
+    }
+
+    const D_TYPE divisor = D_TYPE(vals[0]);
+
+    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
+        data_d[rowx*p.KX + col] /= divisor;
+    }
+}
+"""
+
+# ROPE
+rope_src = """
+#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout(local_size_x = 1, local_size_y = 256, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer Y {int data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
+
+layout (push_constant) uniform parameter {
+    uint ncols;
+    float freq_scale;
+    uint p_delta_rows;
+    float freq_base;
+    float ext_factor;
+    float attn_factor;
+    float corr_dims[4];
+} p;
+
+float rope_yarn_ramp(const float low, const float high, const uint i0) {
+    const float y = (i0 / 2 - low) / max(0.001f, high - low);
+    return 1.0f - min(1.0f, max(0.0f, y));
+}
+
+void rope_yarn(const float theta_extrap, const uint i0, out float cos_theta, out float sin_theta) {
+    float mscale = p.attn_factor;
+    // Get n-d rotational scaling corrected for extrapolation
+    float theta_interp = p.freq_scale * theta_extrap;
+    float theta = theta_interp;
+    if (p.ext_factor != 0.0f) {
+        float ramp_mix = rope_yarn_ramp(p.corr_dims[0], p.corr_dims[1], i0) * p.ext_factor;
+        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+
+        // Get n-d magnitude scaling corrected for interpolation
+        mscale *= 1.0f + 0.1f * log(1.0f / p.freq_scale);
+    }
+    cos_theta = cos(theta) * mscale;
+    sin_theta = sin(theta) * mscale;
+}
+
+void main() {
+    const uint col = gl_GlobalInvocationID.y * 2;
+    const uint row = gl_GlobalInvocationID.x;
+
+    if (col >= p.ncols) {
+        return;
+    }
+
+    const uint i = row*p.ncols + col;
+    const uint i2 = row/p.p_delta_rows;
+
+    const int pos = data_b[i2];
+    const float theta_base = pos * pow(p.freq_base, -float(col)/p.ncols);
+
+    float cos_theta, sin_theta;
+    rope_yarn(theta_base, col, cos_theta, sin_theta);
+
+    const float x0 = float(data_a[i + 0]);
+    const float x1 = float(data_a[i + 1]);
+
+    data_d[i + 0] = D_TYPE(x0*cos_theta - x1*sin_theta);
+    data_d[i + 1] = D_TYPE(x0*sin_theta + x1*cos_theta);
+}
+"""
+
+rope_neox_src = """
+#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout(local_size_x = 1, local_size_y = 256, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer Y {int data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
+
+layout (push_constant) uniform parameter {
+    uint ncols;
+    uint ndims;
+    float freq_scale;
+    uint p_delta_rows;
+    float freq_base;
+    float ext_factor;
+    float attn_factor;
+    float corr_dims[4];
+    float theta_scale;
+    float inv_ndims;
+} p;
+
+float rope_yarn_ramp(const float low, const float high, const uint i0) {
+    const float y = (i0 / 2 - low) / max(0.001f, high - low);
+    return 1.0f - min(1.0f, max(0.0f, y));
+}
+
+void rope_yarn(const float theta_extrap, const uint i0, out float cos_theta, out float sin_theta) {
+    float mscale = p.attn_factor;
+    // Get n-d rotational scaling corrected for extrapolation
+    float theta_interp = p.freq_scale * theta_extrap;
+    float theta = theta_interp;
+    if (p.ext_factor != 0.0f) {
+        float ramp_mix = rope_yarn_ramp(p.corr_dims[0], p.corr_dims[1], i0) * p.ext_factor;
+        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+
+        // Get n-d magnitude scaling corrected for interpolation
+        mscale *= 1.0f + 0.1f * log(1.0f / p.freq_scale);
+    }
+    cos_theta = cos(theta) * mscale;
+    sin_theta = sin(theta) * mscale;
+}
+
+void main() {
+    const uint col = gl_GlobalInvocationID.y * 2;
+    const uint row = gl_GlobalInvocationID.x;
+
+    if (col >= p.ncols) {
+        return;
+    }
+
+    const uint ib = col / p.ndims;
+    const uint ic = col % p.ndims;
+
+    if (ib > 0) {
+        const uint i = row*p.ncols + ib*p.ndims + ic;
+
+        data_d[i + 0] = data_a[i + 0];
+        data_d[i + 1] = data_a[i + 1];
+
+        return;
+    }
+
+    const uint i  = row*p.ncols + ib*p.ndims + ic/2;
+    const uint i2 = row/p.p_delta_rows;
+
+    const float cur_rot = p.inv_ndims * ic - ib;
+
+    const int pos = data_b[i2];
+    const float theta_base = pos*p.freq_scale*pow(p.theta_scale, col/2.0f);
+
+    float cos_theta, sin_theta;
+    rope_yarn(theta_base, uint(cur_rot), cos_theta, sin_theta);
+
+    const float x0 = float(data_a[i + 0]);
+    const float x1 = float(data_a[i + p.ndims/2]);
+
+    data_d[i + 0]        = D_TYPE(x0*cos_theta - x1*sin_theta);
+    data_d[i + p.ndims/2] = D_TYPE(x0*sin_theta + x1*cos_theta);
+}
+"""
+
+argsort_src = """
+#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout(local_size_x = 1024, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1)          buffer D {int data_d[];};
+
+layout (push_constant) uniform parameter {
+    uint ncols;
+    bool ascending;
+} p;
+
+void swap(uint idx0, uint idx1) {
+    int tmp = data_d[idx0];
+    data_d[idx0] = data_d[idx1];
+    data_d[idx1] = tmp;
+}
+
+void main() {
+    // bitonic sort
+    const int col = int(gl_LocalInvocationID.x);
+    const uint row = gl_WorkGroupID.y;
+
+    if (col >= p.ncols) {
+        return;
+    }
+
+    const uint a_idx = row * p.ncols;
+    const uint d_idx = row * p.ncols;
+
+    // initialize indices
+    if (col < p.ncols) {
+        data_d[col] = col;
+    }
+    barrier();
+
+    for (uint k = 2; k <= p.ncols; k *= 2) {
+        for (uint j = k / 2; j > 0; j /= 2) {
+            const uint ixj = col ^ j;
+            if (ixj > col) {
+                if ((col & k) == 0) {
+                    if (p.ascending ? data_a[a_idx + data_d[d_idx + col]] > data_a[a_idx + data_d[d_idx + ixj]] : data_a[a_idx + data_d[d_idx + col]] < data_a[a_idx + data_d[d_idx + ixj]]) {
+                        swap(d_idx + col, d_idx + ixj);
+                    }
+                } else {
+                    if (p.ascending ? data_a[a_idx + data_d[d_idx + col]] < data_a[a_idx + data_d[d_idx + ixj]] : data_a[a_idx + data_d[d_idx + col]] > data_a[a_idx + data_d[d_idx + ixj]]) {
+                        swap(d_idx + col, d_idx + ixj);
+                    }
+                }
+            }
+            barrier();
+        }
+    }
+}
+"""
+
+GLSLC = "glslc"
+
+VK_NUM_TYPES = 16
+
+GGML_TYPE_F32  = 0
+GGML_TYPE_F16  = 1
+GGML_TYPE_Q4_0 = 2
+GGML_TYPE_Q4_1 = 3
+GGML_TYPE_Q5_0 = 6
+GGML_TYPE_Q5_1 = 7
+GGML_TYPE_Q8_0 = 8
+GGML_TYPE_Q8_1 = 9
+GGML_TYPE_Q2_K = 10
+GGML_TYPE_Q3_K = 11
+GGML_TYPE_Q4_K = 12
+GGML_TYPE_Q5_K = 13
+GGML_TYPE_Q6_K = 14
+GGML_TYPE_Q8_K = 15
+
+
+type_names = {
+    GGML_TYPE_F32: "f32",
+    GGML_TYPE_F16: "f16",
+    GGML_TYPE_Q4_0: "q4_0",
+    GGML_TYPE_Q4_1: "q4_1",
+    GGML_TYPE_Q5_0: "q5_0",
+    GGML_TYPE_Q5_1: "q5_1",
+    GGML_TYPE_Q8_0: "q8_0",
+    GGML_TYPE_Q8_1: "q8_1",
+    GGML_TYPE_Q2_K: "q2_K",
+    GGML_TYPE_Q3_K: "q3_K",
+    GGML_TYPE_Q4_K: "q4_K",
+    GGML_TYPE_Q5_K: "q5_K",
+    GGML_TYPE_Q6_K: "q6_K",
+    GGML_TYPE_Q8_K: "q8_K",
+}
+
+K_QUANTS_PER_ITERATION = 2
+
+ASYNCIO_CONCURRENCY = 64
+
+output_dir = gettempdir()
+
+lock = asyncio.Lock()
+shader_fnames = []
+
+
+async def string_to_spv(name, code, defines, fp16=True):
+    f = NamedTemporaryFile(mode="w", delete=False)
+    f.write(code)
+    f.flush()
+
+    name = f"{name}{'_fp32' if not fp16 else ''}"
+    fname = os.path.join(output_dir, f"{name}.comp")
+
+    cmd = [GLSLC, "-fshader-stage=compute", "--target-env=vulkan1.2", "-O", f.name, "-o", fname]
+
+    cmd.extend([f"-D{key}={value}" for key, value in defines.items()])
+
+    proc = await asyncio.create_subprocess_exec(*cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE)
+
+    stdout, stderr = await proc.communicate()
+
+    stdout = stdout.decode()
+    error = stderr.decode()
+
+    if proc.returncode:
+        # Generate preprocessed code
+        cmd = [GLSLC, "-E", f.name]
+        cmd.extend([f"-D{key}={value}" for key, value in defines.items()])
+
+        proc = await asyncio.create_subprocess_exec(*cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE)
+
+        stdout, stderr = await proc.communicate()
+
+        print(" ".join(cmd))
+
+        if proc.returncode:
+            raise RuntimeError(f"{name=} {f.name=} {stdout=} {stderr=}")
+
+        preprocessed_code = stdout.decode()
+
+        cmd.extend([f"-D{key}={value}" for key, value in defines.items()])
+        code_with_lines = "\n".join([f"{i + 1}: {line}" for i, line in enumerate(preprocessed_code.splitlines())])
+        print(f"ERROR compiling {name}\n\n{code_with_lines}\n\n{error}")
+        f.close()
+        os.remove(f.name)
+        sys.exit(proc.returncode)
+
+    f.close()
+    os.remove(f.name)
+
+    async with lock:
+        shader_fnames.append((name, fname))
+
+
+async def main():
+    print("ggml_vulkan: Generating and compiling shaders to SPIR-V")
+
+    tasks = []
+
+    stream = []
+
+    for fp16 in (False, True):
+        # mulmat
+        if fp16:
+            shader_float_type = shader_f16
+            load_vec = "8"
+            vec_type_f16 = "f16mat2x4"
+            vec_type = "mat2x4"
+        else:
+            shader_float_type = shader_f32
+            load_vec = "4"
+            vec_type_f16 = "f16vec4"
+            vec_type = "vec4"
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_float_type, mulmat_body1, mulmat_load_scalar, mulmat_body2))
+        tasks.append(string_to_spv("matmul_f32", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_f32_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        tasks.append(string_to_spv("matmul_f16", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_f16_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16))
+
+        tasks.append(string_to_spv("matmul_f16_f32", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_f16_f32_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_0_defines, mulmat_body1, mulmat_load_q4_0, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q4_0_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q4_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q4_0_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_1_defines, mulmat_body1, mulmat_load_q4_1, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q4_1_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q4_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q4_1_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_0_defines, mulmat_body1, mulmat_load_q5_0, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q5_0_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q5_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q5_0_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_1_defines, mulmat_body1, mulmat_load_q5_1, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q5_1_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q5_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q5_1_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q8_0_defines, mulmat_body1, mulmat_load_q8_0, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q8_0_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q8_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q8_0_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q8_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q2_K_defines, mulmat_body1, mulmat_load_q2_K, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q2_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q2_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q2_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q2_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q3_K_defines, mulmat_body1, mulmat_load_q3_K, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q3_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q3_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q3_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q3_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_K_defines, mulmat_body1, mulmat_load_q4_K, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q4_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q4_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q4_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_K_defines, mulmat_body1, mulmat_load_q5_K, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q5_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q5_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q5_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+        stream.clear()
+        stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q6_K_defines, mulmat_body1, mulmat_load_q6_K, mulmat_body2))
+        tasks.append(string_to_spv("matmul_q6_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q6_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
+        tasks.append(string_to_spv("matmul_q6_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q6_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16))
+
+    # Shaders where precision is needed, so no fp16 version
+
+    # mul mat vec
+    for i in range(0, VK_NUM_TYPES):
+        stream.clear()
+        stream.extend((mul_mat_vec_head, shader_int8_ext, shader_f32))
+
+        if i == GGML_TYPE_F16:
+            stream.extend((shader_f16_defines, shader_float_dequant_func, mul_mat_vec_body))
+        elif i == GGML_TYPE_Q4_0:
+            stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, mul_mat_vec_body))
+        elif i == GGML_TYPE_Q4_1:
+            stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, mul_mat_vec_body))
+        elif i == GGML_TYPE_Q5_0:
+            stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, mul_mat_vec_body))
+        elif i == GGML_TYPE_Q5_1:
+            stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, mul_mat_vec_body))
+        elif i == GGML_TYPE_Q8_0:
+            stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, mul_mat_vec_body))
+        elif i == GGML_TYPE_Q2_K:
+            stream.extend((shader_q2_K_defines, mul_mat_vec_q2_K_body))
+        elif i == GGML_TYPE_Q3_K:
+            stream.extend((shader_q3_K_defines, mul_mat_vec_q3_K_body))
+        elif i == GGML_TYPE_Q4_K:
+            stream.extend((shader_q4_K_defines, mul_mat_vec_q4_K_body))
+        elif i == GGML_TYPE_Q5_K:
+            stream.extend((shader_q5_K_defines, mul_mat_vec_q5_K_body))
+        elif i == GGML_TYPE_Q6_K:
+            stream.extend((shader_q6_K_defines, mul_mat_vec_q6_K_body))
+        else:
+            continue
+
+        tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION}))
+
+    # Dequant shaders
+    for i in range(0, VK_NUM_TYPES):
+        stream.clear()
+
+        stream.extend((dequant_head, shader_int8_ext, shader_f32))
+
+        if i == GGML_TYPE_F32:
+            stream.append(dequant_f32_body)
+        elif i == GGML_TYPE_Q4_0:
+            stream.extend((shader_q4_0_defines, dequant_q4_0_body))
+        elif i == GGML_TYPE_Q4_1:
+            stream.extend((shader_q4_1_defines, dequant_q4_1_body))
+        elif i == GGML_TYPE_Q5_0:
+            stream.extend((shader_q5_0_defines, dequant_q5_0_body))
+        elif i == GGML_TYPE_Q5_1:
+            stream.extend((shader_q5_1_defines, dequant_q5_1_body))
+        elif i == GGML_TYPE_Q8_0:
+            stream.extend((shader_q8_0_defines, dequant_q8_0_body))
+        elif i == GGML_TYPE_Q2_K:
+            stream.extend((shader_q2_K_defines, dequant_q2_K_body))
+        elif i == GGML_TYPE_Q3_K:
+            stream.extend((shader_q3_K_defines, dequant_q3_K_body))
+        elif i == GGML_TYPE_Q4_K:
+            stream.extend((shader_q4_K_defines, dequant_q4_K_body))
+        elif i == GGML_TYPE_Q5_K:
+            stream.extend((shader_q5_K_defines, dequant_q5_K_body))
+        elif i == GGML_TYPE_Q6_K:
+            stream.extend((shader_q6_K_defines, dequant_q6_K_body))
+        else:
+            continue
+
+        tasks.append(string_to_spv(f"dequant_{type_names[i]}", "".join(stream), {"D_TYPE": "float16_t"}))
+
+    # get_rows
+    for i in range(0, VK_NUM_TYPES):
+        stream.clear()
+        stream.extend((generic_binary_op_head, shader_int8_ext, shader_f32))
+        optimization_workaround = False
+
+        if i == GGML_TYPE_F32:
+            stream.extend((shader_f32_defines, generic_binary_op_funcs, get_rows_float_body))
+        elif i == GGML_TYPE_F16:
+            stream.extend((shader_f16_defines, generic_binary_op_funcs, get_rows_float_body))
+            optimization_workaround = True
+        elif i == GGML_TYPE_Q4_0:
+            stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, generic_binary_op_funcs, get_rows_body))
+        elif i == GGML_TYPE_Q4_1:
+            stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, generic_binary_op_funcs, get_rows_body))
+        elif i == GGML_TYPE_Q5_0:
+            stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, generic_binary_op_funcs, get_rows_body))
+        elif i == GGML_TYPE_Q5_1:
+            stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, generic_binary_op_funcs, get_rows_body))
+        elif i == GGML_TYPE_Q8_0:
+            stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, generic_binary_op_funcs, get_rows_body))
+        else:
+            continue
+
+        if optimization_workaround:
+            tasks.append(string_to_spv(f"get_rows_{type_names[i]}", "".join(stream), {"B_TYPE": "int", "D_TYPE": "float16_t", "OPTIMIZATION_ERROR_WORKAROUND": "1"}))
+        else:
+            tasks.append(string_to_spv(f"get_rows_{type_names[i]}", "".join(stream), {"B_TYPE": "int", "D_TYPE": "float16_t"}))
+        tasks.append(string_to_spv(f"get_rows_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "int", "D_TYPE": "float"}))
+
+    tasks.append(string_to_spv("mul_mat_vec_p021_f16_f32", mul_mat_p021_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("mul_mat_vec_nc_f16_f32", mul_mat_nc_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}))
+
+    # Norms
+    tasks.append(string_to_spv("norm_f32", f"{generic_head}\n{shader_f32}\n{norm_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("rms_norm_f32", f"{generic_head}\n{shader_f32}\n{rms_norm_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
+
+    tasks.append(string_to_spv("cpy_f32_f32", f"{generic_unary_op_combined}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("cpy_f32_f16", f"{generic_unary_op_combined}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float16_t"}))
+    tasks.append(string_to_spv("cpy_f16_f16", f"{generic_unary_op_combined}\n{cpy_f16_f16_end}", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
+
+    tasks.append(string_to_spv("add_f32", f"{generic_binary_op_combined}\n{add_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
+
+    tasks.append(string_to_spv("split_k_reduce", mulmat_split_k_reduce_src, {}))
+    tasks.append(string_to_spv("mul_f32", f"{generic_binary_op_combined}\n{mul_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
+
+    tasks.append(string_to_spv("scale_f32", f"{generic_unary_op_combined}\n{scale_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
+
+    tasks.append(string_to_spv("sqr_f32", f"{generic_unary_op_combined}\n{sqr_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
+
+    tasks.append(string_to_spv("clamp_f32", f"{generic_unary_op_combined}\n{clamp_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
+
+    tasks.append(string_to_spv("gelu_f32", f"{generic_head}\n{shader_f32}\n{gelu_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("silu_f32", f"{generic_head}\n{shader_f32}\n{silu_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("relu_f32", f"{generic_head}\n{shader_f32}\n{relu_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
+
+    tasks.append(string_to_spv("diag_mask_inf_f32", f"{diag_mask_inf_head}\n{shader_f32}\n{diag_mask_inf_body}", {"A_TYPE": "float", "D_TYPE": "float"}))
+
+    tasks.append(string_to_spv("soft_max_f32", f"{soft_max_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "C_TYPE": "float", "D_TYPE": "float"}))
+
+    tasks.append(string_to_spv("rope_f32", rope_src, {"A_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("rope_f16", rope_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
+
+    tasks.append(string_to_spv("rope_neox_f32", rope_neox_src, {"A_TYPE": "float", "D_TYPE": "float"}))
+    tasks.append(string_to_spv("rope_neox_f16", rope_neox_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
+
+    tasks.append(string_to_spv("argsort_f32", argsort_src, {"A_TYPE": "float"}))
+
+    # Helper to decorate tasks with semaphore acquisition.
+    async def withSemaphore(sem, task):
+        async with sem:
+            return await task
+
+    # Run tasks concurrently guarded by a concurrency limit.
+    sem = asyncio.Semaphore(ASYNCIO_CONCURRENCY)
+    await asyncio.gather(*(withSemaphore(sem, task) for task in tasks))
+
+    with open("ggml-vulkan-shaders.hpp", "w") as f:
+        f.write("#include <cstdint>\n\n")
+        for name, path in sorted(shader_fnames):
+
+            with open(path, "rb") as spv:
+                counter = 0
+                newline_counter = 0
+                f.write(f"unsigned char {name}_data[] = {{\n")
+                for val in spv.read():
+                    f.write(f"0x{val:02x},")
+                    newline_counter += 1
+                    counter += 1
+                    if newline_counter >= 12:
+                        newline_counter = 0
+                        f.write("\n")
+            f.write("\n};\n")
+            f.write(f"const uint64_t {name}_len = {counter};\n\n")
+            os.remove(path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="GGML Vulkan Shader Generator")
+
+    parser.add_argument("--glslc", help="Path to glslc")
+
+    args = parser.parse_args()
+
+    if args.glslc:
+        GLSLC = args.glslc
+
+    asyncio.run(main())
diff --git a/gguf-py/examples/reader.py b/gguf-py/examples/reader.py
new file mode 100644
index 000000000..62e0769da
--- /dev/null
+++ b/gguf-py/examples/reader.py
@@ -0,0 +1,45 @@
+#!/usr/bin/env python3
+import sys
+from pathlib import Path
+from gguf.gguf_reader import GGUFReader
+
+
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+
+def read_gguf_file(gguf_file_path):
+    """
+    Reads and prints key-value pairs and tensor information from a GGUF file in an improved format.
+
+    Parameters:
+    - gguf_file_path: Path to the GGUF file.
+    """
+
+    reader = GGUFReader(gguf_file_path)
+
+    # List all key-value pairs in a columnized format
+    print("Key-Value Pairs:")
+    max_key_length = max(len(key) for key in reader.fields.keys())
+    for key, field in reader.fields.items():
+        value = field.parts[field.data[0]]
+        print(f"{key:{max_key_length}} : {value}")
+    print("----")
+
+    # List all tensors
+    print("Tensors:")
+    tensor_info_format = "{:<30} | Shape: {:<15} | Size: {:<12} | Quantization: {}"
+    print(tensor_info_format.format("Tensor Name", "Shape", "Size", "Quantization"))
+    print("-" * 80)
+    for tensor in reader.tensors:
+        shape_str = "x".join(map(str, tensor.shape))
+        size_str = str(tensor.n_elements)
+        quantization_str = tensor.tensor_type.name
+        print(tensor_info_format.format(tensor.name, shape_str, size_str, quantization_str))
+
+
+if __name__ == '__main__':
+    if len(sys.argv) < 2:
+        print("Usage: reader.py <path_to_gguf_file>")
+        sys.exit(1)
+    gguf_file_path = sys.argv[1]
+    read_gguf_file(gguf_file_path)
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index f0a1c51f8..c44d8abeb 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -24,6 +24,7 @@ class Keys:
         ALIGNMENT            = "general.alignment"
         NAME                 = "general.name"
         AUTHOR               = "general.author"
+        VERSION              = "general.version"
         URL                  = "general.url"
         DESCRIPTION          = "general.description"
         LICENSE              = "general.license"
@@ -32,6 +33,7 @@ class Keys:
         FILE_TYPE            = "general.file_type"
 
     class LLM:
+        VOCAB_SIZE            = "{arch}.vocab_size"
         CONTEXT_LENGTH        = "{arch}.context_length"
         EMBEDDING_LENGTH      = "{arch}.embedding_length"
         BLOCK_COUNT           = "{arch}.block_count"
@@ -40,6 +42,8 @@ class Keys:
         TENSOR_DATA_LAYOUT    = "{arch}.tensor_data_layout"
         EXPERT_COUNT          = "{arch}.expert_count"
         EXPERT_USED_COUNT     = "{arch}.expert_used_count"
+        POOLING_TYPE          = "{arch}.pooling_type"
+        LOGIT_SCALE           = "{arch}.logit_scale"
 
     class Attention:
         HEAD_COUNT        = "{arch}.attention.head_count"
@@ -50,6 +54,7 @@ class Keys:
         VALUE_LENGTH      = "{arch}.attention.value_length"
         LAYERNORM_EPS     = "{arch}.attention.layer_norm_epsilon"
         LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
+        CAUSAL            = "{arch}.attention.causal"
 
     class Rope:
         DIMENSION_COUNT      = "{arch}.rope.dimension_count"
@@ -59,22 +64,32 @@ class Keys:
         SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length"
         SCALING_FINETUNED    = "{arch}.rope.scaling.finetuned"
 
+    class SSM:
+        CONV_KERNEL    = "{arch}.ssm.conv_kernel"
+        INNER_SIZE     = "{arch}.ssm.inner_size"
+        STATE_SIZE     = "{arch}.ssm.state_size"
+        TIME_STEP_RANK = "{arch}.ssm.time_step_rank"
+
     class Tokenizer:
-        MODEL         = "tokenizer.ggml.model"
-        LIST          = "tokenizer.ggml.tokens"
-        TOKEN_TYPE    = "tokenizer.ggml.token_type"
-        SCORES        = "tokenizer.ggml.scores"
-        MERGES        = "tokenizer.ggml.merges"
-        BOS_ID        = "tokenizer.ggml.bos_token_id"
-        EOS_ID        = "tokenizer.ggml.eos_token_id"
-        UNK_ID        = "tokenizer.ggml.unknown_token_id"
-        SEP_ID        = "tokenizer.ggml.seperator_token_id"
-        PAD_ID        = "tokenizer.ggml.padding_token_id"
-        ADD_BOS       = "tokenizer.ggml.add_bos_token"
-        ADD_EOS       = "tokenizer.ggml.add_eos_token"
-        HF_JSON       = "tokenizer.huggingface.json"
-        RWKV          = "tokenizer.rwkv.world"
-        CHAT_TEMPLATE = "tokenizer.chat_template"
+        MODEL            = "tokenizer.ggml.model"
+        LIST             = "tokenizer.ggml.tokens"
+        TOKEN_TYPE       = "tokenizer.ggml.token_type"
+        TOKEN_TYPE_COUNT = "tokenizer.ggml.token_type_count"  # for BERT-style token types
+        SCORES           = "tokenizer.ggml.scores"
+        MERGES           = "tokenizer.ggml.merges"
+        BOS_ID           = "tokenizer.ggml.bos_token_id"
+        EOS_ID           = "tokenizer.ggml.eos_token_id"
+        UNK_ID           = "tokenizer.ggml.unknown_token_id"
+        SEP_ID           = "tokenizer.ggml.seperator_token_id"
+        PAD_ID           = "tokenizer.ggml.padding_token_id"
+        CLS_ID           = "tokenizer.ggml.cls_token_id"
+        MASK_ID          = "tokenizer.ggml.mask_token_id"
+        ADD_BOS          = "tokenizer.ggml.add_bos_token"
+        ADD_EOS          = "tokenizer.ggml.add_eos_token"
+        ADD_PREFIX       = "tokenizer.ggml.add_space_prefix"
+        HF_JSON          = "tokenizer.huggingface.json"
+        RWKV             = "tokenizer.rwkv.world"
+        CHAT_TEMPLATE    = "tokenizer.chat_template"
 
 
 #
@@ -83,22 +98,34 @@ class Keys:
 
 
 class MODEL_ARCH(IntEnum):
-    LLAMA     = auto()
-    FALCON    = auto()
-    BAICHUAN  = auto()
-    GPT2      = auto()
-    GPTJ      = auto()
-    GPTNEOX   = auto()
-    MPT       = auto()
-    STARCODER = auto()
-    PERSIMMON = auto()
-    REFACT    = auto()
-    BERT      = auto()
-    BLOOM     = auto()
-    STABLELM  = auto()
-    QWEN      = auto()
-    PHI2      = auto()
-    PLAMO     = auto()
+    LLAMA      = auto()
+    FALCON     = auto()
+    BAICHUAN   = auto()
+    GROK       = auto()
+    GPT2       = auto()
+    GPTJ       = auto()
+    GPTNEOX    = auto()
+    MPT        = auto()
+    STARCODER  = auto()
+    PERSIMMON  = auto()
+    REFACT     = auto()
+    BERT       = auto()
+    NOMIC_BERT = auto()
+    BLOOM      = auto()
+    STABLELM   = auto()
+    QWEN       = auto()
+    QWEN2      = auto()
+    PHI2       = auto()
+    PLAMO      = auto()
+    CODESHELL  = auto()
+    ORION      = auto()
+    INTERNLM2  = auto()
+    MINICPM    = auto()
+    GEMMA      = auto()
+    STARCODER2 = auto()
+    MAMBA      = auto()
+    XVERSE     = auto()
+    COMMAND_R  = auto()
 
 
 class MODEL_TENSOR(IntEnum):
@@ -116,6 +143,7 @@ class MODEL_TENSOR(IntEnum):
     ATTN_OUT        = auto()
     ATTN_NORM       = auto()
     ATTN_NORM_2     = auto()
+    ATTN_OUT_NORM   = auto()
     ATTN_ROT_EMBD   = auto()
     FFN_GATE_INP    = auto()
     FFN_NORM        = auto()
@@ -128,12 +156,21 @@ class MODEL_TENSOR(IntEnum):
     FFN_UP_EXP      = auto()
     ATTN_Q_NORM     = auto()
     ATTN_K_NORM     = auto()
+    LAYER_OUT_NORM  = auto()
+    SSM_IN          = auto()
+    SSM_CONV1D      = auto()
+    SSM_X           = auto()
+    SSM_DT          = auto()
+    SSM_A           = auto()
+    SSM_D           = auto()
+    SSM_OUT         = auto()
 
 
 MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.LLAMA:          "llama",
     MODEL_ARCH.FALCON:         "falcon",
     MODEL_ARCH.BAICHUAN:       "baichuan",
+    MODEL_ARCH.GROK:           "grok",
     MODEL_ARCH.GPT2:           "gpt2",
     MODEL_ARCH.GPTJ:           "gptj",
     MODEL_ARCH.GPTNEOX:        "gptneox",
@@ -142,11 +179,22 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.PERSIMMON:      "persimmon",
     MODEL_ARCH.REFACT:         "refact",
     MODEL_ARCH.BERT:           "bert",
+    MODEL_ARCH.NOMIC_BERT:     "nomic-bert",
     MODEL_ARCH.BLOOM:          "bloom",
     MODEL_ARCH.STABLELM:       "stablelm",
     MODEL_ARCH.QWEN:           "qwen",
+    MODEL_ARCH.QWEN2:          "qwen2",
     MODEL_ARCH.PHI2:           "phi2",
     MODEL_ARCH.PLAMO:          "plamo",
+    MODEL_ARCH.CODESHELL:      "codeshell",
+    MODEL_ARCH.ORION:          "orion",
+    MODEL_ARCH.INTERNLM2:      "internlm2",
+    MODEL_ARCH.MINICPM:        "minicpm",
+    MODEL_ARCH.GEMMA:          "gemma",
+    MODEL_ARCH.STARCODER2:     "starcoder2",
+    MODEL_ARCH.MAMBA:          "mamba",
+    MODEL_ARCH.XVERSE:         "xverse",
+    MODEL_ARCH.COMMAND_R:      "command-r",
 }
 
 TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@@ -167,15 +215,24 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.ATTN_ROT_EMBD:   "blk.{bid}.attn_rot_embd",
     MODEL_TENSOR.ATTN_Q_NORM:     "blk.{bid}.attn_q_norm",
     MODEL_TENSOR.ATTN_K_NORM:     "blk.{bid}.attn_k_norm",
+    MODEL_TENSOR.ATTN_OUT_NORM:   "blk.{bid}.attn_output_norm",
     MODEL_TENSOR.FFN_GATE_INP:    "blk.{bid}.ffn_gate_inp",
     MODEL_TENSOR.FFN_NORM:        "blk.{bid}.ffn_norm",
     MODEL_TENSOR.FFN_GATE:        "blk.{bid}.ffn_gate",
     MODEL_TENSOR.FFN_DOWN:        "blk.{bid}.ffn_down",
     MODEL_TENSOR.FFN_UP:          "blk.{bid}.ffn_up",
     MODEL_TENSOR.FFN_ACT:         "blk.{bid}.ffn",
-    MODEL_TENSOR.FFN_GATE_EXP:    "blk.{bid}.ffn_gate.{xid}",
-    MODEL_TENSOR.FFN_DOWN_EXP:    "blk.{bid}.ffn_down.{xid}",
-    MODEL_TENSOR.FFN_UP_EXP:      "blk.{bid}.ffn_up.{xid}",
+    MODEL_TENSOR.FFN_GATE_EXP:    "blk.{bid}.ffn_gate_exps",
+    MODEL_TENSOR.FFN_DOWN_EXP:    "blk.{bid}.ffn_down_exps",
+    MODEL_TENSOR.FFN_UP_EXP:      "blk.{bid}.ffn_up_exps",
+    MODEL_TENSOR.LAYER_OUT_NORM:  "blk.{bid}.layer_output_norm",
+    MODEL_TENSOR.SSM_IN:          "blk.{bid}.ssm_in",
+    MODEL_TENSOR.SSM_CONV1D:      "blk.{bid}.ssm_conv1d",
+    MODEL_TENSOR.SSM_X:           "blk.{bid}.ssm_x",
+    MODEL_TENSOR.SSM_DT:          "blk.{bid}.ssm_dt",
+    MODEL_TENSOR.SSM_A:           "blk.{bid}.ssm_a",
+    MODEL_TENSOR.SSM_D:           "blk.{bid}.ssm_d",
+    MODEL_TENSOR.SSM_OUT:         "blk.{bid}.ssm_out",
 }
 
 MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@@ -199,6 +256,28 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN_EXP,
         MODEL_TENSOR.FFN_UP_EXP,
     ],
+    MODEL_ARCH.GROK: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.ATTN_OUT_NORM,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.LAYER_OUT_NORM,
+    ],
     MODEL_ARCH.GPTNEOX: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -251,17 +330,32 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
     ],
     MODEL_ARCH.BERT: [
         MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.TOKEN_EMBD_NORM,
         MODEL_TENSOR.TOKEN_TYPES,
         MODEL_TENSOR.POS_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
-        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_OUT_NORM,
         MODEL_TENSOR.ATTN_Q,
         MODEL_TENSOR.ATTN_K,
         MODEL_TENSOR.ATTN_V,
         MODEL_TENSOR.ATTN_OUT,
-        MODEL_TENSOR.FFN_NORM,
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.LAYER_OUT_NORM,
+    ],
+    MODEL_ARCH.NOMIC_BERT: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.TOKEN_EMBD_NORM,
+        MODEL_TENSOR.TOKEN_TYPES,
+        MODEL_TENSOR.POS_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ATTN_OUT_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.LAYER_OUT_NORM,
     ],
     MODEL_ARCH.MPT: [
         MODEL_TENSOR.TOKEN_EMBD,
@@ -274,6 +368,9 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
         MODEL_TENSOR.FFN_ACT,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.POS_EMBD,
     ],
     MODEL_ARCH.GPTJ: [
         MODEL_TENSOR.TOKEN_EMBD,
@@ -356,6 +453,20 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.QWEN2: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
     MODEL_ARCH.PLAMO: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -389,11 +500,146 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.OUTPUT,
         MODEL_TENSOR.ATTN_NORM,
         MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
         MODEL_TENSOR.ATTN_OUT,
         MODEL_TENSOR.FFN_NORM,
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
-    ]
+    ],
+    MODEL_ARCH.CODESHELL: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.POS_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
+    MODEL_ARCH.ORION: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
+    MODEL_ARCH.INTERNLM2: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
+    MODEL_ARCH.MINICPM: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+    ],
+    MODEL_ARCH.GEMMA: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_NORM,
+    ],
+    MODEL_ARCH.STARCODER2: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
+    MODEL_ARCH.MAMBA: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.SSM_IN,
+        MODEL_TENSOR.SSM_CONV1D,
+        MODEL_TENSOR.SSM_X,
+        MODEL_TENSOR.SSM_DT,
+        MODEL_TENSOR.SSM_A,
+        MODEL_TENSOR.SSM_D,
+        MODEL_TENSOR.SSM_OUT,
+    ],
+    MODEL_ARCH.XVERSE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
+    MODEL_ARCH.COMMAND_R: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
     # TODO
 }
 
@@ -414,6 +660,22 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.ROPE_FREQS,
         MODEL_TENSOR.ATTN_ROT_EMBD,
     ],
+    MODEL_ARCH.CODESHELL: [
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+    ],
+    MODEL_ARCH.ORION: [
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+    ],
+    MODEL_ARCH.STARCODER2: [
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+    ],
+    MODEL_ARCH.XVERSE: [
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+    ],
 }
 
 #
@@ -436,21 +698,41 @@ class RopeScalingType(Enum):
     YARN   = 'yarn'
 
 
+class PoolingType(IntEnum):
+    NONE = 0
+    MEAN = 1
+    CLS  = 2
+
+
 class GGMLQuantizationType(IntEnum):
-    F32  = 0
-    F16  = 1
-    Q4_0 = 2
-    Q4_1 = 3
-    Q5_0 = 6
-    Q5_1 = 7
-    Q8_0 = 8
-    Q8_1 = 9
-    Q2_K = 10
-    Q3_K = 11
-    Q4_K = 12
-    Q5_K = 13
-    Q6_K = 14
-    Q8_K = 15
+    F32     = 0
+    F16     = 1
+    Q4_0    = 2
+    Q4_1    = 3
+    Q5_0    = 6
+    Q5_1    = 7
+    Q8_0    = 8
+    Q8_1    = 9
+    Q2_K    = 10
+    Q3_K    = 11
+    Q4_K    = 12
+    Q5_K    = 13
+    Q6_K    = 14
+    Q8_K    = 15
+    IQ2_XXS = 16
+    IQ2_XS  = 17
+    IQ3_XXS = 18
+    IQ1_S   = 19
+    IQ4_NL  = 20
+    IQ3_S   = 21
+    IQ2_S   = 22
+    IQ4_XS  = 23
+    I8      = 24
+    I16     = 25
+    I32     = 26
+    I64     = 27
+    F64     = 28
+    IQ1_M   = 29
 
 
 class GGUFEndian(IntEnum):
@@ -495,20 +777,33 @@ class GGUFValueType(IntEnum):
 QK_K = 256
 # Items here are (block size, type size)
 GGML_QUANT_SIZES = {
-    GGMLQuantizationType.F32:  (1, 4),
-    GGMLQuantizationType.F16:  (1, 2),
-    GGMLQuantizationType.Q4_0: (32, 2 + 16),
-    GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16),
-    GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16),
-    GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16),
-    GGMLQuantizationType.Q8_0: (32, 2 + 32),
-    GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32),
-    GGMLQuantizationType.Q2_K: (256, 2 + 2 + QK_K // 16 + QK_K // 4),
-    GGMLQuantizationType.Q3_K: (256, 2 + QK_K // 4 + QK_K // 8 + 12),
-    GGMLQuantizationType.Q4_K: (256, 2 + 2 + QK_K // 2 + 12),
-    GGMLQuantizationType.Q5_K: (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12),
-    GGMLQuantizationType.Q6_K: (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16),
-    GGMLQuantizationType.Q8_K: (256, 4 + QK_K + QK_K // 8),
+    GGMLQuantizationType.F32:     (1, 4),
+    GGMLQuantizationType.F16:     (1, 2),
+    GGMLQuantizationType.Q4_0:    (32, 2 + 16),
+    GGMLQuantizationType.Q4_1:    (32, 2 + 2 + 16),
+    GGMLQuantizationType.Q5_0:    (32, 2 + 4 + 16),
+    GGMLQuantizationType.Q5_1:    (32, 2 + 2 + 4 + 16),
+    GGMLQuantizationType.Q8_0:    (32, 2 + 32),
+    GGMLQuantizationType.Q8_1:    (32, 4 + 4 + 32),
+    GGMLQuantizationType.Q2_K:    (256, 2 + 2 + QK_K // 16 + QK_K // 4),
+    GGMLQuantizationType.Q3_K:    (256, 2 + QK_K // 4 + QK_K // 8 + 12),
+    GGMLQuantizationType.Q4_K:    (256, 2 + 2 + QK_K // 2 + 12),
+    GGMLQuantizationType.Q5_K:    (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12),
+    GGMLQuantizationType.Q6_K:    (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16),
+    GGMLQuantizationType.Q8_K:    (256, 4 + QK_K + QK_K // 8),
+    GGMLQuantizationType.IQ2_XXS: (256, 2 + QK_K // 4),
+    GGMLQuantizationType.IQ2_XS:  (256, 2 + QK_K // 4 + QK_K // 32),
+    GGMLQuantizationType.IQ3_XXS: (256, 2 + QK_K // 4 + QK_K // 8),
+    GGMLQuantizationType.IQ1_S:   (256, 2 + QK_K // 8 + QK_K // 16),
+    GGMLQuantizationType.IQ4_NL:  (32, 2 + 16),
+    GGMLQuantizationType.IQ3_S:   (256, 2 + QK_K // 4 + QK_K // 8 + QK_K // 32 + 4),
+    GGMLQuantizationType.IQ2_S:   (256, 2 + QK_K // 4 + QK_K // 16),
+    GGMLQuantizationType.IQ4_XS:  (256, 2 + 2 + QK_K // 2 + QK_K // 64),
+    GGMLQuantizationType.I8:      (1, 1),
+    GGMLQuantizationType.I16:     (1, 2),
+    GGMLQuantizationType.I32:     (1, 4),
+    GGMLQuantizationType.I64:     (1, 8),
+    GGMLQuantizationType.F64:     (1, 8),
 }
 
 
@@ -528,6 +823,7 @@ KEY_GENERAL_SOURCE_HF_REPO       = Keys.General.SOURCE_HF_REPO
 KEY_GENERAL_FILE_TYPE            = Keys.General.FILE_TYPE
 
 # LLM
+KEY_VOCAB_SIZE            = Keys.LLM.VOCAB_SIZE
 KEY_CONTEXT_LENGTH        = Keys.LLM.CONTEXT_LENGTH
 KEY_EMBEDDING_LENGTH      = Keys.LLM.EMBEDDING_LENGTH
 KEY_BLOCK_COUNT           = Keys.LLM.BLOCK_COUNT
@@ -551,6 +847,12 @@ KEY_ROPE_SCALING_FACTOR       = Keys.Rope.SCALING_FACTOR
 KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN
 KEY_ROPE_SCALING_FINETUNED    = Keys.Rope.SCALING_FINETUNED
 
+# SSM
+KEY_SSM_CONV_KERNEL    = Keys.SSM.CONV_KERNEL
+KEY_SSM_INNER_SIZE     = Keys.SSM.INNER_SIZE
+KEY_SSM_STATE_SIZE     = Keys.SSM.STATE_SIZE
+KEY_SSM_TIME_STEP_RANK = Keys.SSM.TIME_STEP_RANK
+
 # tokenization
 KEY_TOKENIZER_MODEL      = Keys.Tokenizer.MODEL
 KEY_TOKENIZER_LIST       = Keys.Tokenizer.LIST
@@ -562,5 +864,7 @@ KEY_TOKENIZER_EOS_ID     = Keys.Tokenizer.EOS_ID
 KEY_TOKENIZER_UNK_ID     = Keys.Tokenizer.UNK_ID
 KEY_TOKENIZER_SEP_ID     = Keys.Tokenizer.SEP_ID
 KEY_TOKENIZER_PAD_ID     = Keys.Tokenizer.PAD_ID
+KEY_TOKENIZER_CLS_ID     = Keys.Tokenizer.CLS_ID
+KEY_TOKENIZER_MASK_ID    = Keys.Tokenizer.MASK_ID
 KEY_TOKENIZER_HF_JSON    = Keys.Tokenizer.HF_JSON
 KEY_TOKENIZER_RWKV       = Keys.Tokenizer.RWKV
diff --git a/gguf-py/gguf/gguf_reader.py b/gguf-py/gguf/gguf_reader.py
index 8682765ed..33afac552 100644
--- a/gguf-py/gguf/gguf_reader.py
+++ b/gguf-py/gguf/gguf_reader.py
@@ -107,7 +107,7 @@ class GGUFReader:
         offs, tensors_fields = self._build_tensors_fields(offs, tensor_count)
         new_align = self.fields.get('general.alignment')
         if new_align is not None:
-            if new_align.types != [GGUFValueType.UINT64]:
+            if new_align.types != [GGUFValueType.UINT32]:
                 raise ValueError('Bad type for general.alignment field')
             self.alignment = new_align.parts[-1][0]
         padding = offs % self.alignment
@@ -242,12 +242,27 @@ class GGUFReader:
             n_bytes = n_elems * type_size // block_size
             data_offs = int(start_offs + offset_tensor[0])
             item_type: npt.DTypeLike
-            if ggml_type == GGMLQuantizationType.F32:
-                item_count = n_elems
-                item_type = np.float32
-            elif ggml_type == GGMLQuantizationType.F16:
+            if ggml_type == GGMLQuantizationType.F16:
                 item_count = n_elems
                 item_type = np.float16
+            elif ggml_type == GGMLQuantizationType.F32:
+                item_count = n_elems
+                item_type = np.float32
+            elif ggml_type == GGMLQuantizationType.F64:
+                item_count = n_elems
+                item_type = np.float64
+            elif ggml_type == GGMLQuantizationType.I8:
+                item_count = n_elems
+                item_type = np.int8
+            elif ggml_type == GGMLQuantizationType.I16:
+                item_count = n_elems
+                item_type = np.int16
+            elif ggml_type == GGMLQuantizationType.I32:
+                item_count = n_elems
+                item_type = np.int32
+            elif ggml_type == GGMLQuantizationType.I64:
+                item_count = n_elems
+                item_type = np.int64
             else:
                 item_count = n_bytes
                 item_type = np.uint8
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index d93aaa877..f4c440766 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -19,6 +19,7 @@ from .constants import (
     GGUFValueType,
     Keys,
     RopeScalingType,
+    PoolingType,
     TokenType,
 )
 
@@ -195,9 +196,6 @@ class GGUFWriter:
         if self.state is not WriterState.EMPTY:
             raise ValueError(f'Expected output file to be empty, got {self.state}')
 
-        if raw_dtype is None and tensor_dtype not in (np.float32, np.float16):
-            raise ValueError("Only F32 and F16 tensors are supported for now")
-
         encoded_name = name.encode("utf8")
         self.ti_data += self._pack("Q", len(encoded_name))
         self.ti_data += encoded_name
@@ -206,7 +204,22 @@ class GGUFWriter:
         for i in range(n_dims):
             self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i])
         if raw_dtype is None:
-            dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
+            if tensor_dtype == np.float16:
+                dtype = GGMLQuantizationType.F16
+            elif tensor_dtype == np.float32:
+                dtype = GGMLQuantizationType.F32
+            elif tensor_dtype == np.float64:
+                dtype = GGMLQuantizationType.F64
+            elif tensor_dtype == np.int8:
+                dtype = GGMLQuantizationType.I8
+            elif tensor_dtype == np.int16:
+                dtype = GGMLQuantizationType.I16
+            elif tensor_dtype == np.int32:
+                dtype = GGMLQuantizationType.I32
+            elif tensor_dtype == np.int64:
+                dtype = GGMLQuantizationType.I64
+            else:
+                raise ValueError("Only F16, F32, F64, I8, I16, I32, I64 tensors are supported for now")
         else:
             dtype = raw_dtype
         self.ti_data += self._pack("I", dtype)
@@ -283,6 +296,9 @@ class GGUFWriter:
     def add_author(self, author: str) -> None:
         self.add_string(Keys.General.AUTHOR, author)
 
+    def add_version(self, version: str) -> None:
+        self.add_string(Keys.General.VERSION, version)
+
     def add_tensor_data_layout(self, layout: str) -> None:
         self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
 
@@ -292,6 +308,9 @@ class GGUFWriter:
     def add_description(self, description: str) -> None:
         self.add_string(Keys.General.DESCRIPTION, description)
 
+    def add_licence(self, licence: str) -> None:
+        self.add_string(Keys.General.LICENSE, licence)
+
     def add_source_url(self, url: str) -> None:
         self.add_string(Keys.General.SOURCE_URL, url)
 
@@ -312,6 +331,9 @@ class GGUFWriter:
         self.data_alignment = alignment
         self.add_uint32(Keys.General.ALIGNMENT, alignment)
 
+    def add_vocab_size(self, size: int) -> None:
+        self.add_uint32(Keys.LLM.VOCAB_SIZE.format(arch=self.arch), size)
+
     def add_context_length(self, length: int) -> None:
         self.add_uint32(Keys.LLM.CONTEXT_LENGTH.format(arch=self.arch), length)
 
@@ -345,6 +367,9 @@ class GGUFWriter:
     def add_clamp_kqv(self, value: float) -> None:
         self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value)
 
+    def add_logit_scale(self, value: float) -> None:
+        self.add_float32(Keys.LLM.LOGIT_SCALE.format(arch=self.arch), value)
+
     def add_expert_count(self, count: int) -> None:
         self.add_uint32(Keys.LLM.EXPERT_COUNT.format(arch=self.arch), count)
 
@@ -357,6 +382,12 @@ class GGUFWriter:
     def add_layer_norm_rms_eps(self, value: float) -> None:
         self.add_float32(Keys.Attention.LAYERNORM_RMS_EPS.format(arch=self.arch), value)
 
+    def add_causal_attention(self, value: bool) -> None:
+        self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value)
+
+    def add_pooling_type(self, value: PoolingType) -> None:
+        self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value)
+
     def add_rope_dimension_count(self, count: int) -> None:
         self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count)
 
@@ -375,6 +406,18 @@ class GGUFWriter:
     def add_rope_scaling_finetuned(self, value: bool) -> None:
         self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value)
 
+    def add_ssm_conv_kernel(self, value: int) -> None:
+        self.add_uint32(Keys.SSM.CONV_KERNEL.format(arch=self.arch), value)
+
+    def add_ssm_inner_size(self, value: int) -> None:
+        self.add_uint32(Keys.SSM.INNER_SIZE.format(arch=self.arch), value)
+
+    def add_ssm_state_size(self, value: int) -> None:
+        self.add_uint32(Keys.SSM.STATE_SIZE.format(arch=self.arch), value)
+
+    def add_ssm_time_step_rank(self, value: int) -> None:
+        self.add_uint32(Keys.SSM.TIME_STEP_RANK.format(arch=self.arch), value)
+
     def add_tokenizer_model(self, model: str) -> None:
         self.add_string(Keys.Tokenizer.MODEL, model)
 
@@ -387,6 +430,9 @@ class GGUFWriter:
     def add_token_types(self, types: Sequence[TokenType] | Sequence[int]) -> None:
         self.add_array(Keys.Tokenizer.TOKEN_TYPE, types)
 
+    def add_token_type_count(self, value: int) -> None:
+        self.add_uint32(Keys.Tokenizer.TOKEN_TYPE_COUNT, value)
+
     def add_token_scores(self, scores: Sequence[float]) -> None:
         self.add_array(Keys.Tokenizer.SCORES, scores)
 
@@ -405,12 +451,21 @@ class GGUFWriter:
     def add_pad_token_id(self, id: int) -> None:
         self.add_uint32(Keys.Tokenizer.PAD_ID, id)
 
+    def add_cls_token_id(self, id: int) -> None:
+        self.add_uint32(Keys.Tokenizer.CLS_ID, id)
+
+    def add_mask_token_id(self, id: int) -> None:
+        self.add_uint32(Keys.Tokenizer.MASK_ID, id)
+
     def add_add_bos_token(self, value: bool) -> None:
         self.add_bool(Keys.Tokenizer.ADD_BOS, value)
 
     def add_add_eos_token(self, value: bool) -> None:
         self.add_bool(Keys.Tokenizer.ADD_EOS, value)
 
+    def add_add_space_prefix(self, value: bool) -> None:
+        self.add_bool(Keys.Tokenizer.ADD_PREFIX, value)
+
     def add_chat_template(self, value: str) -> None:
         self.add_string(Keys.Tokenizer.CHAT_TEMPLATE, value)
 
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 80c1d5449..345b1b0c7 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -15,20 +15,27 @@ class TensorNameMap:
             "word_embeddings",                           # bloom
             "model.embed_tokens",                        # llama-hf
             "tok_embeddings",                            # llama-pth
-            "embeddings.word_embeddings",                # bert
+            "embeddings.word_embeddings",                # bert nomic-bert
             "language_model.embedding.word_embeddings",  # persimmon
             "wte",                                       # gpt2
             "transformer.embd.wte",                      # phi2
+            "model.tok_embeddings",                      # internlm2
+            "model.embedding",                           # mamba-qbert
+            "backbone.embedding",                        # mamba
+            "backbone.embeddings",                       # mamba-hf
+            "transformer.in_out_embed",                  # Grok
         ),
 
         # Token type embeddings
         MODEL_TENSOR.TOKEN_TYPES: (
-            "embeddings.token_type_embeddings",  # bert
+            "embeddings.token_type_embeddings",  # bert nomic-bert
         ),
 
         # Normalization of token embeddings
         MODEL_TENSOR.TOKEN_EMBD_NORM: (
             "word_embeddings_layernorm",  # bloom
+            "embeddings.LayerNorm",       # bert
+            "emb_ln",                     # nomic-bert
         ),
 
         # Position embeddings
@@ -41,8 +48,8 @@ class TensorNameMap:
         # Output
         MODEL_TENSOR.OUTPUT: (
             "embed_out",                 # gptneox
-            "lm_head",                   # gpt2 mpt falcon llama-hf baichuan qwen
-            "output",                    # llama-pth bloom
+            "lm_head",                   # gpt2 mpt falcon llama-hf baichuan qwen mamba
+            "output",                    # llama-pth bloom internlm2
             "word_embeddings_for_head",  # persimmon
             "lm_head.linear",            # phi2
         ),
@@ -51,13 +58,16 @@ class TensorNameMap:
         MODEL_TENSOR.OUTPUT_NORM: (
             "gpt_neox.final_layer_norm",               # gptneox
             "transformer.ln_f",                        # gpt2 gpt-j falcon
-            "model.norm",                              # llama-hf baichuan
+            "model.norm",                              # llama-hf baichuan internlm2
             "norm",                                    # llama-pth
-            "embeddings.LayerNorm",                    # bert
             "transformer.norm_f",                      # mpt
             "ln_f",                                    # refact bloom qwen gpt2
             "language_model.encoder.final_layernorm",  # persimmon
+            "model.final_layernorm",                   # persimmon
             "lm_head.ln",                              # phi2
+            "model.norm_f",                            # mamba-qbert
+            "backbone.norm_f",                         # mamba
+            "transformer.rms_norm",                    # Grok
         ),
 
         # Rope frequencies
@@ -77,12 +87,15 @@ class TensorNameMap:
             "transformer.h.{bid}.ln_mlp",                           # falcon40b
             "model.layers.{bid}.input_layernorm",                   # llama-hf
             "layers.{bid}.attention_norm",                          # llama-pth
-            "encoder.layer.{bid}.attention.output.LayerNorm",       # bert
             "language_model.encoder.layers.{bid}.input_layernorm",  # persimmon
             "model.layers.{bid}.ln1",                               # yi
             "h.{bid}.ln_1",                                         # gpt2
             "transformer.h.{bid}.ln",                               # phi2
             "model.layers.layers.{bid}.norm",                       # plamo
+            "model.layers.{bid}.attention_norm",                    # internlm2
+            "model.layers.{bid}.norm",                              # mamba-qbert
+            "backbone.layers.{bid}.norm",                           # mamba
+            "transformer.decoder_layer.{bid}.rms_norm",             # Grok
         ),
 
         # Attention norm 2
@@ -98,35 +111,43 @@ class TensorNameMap:
             "transformer.h.{bid}.self_attention.query_key_value",                  # falcon
             "h.{bid}.self_attention.query_key_value",                              # bloom
             "language_model.encoder.layers.{bid}.self_attention.query_key_value",  # persimmon
+            "model.layers.{bid}.self_attn.query_key_value",                        # persimmon
             "h.{bid}.attn.c_attn",                                                 # gpt2
             "transformer.h.{bid}.mixer.Wqkv",                                      # phi2
+            "encoder.layers.{bid}.attn.Wqkv",                                      # nomic-bert
         ),
 
         # Attention query
         MODEL_TENSOR.ATTN_Q: (
-            "model.layers.{bid}.self_attn.q_proj",         # llama-hf
-            "layers.{bid}.attention.wq",                   # llama-pth
-            "encoder.layer.{bid}.attention.self.query",    # bert
-            "transformer.h.{bid}.attn.q_proj",             # gpt-j
-            "model.layers.layers.{bid}.self_attn.q_proj",  # plamo
+            "model.layers.{bid}.self_attn.q_proj",                       # llama-hf
+            "layers.{bid}.attention.wq",                                 # llama-pth
+            "encoder.layer.{bid}.attention.self.query",                  # bert
+            "transformer.h.{bid}.attn.q_proj",                           # gpt-j
+            "model.layers.layers.{bid}.self_attn.q_proj",                # plamo
+            "model.layers.{bid}.attention.wq",                           # internlm2
+            "transformer.decoder_layer.{bid}.multi_head_attention.query" # Grok
         ),
 
         # Attention key
         MODEL_TENSOR.ATTN_K: (
-            "model.layers.{bid}.self_attn.k_proj",         # llama-hf
-            "layers.{bid}.attention.wk",                   # llama-pth
-            "encoder.layer.{bid}.attention.self.key",      # bert
-            "transformer.h.{bid}.attn.k_proj",             # gpt-j
-            "model.layers.layers.{bid}.self_attn.k_proj",  # plamo
+            "model.layers.{bid}.self_attn.k_proj",                     # llama-hf
+            "layers.{bid}.attention.wk",                               # llama-pth
+            "encoder.layer.{bid}.attention.self.key",                  # bert
+            "transformer.h.{bid}.attn.k_proj",                         # gpt-j
+            "model.layers.layers.{bid}.self_attn.k_proj",              # plamo
+            "model.layers.{bid}.attention.wk",                         # internlm2
+            "transformer.decoder_layer.{bid}.multi_head_attention.key" # Grok
         ),
 
         # Attention value
         MODEL_TENSOR.ATTN_V: (
-            "model.layers.{bid}.self_attn.v_proj",         # llama-hf
-            "layers.{bid}.attention.wv",                   # llama-pth
-            "encoder.layer.{bid}.attention.self.value",    # bert
-            "transformer.h.{bid}.attn.v_proj",             # gpt-j
-            "model.layers.layers.{bid}.self_attn.v_proj",  # plamo
+            "model.layers.{bid}.self_attn.v_proj",                       # llama-hf
+            "layers.{bid}.attention.wv",                                 # llama-pth
+            "encoder.layer.{bid}.attention.self.value",                  # bert
+            "transformer.h.{bid}.attn.v_proj",                           # gpt-j
+            "model.layers.layers.{bid}.self_attn.v_proj",                # plamo
+            "model.layers.{bid}.attention.wv",                           # internlm2
+            "transformer.decoder_layer.{bid}.multi_head_attention.value" # Grok
         ),
 
         # Attention output
@@ -141,9 +162,20 @@ class TensorNameMap:
             "encoder.layer.{bid}.attention.output.dense",                # bert
             "transformer.h.{bid}.attn.out_proj",                         # gpt-j
             "language_model.encoder.layers.{bid}.self_attention.dense",  # persimmon
+            "model.layers.{bid}.self_attn.dense",                        # persimmon
             "h.{bid}.attn.c_proj",                                       # gpt2
             "transformer.h.{bid}.mixer.out_proj",                        # phi2
             "model.layers.layers.{bid}.self_attn.o_proj",                # plamo
+            "model.layers.{bid}.attention.wo",                           # internlm2
+            "encoder.layers.{bid}.attn.out_proj",                        # nomic-bert
+            "transformer.decoder_layer.{bid}.multi_head_attention.linear"# Grok
+        ),
+
+        # Attention output norm
+        MODEL_TENSOR.ATTN_OUT_NORM: (
+            "encoder.layer.{bid}.attention.output.LayerNorm",  # bert
+            "encoder.layers.{bid}.norm1",                      # nomic-bert
+            "transformer.decoder_layer.{bid}.rms_norm_1",      # Grok
         ),
 
         # Rotary embeddings
@@ -151,6 +183,7 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.rotary_emb.inv_freq",        # llama-hf
             "layers.{bid}.attention.inner_attention.rope.freqs",       # llama-pth
             "model.layers.layers.{bid}.self_attn.rotary_emb.inv_freq", # plamo
+            "transformer.h.{bid}.attn.rotary_emb.inv_freq",            # codeshell
         ),
 
         # Feed-forward norm
@@ -161,15 +194,17 @@ class TensorNameMap:
             "transformer.blocks.{bid}.norm_2",                               # mpt
             "model.layers.{bid}.post_attention_layernorm",                   # llama-hf
             "layers.{bid}.ffn_norm",                                         # llama-pth
-            "encoder.layer.{bid}.output.LayerNorm",                          # bert
             "language_model.encoder.layers.{bid}.post_attention_layernorm",  # persimmon
             "model.layers.{bid}.ln2",                                        # yi
             "h.{bid}.ln_2",                                                  # gpt2
+            "model.layers.{bid}.ffn_norm",                                   # internlm2
+            "transformer.decoder_layer.{bid}.rms_norm_2",                    # Grok
         ),
 
         MODEL_TENSOR.FFN_GATE_INP: (
             "layers.{bid}.feed_forward.gate",           # mixtral
             "model.layers.{bid}.block_sparse_moe.gate", # mixtral
+            "transformer.decoder_layer.{bid}.router"    # Grok
         ),
 
         # Feed-forward up
@@ -184,15 +219,20 @@ class TensorNameMap:
             "encoder.layer.{bid}.intermediate.dense",                 # bert
             "transformer.h.{bid}.mlp.fc_in",                          # gpt-j
             "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h",  # persimmon
+            "model.layers.{bid}.mlp.dense_h_to_4h",                   # persimmon
             "transformer.h.{bid}.mlp.w1",                             # qwen
             "h.{bid}.mlp.c_fc",                                       # gpt2
             "transformer.h.{bid}.mlp.fc1",                            # phi2
+            "model.layers.{bid}.mlp.fc1",                             # phi2
             "model.layers.layers.{bid}.mlp.up_proj",                  # plamo
+            "model.layers.{bid}.feed_forward.w3",                     # internlm2
+            "encoder.layers.{bid}.mlp.fc11",                          # nomic-bert
+            "model.layers.{bid}.mlp.c_fc",                            # starcoder2
         ),
 
         MODEL_TENSOR.FFN_UP_EXP: (
-            "layers.{bid}.feed_forward.experts.{xid}.w3",           # mixtral
-            "model.layers.{bid}.block_sparse_moe.experts.{xid}.w3", # mixtral
+            "layers.{bid}.feed_forward.experts.w3",                 # mixtral (merged)
+            "transformer.decoder_layer.{bid}.moe.linear_v",         # Grok (merged)
         ),
 
         # AWQ-activation gate
@@ -206,11 +246,13 @@ class TensorNameMap:
             "layers.{bid}.feed_forward.w1",               # llama-pth
             "transformer.h.{bid}.mlp.w2",                 # qwen
             "model.layers.layers.{bid}.mlp.gate_proj",    # plamo
+            "model.layers.{bid}.feed_forward.w1",         # internlm2
+            "encoder.layers.{bid}.mlp.fc12",              # nomic-bert
         ),
 
         MODEL_TENSOR.FFN_GATE_EXP: (
-            "layers.{bid}.feed_forward.experts.{xid}.w1",           # mixtral
-            "model.layers.{bid}.block_sparse_moe.experts.{xid}.w1", # mixtral
+            "layers.{bid}.feed_forward.experts.w1",                 # mixtral (merged)
+            "transformer.decoder_layer.{bid}.moe.linear"            # Grok (merged)
         ),
 
         # Feed-forward down
@@ -225,27 +267,77 @@ class TensorNameMap:
             "encoder.layer.{bid}.output.dense",                       # bert
             "transformer.h.{bid}.mlp.fc_out",                         # gpt-j
             "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h",  # persimmon
+            "model.layers.{bid}.mlp.dense_4h_to_h",                   # persimmon
             "h.{bid}.mlp.c_proj",                                     # gpt2
             "transformer.h.{bid}.mlp.fc2",                            # phi2
+            "model.layers.{bid}.mlp.fc2",                             # phi2
             "model.layers.layers.{bid}.mlp.down_proj",                # plamo
+            "model.layers.{bid}.feed_forward.w2",                     # internlm2
+            "encoder.layers.{bid}.mlp.fc2",                           # nomic-bert
+            "model.layers.{bid}.mlp.c_proj",                          # starcoder2
         ),
 
         MODEL_TENSOR.FFN_DOWN_EXP: (
-            "layers.{bid}.feed_forward.experts.{xid}.w2",           # mixtral
-            "model.layers.{bid}.block_sparse_moe.experts.{xid}.w2", # mixtral
+            "layers.{bid}.feed_forward.experts.w2",                 # mixtral (merged)
+            "transformer.decoder_layer.{bid}.moe.linear_1",         # Grok (merged)
         ),
 
         MODEL_TENSOR.ATTN_Q_NORM: (
             "language_model.encoder.layers.{bid}.self_attention.q_layernorm",
+            "model.layers.{bid}.self_attn.q_layernorm",                       # persimmon
+            "transformer.blocks.{bid}.attn.q_ln",                             # sea-lion
         ),
 
         MODEL_TENSOR.ATTN_K_NORM: (
             "language_model.encoder.layers.{bid}.self_attention.k_layernorm",
+            "model.layers.{bid}.self_attn.k_layernorm",                       # persimmon
+            "transformer.blocks.{bid}.attn.k_ln",                             # sea-lion
         ),
 
         MODEL_TENSOR.ROPE_FREQS: (
             "language_model.encoder.layers.{bid}.self_attention.rotary_emb.inv_freq",  # persimmon
         ),
+
+        MODEL_TENSOR.LAYER_OUT_NORM: (
+            "encoder.layer.{bid}.output.LayerNorm",         # bert
+            "encoder.layers.{bid}.norm2",                   # nomic-bert
+            "transformer.decoder_layer.{bid}.rms_norm_3",   # Grok
+        ),
+
+        MODEL_TENSOR.SSM_IN: (
+            "model.layers.{bid}.in_proj",
+            "backbone.layers.{bid}.mixer.in_proj",
+        ),
+
+        MODEL_TENSOR.SSM_CONV1D: (
+            "model.layers.{bid}.conv1d",
+            "backbone.layers.{bid}.mixer.conv1d",
+        ),
+
+        MODEL_TENSOR.SSM_X: (
+            "model.layers.{bid}.x_proj",
+            "backbone.layers.{bid}.mixer.x_proj",
+        ),
+
+        MODEL_TENSOR.SSM_DT: (
+            "model.layers.{bid}.dt_proj",
+            "backbone.layers.{bid}.mixer.dt_proj",
+        ),
+
+        MODEL_TENSOR.SSM_A: (
+            "model.layers.{bid}.A_log",
+            "backbone.layers.{bid}.mixer.A_log",
+        ),
+
+        MODEL_TENSOR.SSM_D: (
+            "model.layers.{bid}.D",
+            "backbone.layers.{bid}.mixer.D",
+        ),
+
+        MODEL_TENSOR.SSM_OUT: (
+            "model.layers.{bid}.out_proj",
+            "backbone.layers.{bid}.mixer.out_proj",
+        ),
     }
 
     mapping: dict[str, tuple[MODEL_TENSOR, str]]
diff --git a/gguf-py/gguf/vocab.py b/gguf-py/gguf/vocab.py
index cd1942975..a23136b18 100644
--- a/gguf-py/gguf/vocab.py
+++ b/gguf-py/gguf/vocab.py
@@ -29,7 +29,7 @@ class SpecialVocab:
         if special_token_types is not None:
             self.special_token_types = special_token_types
         else:
-            self.special_token_types = ('bos', 'eos', 'unk', 'sep', 'pad')
+            self.special_token_types = ('bos', 'eos', 'unk', 'sep', 'pad', 'cls', 'mask')
         self._load(Path(path))
 
     def __repr__(self) -> str:
@@ -152,10 +152,6 @@ class SpecialVocab:
             add_entry = tokenizer_config.get(f'add_{typ}_token')
             if isinstance(add_entry, bool):
                 self.add_special_token[typ] = add_entry
-            if not added_tokens:
-                # We will need this to get the content for the token, so if it's empty
-                # may as well just give up.
-                continue
             entry = tokenizer_config.get(f'{typ}_token')
             if isinstance(entry, str):
                 tc_content = entry
diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml
index 9789c2c87..13cbfffbc 100644
--- a/gguf-py/pyproject.toml
+++ b/gguf-py/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "gguf"
-version = "0.7.0"
+version = "0.9.0"
 description = "Read and write ML models in GGUF for GGML"
 authors = ["GGML <ggml@ggml.ai>"]
 packages = [
diff --git a/grammars/json.gbnf b/grammars/json.gbnf
index a9537cdf9..a8a80752e 100644
--- a/grammars/json.gbnf
+++ b/grammars/json.gbnf
@@ -15,7 +15,7 @@ array  ::=
 
 string ::=
   "\"" (
-    [^"\\] |
+    [^"\\\x7F\x00-\x1F] |
     "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
   )* "\"" ws
 
diff --git a/grammars/json_arr.gbnf b/grammars/json_arr.gbnf
index ef53e77a0..31a3202f8 100644
--- a/grammars/json_arr.gbnf
+++ b/grammars/json_arr.gbnf
@@ -24,7 +24,7 @@ array  ::=
 
 string ::=
   "\"" (
-    [^"\\] |
+    [^"\\\x7F\x00-\x1F] |
     "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
   )* "\"" ws
 
diff --git a/kompute b/kompute
new file mode 160000
index 000000000..4565194ed
--- /dev/null
+++ b/kompute
@@ -0,0 +1 @@
+Subproject commit 4565194ed7c32d1d2efa32ceab4d3c6cae006306
diff --git a/kompute-shaders/common.comp b/kompute-shaders/common.comp
new file mode 100644
index 000000000..62d62b025
--- /dev/null
+++ b/kompute-shaders/common.comp
@@ -0,0 +1,102 @@
+#extension GL_EXT_shader_16bit_storage: require
+#extension GL_EXT_shader_8bit_storage: require
+#extension GL_EXT_shader_explicit_arithmetic_types_float16: require
+#extension GL_EXT_shader_explicit_arithmetic_types_int8: require
+#extension GL_EXT_shader_explicit_arithmetic_types_int16: require
+#extension GL_EXT_control_flow_attributes: enable
+#extension GL_KHR_shader_subgroup_arithmetic : require
+#extension GL_EXT_debug_printf : enable
+
+#define QK4_0 32
+#define QK4_1 32
+
+#define GELU_COEF_A 0.044715
+#define SQRT_2_OVER_PI 0.79788456080286535587989211986876
+#define TWOPI_F 6.283185307179586f
+
+#define QK_K 256
+
+#define u8BufToU16(buf, idx) (((uint16_t(buf[idx + 1]) << 8)) | buf[idx])
+#define u8BufToFloat16(buf, idx) uint16BitsToHalf u8BufToU16(buf, idx)
+#define u8BufToU32(buf, idx) (((uint32_t u8BufToU16(buf, idx + 2) << 8 | buf[idx + 1]) << 8) | buf[idx])
+#define u8BufToFloat(buf, idx) uintBitsToFloat u8BufToU32(buf, idx)
+
+#define sizeof_block_q4_0 0x12
+struct block_q4_0 {
+    float16_t d;
+    uint8_t qs[QK4_0 / 2];
+};
+mat4 dequantize_q4_0(const block_q4_0 xb, uint il) {
+    const float d1 = il != 0 ? (xb.d / 16.f) : xb.d;
+    const float d2 = d1 / 256.f;
+    const float md = -8.f * xb.d;
+    const uint16_t mask0 = il != 0 ? uint16_t(0x00F0) : uint16_t(0x000F);
+    const uint16_t mask1 = mask0 << 8;
+
+    mat4 reg;
+    for (int i=0;i<8;i++) {
+        uint16_t b = (uint16_t(xb.qs[2 * i + 1]) << 8) | uint16_t(xb.qs[2 * i]);
+        reg[i/2][2*(i%2)+0] = d1 * (b & mask0) + md;
+        reg[i/2][2*(i%2)+1] = d2 * (b & mask1) + md;
+    }
+    return reg;
+}
+
+#define sizeof_block_q4_1 0x14
+struct block_q4_1 {
+    float16_t d;
+    float16_t m;
+    uint8_t qs[QK4_1 / 2];
+};
+mat4 dequantize_q4_1(const block_q4_1 xb, uint il) {
+    const float d1 = il != 0 ? (xb.d / 16.f) : xb.d;
+    const float d2 = d1 / 256.f;
+    const float  m = xb.m;
+    const uint16_t mask0 = il != 0 ? uint16_t(0x00F0) : uint16_t(0x000F);
+    const uint16_t mask1 = mask0 << 8;
+
+    mat4 reg;
+    for (int i=0;i<8;i++) {
+        uint16_t b = (uint16_t(xb.qs[2 * i + 1]) << 8) | uint16_t(xb.qs[2 * i]);
+        reg[i/2][2*(i%2)+0] = ((b & mask0) * d1) + m;
+        reg[i/2][2*(i%2)+1] = ((b & mask1) * d2) + m;
+    }
+    return reg;
+}
+
+#define sizeof_block_q6_k 210
+struct block_q6_k {
+    uint8_t ql[QK_K/2];      // quants, lower 4 bits
+    uint8_t qh[QK_K/4];      // quants, upper 2 bits
+    int8_t  scales[QK_K/16]; // scales, quantized with 8 bits
+    float16_t d;             // super-block scale
+};
+mat4 dequantize_q6_k(const block_q6_k xb, uint il) {
+    const float16_t d_all = xb.d;
+
+    const uint qlIndex = 64*(il/8) + 32*((il/2)&1) + 16*(il&1);
+    const uint qhIndex = 32*(il/8) + 16*(il&1);
+    float16_t sc = xb.scales[(il%2) + 2 * ((il/2))];
+    il = (il/2) & 3;
+
+    const uint16_t  kmask1 = il>1 ? uint16_t(il>2 ? 192 : 48) : uint16_t(il>0 ? 12 : 3);
+    const uint16_t  kmask2 = il>1 ? uint8_t(0xF0)             : uint8_t(0x0F);
+    const float16_t coef   = il>1 ? float16_t(1.f/16.f)       : float16_t(1.f);
+    const float16_t ml = float16_t(d_all * sc * 32.f);
+    const float16_t dl = float16_t(d_all * sc * coef);
+    mat4 reg;
+    for (int i = 0; i < 16; ++i) {
+        const float16_t q = (il&1) != 0 ? ((xb.ql[qlIndex + i] & kmask2) | ((xb.qh[qhIndex + i] & kmask1) << 2))
+                                        : ((xb.ql[qlIndex + i] & kmask2) | ((xb.qh[qhIndex + i] & kmask1) << 4));
+        reg[i/4][i%4] = dl * q - ml;
+    }
+    return reg;
+}
+
+
+#define QK8_0 32
+// struct block_q8_0 {
+//     float16_t d;         // delta
+//     int8_t    qs[QK8_0]; // quants
+// };
+#define sizeof_block_q8_0 34
diff --git a/kompute-shaders/op_add.comp b/kompute-shaders/op_add.comp
new file mode 100644
index 000000000..b7b76a79d
--- /dev/null
+++ b/kompute-shaders/op_add.comp
@@ -0,0 +1,58 @@
+#version 450
+
+#include "common.comp"
+
+layout(local_size_x = 1024) in;
+
+layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; };
+layout(binding = 1) buffer restrict readonly tensorInB { float inB[]; };
+layout(binding = 2) buffer restrict writeonly tensorOut { float out_[]; };
+
+layout(push_constant) uniform PushConstants {
+    uint inAOff;
+    uint inBOff;
+    uint outOff;
+    int ne00;
+    int nb00;
+    int nb01;
+    int nb02;
+    int nb03;
+    int ne10;
+    int ne11;
+    int ne12;
+    int ne13;
+    int nb10;
+    int nb11;
+    int nb12;
+    int nb13;
+    int ne0;
+    int nb0;
+    int nb1;
+    int nb2;
+    int nb3;
+  //int offs; // TODO: needed for GGML_OP_ACC, see metal code
+} pcs;
+
+// general-purpose kernel for addition of two tensors
+// pros: works for non-contiguous tensors, supports broadcast across dims 1, 2 and 3
+// cons: not very efficient
+void main() {
+    const uint i03 = gl_WorkGroupID.z;
+    const uint i02 = gl_WorkGroupID.y;
+    const uint i01 = gl_WorkGroupID.x;
+
+    const uint i13 = i03 % pcs.ne13;
+    const uint i12 = i02 % pcs.ne12;
+    const uint i11 = i01 % pcs.ne11;
+
+    int offs = 0; // TMP (see above)
+
+    uint src0_off = uint((i03*pcs.nb03 + i02*pcs.nb02 + i01*pcs.nb01 + offs) / 4);
+    uint src1_off = uint((i13*pcs.nb13 + i12*pcs.nb12 + i11*pcs.nb11       ) / 4);
+    uint dst_off  = uint((i03*pcs.nb3  + i02*pcs.nb2  + i01*pcs.nb1  + offs) / 4);
+
+    for (uint i0 = gl_LocalInvocationID.x; i0 < pcs.ne0; i0 += gl_WorkGroupSize.x) {
+        const uint i10 = i0 % pcs.ne10;
+        out_[pcs.outOff + dst_off + i0] = inA[pcs.inAOff + src0_off + i0] + inB[pcs.inBOff + src1_off + i10];
+    }
+}
diff --git a/kompute-shaders/op_addrow.comp b/kompute-shaders/op_addrow.comp
new file mode 100644
index 000000000..2376a6b8f
--- /dev/null
+++ b/kompute-shaders/op_addrow.comp
@@ -0,0 +1,25 @@
+#version 450
+
+#include "common.comp"
+
+layout(local_size_x = 1) in;
+
+layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; };
+layout(binding = 1) buffer restrict readonly tensorInB { float inB[]; };
+layout(binding = 2) buffer restrict writeonly tensorOut { float out_[]; };
+
+layout(push_constant) uniform PushConstants {
+    uint inAOff;
+    uint inBOff;
+    uint outOff;
+    uint row;
+} pcs;
+
+void main() {
+    const uint baseIndex = gl_WorkGroupID.x * 4;
+
+    for (uint x = 0; x < 4; x++) {
+        const uint i = baseIndex + x;
+        out_[i + pcs.outOff] = inA[i + pcs.inAOff] + inB[(i % pcs.row) + pcs.inBOff];
+    }
+}
diff --git a/kompute-shaders/op_cpy_f16_f16.comp b/kompute-shaders/op_cpy_f16_f16.comp
new file mode 100644
index 000000000..d57247d2d
--- /dev/null
+++ b/kompute-shaders/op_cpy_f16_f16.comp
@@ -0,0 +1,52 @@
+#version 450
+
+#include "common.comp"
+
+#define IN_TYPE float16_t
+#define IN_TYPE_SIZE 2
+#define OUT_TYPE float16_t
+#define OUT_TYPE_SIZE 2
+
+layout(local_size_x = 1024) in;
+
+layout (binding = 0) readonly buffer tensorIn { IN_TYPE in_[]; };
+layout (binding = 1) writeonly buffer tensorOut { OUT_TYPE out_[]; };
+
+layout (push_constant) uniform parameter {
+    uint inOff;
+    uint outOff;
+    int ne00;
+    int ne01;
+    int ne02;
+    uint nb00;
+    uint nb01;
+    uint nb02;
+    uint nb03;
+    int ne0;
+    int ne1;
+    int ne2;
+    uint nb0;
+    uint nb1;
+    uint nb2;
+    uint nb3;
+} pcs;
+
+void main() {
+    const uint i03 = gl_WorkGroupID.z;
+    const uint i02 = gl_WorkGroupID.y;
+    const uint i01 = gl_WorkGroupID.x;
+
+    const int n = int(i03)*pcs.ne02*pcs.ne01*pcs.ne00 + int(i02)*pcs.ne01*pcs.ne00 + int(i01)*pcs.ne00;
+
+    const int i3 = n / (pcs.ne2*pcs.ne1*pcs.ne0);
+    const int i2 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0) / (pcs.ne1*pcs.ne0);
+    const int i1 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0) / pcs.ne0;
+    const int i0 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0 - i1*pcs.ne0);
+
+    const uint dst_data = (i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / OUT_TYPE_SIZE + pcs.outOff; // Based from out_
+
+    for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) {
+        const uint src = uint((i03*pcs.nb03 + i02*pcs.nb02 + i01*pcs.nb01 + i00*pcs.nb00) / IN_TYPE_SIZE) + pcs.inOff; // Based from in_
+        out_[dst_data+i00] = OUT_TYPE(in_[src]);
+    }
+}
diff --git a/kompute-shaders/op_cpy_f16_f32.comp b/kompute-shaders/op_cpy_f16_f32.comp
new file mode 100644
index 000000000..b568bcd7b
--- /dev/null
+++ b/kompute-shaders/op_cpy_f16_f32.comp
@@ -0,0 +1,52 @@
+#version 450
+
+#include "common.comp"
+
+#define IN_TYPE float16_t
+#define IN_TYPE_SIZE 2
+#define OUT_TYPE float
+#define OUT_TYPE_SIZE 4
+
+layout(local_size_x = 1024) in;
+
+layout (binding = 0) readonly buffer tensorIn { IN_TYPE in_[]; };
+layout (binding = 1) writeonly buffer tensorOut { OUT_TYPE out_[]; };
+
+layout (push_constant) uniform parameter {
+    uint inOff;
+    uint outOff;
+    int ne00;
+    int ne01;
+    int ne02;
+    uint nb00;
+    uint nb01;
+    uint nb02;
+    uint nb03;
+    int ne0;
+    int ne1;
+    int ne2;
+    uint nb0;
+    uint nb1;
+    uint nb2;
+    uint nb3;
+} pcs;
+
+void main() {
+    const uint i03 = gl_WorkGroupID.z;
+    const uint i02 = gl_WorkGroupID.y;
+    const uint i01 = gl_WorkGroupID.x;
+
+    const int n = int(i03)*pcs.ne02*pcs.ne01*pcs.ne00 + int(i02)*pcs.ne01*pcs.ne00 + int(i01)*pcs.ne00;
+
+    const int i3 = n / (pcs.ne2*pcs.ne1*pcs.ne0);
+    const int i2 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0) / (pcs.ne1*pcs.ne0);
+    const int i1 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0) / pcs.ne0;
+    const int i0 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0 - i1*pcs.ne0);
+
+    const uint dst_data = (i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / OUT_TYPE_SIZE + pcs.outOff; // Based from out_
+
+    for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) {
+        const uint src = uint((i03*pcs.nb03 + i02*pcs.nb02 + i01*pcs.nb01 + i00*pcs.nb00) / IN_TYPE_SIZE) + pcs.inOff; // Based from in_
+        out_[dst_data+i00] = OUT_TYPE(in_[src]);
+    }
+}
diff --git a/kompute-shaders/op_cpy_f32_f16.comp b/kompute-shaders/op_cpy_f32_f16.comp
new file mode 100644
index 000000000..99b228343
--- /dev/null
+++ b/kompute-shaders/op_cpy_f32_f16.comp
@@ -0,0 +1,52 @@
+#version 450
+
+#include "common.comp"
+
+#define IN_TYPE float
+#define IN_TYPE_SIZE 4
+#define OUT_TYPE float16_t
+#define OUT_TYPE_SIZE 2
+
+layout(local_size_x = 1024) in;
+
+layout (binding = 0) readonly buffer tensorIn { IN_TYPE in_[]; };
+layout (binding = 1) writeonly buffer tensorOut { OUT_TYPE out_[]; };
+
+layout (push_constant) uniform parameter {
+    uint inOff;
+    uint outOff;
+    int ne00;
+    int ne01;
+    int ne02;
+    uint nb00;
+    uint nb01;
+    uint nb02;
+    uint nb03;
+    int ne0;
+    int ne1;
+    int ne2;
+    uint nb0;
+    uint nb1;
+    uint nb2;
+    uint nb3;
+} pcs;
+
+void main() {
+    const uint i03 = gl_WorkGroupID.z;
+    const uint i02 = gl_WorkGroupID.y;
+    const uint i01 = gl_WorkGroupID.x;
+
+    const int n = int(i03)*pcs.ne02*pcs.ne01*pcs.ne00 + int(i02)*pcs.ne01*pcs.ne00 + int(i01)*pcs.ne00;
+
+    const int i3 = n / (pcs.ne2*pcs.ne1*pcs.ne0);
+    const int i2 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0) / (pcs.ne1*pcs.ne0);
+    const int i1 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0) / pcs.ne0;
+    const int i0 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0 - i1*pcs.ne0);
+
+    const uint dst_data = (i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / OUT_TYPE_SIZE + pcs.outOff; // Based from out_
+
+    for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) {
+        const uint src = uint((i03*pcs.nb03 + i02*pcs.nb02 + i01*pcs.nb01 + i00*pcs.nb00) / IN_TYPE_SIZE) + pcs.inOff; // Based from in_
+        out_[dst_data+i00] = OUT_TYPE(in_[src]);
+    }
+}
diff --git a/kompute-shaders/op_cpy_f32_f32.comp b/kompute-shaders/op_cpy_f32_f32.comp
new file mode 100644
index 000000000..2fc998492
--- /dev/null
+++ b/kompute-shaders/op_cpy_f32_f32.comp
@@ -0,0 +1,52 @@
+#version 450
+
+#include "common.comp"
+
+#define IN_TYPE float
+#define IN_TYPE_SIZE 4
+#define OUT_TYPE float
+#define OUT_TYPE_SIZE 4
+
+layout(local_size_x = 1024) in;
+
+layout (binding = 0) readonly buffer tensorIn { IN_TYPE in_[]; };
+layout (binding = 1) writeonly buffer tensorOut { OUT_TYPE out_[]; };
+
+layout (push_constant) uniform parameter {
+    uint inOff;
+    uint outOff;
+    int ne00;
+    int ne01;
+    int ne02;
+    uint nb00;
+    uint nb01;
+    uint nb02;
+    uint nb03;
+    int ne0;
+    int ne1;
+    int ne2;
+    uint nb0;
+    uint nb1;
+    uint nb2;
+    uint nb3;
+} pcs;
+
+void main() {
+    const uint i03 = gl_WorkGroupID.z;
+    const uint i02 = gl_WorkGroupID.y;
+    const uint i01 = gl_WorkGroupID.x;
+
+    const int n = int(i03)*pcs.ne02*pcs.ne01*pcs.ne00 + int(i02)*pcs.ne01*pcs.ne00 + int(i01)*pcs.ne00;
+
+    const int i3 = n / (pcs.ne2*pcs.ne1*pcs.ne0);
+    const int i2 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0) / (pcs.ne1*pcs.ne0);
+    const int i1 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0) / pcs.ne0;
+    const int i0 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0 - i1*pcs.ne0);
+
+    const uint dst_data = (i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / OUT_TYPE_SIZE + pcs.outOff; // Based from out_
+
+    for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) {
+        const uint src = uint((i03*pcs.nb03 + i02*pcs.nb02 + i01*pcs.nb01 + i00*pcs.nb00) / IN_TYPE_SIZE) + pcs.inOff; // Based from in_
+        out_[dst_data+i00] = OUT_TYPE(in_[src]);
+    }
+}
diff --git a/kompute-shaders/op_diagmask.comp b/kompute-shaders/op_diagmask.comp
new file mode 100644
index 000000000..291c3fc18
--- /dev/null
+++ b/kompute-shaders/op_diagmask.comp
@@ -0,0 +1,30 @@
+#version 450
+
+#include "common.comp"
+
+layout(local_size_x = 1) in;
+
+layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; };
+layout(binding = 1) buffer restrict writeonly tensorOut { float out_[]; };
+
+layout(push_constant) uniform PushConstants {
+    uint inOff;
+    uint outOff;
+    uint n_past;
+    int ne00;
+    int ne01;
+} pcs;
+
+void main() {
+    const uint i02 = gl_WorkGroupID.z;
+    const uint i01 = gl_WorkGroupID.y;
+    const uint i00 = gl_WorkGroupID.x;
+
+    const uint index = i02*pcs.ne01*pcs.ne00 + i01*pcs.ne00 + i00;
+
+    if (i00 > pcs.n_past + i01) {
+        out_[index + pcs.outOff] = uintBitsToFloat(0xFF800000);
+    } else {
+        out_[index + pcs.outOff] = in_[index + pcs.inOff];
+    }
+}
diff --git a/kompute-shaders/op_gelu.comp b/kompute-shaders/op_gelu.comp
new file mode 100644
index 000000000..9d8c53710
--- /dev/null
+++ b/kompute-shaders/op_gelu.comp
@@ -0,0 +1,22 @@
+#version 450
+
+#include "common.comp"
+
+layout(local_size_x = 1) in;
+
+layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; };
+layout(binding = 1) buffer restrict writeonly tensorOut { float out_[]; };
+layout(push_constant) uniform PushConstants {
+    uint inOff;
+    uint outOff;
+} pcs;
+
+void main() {
+    const uint baseIndex = gl_WorkGroupID.x * 8;
+
+    for (uint x = 0; x < 8; x++) {
+        const uint i = baseIndex + x;
+        const float y = in_[i + pcs.inOff];
+        out_[i + pcs.outOff] = 0.5*y*(1.0 + tanh(clamp(SQRT_2_OVER_PI*y*(1.0 + GELU_COEF_A*y*y), -15.0, 15.0)));
+    }
+}
diff --git a/kompute-shaders/op_getrows.comp b/kompute-shaders/op_getrows.comp
new file mode 100644
index 000000000..1a5581b23
--- /dev/null
+++ b/kompute-shaders/op_getrows.comp
@@ -0,0 +1,17 @@
+void main() {
+    const uint i = gl_WorkGroupID.x;
+    const int r = inB[i + pcs.inBOff];
+
+    int z = 0;
+    for (uint ind = gl_LocalInvocationID.x; ind < pcs.ne00/16; ind += gl_WorkGroupSize.x) {
+        const uint inIndex = (r * pcs.nb01 + pcs.inAOff) + ind/NL * SIZE_OF_BLOCK;
+        const mat4 result = dequantize_block(inIndex, ind%NL);
+        for (uint j = 0; j < 4; ++j) {
+            for (uint k = 0; k < 4; ++k) {
+                const uint outIndex = i * pcs.nb1/BYTES_FOR_TYPE + pcs.outOff + z;
+                out_[outIndex] = result[j][k];
+                ++z;
+            }
+        }
+    }
+}
diff --git a/kompute-shaders/op_getrows_f16.comp b/kompute-shaders/op_getrows_f16.comp
new file mode 100644
index 000000000..48c936108
--- /dev/null
+++ b/kompute-shaders/op_getrows_f16.comp
@@ -0,0 +1,31 @@
+#version 450
+
+#include "common.comp"
+
+layout(local_size_x = 1) in;
+
+layout (binding = 0) readonly buffer tensorInA { float16_t inA[]; };
+layout (binding = 1) readonly buffer tensorInB { int inB[]; };
+layout (binding = 2) writeonly buffer tensorOut { float out_[]; };
+
+layout (push_constant) uniform parameter {
+    uint inAOff;
+    uint inBOff;
+    uint outOff;
+    int ne00;
+    int nb01;
+    int nb1;
+} pcs;
+
+void dequantize_row_f16(uint x /*Based from inA unaligned*/, uint y /*Based from out_*/, int k) {
+    for (int j = 0; j < k; j++) {
+        out_[y + j] = inA[x + j];
+    }
+}
+
+void main() {
+    const uint i = gl_WorkGroupID.x;
+    const int r = inB[i + pcs.inBOff];
+
+    dequantize_row_f16(r*pcs.nb01/2/*bytes for float16*/ + pcs.inAOff, i*pcs.nb1/4 + pcs.outOff, pcs.ne00);
+}
diff --git a/kompute-shaders/op_getrows_q4_0.comp b/kompute-shaders/op_getrows_q4_0.comp
new file mode 100644
index 000000000..32b2e891e
--- /dev/null
+++ b/kompute-shaders/op_getrows_q4_0.comp
@@ -0,0 +1,38 @@
+#version 450
+
+#include "common.comp"
+
+#define NL 2
+#define BYTES_FOR_TYPE 4 /*bytes for float*/
+#define SIZE_OF_BLOCK sizeof_block_q4_0
+
+layout(local_size_x = 1) in;
+
+layout (binding = 0) readonly buffer tensorInA { uint8_t inA[]; };
+layout (binding = 1) readonly buffer tensorInB { int inB[]; };
+layout (binding = 2) writeonly buffer tensorOut { float out_[]; };
+
+layout (push_constant) uniform parameter {
+    uint inAOff;
+    uint inBOff;
+    uint outOff;
+    int ne00;
+    int nb01;
+    int nb1;
+} pcs;
+
+block_q4_0 get_unaligned_block_q4_0(uint index) {
+    block_q4_0 fres;
+    fres.d = u8BufToFloat16(inA, index);
+    [[unroll]] for (uint it = 0; it != QK4_0 / 2; it++) {
+        fres.qs[it] = inA[index+2+it];
+    }
+    return fres;
+}
+
+mat4 dequantize_block(uint index, uint il) {
+    const block_q4_0 block = get_unaligned_block_q4_0(index);
+    return dequantize_q4_0(block, il);
+}
+
+#include "op_getrows.comp"
diff --git a/kompute-shaders/op_getrows_q4_1.comp b/kompute-shaders/op_getrows_q4_1.comp
new file mode 100644
index 000000000..87f2fbe17
--- /dev/null
+++ b/kompute-shaders/op_getrows_q4_1.comp
@@ -0,0 +1,39 @@
+#version 450
+
+#include "common.comp"
+
+#define NL 2
+#define BYTES_FOR_TYPE 4 /*bytes for float*/
+#define SIZE_OF_BLOCK sizeof_block_q4_1
+
+layout(local_size_x = 1) in;
+
+layout (binding = 0) readonly buffer tensorInA { uint8_t inA[]; };
+layout (binding = 1) readonly buffer tensorInB { int inB[]; };
+layout (binding = 2) writeonly buffer tensorOut { float out_[]; };
+
+layout (push_constant) uniform parameter {
+    uint inAOff;
+    uint inBOff;
+    uint outOff;
+    int ne00;
+    int nb01;
+    int nb1;
+} pcs;
+
+block_q4_1 get_unaligned_block_q4_1(uint index) {
+    block_q4_1 fres;
+    fres.d = u8BufToFloat16(inA, index);
+    fres.m = u8BufToFloat16(inA, index+2);
+    [[unroll]] for (uint it = 0; it != QK4_1 / 2; it++) {
+        fres.qs[it] = inA[index+4+it];
+    }
+    return fres;
+}
+
+mat4 dequantize_block(uint index, uint il) {
+    const block_q4_1 block = get_unaligned_block_q4_1(index);
+    return dequantize_q4_1(block, il);
+}
+
+#include "op_getrows.comp"
diff --git a/kompute-shaders/op_getrows_q6_k.comp b/kompute-shaders/op_getrows_q6_k.comp
new file mode 100644
index 000000000..9ce3545d1
--- /dev/null
+++ b/kompute-shaders/op_getrows_q6_k.comp
@@ -0,0 +1,44 @@
+#version 450
+
+#include "common.comp"
+
+#define NL 16
+#define BYTES_FOR_TYPE 4 /*bytes for float*/
+#define SIZE_OF_BLOCK sizeof_block_q6_k
+
+layout(local_size_x = 1) in;
+
+layout (binding = 0) readonly buffer tensorInA { uint8_t inA[]; };
+layout (binding = 1) readonly buffer tensorInB { int inB[]; };
+layout (binding = 2) writeonly buffer tensorOut { float out_[]; };
+
+layout (push_constant) uniform parameter {
+    uint inAOff;
+    uint inBOff;
+    uint outOff;
+    int ne00;
+    int nb01;
+    int nb1;
+} pcs;
+
+block_q6_k get_unaligned_block_q6_k(uint index) {
+    block_q6_k fres;
+    [[unroll]] for (uint it = 0; it != QK_K / 2; it++) {
+        fres.ql[it] = inA[index + it];
+    }
+    [[unroll]] for (uint it = 0; it != QK_K / 4; it++) {
+        fres.qh[it] = inA[index + QK_K/2 + it];
+    }
+    [[unroll]] for (uint it = 0; it != QK_K / 16; it++) {
+        fres.scales[it] = int8_t(inA[index + QK_K/2 + QK_K/4 + it]);
+    }
+    fres.d = u8BufToFloat16(inA, index + QK_K/2 + QK_K/4 + QK_K/16);
+    return fres;
+}
+
+mat4 dequantize_block(uint index, uint il) {
+    const block_q6_k block = get_unaligned_block_q6_k(index);
+    return dequantize_q6_k(block, il);
+}
+
+#include "op_getrows.comp"
diff --git a/kompute-shaders/op_mul.comp b/kompute-shaders/op_mul.comp
new file mode 100644
index 000000000..c92647c4d
--- /dev/null
+++ b/kompute-shaders/op_mul.comp
@@ -0,0 +1,52 @@
+#version 450
+
+#include "common.comp"
+
+layout(local_size_x = 1024) in;
+
+layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; };
+layout(binding = 1) buffer restrict readonly tensorInB { float inB[]; };
+layout(binding = 2) buffer restrict writeonly tensorOut { float out_[]; };
+
+layout(push_constant) uniform PushConstants {
+    uint inAOff;
+    uint inBOff;
+    uint outOff;
+    int ne00;
+    int nb00;
+    int nb01;
+    int nb02;
+    int nb03;
+    int ne10;
+    int ne11;
+    int ne12;
+    int ne13;
+    int nb10;
+    int nb11;
+    int nb12;
+    int nb13;
+    int ne0;
+    int nb0;
+    int nb1;
+    int nb2;
+    int nb3;
+} pcs;
+
+void main() {
+    const uint i03 = gl_WorkGroupID.z;
+    const uint i02 = gl_WorkGroupID.y;
+    const uint i01 = gl_WorkGroupID.x;
+
+    const uint i13 = i03 % pcs.ne13;
+    const uint i12 = i02 % pcs.ne12;
+    const uint i11 = i01 % pcs.ne11;
+
+    uint src0_off = uint((i03*pcs.nb03 + i02*pcs.nb02 + i01*pcs.nb01) / 4);
+    uint src1_off = uint((i13*pcs.nb13 + i12*pcs.nb12 + i11*pcs.nb11) / 4);
+    uint dst_off  = uint((i03*pcs.nb3  + i02*pcs.nb2  + i01*pcs.nb1)  / 4);
+
+    for (uint i0 = gl_LocalInvocationID.x; i0 < pcs.ne0; i0 += gl_WorkGroupSize.x) {
+        const uint i10 = i0 % pcs.ne10;
+        out_[pcs.outOff + dst_off + i0] = inA[pcs.inAOff + src0_off + i0] * inB[pcs.inBOff + src1_off + i10];
+    }
+}
diff --git a/kompute-shaders/op_mul_mat_f16.comp b/kompute-shaders/op_mul_mat_f16.comp
new file mode 100644
index 000000000..8f0a9031f
--- /dev/null
+++ b/kompute-shaders/op_mul_mat_f16.comp
@@ -0,0 +1,67 @@
+#version 450
+
+#include "common.comp"
+
+#extension GL_KHR_shader_subgroup_arithmetic : require
+
+layout(local_size_x_id = 0) in;
+
+layout (binding = 0) readonly buffer tensorInA { float16_t inA[]; };
+layout (binding = 1) readonly buffer tensorInB { float inB[]; };
+layout (binding = 2) writeonly buffer tensorOut { float out_[]; };
+
+layout (push_constant) uniform parameter {
+    uint inAOff;
+    uint inBOff;
+    uint outOff;
+    int ne00;
+    int ne01;
+    int ne02;
+    uint nb00;
+    uint nb01;
+    uint nb02;
+    int ne10;
+    int ne11;
+    int ne12;
+    uint nb10;
+    uint nb11;
+    uint nb12;
+    int ne0;
+    int ne1;
+    uint r2;
+    uint r3;
+} pcs;
+
+#define N_F16_F32 4
+
+void main() {
+    const uint r0 = gl_WorkGroupID.x;
+    const uint rb = gl_WorkGroupID.y*N_F16_F32;
+    const uint im = gl_WorkGroupID.z;
+
+    const uint i12 = im%pcs.ne12;
+    const uint i13 = im/pcs.ne12;
+
+    const uint offset0 = r0*pcs.nb01 + (i12/pcs.r2)*pcs.nb02 + (i13/pcs.r3)*pcs.nb02*pcs.ne02;
+
+    const uint x = offset0 / 2 + pcs.inAOff; // Based from inA
+
+    for (uint row = 0; row < N_F16_F32; ++row) {
+        uint r1 = rb + row;
+        if (r1 >= pcs.ne11) {
+            break;
+        }
+
+        const uint y = (r1*pcs.nb11 + im*pcs.nb12) / 4 + pcs.inBOff; // Based from inB
+
+        float sumf = 0;
+        for (uint i = gl_SubgroupInvocationID.x; i < pcs.ne00; i += gl_SubgroupSize) {
+            sumf += float(inA[x+i]) * float(inB[y+i]);
+        }
+
+        const float all_sum = subgroupAdd(sumf);
+        if (subgroupElect()) {
+            out_[im*pcs.ne1*pcs.ne0 + r1*pcs.ne0 + r0 + pcs.outOff] = all_sum;
+        }
+    }
+}
diff --git a/kompute-shaders/op_mul_mat_mat_f32.comp b/kompute-shaders/op_mul_mat_mat_f32.comp
new file mode 100644
index 000000000..d1ca4ad6c
--- /dev/null
+++ b/kompute-shaders/op_mul_mat_mat_f32.comp
@@ -0,0 +1,51 @@
+#version 450
+
+#include "common.comp"
+
+#extension GL_KHR_shader_subgroup_arithmetic : require
+#extension GL_EXT_debug_printf : enable
+
+// device subgroup size
+layout (local_size_x_id = 0) in;
+
+layout(binding = 0) readonly buffer tensorInA { float inA[]; };
+layout(binding = 1) readonly buffer tensorInB { float inB[]; };
+layout(binding = 2) writeonly buffer tensorOut { float out_[]; };
+
+layout(push_constant) uniform parameter {
+  uint inAOff;
+  uint inBOff;
+  uint outOff;
+  int ne00;
+  int ne01;
+  int ne02;
+  int ne11;
+  int ne12;
+  uint nb01;
+  uint nb02;
+  uint nb11;
+  uint nb12;
+  uint nb1;
+  uint nb2;
+}
+pcs;
+
+
+void main() {
+  uvec3 gid = gl_WorkGroupID;
+
+  uint bc_ab = pcs.ne12 > pcs.ne02 ? gid.z / (pcs.ne12 / pcs.ne02) : gid.z;
+  uint bc_ba = pcs.ne02 > pcs.ne12 ? gid.z / (pcs.ne02 / pcs.ne12) : gid.z;
+
+  const uint x = (gid.x*pcs.nb01 + bc_ab*pcs.nb02) / 4 + pcs.inAOff; // Based from inA
+  const uint y = (gid.y*pcs.nb11 + bc_ba*pcs.nb12) / 4 + pcs.inBOff; // based from inB
+  float sum = 0.0f;
+  for (uint i = gl_SubgroupInvocationID.x; i < pcs.ne00; i += gl_SubgroupSize) {
+      sum += float(inA[x+i]) * float(inB[y+i]);
+  }
+
+  const float all_sum = subgroupAdd(sum);
+  if (subgroupElect()) {
+    out_[gid.z*(pcs.nb2/4) + gid.y*(pcs.nb1/4) + gid.x + pcs.outOff] = all_sum;
+  }
+}
diff --git a/kompute-shaders/op_mul_mat_q4_0.comp b/kompute-shaders/op_mul_mat_q4_0.comp
new file mode 100644
index 000000000..b0cea8bbe
--- /dev/null
+++ b/kompute-shaders/op_mul_mat_q4_0.comp
@@ -0,0 +1,33 @@
+#version 450
+
+#include "common.comp"
+
+#define BLOCKS_IN_QUANT QK4_0
+#define SIZE_OF_BLOCK sizeof_block_q4_0
+#define N_ROWS 4
+
+#include "op_mul_mv_q_n_pre.comp"
+
+// The q4_0 version of this function
+float block_q_n_dot_y(uint block_index, uint yb, uint il) {
+    vec2 acc = vec2(0.0, 0.0);
+    const uint index = (block_index) * SIZE_OF_BLOCK + pcs.inAOff;
+    float d = float(u8BufToFloat16(inA, index));
+    float sumy = 0.0f;
+    for (int i = 0; i < BLOCKS_IN_QUANT/4; i+=2) {
+        const uint16_t b = u8BufToU16(inA, index + 2 + il + i);
+
+        const float yl0 = inB[yb + i];
+        const float yl1 = inB[yb + i + 1];
+        const float yl8 = inB[yb + i + BLOCKS_IN_QUANT/2];
+        const float yl9 = inB[yb + i + BLOCKS_IN_QUANT/2 + 1];
+
+        sumy += yl0 + yl1 + yl8 + yl9;
+
+        acc[0] += yl0 * (b & 0x000F) + yl1 / 256.f * (b & 0x0F00);
+        acc[1] += yl8 / 16.f * (b & 0x00F0) + yl9 / 4096.f * (b & 0xF000);
+    }
+    return d * (sumy * -8.f + acc[0] + acc[1]);
+}
+
+#include "op_mul_mv_q_n.comp"
diff --git a/kompute-shaders/op_mul_mat_q4_1.comp b/kompute-shaders/op_mul_mat_q4_1.comp
new file mode 100644
index 000000000..8582c61a3
--- /dev/null
+++ b/kompute-shaders/op_mul_mat_q4_1.comp
@@ -0,0 +1,35 @@
+#version 450
+
+#include "common.comp"
+
+#define BLOCKS_IN_QUANT QK4_1
+#define SIZE_OF_BLOCK sizeof_block_q4_1
+#define N_ROWS 4
+
+#include "op_mul_mv_q_n_pre.comp"
+
+// The q4_1 version of this function
+float block_q_n_dot_y(uint block_index, uint yb, uint il) {
+    vec2 acc = vec2(0.0, 0.0);
+    const uint index = (block_index) * SIZE_OF_BLOCK + pcs.inAOff;
+    float d = float(u8BufToFloat16(inA, index));
+    float m = float(u8BufToFloat16(inA, index+2));
+
+    float sumy = 0.0f;
+    for (int i = 0; i < BLOCKS_IN_QUANT/4; i+=2) {
+        const uint16_t b = u8BufToU16(inA, index + 4 + il + i);
+
+        const float yl0 = inB[yb + i];
+        const float yl1 = inB[yb + i + 1];
+        const float yl8 = inB[yb + i + BLOCKS_IN_QUANT/2];
+        const float yl9 = inB[yb + i + BLOCKS_IN_QUANT/2 + 1];
+
+        sumy += yl0 + yl1 + yl8 + yl9;
+
+        acc[0] += yl0 * (b & 0x000F) + yl1 / 256.f * (b & 0x0F00);
+        acc[1] += yl8 / 16.f * (b & 0x00F0) + yl9 / 4096.f * (b & 0xF000);
+    }
+    return d * (acc[0] + acc[1]) + sumy * m;
+}
+
+#include "op_mul_mv_q_n.comp"
diff --git a/kompute-shaders/op_mul_mat_q6_k.comp b/kompute-shaders/op_mul_mat_q6_k.comp
new file mode 100644
index 000000000..c9baebdf4
--- /dev/null
+++ b/kompute-shaders/op_mul_mat_q6_k.comp
@@ -0,0 +1,94 @@
+#version 450
+
+#include "common.comp"
+
+#define SIZE_OF_BLOCK sizeof_block_q6_k
+
+layout(local_size_x_id = 0) in;
+layout(local_size_y_id = 1) in;
+layout(local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer tensorInA { uint8_t inA[]; };
+layout (binding = 1) readonly buffer tensorInB { float inB[]; };
+layout (binding = 2) writeonly buffer tensorOut { float out_[]; };
+
+layout (push_constant) uniform parameter {
+    uint inAOff;
+    uint inBOff;
+    uint outOff;
+    int ne00;
+    int ne10;
+    int ne0;
+    int ne1;
+    int ne01;
+    int gqa;
+} pcs;
+
+void main() {
+    const uint8_t kmask1 = uint8_t(0x03);
+    const uint8_t kmask2 = uint8_t(0x0C);
+    const uint8_t kmask3 = uint8_t(0x30);
+    const uint8_t kmask4 = uint8_t(0xC0);
+
+    const uint nb = pcs.ne00/QK_K;
+
+    const uint r0 = gl_WorkGroupID.x;
+    const uint r1 = gl_WorkGroupID.y;
+    const uint r2 = gl_WorkGroupID.z;
+
+    const uint row = (r0 * gl_NumSubgroups + gl_SubgroupID);
+    const uint offset0 = r2/pcs.gqa*(nb*pcs.ne0);
+    const uint x = row * nb + offset0; // Based from inA without base offset
+    const uint yy = r1*pcs.ne10 + r2*pcs.ne00*pcs.ne1+pcs.inBOff; // Based from inB
+
+    float sumf = 0;
+
+    // bits of invocation ID for gl_SubgroupSize=32:
+    //  x   x   x   x   x
+    //  4   3   2   1   0
+    // (     tid     ) ix
+    //  ip (   il    )
+
+    const uint block_stride = gl_SubgroupSize / 16;         // number of blocks each subgroup processes
+    const uint tid  = gl_SubgroupInvocationID/block_stride; // first block_stride groups have tid=0
+    const uint ix   = gl_SubgroupInvocationID%block_stride; // first block is 0..block_stride-1
+    const uint ip   = tid/8;        // first or second half of block (0 or 1)
+    const uint il   = tid%8;        // each half has 8 parts, one per scale
+    const uint n    = 4;            // 4 scales at a time (and 4 sums)
+    const uint l0   = n*il;         // offset into half-block, 0..28
+    const uint is   = 8*ip + l0/16; // 0, 1, 8, 9
+
+    const uint y_offset = 128*ip + l0;
+    const uint q_offset_l = 64*ip + l0;
+    const uint q_offset_h = 32*ip + l0;
+
+    for (uint i = ix; i < nb; i += block_stride) {
+
+        const uint baseIndex = (x + i) * SIZE_OF_BLOCK + pcs.inAOff;
+
+        const uint qlIndex = q_offset_l;
+        const uint q2Index = qlIndex + QK_K/8;
+        const uint qhIndex = q_offset_h;
+        const uint y = yy + i * QK_K + y_offset;
+
+        float sums[4] = {0.0f, 0.0f, 0.0f, 0.0f};
+        for (uint l = 0; l < n; ++l) {
+            const uint8_t currentQ1 = inA[baseIndex + qlIndex + l];
+            const uint8_t currentQ2 = inA[baseIndex + q2Index + l];
+            const uint8_t currentQh = inA[baseIndex + QK_K/2 + qhIndex + l];
+
+            sums[0] += inB[y+l+ 0] * (int8_t((currentQ1 & 0xF) | ((currentQh & kmask1) << 4)) - 32);
+            sums[1] += inB[y+l+32] * (int8_t((currentQ2 & 0xF) | ((currentQh & kmask2) << 2)) - 32);
+            sums[2] += inB[y+l+64] * (int8_t((currentQ1  >> 4) | ((currentQh & kmask3) << 0)) - 32);
+            sums[3] += inB[y+l+96] * (int8_t((currentQ2  >> 4) | ((currentQh & kmask4) >> 2)) - 32);
+        }
+
+        float d = u8BufToFloat16(inA, baseIndex + QK_K/2 + QK_K/4 + QK_K/16);
+        sumf += d * (sums[0] * int8_t(inA[baseIndex + QK_K/2 + QK_K/4 + is]) + sums[1] * int8_t(inA[baseIndex + QK_K/2 + QK_K/4 + 2 + is]) + sums[2] * int8_t(inA[baseIndex + QK_K/2 + QK_K/4 + 4 + is]) + sums[3] * int8_t(inA[baseIndex + QK_K/2 + QK_K/4 + 6 + is]));
+    }
+
+    const float tot = subgroupAdd(sumf);
+    if (subgroupElect()) {
+        out_[r1*pcs.ne0 + r2*pcs.ne0*pcs.ne1 + row + pcs.outOff] = tot;
+    }
+}
diff --git a/kompute-shaders/op_mul_mat_q8_0.comp b/kompute-shaders/op_mul_mat_q8_0.comp
new file mode 100644
index 000000000..34d015e90
--- /dev/null
+++ b/kompute-shaders/op_mul_mat_q8_0.comp
@@ -0,0 +1,73 @@
+#version 450
+
+#include "common.comp"
+
+#include "op_mul_mv_q_n_pre.comp"
+
+#define SIZE_OF_D 2
+
+#define N_DST 4 // each SIMD group works on 4 rows
+#define N_SIMDGROUP 2 // number of SIMD groups in a thread group
+#define N_SIMDWIDTH 32 // assuming SIMD group size is 32
+
+#define NB_Q8_0 8
+
+void main() {
+    // NB: hack to make compatible with AMD GPUs that have a subgroup size of 64
+    if (gl_SubgroupInvocationID > 31)
+        return;
+
+    const int nr  = N_DST;
+    const int nsg = N_SIMDGROUP;
+    const int nw  = N_SIMDWIDTH;
+
+    const int nb = pcs.ne00/QK8_0;
+    const uint r0 = gl_WorkGroupID.x;
+    const uint r1 = gl_WorkGroupID.y;
+    const uint im = gl_WorkGroupID.z;
+
+    const uint first_row = (r0 * nsg + gl_SubgroupID) * nr;
+
+    const uint i12 = im%pcs.ne12;
+    const uint i13 = im/pcs.ne12;
+
+    const uint offset0 = first_row * nb + (i12/pcs.r2)*(nb*pcs.ne01) + (i13/pcs.r3)*(nb*pcs.ne01*pcs.ne02);
+
+    const uint x = offset0*sizeof_block_q8_0 + pcs.inAOff; // Based from inA
+    const uint y = r1*pcs.ne10 + im*pcs.ne00*pcs.ne1 + pcs.inBOff; // based from inB
+
+    float yl[NB_Q8_0];
+    float sumf[N_DST]={0.f, 0.f, 0.f, 0.f};
+
+    const uint ix = gl_SubgroupInvocationID.x/4;
+    const uint il = gl_SubgroupInvocationID.x%4;
+
+    uint yb = y + ix * QK8_0 + NB_Q8_0*il;
+
+    // each thread in a SIMD group deals with NB_Q8_0 quants at a time
+    for (uint ib = ix; ib < nb; ib += nw/4) {
+        for (int i = 0; i < NB_Q8_0; ++i) {
+            yl[i] = inB[yb + i];
+        }
+
+        for (int row = 0; row < nr; row++) {
+            const uint block_offset = (ib+row*nb) * sizeof_block_q8_0;
+            float sumq = 0.f;
+            for (int iq = 0; iq < NB_Q8_0; ++iq) {
+                const int8_t qs_iq = int8_t(inA[x + block_offset + SIZE_OF_D + NB_Q8_0*il + iq]);
+                sumq += qs_iq * yl[iq];
+            }
+            const float16_t d = u8BufToFloat16(inA, x + block_offset);
+            sumf[row] += sumq*d;
+        }
+
+        yb += NB_Q8_0 * nw;
+    }
+
+    for (int row = 0; row < nr; ++row) {
+        const float tot = subgroupAdd(sumf[row]);
+        if (subgroupElect() && first_row + row < pcs.ne01) {
+            out_[r1*pcs.ne0 + im*pcs.ne0*pcs.ne1 + first_row + row] = tot;
+        }
+    }
+}
diff --git a/kompute-shaders/op_mul_mv_q_n.comp b/kompute-shaders/op_mul_mv_q_n.comp
new file mode 100644
index 000000000..440b5ab2c
--- /dev/null
+++ b/kompute-shaders/op_mul_mv_q_n.comp
@@ -0,0 +1,48 @@
+void main() {
+    // NB: hack to make compatible with AMD GPUs that have a subgroup size of 64
+    if (gl_SubgroupInvocationID > 31)
+        return;
+
+    const uint nb = uint(pcs.ne00/BLOCKS_IN_QUANT);
+
+    const uint r0 = gl_WorkGroupID.x;
+    const uint r1 = gl_WorkGroupID.y;
+    const uint im = gl_WorkGroupID.z;
+
+    const uint first_row = (r0 * gl_NumSubgroups + gl_SubgroupID) * N_ROWS;
+
+    const uint i12 = im%pcs.ne12;
+    const uint i13 = im/pcs.ne12;
+
+    const uint offset0 = first_row * nb + (i12/pcs.r2)*(nb*pcs.ne01) + (i13/pcs.r3)*(nb*pcs.ne01*pcs.ne02);
+
+    const uint x = offset0; // Based from inA without base offset
+    const uint y = r1*uint(pcs.ne10)+im*pcs.ne00*pcs.ne1+pcs.inBOff; // Based from inB
+
+    float sumf[N_ROWS] = {0.0f, 0.0f, 0.0f, 0.0f};
+
+    const uint ix = gl_SubgroupInvocationID/2;
+    const uint il = (BLOCKS_IN_QUANT/4)*(gl_SubgroupInvocationID%2);
+
+    uint yb = y + ix * BLOCKS_IN_QUANT + il;
+
+    //debugPrintfEXT("gl_NumSubgroups=%d, gl_SubgroupID=%d, gl_SubgroupInvocationID=%d, glSubgroupSize=%d, gl_WorkGroupSize.x=%d, gl_WorkGroupSize.y=%d, gl_WorkGroupSize.z=%d\n",
+    //    gl_NumSubgroups, gl_SubgroupID, gl_SubgroupInvocationID, gl_SubgroupSize,
+    //    gl_WorkGroupSize.x, gl_WorkGroupSize.y, gl_WorkGroupSize.z);
+
+    for (uint ib = ix; ib < nb; ib += 16) {
+        for (int row = 0; row < N_ROWS; row++) {
+            const uint block_index = x + ib + row * nb;
+            sumf[row] += block_q_n_dot_y(block_index, yb, il);
+        }
+
+        yb += BLOCKS_IN_QUANT * 16;
+    }
+
+    for (int row = 0; row < N_ROWS; ++row) {
+        const float tot = subgroupAdd(sumf[row]);
+        if (first_row + row < pcs.ne01 && subgroupElect()) {
+            out_[r1*pcs.ne0 + im*pcs.ne0*pcs.ne1 + first_row + row + pcs.outOff] = tot;
+        }
+    }
+}
diff --git a/kompute-shaders/op_mul_mv_q_n_pre.comp b/kompute-shaders/op_mul_mv_q_n_pre.comp
new file mode 100644
index 000000000..7912b09ac
--- /dev/null
+++ b/kompute-shaders/op_mul_mv_q_n_pre.comp
@@ -0,0 +1,22 @@
+layout(local_size_x_id = 0) in;
+layout(local_size_y = 1) in;
+layout(local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer tensorInA { uint8_t inA[]; };
+layout (binding = 1) readonly buffer tensorInB { float inB[]; };
+layout (binding = 2) writeonly buffer tensorOut { float out_[]; };
+
+layout (push_constant) uniform parameter {
+    uint inAOff;
+    uint inBOff;
+    uint outOff;
+    int  ne00;
+    int  ne01;
+    int  ne02;
+    int  ne10;
+    int  ne12;
+    int  ne0;
+    int  ne1;
+    uint r2;
+    uint r3;
+} pcs;
diff --git a/kompute-shaders/op_norm.comp b/kompute-shaders/op_norm.comp
new file mode 100644
index 000000000..ad0c3c01b
--- /dev/null
+++ b/kompute-shaders/op_norm.comp
@@ -0,0 +1,84 @@
+#version 450
+
+#include "common.comp"
+
+layout(local_size_x = 256) in;
+
+layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; };
+layout(binding = 1) buffer restrict tensorOut { float out_[]; };
+
+layout(push_constant) uniform PushConstants {
+    uint inOff;
+    uint outOff;
+    uint ne00;
+    uint nb01;
+    float eps;
+} pcs;
+
+shared float sum[gl_WorkGroupSize.x];
+
+void main() {
+    const uint x = (gl_WorkGroupID.x*pcs.nb01/4) + pcs.inOff; // Based from in_
+    // MEAN
+    // parallel sum
+    sum[gl_LocalInvocationID.x] = 0.0;
+    for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) {
+        sum[gl_LocalInvocationID.x] += in_[x+i00];
+    }
+
+    // reduce
+    barrier();
+    memoryBarrierShared();
+    [[unroll]] for (uint i = gl_WorkGroupSize.x/2; i > 0; i /= 2) {
+        if (gl_LocalInvocationID.x < i) {
+            sum[gl_LocalInvocationID.x] += sum[gl_LocalInvocationID.x + i];
+        }
+        barrier();
+        memoryBarrierShared();
+    }
+
+    // broadcast
+    if (gl_LocalInvocationID.x == 0) {
+        sum[0] /= float(pcs.ne00);
+    }
+    barrier();
+    memoryBarrierShared();
+    const float mean = sum[0];
+
+    // recenter
+    const uint y = (gl_WorkGroupID.x*pcs.ne00) + pcs.outOff; // Based from out_
+    for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) {
+        out_[y+i00] = in_[x+i00] - mean;
+    }
+
+    // VARIANCE
+    // parallel sum
+    sum[gl_LocalInvocationID.x] = 0.0;
+    for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) {
+        sum[gl_LocalInvocationID.x] += out_[y+i00] * out_[y+i00];
+    }
+
+    // reduce
+    barrier();
+    memoryBarrierShared();
+    [[unroll]] for (uint i = gl_WorkGroupSize.x/2; i > 0; i /= 2) {
+        if (gl_LocalInvocationID.x < i) {
+            sum[gl_LocalInvocationID.x] += sum[gl_LocalInvocationID.x + i];
+        }
+        barrier();
+        memoryBarrierShared();
+    }
+
+    // broadcast
+    if (gl_LocalInvocationID.x == 0) {
+        sum[0] /= float(pcs.ne00);
+    }
+    barrier();
+    memoryBarrierShared();
+    const float variance = sum[0];
+
+    const float scale = 1.0f/sqrt(variance + pcs.eps);
+    for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) {
+        out_[y+i00] *= scale;
+    }
+}
diff --git a/kompute-shaders/op_relu.comp b/kompute-shaders/op_relu.comp
new file mode 100644
index 000000000..52a601fe6
--- /dev/null
+++ b/kompute-shaders/op_relu.comp
@@ -0,0 +1,21 @@
+#version 450
+
+#include "common.comp"
+
+layout(local_size_x = 1) in;
+
+layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; };
+layout(binding = 1) buffer restrict writeonly tensorOut { float out_[]; };
+layout(push_constant) uniform PushConstants {
+    uint inOff;
+    uint outOff;
+} pcs;
+
+void main() {
+    const uint baseIndex = gl_WorkGroupID.x * 4;
+
+    for (uint x = 0; x < 4; x++) {
+        const uint i = baseIndex + x;
+        out_[i + pcs.outOff] = max(0.0, in_[i + pcs.inOff]);
+    }
+}
diff --git a/kompute-shaders/op_rmsnorm.comp b/kompute-shaders/op_rmsnorm.comp
new file mode 100644
index 000000000..da658c160
--- /dev/null
+++ b/kompute-shaders/op_rmsnorm.comp
@@ -0,0 +1,53 @@
+#version 450
+
+#include "common.comp"
+
+layout(local_size_x = 512) in;
+
+layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; };
+layout(binding = 1) buffer restrict tensorOut { float out_[]; };
+
+layout(push_constant) uniform PushConstants {
+    uint inOff;
+    uint outOff;
+    uint ne00;
+    uint nb01;
+    float eps;
+} pcs;
+
+shared float sum[gl_WorkGroupSize.x];
+
+void main() {
+    const uint x = (gl_WorkGroupID.x*pcs.nb01/4) + pcs.inOff; // Based from in_
+
+    // parallel sum
+    sum[gl_LocalInvocationID.x] = 0.0;
+    for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) {
+        sum[gl_LocalInvocationID.x] += in_[x+i00] * in_[x+i00];
+    }
+
+    // reduce
+    barrier();
+    memoryBarrierShared();
+    [[unroll]] for (uint i = gl_WorkGroupSize.x/2; i > 0; i /= 2) {
+        if (gl_LocalInvocationID.x < i) {
+            sum[gl_LocalInvocationID.x] += sum[gl_LocalInvocationID.x + i];
+        }
+        barrier();
+        memoryBarrierShared();
+    }
+
+    // broadcast
+    if (gl_LocalInvocationID.x == 0) {
+        sum[0] /= float(pcs.ne00);
+    }
+    barrier();
+    memoryBarrierShared();
+
+    const float scale = 1.0f/sqrt(sum[0] + pcs.eps);
+
+    const uint y = (gl_WorkGroupID.x*pcs.ne00) + pcs.outOff; // Based from out_
+    for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) {
+        out_[y+i00] = in_[x+i00] * scale;
+    }
+}
diff --git a/kompute-shaders/op_rope_f16.comp b/kompute-shaders/op_rope_f16.comp
new file mode 100644
index 000000000..b44622584
--- /dev/null
+++ b/kompute-shaders/op_rope_f16.comp
@@ -0,0 +1,73 @@
+#version 450
+
+#include "rope_common.comp"
+
+layout(binding = 0) buffer restrict readonly  tensorInA { float16_t inA[]; };
+layout(binding = 1) buffer restrict readonly  tensorInB { int       inB[]; };
+layout(binding = 2) buffer restrict writeonly tensorOut { float16_t out_[]; };
+
+void main() {
+    const uint i3 = gl_WorkGroupID.z;
+    const uint i2 = gl_WorkGroupID.y;
+    const uint i1 = gl_WorkGroupID.x;
+
+    const bool is_neox = (pcs.mode & 2) != 0;
+
+    float corr_dims[2];
+    rope_yarn_corr_dims(pcs.n_dims, pcs.n_orig_ctx, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims);
+
+    const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims);
+
+    const int p = inB[pcs.inBOff + i2];
+
+    float theta = float(p);
+
+    if (!is_neox) {
+        for (uint i0 = 0; i0 < pcs.ne0; i0 += 2) {
+            float cos_theta, sin_theta;
+            rope_yarn(theta, pcs.freq_scale, corr_dims, i0, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta);
+
+            theta *= theta_scale;
+
+            const uint src      = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in
+            const uint dst_data = uint((i3*pcs.nb3  + i2*pcs.nb2  + i1*pcs.nb1  + i0*pcs.nb0)  / 2) + pcs.outOff; // Based from out_
+
+            const float x0 = float(inA[src]);
+            const float x1 = float(inA[src+1]);
+
+            out_[dst_data]   = float16_t(x0*cos_theta - x1*sin_theta);
+            out_[dst_data+1] = float16_t(x0*sin_theta + x1*cos_theta);
+        }
+    } else {
+        const float inv_ndims = -1.f/pcs.n_dims;
+        for (uint ic = 0; ic < pcs.n_dims; ic += 2) {
+            const uint cur_rot = ic;
+
+            float cos_theta, sin_theta;
+            rope_yarn(theta, pcs.freq_scale, corr_dims, cur_rot, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta);
+
+            theta *= theta_scale;
+
+            const uint i0 = ic/2;
+
+            const uint src      = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in
+            const uint dst_data = uint((i3*pcs.nb3  + i2*pcs.nb2  + i1*pcs.nb1  + i0*pcs.nb0)  / 2) + pcs.outOff; // Based from out_
+
+            const float x0 = float(inA[src]);
+            const float x1 = float(inA[src+pcs.n_dims/2]);
+
+            out_[dst_data]              = float16_t(x0*cos_theta - x1*sin_theta);
+            out_[dst_data+pcs.n_dims/2] = float16_t(x0*sin_theta + x1*cos_theta);
+        }
+
+        for (uint ic = pcs.n_dims; ic < pcs.ne0; ic += 2) {
+            const uint i0 = ic;
+
+            const uint src      = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in
+            const uint dst_data = uint((i3*pcs.nb3  + i2*pcs.nb2  + i1*pcs.nb1  + i0*pcs.nb0)  / 2) + pcs.outOff; // Based from out_
+
+            out_[dst_data + 0] = inA[src + 0];
+            out_[dst_data + 1] = inA[src + 1];
+        }
+    }
+}
diff --git a/kompute-shaders/op_rope_f32.comp b/kompute-shaders/op_rope_f32.comp
new file mode 100644
index 000000000..2c0235d75
--- /dev/null
+++ b/kompute-shaders/op_rope_f32.comp
@@ -0,0 +1,73 @@
+#version 450
+
+#include "rope_common.comp"
+
+layout(binding = 0) buffer restrict readonly  tensorInA { float inA[]; };
+layout(binding = 1) buffer restrict readonly  tensorInB { int   inB[]; };
+layout(binding = 2) buffer restrict writeonly tensorOut { float out_[]; };
+
+void main() {
+    const uint i3 = gl_WorkGroupID.z;
+    const uint i2 = gl_WorkGroupID.y;
+    const uint i1 = gl_WorkGroupID.x;
+
+    const bool is_neox = (pcs.mode & 2) != 0;
+
+    float corr_dims[2];
+    rope_yarn_corr_dims(pcs.n_dims, pcs.n_orig_ctx, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims);
+
+    const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims);
+
+    const int p = inB[pcs.inBOff + i2];
+
+    float theta = float(p);
+
+    if (!is_neox) {
+        for (uint i0 = 0; i0 < pcs.ne0; i0 += 2) {
+            float cos_theta, sin_theta;
+            rope_yarn(theta, pcs.freq_scale, corr_dims, i0, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta);
+
+            theta *= theta_scale;
+
+            const uint src      = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in
+            const uint dst_data = uint((i3*pcs.nb3  + i2*pcs.nb2  + i1*pcs.nb1  + i0*pcs.nb0)  / 4) + pcs.outOff; // Based from out_
+
+            const float x0 = inA[src];
+            const float x1 = inA[src+1];
+
+            out_[dst_data]   = x0*cos_theta - x1*sin_theta;
+            out_[dst_data+1] = x0*sin_theta + x1*cos_theta;
+        }
+    } else {
+        const float inv_ndims = -1.f/pcs.n_dims;
+        for (uint ic = 0; ic < pcs.n_dims; ic += 2) {
+            const uint cur_rot = ic;
+
+            float cos_theta, sin_theta;
+            rope_yarn(theta, pcs.freq_scale, corr_dims, cur_rot, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta);
+
+            theta *= theta_scale;
+
+            const uint i0 = ic/2;
+
+            const uint src      = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in
+            const uint dst_data = uint((i3*pcs.nb3  + i2*pcs.nb2  + i1*pcs.nb1  + i0*pcs.nb0)  / 4) + pcs.outOff; // Based from out_
+
+            const float x0 = inA[src];
+            const float x1 = inA[src+pcs.n_dims/2];
+
+            out_[dst_data] = x0*cos_theta - x1*sin_theta;
+            out_[dst_data+pcs.n_dims/2] = x0*sin_theta + x1*cos_theta;
+        }
+
+        for (uint ic = pcs.n_dims; ic < pcs.ne0; ic += 2) {
+            const uint i0 = ic;
+
+            const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in
+            const uint dst_data = uint((i3*pcs.nb3  + i2*pcs.nb2  + i1*pcs.nb1  + i0*pcs.nb0) / 4) + pcs.outOff; // Based from out_
+
+            out_[dst_data + 0] = inA[src + 0];
+            out_[dst_data + 1] = inA[src + 1];
+        }
+    }
+}
diff --git a/kompute-shaders/op_scale.comp b/kompute-shaders/op_scale.comp
new file mode 100644
index 000000000..bdae26738
--- /dev/null
+++ b/kompute-shaders/op_scale.comp
@@ -0,0 +1,19 @@
+#version 450
+
+#include "common.comp"
+
+layout(local_size_x = 1) in;
+
+layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; };
+layout(binding = 1) buffer restrict writeonly tensorOut { float out_[]; };
+
+layout(push_constant) uniform PushConstants {
+    uint inOff;
+    uint outOff;
+    float scale;
+} pcs;
+
+void main() {
+    const uint i = gl_WorkGroupID.x;
+    out_[i + pcs.outOff] = in_[i + pcs.inOff] * pcs.scale;
+}
diff --git a/kompute-shaders/op_scale_8.comp b/kompute-shaders/op_scale_8.comp
new file mode 100644
index 000000000..ada69754b
--- /dev/null
+++ b/kompute-shaders/op_scale_8.comp
@@ -0,0 +1,23 @@
+#version 450
+
+#include "common.comp"
+
+layout(local_size_x = 1) in;
+
+layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; };
+layout(binding = 1) buffer restrict writeonly tensorOut { float out_[]; };
+
+layout(push_constant) uniform PushConstants {
+    uint inOff;
+    uint outOff;
+    float scale;
+} pcs;
+
+void main() {
+    const uint baseIndex = gl_WorkGroupID.x * 8;
+
+    for (uint x = 0; x < 8; x++) {
+        const uint i = baseIndex + x;
+        out_[i + pcs.outOff] = in_[i + pcs.inOff] * pcs.scale;
+    }
+}
diff --git a/kompute-shaders/op_silu.comp b/kompute-shaders/op_silu.comp
new file mode 100644
index 000000000..0fb8e4b74
--- /dev/null
+++ b/kompute-shaders/op_silu.comp
@@ -0,0 +1,22 @@
+#version 450
+
+#include "common.comp"
+
+layout(local_size_x = 1) in;
+
+layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; };
+layout(binding = 1) buffer restrict writeonly tensorOut { float out_[]; };
+layout(push_constant) uniform PushConstants {
+    uint inOff;
+    uint outOff;
+} pcs;
+
+void main() {
+    const uint baseIndex = gl_WorkGroupID.x * 4;
+
+    for (uint x = 0; x < 4; x++) {
+        const uint i = baseIndex + x;
+        const float y = in_[i + pcs.inOff];
+        out_[i + pcs.outOff] = y / (1.0 + exp(-y));
+    }
+}
diff --git a/kompute-shaders/op_softmax.comp b/kompute-shaders/op_softmax.comp
new file mode 100644
index 000000000..7bc9176ca
--- /dev/null
+++ b/kompute-shaders/op_softmax.comp
@@ -0,0 +1,56 @@
+// TODO: implement multi-simd softmax (llama.cpp commit e16b9fa4)
+
+#version 450
+
+#include "common.comp"
+
+layout(local_size_x_id = 0) in;
+
+layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; };
+layout(binding = 1) buffer restrict readonly tensorInB { float inB[]; };
+layout(binding = 2) buffer restrict writeonly tensorOut { float out_[]; };
+
+layout(push_constant) uniform PushConstants {
+    uint inAOff;
+    uint inBOff;
+    uint outOff;
+    int ne00;
+    int ne01;
+    int ne02;
+    float scale;
+    int mask;
+} pcs;
+
+void main() {
+    if (gl_SubgroupInvocationID > 31)
+        return;
+
+    const uint i03 = gl_WorkGroupID.z;
+    const uint i02 = gl_WorkGroupID.y;
+    const uint i01 = gl_WorkGroupID.x;
+
+    const uint extra_off = i03*pcs.ne02*pcs.ne01*pcs.ne00 + i02*pcs.ne01*pcs.ne00 + i01*pcs.ne00;
+    const uint psrc0 = extra_off + pcs.inAOff; // Based from inA
+    const uint pmask = i01*pcs.ne00 + pcs.inBOff; // Based from inB
+    const uint pdst = extra_off + pcs.outOff; // Based from out_
+
+    // parallel max
+    float localMax = uintBitsToFloat(0xFF800000);
+    for (uint i00 = gl_SubgroupInvocationID.x; i00 < pcs.ne00; i00 += 32) {
+        localMax = max(localMax, inA[psrc0 + i00]*pcs.scale + (pcs.mask!=0 ? inB[pmask + i00] : 0.0f));
+    }
+    float max_ = subgroupMax(localMax);
+
+    // parallel sum
+    float localSum = 0.0f;
+    for (uint i00 = gl_SubgroupInvocationID.x; i00 < pcs.ne00; i00 += 32) {
+        const float exp_psrc0 = exp(inA[psrc0 + i00]*pcs.scale + (pcs.mask!=0 ? inB[pmask + i00] : 0.0f) - max_);
+        localSum += exp_psrc0;
+        out_[pdst + i00] = exp_psrc0;
+    }
+
+    const float sum = subgroupAdd(localSum);
+    for (uint i00 = gl_SubgroupInvocationID.x; i00 < pcs.ne00; i00 += 32) {
+        out_[pdst + i00] /= sum;
+    }
+}
diff --git a/kompute-shaders/rope_common.comp b/kompute-shaders/rope_common.comp
new file mode 100644
index 000000000..57ba6597a
--- /dev/null
+++ b/kompute-shaders/rope_common.comp
@@ -0,0 +1,67 @@
+#include "common.comp"
+
+// TODO: use a local size of 32 or more (Metal uses 1024)
+layout(local_size_x = 1) in;
+
+layout (push_constant) uniform parameter {
+    uint inAOff;
+    uint inBOff;
+    uint outOff;
+    int n_dims;
+    int mode;
+    int n_orig_ctx;
+    float freq_base;
+    float freq_scale;
+    float ext_factor;
+    float attn_factor;
+    float beta_fast;
+    float beta_slow;
+    uint nb00;
+    uint nb01;
+    uint nb02;
+    uint nb03;
+    int ne0;
+    uint nb0;
+    uint nb1;
+    uint nb2;
+    uint nb3;
+} pcs;
+
+float rope_yarn_ramp(const float low, const float high, const float i0) {
+    const float y = (i0 / 2 - low) / max(0.001f, high - low);
+    return 1.0f - min(1.0f, max(0.0f, y));
+}
+
+// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
+// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
+void rope_yarn(
+    float theta_extrap, float freq_scale, float corr_dims[2], float i0, float ext_factor, float mscale,
+    out float cos_theta, out float sin_theta
+) {
+    // Get n-d rotational scaling corrected for extrapolation
+    float theta_interp = freq_scale * theta_extrap;
+    float theta = theta_interp;
+    if (ext_factor != 0.0f) {
+        float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor;
+        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+
+        // Get n-d magnitude scaling corrected for interpolation
+        mscale *= 1.0f + 0.1f * log(1.0f / freq_scale);
+    }
+    cos_theta = cos(theta) * mscale;
+    sin_theta = sin(theta) * mscale;
+}
+
+// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get
+// `corr_fac(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))`
+float rope_yarn_corr_factor(int n_dims, int n_orig_ctx, float n_rot, float base) {
+    return n_dims * log(n_orig_ctx / (n_rot * TWOPI_F)) / (2 * log(base));
+}
+
+void rope_yarn_corr_dims(
+    int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, out float dims[2]
+) {
+    // start and end correction dims
+    dims[0] = max(0.0f,         floor(rope_yarn_corr_factor(n_dims, n_orig_ctx, beta_fast, freq_base)));
+    dims[1] = min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_orig_ctx, beta_slow, freq_base)));
+}
diff --git a/llama.cpp b/llama.cpp
index 8e0717db9..217726184 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -1,5 +1,4 @@
 #define LLAMA_API_INTERNAL
-//#define LLAMA_GGML_BACKEND_CUDA_TEST // for testing only - enables ggml-cuda through ggml-backend, disables partial offloading
 #include "llama.h"
 
 #include "unicode.h"
@@ -8,10 +7,16 @@
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
 
-#ifdef GGML_USE_CUBLAS
+#ifdef GGML_USE_CUDA
 #  include "ggml-cuda.h"
 #elif defined(GGML_USE_CLBLAST)
 #  include "ggml-opencl.h"
+#elif defined(GGML_USE_VULKAN)
+#  include "ggml-vulkan.h"
+#elif defined(GGML_USE_SYCL)
+#  include "ggml-sycl.h"
+#elif defined(GGML_USE_KOMPUTE)
+#   include "ggml-kompute.h"
 #endif
 
 #ifdef GGML_USE_METAL
@@ -47,12 +52,17 @@
         #define NOMINMAX
     #endif
     #include <windows.h>
+    #ifndef PATH_MAX
+        #define PATH_MAX MAX_PATH
+    #endif
     #include <io.h>
 #endif
 
 #include <algorithm>
 #include <array>
 #include <cassert>
+#include <cctype>
+#include <cfloat>
 #include <cinttypes>
 #include <climits>
 #include <cmath>
@@ -66,6 +76,7 @@
 #include <fstream>
 #include <functional>
 #include <initializer_list>
+#include <locale>
 #include <map>
 #include <memory>
 #include <mutex>
@@ -96,6 +107,7 @@
 #define LLAMA_MAX_NODES   8192
 #define LLAMA_MAX_EXPERTS 8
 
+
 //
 // logging
 //
@@ -152,10 +164,6 @@ static bool is_float_close(float a, float b, float abs_tol) {
     return std::fabs(b - a) <= abs_tol;
 }
 
-#ifdef GGML_USE_CPU_HBM
-#include <hbwmalloc.h>
-#endif
-
 static void zeros(std::ofstream & file, size_t n) {
     char zero = 0;
     for (size_t i = 0; i < n; ++i) {
@@ -187,6 +195,7 @@ enum llm_arch {
     LLM_ARCH_LLAMA,
     LLM_ARCH_FALCON,
     LLM_ARCH_BAICHUAN,
+    LLM_ARCH_GROK,
     LLM_ARCH_GPT2,
     LLM_ARCH_GPTJ,
     LLM_ARCH_GPTNEOX,
@@ -194,30 +203,56 @@ enum llm_arch {
     LLM_ARCH_STARCODER,
     LLM_ARCH_PERSIMMON,
     LLM_ARCH_REFACT,
+    LLM_ARCH_BERT,
+    LLM_ARCH_NOMIC_BERT,
     LLM_ARCH_BLOOM,
     LLM_ARCH_STABLELM,
     LLM_ARCH_QWEN,
+    LLM_ARCH_QWEN2,
     LLM_ARCH_PHI2,
     LLM_ARCH_PLAMO,
+    LLM_ARCH_CODESHELL,
+    LLM_ARCH_ORION,
+    LLM_ARCH_INTERNLM2,
+    LLM_ARCH_MINICPM,
+    LLM_ARCH_GEMMA,
+    LLM_ARCH_STARCODER2,
+    LLM_ARCH_MAMBA,
+    LLM_ARCH_XVERSE,
+    LLM_ARCH_COMMAND_R,
     LLM_ARCH_UNKNOWN,
 };
 
-static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
-    { LLM_ARCH_LLAMA,           "llama"     },
-    { LLM_ARCH_FALCON,          "falcon"    },
-    { LLM_ARCH_GPT2,            "gpt2"      },
-    { LLM_ARCH_GPTJ,            "gptj"      },
-    { LLM_ARCH_GPTNEOX,         "gptneox"   },
-    { LLM_ARCH_MPT,             "mpt"       },
-    { LLM_ARCH_BAICHUAN,        "baichuan"  },
-    { LLM_ARCH_STARCODER,       "starcoder" },
-    { LLM_ARCH_PERSIMMON,       "persimmon" },
-    { LLM_ARCH_REFACT,          "refact"    },
-    { LLM_ARCH_BLOOM,           "bloom"     },
-    { LLM_ARCH_STABLELM,        "stablelm"  },
-    { LLM_ARCH_QWEN,            "qwen"      },
-    { LLM_ARCH_PHI2,            "phi2"      },
-    { LLM_ARCH_PLAMO,           "plamo"     },
+static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
+    { LLM_ARCH_LLAMA,           "llama"      },
+    { LLM_ARCH_FALCON,          "falcon"     },
+    { LLM_ARCH_GROK,            "grok"       },
+    { LLM_ARCH_GPT2,            "gpt2"       },
+    { LLM_ARCH_GPTJ,            "gptj"       },
+    { LLM_ARCH_GPTNEOX,         "gptneox"    },
+    { LLM_ARCH_MPT,             "mpt"        },
+    { LLM_ARCH_BAICHUAN,        "baichuan"   },
+    { LLM_ARCH_STARCODER,       "starcoder"  },
+    { LLM_ARCH_PERSIMMON,       "persimmon"  },
+    { LLM_ARCH_REFACT,          "refact"     },
+    { LLM_ARCH_BERT,            "bert"       },
+    { LLM_ARCH_NOMIC_BERT,      "nomic-bert" },
+    { LLM_ARCH_BLOOM,           "bloom"      },
+    { LLM_ARCH_STABLELM,        "stablelm"   },
+    { LLM_ARCH_QWEN,            "qwen"       },
+    { LLM_ARCH_QWEN2,           "qwen2"      },
+    { LLM_ARCH_PHI2,            "phi2"       },
+    { LLM_ARCH_PLAMO,           "plamo"      },
+    { LLM_ARCH_CODESHELL,       "codeshell"  },
+    { LLM_ARCH_ORION,           "orion"      },
+    { LLM_ARCH_INTERNLM2,       "internlm2"  },
+    { LLM_ARCH_MINICPM,         "minicpm"    },
+    { LLM_ARCH_GEMMA,           "gemma"      },
+    { LLM_ARCH_STARCODER2,      "starcoder2" },
+    { LLM_ARCH_MAMBA,           "mamba"      },
+    { LLM_ARCH_XVERSE,          "xverse"     },
+    { LLM_ARCH_COMMAND_R,       "command-r"  },
+    { LLM_ARCH_UNKNOWN,         "(unknown)"  },
 };
 
 enum llm_kv {
@@ -226,12 +261,14 @@ enum llm_kv {
     LLM_KV_GENERAL_ALIGNMENT,
     LLM_KV_GENERAL_NAME,
     LLM_KV_GENERAL_AUTHOR,
+    LLM_KV_GENERAL_VERSION,
     LLM_KV_GENERAL_URL,
     LLM_KV_GENERAL_DESCRIPTION,
     LLM_KV_GENERAL_LICENSE,
     LLM_KV_GENERAL_SOURCE_URL,
     LLM_KV_GENERAL_SOURCE_HF_REPO,
 
+    LLM_KV_VOCAB_SIZE,
     LLM_KV_CONTEXT_LENGTH,
     LLM_KV_EMBEDDING_LENGTH,
     LLM_KV_BLOCK_COUNT,
@@ -240,6 +277,8 @@ enum llm_kv {
     LLM_KV_TENSOR_DATA_LAYOUT,
     LLM_KV_EXPERT_COUNT,
     LLM_KV_EXPERT_USED_COUNT,
+    LLM_KV_POOLING_TYPE,
+    LLM_KV_LOGIT_SCALE,
 
     LLM_KV_ATTENTION_HEAD_COUNT,
     LLM_KV_ATTENTION_HEAD_COUNT_KV,
@@ -249,6 +288,7 @@ enum llm_kv {
     LLM_KV_ATTENTION_VALUE_LENGTH,
     LLM_KV_ATTENTION_LAYERNORM_EPS,
     LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,
+    LLM_KV_ATTENTION_CAUSAL,
 
     LLM_KV_ROPE_DIMENSION_COUNT,
     LLM_KV_ROPE_FREQ_BASE,
@@ -258,9 +298,19 @@ enum llm_kv {
     LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,
     LLM_KV_ROPE_SCALING_FINETUNED,
 
+    LLM_KV_SPLIT_NO,
+    LLM_KV_SPLIT_COUNT,
+    LLM_KV_SPLIT_TENSORS_COUNT,
+
+    LLM_KV_SSM_INNER_SIZE,
+    LLM_KV_SSM_CONV_KERNEL,
+    LLM_KV_SSM_STATE_SIZE,
+    LLM_KV_SSM_TIME_STEP_RANK,
+
     LLM_KV_TOKENIZER_MODEL,
     LLM_KV_TOKENIZER_LIST,
     LLM_KV_TOKENIZER_TOKEN_TYPE,
+    LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,
     LLM_KV_TOKENIZER_SCORES,
     LLM_KV_TOKENIZER_MERGES,
     LLM_KV_TOKENIZER_BOS_ID,
@@ -270,22 +320,25 @@ enum llm_kv {
     LLM_KV_TOKENIZER_PAD_ID,
     LLM_KV_TOKENIZER_ADD_BOS,
     LLM_KV_TOKENIZER_ADD_EOS,
+    LLM_KV_TOKENIZER_ADD_PREFIX,
     LLM_KV_TOKENIZER_HF_JSON,
     LLM_KV_TOKENIZER_RWKV,
 };
 
-static std::map<llm_kv, std::string> LLM_KV_NAMES = {
+static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_GENERAL_ARCHITECTURE,          "general.architecture"                  },
     { LLM_KV_GENERAL_QUANTIZATION_VERSION,  "general.quantization_version"          },
     { LLM_KV_GENERAL_ALIGNMENT,             "general.alignment"                     },
     { LLM_KV_GENERAL_NAME,                  "general.name"                          },
     { LLM_KV_GENERAL_AUTHOR,                "general.author"                        },
+    { LLM_KV_GENERAL_VERSION,               "general.version"                       },
     { LLM_KV_GENERAL_URL,                   "general.url"                           },
     { LLM_KV_GENERAL_DESCRIPTION,           "general.description"                   },
     { LLM_KV_GENERAL_LICENSE,               "general.license"                       },
     { LLM_KV_GENERAL_SOURCE_URL,            "general.source.url"                    },
     { LLM_KV_GENERAL_SOURCE_HF_REPO,        "general.source.huggingface.repository" },
 
+    { LLM_KV_VOCAB_SIZE,                    "%s.vocab_size"            },
     { LLM_KV_CONTEXT_LENGTH,                "%s.context_length"        },
     { LLM_KV_EMBEDDING_LENGTH,              "%s.embedding_length"      },
     { LLM_KV_BLOCK_COUNT,                   "%s.block_count"           },
@@ -294,6 +347,8 @@ static std::map<llm_kv, std::string> LLM_KV_NAMES = {
     { LLM_KV_TENSOR_DATA_LAYOUT,            "%s.tensor_data_layout"    },
     { LLM_KV_EXPERT_COUNT,                  "%s.expert_count"          },
     { LLM_KV_EXPERT_USED_COUNT,             "%s.expert_used_count"     },
+    { LLM_KV_POOLING_TYPE ,                 "%s.pooling_type"          },
+    { LLM_KV_LOGIT_SCALE,                   "%s.logit_scale"           },
 
     { LLM_KV_ATTENTION_HEAD_COUNT,          "%s.attention.head_count"             },
     { LLM_KV_ATTENTION_HEAD_COUNT_KV,       "%s.attention.head_count_kv"          },
@@ -303,6 +358,7 @@ static std::map<llm_kv, std::string> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_VALUE_LENGTH,        "%s.attention.value_length"           },
     { LLM_KV_ATTENTION_LAYERNORM_EPS,       "%s.attention.layer_norm_epsilon"     },
     { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,   "%s.attention.layer_norm_rms_epsilon" },
+    { LLM_KV_ATTENTION_CAUSAL,              "%s.attention.causal"                 },
 
     { LLM_KV_ROPE_DIMENSION_COUNT,          "%s.rope.dimension_count"                 },
     { LLM_KV_ROPE_FREQ_BASE,                "%s.rope.freq_base"                       },
@@ -312,9 +368,19 @@ static std::map<llm_kv, std::string> LLM_KV_NAMES = {
     { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,     "%s.rope.scaling.original_context_length" },
     { LLM_KV_ROPE_SCALING_FINETUNED,        "%s.rope.scaling.finetuned"               },
 
+    { LLM_KV_SPLIT_NO,                      "split.no"            },
+    { LLM_KV_SPLIT_COUNT,                   "split.count"         },
+    { LLM_KV_SPLIT_TENSORS_COUNT,           "split.tensors.count" },
+
+    { LLM_KV_SSM_CONV_KERNEL,               "%s.ssm.conv_kernel"    },
+    { LLM_KV_SSM_INNER_SIZE,                "%s.ssm.inner_size"     },
+    { LLM_KV_SSM_STATE_SIZE,                "%s.ssm.state_size"     },
+    { LLM_KV_SSM_TIME_STEP_RANK,            "%s.ssm.time_step_rank" },
+
     { LLM_KV_TOKENIZER_MODEL,               "tokenizer.ggml.model"              },
     { LLM_KV_TOKENIZER_LIST,                "tokenizer.ggml.tokens"             },
     { LLM_KV_TOKENIZER_TOKEN_TYPE,          "tokenizer.ggml.token_type"         },
+    { LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,    "tokenizer.ggml.token_type_count"   },
     { LLM_KV_TOKENIZER_SCORES,              "tokenizer.ggml.scores"             },
     { LLM_KV_TOKENIZER_MERGES,              "tokenizer.ggml.merges"             },
     { LLM_KV_TOKENIZER_BOS_ID,              "tokenizer.ggml.bos_token_id"       },
@@ -324,6 +390,7 @@ static std::map<llm_kv, std::string> LLM_KV_NAMES = {
     { LLM_KV_TOKENIZER_PAD_ID,              "tokenizer.ggml.padding_token_id"   },
     { LLM_KV_TOKENIZER_ADD_BOS,             "tokenizer.ggml.add_bos_token"      },
     { LLM_KV_TOKENIZER_ADD_EOS,             "tokenizer.ggml.add_eos_token"      },
+    { LLM_KV_TOKENIZER_ADD_PREFIX,          "tokenizer.ggml.add_space_prefix"   },
     { LLM_KV_TOKENIZER_HF_JSON,             "tokenizer.huggingface.json"        },
     { LLM_KV_TOKENIZER_RWKV,                "tokenizer.rwkv.world"              },
 };
@@ -334,13 +401,14 @@ struct LLM_KV {
     llm_arch arch;
 
     std::string operator()(llm_kv kv) const {
-        return ::format(LLM_KV_NAMES[kv].c_str(), LLM_ARCH_NAMES[arch].c_str());
+        return ::format(LLM_KV_NAMES.at(kv), LLM_ARCH_NAMES.at(arch));
     }
 };
 
 enum llm_tensor {
     LLM_TENSOR_TOKEN_EMBD,
     LLM_TENSOR_TOKEN_EMBD_NORM,
+    LLM_TENSOR_TOKEN_TYPES,
     LLM_TENSOR_POS_EMBD,
     LLM_TENSOR_OUTPUT,
     LLM_TENSOR_OUTPUT_NORM,
@@ -352,6 +420,7 @@ enum llm_tensor {
     LLM_TENSOR_ATTN_OUT,
     LLM_TENSOR_ATTN_NORM,
     LLM_TENSOR_ATTN_NORM_2,
+    LLM_TENSOR_ATTN_OUT_NORM,
     LLM_TENSOR_ATTN_ROT_EMBD,
     LLM_TENSOR_FFN_GATE_INP,
     LLM_TENSOR_FFN_NORM,
@@ -359,14 +428,25 @@ enum llm_tensor {
     LLM_TENSOR_FFN_DOWN,
     LLM_TENSOR_FFN_UP,
     LLM_TENSOR_FFN_ACT,
-    LLM_TENSOR_FFN_DOWN_EXP,
+    LLM_TENSOR_FFN_DOWN_EXP,  // split experts for backward compatibility
     LLM_TENSOR_FFN_GATE_EXP,
     LLM_TENSOR_FFN_UP_EXP,
+    LLM_TENSOR_FFN_DOWN_EXPS, // merged experts
+    LLM_TENSOR_FFN_GATE_EXPS,
+    LLM_TENSOR_FFN_UP_EXPS,
     LLM_TENSOR_ATTN_Q_NORM,
     LLM_TENSOR_ATTN_K_NORM,
+    LLM_TENSOR_LAYER_OUT_NORM,
+    LLM_TENSOR_SSM_IN,
+    LLM_TENSOR_SSM_CONV1D,
+    LLM_TENSOR_SSM_X,
+    LLM_TENSOR_SSM_DT,
+    LLM_TENSOR_SSM_A,
+    LLM_TENSOR_SSM_D,
+    LLM_TENSOR_SSM_OUT,
 };
 
-static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
+static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
     {
         LLM_ARCH_LLAMA,
         {
@@ -388,6 +468,9 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
             { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
             { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
             { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
+            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
         },
     },
     {
@@ -423,6 +506,31 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_GROK,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
+            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
+            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
+            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
+            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
+            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
+        },
+    },
     {
         LLM_ARCH_GPT2,
         {
@@ -480,7 +588,7 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
         {
             { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
             { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_OUTPUT,          "output"},
             { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
             { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
             { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
@@ -488,6 +596,9 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
             { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
             { LLM_TENSOR_FFN_ACT,         "blk.%d.ffn.act" },
+            { LLM_TENSOR_POS_EMBD,        "position_embd" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm"},
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm"},
         },
     },
     {
@@ -522,6 +633,38 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_BERT,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
+            { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
+            { LLM_TENSOR_POS_EMBD,        "position_embd" },
+            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_NOMIC_BERT,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
+            { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
+            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
     {
         LLM_ARCH_BLOOM,
         {
@@ -571,6 +714,23 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_QWEN2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
     {
         LLM_ARCH_PHI2,
         {
@@ -579,6 +739,9 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
             { LLM_TENSOR_OUTPUT,          "output" },
             { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
             { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
             { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
             { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
@@ -602,7 +765,169 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
-
+    {
+        LLM_ARCH_CODESHELL,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_ORION,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_INTERNLM2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_MINICPM,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
+            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
+            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
+        },
+    },
+    {
+        LLM_ARCH_GEMMA,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_STARCODER2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_MAMBA,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_SSM_IN,          "blk.%d.ssm_in" },
+            { LLM_TENSOR_SSM_CONV1D,      "blk.%d.ssm_conv1d" },
+            { LLM_TENSOR_SSM_X,           "blk.%d.ssm_x" },
+            { LLM_TENSOR_SSM_DT,          "blk.%d.ssm_dt" },
+            { LLM_TENSOR_SSM_A,           "blk.%d.ssm_a" },
+            { LLM_TENSOR_SSM_D,           "blk.%d.ssm_d" },
+            { LLM_TENSOR_SSM_OUT,         "blk.%d.ssm_out" },
+        },
+    },
+    {
+        LLM_ARCH_XVERSE,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_COMMAND_R,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
     {
         LLM_ARCH_UNKNOWN,
         {
@@ -636,23 +961,38 @@ struct LLM_TN {
     llm_arch arch;
 
     std::string operator()(llm_tensor tensor) const {
-        return LLM_TENSOR_NAMES[arch].at(tensor);
+        if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
+            return "__missing__";
+        }
+        return LLM_TENSOR_NAMES.at(arch).at(tensor);
     }
 
     std::string operator()(llm_tensor tensor, const std::string & suffix) const {
-        return LLM_TENSOR_NAMES[arch].at(tensor) + "." + suffix;
+        if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
+            return "__missing__";
+        }
+        return LLM_TENSOR_NAMES.at(arch).at(tensor) + "." + suffix;
     }
 
     std::string operator()(llm_tensor tensor, int bid) const {
-        return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid);
+        if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
+            return "__missing__";
+        }
+        return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid);
     }
 
     std::string operator()(llm_tensor tensor, const std::string & suffix, int bid) const {
-        return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid) + "." + suffix;
+        if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
+            return "__missing__";
+        }
+        return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid) + "." + suffix;
     }
 
     std::string operator()(llm_tensor tensor, const std::string & suffix, int bid, int xid) const {
-        return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid, xid) + "." + suffix;
+        if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
+            return "__missing__";
+        }
+        return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid, xid) + "." + suffix;
     }
 };
 
@@ -660,20 +1000,20 @@ struct LLM_TN {
 // gguf helpers
 //
 
-static std::map<int8_t, std::string> LLAMA_ROPE_SCALING_TYPES = {
-    { LLAMA_ROPE_SCALING_NONE,   "none"   },
-    { LLAMA_ROPE_SCALING_LINEAR, "linear" },
-    { LLAMA_ROPE_SCALING_YARN,   "yarn"   },
+static const std::map<llama_rope_scaling_type, const char *> LLAMA_ROPE_SCALING_TYPES = {
+    { LLAMA_ROPE_SCALING_TYPE_NONE,   "none"   },
+    { LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" },
+    { LLAMA_ROPE_SCALING_TYPE_YARN,   "yarn"   },
 };
 
-static int8_t llama_rope_scaling_type_from_string(const std::string & name) {
+static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::string & name) {
     for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
         if (kv.second == name) {
-            return kv.first;
+            return (llama_rope_scaling_type) kv.first;
         }
     }
 
-    return LLAMA_ROPE_SCALING_UNSPECIFIED;
+    return LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
 }
 
 static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) {
@@ -730,21 +1070,6 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
     }
 }
 
-//
-// ggml helpers
-//
-
-static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
-    struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
-
-    if (plan.work_size > 0) {
-        buf.resize(plan.work_size);
-        plan.work_data = buf.data();
-    }
-
-    ggml_graph_compute(graph, &plan);
-}
-
 //
 // llama helpers
 //
@@ -775,7 +1100,7 @@ struct llama_file {
     size_t size;
 
     llama_file(const char * fname, const char * mode) {
-        fp = std::fopen(fname, mode);
+        fp = ggml_fopen(fname, mode);
         if (fp == NULL) {
             throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
         }
@@ -844,6 +1169,7 @@ struct llama_file {
         }
     }
 };
+using llama_files = std::vector<std::unique_ptr<llama_file>>;
 
 struct llama_mmap {
     void * addr;
@@ -862,7 +1188,7 @@ struct llama_mmap {
         int fd = fileno(file->fp);
         int flags = MAP_SHARED;
         // prefetch/readahead impairs performance on NUMA systems
-        if (numa) { prefetch = 0; }
+        if (numa)  { prefetch = 0; }
 #ifdef __linux__
         // advise the kernel to read the file sequentially (increases readahead)
         if (posix_fadvise(fd, 0, 0, POSIX_FADV_SEQUENTIAL)) {
@@ -989,6 +1315,7 @@ struct llama_mmap {
         }
 
         if (prefetch > 0) {
+#if _WIN32_WINNT >= 0x602
             // PrefetchVirtualMemory is only present on Windows 8 and above, so we dynamically load it
             BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG);
             HMODULE hKernel32 = GetModuleHandleW(L"kernel32.dll");
@@ -1006,6 +1333,9 @@ struct llama_mmap {
                             llama_format_win_err(GetLastError()).c_str());
                 }
             }
+#else
+            throw std::runtime_error("PrefetchVirtualMemory unavailable");
+#endif
         }
     }
 
@@ -1040,6 +1370,7 @@ struct llama_mmap {
     }
 #endif
 };
+using llama_mmaps = std::vector<std::unique_ptr<llama_mmap>>;
 
 // Represents some region of memory being locked using mlock or VirtualLock;
 // will automatically unlock on destruction.
@@ -1089,10 +1420,10 @@ struct llama_mlock {
     #ifdef __APPLE__
         #define MLOCK_SUGGESTION \
             "Try increasing the sysctl values 'vm.user_wire_limit' and 'vm.global_user_wire_limit' and/or " \
-            "decreasing 'vm.global_no_user_wire_amount'.  Also try increasing RLIMIT_MLOCK (ulimit -l).\n"
+            "decreasing 'vm.global_no_user_wire_amount'.  Also try increasing RLIMIT_MEMLOCK (ulimit -l).\n"
     #else
         #define MLOCK_SUGGESTION \
-            "Try increasing RLIMIT_MLOCK ('ulimit -l' as root).\n"
+            "Try increasing RLIMIT_MEMLOCK ('ulimit -l' as root).\n"
     #endif
 
     bool raw_lock(const void * addr, size_t size) const {
@@ -1112,7 +1443,7 @@ struct llama_mlock {
             suggest = false;
         }
 
-        fprintf(stderr, "warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s",
+        LLAMA_LOG_WARN("warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s",
                 size, this->size, errmsg, suggest ? MLOCK_SUGGESTION : "");
         return false;
     }
@@ -1121,7 +1452,7 @@ struct llama_mlock {
 
     static void raw_unlock(void * addr, size_t size) {
         if (munlock(addr, size)) {
-            fprintf(stderr, "warning: failed to munlock buffer: %s\n", std::strerror(errno));
+            LLAMA_LOG_WARN("warning: failed to munlock buffer: %s\n", std::strerror(errno));
         }
     }
 #elif defined(_WIN32)
@@ -1139,7 +1470,7 @@ struct llama_mlock {
                 return true;
             }
             if (tries == 2) {
-                fprintf(stderr, "warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n",
+                LLAMA_LOG_WARN("warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n",
                     len, size, llama_format_win_err(GetLastError()).c_str());
                 return false;
             }
@@ -1148,7 +1479,7 @@ struct llama_mlock {
             // set size and try again.
             SIZE_T min_ws_size, max_ws_size;
             if (!GetProcessWorkingSetSize(GetCurrentProcess(), &min_ws_size, &max_ws_size)) {
-                fprintf(stderr, "warning: GetProcessWorkingSetSize failed: %s\n",
+                LLAMA_LOG_WARN("warning: GetProcessWorkingSetSize failed: %s\n",
                         llama_format_win_err(GetLastError()).c_str());
                 return false;
             }
@@ -1161,7 +1492,7 @@ struct llama_mlock {
             min_ws_size += increment;
             max_ws_size += increment;
             if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) {
-                fprintf(stderr, "warning: SetProcessWorkingSetSize failed: %s\n",
+                LLAMA_LOG_WARN("warning: SetProcessWorkingSetSize failed: %s\n",
                         llama_format_win_err(GetLastError()).c_str());
                 return false;
             }
@@ -1170,7 +1501,7 @@ struct llama_mlock {
 
     static void raw_unlock(void * ptr, size_t len) {
         if (!VirtualUnlock(ptr, len)) {
-            fprintf(stderr, "warning: failed to VirtualUnlock buffer: %s\n",
+            LLAMA_LOG_WARN("warning: failed to VirtualUnlock buffer: %s\n",
                     llama_format_win_err(GetLastError()).c_str());
         }
     }
@@ -1182,19 +1513,14 @@ struct llama_mlock {
     }
 
     bool raw_lock(const void * addr, size_t len) const {
-        fprintf(stderr, "warning: mlock not supported on this system\n");
+        LLAMA_LOG_WARN("warning: mlock not supported on this system\n");
         return false;
     }
 
     static void raw_unlock(const void * addr, size_t len) {}
 #endif
 };
-
-typedef void (*offload_func_t)(struct ggml_tensor * tensor);
-
-static void ggml_offload_nop(struct ggml_tensor * tensor) {
-    (void) tensor;
-}
+using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;
 
 static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
     std::vector<char> result(8, 0);
@@ -1211,30 +1537,117 @@ static std::string llama_token_to_piece(const struct llama_context * ctx, llama_
     return std::string(result.data(), result.size());
 }
 
-static ggml_backend_buffer_type_t llama_default_buffer_type(int n_gpu_layers) {
+static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer) {
     ggml_backend_buffer_type_t buft = nullptr;
 
-#ifdef GGML_USE_METAL
-    if (n_gpu_layers > 0) {
-        buft = ggml_backend_metal_buffer_type();
+#if defined(GGML_USE_CUDA)
+    // host buffers should only be used when data is expected to be copied to/from the GPU
+    if (host_buffer) {
+        buft = ggml_backend_cuda_host_buffer_type();
     }
-#elif defined(GGML_USE_CUBLAS) && defined(LLAMA_GGML_BACKEND_CUDA_TEST)
-    if (n_gpu_layers > 0) {
-        buft = ggml_backend_cuda_buffer_type(0);
+#elif defined(GGML_USE_SYCL)
+    if (host_buffer) {
+        buft = ggml_backend_sycl_host_buffer_type();
     }
-#elif defined(GGML_USE_CUBLAS)
-    buft = ggml_backend_cuda_host_buffer_type();
 #elif defined(GGML_USE_CPU_HBM)
     buft = ggml_backend_cpu_hbm_buffer_type();
+#elif defined(GGML_USE_VULKAN)
+    if (host_buffer) {
+        buft = ggml_backend_vk_host_buffer_type();
+    }
 #endif
 
     if (buft == nullptr) {
         buft = ggml_backend_cpu_buffer_type();
     }
-
     return buft;
 
-    GGML_UNUSED(n_gpu_layers);
+    GGML_UNUSED(host_buffer);
+}
+
+static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) {
+    ggml_backend_buffer_type_t buft = nullptr;
+
+#ifdef GGML_USE_METAL
+    buft = ggml_backend_metal_buffer_type();
+#elif defined(GGML_USE_CUDA)
+    buft = ggml_backend_cuda_buffer_type(gpu);
+#elif defined(GGML_USE_VULKAN)
+    buft = ggml_backend_vk_buffer_type(gpu);
+#elif defined(GGML_USE_SYCL)
+    buft = ggml_backend_sycl_buffer_type(gpu);
+#elif defined(GGML_USE_CLBLAST)
+    buft = ggml_backend_opencl_buffer_type();
+#elif defined(GGML_USE_KOMPUTE)
+    buft = ggml_backend_kompute_buffer_type(gpu);
+    if (buft == nullptr) {
+        LLAMA_LOG_WARN("%s: cannot use GPU %d, check `vulkaninfo --summary`\n", __func__, gpu);
+    }
+#endif
+
+    if (buft == nullptr) {
+        buft = llama_default_buffer_type_cpu(true);
+    }
+    return buft;
+
+    GGML_UNUSED(gpu);
+}
+
+static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_gpu, const float * tensor_split) {
+    ggml_backend_buffer_type_t buft = nullptr;
+
+#ifdef GGML_USE_CUDA
+    if (ggml_backend_cuda_get_device_count() > 1) {
+        buft = ggml_backend_cuda_split_buffer_type(tensor_split);
+    }
+#endif
+
+#ifdef GGML_USE_SYCL
+    if (ggml_backend_sycl_get_device_count() > 1) {
+        buft = ggml_backend_sycl_split_buffer_type(tensor_split);
+    }
+#endif
+
+    if (buft == nullptr) {
+        buft = llama_default_buffer_type_offload(fallback_gpu);
+    }
+    return buft;
+
+    GGML_UNUSED(tensor_split);
+}
+
+static size_t llama_get_device_count() {
+#if defined(GGML_USE_CUDA)
+    return ggml_backend_cuda_get_device_count();
+#elif defined(GGML_USE_SYCL)
+    return ggml_backend_sycl_get_device_count();
+#elif defined(GGML_USE_VULKAN)
+    return ggml_backend_vk_get_device_count();
+#else
+    return 1;
+#endif
+}
+
+static size_t llama_get_device_memory(int device) {
+#if defined(GGML_USE_CUDA)
+    size_t total;
+    size_t free;
+    ggml_backend_cuda_get_device_memory(device, &total, &free);
+    return free;
+#elif defined(GGML_USE_SYCL)
+    size_t total;
+    size_t free;
+    ggml_backend_sycl_get_device_memory(device, &total, &free);
+    return free;
+#elif defined(GGML_USE_VULKAN)
+    size_t total;
+    size_t free;
+    ggml_backend_vk_get_device_memory(device, &total, &free);
+    return free;
+#else
+    return 1;
+    GGML_UNUSED(device);
+#endif
 }
 
 //
@@ -1244,7 +1657,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type(int n_gpu_layers) {
 struct llama_state {
     llama_state() {
 #ifdef GGML_USE_METAL
-        ggml_metal_log_set_callback(log_callback, log_callback_user_data);
+        ggml_backend_metal_log_set_callback(log_callback, log_callback_user_data);
 #endif
     }
 
@@ -1258,17 +1671,30 @@ static llama_state g_state;
 // available llama models
 enum e_model {
     MODEL_UNKNOWN,
+    MODEL_17M,
+    MODEL_22M,
+    MODEL_33M,
+    MODEL_109M,
+    MODEL_137M,
+    MODEL_335M,
+    MODEL_0_5B,
     MODEL_1B,
+    MODEL_2B,
     MODEL_3B,
+    MODEL_4B,
     MODEL_7B,
     MODEL_8B,
     MODEL_13B,
+    MODEL_14B,
     MODEL_15B,
+    MODEL_20B,
     MODEL_30B,
     MODEL_34B,
+    MODEL_35B,
     MODEL_40B,
     MODEL_65B,
     MODEL_70B,
+    MODEL_314B,
     MODEL_SMALL,
     MODEL_MEDIUM,
     MODEL_LARGE,
@@ -1280,7 +1706,9 @@ static const size_t MiB = 1024*kiB;
 static const size_t GiB = 1024*MiB;
 
 struct llama_hparams {
-    bool     vocab_only;
+    bool vocab_only;
+    bool rope_finetuned;
+
     uint32_t n_vocab;
     uint32_t n_ctx_train; // context size the model was trained on
     uint32_t n_embd;
@@ -1293,6 +1721,7 @@ struct llama_hparams {
     uint32_t n_ff;
     uint32_t n_expert = 0;
     uint32_t n_expert_used = 0;
+    uint32_t n_vocab_type = 0; // for BERT-style token types
 
     float f_norm_eps;
     float f_norm_rms_eps;
@@ -1300,12 +1729,23 @@ struct llama_hparams {
     float    rope_freq_base_train;
     float    rope_freq_scale_train;
     uint32_t n_yarn_orig_ctx;
-    int8_t   rope_scaling_type_train : 3;
-    bool     rope_finetuned : 1;
 
-    float f_clamp_kqv;
-    float f_max_alibi_bias;
+    // for State Space Models
+    uint32_t ssm_d_conv  = 0;
+    uint32_t ssm_d_inner = 0;
+    uint32_t ssm_d_state = 0;
+    uint32_t ssm_dt_rank = 0;
 
+    float f_clamp_kqv      = 0.0f;
+    float f_max_alibi_bias = 0.0f;
+    float f_logit_scale    = 0.0f;
+
+    bool causal_attn = true;
+    bool need_kq_pos = false;
+
+    enum llama_pooling_type      pooling_type            = LLAMA_POOLING_TYPE_NONE;
+    enum llama_rope_type         rope_type               = LLAMA_ROPE_TYPE_NONE;
+    enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
 
     bool operator!=(const llama_hparams & other) const {
         if (this->vocab_only    != other.vocab_only)    return true;
@@ -1325,6 +1765,11 @@ struct llama_hparams {
         if (this->rope_finetuned  != other.rope_finetuned)  return true;
         if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true;
 
+        if (this->ssm_d_conv  != other.ssm_d_conv)  return true;
+        if (this->ssm_d_inner != other.ssm_d_inner) return true;
+        if (this->ssm_d_state != other.ssm_d_state) return true;
+        if (this->ssm_dt_rank != other.ssm_dt_rank) return true;
+
         const float EPSILON = 1e-9f;
 
         if (!is_float_close(this->f_norm_eps,            other.f_norm_eps,            EPSILON)) return true;
@@ -1336,6 +1781,9 @@ struct llama_hparams {
     }
 
     uint32_t n_gqa() const {
+        if (n_head_kv == 0) {
+            return 0;
+        }
         return n_head/n_head_kv;
     }
 
@@ -1346,16 +1794,30 @@ struct llama_hparams {
     uint32_t n_embd_v_gqa() const { // dimension of value embeddings across all k-v heads
         return n_embd_head_v * n_head_kv;
     }
+
+    uint32_t n_embd_k_s() const { // dimension of the rolling state embeddings
+        // corresponds to Mamba's conv_states size
+        // TODO: maybe support other convolution strides than 1
+        // NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed
+        return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner;
+    }
+
+    uint32_t n_embd_v_s() const { // dimension of the recurrent state embeddings
+        // corresponds to Mamba's ssm_states size
+        return ssm_d_state * ssm_d_inner;
+    }
 };
 
 struct llama_cparams {
-    uint32_t n_ctx;       // context size used during inference
+    uint32_t n_ctx;           // context size used during inference
     uint32_t n_batch;
+    uint32_t n_ubatch;
+    uint32_t n_seq_max;
     uint32_t n_threads;       // number of threads to use for generation
     uint32_t n_threads_batch; // number of threads to use for batch processing
 
-    float    rope_freq_base;
-    float    rope_freq_scale;
+    float rope_freq_base;
+    float rope_freq_scale;
 
     uint32_t n_yarn_orig_ctx;
     // These hyperparameters are not exposed in GGUF, because all
@@ -1364,9 +1826,16 @@ struct llama_cparams {
     float yarn_attn_factor;
     float yarn_beta_fast;
     float yarn_beta_slow;
+    float defrag_thold;
 
-    bool mul_mat_q;
+    bool embeddings;
+    bool causal_attn;
     bool offload_kqv;
+
+    enum llama_pooling_type pooling_type;
+
+    ggml_backend_sched_eval_callback cb_eval;
+    void * cb_eval_user_data;
 };
 
 struct llama_layer {
@@ -1379,6 +1848,8 @@ struct llama_layer {
     struct ggml_tensor * attn_q_norm_b;
     struct ggml_tensor * attn_k_norm;
     struct ggml_tensor * attn_k_norm_b;
+    struct ggml_tensor * attn_out_norm;
+    struct ggml_tensor * attn_out_norm_b;
 
     // attention
     struct ggml_tensor * wq;
@@ -1397,6 +1868,8 @@ struct llama_layer {
     // normalization
     struct ggml_tensor * ffn_norm;
     struct ggml_tensor * ffn_norm_b;
+    struct ggml_tensor * layer_out_norm;
+    struct ggml_tensor * layer_out_norm_b;
 
     // ff
     struct ggml_tensor * ffn_gate; // w1
@@ -1405,30 +1878,58 @@ struct llama_layer {
 
     // ff MoE
     struct ggml_tensor * ffn_gate_inp;
-    struct ggml_tensor * ffn_gate_exp[LLAMA_MAX_EXPERTS];
-    struct ggml_tensor * ffn_down_exp[LLAMA_MAX_EXPERTS];
-    struct ggml_tensor * ffn_up_exp  [LLAMA_MAX_EXPERTS];
+    struct ggml_tensor * ffn_gate_exps;
+    struct ggml_tensor * ffn_down_exps;
+    struct ggml_tensor * ffn_up_exps ;
 
     // ff bias
     struct ggml_tensor * ffn_down_b; // b2
     struct ggml_tensor * ffn_up_b;   // b3
     struct ggml_tensor * ffn_act;
+
+    // mamba proj
+    struct ggml_tensor * ssm_in;
+    struct ggml_tensor * ssm_x;
+    struct ggml_tensor * ssm_dt;
+    struct ggml_tensor * ssm_out;
+
+    // mamba
+    struct ggml_tensor * ssm_conv1d;
+    struct ggml_tensor * ssm_a;
+    struct ggml_tensor * ssm_d;
+
+    // mamba bias
+    struct ggml_tensor * ssm_conv1d_b;
+    struct ggml_tensor * ssm_dt_b;
 };
 
 struct llama_kv_cell {
     llama_pos pos   = -1;
     llama_pos delta = 0;
+    int32_t   src   = 0; // used by recurrent state models to copy states
 
     std::set<llama_seq_id> seq_id;
 
     bool has_seq_id(const llama_seq_id & id) const {
         return seq_id.find(id) != seq_id.end();
     }
+
+    bool is_empty() const {
+        return seq_id.empty();
+    }
+
+    bool is_same_seq(const llama_kv_cell & other) const {
+        return seq_id == other.seq_id;
+    }
 };
 
 // ring-buffer of cached KV data
 struct llama_kv_cache {
     bool has_shift = false;
+    bool do_defrag = false;
+    bool do_copy   = false;
+    // with recurrent state models, a cell can hold the state for more than one past token
+    bool recurrent = false;
 
     // Note: The value of head isn't only used to optimize searching
     // for a free KV slot. llama_decode_internal also uses it, so it
@@ -1440,29 +1941,57 @@ struct llama_kv_cache {
     // computed before each graph build
     uint32_t n = 0;
 
+    ggml_type type_k = GGML_TYPE_F16;
+    ggml_type type_v = GGML_TYPE_F16;
+
     std::vector<llama_kv_cell> cells;
 
     std::vector<struct ggml_tensor *> k_l; // per layer
     std::vector<struct ggml_tensor *> v_l;
 
-    struct ggml_context * ctx = NULL;
+    std::vector<struct ggml_context *> ctxs;
+    std::vector<ggml_backend_buffer_t> bufs;
 
-    ggml_backend_buffer_t buf = NULL;
+    size_t total_size() const {
+        size_t size = 0;
+        for (ggml_backend_buffer_t buf : bufs) {
+            size += ggml_backend_buffer_get_size(buf);
+        }
+        return size;
+    }
 
     ~llama_kv_cache() {
-#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
-        if (ggml_cublas_loaded()) {
-            for (size_t i = 0; i < k_l.size(); ++i) {
-                ggml_cuda_free_data(k_l[i]);
-                ggml_cuda_free_data(v_l[i]);
-            }
-        }
-#endif
-        if (ctx) {
+        for (struct ggml_context * ctx : ctxs) {
             ggml_free(ctx);
         }
+        for (ggml_backend_buffer_t buf : bufs) {
+            ggml_backend_buffer_free(buf);
+        }
+    }
+};
 
-        ggml_backend_buffer_free(buf);
+struct llama_control_vector {
+    std::vector<struct ggml_tensor *> tensors; // per layer
+    std::vector<struct ggml_context *> ctxs;
+    std::vector<ggml_backend_buffer_t> bufs;
+
+    int32_t layer_start = -1;
+    int32_t layer_end   = -1;
+
+    ggml_tensor * tensor_for(int il) const {
+        if (il < 0 || il < layer_start || il > layer_end || (size_t) il >= tensors.size()) {
+            return nullptr;
+        }
+        return tensors[il];
+    }
+
+    ~llama_control_vector() {
+        for (struct ggml_context * ctx : ctxs) {
+            ggml_free(ctx);
+        }
+        for (ggml_backend_buffer_t buf : bufs) {
+            ggml_backend_buffer_free(buf);
+        }
     }
 };
 
@@ -1502,6 +2031,8 @@ struct llama_vocab {
     id special_suffix_id = 32008;
     id special_eot_id    = 32010;
 
+    bool add_space_prefix = true;
+
     int find_bpe_rank(const std::string & token_left, const std::string & token_right) const {
         GGML_ASSERT(token_left.find(' ') == std::string::npos);
         GGML_ASSERT(token_left.find('\n') == std::string::npos);
@@ -1528,6 +2059,7 @@ struct llama_model {
     llama_vocab   vocab;
 
     struct ggml_tensor * tok_embd;
+    struct ggml_tensor * type_embd;
     struct ggml_tensor * pos_embd;
     struct ggml_tensor * tok_norm;
     struct ggml_tensor * tok_norm_b;
@@ -1539,23 +2071,39 @@ struct llama_model {
 
     std::vector<llama_layer> layers;
 
+    llama_split_mode split_mode;
+    int main_gpu;
     int n_gpu_layers;
 
     // gguf metadata
     std::unordered_map<std::string, std::string> gguf_kv;
 
-    // context
-    struct ggml_context * ctx = NULL;
+    // layer -> buffer type mapping
+    struct layer_buft {
+        layer_buft() : buft_matrix(nullptr), buft(nullptr) {}
+        layer_buft(ggml_backend_buffer_type_t matrix) : buft_matrix(matrix), buft(matrix) {}
+        layer_buft(ggml_backend_buffer_type_t matrix, ggml_backend_buffer_type_t other) : buft_matrix(matrix), buft(other) {}
 
-    // the model memory buffer
-    ggml_backend_buffer_t buf = NULL;
+        ggml_backend_buffer_type_t buft_matrix; // matrices only - used by split buffers and backends that support only matrix multiplication
+        ggml_backend_buffer_type_t buft;        // everything else
+    };
 
-    // model memory mapped file
-    std::unique_ptr<llama_mmap> mapping;
+    layer_buft buft_input;
+    layer_buft buft_output;
+    std::vector<layer_buft> buft_layer;
+
+    // contexts where the model tensors metadata is stored
+    std::vector<struct ggml_context *> ctxs;
+
+    // the model memory buffers for the tensor data
+    std::vector<ggml_backend_buffer_t> bufs;
+
+    // model memory mapped files
+    llama_mmaps mappings;
 
     // objects representing data potentially being locked in memory
-    llama_mlock mlock_buf;
-    llama_mlock mlock_mmap;
+    llama_mlocks mlock_bufs;
+    llama_mlocks mlock_mmaps;
 
     // for quantize-stats only
     std::vector<std::pair<std::string, struct ggml_tensor *>> tensors_by_name;
@@ -1564,39 +2112,39 @@ struct llama_model {
     int64_t t_start_us = 0;
 
     ~llama_model() {
-#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
-        if (ggml_cublas_loaded()) {
-            for (size_t i = 0; i < tensors_by_name.size(); ++i) {
-                ggml_cuda_free_data(tensors_by_name[i].second);
-            }
-            ggml_cuda_free_scratch();
-        }
-#endif
-
-#if defined(GGML_USE_CLBLAST)
-        for (size_t i = 0; i < tensors_by_name.size(); ++i) {
-            ggml_cl_free_data(tensors_by_name[i].second);
-        }
-#endif
-        if (ctx) {
+        for (struct ggml_context * ctx : ctxs) {
             ggml_free(ctx);
         }
-
-        ggml_backend_buffer_free(buf);
+        for (ggml_backend_buffer_t buf : bufs) {
+#ifdef GGML_USE_CUDA
+            if (ggml_backend_buffer_get_type(buf) == ggml_backend_cpu_buffer_type()) {
+                ggml_backend_cuda_unregister_host_buffer(ggml_backend_buffer_get_base(buf));
+            }
+#endif
+            ggml_backend_buffer_free(buf);
+        }
     }
 };
 
 struct llama_context {
     llama_context(const llama_model & model) : model(model), t_start_us(model.t_start_us), t_load_us(model.t_load_us) {}
     ~llama_context() {
-        ggml_allocr_free(alloc);
-        ggml_backend_buffer_free(buf_alloc);
-        ggml_backend_free(backend);
+        ggml_backend_sched_free(sched);
+
+        for (ggml_backend_t backend : backends) {
+            ggml_backend_free(backend);
+        }
+
+        ggml_backend_buffer_free(buf_output);
     }
 
     llama_cparams cparams;
 
-    ggml_backend_t backend = nullptr;
+    std::vector<ggml_backend_t> backends;
+#ifdef GGML_USE_METAL
+    ggml_backend_t backend_metal = nullptr;
+#endif
+    ggml_backend_t backend_cpu = nullptr;
 
     const llama_model & model;
 
@@ -1613,28 +2161,58 @@ struct llama_context {
     int64_t t_p_eval_us = 0;
     int64_t t_eval_us   = 0;
 
+    int64_t t_compute_start_us = 0;
+    int64_t n_queued_tokens = 0;
+
     int32_t n_sample = 0; // number of tokens sampled
     int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
     int32_t n_eval   = 0; // number of eval calls
 
-    // decode output (2-dimensional array: [n_tokens][n_vocab])
-    std::vector<float> logits;
-#ifndef NDEBUG
-    // guard against access to unset logits
-    std::vector<bool>  logits_valid;
-#endif
+    // host buffer for the model output (logits and embeddings)
+    ggml_backend_buffer_t buf_output = nullptr;
+
+    // decode output (2-dimensional array: [n_outputs][n_vocab])
+    size_t  logits_size = 0; // capacity (of floats) for logits
+    float * logits      = nullptr;
+
+    std::vector<int32_t> output_ids; // map batch token positions to ids of the logits and embd buffers
+    size_t  output_size = 0; // capacity (of tokens positions) for the output buffers
+    int32_t n_outputs   = 0; // number of actually-used outputs in the current ubatch
+
     bool logits_all = false;
 
-    // input embedding (1-dimensional array: [n_embd])
-    std::vector<float> embedding;
+    // embeddings output (2-dimensional array: [n_outputs][n_embd])
+    // populated only when pooling_type == LLAMA_POOLING_TYPE_NONE
+    size_t  embd_size = 0; // capacity (of floats) for embeddings
+    float * embd      = nullptr;
+
+    // sequence embeddings output (map of [n_embd] vectors)
+    // populated only when pooling_type != LLAMA_POOLING_TYPE_NONE
+    std::map<llama_seq_id, std::vector<float>> embd_seq;
 
     // memory buffers used to evaluate the model
     std::vector<uint8_t> buf_compute_meta;
-    ggml_backend_buffer_t buf_alloc = NULL;
-    ggml_allocr * alloc = NULL;
+    ggml_backend_sched_t sched = nullptr;
 
-    // temporary buffer for copying data to/from the backend
-    std::vector<no_init<uint8_t>> buf_copy;
+    ggml_abort_callback abort_callback      = nullptr;
+    void *              abort_callback_data = nullptr;
+
+    // input tensors
+    struct ggml_tensor * inp_tokens;    // I32 [n_batch]
+    struct ggml_tensor * inp_embd;      // F32 [n_embd, n_batch]
+    struct ggml_tensor * inp_pos;       // I32 [n_batch]
+    struct ggml_tensor * inp_out_ids;   // I32 [n_outputs]
+    struct ggml_tensor * inp_KQ_mask;   // F32 [kv_size, n_batch]
+    struct ggml_tensor * inp_KQ_pos;    // F32 [n_kv]
+    struct ggml_tensor * inp_K_shift;   // I32 [kv_size]
+    struct ggml_tensor * inp_mean;      // F32 [n_batch, n_batch]
+    struct ggml_tensor * inp_cls;       // I32 [n_batch]
+    struct ggml_tensor * inp_s_copy;    // I32 [kv_size]
+    struct ggml_tensor * inp_s_mask;    // F32 [1, n_kv]
+    struct ggml_tensor * inp_s_seq;     // I32 [n_kv, n_batch]
+
+    // control vectors
+    struct llama_control_vector cvec;
 
 #ifdef GGML_USE_MPI
     ggml_mpi_context * ctx_mpi = NULL;
@@ -1646,82 +2224,106 @@ struct llama_context {
 //
 
 static bool llama_kv_cache_init(
-        const struct llama_hparams & hparams,
              struct llama_kv_cache & cache,
-                         ggml_type   ktype,
-                         ggml_type   vtype,
-                          uint32_t   n_ctx,
-                               int   n_gpu_layers,
+                 const llama_model & model,
+                         ggml_type   type_k,
+                         ggml_type   type_v,
+                          uint32_t   kv_size,
                               bool   offload) {
-    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa();
-    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
-    const uint32_t n_layer      = hparams.n_layer;
+    const struct llama_hparams & hparams = model.hparams;
+
+    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
+    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
+    const int64_t  n_layer      = hparams.n_layer;
 
     cache.has_shift = false;
 
+    // TODO: find a nicer way to add other recurrent model architectures
+    cache.recurrent = model.arch == LLM_ARCH_MAMBA;
+
+    // TODO: support mixed reccurent Transformer architectues
+    // NOTE: (!a || b) is a logical implication (a -> b)
+    GGML_ASSERT(!cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_s());
+    GGML_ASSERT(!cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_s());
+    GGML_ASSERT( cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_gqa());
+    GGML_ASSERT( cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_gqa());
+
     cache.head = 0;
-    cache.size = n_ctx;
+    cache.size = kv_size;
     cache.used = 0;
 
+    cache.type_k = type_k;
+    cache.type_v = type_v;
+
     cache.cells.clear();
-    cache.cells.resize(n_ctx);
+    cache.cells.resize(kv_size);
 
-    struct ggml_init_params params;
-    params.mem_size   = 2u*n_layer*ggml_tensor_overhead();
-    params.mem_buffer = NULL;
-    params.no_alloc   = true;
+    if (cache.recurrent) {
+        // init state copy sources
+        for (uint32_t i = 0; i < cache.size; ++i) {
+            cache.cells[i].src = i;
+        }
+    }
 
-    cache.ctx = ggml_init(params);
+#ifdef GGML_USE_CLBLAST
+    offload = false;
+#endif
 
-    size_t vram_kv_cache = 0;
+    // count used buffer types
+    std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
+    if (offload) {
+        for (int64_t i = 0; i < n_layer; ++i) {
+            buft_layer_count[model.buft_layer[i].buft]++;
+        }
+    } else {
+        buft_layer_count[llama_default_buffer_type_cpu(true)] = n_layer;
+    }
 
-    if (!cache.ctx) {
-        LLAMA_LOG_ERROR("%s: failed to allocate memory for kv cache\n", __func__);
-        return false;
+    // create a context for each buffer type
+    std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
+    for (auto & it : buft_layer_count) {
+        int n_layers = it.second;
+        struct ggml_init_params params = {
+            /*.mem_size   =*/ 2u*n_layers*ggml_tensor_overhead(),
+            /*.mem_buffer =*/ NULL,
+            /*.no_alloc   =*/ true,
+        };
+        ggml_context * ctx = ggml_init(params);
+        if (!ctx) {
+            LLAMA_LOG_ERROR("%s: failed to allocate context for kv cache\n", __func__);
+            return false;
+        }
+        ctx_map[it.first] = ctx;
+        cache.ctxs.push_back(ctx);
     }
 
     cache.k_l.reserve(n_layer);
     cache.v_l.reserve(n_layer);
 
-    const int i_gpu_start = (int) n_layer - n_gpu_layers;
-
     for (int i = 0; i < (int) n_layer; i++) {
-        ggml_tensor * k = ggml_new_tensor_1d(cache.ctx, ktype, n_embd_k_gqa*n_ctx);
-        ggml_tensor * v = ggml_new_tensor_1d(cache.ctx, vtype, n_embd_v_gqa*n_ctx);
+        struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front();
+        ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
+        ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size);
         ggml_format_name(k, "cache_k_l%d", i);
         ggml_format_name(v, "cache_v_l%d", i);
         cache.k_l.push_back(k);
         cache.v_l.push_back(v);
-#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
-        if (i >= i_gpu_start) {
-            if (offload) {
-                ggml_cuda_assign_buffers_no_scratch(k);
-                ggml_cuda_assign_buffers_no_scratch(v);
-                vram_kv_cache += ggml_nbytes(k);
-                vram_kv_cache += ggml_nbytes(v);
-                // HACK: mark tensor as allocated
-                k->data = v->data = (void *)(uintptr_t)1;
-            }
+    }
+
+    // allocate tensors and initialize the buffers to avoid NaNs in the padding
+    for (auto it : ctx_map) {
+        ggml_backend_buffer_type_t buft = it.first;
+        ggml_context * ctx = it.second;
+        ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
+        if (!buf) {
+            LLAMA_LOG_ERROR("%s: failed to allocate buffer for kv cache\n", __func__);
+            return false;
         }
-#endif // GGML_USE_CUBLAS
+        ggml_backend_buffer_clear(buf, 0);
+        LLAMA_LOG_INFO("%s: %10s KV buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0);
+        cache.bufs.push_back(buf);
     }
 
-    // allocate tensors
-    cache.buf = ggml_backend_alloc_ctx_tensors_from_buft(cache.ctx, llama_default_buffer_type(n_gpu_layers));
-
-    // buf may be NULL with full offload
-    if (cache.buf) {
-        // initialize the buffer to avoid NaNs in the padding
-        ggml_backend_buffer_clear(cache.buf, 0);
-    }
-
-    if (vram_kv_cache > 0) {
-        LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MB\n", __func__, vram_kv_cache / 1024.0 / 1024.0);
-    }
-
-    GGML_UNUSED(i_gpu_start);
-    GGML_UNUSED(offload);
-
     return true;
 }
 
@@ -1735,6 +2337,54 @@ static bool llama_kv_cache_find_slot(
     const uint32_t n_ctx    = cache.size;
     const uint32_t n_tokens = batch.n_tokens;
 
+    if (cache.recurrent) {
+        // For recurrent state architectures (like Mamba),
+        // each KV cache cell can store the state for a whole sequence.
+
+        llama_seq_id min = cache.size - 1;
+        llama_seq_id max = 0;
+
+        for (uint32_t i = 0; i < n_tokens; ++i) {
+            for (int32_t j = 0; j < batch.n_seq_id[i]; ++j) {
+                llama_seq_id seq_id = batch.seq_id[i][j];
+                // make sure it's a valid seq_id
+                if ((uint32_t) seq_id < cache.size) {
+                    if (seq_id > max) {
+                        max = seq_id;
+                    }
+                    if (seq_id < min) {
+                        min = seq_id;
+                    }
+                    // Assuming the tokens are in-order
+                    if (batch.pos[i] != cache.cells[seq_id].pos + 1) {
+                        // What should happen when the pos backtracks or skips a value?
+                        // Clearing the state mid-batch would require special-casing which isn't done.
+                        LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d\n",
+                            __func__, batch.pos[i], cache.cells[seq_id].pos, seq_id);
+                    }
+                    if (cache.cells[seq_id].pos < 0 && 0 <= batch.pos[i]) {
+                        cache.used += 1;
+                    }
+                    cache.cells[seq_id].pos = batch.pos[i];
+                    // NOTE: seq_ids are not inserted here; they are handled when the input tensors are set
+                } else {
+                    // too big seq_id
+                    // TODO: would it be possible to resize the KV cache size instead?
+                    LLAMA_LOG_ERROR("%s: seq_id=%d >= kv_size=%d Try using a bigger --parallel value\n", __func__, seq_id, cache.size);
+                    return false;
+                }
+            }
+        }
+
+        // allow getting the range of used cells, from head to head + n
+        cache.head = min;
+        cache.n    = max - min + 1;
+
+        // sanity check
+        return max >= min;
+    }
+    // otherwise, one cell per token.
+
     if (n_tokens > n_ctx) {
         LLAMA_LOG_ERROR("%s: n_tokens=%d > n_ctx=%d\n", __func__, n_tokens, n_ctx);
         return false;
@@ -1783,10 +2433,12 @@ static bool llama_kv_cache_find_slot(
 }
 
 // find how many cells are currently in use
-static int32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) {
-    for (uint32_t i = cache.size - 1; i > 0; --i) {
-        if (cache.cells[i].pos >= 0 && !cache.cells[i].seq_id.empty()) {
-            return i + 1;
+static uint32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) {
+    for (uint32_t i = cache.size; i > 0; --i) {
+        const llama_kv_cell & cell = cache.cells[i - 1];
+
+        if (cell.pos >= 0 && !cell.is_empty()) {
+            return i;
         }
     }
 
@@ -1802,7 +2454,7 @@ static void llama_kv_cache_clear(struct llama_kv_cache & cache) {
     cache.used = 0;
 }
 
-static void llama_kv_cache_seq_rm(
+static bool llama_kv_cache_seq_rm(
         struct llama_kv_cache & cache,
                  llama_seq_id   seq_id,
                     llama_pos   p0,
@@ -1812,6 +2464,25 @@ static void llama_kv_cache_seq_rm(
     if (p0 < 0) p0 = 0;
     if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
 
+    // models like Mamba can't have a state partially erased
+    if (cache.recurrent) {
+        if (seq_id >= (int64_t) cache.size) {
+            // could be fatal
+            return false;
+        }
+        if (0 <= seq_id) {
+            // partial intersection is invalid
+            if ((0 < p0 && p0 <= cache.cells[seq_id].pos) || (0 < p1 && p1 <= cache.cells[seq_id].pos)) {
+                return false;
+            }
+        } else {
+            // seq_id is negative, then the range should include everything or nothing
+            if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits<llama_pos>::max())) {
+                return false;
+            }
+        }
+    }
+
     for (uint32_t i = 0; i < cache.size; ++i) {
         if (cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
             if (seq_id < 0) {
@@ -1821,7 +2492,7 @@ static void llama_kv_cache_seq_rm(
             } else {
                 continue;
             }
-            if (cache.cells[i].seq_id.empty()) {
+            if (cache.cells[i].is_empty()) {
                 // keep count of the number of used cells
                 if (cache.cells[i].pos >= 0) cache.used--;
 
@@ -1833,6 +2504,8 @@ static void llama_kv_cache_seq_rm(
 
     // If we freed up a slot, set head to it so searching can start there.
     if (new_head != cache.size && new_head < cache.head) cache.head = new_head;
+
+    return true;
 }
 
 static void llama_kv_cache_seq_cp(
@@ -1844,6 +2517,29 @@ static void llama_kv_cache_seq_cp(
     if (p0 < 0) p0 = 0;
     if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
 
+    if (cache.recurrent) {
+        if ((uint32_t) seq_id_dst < cache.size && (uint32_t) seq_id_src < cache.size) {
+            seq_id_src = cache.cells[seq_id_src].src;
+            GGML_ASSERT((uint32_t) seq_id_src < cache.size);
+            // intent to "copy from"
+            // supports copy chains thanks to taking the source of the source
+            cache.cells[seq_id_dst].src = seq_id_src;
+
+            // preserve the "keep or clear" status of the copied sequence
+            if (cache.cells[seq_id_src].has_seq_id(seq_id_src)) {
+                cache.cells[seq_id_dst].seq_id.insert(seq_id_dst);
+            } else {
+                cache.cells[seq_id_dst].seq_id.erase(seq_id_dst);
+            }
+
+            cache.do_copy = true;
+
+            cache.cells[seq_id_dst].pos = cache.cells[seq_id_src].pos;
+        }
+        return;
+    }
+    // otherwise, this is the KV cache of a Transformer-like model
+
     cache.head = 0;
 
     for (uint32_t i = 0; i < cache.size; ++i) {
@@ -1872,7 +2568,7 @@ static void llama_kv_cache_seq_keep(struct llama_kv_cache & cache, llama_seq_id
     if (new_head != cache.size && new_head < cache.head) cache.head = new_head;
 }
 
-static void llama_kv_cache_seq_shift(
+static void llama_kv_cache_seq_add(
         struct llama_kv_cache & cache,
                  llama_seq_id   seq_id,
                     llama_pos   p0,
@@ -1883,6 +2579,17 @@ static void llama_kv_cache_seq_shift(
     if (p0 < 0) p0 = 0;
     if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
 
+    if (cache.recurrent) {
+        // for Mamba-like models, only the pos needs to be shifted
+        if (0 <= seq_id && seq_id < (int64_t) cache.size) {
+            llama_kv_cell & cell = cache.cells[seq_id];
+            if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
+                cell.pos += delta;
+            }
+        }
+        return;
+    }
+
     for (uint32_t i = 0; i < cache.size; ++i) {
         if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
             cache.has_shift = true;
@@ -1890,10 +2597,14 @@ static void llama_kv_cache_seq_shift(
             cache.cells[i].delta += delta;
 
             if (cache.cells[i].pos < 0) {
-                if (!cache.cells[i].seq_id.empty()) cache.used--;
+                if (!cache.cells[i].is_empty()) {
+                    cache.used--;
+                }
                 cache.cells[i].pos = -1;
                 cache.cells[i].seq_id.clear();
-                if (new_head == cache.size) new_head = i;
+                if (new_head == cache.size) {
+                    new_head = i;
+                }
             }
         }
     }
@@ -1912,6 +2623,17 @@ static void llama_kv_cache_seq_div(
     if (p0 < 0) p0 = 0;
     if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
 
+    if (cache.recurrent) {
+        // for Mamba-like models, only the pos needs to be changed
+        if (0 <= seq_id && seq_id < (int64_t) cache.size) {
+            llama_kv_cell & cell = cache.cells[seq_id];
+            if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
+                cell.pos /= d;
+            }
+        }
+        return;
+    }
+
     for (uint32_t i = 0; i < cache.size; ++i) {
         if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
             cache.has_shift = true;
@@ -1925,6 +2647,22 @@ static void llama_kv_cache_seq_div(
     }
 }
 
+static llama_pos llama_kv_cache_seq_pos_max(struct llama_kv_cache & cache, llama_seq_id seq_id) {
+    llama_pos result = 0;
+
+    for (uint32_t i = 0; i < cache.size; ++i) {
+        if (cache.cells[i].has_seq_id(seq_id)) {
+            result = std::max(result, cache.cells[i].pos);
+        }
+    }
+
+    return result;
+}
+
+static void llama_kv_cache_defrag(struct llama_kv_cache & cache) {
+    cache.do_defrag = true;
+}
+
 //
 // model loading and saving
 //
@@ -1996,7 +2734,7 @@ namespace GGUFMeta {
         }
     };
 
-    struct ArrayInfo{
+    struct ArrayInfo {
         const gguf_type gt;
         const size_t length;
         const void * data;
@@ -2015,7 +2753,7 @@ namespace GGUFMeta {
     };
 
     template<typename T>
-    class GKV: public GKV_Base<T> {
+    class GKV : public GKV_Base<T> {
         GKV() = delete;
 
         public:
@@ -2031,46 +2769,46 @@ namespace GGUFMeta {
 
         static const char * override_type_to_str(const llama_model_kv_override_type ty) {
             switch (ty) {
-                case LLAMA_KV_OVERRIDE_BOOL:  return "bool";
-                case LLAMA_KV_OVERRIDE_INT:   return "int";
-                case LLAMA_KV_OVERRIDE_FLOAT: return "float";
+                case LLAMA_KV_OVERRIDE_TYPE_BOOL:  return "bool";
+                case LLAMA_KV_OVERRIDE_TYPE_INT:   return "int";
+                case LLAMA_KV_OVERRIDE_TYPE_FLOAT: return "float";
             }
             return "unknown";
         }
 
-        static bool validate_override(const llama_model_kv_override_type expected_type, const struct llama_model_kv_override *override) {
-            if (!override) { return false; }
-            if (override->tag == expected_type) {
+        static bool validate_override(const llama_model_kv_override_type expected_type, const struct llama_model_kv_override * ovrd) {
+            if (!ovrd) { return false; }
+            if (ovrd->tag == expected_type) {
                 LLAMA_LOG_INFO("%s: Using metadata override (%5s) '%s' = ",
-                    __func__, override_type_to_str(override->tag), override->key);
-                switch (override->tag) {
-                    case LLAMA_KV_OVERRIDE_BOOL:  {
-                        printf("%s\n", override->bool_value ? "true" : "false");
+                    __func__, override_type_to_str(ovrd->tag), ovrd->key);
+                switch (ovrd->tag) {
+                    case LLAMA_KV_OVERRIDE_TYPE_BOOL:  {
+                        LLAMA_LOG_INFO("%s\n", ovrd->bool_value ? "true" : "false");
                     } break;
-                    case LLAMA_KV_OVERRIDE_INT:   {
-                        printf("%" PRId64 "\n", override->int_value);
+                    case LLAMA_KV_OVERRIDE_TYPE_INT:   {
+                        LLAMA_LOG_INFO("%" PRId64 "\n", ovrd->int_value);
                     } break;
-                    case LLAMA_KV_OVERRIDE_FLOAT: {
-                        printf("%.6f\n", override->float_value);
+                    case LLAMA_KV_OVERRIDE_TYPE_FLOAT: {
+                        LLAMA_LOG_INFO("%.6f\n", ovrd->float_value);
                     } break;
                     default:
                         // Shouldn't be possible to end up here, but just in case...
                         throw std::runtime_error(
                             format("Unsupported attempt to override %s type for metadata key %s\n",
-                                override_type_to_str(override->tag), override->key));
+                                override_type_to_str(ovrd->tag), ovrd->key));
                 }
                 return true;
             }
             LLAMA_LOG_WARN("%s: Warning: Bad metadata override type for key '%s', expected %s but got %s\n",
-                __func__, override->key, override_type_to_str(expected_type), override_type_to_str(override->tag));
+                __func__, ovrd->key, override_type_to_str(expected_type), override_type_to_str(ovrd->tag));
             return false;
         }
 
         template<typename OT>
         static typename std::enable_if<std::is_same<OT, bool>::value, bool>::type
-        try_override(OT & target, const struct llama_model_kv_override *override) {
-            if (validate_override(LLAMA_KV_OVERRIDE_BOOL, override)) {
-                target = override->bool_value;
+        try_override(OT & target, const struct llama_model_kv_override * ovrd) {
+            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_BOOL, ovrd)) {
+                target = ovrd->bool_value;
                 return true;
             }
             return false;
@@ -2078,9 +2816,9 @@ namespace GGUFMeta {
 
         template<typename OT>
         static typename std::enable_if<!std::is_same<OT, bool>::value && std::is_integral<OT>::value, bool>::type
-        try_override(OT & target, const struct llama_model_kv_override *override) {
-            if (validate_override(LLAMA_KV_OVERRIDE_INT, override)) {
-                target = override->int_value;
+        try_override(OT & target, const struct llama_model_kv_override * ovrd) {
+            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_INT, ovrd)) {
+                target = ovrd->int_value;
                 return true;
             }
             return false;
@@ -2088,9 +2826,9 @@ namespace GGUFMeta {
 
         template<typename OT>
         static typename std::enable_if<std::is_floating_point<OT>::value, bool>::type
-        try_override(T & target, const struct llama_model_kv_override *override) {
-            if (validate_override(LLAMA_KV_OVERRIDE_FLOAT, override)) {
-                target = override->float_value;
+        try_override(T & target, const struct llama_model_kv_override * ovrd) {
+            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_FLOAT, ovrd)) {
+                target = ovrd->float_value;
                 return true;
             }
             return false;
@@ -2098,17 +2836,17 @@ namespace GGUFMeta {
 
         template<typename OT>
         static typename std::enable_if<std::is_same<OT, std::string>::value, bool>::type
-        try_override(T & target, const struct llama_model_kv_override *override) {
+        try_override(T & target, const struct llama_model_kv_override * ovrd) {
             (void)target;
-            (void)override;
-            if (!override) { return false; }
+            (void)ovrd;
+            if (!ovrd) { return false; }
             // Currently, we should never end up here so it would be a bug if we do.
             throw std::runtime_error(format("Unsupported attempt to override string type for metadata key %s\n",
-                override ? override->key : "NULL"));
+                ovrd ? ovrd->key : "NULL"));
         }
 
-        static bool set(const gguf_context * ctx, const int k, T & target, const struct llama_model_kv_override *override = nullptr) {
-            if (try_override<T>(target, override)) {
+        static bool set(const gguf_context * ctx, const int k, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
+            if (try_override<T>(target, ovrd)) {
                 return true;
             }
             if (k < 0) { return false; }
@@ -2116,16 +2854,18 @@ namespace GGUFMeta {
             return true;
         }
 
-        static bool set(const gguf_context * ctx, const char * key, T & target, const struct llama_model_kv_override *override = nullptr) {
-            return set(ctx, gguf_find_key(ctx, key), target, override);
+        static bool set(const gguf_context * ctx, const char * key, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
+            return set(ctx, gguf_find_key(ctx, key), target, ovrd);
         }
 
-        static bool set(const gguf_context * ctx, const std::string & key, T & target, const struct llama_model_kv_override *override = nullptr) {
-            return set(ctx, key.c_str(), target, override);
+        static bool set(const gguf_context * ctx, const std::string & key, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
+            return set(ctx, key.c_str(), target, ovrd);
         }
     };
 }
 
+using llama_buf_map = std::unordered_map<uint32_t, ggml_backend_buffer_t>;
+
 struct llama_model_loader {
     int n_kv      = 0;
     int n_tensors = 0;
@@ -2136,24 +2876,39 @@ struct llama_model_loader {
 
     bool use_mmap = false;
 
-    llama_file  file;
+    llama_files files;
     llama_ftype ftype;
     llama_fver  fver;
 
-    std::unique_ptr<llama_mmap> mapping;
+    llama_mmaps mappings;
+
+    // Holds information on a model weight
+    struct llama_tensor_weight {
+        uint16_t  idx; // source file index
+        size_t   offs; // tensor data offset in the original file
+
+        ggml_tensor * tensor;
+
+        llama_tensor_weight(uint16_t idx, const char * name, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) {
+            const int tensor_idx = gguf_find_tensor(gguf_ctx, name);
+            offs = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, tensor_idx);
+        }
+    };
+    std::vector<llama_tensor_weight> weights;
+
     std::unordered_map<std::string, struct llama_model_kv_override> kv_overrides;
 
-    struct gguf_context * ctx_gguf = NULL;
-    struct ggml_context * ctx_meta = NULL;
+    struct gguf_context * meta = NULL;
+    std::vector<ggml_context *> contexts;
 
     std::string arch_name;
     LLM_KV      llm_kv    = LLM_KV(LLM_ARCH_UNKNOWN);
 
-    llama_model_loader(const std::string & fname, bool use_mmap, const struct llama_model_kv_override * param_overrides_p) : file(fname.c_str(), "rb") {
-        struct gguf_init_params params = {
-            /*.no_alloc = */ true,
-            /*.ctx      = */ &ctx_meta,
-        };
+    llama_model_loader(const std::string & fname, bool use_mmap, const struct llama_model_kv_override * param_overrides_p) {
+        int trace = 0;
+        if (getenv("LLAMA_TRACE")) {
+            trace = atoi(getenv("LLAMA_TRACE"));
+        }
 
         if (param_overrides_p != nullptr) {
             for (const struct llama_model_kv_override *p = param_overrides_p; p->key[0] != 0; p++) {
@@ -2161,24 +2916,93 @@ struct llama_model_loader {
             }
         }
 
-        ctx_gguf = gguf_init_from_file(fname.c_str(), params);
-        if (!ctx_gguf) {
+        struct ggml_context * ctx = NULL;
+        struct gguf_init_params params = {
+            /*.no_alloc = */ true,
+            /*.ctx      = */ &ctx,
+        };
+
+        meta = gguf_init_from_file(fname.c_str(), params);
+        if (!meta) {
             throw std::runtime_error(format("%s: failed to load model from %s\n", __func__, fname.c_str()));
         }
 
         get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false);
         llm_kv = LLM_KV(llm_arch_from_string(arch_name));
 
-        n_kv      = gguf_get_n_kv(ctx_gguf);
-        n_tensors = gguf_get_n_tensors(ctx_gguf);
+        // Save tensors data offset of the main file.
+        // For subsidiary files, `meta` tensor data offset must not be used,
+        // so we build a unified tensors index for weights.
+        for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
+            weights.emplace_back(0, cur->name, meta, cur);
+        }
+        files.emplace_back(new llama_file(fname.c_str(), "rb"));
+        contexts.emplace_back(ctx);
 
-        fver = (enum llama_fver ) gguf_get_version(ctx_gguf);
+        uint16_t n_split = 0;
+        get_key(llm_kv(LLM_KV_SPLIT_COUNT), n_split, false);
 
-        for (int i = 0; i < n_tensors; i++) {
-            const char * name = gguf_get_tensor_name(ctx_gguf, i);
-            struct ggml_tensor * t = ggml_get_tensor(ctx_meta, name);
-            n_elements += ggml_nelements(t);
-            n_bytes    += ggml_nbytes(t);
+        // Load additional GGML contexts
+        if (n_split > 1) {
+            uint16_t idx = 0;
+            get_key(llm_kv(LLM_KV_SPLIT_NO), idx);
+            if (idx != 0) {
+                throw std::runtime_error(format("illegal split file: %d, model must be loaded with the first split", idx));
+            }
+
+            char split_prefix[PATH_MAX] = {0};
+            if (!llama_split_prefix(split_prefix, sizeof(split_prefix), fname.c_str(), idx, n_split)) {
+                throw std::runtime_error(format("invalid split file: %s", fname.c_str()));
+            }
+
+            if (trace > 0) {
+                LLAMA_LOG_INFO("%s: loading additional %d GGUFs\n", __func__, n_split);
+            }
+
+            char split_path[PATH_MAX] = {0};
+            for (idx = 1; idx < n_split; idx++) {
+                llama_split_path(split_path, sizeof(split_path), split_prefix, idx, n_split);
+
+                struct gguf_init_params split_params = {
+                    /*.no_alloc = */ true,
+                    /*.ctx      = */ &ctx,
+                };
+                struct gguf_context * ctx_gguf = gguf_init_from_file(split_path, split_params);
+                if (!ctx_gguf) {
+                    throw std::runtime_error(format("%s: failed to load GGUF split from %s\n", __func__, split_path));
+                }
+
+                // Save tensors data offset info of the shard.
+                for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
+                    weights.emplace_back(idx, cur->name, ctx_gguf, cur);
+                }
+                files.emplace_back(new llama_file(split_path, "rb"));
+                contexts.emplace_back(ctx);
+
+                gguf_free(ctx_gguf);
+            }
+
+            get_key(llm_kv(LLM_KV_SPLIT_TENSORS_COUNT), n_tensors);
+
+            // sanity check
+            {
+                const int n_tensors_loaded = (int) weights.size();
+                if (n_tensors != n_tensors_loaded) {
+                    throw std::runtime_error(format("corrupted model: %d tensors expected but %d found", n_tensors, n_tensors_loaded));
+                }
+            }
+
+            LLAMA_LOG_INFO("%s: additional %d GGUFs metadata loaded.\n",  __func__, n_split - 1);
+        }
+
+        n_kv      = gguf_get_n_kv(meta);
+        n_tensors = weights.size();
+
+        fver = (enum llama_fver) gguf_get_version(meta);
+
+        for (auto & w : weights) {
+            n_elements += ggml_nelements(w.tensor);
+            n_bytes    += ggml_nbytes(w.tensor);
         }
 
         LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
@@ -2193,7 +3017,8 @@ struct llama_model_loader {
             enum ggml_type type_max = GGML_TYPE_F32;
 
             for (int i = 0; i < n_tensors; i++) {
-                enum ggml_type type = gguf_get_tensor_type(ctx_gguf, i);
+                const ggml_tensor * tensor = weights.at(i).tensor;
+                enum ggml_type type = tensor->type;
 
                 n_type[type]++;
 
@@ -2202,27 +3027,34 @@ struct llama_model_loader {
                     type_max   = type;
                 }
 
-                // TODO: make runtime configurable
-#if 0
-                struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
-                LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, ggml_get_name(meta), ggml_type_name(type), llama_format_tensor_shape(meta).c_str());
-#endif
+                if (trace > 0) {
+                    const uint16_t sid = weights.at(i).idx;
+                    LLAMA_LOG_INFO("%s: - tensor %4d, split %2d: %32s %-8s [ %s ]\n", __func__, i, sid, ggml_get_name(tensor), ggml_type_name(type), llama_format_tensor_shape(tensor).c_str());
+                }
             }
 
             switch (type_max) {
-                case GGML_TYPE_F32:  ftype = LLAMA_FTYPE_ALL_F32;       break;
-                case GGML_TYPE_F16:  ftype = LLAMA_FTYPE_MOSTLY_F16;    break;
-                case GGML_TYPE_Q4_0: ftype = LLAMA_FTYPE_MOSTLY_Q4_0;   break;
-                case GGML_TYPE_Q4_1: ftype = LLAMA_FTYPE_MOSTLY_Q4_1;   break;
-                case GGML_TYPE_Q5_0: ftype = LLAMA_FTYPE_MOSTLY_Q5_0;   break;
-                case GGML_TYPE_Q5_1: ftype = LLAMA_FTYPE_MOSTLY_Q5_1;   break;
-                case GGML_TYPE_Q8_0: ftype = LLAMA_FTYPE_MOSTLY_Q8_0;   break;
-                case GGML_TYPE_Q2_K: ftype = LLAMA_FTYPE_MOSTLY_Q2_K;   break;
-                case GGML_TYPE_Q3_K: ftype = LLAMA_FTYPE_MOSTLY_Q3_K_M; break;
-                case GGML_TYPE_Q4_K: ftype = LLAMA_FTYPE_MOSTLY_Q4_K_M; break;
-                case GGML_TYPE_Q5_K: ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M; break;
-                case GGML_TYPE_Q6_K: ftype = LLAMA_FTYPE_MOSTLY_Q6_K;   break;
+                case GGML_TYPE_F32:     ftype = LLAMA_FTYPE_ALL_F32;        break;
+                case GGML_TYPE_F16:     ftype = LLAMA_FTYPE_MOSTLY_F16;     break;
+                case GGML_TYPE_Q4_0:    ftype = LLAMA_FTYPE_MOSTLY_Q4_0;    break;
+                case GGML_TYPE_Q4_1:    ftype = LLAMA_FTYPE_MOSTLY_Q4_1;    break;
+                case GGML_TYPE_Q5_0:    ftype = LLAMA_FTYPE_MOSTLY_Q5_0;    break;
+                case GGML_TYPE_Q5_1:    ftype = LLAMA_FTYPE_MOSTLY_Q5_1;    break;
+                case GGML_TYPE_Q8_0:    ftype = LLAMA_FTYPE_MOSTLY_Q8_0;    break;
+                case GGML_TYPE_Q2_K:    ftype = LLAMA_FTYPE_MOSTLY_Q2_K;    break;
+                case GGML_TYPE_Q3_K:    ftype = LLAMA_FTYPE_MOSTLY_Q3_K_M;  break;
+                case GGML_TYPE_Q4_K:    ftype = LLAMA_FTYPE_MOSTLY_Q4_K_M;  break;
+                case GGML_TYPE_Q5_K:    ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M;  break;
+                case GGML_TYPE_Q6_K:    ftype = LLAMA_FTYPE_MOSTLY_Q6_K;    break;
                 case GGML_TYPE_IQ2_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS; break;
+                case GGML_TYPE_IQ2_XS:  ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS;  break;
+                case GGML_TYPE_IQ2_S:   ftype = LLAMA_FTYPE_MOSTLY_IQ2_S;   break;
+                case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break;
+                case GGML_TYPE_IQ1_S:   ftype = LLAMA_FTYPE_MOSTLY_IQ1_S;   break;
+                case GGML_TYPE_IQ1_M:   ftype = LLAMA_FTYPE_MOSTLY_IQ1_M;   break;
+                case GGML_TYPE_IQ4_NL:  ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL;  break;
+                case GGML_TYPE_IQ4_XS:  ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS;  break;
+                case GGML_TYPE_IQ3_S:   ftype = LLAMA_FTYPE_MOSTLY_IQ3_S;   break;
                 default:
                     {
                         LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max));
@@ -2234,22 +3066,23 @@ struct llama_model_loader {
             ftype = (llama_ftype) (ftype | LLAMA_FTYPE_GUESSED);
 
             {
-                const int kid = gguf_find_key(ctx_gguf, "general.file_type");
+                const int kid = gguf_find_key(meta, "general.file_type");
                 if (kid >= 0) {
-                    ftype = (llama_ftype) gguf_get_val_u32(ctx_gguf, kid);
+                    ftype = (llama_ftype) gguf_get_val_u32(meta, kid);
                 }
             }
 
             LLAMA_LOG_INFO("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
+
             for (int i = 0; i < n_kv; i++) {
-                const char * name           = gguf_get_key(ctx_gguf, i);
-                const enum gguf_type type   = gguf_get_kv_type(ctx_gguf, i);
+                const char * name           = gguf_get_key(meta, i);
+                const enum gguf_type type   = gguf_get_kv_type(meta, i);
                 const std::string type_name =
                     type == GGUF_TYPE_ARRAY
-                    ? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(ctx_gguf, i)), gguf_get_arr_n(ctx_gguf, i))
+                    ? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(meta, i)), gguf_get_arr_n(meta, i))
                     : gguf_type_name(type);
 
-                std::string value          = gguf_kv_to_str(ctx_gguf, i);
+                std::string value          = gguf_kv_to_str(meta, i);
                 const size_t MAX_VALUE_LEN = 40;
                 if (value.size() > MAX_VALUE_LEN) {
                     value = format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str());
@@ -2278,18 +3111,18 @@ struct llama_model_loader {
     }
 
     ~llama_model_loader() {
-        if (ctx_gguf) {
-            gguf_free(ctx_gguf);
+        if (meta) {
+            gguf_free(meta);
         }
-        if (ctx_meta) {
-            ggml_free(ctx_meta);
+        for (auto * ctx : contexts) {
+            ggml_free(ctx);
         }
     }
 
     template<typename T>
     typename std::enable_if<std::is_integral<T>::value, bool>::type
     get_arr_n(const std::string & key, T & result, const bool required = true) {
-        const int kid = gguf_find_key(ctx_gguf, key.c_str());
+        const int kid = gguf_find_key(meta, key.c_str());
 
         if (kid < 0) {
             if (required) {
@@ -2299,7 +3132,7 @@ struct llama_model_loader {
         }
 
         struct GGUFMeta::ArrayInfo arr_info =
-            GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(ctx_gguf, kid);
+            GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta, kid);
 
 
         result = arr_info.length;
@@ -2319,7 +3152,7 @@ struct llama_model_loader {
         const struct llama_model_kv_override * override =
             it != kv_overrides.end() ? &it->second : nullptr;
 
-        const bool found = GGUFMeta::GKV<T>::set(ctx_gguf, key, result, override);
+        const bool found = GGUFMeta::GKV<T>::set(meta, key, result, override);
 
         if (required && !found) {
             throw std::runtime_error(format("key not found in model: %s", key.c_str()));
@@ -2342,29 +3175,57 @@ struct llama_model_loader {
     }
 
     const char * get_tensor_name(int i) const {
-        return gguf_get_tensor_name(ctx_gguf, i);
+        return weights.at(i).tensor->name;
+    }
+
+    const llama_tensor_weight * get_weight(const char * name) const {
+        for (const auto & weight : weights) {
+            if (strcmp(name, weight.tensor->name) == 0) {
+                return &weight;
+            }
+        }
+        return nullptr;
+    }
+
+    const llama_tensor_weight & require_weight(const char * name) const {
+        const llama_tensor_weight * weight = get_weight(name);
+        if (!weight) {
+            throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name));
+        }
+        return *weight;
     }
 
     struct ggml_tensor * get_tensor_meta(const char * name) const {
-        return ggml_get_tensor(ctx_meta, name);
+        const auto * weight = get_weight(name);
+        if (!weight) {
+            return nullptr;
+        }
+        return weight->tensor;
+    }
+
+    struct ggml_tensor * require_tensor_meta(const char * name) const {
+        struct ggml_tensor * tensor = get_tensor_meta(name);
+        if (!tensor) {
+            throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name));
+        }
+        return tensor;
     }
 
     struct ggml_tensor * get_tensor_meta(int i) const {
         return get_tensor_meta(get_tensor_name(i));
     }
 
-    struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, struct ggml_tensor * meta, ggml_backend_type backend) {
-        struct ggml_tensor * tensor = ggml_dup_tensor(ctx, meta);
-        tensor->backend = backend; // TODO: ggml_set_backend
-        ggml_set_name(tensor, ggml_get_name(meta));
+    struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, const struct ggml_tensor * cur) {
+        struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur);
+        ggml_set_name(tensor, ggml_get_name(cur));
 
         n_created++;
 
         return tensor;
     }
 
-    struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, ggml_backend_type backend, bool required = true) {
-        struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, name.c_str());
+    const struct ggml_tensor * check_tensor_dims(const std::string & name, const std::vector<int64_t> & ne, bool required) const {
+        const struct ggml_tensor * cur = get_tensor_meta(name.c_str());
 
         if (cur == NULL) {
             if (!required) {
@@ -2373,16 +3234,10 @@ struct llama_model_loader {
             throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
         }
 
-        if (backend == GGML_BACKEND_GPU_SPLIT) {
-            if (ne.size() == 1) {
-                throw std::runtime_error(format("%s: 1-dimensional tensor '%s' cannot be split on the GPU", __func__, name.c_str()));
-            }
-        }
-
         {
             bool is_ok = true;
-            for (size_t i = 0; i < ne.size(); ++i) {
-                if (ne[i] != cur->ne[i]) {
+            for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
+                if ((i < ne.size() && ne[i] != cur->ne[i]) || (i >= ne.size() && cur->ne[i] != 1)) {
                     is_ok = false;
                     break;
                 }
@@ -2396,7 +3251,45 @@ struct llama_model_loader {
             }
         }
 
-        return create_tensor_for(ctx, cur, backend);
+        return cur;
+    }
+
+    struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, bool required = true) {
+        const struct ggml_tensor * cur = check_tensor_dims(name, ne, required);
+
+        if (cur == NULL) {
+            return NULL;
+        }
+
+        return create_tensor_for(ctx, cur);
+    }
+
+    struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::vector<int64_t> & ne, size_t offset, bool required = true) {
+        const struct ggml_tensor * cur = check_tensor_dims(name, ne, required);
+
+        if (cur == NULL) {
+            return NULL;
+        }
+
+        if (cur->type != base->type) {
+            throw std::runtime_error(format("%s: tensor '%s' has wrong type; expected %s, got %s", __func__, name.c_str(), ggml_type_name(base->type), ggml_type_name(cur->type)));
+        }
+
+        std::array<int64_t, GGML_MAX_DIMS> dims;
+        for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
+            dims[i] = i < ne.size() ? ne[i] : 1;
+        }
+
+        struct ggml_tensor * tensor = ggml_view_4d(ctx, base,
+                                        dims[0], dims[1], dims[2], dims[3],
+                                        cur->nb[1], cur->nb[2], cur->nb[3],
+                                        offset);
+
+        ggml_set_name(tensor, name.c_str());
+
+        n_created++;
+
+        return tensor;
     }
 
     void done_getting_tensors() const {
@@ -2405,83 +3298,92 @@ struct llama_model_loader {
         }
     }
 
-    size_t file_offset(const char * name) const {
-        const int idx = gguf_find_tensor(ctx_gguf, name);
-
-        if (idx < 0) {
-            throw std::runtime_error(format("%s: tensor '%s' not found in the file", __func__, name));
+    void init_mappings(bool prefetch = true, llama_mlocks * mlock_mmaps = nullptr) {
+        if (use_mmap) {
+            mappings.reserve(files.size());
+            mmaps_used.reserve(files.size());
+            for (const auto & file : files) {
+                std::unique_ptr<llama_mmap> mapping(new llama_mmap(file.get(), prefetch ? -1 : 0, ggml_is_numa()));
+                mmaps_used.emplace_back(mapping->size, 0);
+                if (mlock_mmaps) {
+                    std::unique_ptr<llama_mlock> mlock_mmap(new llama_mlock());
+                    mlock_mmap->init(mapping->addr);
+                    mlock_mmaps->emplace_back(std::move(mlock_mmap));
+                }
+                mappings.emplace_back(std::move(mapping));
+            }
         }
 
-        return gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, idx);
+        // compute the total size of all tensors for progress reporting
+        for (auto & w : weights) {
+            size_data += ggml_nbytes(w.tensor);
+        }
     }
 
-    void init_mapping(bool prefetch = true) {
-        /*
-        // prefetch only CPU tensors
-        if (use_mmap) {
-            size_t size_pref = 0; // prefetch
+    void get_mapping_range(size_t * first, size_t * last, void ** addr, int idx, ggml_context * ctx) const {
+        GGML_ASSERT(!mappings.empty());
+        const auto & mapping = mappings.at(idx);
 
-            for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
-                struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i));
-                if (cur->backend == GGML_BACKEND_CPU) {
-                    size_t tensor_end = gguf_get_tensor_offset(ctx_gguf, i) + ggml_nbytes(cur);
-                    size_pref = std::max(size_pref, tensor_end);
+        *first = mapping->size;
+        *last  = 0;
+        *addr = mapping->addr;
+        for (ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor; tensor = ggml_get_next_tensor(ctx, tensor)) {
+            try {
+                const auto * weight = get_weight(ggml_get_name(tensor));
+                if (!weight) {
+                    continue;
                 }
+                if (weight->idx != idx) {
+                    continue;
+                }
+                *first = std::min(*first, weight->offs);
+                *last  = std::max(*last,  weight->offs + ggml_nbytes(tensor));
+            } catch(...) {
+                // the tensor is not in the model
             }
-            mapping.reset(new llama_mmap(&file, gguf_get_data_offset(ctx_gguf) + size_pref, ggml_is_numa()));
-        }
-        */
-        // prefetch the whole file - all the data is needed anyway
-        if (use_mmap) {
-            mapping.reset(new llama_mmap(&file, prefetch ? -1 : 0, ggml_is_numa()));
         }
     }
 
     // for backwards compatibility, does not support ggml-backend
     void load_data_for(struct ggml_tensor * cur) const {
-        const size_t offs = file_offset(ggml_get_name(cur));
+        const auto & w = require_weight(ggml_get_name(cur));
 
-        if (use_mmap && mapping) {
-            GGML_ASSERT(cur->data == nullptr);
-            cur->data = (uint8_t *)mapping->addr + offs;
+        if (use_mmap) {
+            const auto & mapping = mappings.at(w.idx);
+            if (cur->data == nullptr) {
+                cur->data = (uint8_t *)mapping->addr + w.offs;
+            } else {
+                memcpy(cur->data, (uint8_t *)mapping->addr + w.offs, ggml_nbytes(cur));
+            }
         } else {
             GGML_ASSERT(cur->data != nullptr);
-            file.seek(offs, SEEK_SET);
-            file.read_raw(cur->data, ggml_nbytes(cur));
+            GGML_ASSERT(w.idx < files.size());
+            const auto & file = files.at(w.idx);
+            file->seek(w.offs, SEEK_SET);
+            file->read_raw(cur->data, ggml_nbytes(cur));
         }
     }
 
+    size_t size_done = 0;
+    size_t size_data = 0;
+    std::vector<std::pair<size_t, size_t>> mmaps_used;
+
     // Returns false if cancelled by progress_callback
-    bool load_all_data(struct ggml_context * ctx, llama_progress_callback progress_callback, void * progress_callback_user_data, ggml_backend_buffer_t buf_mmap, llama_mlock * lmlock) const {
-        size_t size_data = 0;
-
-        for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
-            struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i));
-            size_data += ggml_nbytes(cur);
-        }
-
-        if (use_mmap && buf_mmap) {
-            if (lmlock) {
-                lmlock->init(mapping->addr);
-            }
-        }
-
-#if (defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)) || defined(GGML_USE_CLBLAST)
-        const bool legacy_offload = true;
-#else
-        const bool legacy_offload = false;
-#endif
+    bool load_all_data(
+            struct ggml_context * ctx,
+            llama_buf_map & bufs_mmap,
+            llama_mlocks * lmlocks,
+            llama_progress_callback progress_callback,
+            void * progress_callback_user_data) {
+        GGML_ASSERT(size_data != 0 && "call init_mappings() first");
 
         std::vector<no_init<uint8_t>> read_buf;
-
-        size_t size_done = 0;
-
-        size_t mmap_first = -1;
-        size_t mmap_last  = 0;
-
-        for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
-            struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i));
-            GGML_ASSERT(cur); // unused tensors should have been caught by load_data already
+        for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
+            const auto * weight = get_weight(ggml_get_name(cur));
+            if (weight == nullptr) {
+                // this can happen with split experts models
+                continue;
+            }
 
             if (progress_callback) {
                 if (!progress_callback((float) size_done / size_data, progress_callback_user_data)) {
@@ -2489,78 +3391,87 @@ struct llama_model_loader {
                 }
             }
 
-            const size_t offs = file_offset(ggml_get_name(cur));
+            size_t n_size = ggml_nbytes(cur);
 
-            if (!legacy_offload || cur->backend == GGML_BACKEND_CPU) {
-                if (use_mmap && mapping) {
-                    if (buf_mmap) {
-                        ggml_backend_tensor_alloc(buf_mmap, cur, (uint8_t *) mapping->addr + offs);
-                        if (lmlock) {
-                            lmlock->grow_to(offs + ggml_nbytes(cur));
-                        }
-                        mmap_first = std::min(mmap_first, offs);
-                        mmap_last  = std::max(mmap_last,  offs + ggml_nbytes(cur));
-                    } else {
-                        ggml_backend_tensor_set(cur, (uint8_t *) mapping->addr + offs, 0, ggml_nbytes(cur));
+            if (use_mmap) {
+                const auto & mapping = mappings.at(weight->idx);
+                ggml_backend_buffer_t buf_mmap = nullptr;
+                if (bufs_mmap.count(weight->idx)) {
+                    buf_mmap = bufs_mmap.at(weight->idx);
+                }
+                GGML_ASSERT(buf_mmap || cur->data); // either we have a buffer to allocate the tensor in, or it is already allocated
+                if (buf_mmap && cur->data == nullptr) {
+                    ggml_backend_tensor_alloc(buf_mmap, cur, (uint8_t *) mapping->addr + weight->offs);
+                    if (lmlocks) {
+                        const auto & lmlock = lmlocks->at(weight->idx);
+                        lmlock->grow_to(weight->offs + ggml_nbytes(cur));
                     }
+
+                    auto & mmap_used = mmaps_used[weight->idx];
+                    mmap_used.first  = std::min(mmap_used.first,  weight->offs);
+                    mmap_used.second = std::max(mmap_used.second, weight->offs + n_size);
                 } else {
-                    if (ggml_backend_buffer_is_host(cur->buffer)) {
-                        file.seek(offs, SEEK_SET);
-                        file.read_raw(cur->data, ggml_nbytes(cur));
-                    } else {
-                        read_buf.resize(ggml_nbytes(cur));
-                        file.seek(offs, SEEK_SET);
-                        file.read_raw(read_buf.data(), ggml_nbytes(cur));
-                        ggml_backend_tensor_set(cur, read_buf.data(), 0, ggml_nbytes(cur));
-                    }
+                    ggml_backend_tensor_set(cur, (uint8_t *) mapping->addr + weight->offs, 0, n_size);
                 }
             } else {
-                // HACK: mark tensor as allocated
-                cur->data = (void *)(uintptr_t)1;
-                void * data;
-                if (use_mmap && mapping) {
-                    data = (uint8_t *) mapping->addr + offs;
+                GGML_ASSERT(weight->idx < files.size());
+                const auto & file = files.at(weight->idx);
+                if (ggml_backend_buffer_is_host(cur->buffer)) {
+                    file->seek(weight->offs, SEEK_SET);
+                    file->read_raw(cur->data, ggml_nbytes(cur));
                 } else {
                     read_buf.resize(ggml_nbytes(cur));
-                    file.seek(offs, SEEK_SET);
-                    file.read_raw(read_buf.data(), ggml_nbytes(cur));
-                    data = read_buf.data();
+                    file->seek(weight->offs, SEEK_SET);
+                    file->read_raw(read_buf.data(), ggml_nbytes(cur));
+                    ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size);
                 }
-
-#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
-                ggml_cuda_transform_tensor(data, cur);
-#elif defined(GGML_USE_CLBLAST)
-                GGML_ASSERT(cur->backend == GGML_BACKEND_GPU);
-                ggml_cl_transform_tensor(data, cur);
-#else
-                GGML_ASSERT(!"GPU tensor without a GPU backend");
-                GGML_UNUSED(data);
-#endif
             }
 
-            size_done += ggml_nbytes(cur);
+            size_done += n_size;
         }
 
-        // unmap offloaded tensors and metadata
-        if (use_mmap && mapping) {
-            mapping->unmap_fragment(0, mmap_first);
-            mapping->unmap_fragment(mmap_last, mapping->size);
+        // check if this is the last call and do final cleanup
+        if (size_done >= size_data) {
+            // unmap offloaded tensors and metadata
+            if (use_mmap) {
+                for (uint32_t idx = 0; idx < mappings.size(); idx++) {
+                    const auto & mmap_used = mmaps_used.at(idx);
+                    auto & mapping = mappings.at(idx);
+                    mapping->unmap_fragment(0, mmap_used.first);
+                    if (mmap_used.second != 0) {
+                        mapping->unmap_fragment(mmap_used.second, mapping->size);
+                    }
+                }
+            }
+            if (progress_callback) {
+                // Even though the model is done loading, we still honor
+                // cancellation since we need to free allocations.
+                return progress_callback(1.0f, progress_callback_user_data);
+            }
         }
 
-        if (progress_callback) {
-            // Even though the model is done loading, we still honor
-            // cancellation since we need to free allocations.
-            return progress_callback(1.0f, progress_callback_user_data);
-        }
         return true;
     }
 };
 
+template<>
+bool llama_model_loader::get_key(const enum llm_kv kid, enum llama_pooling_type & result, const bool required) {
+    uint32_t tmp;
+    const bool found = get_key(kid, tmp, required);
+    if (found) {
+        result = (enum llama_pooling_type) tmp;
+    } else {
+        result = LLAMA_POOLING_TYPE_UNSPECIFIED;
+    }
+    return found;
+}
+
+
 //
 // load LLaMA models
 //
 
-static std::string llama_model_arch_name(llm_arch arch) {
+static const char * llama_model_arch_name(llm_arch arch) {
     auto it = LLM_ARCH_NAMES.find(arch);
     if (it == LLM_ARCH_NAMES.end()) {
         return "unknown";
@@ -2585,7 +3496,8 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
         case LLAMA_FTYPE_MOSTLY_Q8_0: return "Q8_0";
 
         // K-quants
-        case LLAMA_FTYPE_MOSTLY_Q2_K:   return "Q2_K";
+        case LLAMA_FTYPE_MOSTLY_Q2_K:   return "Q2_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q2_K_S: return "Q2_K - Small";
         case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "Q3_K - Small";
         case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "Q3_K - Medium";
         case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "Q3_K - Large";
@@ -2594,7 +3506,18 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
         case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "Q5_K - Small";
         case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "Q5_K - Medium";
         case LLAMA_FTYPE_MOSTLY_Q6_K:   return "Q6_K";
-        case LLAMA_FTYPE_MOSTLY_IQ2_XXS:return "IQ2_XSS - 2.0625 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ2_XXS:return "IQ2_XXS - 2.0625 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ2_XS: return "IQ2_XS - 2.3125 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ2_S:  return "IQ2_S - 2.5 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ2_M:  return "IQ2_M - 2.7 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_XS: return "IQ3_XS - 3.3 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XXS - 3.0625 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ1_S  :return "IQ1_S - 1.5625 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ1_M  :return "IQ1_M - 1.75 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ4_XS: return "IQ4_XS - 4.25 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_S:  return "IQ3_S - 3.4375 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_M:  return "IQ3_S mix - 3.66 bpw";
 
         default: return "unknown, may not work";
     }
@@ -2602,17 +3525,27 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
 
 static const char * llama_model_type_name(e_model type) {
     switch (type) {
+        case MODEL_22M:    return "22M";
+        case MODEL_33M:    return "33M";
+        case MODEL_109M:   return "109M";
+        case MODEL_137M:   return "137M";
+        case MODEL_0_5B:   return "0.5B";
         case MODEL_1B:     return "1B";
+        case MODEL_2B:     return "2B";
         case MODEL_3B:     return "3B";
         case MODEL_7B:     return "7B";
         case MODEL_8B:     return "8B";
         case MODEL_13B:    return "13B";
+        case MODEL_14B:    return "14B";
         case MODEL_15B:    return "15B";
+        case MODEL_20B:    return "20B";
         case MODEL_30B:    return "30B";
         case MODEL_34B:    return "34B";
+        case MODEL_35B:    return "35B";
         case MODEL_40B:    return "40B";
         case MODEL_65B:    return "65B";
         case MODEL_70B:    return "70B";
+        case MODEL_314B:   return "314B";
         case MODEL_SMALL:  return "0.1B";
         case MODEL_MEDIUM: return "0.4B";
         case MODEL_LARGE:  return "0.8B";
@@ -2621,6 +3554,16 @@ static const char * llama_model_type_name(e_model type) {
     }
 }
 
+static const char * llama_model_vocab_type_name(enum llama_vocab_type type){
+    switch (type) {
+        case LLAMA_VOCAB_TYPE_NONE: return "no vocab";
+        case LLAMA_VOCAB_TYPE_SPM:  return "SPM";
+        case LLAMA_VOCAB_TYPE_BPE:  return "BPE";
+        case LLAMA_VOCAB_TYPE_WPM:  return "WPM";
+        default:                    return "unknown";
+    }
+}
+
 static void llm_load_arch(llama_model_loader & ml, llama_model & model) {
     model.arch = ml.get_arch();
     if (model.arch == LLM_ARCH_UNKNOWN) {
@@ -2632,7 +3575,7 @@ static void llm_load_hparams(
         llama_model_loader & ml,
         llama_model & model) {
     auto & hparams = model.hparams;
-    const gguf_context * ctx = ml.ctx_gguf;
+    const gguf_context * ctx = ml.meta;
 
     // get metadata as string
     for (int i = 0; i < gguf_get_n_kv(ctx); i++) {
@@ -2649,14 +3592,14 @@ static void llm_load_hparams(
     ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);
 
     // get hparams kv
-    ml.get_arr_n(LLM_KV_TOKENIZER_LIST,       hparams.n_vocab);
-    ml.get_key  (LLM_KV_CONTEXT_LENGTH,       hparams.n_ctx_train);
-    ml.get_key  (LLM_KV_EMBEDDING_LENGTH,     hparams.n_embd);
-    ml.get_key  (LLM_KV_FEED_FORWARD_LENGTH,  hparams.n_ff);
-    ml.get_key  (LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head);
-    ml.get_key  (LLM_KV_BLOCK_COUNT,          hparams.n_layer);
-    ml.get_key  (LLM_KV_EXPERT_COUNT,         hparams.n_expert,      false);
-    ml.get_key  (LLM_KV_EXPERT_USED_COUNT,    hparams.n_expert_used, false);
+    ml.get_key(LLM_KV_VOCAB_SIZE,           hparams.n_vocab,       false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab);
+    ml.get_key(LLM_KV_CONTEXT_LENGTH,       hparams.n_ctx_train);
+    ml.get_key(LLM_KV_EMBEDDING_LENGTH,     hparams.n_embd);
+    ml.get_key(LLM_KV_FEED_FORWARD_LENGTH,  hparams.n_ff);
+    ml.get_key(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head);
+    ml.get_key(LLM_KV_BLOCK_COUNT,          hparams.n_layer);
+    ml.get_key(LLM_KV_EXPERT_COUNT,         hparams.n_expert,      false);
+    ml.get_key(LLM_KV_EXPERT_USED_COUNT,    hparams.n_expert_used, false);
 
     GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS);
     GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert);
@@ -2684,7 +3627,7 @@ static void llm_load_hparams(
     std::string rope_scaling("linear");
     ml.get_key(LLM_KV_ROPE_SCALING_TYPE, rope_scaling, false);
     hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling);
-    GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_UNSPECIFIED);
+    GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED);
 
     // rope_freq_scale (inverse of the kv) is optional
     float ropescale = 0.0f;
@@ -2696,7 +3639,7 @@ static void llm_load_hparams(
 
     // sanity check for n_rot (optional)
     {
-        hparams.n_rot = hparams.n_embd / hparams.n_head;
+        hparams.n_rot = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head;
 
         ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
 
@@ -2709,10 +3652,10 @@ static void llm_load_hparams(
         // gpt-j n_rot = rotary_dim
     }
 
-    hparams.n_embd_head_k = hparams.n_embd / hparams.n_head;
+    hparams.n_embd_head_k = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head;
     ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false);
 
-    hparams.n_embd_head_v = hparams.n_embd / hparams.n_head;
+    hparams.n_embd_head_v = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head;
     ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
 
     // arch-specific KVs
@@ -2732,6 +3675,24 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_MINICPM:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 40: model.type = e_model::MODEL_2B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_GROK:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 64: model.type = e_model::MODEL_314B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_FALCON:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@@ -2750,6 +3711,11 @@ static void llm_load_hparams(
                     case 40: model.type = e_model::MODEL_13B; break;
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
+
+                if (model.type == e_model::MODEL_13B) {
+                    // TODO: become GGUF KV parameter
+                    hparams.f_max_alibi_bias = 8.0f;
+                }
             } break;
         case LLM_ARCH_STARCODER:
             {
@@ -2777,6 +3743,41 @@ static void llm_load_hparams(
                     case 32: model.type = e_model::MODEL_1B; break;
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
+
+                // TODO: become GGUF KV parameter
+                hparams.f_max_alibi_bias = 8.0f;
+            } break;
+        case LLM_ARCH_BERT:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
+                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
+                ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
+                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type, false);
+
+                switch (hparams.n_layer) {
+                    case 3:
+                        model.type = e_model::MODEL_17M; break; // bge-micro
+                    case 6:
+                        model.type = e_model::MODEL_22M; break; // MiniLM-L6
+                    case 12:
+                        switch (hparams.n_embd) {
+                            case 384: model.type = e_model::MODEL_33M; break; // MiniLM-L12, bge-small
+                            case 768: model.type = e_model::MODEL_109M; break; // bge-base
+                        } break;
+                    case 24:
+                        model.type = e_model::MODEL_335M; break; // bge-large
+                }
+            } break;
+        case LLM_ARCH_NOMIC_BERT:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
+                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
+                ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
+                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type);
+
+                if (hparams.n_layer == 12 && hparams.n_embd == 768) {
+                    model.type = e_model::MODEL_137M;
+                }
             } break;
         case LLM_ARCH_BLOOM:
             {
@@ -2790,11 +3791,12 @@ static void llm_load_hparams(
                             case 4096: model.type = e_model::MODEL_7B; break;
                         } break;
                 }
+
+                // TODO: become GGUF KV parameter
+                hparams.f_max_alibi_bias = 8.0f;
             } break;
         case LLM_ARCH_MPT:
             {
-                hparams.f_clamp_kqv = 0.0f;
-
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,  hparams.f_norm_eps);
                 ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV,      hparams.f_clamp_kqv, false);
                 ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
@@ -2810,6 +3812,7 @@ static void llm_load_hparams(
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
 
                 switch (hparams.n_layer) {
+                    case 24: model.type = e_model::MODEL_1B; break;
                     case 32: model.type = e_model::MODEL_3B; break;
                     default: model.type = e_model::MODEL_UNKNOWN;
                }
@@ -2824,11 +3827,23 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_QWEN2:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                switch (hparams.n_layer) {
+                    case 24: model.type = hparams.n_embd == 1024 ? e_model::MODEL_0_5B : e_model::MODEL_1B; break;
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 40: model.type = hparams.n_head == 20 ? e_model::MODEL_4B : e_model::MODEL_13B; break;
+                    case 80: model.type = e_model::MODEL_70B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_PHI2:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
 
                 switch (hparams.n_layer) {
+                    case 24: model.type = e_model::MODEL_1B; break;
                     case 32: model.type = e_model::MODEL_3B; break;
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
@@ -2853,11 +3868,111 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_CODESHELL:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                switch (hparams.n_layer) {
+                    case 42: model.type = e_model::MODEL_SMALL; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_ORION:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
 
+                switch (hparams.n_layer) {
+                    case 40: model.type = e_model::MODEL_14B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_INTERNLM2:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 48: model.type = e_model::MODEL_20B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_GEMMA:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 18: model.type = e_model::MODEL_2B; break;
+                    case 28: model.type = e_model::MODEL_7B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+               }
+            } break;
+        case LLM_ARCH_STARCODER2:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                switch (hparams.n_layer) {
+                    case 30: model.type = e_model::MODEL_3B; break;
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 40: model.type = e_model::MODEL_15B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_MAMBA:
+            {
+                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
+                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
+                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
+                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                    case 24:
+                        switch (hparams.n_embd) {
+                            case 768: model.type = e_model::MODEL_SMALL; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 48:
+                        switch (hparams.n_embd) {
+                            case 1024: model.type = e_model::MODEL_MEDIUM; break;
+                            case 1536: model.type = e_model::MODEL_LARGE; break;
+                            case 2048: model.type = e_model::MODEL_XL; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 64:
+                        switch (hparams.n_embd) {
+                            case 2560: model.type = e_model::MODEL_3B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_XVERSE:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                switch (hparams.n_layer) {
+                    case 32: model.type = e_model::MODEL_7B; break;
+                    case 40: model.type = e_model::MODEL_13B; break;
+                    case 80: model.type = e_model::MODEL_65B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_COMMAND_R:
+            {
+                ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                switch (hparams.n_layer) {
+                    case 40: model.type = e_model::MODEL_35B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
         default: (void)0;
     }
 
     model.ftype = ml.ftype;
+
+    if (hparams.f_max_alibi_bias > 0.0f) {
+        hparams.need_kq_pos = true;
+    }
+
+    hparams.rope_type = llama_rope_type(&model);
 }
 
 // TODO: This should probably be in llama.h
@@ -2869,34 +3984,29 @@ static void llm_load_vocab(
         llama_model & model) {
     auto & vocab = model.vocab;
 
-    struct gguf_context * ctx = ml.ctx_gguf;
+    struct gguf_context * ctx = ml.meta;
 
     const auto kv = LLM_KV(model.arch);
 
-    const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str());
-    if (token_idx == -1) {
-        throw std::runtime_error("cannot find tokenizer vocab in model file\n");
-    }
-
-    const float * scores = nullptr;
-    const int score_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_SCORES).c_str());
-    if (score_idx != -1) {
-        scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
-    }
-
-    const int * toktypes = nullptr;
-    const int toktype_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str());
-    if (toktype_idx != -1) {
-        toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
-    }
-
     // determine vocab type
     {
         std::string tokenizer_name;
 
         ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_name);
 
-        if (tokenizer_name == "llama") {
+        if (tokenizer_name == "no_vocab") {
+            vocab.type = LLAMA_VOCAB_TYPE_NONE;
+
+            // default special tokens
+            vocab.special_bos_id = -1;
+            vocab.special_eos_id = -1;
+            vocab.special_unk_id = -1;
+            vocab.special_sep_id = -1;
+            vocab.special_pad_id = -1;
+            vocab.linefeed_id    = -1;
+
+            return;
+        } else if (tokenizer_name == "llama") {
             vocab.type = LLAMA_VOCAB_TYPE_SPM;
 
             // default special tokens
@@ -2905,6 +4015,11 @@ static void llm_load_vocab(
             vocab.special_unk_id = 0;
             vocab.special_sep_id = -1;
             vocab.special_pad_id = -1;
+
+            const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
+            if (add_space_prefix_keyidx != -1) {
+                vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
+            } // The default value of add_space_prefix is true.
         } else if (tokenizer_name == "gpt2") {
             vocab.type = LLAMA_VOCAB_TYPE_BPE;
 
@@ -2918,7 +4033,7 @@ static void llm_load_vocab(
 
             for (int i = 0; i < n_merges; i++) {
                 const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
-                GGML_ASSERT(codepoints_from_utf8(word).size() > 0);
+                GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
 
                 std::string first;
                 std::string second;
@@ -2939,6 +4054,16 @@ static void llm_load_vocab(
             vocab.special_unk_id = -1;
             vocab.special_sep_id = -1;
             vocab.special_pad_id = -1;
+        } else if (tokenizer_name == "bert") {
+            vocab.type = LLAMA_VOCAB_TYPE_WPM;
+
+            // default special tokens
+            vocab.special_bos_id = 101;
+            vocab.special_eos_id = 102;
+            vocab.special_unk_id = 100;
+            vocab.special_sep_id = -1;
+            vocab.special_pad_id = -1;
+            vocab.add_space_prefix = false;
         } else {
             LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_name.c_str());
             LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__);
@@ -2947,13 +4072,30 @@ static void llm_load_vocab(
         }
     }
 
+    const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str());
+    if (token_idx == -1) {
+        throw std::runtime_error("cannot find tokenizer vocab in model file\n");
+    }
+
+    const float * scores = nullptr;
+    const int score_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_SCORES).c_str());
+    if (score_idx != -1) {
+        scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
+    }
+
+    const int * toktypes = nullptr;
+    const int toktype_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str());
+    if (toktype_idx != -1) {
+        toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
+    }
+
     const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);
 
     vocab.id_to_token.resize(n_vocab);
 
     for (uint32_t i = 0; i < n_vocab; i++) {
         std::string word = gguf_get_arr_str(ctx, token_idx, i);
-        GGML_ASSERT(codepoints_from_utf8(word).size() > 0);
+        GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
 
         vocab.token_to_id[word] = i;
 
@@ -2966,9 +4108,16 @@ static void llm_load_vocab(
 
     // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
     if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
-        vocab.linefeed_id = llama_byte_to_token(vocab, '\n');
+        try {
+            vocab.linefeed_id = llama_byte_to_token(vocab, '\n');
+        } catch (const std::exception & e) {
+            LLAMA_LOG_WARN("%s: SPM vocabulary, but newline token not found: %s! Using special_pad_id instead.", __func__, e.what());
+            vocab.linefeed_id = vocab.special_pad_id;
+        }
+    } else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) {
+        vocab.linefeed_id = vocab.special_pad_id;
     } else {
-        const std::vector<int> ids = llama_tokenize_internal(vocab, "\u010A", false);
+        const std::vector<int> ids = llama_tokenize_internal(vocab, "\xC4\x8A", false); // U+010A
         GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
         vocab.linefeed_id = ids[0];
     }
@@ -3112,12 +4261,12 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     const auto & hparams = model.hparams;
     const auto & vocab   = model.vocab;
 
-    const auto rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
+    const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
 
     // hparams
     LLAMA_LOG_INFO("%s: format           = %s\n",     __func__, llama_file_version_name(ml.fver));
-    LLAMA_LOG_INFO("%s: arch             = %s\n",     __func__, LLM_ARCH_NAMES.at(model.arch).c_str());
-    LLAMA_LOG_INFO("%s: vocab type       = %s\n",     __func__, vocab.type == LLAMA_VOCAB_TYPE_SPM ? "SPM" : "BPE"); // TODO: fix
+    LLAMA_LOG_INFO("%s: arch             = %s\n",     __func__, LLM_ARCH_NAMES.at(model.arch));
+    LLAMA_LOG_INFO("%s: vocab type       = %s\n",     __func__, llama_model_vocab_type_name(vocab.type));
     LLAMA_LOG_INFO("%s: n_vocab          = %u\n",     __func__, hparams.n_vocab);
     LLAMA_LOG_INFO("%s: n_merges         = %u\n",     __func__, (int) vocab.bpe_ranks.size());
     LLAMA_LOG_INFO("%s: n_ctx_train      = %u\n",     __func__, hparams.n_ctx_train);
@@ -3135,17 +4284,33 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     LLAMA_LOG_INFO("%s: f_norm_rms_eps   = %.1e\n",   __func__, hparams.f_norm_rms_eps);
     LLAMA_LOG_INFO("%s: f_clamp_kqv      = %.1e\n",   __func__, hparams.f_clamp_kqv);
     LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n",   __func__, hparams.f_max_alibi_bias);
+    LLAMA_LOG_INFO("%s: f_logit_scale    = %.1e\n",   __func__, hparams.f_logit_scale);
     LLAMA_LOG_INFO("%s: n_ff             = %u\n",     __func__, hparams.n_ff);
     LLAMA_LOG_INFO("%s: n_expert         = %u\n",     __func__, hparams.n_expert);
     LLAMA_LOG_INFO("%s: n_expert_used    = %u\n",     __func__, hparams.n_expert_used);
-    LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type.c_str());
+    LLAMA_LOG_INFO("%s: causal attn      = %d\n",     __func__, hparams.causal_attn);
+    LLAMA_LOG_INFO("%s: pooling type     = %d\n",     __func__, hparams.pooling_type);
+    LLAMA_LOG_INFO("%s: rope type        = %d\n",     __func__, hparams.rope_type);
+    LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type);
     LLAMA_LOG_INFO("%s: freq_base_train  = %.1f\n",   __func__, hparams.rope_freq_base_train);
     LLAMA_LOG_INFO("%s: freq_scale_train = %g\n",     __func__, hparams.rope_freq_scale_train);
     LLAMA_LOG_INFO("%s: n_yarn_orig_ctx  = %u\n",     __func__, hparams.n_yarn_orig_ctx);
     LLAMA_LOG_INFO("%s: rope_finetuned   = %s\n",     __func__, hparams.rope_finetuned ? "yes" : "unknown");
+    LLAMA_LOG_INFO("%s: ssm_d_conv       = %u\n",     __func__, hparams.ssm_d_conv);
+    LLAMA_LOG_INFO("%s: ssm_d_inner      = %u\n",     __func__, hparams.ssm_d_inner);
+    LLAMA_LOG_INFO("%s: ssm_d_state      = %u\n",     __func__, hparams.ssm_d_state);
+    LLAMA_LOG_INFO("%s: ssm_dt_rank      = %u\n",     __func__, hparams.ssm_dt_rank);
     LLAMA_LOG_INFO("%s: model type       = %s\n",     __func__, llama_model_type_name(model.type));
     LLAMA_LOG_INFO("%s: model ftype      = %s\n",     __func__, llama_model_ftype_name(model.ftype).c_str());
-    LLAMA_LOG_INFO("%s: model params     = %.2f B\n", __func__, ml.n_elements*1e-9);
+    if (ml.n_elements >= 1e12) {
+        LLAMA_LOG_INFO("%s: model params     = %.2f T\n", __func__, ml.n_elements*1e-12);
+    } else if (ml.n_elements >= 1e9) {
+        LLAMA_LOG_INFO("%s: model params     = %.2f B\n", __func__, ml.n_elements*1e-9);
+    } else if (ml.n_elements >= 1e6) {
+        LLAMA_LOG_INFO("%s: model params     = %.2f M\n", __func__, ml.n_elements*1e-6);
+    } else {
+        LLAMA_LOG_INFO("%s: model params     = %.2f K\n", __func__, ml.n_elements*1e-3);
+    }
     if (ml.n_bytes < GiB) {
         LLAMA_LOG_INFO("%s: model size       = %.2f MiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0,        ml.n_bytes*8.0/ml.n_elements);
     } else {
@@ -3169,6 +4334,7 @@ static bool llm_load_tensors(
         llama_model_loader & ml,
         llama_model & model,
         int n_gpu_layers,
+        enum llama_split_mode split_mode,
         int main_gpu,
         const float * tensor_split,
         bool use_mlock,
@@ -3176,702 +4342,1078 @@ static bool llm_load_tensors(
         void * progress_callback_user_data) {
     model.t_start_us = ggml_time_us();
 
-    auto & ctx     = model.ctx;
     auto & hparams = model.hparams;
 
+    model.split_mode   = split_mode;
+    model.main_gpu     = main_gpu;
     model.n_gpu_layers = n_gpu_layers;
 
-    size_t ctx_size = ggml_tensor_overhead() * ml.n_tensors;
+    const int64_t n_layer     = hparams.n_layer;
+    const int64_t i_gpu_start = std::max((int64_t) hparams.n_layer - n_gpu_layers, (int64_t) 0);
+    bool use_mmap_buffer = true;
 
-    LLAMA_LOG_INFO("%s: ggml ctx size       = %7.2f MiB\n", __func__, ctx_size/1024.0/1024.0);
+    // there is very little benefit to offloading the input layer, so always keep it on the CPU
+    model.buft_input = llama_default_buffer_type_cpu(true);
+    //model.buft_input = llama_default_buffer_type_offload(main_gpu);
 
-    // create the ggml context
-    {
+    model.buft_layer.resize(n_layer);
+
+    // assign cpu layers
+    for (int64_t i = 0; i < i_gpu_start; ++i) {
+        model.buft_layer[i] = llama_default_buffer_type_cpu(true);
+    }
+
+    if (split_mode == LLAMA_SPLIT_MODE_LAYER) {
+        // calculate the split points
+        int device_count = llama_get_device_count();
+        bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; });
+        std::vector<float> splits(device_count);
+        if (all_zero) {
+            // default split, by free memory
+            for (int i = 0; i < device_count; ++i) {
+                splits[i] = llama_get_device_memory(i);
+            }
+        } else {
+            std::copy(tensor_split, tensor_split + device_count, splits.begin());
+        }
+
+        // sum and normalize the splits to get the split points
+        float split_sum = 0.0f;
+        for (int i = 0; i < device_count; ++i) {
+            split_sum += splits[i];
+            splits[i] = split_sum;
+        }
+        for (int i = 0; i < device_count; ++i) {
+            splits[i] /= split_sum;
+        }
+
+        // assign the repeating layers to the devices according to the splits
+        int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1);
+        for (int64_t i = i_gpu_start; i < n_layer; ++i) {
+            int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin();
+            model.buft_layer[i] = llama_default_buffer_type_offload(layer_gpu);
+        }
+        // assign the output layer
+        if (n_gpu_layers > n_layer) {
+            int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits.begin();
+            model.buft_output = llama_default_buffer_type_offload(layer_gpu);
+        } else {
+            model.buft_output = llama_default_buffer_type_cpu(true);
+        }
+    } else {
+        ggml_backend_buffer_type_t split_buft;
+        if (split_mode == LLAMA_SPLIT_MODE_ROW) {
+            split_buft = llama_default_buffer_type_split(main_gpu, tensor_split);
+        } else {
+            // LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_LAYER in backends where it is not supported
+            split_buft = llama_default_buffer_type_offload(main_gpu);
+        }
+        // assign the repeating layers
+        for (int64_t i = i_gpu_start; i < n_layer; ++i) {
+            model.buft_layer[i] = {
+                split_buft,
+                llama_default_buffer_type_offload(main_gpu)
+            };
+        }
+        // assign the output layer
+        if (n_gpu_layers > n_layer) {
+            model.buft_output = {
+                split_buft,
+                llama_default_buffer_type_offload(main_gpu)
+            };
+        } else {
+            model.buft_output = llama_default_buffer_type_cpu(true);
+        }
+    }
+
+    // count used buffer types
+    std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
+    buft_layer_count[model.buft_input.buft]++;
+    buft_layer_count[model.buft_input.buft_matrix]++;
+    buft_layer_count[model.buft_output.buft]++;
+    buft_layer_count[model.buft_output.buft_matrix]++;
+    for (int64_t i = 0; i < n_layer; ++i) {
+        buft_layer_count[model.buft_layer[i].buft]++;
+        buft_layer_count[model.buft_layer[i].buft_matrix]++;
+    }
+
+    // create one context per buffer type
+    size_t ctx_size = ggml_tensor_overhead()*(ml.n_tensors + 1); // +1 for models where tok_embd is duplicated as output
+
+    // for moe merged tensors
+    ctx_size += ggml_tensor_overhead()*hparams.n_expert*n_layer;
+
+    std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
+    for (auto & it : buft_layer_count) {
         struct ggml_init_params params = {
             /*.mem_size   =*/ ctx_size,
             /*.mem_buffer =*/ NULL,
             /*.no_alloc   =*/ true,
         };
-
-        model.ctx = ggml_init(params);
-        if (!model.ctx) {
-            throw std::runtime_error(format("ggml_init() failed"));
+        ggml_context * ctx = ggml_init(params);
+        if (!ctx) {
+            throw std::runtime_error(format("failed to create context"));
         }
+        ctx_map[it.first] = ctx;
+        model.ctxs.push_back(ctx);
     }
 
-    (void) main_gpu;
-
-    enum ggml_backend_type llama_backend_offload       = GGML_BACKEND_CPU;
-    enum ggml_backend_type llama_backend_offload_split = GGML_BACKEND_CPU;
-
-#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
-    if (ggml_cublas_loaded()) {
-        LLAMA_LOG_INFO("%s: using " GGML_CUDA_NAME " for GPU acceleration\n", __func__);
-        ggml_cuda_set_main_device(main_gpu);
-
-        llama_backend_offload       = GGML_BACKEND_GPU;
-        llama_backend_offload_split = GGML_BACKEND_GPU_SPLIT;
-    }
-#elif defined(GGML_USE_CLBLAST)
-        LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__);
-        llama_backend_offload       = GGML_BACKEND_GPU;
-        llama_backend_offload_split = GGML_BACKEND_GPU;
-#endif
+    LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MiB\n", __func__, model.ctxs.size()*ctx_size/1024.0/1024.0);
 
     // create tensors for the weights
     {
         const int64_t n_embd       = hparams.n_embd;
         const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
         const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
-        const int64_t n_layer      = hparams.n_layer;
+        const int64_t n_embd_gqa   = n_embd_v_gqa;
         const int64_t n_vocab      = hparams.n_vocab;
+        const int64_t n_vocab_type = hparams.n_vocab_type;
+        const int64_t n_ff         = hparams.n_ff;
+        const int64_t n_expert     = hparams.n_expert;
+
+        if (n_expert > 0 && hparams.n_expert_used == 0) {
+            throw std::runtime_error("model has expert layers but no expert layers are used");
+        }
+
+        GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
+
+        ggml_context * ctx_input        = ctx_map.at(model.buft_input.buft);
+        ggml_context * ctx_output       = ctx_map.at(model.buft_output.buft);
+        ggml_context * ctx_output_split = ctx_map.at(model.buft_output.buft_matrix);
+        auto ctx_for_layer              = [&](int i) { return ctx_map.at(model.buft_layer[i].buft); };
+        auto ctx_for_layer_split        = [&](int i) { return ctx_map.at(model.buft_layer[i].buft_matrix); };
+
+        model.layers.resize(n_layer);
 
         const auto tn = LLM_TN(model.arch);
         switch (model.arch) {
             case LLM_ARCH_LLAMA:
             case LLM_ARCH_REFACT:
+            case LLM_ARCH_MINICPM:
                 {
-                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
 
                     // output
                     {
-                        ggml_backend_type backend_norm;
-                        ggml_backend_type backend_output;
-
-                        if (n_gpu_layers > int(n_layer)) {
-                            backend_norm   = llama_backend_offload;
-                            backend_output = llama_backend_offload_split;
-                        } else {
-                            backend_norm   = GGML_BACKEND_CPU;
-                            backend_output = GGML_BACKEND_CPU;
+                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        if (model.arch != LLM_ARCH_MINICPM){
+                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
+                            // if output is NULL, init from the input tok embed
+                            if (model.output == NULL) {
+                                model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                                ml.n_created--; // artificial tensor
+                                ml.size_data += ggml_nbytes(model.output);
+                            }
                         }
-
-                        model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
-                        model.output      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff      = hparams.n_ff;
-                    const int64_t n_embd_gqa = n_embd_v_gqa;
-                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
-                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
-
-                    const int i_gpu_start = n_layer - n_gpu_layers;
-
-                    model.layers.resize(n_layer);
-
-                    for (uint32_t i = 0; i < n_layer; ++i) {
-                        const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
-                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
                         auto & layer = model.layers[i];
 
-                        layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
 
-                        layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd},     backend_split);
-                        layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, backend_split);
-                        layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, backend_split);
-                        layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},     backend_split);
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
 
                         // optional bias tensors
-                        layer.bq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     backend, false);
-                        layer.bk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, backend, false);
-                        layer.bv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, backend, false);
-                        layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd},     backend, false);
+                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     false);
+                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, false);
+                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, false);
+                        layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd},     false);
 
-                        layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
 
-                        layer.ffn_gate_inp = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd}, backend, false);
-
-                        if (layer.ffn_gate_inp == nullptr) {
-                            GGML_ASSERT(hparams.n_expert      == 0);
-                            GGML_ASSERT(hparams.n_expert_used == 0);
-
-                            layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, backend_split);
-                            layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
-                            layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+                        if (n_expert == 0) {
+                            layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                            layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                            layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                         } else {
-                            GGML_ASSERT(hparams.n_expert      > 0);
-                            GGML_ASSERT(hparams.n_expert_used > 0);
+                            layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
 
-                            // MoE branch
-                            for (uint32_t x = 0; x < hparams.n_expert; ++x) {
-                                layer.ffn_gate_exp[x] = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), {n_embd,   n_ff}, backend_split);
-                                layer.ffn_down_exp[x] = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), {  n_ff, n_embd}, backend_split);
-                                layer.ffn_up_exp[x]   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP_EXP,   "weight", i, x), {n_embd,   n_ff}, backend_split);
+                            layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, false);
+                            if (layer.ffn_gate_exps) {
+                                layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert});
+                                layer.ffn_up_exps   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert});
+                            } else {
+                                // merge split expert into a single tensor for compatibility with older models
+                                // requires disabling mmap
+                                use_mmap_buffer = false;
+
+                                ggml_type type_gate = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, 0).c_str())->type;
+                                ggml_type type_down = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, 0).c_str())->type;
+                                ggml_type type_up   = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_UP_EXP,   "weight", i, 0).c_str())->type;
+
+                                layer.ffn_gate_exps = ggml_new_tensor_3d(ctx_split, type_gate, n_embd,   n_ff, n_expert);
+                                layer.ffn_down_exps = ggml_new_tensor_3d(ctx_split, type_down,   n_ff, n_embd, n_expert);
+                                layer.ffn_up_exps   = ggml_new_tensor_3d(ctx_split, type_up,   n_embd,   n_ff, n_expert);
+
+                                ggml_set_name(layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i).c_str());
+                                ggml_set_name(layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i).c_str());
+                                ggml_set_name(layer.ffn_up_exps,   tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i).c_str());
+
+                                for (uint32_t x = 0; x < n_expert; ++x) {
+                                    // the individual experts are loaded into a view of the merged tensor
+                                    ml.create_tensor_as_view(ctx_split, layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_gate_exps->nb[2]*x);
+                                    ml.create_tensor_as_view(ctx_split, layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd }, layer.ffn_down_exps->nb[2]*x);
+                                    ml.create_tensor_as_view(ctx_split, layer.ffn_up_exps,   tn(LLM_TENSOR_FFN_UP_EXP,   "weight", i, x), { n_embd, n_ff }, layer.ffn_up_exps->nb[2]*x);
+                                }
                             }
                         }
                     }
                 } break;
-            case LLM_ARCH_BAICHUAN:
+            case LLM_ARCH_GROK:
                 {
-                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
-                    {
-                        ggml_backend_type backend_norm;
-                        ggml_backend_type backend_output;
-
-                        if (n_gpu_layers > int(n_layer)) {
-                            backend_norm   = llama_backend_offload;
-                            backend_output = llama_backend_offload_split;
-                        } else {
-                            backend_norm   = GGML_BACKEND_CPU;
-                            backend_output = GGML_BACKEND_CPU;
-                        }
-
-                        model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
-                        model.output      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
+                    if (n_expert == 0) {
+                        throw std::runtime_error("Grok model cannot have zero experts");
                     }
 
-                    const uint32_t n_ff      = hparams.n_ff;
-                    const int64_t n_embd_gqa = n_embd_v_gqa;
-                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
-                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
 
-                    const int i_gpu_start = n_layer - n_gpu_layers;
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, false);
+                        // if output is NULL, init from the input tok embed
+                        if (model.output == NULL) {
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                            ml.n_created--; // artificial tensor
+                            ml.size_data += ggml_nbytes(model.output);
+                        }
+                    }
 
-                    model.layers.resize(n_layer);
-
-                    for (uint32_t i = 0; i < n_layer; ++i) {
-                        const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
-                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
                         auto & layer = model.layers[i];
 
-                        layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
 
-                        layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd},     backend_split);
-                        layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, backend_split);
-                        layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, backend_split);
-                        layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},     backend_split);
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
 
-                        layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
+                        layer.attn_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
 
-                        layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, backend_split);
-                        layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
-                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+
+                        layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
+
+                        layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false);
+                        if (layer.ffn_gate_exps) {
+                            layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert});
+                            layer.ffn_up_exps   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert});
+                        } else {
+                            // merge split expert into a single tensor for compatibility with older models
+                            // requires disabling mmap
+                            use_mmap_buffer = false;
+
+                            ggml_type type_gate = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, 0).c_str())->type;
+                            ggml_type type_down = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, 0).c_str())->type;
+                            ggml_type type_up   = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_UP_EXP,   "weight", i, 0).c_str())->type;
+
+                            layer.ffn_gate_exps = ggml_new_tensor_3d(ctx_split, type_gate, n_embd,   n_ff, n_expert);
+                            layer.ffn_down_exps = ggml_new_tensor_3d(ctx_split, type_down,   n_ff, n_embd, n_expert);
+                            layer.ffn_up_exps   = ggml_new_tensor_3d(ctx_split, type_up,   n_embd,   n_ff, n_expert);
+
+                            ggml_set_name(layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i).c_str());
+                            ggml_set_name(layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i).c_str());
+                            ggml_set_name(layer.ffn_up_exps,   tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i).c_str());
+
+                            for (uint32_t x = 0; x < n_expert; ++x) {
+                                // the individual experts are loaded into a view of the merged tensor
+                                ml.create_tensor_as_view(ctx_split, layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_gate_exps->nb[2]*x);
+                                ml.create_tensor_as_view(ctx_split, layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd }, layer.ffn_down_exps->nb[2]*x);
+                                ml.create_tensor_as_view(ctx_split, layer.ffn_up_exps,   tn(LLM_TENSOR_FFN_UP_EXP,   "weight", i, x), { n_embd, n_ff }, layer.ffn_up_exps->nb[2]*x);
+                            }
+                        }
+
+                        layer.layer_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
+                    }
+                } break;
+            case LLM_ARCH_BAICHUAN:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                     }
                 } break;
             case LLM_ARCH_FALCON:
                 {
-                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
 
                     // output
                     {
-                        ggml_backend_type backend_norm;
-                        ggml_backend_type backend_output;
+                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
 
-                        if (n_gpu_layers > int(n_layer)) {
-                            backend_norm   = llama_backend_offload;
-                            backend_output = llama_backend_offload_split;
-                        } else {
-                            backend_norm   = GGML_BACKEND_CPU;
-                            backend_output = GGML_BACKEND_CPU;
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, false);
+                        if (!model.output) {
+                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU
+                            ml.n_created--; // artificial tensor
+                            ml.size_data += ggml_nbytes(model.output);
                         }
-
-                        model.output_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
-                        model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd},          backend_norm);
-                        model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff      = hparams.n_ff;
-                    const int64_t n_embd_gqa = n_embd_v_gqa;
-                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
-                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
-
-                    const int i_gpu_start = n_layer - n_gpu_layers;
-
-                    model.layers.resize(n_layer);
-
-                    for (uint32_t i = 0; i < n_layer; ++i) {
-                        const ggml_backend_type backend       = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
-                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
                         auto & layer = model.layers[i];
 
-                        layer.attn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, backend);
-                        layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, backend);
+                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});
 
-                        if (gguf_find_tensor(ml.ctx_gguf, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i).c_str()) >= 0) {
-                            layer.attn_norm_2   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, backend);
-                            layer.attn_norm_2_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, backend);
-                        }
+                        layer.attn_norm_2   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, false);
+                        layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, false);
 
-                        layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
-                        layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},                backend_split);
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
 
-                        layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
-                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                     }
                 } break;
             case LLM_ARCH_STARCODER:
                 {
-                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab},             GGML_BACKEND_CPU);
-                    model.pos_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"),   {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU);
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                    model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, hparams.n_ctx_train});
 
                     // output
                     {
-                        ggml_backend_type backend_norm;
-                        ggml_backend_type backend_output;
-
-                        if (n_gpu_layers > int(n_layer)) {
-                            backend_norm   = llama_backend_offload;
-                            backend_output = llama_backend_offload_split;
-                        } else {
-                            backend_norm   = GGML_BACKEND_CPU;
-                            backend_output = GGML_BACKEND_CPU;
-                        }
-
-                        model.output_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
-                        model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd},          backend_norm);
-                        model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
+                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                     }
 
-                    const uint32_t n_ff      = hparams.n_ff;
-                    const int64_t n_embd_gqa = n_embd_v_gqa;
-                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
-                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
-
-                    const int i_gpu_start = n_layer - n_gpu_layers;
-
-                    model.layers.resize(n_layer);
-
-                    for (uint32_t i = 0; i < n_layer; ++i) {
-                        const ggml_backend_type backend       = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
-                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
                         auto & layer = model.layers[i];
 
-                        layer.attn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, backend);
-                        layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, backend);
+                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});
 
-                        layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
-                        layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa},         backend);
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa});
 
-                        layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},   backend_split);
-                        layer.bo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd},           backend);
+                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});
 
-                        layer.ffn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
-                        layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, backend);
+                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});
 
-                        layer.ffn_down   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
-                        layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd},       backend);
+                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});
 
-                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, backend_split);
-                        layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "bias", i),           {n_ff}, backend);
+                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i),   {n_embd, n_ff});
+                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i),     {n_ff});
                     }
                 } break;
             case LLM_ARCH_PERSIMMON:
                 {
-                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"),  {n_embd, n_vocab}, GGML_BACKEND_CPU);
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"),  {n_embd, n_vocab});
 
                     {
-                        ggml_backend_type backend_norm;
-                        ggml_backend_type backend_output;
-
-                        if (n_gpu_layers > int(n_layer)) {
-                            backend_norm   = llama_backend_offload;
-                            backend_output = llama_backend_offload_split;
-                        } else {
-                            backend_norm   = GGML_BACKEND_CPU;
-                            backend_output = GGML_BACKEND_CPU;
-                        }
-
-                        model.output_norm    = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
-                        model.output_norm_b  = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd},          backend_norm);
-                        model.output         = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
+                        model.output_norm    = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output_norm_b  = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
+                        model.output         = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                     }
 
-                    const uint32_t n_ff      = hparams.n_ff;
-                    const int64_t n_embd_gqa = n_embd_v_gqa;
-                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
-                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
-                    const int i_gpu_start = n_layer - n_gpu_layers;
-                    model.layers.resize(n_layer);
-                    for (uint32_t i = 0; i < n_layer; ++i) {
-                        const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload;
-                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split;
                         auto & layer = model.layers[i];
-                        layer.attn_norm     = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, backend);
-                        layer.attn_norm_b   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "bias",   i), {n_embd}, backend);
-                        layer.wqkv          = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV,    "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
-                        layer.bqkv          = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV,    "bias",   i), {n_embd + 2*n_embd_gqa},         backend);
-                        layer.wo            = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT,    "weight", i), {n_embd, n_embd},   backend_split);
-                        layer.bo            = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT,    "bias",   i), {n_embd},           backend);
-                        layer.ffn_down      = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN,    "weight", i), {n_ff, n_embd}, backend_split);
-                        layer.ffn_down_b    = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN,    "bias",   i), {n_embd},       backend);
-                        layer.ffn_up        = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,      "weight", i), {n_embd,   n_ff}, backend_split);
-                        layer.ffn_up_b      = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,      "bias",   i), {n_ff},           backend);
-                        layer.ffn_norm      = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM,    "weight", i), {n_embd}, backend);
-                        layer.ffn_norm_b    = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM,    "bias",   i), {n_embd}, backend);
-                        layer.attn_q_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64}, backend);
-                        layer.attn_q_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {64}, backend);
-                        layer.attn_k_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64}, backend);
-                        layer.attn_k_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {64}, backend);
+
+                        layer.attn_norm     = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd});
+                        layer.attn_norm_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,   "bias",   i), {n_embd});
+
+                        layer.wqkv          = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV,    "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+                        layer.bqkv          = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV,    "bias",   i), {n_embd + 2*n_embd_gqa});
+
+                        layer.wo            = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT,    "weight", i), {n_embd, n_embd});
+                        layer.bo            = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT,    "bias",   i), {n_embd});
+
+                        layer.ffn_down      = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN,    "weight", i), {n_ff, n_embd});
+                        layer.ffn_down_b    = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN,    "bias",   i), {n_embd});
+
+                        layer.ffn_up        = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,      "weight", i), {n_embd, n_ff});
+                        layer.ffn_up_b      = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,      "bias",   i), {n_ff});
+
+                        layer.ffn_norm      = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM,    "weight", i), {n_embd});
+                        layer.ffn_norm_b    = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM,    "bias",   i), {n_embd});
+
+                        layer.attn_q_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64});
+                        layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {64});
+
+                        layer.attn_k_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64});
+                        layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {64});
+                    }
+                } break;
+            case LLM_ARCH_BERT:
+            case LLM_ARCH_NOMIC_BERT:
+                {
+                    model.tok_embd     = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab});
+                    model.type_embd    = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type});
+                    if (model.arch == LLM_ARCH_BERT) {
+                        model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,    "weight"), {n_embd, hparams.n_ctx_train});
+                    }
+
+                    model.tok_norm   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
+                    model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd});
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        if (model.arch == LLM_ARCH_BERT) {
+                            layer.wq   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                            layer.bq   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i),   {n_embd});
+
+                            layer.wk   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                            layer.bk   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i),   {n_embd_gqa});
+
+                            layer.wv   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                            layer.bv   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i),   {n_embd_gqa});
+                        } else {
+                            layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+                        }
+
+                        layer.wo              = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {n_embd, n_embd});
+
+                        layer.attn_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
+                        layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i),   {n_embd});
+
+                        layer.ffn_up          = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,        "weight", i), {n_embd, n_ff});
+                        layer.ffn_down        = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN,      "weight", i), {n_ff, n_embd});
+
+                        if (model.arch == LLM_ARCH_BERT) {
+                            layer.bo         = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});
+                            layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff});
+
+                            layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});
+                        } else {
+                            layer.ffn_gate   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                        }
+
+                        layer.layer_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
+                        layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i),   {n_embd});
                     }
                 } break;
             case LLM_ARCH_BLOOM:
                 {
-                    model.tok_embd   = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
-                    model.tok_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd},          GGML_BACKEND_CPU);
-                    model.tok_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd},          GGML_BACKEND_CPU);
+                    model.tok_embd   = ml.create_tensor(ctx_input,  tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab});
+                    model.tok_norm   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
+                    model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd});
 
                     // output
                     {
-                        ggml_backend_type backend_norm;
-                        ggml_backend_type backend_output;
-
-                        if (n_gpu_layers > int(n_layer)) {
-                            backend_norm   = llama_backend_offload;
-                            backend_output = llama_backend_offload_split;
-                        } else {
-                            backend_norm   = GGML_BACKEND_CPU;
-                            backend_output = GGML_BACKEND_CPU;
-                        }
-
-                        model.output_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
-                        model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd},          backend_norm);
-                        model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
+                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                     }
 
-                    const uint32_t n_ff      = hparams.n_ff;
-                    const int64_t n_embd_gqa = n_embd_v_gqa;
-                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
-                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
-
-                    const int i_gpu_start = n_layer - n_gpu_layers;
-
-                    model.layers.resize(n_layer);
-
-                    for (uint32_t i = 0; i < n_layer; ++i) {
-                        const ggml_backend_type backend       = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
-                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
                         auto & layer = model.layers[i];
 
-                        layer.attn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, backend);
-                        layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, backend);
+                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});
 
-                        layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
-                        layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa},         backend);
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa});
 
-                        layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},                backend_split);
-                        layer.bo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd},                        backend);
+                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});
 
-                        layer.ffn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
-                        layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, backend);
+                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});
 
-                        layer.ffn_down   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
-                        layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd},       backend);
+                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});
 
-                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
-                        layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff},           backend);
+                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff});
+                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff});
                     }
                 } break;
             case LLM_ARCH_MPT:
                 {
-                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                    model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, hparams.n_ctx_train}, false);
+
                     // output
                     {
-                        ggml_backend_type backend_norm;
-                        ggml_backend_type backend_output;
+                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, false);
 
-                        if (n_gpu_layers > int(n_layer)) {
-                            backend_norm   = llama_backend_offload;
-                            backend_output = llama_backend_offload_split;
-                        } else {
-                            backend_norm   = GGML_BACKEND_CPU;
-                            backend_output = GGML_BACKEND_CPU;
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, false);
+                        if (!model.output) {
+                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU
+                            ml.n_created--; // artificial tensor
+                            ml.size_data += ggml_nbytes(model.output);
                         }
-
-                        model.output_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
-                        model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
                     }
 
-                    const uint32_t n_ff      = hparams.n_ff;
-                    const int64_t n_embd_gqa = n_embd_v_gqa;
-                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
-                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
-
-                    const int i_gpu_start = n_layer - n_gpu_layers;
-
-                    model.layers.resize(n_layer);
-
-                    for (uint32_t i = 0; i < n_layer; ++i) {
-                        const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
-                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
                         auto & layer = model.layers[i];
 
-                        layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
-                        layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
-                        layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},                backend_split);
+                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, false);
 
-                        layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, false);
 
-                        layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
-                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, false);
+
+                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, false);
+
+                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, false);
+
+                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, false);
+
+                        layer.attn_q_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, false);
+                        layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {n_embd}, false);
+
+                        layer.attn_k_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, false);
+                        layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {n_embd}, false);
 
                         // AWQ ScaleActivation layer
-                        layer.ffn_act = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, backend, false);
+                        layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, false);
                     }
                 } break;
             case LLM_ARCH_STABLELM:
                 {
-                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
 
                     // output
                     {
-                        ggml_backend_type backend_norm;
-                        ggml_backend_type backend_output;
-
-                        if (n_gpu_layers > int(n_layer)) {
-                            backend_norm   = llama_backend_offload;
-                            backend_output = llama_backend_offload_split;
-                        } else {
-                            backend_norm   = GGML_BACKEND_CPU;
-                            backend_output = GGML_BACKEND_CPU;
-                        }
-
-                        model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd},          backend_norm);
-                        model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
-                        model.output      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
+                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
+                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                     }
 
-                    const uint32_t n_ff      = hparams.n_ff;
-                    const int64_t n_embd_gqa = n_embd_v_gqa;
-                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
-                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
-
-                    const int i_gpu_start = n_layer - n_gpu_layers;
-
-                    model.layers.resize(n_layer);
-
-                    for (uint32_t i = 0; i < n_layer; ++i) {
-                        /*
-                        llama_model_loader: - tensor    4:         blk.0.attn_output.weight f16      [  2560,  2560,     1,     1 ]
-                        */
-                        const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
-                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
                         auto & layer = model.layers[i];
 
-                        layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
-                        layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
+                        layer.attn_norm =   ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
 
-                        layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd},     backend_split);
-                        layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, backend_split);
-                        layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, backend_split);
-                        layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},     backend_split);
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
 
-                        layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
-                        layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend);
+                        // optional bias tensors, present in Stable LM 2 1.6B
+                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     false);
+                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, false);
+                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, false);
 
-                        layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, backend_split);
-                        layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
-                        layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});
+
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                     }
                 } break;
             case LLM_ARCH_QWEN:
                 {
-                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
                     {
-                        ggml_backend_type backend_norm;
-                        ggml_backend_type backend_output;
+                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                    }
 
-                        if (n_gpu_layers > int(n_layer)) {
-                            backend_norm   = llama_backend_offload;
-                            backend_output = llama_backend_offload_split;
-                        } else {
-                            backend_norm   = GGML_BACKEND_CPU;
-                            backend_output = GGML_BACKEND_CPU;
-                        }
-
-                        model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
-                        model.output      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
-                   }
-
-                    const uint32_t n_ff = hparams.n_ff / 2;
-
-                    const int i_gpu_start = n_layer - n_gpu_layers;
-
-                    model.layers.resize(n_layer);
-
-                    for (uint32_t i = 0; i < n_layer; ++i) {
-                        const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
-                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
                         auto & layer = model.layers[i];
 
-                        layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
 
-                        layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd * 3}, backend_split);
-                        layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd * 3},         backend);
-                        layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},     backend_split);
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd*3});
+                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd*3});
+                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
 
-                        layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
 
-                        layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, backend_split);
-                        layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
-                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff/2});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff/2, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff/2});
+                    }
+                } break;
+            case LLM_ARCH_QWEN2:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+
+                        // optional bias tensors
+                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd});
+                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa});
+                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa});
+
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                     }
                 } break;
             case LLM_ARCH_PHI2:
                 {
-                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
 
                     // output
                     {
-                        ggml_backend_type backend_norm;
-                        ggml_backend_type backend_output;
-
-                        if (n_gpu_layers > int(n_layer)) {
-                            backend_norm   = llama_backend_offload;
-                            backend_output = llama_backend_offload;
-                        } else {
-                            backend_norm   = GGML_BACKEND_CPU;
-                            backend_output = GGML_BACKEND_CPU;
-                        }
-
-                        model.output_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
-                        model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd},          backend_norm);
-                        model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
-                        model.output_b      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "bias"),   {n_vocab},         backend_output);
+                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                        model.output_b      = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT,      "bias"),   {n_vocab});
                     }
 
-                    const uint32_t n_ff      = hparams.n_ff;
-                    const int64_t n_embd_gqa = n_embd_v_gqa;
-                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
-                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
-
-                    const int i_gpu_start = n_layer - n_gpu_layers;
-
-                    model.layers.resize(n_layer);
-
-                    for (uint32_t i = 0; i < n_layer; ++i) {
-                        const ggml_backend_type backend       = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
-                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
                         auto & layer = model.layers[i];
 
-                        layer.attn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
-                        layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, backend);
+                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});
 
-                        layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
-                        layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa},         backend);
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, false);
+                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, false);
 
-                        layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
-                        layer.bo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd},         backend);
+                        if (layer.wqkv == nullptr) {
+                            layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
+                            layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i),   {n_embd});
 
-                        layer.ffn_down   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
-                        layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd},       backend);
+                            layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
+                            layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i),   {n_embd_gqa});
 
-                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
-                        layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i),   {n_ff},         backend);
+                            layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
+                            layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i),   {n_embd_gqa});
+                        }
+
+                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});
+
+                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});
+
+                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff});
+                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff});
                     }
                 } break;
             case LLM_ARCH_PLAMO:
                 {
-                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
 
                     // output
                     {
-                        ggml_backend_type backend_norm;
-                        ggml_backend_type backend_output;
-
-                        if (n_gpu_layers > int(n_layer)) {
-                            backend_norm   = llama_backend_offload;
-                            backend_output = llama_backend_offload_split;
-                        } else {
-                            backend_norm   = GGML_BACKEND_CPU;
-                            backend_output = GGML_BACKEND_CPU;
-                        }
-
-                        model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
-                        model.output      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
+                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                     }
 
-                    const uint32_t n_ff      = hparams.n_ff;
-                    const int64_t n_embd_gqa = n_embd_v_gqa;
-                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
-                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
-
-                    const int i_gpu_start = n_layer - n_gpu_layers;
-
-                    model.layers.resize(n_layer);
-
-                    for (uint32_t i = 0; i < n_layer; ++i) {
-                        const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
-                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
                         auto & layer = model.layers[i];
 
-                        layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
 
-                        layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd},     backend_split);
-                        layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, backend_split);
-                        layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, backend_split);
-                        layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},     backend_split);
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
 
-                        layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, backend_split);
-                        layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
-                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                     }
                 } break;
             case LLM_ARCH_GPT2:
                 {
-                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab},             GGML_BACKEND_CPU);
-                    model.pos_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"),   {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU);
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                    model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,   "weight"),   {n_embd, hparams.n_ctx_train});
 
                     // output
                     {
-                        ggml_backend_type backend_norm;
-                        ggml_backend_type backend_output;
-
-                        if (n_gpu_layers > int(n_layer)) {
-                            backend_norm   = llama_backend_offload;
-                            backend_output = llama_backend_offload_split;
-                        } else {
-                            backend_norm   = GGML_BACKEND_CPU;
-                            backend_output = GGML_BACKEND_CPU;
-                        }
-
-                        model.output_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
-                        model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd},          backend_norm);
-                        model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);
+                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                     }
 
-                    const uint32_t n_ff      = hparams.n_ff;
-                    const int64_t n_embd_gqa = n_embd_v_gqa;
-                    GGML_ASSERT(n_embd_gqa == n_embd / hparams.n_gqa());
-                    GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
-
-                    const int i_gpu_start = n_layer - n_gpu_layers;
-
-                    model.layers.resize(n_layer);
-
-                    for (uint32_t i = 0; i < n_layer; ++i) {
-                        const ggml_backend_type backend       = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
-                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
 
                         auto & layer = model.layers[i];
 
-                        layer.attn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, backend);
-                        layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, backend);
+                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd});
+                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd});
 
-                        layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
-                        layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa},         backend);
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa});
 
-                        layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},   backend_split);
-                        layer.bo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd},           backend);
+                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});
 
-                        layer.ffn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
-                        layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, backend);
+                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});
 
-                        layer.ffn_down   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
-                        layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd},       backend);
+                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});
 
-                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, backend_split);
-                        layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "bias", i),           {n_ff}, backend);
+                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff});
+                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff});
+                    }
+                } break;
+            case LLM_ARCH_CODESHELL:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    {
+                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});
+
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa});
+
+                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});
+
+                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});
+
+                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});
+
+                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i),   {n_embd, n_ff});
+                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i),     {n_ff});
+                    }
+                } break;
+            case LLM_ARCH_ORION:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                    {
+                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                    }
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});
+
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+
+                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});
+
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+                    }
+                } break;
+            case LLM_ARCH_INTERNLM2:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        // layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+                    }
+                } break;
+            case LLM_ARCH_GEMMA:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                    model.output      = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}); // same as tok_embd, duplicated to allow offloading
+                    ml.n_created--; // artificial tensor
+                    ml.size_data += ggml_nbytes(model.output);
+
+                    const int64_t n_ff          = hparams.n_ff;
+                    const int64_t n_embd_head_k = hparams.n_embd_head_k;
+                    const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
+                    const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa();
+
+                    for (uint32_t i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * hparams.n_head});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * hparams.n_head, n_embd});
+
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                    }
+                } break;
+            case LLM_ARCH_STARCODER2:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    {
+                        model.output_norm   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
+
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
+                        // if output is NULL, init from the input tok embed
+                        if (model.output == NULL) {
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                            ml.n_created--; // artificial tensor
+                            ml.size_data += ggml_nbytes(model.output);
+                        }
+
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});
+
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+
+                        // optional bias tensors
+                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd});
+                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa});
+                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa});
+                        layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
+
+                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});
+
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+
+                        // optional bias tensors
+                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
+                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP ,  "bias", i), {  n_ff});
+                    }
+                } break;
+            case LLM_ARCH_MAMBA:
+                {
+                    const int64_t d_conv  = hparams.ssm_d_conv;
+                    const int64_t d_inner = hparams.ssm_d_inner;
+                    const int64_t d_state = hparams.ssm_d_state;
+                    const int64_t dt_rank = hparams.ssm_dt_rank;
+                    // only an expansion factor of 2 is supported for now
+                    GGML_ASSERT(2 * n_embd == d_inner);
+
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
+                        // if output is NULL, init from the input tok embed, duplicated to allow offloading
+                        if (model.output == NULL) {
+                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                            ml.n_created--; // artificial tensor
+                            ml.size_data += ggml_nbytes(model.output);
+                        }
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        // norm
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+
+                        layer.ssm_in = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner});
+
+                        layer.ssm_conv1d = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner});
+                        layer.ssm_conv1d_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner});
+
+                        layer.ssm_x = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state});
+
+                        layer.ssm_dt = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner});
+                        layer.ssm_dt_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner});
+
+                        // no "weight" suffix for these
+                        layer.ssm_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner});
+                        layer.ssm_d = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_D, i), {d_inner});
+
+                        // out_proj
+                        layer.ssm_out = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd});
+                    }
+                } break;
+            case LLM_ARCH_XVERSE:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                    }
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+                        auto & layer = model.layers[i];
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+                    }
+                } break;
+            case LLM_ARCH_COMMAND_R:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        // init output from the input tok embed
+                        model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+                        ml.n_created--; // artificial tensor
+                        ml.size_data += ggml_nbytes(model.output);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                     }
                 } break;
             default:
@@ -3881,78 +5423,100 @@ static bool llm_load_tensors(
 
     ml.done_getting_tensors();
 
-    ml.init_mapping();
+    ml.init_mappings(true, use_mlock ? &model.mlock_mmaps : nullptr);
+    model.mappings.reserve(ml.mappings.size());
 
-    // allocate tensors
-    size_t vram_weights = 0;
-    size_t buf_size = 0;
+    // create the backend buffers
+    std::vector<std::pair<ggml_context *, llama_buf_map>> ctx_bufs;
+    ctx_bufs.reserve(ctx_map.size());
 
-    ggml_backend_buffer_type_t buft = llama_default_buffer_type(n_gpu_layers);
+    // Ensure we have enough capacity for the maximum backend buffer we will potentially create
+    size_t n_max_backend_buffer = ctx_map.size() * ml.files.size();
+    model.bufs.reserve(n_max_backend_buffer);
 
-    for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
-        // GGML_BACKEND_GPU tensors are for CUDA and OpenCL only, which are handled separately without ggml-backend
-        if (t->backend == GGML_BACKEND_CPU) {
-            buf_size += GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), ggml_backend_buft_get_alignment(buft));
-        } else {
-            vram_weights += ggml_nbytes(t);
-        }
-    }
+    for (auto & it : ctx_map) {
+        ggml_backend_buffer_type_t buft = it.first;
+        ggml_context * ctx              = it.second;
 
-    // create backend buffer
-    ggml_backend_buffer_t buf_mmap = nullptr;
+        llama_buf_map bufs;
+        bufs.reserve(n_max_backend_buffer);
 
-#ifdef GGML_USE_METAL
-    if (n_gpu_layers > 0) {
-        if (ml.use_mmap) {
-            const size_t max_size = ggml_get_max_tensor_size(ctx);
-            model.buf = ggml_backend_metal_buffer_from_ptr(ml.mapping->addr, ml.mapping->size, max_size);
-            buf_mmap = model.buf;
-        } else {
-            model.buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_metal_buffer_type());
-        }
-    }
-#elif defined(GGML_USE_CUBLAS) && defined(LLAMA_GGML_BACKEND_CUDA_TEST)
-    // for testing only
-    if (n_gpu_layers > 0) {
-        model.buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_cuda_buffer_type(0));
-    }
-#endif
-
-    if (model.buf == nullptr) {
-        // CPU backend, and indirectly CUDA and OpenCL
-        if (ml.use_mmap) {
-            model.buf = ggml_backend_cpu_buffer_from_ptr(ml.mapping->addr, ml.mapping->size);
-            buf_mmap = model.buf;
-        } else {
-            // allocate only CPU tensors
-            model.buf = ggml_backend_buft_alloc_buffer(buft, buf_size);
-            ggml_tallocr_t alloc = ggml_tallocr_new_from_buffer(model.buf);
-            for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
-                if (t->backend == GGML_BACKEND_CPU) {
-                    ggml_tallocr_alloc(alloc, t);
+        // only the mmap region containing the tensors in the model is mapped to the backend buffer
+        // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
+        // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
+        if (ml.use_mmap && use_mmap_buffer && buft == llama_default_buffer_type_cpu(true)) {
+            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
+                void * addr = nullptr;
+                size_t first, last;
+                ml.get_mapping_range(&first, &last, &addr, idx, ctx);
+                if (first >= last) {
+                    continue;
                 }
+                ggml_backend_buffer_t buf = ggml_backend_cpu_buffer_from_ptr((char *) addr + first, last - first);
+                if (buf == nullptr) {
+                    throw std::runtime_error("unable to allocate backend CPU buffer");
+                }
+                model.bufs.push_back(buf);
+                bufs.emplace(idx, buf);
+#ifdef GGML_USE_CUDA
+                if (n_layer >= n_gpu_layers) {
+                    ggml_backend_cuda_register_host_buffer(
+                        ggml_backend_buffer_get_base(buf),
+                        ggml_backend_buffer_get_size(buf));
+                }
+#endif
             }
-            ggml_tallocr_free(alloc);
         }
+#ifdef GGML_USE_METAL
+        else if (ml.use_mmap && use_mmap_buffer && buft == ggml_backend_metal_buffer_type()) {
+            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
+                const size_t max_size = ggml_get_max_tensor_size(ctx);
+                void * addr = nullptr;
+                size_t first, last;
+                ml.get_mapping_range(&first, &last, &addr, idx, ctx);
+                if (first >= last) {
+                    continue;
+                }
+                ggml_backend_buffer_t buf = ggml_backend_metal_buffer_from_ptr((char *) addr + first, last - first, max_size);
+                if (buf == nullptr) {
+                    throw std::runtime_error("unable to allocate backend metal buffer");
+                }
+                model.bufs.push_back(buf);
+                bufs.emplace(idx, buf);
+            }
+        }
+#endif
+        else {
+            ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
+            if (buf == nullptr) {
+                throw std::runtime_error("unable to allocate backend buffer");
+            }
+            model.bufs.push_back(buf);
+            if (use_mlock && ggml_backend_buffer_is_host(buf)) {
+                model.mlock_bufs.emplace_back(new llama_mlock);
+                auto & mlock_buf = model.mlock_bufs.back();
+                mlock_buf->init   (ggml_backend_buffer_get_base(buf));
+                mlock_buf->grow_to(ggml_backend_buffer_get_size(buf));
+            }
+            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
+                bufs.emplace(idx, buf);
+            }
+        }
+
+        if (bufs.empty()) {
+            throw std::runtime_error("failed to allocate buffer");
+        }
+
+        for (auto & buf : bufs) {
+            // indicate that this buffer contains weights
+            // this is used by ggml_backend_sched to improve op scheduling -> ops that use a weight are preferably scheduled to the backend that contains the weight
+            ggml_backend_buffer_set_usage(buf.second, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
+        }
+
+        ctx_bufs.emplace_back(ctx, bufs);
     }
 
-    if (use_mlock && ggml_backend_buffer_is_host(model.buf)) {
-        model.mlock_buf.init   (ggml_backend_buffer_get_base(model.buf));
-        model.mlock_buf.grow_to(ggml_backend_buffer_get_size(model.buf));
-    }
-
-    // print memory requirements
-    {
-        size_t sys_mem_required = ctx_size + buf_size;
-
-        if (sys_mem_required > 0) {
-            LLAMA_LOG_INFO("%s: system memory used  = %7.2f MiB\n", __func__, sys_mem_required / 1024.0 / 1024.0);
-        }
-        if (vram_weights > 0) {
-            LLAMA_LOG_INFO("%s: VRAM used           = %7.2f MiB\n", __func__, vram_weights / 1024.0 / 1024.0);
-        }
-
-#if (defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)) || defined(GGML_USE_CLBLAST)
+    if (llama_supports_gpu_offload()) {
         const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
 
         LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu);
@@ -3964,26 +5528,34 @@ static bool llm_load_tensors(
         const int max_offloadable_layers       = hparams.n_layer + 1;
 
         LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
-#endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
     }
 
-#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
-    ggml_cuda_set_tensor_split(tensor_split);
-#else
-    GGML_UNUSED(tensor_split);
-#endif // GGML_USE_CUBLAS
+    // print memory requirements
+    for (ggml_backend_buffer_t buf : model.bufs) {
+        LLAMA_LOG_INFO("%s: %10s buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0);
+    }
 
     // populate tensors_by_name
-    for (int i = 0; i < ml.n_tensors; ++i) {
-        struct ggml_tensor * cur = ggml_get_tensor(ctx, ml.get_tensor_name(i));
-        model.tensors_by_name.emplace_back(ggml_get_name(cur), cur);
+    for (ggml_context * ctx : model.ctxs) {
+        for (auto * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
+            model.tensors_by_name.emplace_back(ggml_get_name(cur), cur);
+        }
     }
 
-    if (!ml.load_all_data(ctx, progress_callback, progress_callback_user_data, buf_mmap, use_mlock ? &model.mlock_mmap : NULL)) {
-        return false;
+    // load tensor data
+    for (auto & it : ctx_bufs) {
+        ggml_context * ctx = it.first;
+        auto & bufs = it.second;
+        if (!ml.load_all_data(ctx, bufs, use_mlock ? &model.mlock_mmaps : NULL, progress_callback, progress_callback_user_data)) {
+            return false;
+        }
     }
 
-    model.mapping = std::move(ml.mapping);
+    if (use_mmap_buffer) {
+        for (auto & mapping : ml.mappings) {
+            model.mappings.emplace_back(std::move(mapping));
+        }
+    }
 
     // loading time will be recalculate after the first eval, so
     // we take page faults deferred by mmap() into consideration
@@ -3992,19 +5564,32 @@ static bool llm_load_tensors(
 }
 
 // Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
-static int llama_model_load(const std::string & fname, llama_model & model, const llama_model_params & params) {
+static int llama_model_load(const std::string & fname, llama_model & model, llama_model_params & params) {
     try {
         llama_model_loader ml(fname, params.use_mmap, params.kv_overrides);
 
         model.hparams.vocab_only = params.vocab_only;
 
-        llm_load_arch   (ml, model);
-        llm_load_hparams(ml, model);
-        llm_load_vocab  (ml, model);
+        try {
+            llm_load_arch(ml, model);
+        } catch(const std::exception & e) {
+            throw std::runtime_error("error loading model architecture: " + std::string(e.what()));
+        }
+        try {
+            llm_load_hparams(ml, model);
+        } catch(const std::exception & e) {
+            throw std::runtime_error("error loading model hyperparameters: " + std::string(e.what()));
+        }
+        try {
+            llm_load_vocab(ml, model);
+        } catch(const std::exception & e) {
+            throw std::runtime_error("error loading model vocabulary: " + std::string(e.what()));
+        }
 
         llm_load_print_meta(ml, model);
 
-        if (model.hparams.n_vocab != model.vocab.id_to_token.size()) {
+        if (model.vocab.type != LLAMA_VOCAB_TYPE_NONE &&
+            model.hparams.n_vocab != model.vocab.id_to_token.size()) {
             throw std::runtime_error("vocab size mismatch");
         }
 
@@ -4013,14 +5598,40 @@ static int llama_model_load(const std::string & fname, llama_model & model, cons
             return 0;
         }
 
+#ifdef GGML_USE_KOMPUTE
+        if (params.n_gpu_layers > 0 && (
+            !(model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON)
+            || !(
+                model.ftype == LLAMA_FTYPE_ALL_F32 ||
+                model.ftype == LLAMA_FTYPE_MOSTLY_F16 ||
+                model.ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ||
+                model.ftype == LLAMA_FTYPE_MOSTLY_Q4_1
+            )
+        )) {
+            // TODO(cebtenzzre): propagate this error outside of llama_load_model_from_file
+            LLAMA_LOG_WARN("%s: disabling Kompute due to unsupported model arch or quantization\n", __func__);
+            params.n_gpu_layers = 0;
+        }
+#endif
+
+#ifdef GGML_USE_SYCL
+        if (params.split_mode == LLAMA_SPLIT_MODE_NONE) {
+            ggml_backend_sycl_set_single_device_mode(params.main_gpu);
+            //SYCL use device index (0, 1, 2) directly, uer input device id, then convert to device index.
+            params.main_gpu = ggml_backend_sycl_get_device_index(params.main_gpu);
+        } else {
+            ggml_backend_sycl_set_mul_device_mode();
+        }
+#endif
+
         if (!llm_load_tensors(
-            ml, model, params.n_gpu_layers, params.main_gpu, params.tensor_split, params.use_mlock,
+            ml, model, params.n_gpu_layers, params.split_mode,  params.main_gpu, params.tensor_split, params.use_mlock,
             params.progress_callback, params.progress_callback_user_data
         )) {
             return -2;
         }
     } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("error loading model: %s\n", err.what());
+        LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what());
         return -1;
     }
 
@@ -4033,12 +5644,6 @@ static int llama_model_load(const std::string & fname, llama_model & model, cons
 
 using llm_build_cb = std::function<void(struct ggml_tensor * cur, const char * name, int nl)>;
 
-enum llm_rope_type {
-    LLM_ROPE,
-    LLM_ROPE_NEOX,
-    LLM_ROPE_GLM,
-};
-
 enum llm_ffn_op_type {
     LLM_FFN_SILU,
     LLM_FFN_GELU,
@@ -4058,6 +5663,7 @@ enum llm_norm_type {
 
 static struct ggml_tensor * llm_build_inp_embd(
         struct ggml_context * ctx,
+       struct llama_context & lctx,
         const llama_hparams & hparams,
           const llama_batch & batch,
          struct ggml_tensor * tok_embd,
@@ -4067,74 +5673,25 @@ static struct ggml_tensor * llm_build_inp_embd(
     struct ggml_tensor * inpL;
 
     if (batch.token) {
-        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens);
-        cb(inp_tokens, "inp_tokens", -1);
+        lctx.inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens);
+        cb(lctx.inp_tokens, "inp_tokens", -1);
+        ggml_set_input(lctx.inp_tokens);
 
-        inpL = ggml_get_rows(ctx, tok_embd, inp_tokens);
+        inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens);
     } else {
 #ifdef GGML_USE_MPI
         GGML_ASSERT(false && "not implemented");
 #endif
-
-        inpL = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
+        lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
+        inpL = lctx.inp_embd;
+        ggml_set_input(lctx.inp_embd);
     }
 
+    cb(inpL, "inp_embd", -1);
+
     return inpL;
 }
 
-// Persimmon: n_rot = n_embd_head_k/2
-// Other:     n_rot = n_embd_head_k
-static void llm_build_k_shift(
-      struct ggml_context * ctx,
-      const llama_hparams & hparams,
-      const llama_cparams & cparams,
-     const llama_kv_cache & kv,
-       struct ggml_cgraph * graph,
-            llm_rope_type   type,
-                  int64_t   n_ctx,
-                  int       n_rot,
-                  float     freq_base,
-                  float     freq_scale,
-       const llm_build_cb & cb) {
-    const int64_t n_layer       = hparams.n_layer;
-    const int64_t n_head_kv     = hparams.n_head_kv;
-    const int64_t n_embd_head_k = hparams.n_embd_head_k;
-    const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
-    const int32_t n_orig_ctx    = cparams.n_yarn_orig_ctx;
-    const float   ext_factor    = cparams.yarn_ext_factor;
-    const float   attn_factor   = cparams.yarn_attn_factor;
-    const float   beta_fast     = cparams.yarn_beta_fast;
-    const float   beta_slow     = cparams.yarn_beta_slow;
-
-    GGML_ASSERT(n_embd_head_k % n_rot == 0);
-
-    struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_ctx);
-    cb(K_shift, "K_shift", -1);
-
-    int rope_type = 0;
-
-    switch (type) {
-        case LLM_ROPE:      rope_type = 0; break;
-        case LLM_ROPE_NEOX: rope_type = 2; break;
-        case LLM_ROPE_GLM:  rope_type = 4; break;
-    }
-
-    for (int il = 0; il < n_layer; ++il) {
-        struct ggml_tensor * tmp =
-            // we rotate only the first n_rot dimensions
-            ggml_rope_custom_inplace(ctx,
-                    ggml_view_3d(ctx, kv.k_l[il],
-                        n_embd_head_k, n_head_kv, n_ctx,
-                        ggml_row_size(kv.k_l[il]->type, n_embd_head_k),
-                        ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa),
-                        0),
-                    K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
-                    ext_factor, attn_factor, beta_fast, beta_slow);
-        cb(tmp, "K_shifted", il);
-        ggml_build_forward_expand(graph, tmp);
-    }
-}
-
 static void llm_build_kv_store(
         struct ggml_context * ctx,
         const llama_hparams & hparams,
@@ -4150,9 +5707,11 @@ static void llm_build_kv_store(
     const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
     const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
 
+    GGML_ASSERT(kv.size == n_ctx);
+
     // compute the transposed [n_tokens, n_embd] V matrix
-    struct ggml_tensor * v_cur_t = ggml_transpose(ctx, ggml_reshape_2d(ctx, v_cur, n_embd_v_gqa, n_tokens));
-    //struct ggml_tensor * v_cur_t = ggml_transpose(ctx, v_cur); // TODO: reshape above is likely not needed
+    assert(v_cur->ne[0] == n_embd_v_gqa && v_cur->ne[1] == n_tokens);
+    struct ggml_tensor * v_cur_t = ggml_transpose(ctx, v_cur);
     cb(v_cur_t, "v_cur_t", il);
 
     struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k_l[il], n_tokens*n_embd_k_gqa,
@@ -4298,14 +5857,15 @@ static struct ggml_tensor * llm_build_kqv(
           const llama_model & model,
         const llama_hparams & hparams,
        const llama_kv_cache & kv,
+         struct ggml_cgraph * graph,
          struct ggml_tensor * wo,
          struct ggml_tensor * wo_b,
          struct ggml_tensor * q_cur,
          struct ggml_tensor * kq_mask,
+         struct ggml_tensor * kq_pos,
                     int64_t   n_ctx,
                     int32_t   n_tokens,
                     int32_t   n_kv,
-                    float     max_alibi_bias,
                     float     kq_scale,
          const llm_build_cb & cb,
                     int       il) {
@@ -4335,29 +5895,45 @@ static struct ggml_tensor * llm_build_kqv(
         ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
     }
 
-    if (max_alibi_bias > 0.0f) {
-        // temporary branch until we figure out how to handle ggml_alibi through ggml_add
+    if (model.arch == LLM_ARCH_GROK) {
+        // need to do the following:
+        // multiply by attn_output_multiplyer of 0.08838834764831845
+        // and then :
+        // kq = 30 * tanh(kq / 30)
+        // before the softmax below
+
+        //try from phi2
+        //ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
+
+        kq = ggml_tanh(ctx, ggml_scale(ctx, kq, 0.08838834764831845f/30.0f));
+        kq = ggml_scale(ctx, kq, 30);
+    }
+
+#if defined(GGML_USE_KOMPUTE)
+#pragma message("TODO: ALiBi support in ggml_soft_max_ext is not implemented for Kompute")
+#pragma message("      Falling back to ggml_alibi(). Will become an error in Mar 2024")
+#pragma message("ref:  https://github.com/ggerganov/llama.cpp/pull/5488")
+    if (hparams.f_max_alibi_bias > 0.0f) {
         kq = ggml_scale(ctx, kq, kq_scale);
         cb(kq, "kq_scaled", il);
 
-        if (max_alibi_bias > 0.0f) {
-            // TODO: n_head or n_head_kv
-            // TODO: K-shift is likely not working
-            // TODO: change to ggml_add
-            kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, max_alibi_bias);
-            cb(kq, "kq_scaled_alibi", il);
-        }
+        kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, hparams.f_max_alibi_bias);
+        cb(kq, "kq_scaled_alibi", il);
 
         kq = ggml_add(ctx, kq, kq_mask);
         cb(kq, "kq_masked", il);
 
         kq = ggml_soft_max(ctx, kq);
         cb(kq, "kq_soft_max", il);
-    } else {
-        kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_scale);
+    } else
+#endif
+    {
+        kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_pos, kq_scale, hparams.f_max_alibi_bias);
         cb(kq, "kq_soft_max_ext", il);
     }
 
+    GGML_ASSERT(kv.size == n_ctx);
+
     // split cached v into n_head heads
     struct ggml_tensor * v =
         ggml_view_3d(ctx, kv.v_l[il],
@@ -4376,6 +5952,8 @@ static struct ggml_tensor * llm_build_kqv(
     struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_k*n_head, n_tokens);
     cb(cur, "kqv_merged_cont", il);
 
+    ggml_build_forward_expand(graph, cur);
+
     cur = ggml_mul_mat(ctx, wo, cur);
     if (wo_b) {
         cb(cur, "kqv_wo", il);
@@ -4388,8 +5966,47 @@ static struct ggml_tensor * llm_build_kqv(
     return cur;
 }
 
+static struct ggml_tensor * llm_build_kv(
+        struct ggml_context * ctx,
+          const llama_model & model,
+        const llama_hparams & hparams,
+       const llama_kv_cache & kv,
+         struct ggml_cgraph * graph,
+         struct ggml_tensor * wo,
+         struct ggml_tensor * wo_b,
+         struct ggml_tensor * k_cur,
+         struct ggml_tensor * v_cur,
+         struct ggml_tensor * q_cur,
+         struct ggml_tensor * kq_mask,
+         struct ggml_tensor * kq_pos,
+                    int64_t   n_ctx,
+                    int32_t   n_tokens,
+                    int32_t   kv_head,
+                    int32_t   n_kv,
+                    float     kq_scale,
+         const llm_build_cb & cb,
+                    int       il) {
+
+    // these nodes are added to the graph together so that they are not reordered
+    // by doing so, the number of splits in the graph is reduced
+    ggml_build_forward_expand(graph, q_cur);
+    ggml_build_forward_expand(graph, k_cur);
+    ggml_build_forward_expand(graph, v_cur);
+
+    llm_build_kv_store(ctx, hparams, kv, graph, k_cur, v_cur, n_ctx, n_tokens, kv_head, cb, il);
+
+    struct ggml_tensor * cur;
+
+    cur  = llm_build_kqv(ctx, model, hparams, kv, graph, wo, wo_b,
+            q_cur, kq_mask, kq_pos, n_ctx, n_tokens, n_kv, kq_scale, cb, il);
+    cb(cur, "kqv_out", il);
+
+    return cur;
+}
+
 struct llm_build_context {
     const llama_model    & model;
+          llama_context  & lctx;
     const llama_hparams  & hparams;
     const llama_cparams  & cparams;
     const llama_batch    & batch;
@@ -4397,6 +6014,7 @@ struct llm_build_context {
 
     const int64_t n_embd;
     const int64_t n_layer;
+    const int64_t n_rot;
     const int64_t n_ctx;       // user-specified context size (can be different from n_ctx_train)
     const int64_t n_head;
     const int64_t n_head_kv;
@@ -4417,11 +6035,13 @@ struct llm_build_context {
     const float norm_rms_eps;
 
     const int32_t n_tokens;
-    const int32_t n_kv;     // size of KV cache to consider (n_kv <= n_ctx)
+    const int32_t n_kv;     // size of KV cache to consider (n_kv <= kv_self.size)
+    const int32_t n_outputs;
     const int32_t kv_head;  // index of where we store new KV data in the cache
     const int32_t n_orig_ctx;
 
-    const bool do_rope_shift;
+    const enum llama_pooling_type pooling_type;
+    const enum llama_rope_type    rope_type;
 
     const llm_build_cb & cb;
 
@@ -4436,12 +6056,14 @@ struct llm_build_context {
     const llm_build_cb & cb,
                   bool   worst_case) :
         model            (lctx.model),
+        lctx             (lctx),
         hparams          (model.hparams),
         cparams          (lctx.cparams),
         batch            (batch),
         kv_self          (lctx.kv_self),
         n_embd           (hparams.n_embd),
         n_layer          (hparams.n_layer),
+        n_rot            (hparams.n_rot),
         n_ctx            (cparams.n_ctx),
         n_head           (hparams.n_head),
         n_head_kv        (hparams.n_head_kv),
@@ -4460,14 +6082,14 @@ struct llm_build_context {
         norm_eps         (hparams.f_norm_eps),
         norm_rms_eps     (hparams.f_norm_rms_eps),
         n_tokens         (batch.n_tokens),
-        n_kv             (worst_case ? n_ctx            : kv_self.n),
-        kv_head          (worst_case ? n_ctx - n_tokens : kv_self.head),
+        n_kv             (worst_case ? kv_self.size : kv_self.n),
+        n_outputs        (worst_case ? n_tokens : lctx.n_outputs),
+        kv_head          (worst_case ? (kv_self.recurrent ? 0 : kv_self.size - n_tokens) : kv_self.head),
         n_orig_ctx       (cparams.n_yarn_orig_ctx),
-        do_rope_shift    (worst_case || kv_self.has_shift),
+        pooling_type     (cparams.pooling_type),
+        rope_type        (hparams.rope_type),
         cb               (cb),
         buf_compute_meta (lctx.buf_compute_meta) {
-            GGML_ASSERT(!!kv_self.ctx);
-
             // all initializations should be done in init()
         }
 
@@ -4479,6 +6101,19 @@ struct llm_build_context {
         };
 
         ctx0 = ggml_init(params);
+
+        lctx.inp_tokens = nullptr;
+        lctx.inp_embd = nullptr;
+        lctx.inp_pos = nullptr;
+        lctx.inp_out_ids = nullptr;
+        lctx.inp_KQ_mask = nullptr;
+        lctx.inp_KQ_pos = nullptr;
+        lctx.inp_K_shift = nullptr;
+        lctx.inp_mean = nullptr;
+        lctx.inp_cls = nullptr;
+        lctx.inp_s_copy = nullptr;
+        lctx.inp_s_mask = nullptr;
+        lctx.inp_s_seq = nullptr;
     }
 
     void free() {
@@ -4488,9 +6123,178 @@ struct llm_build_context {
         }
     }
 
+    struct ggml_cgraph * build_k_shift() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        GGML_ASSERT(kv_self.size == n_ctx);
+
+        lctx.inp_K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
+        cb(lctx.inp_K_shift, "K_shift", -1);
+        ggml_set_input(lctx.inp_K_shift);
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * tmp =
+                // we rotate only the first n_rot dimensions
+                ggml_rope_custom_inplace(ctx0,
+                        ggml_view_3d(ctx0, kv_self.k_l[il],
+                            n_embd_head_k, n_head_kv, n_ctx,
+                            ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
+                            ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
+                            0),
+                        lctx.inp_K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
+            cb(tmp, "K_shifted", il);
+            ggml_build_forward_expand(gf, tmp);
+        }
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_s_copy() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        GGML_ASSERT(kv_self.recurrent);
+
+        struct ggml_tensor * state_copy = build_inp_s_copy();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size);
+            struct ggml_tensor * ssm_states  = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size);
+
+            conv_states = ggml_get_rows(ctx0, conv_states, state_copy);
+            ssm_states  = ggml_get_rows(ctx0,  ssm_states, state_copy);
+
+            // TODO: name the intermediate tensors with cb()
+
+            ggml_build_forward_expand(gf, ggml_cpy(ctx0, conv_states, kv_self.k_l[il]));
+            ggml_build_forward_expand(gf, ggml_cpy(ctx0,  ssm_states, kv_self.v_l[il]));
+        }
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_defrag(const std::vector<uint32_t> & ids) {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        for (uint32_t i = 0; i < ids.size(); ++i) {
+            const uint32_t id = ids[i];
+
+            if (i == id || id == ids.size()) {
+                continue;
+            }
+
+            uint32_t nm = 1;
+
+            while (i + nm < ids.size() && ids[i + nm] == id + nm) {
+                nm++;
+            }
+
+            for (int il = 0; il < n_layer; ++il) {
+                ggml_tensor * view_k_src = ggml_view_2d(ctx0, kv_self.k_l[il],
+                        n_embd_k_gqa, nm,
+                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
+                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*i));
+
+                ggml_tensor * view_k_dst = ggml_view_2d(ctx0, kv_self.k_l[il],
+                        n_embd_k_gqa, nm,
+                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
+                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*id));
+
+                ggml_tensor * view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il],
+                        nm, n_embd_v_gqa,
+                        ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
+                        ggml_row_size(kv_self.v_l[il]->type, i));
+
+                ggml_tensor * view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il],
+                        nm, n_embd_v_gqa,
+                        ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
+                        ggml_row_size(kv_self.v_l[il]->type, id));
+
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_k_src, view_k_dst));
+                ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_v_src, view_v_dst));
+            }
+
+            i += nm - 1;
+        }
+
+        //LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes);
+
+        return gf;
+    }
+
+    struct ggml_tensor * build_inp_pos() {
+        lctx.inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+        cb(lctx.inp_pos, "inp_pos", -1);
+        ggml_set_input(lctx.inp_pos);
+        return lctx.inp_pos;
+    }
+
+    struct ggml_tensor * build_inp_out_ids() {
+        lctx.inp_out_ids = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_outputs);
+        cb(lctx.inp_out_ids, "inp_out_ids", -1);
+        ggml_set_input(lctx.inp_out_ids);
+        return lctx.inp_out_ids;
+    }
+
+    struct ggml_tensor * build_inp_KQ_mask(bool causal = true) {
+        if (causal) {
+            lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, n_tokens);
+        } else {
+            lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens);
+        }
+        cb(lctx.inp_KQ_mask, "KQ_mask", -1);
+        ggml_set_input(lctx.inp_KQ_mask);
+        return lctx.inp_KQ_mask;
+    }
+
+    struct ggml_tensor * build_inp_KQ_pos() {
+        lctx.inp_KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_kv);
+        cb(lctx.inp_KQ_pos, "KQ_pos", -1);
+        ggml_set_input(lctx.inp_KQ_pos);
+        return lctx.inp_KQ_pos;
+    }
+
+    struct ggml_tensor * build_inp_mean() {
+        lctx.inp_mean = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens);
+        cb(lctx.inp_mean, "inp_mean", -1);
+        ggml_set_input(lctx.inp_mean);
+        return lctx.inp_mean;
+    }
+
+    struct ggml_tensor * build_inp_cls() {
+        lctx.inp_cls = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+        cb(lctx.inp_cls, "inp_cls", -1);
+        ggml_set_input(lctx.inp_cls);
+        return lctx.inp_cls;
+    }
+
+    struct ggml_tensor * build_inp_s_copy() {
+        lctx.inp_s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, kv_self.size);
+        cb(lctx.inp_s_copy, "inp_s_copy", -1);
+        ggml_set_input(lctx.inp_s_copy);
+        return lctx.inp_s_copy;
+    }
+
+    struct ggml_tensor * build_inp_s_mask() {
+        lctx.inp_s_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_kv);
+        cb(lctx.inp_s_mask, "inp_s_mask", -1);
+        ggml_set_input(lctx.inp_s_mask);
+        return lctx.inp_s_mask;
+    }
+
+    struct ggml_tensor * build_inp_s_seq() {
+        lctx.inp_s_seq = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv, n_tokens);
+        cb(lctx.inp_s_seq, "inp_s_seq", -1);
+        ggml_set_input(lctx.inp_s_seq);
+        return lctx.inp_s_seq;
+    }
+
     struct ggml_cgraph * build_llama() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
+        // mutable variable, needed during the last layer of the computation to skip unused tokens
+        int32_t n_tokens = this->n_tokens;
+
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
         GGML_ASSERT(n_embd_head == hparams.n_rot);
@@ -4498,21 +6302,13 @@ struct llm_build_context {
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * inpSA = inpL;
@@ -4548,25 +6344,30 @@ struct llm_build_context {
                 }
 
                 Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
-                    n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale,
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_custom(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
-
-                cur = llm_build_kqv(ctx0, model, hparams, kv_self,
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                n_tokens = n_outputs;
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
             }
 
             struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
@@ -4621,19 +6422,19 @@ struct llm_build_context {
                 for (int i = 0; i < n_expert_used; ++i) {
                     ggml_tensor * cur_expert;
 
-                    ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exp, n_expert, selected_experts, i, cur);
+                    ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exps, selected_experts, i, cur);
                     cb(cur_up, "ffn_moe_up", il);
 
-                    ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exp, n_expert, selected_experts, i, cur);
+                    ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur);
                     cb(cur_gate, "ffn_moe_gate", il);
 
                     cur_gate = ggml_silu(ctx0, cur_gate);
                     cb(cur_gate, "ffn_moe_silu", il);
 
-                    cur_expert = ggml_mul(ctx0, cur_up, cur_gate); // [n_tokens, n_embd]
+                    cur_expert = ggml_mul(ctx0, cur_up, cur_gate);
                     cb(cur_expert, "ffn_moe_gate_par", il);
 
-                    cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exp, n_expert, selected_experts, i, cur_expert); // [n_tokens, n_embd]
+                    cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, selected_experts, i, cur_expert); // [n_tokens, n_embd]
                     cb(cur_expert, "ffn_moe_down", il);
 
                     cur_expert = ggml_mul(ctx0, cur_expert,
@@ -4652,6 +6453,12 @@ struct llm_build_context {
             }
 
             cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
+            if (layer_dir != nullptr) {
+                cur = ggml_add(ctx0, cur, layer_dir);
+            }
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -4679,25 +6486,21 @@ struct llm_build_context {
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
 
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = model.type == MODEL_7B ? build_inp_pos() : nullptr;
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-        cb(KQ_mask, "KQ_mask", -1);
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb);
-        }
+        // positions of the tokens in the KV cache
+        struct ggml_tensor * KQ_pos = build_inp_KQ_pos();
 
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * inpSA = inpL;
@@ -4722,12 +6525,12 @@ struct llm_build_context {
                     case MODEL_7B:
                         Qcur = ggml_rope_custom(
                             ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                            n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale,
+                            n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                             ext_factor, attn_factor, beta_fast, beta_slow
                         );
                         Kcur = ggml_rope_custom(
                             ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                            n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale,
+                            n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                             ext_factor, attn_factor, beta_fast, beta_slow
                         );
                         break;
@@ -4741,15 +6544,16 @@ struct llm_build_context {
                 cb(Qcur, "Qcur", il);
                 cb(Kcur, "Kcur", il);
 
-                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
-
-                // apply ALiBi for 13B model
-                const float max_alibi_bias = model.type == MODEL_13B ? 8.0f : -1.0f;
-
-                cur = llm_build_kqv(ctx0, model, hparams, kv_self,
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                         model.layers[il].wo, NULL,
-                        Qcur, KQ_mask, n_ctx, n_tokens, n_kv, max_alibi_bias, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
             }
 
             struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
@@ -4794,31 +6598,129 @@ struct llm_build_context {
         return gf;
     }
 
+    struct ggml_cgraph * build_xverse() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        // positions of the tokens in the KV cache
+        struct ggml_tensor * KQ_pos = build_inp_KQ_pos();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                        model.layers[il].wo, NULL,
+                        Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,      cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   NULL,
+                        model.layers[il].ffn_gate, NULL,
+                        model.layers[il].ffn_down, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            }
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
     struct ggml_cgraph * build_falcon() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
 
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * attn_norm;
@@ -4858,23 +6760,28 @@ struct llm_build_context {
 
                 // using mode = 2 for neox mode
                 Qcur = ggml_rope_custom(
-                    ctx0, Qcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx,
+                    ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_custom(
-                    ctx0, Kcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx,
+                    ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
-
-                cur = llm_build_kqv(ctx0, model, hparams, kv_self,
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                         model.layers[il].wo, NULL,
-                        Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur       = ggml_get_rows(ctx0,       cur, inp_out_ids);
+                inpL      = ggml_get_rows(ctx0,      inpL, inp_out_ids);
+                attn_norm = ggml_get_rows(ctx0, attn_norm, inp_out_ids);
             }
 
             struct ggml_tensor * ffn_inp = cur;
@@ -4917,6 +6824,214 @@ struct llm_build_context {
         return gf;
     }
 
+    struct ggml_cgraph * build_grok() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        // mutable variable, needed during the last layer of the computation to skip unused tokens
+        int32_t n_tokens = this->n_tokens;
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // multiply by embedding_multiplier_scale of 78.38367176906169
+        inpL = ggml_scale(ctx0, inpL, 78.38367176906169f);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                Qcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                n_tokens = n_outputs;
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            // Grok
+            // if attn_out_norm is present then apply it before adding the input
+            if (model.layers[il].attn_out_norm) {
+                cur = llm_build_norm(ctx0, cur, hparams,
+                        model.layers[il].attn_out_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "attn_out_norm", il);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            // MoE branch
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts]
+            cb(logits, "ffn_moe_logits", il);
+
+            ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts]
+            cb(probs, "ffn_moe_probs", il);
+
+            // select experts
+            ggml_tensor * selected_experts = ggml_top_k(ctx0, probs, n_expert_used); // [n_tokens, num_experts_per_tok]
+            cb(selected_experts->src[0], "ffn_moe_argsort", il);
+
+            ggml_tensor * weights = ggml_get_rows(ctx0,
+                    ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts);
+            cb(weights, "ffn_moe_weights", il);
+
+            weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok]
+
+            ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights);
+            cb(weights_sum, "ffn_moe_weights_sum", il);
+
+            weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok]
+            cb(weights, "ffn_moe_weights_norm", il);
+
+            // compute expert outputs
+            ggml_tensor * moe_out = nullptr;
+
+            for (int i = 0; i < n_expert_used; ++i) {
+                ggml_tensor * cur_expert;
+
+                ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exps, selected_experts, i, cur);
+                cb(cur_up, "ffn_moe_up", il);
+
+                ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur);
+                cb(cur_gate, "ffn_moe_gate", il);
+
+                //GeLU
+                cur_gate = ggml_gelu(ctx0, cur_gate);
+                cb(cur_gate, "ffn_moe_gelu", il);
+
+                cur_expert = ggml_mul(ctx0, cur_up, cur_gate);
+                cb(cur_expert, "ffn_moe_gate_par", il);
+
+                cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, selected_experts, i, cur_expert); // [n_tokens, n_embd]
+                cb(cur_expert, "ffn_moe_down", il);
+
+                cur_expert = ggml_mul(ctx0, cur_expert,
+                        ggml_view_2d(ctx0, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
+                cb(cur_expert, "ffn_moe_weighted", il);
+
+                if (i == 0) {
+                    moe_out = cur_expert;
+                } else {
+                    moe_out = ggml_add(ctx0, moe_out, cur_expert);
+                    cb(moe_out, "ffn_moe_out", il);
+                }
+            }
+
+            cur = moe_out;
+
+            // Grok
+            // if layer_out_norm is present then apply it before adding the input
+            // Idea: maybe ffn_out_norm is a better name
+            if (model.layers[il].layer_out_norm) {
+                cur = llm_build_norm(ctx0, cur, hparams,
+                        model.layers[il].layer_out_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "layer_out_norm", il);
+            }
+
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
+            if (layer_dir != nullptr) {
+                cur = ggml_add(ctx0, cur, layer_dir);
+            }
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+
+        // Grok
+        // multiply logits by output_multiplier_scale of 0.5773502691896257
+
+        cur = ggml_scale(ctx0, cur, 0.5773502691896257f);
+
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
     struct ggml_cgraph * build_starcoder() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
@@ -4925,21 +7040,17 @@ struct llm_build_context {
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
         struct ggml_tensor * cur;
-        struct ggml_tensor * pos;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-        cb(KQ_mask, "KQ_mask", -1);
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
-        pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
+        struct ggml_tensor * pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
         cb(pos, "pos_embd", -1);
 
         inpL = ggml_add(ctx0, inpL, pos);
@@ -4970,12 +7081,16 @@ struct llm_build_context {
 
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
-
-                cur = llm_build_kqv(ctx0, model, hparams, kv_self,
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
             }
 
             // add the input
@@ -5021,27 +7136,19 @@ struct llm_build_context {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        const int64_t n_rot = n_embd_head_k / 2;
+        GGML_ASSERT(n_embd_head   == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head/2 == hparams.n_rot);
 
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
-        cb(inpL, "imp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * residual = inpL;
@@ -5133,13 +7240,13 @@ struct llm_build_context {
                 cb(kpass, "kpass", il);
 
                 struct ggml_tensor * qrotated = ggml_rope_custom(
-                    ctx0, qrot, inp_pos, n_rot, 2, 0, n_orig_ctx,
+                    ctx0, qrot, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(qrotated, "qrotated", il);
 
                 struct ggml_tensor * krotated = ggml_rope_custom(
-                    ctx0, krot, inp_pos, n_rot, 2, 0, n_orig_ctx,
+                    ctx0, krot, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(krotated, "krotated", il);
@@ -5178,13 +7285,16 @@ struct llm_build_context {
                         );
                 cb(Vcur, "Vcur", il);
 
-                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
-
-                // TODO: not tested, could be broken
-                cur = llm_build_kqv(ctx0, model, hparams, kv_self,
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
-                        Q, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Q, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur      = ggml_get_rows(ctx0,      cur, inp_out_ids);
+                residual = ggml_get_rows(ctx0, residual, inp_out_ids);
             }
 
             struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);
@@ -5238,12 +7348,13 @@ struct llm_build_context {
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-        cb(KQ_mask, "KQ_mask", -1);
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        // positions of the tokens in the KV cache
+        struct ggml_tensor * KQ_pos = build_inp_KQ_pos();
 
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * inpSA = inpL;
@@ -5270,12 +7381,16 @@ struct llm_build_context {
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                 cb(Qcur, "Qcur", il);
 
-                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
-
-                cur = llm_build_kqv(ctx0, model, hparams, kv_self,
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                         model.layers[il].wo, NULL,
-                        Qcur, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
             }
 
             struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
@@ -5320,6 +7435,196 @@ struct llm_build_context {
         return gf;
     }
 
+    struct ggml_cgraph * build_bert() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        struct ggml_tensor * inp_pos  = build_inp_pos();
+        struct ggml_tensor * inp_mean = build_inp_mean();
+        struct ggml_tensor * inp_cls  = build_inp_cls();
+
+        // construct input embeddings (token, type, position)
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // token types are hardcoded to zero ("Sentence A")
+        struct ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0);
+        inpL = ggml_add(ctx0, inpL, type_row0);
+        if (model.arch == LLM_ARCH_BERT) {
+            inpL = ggml_add(ctx0, ggml_get_rows(ctx0, model.pos_embd, inp_pos), inpL);
+        }
+        cb(inpL, "inp_embd", -1);
+
+        // embed layer norm
+        inpL = llm_build_norm(ctx0, inpL, hparams, model.tok_norm, model.tok_norm_b, LLM_NORM, cb, -1);
+        cb(inpL, "inp_norm", -1);
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask(false);
+
+        // iterate layers
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * cur = inpL;
+
+            struct ggml_tensor * Qcur;
+            struct ggml_tensor * Kcur;
+            struct ggml_tensor * Vcur;
+
+            // self-attention
+            if (model.arch == LLM_ARCH_BERT) {
+                Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq);
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), model.layers[il].bk);
+                cb(Kcur, "Kcur", il);
+
+                Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), model.layers[il].bv);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+            } else {
+                // compute Q and K and RoPE them
+                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
+
+                Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+                Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+                Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+            }
+
+            struct ggml_tensor * q =                 ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+            struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
+
+            struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+            cb(kq, "kq", il);
+
+            kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, nullptr, 1.0f/sqrtf(float(n_embd_head)), hparams.f_max_alibi_bias);
+            cb(kq, "kq_soft_max_ext", il);
+
+            struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens)));
+            cb(v, "v", il);
+
+            struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_tokens, n_embd_head, n_head_kv), kq);
+            cb(kqv, "kqv", il);
+
+            struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
+            cb(kqv_merged, "kqv_merged", il);
+
+            cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
+            cb(cur, "kqv_merged_cont", il);
+
+            ggml_build_forward_expand(gf, cur);
+
+            cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
+            if (model.layers[il].bo) {
+                cb(cur, "kqv_wo", il);
+            }
+
+            if (model.layers[il].bo) {
+                cur = ggml_add(ctx0, cur, model.layers[il].bo);
+            }
+            cb(cur, "kqv_out", il);
+
+            if (il == n_layer - 1 && pooling_type == LLAMA_POOLING_TYPE_NONE) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            }
+
+            // re-add the layer input
+            cur = ggml_add(ctx0, cur, inpL);
+
+            // attention layer norm
+            cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_out_norm, model.layers[il].attn_out_norm_b, LLM_NORM, cb, il);
+
+            struct ggml_tensor * ffn_inp = cur;
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            if (model.arch == LLM_ARCH_BERT) {
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
+                        NULL,                      NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                        NULL,
+                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+            } else {
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   NULL,
+                        model.layers[il].ffn_gate, NULL,
+                        model.layers[il].ffn_down, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+            }
+            cb(cur, "ffn_out", il);
+
+            // attentions bypass the intermediate layer
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            // output layer norm
+            cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].layer_out_norm, model.layers[il].layer_out_norm_b, LLM_NORM, cb, il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        // final output
+        cur = inpL;
+        cb(cur, "result_embd", -1);
+
+        // pooling layer
+        switch (pooling_type) {
+            case LLAMA_POOLING_TYPE_NONE:
+                {
+                    // nop
+                } break;
+            case LLAMA_POOLING_TYPE_MEAN:
+                {
+                    cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean);
+                    cb(cur, "result_embd_pooled", -1);
+                } break;
+            case LLAMA_POOLING_TYPE_CLS:
+                {
+                    cur = ggml_get_rows(ctx0, cur, inp_cls);
+                    cb(cur, "result_embd_pooled", -1);
+                } break;
+            case LLAMA_POOLING_TYPE_UNSPECIFIED:
+                {
+                    GGML_ASSERT(false && "Invalid pooling type");
+                } break;
+        }
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
     struct ggml_cgraph * build_bloom() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
@@ -5330,12 +7635,13 @@ struct llm_build_context {
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-        cb(KQ_mask, "KQ_mask", -1);
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        // positions of the tokens in the KV cache
+        struct ggml_tensor * KQ_pos = build_inp_KQ_pos();
 
         inpL = llm_build_norm(ctx0, inpL, hparams,
                 model.tok_norm,
@@ -5368,12 +7674,16 @@ struct llm_build_context {
 
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
-
-                cur = llm_build_kqv(ctx0, model, hparams, kv_self,
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
             }
 
             // Add the input
@@ -5423,21 +7733,33 @@ struct llm_build_context {
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
         struct ggml_tensor * cur;
+        struct ggml_tensor * pos;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-        cb(KQ_mask, "KQ_mask", -1);
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        // positions of the tokens in the KV cache
+        struct ggml_tensor * KQ_pos = build_inp_KQ_pos();
+
+        if (model.pos_embd) {
+            // inp_pos - contains the positions
+            struct ggml_tensor * inp_pos = build_inp_pos();
+            pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
+            cb(pos, "pos_embd", -1);
+
+            inpL = ggml_add(ctx0, inpL, pos);
+            cb(inpL, "inpL", -1);
+        }
 
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * attn_norm;
 
             attn_norm = llm_build_norm(ctx0, inpL, hparams,
                     model.layers[il].attn_norm,
-                    NULL,
+                    model.layers[il].attn_norm_b,
                     LLM_NORM, cb, il);
             cb(attn_norm, "attn_norm", il);
 
@@ -5448,6 +7770,11 @@ struct llm_build_context {
                 cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
+                if (model.layers[il].bqkv){
+                    cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                    cb(cur, "bqkv", il);
+                }
+
                 if (hparams.f_clamp_kqv > 0.0f) {
                     cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
                     cb(cur, "wqkv_clamped", il);
@@ -5461,14 +7788,39 @@ struct llm_build_context {
                 cb(Kcur, "Kcur", il);
                 cb(Vcur, "Vcur", il);
 
-                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+                // Q/K Layernorm
+                if (model.layers[il].attn_q_norm) {
+                    Qcur = llm_build_norm(ctx0, Qcur, hparams,
+                            model.layers[il].attn_q_norm,
+                            model.layers[il].attn_q_norm_b,
+                            LLM_NORM, cb, il);
+                    cb(Qcur, "Qcur", il);
 
-                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
+                    Kcur = llm_build_norm(ctx0, Kcur, hparams,
+                            model.layers[il].attn_k_norm,
+                            model.layers[il].attn_k_norm_b,
+                            LLM_NORM, cb, il);
+                    cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kqv(ctx0, model, hparams, kv_self,
-                        model.layers[il].wo, NULL,
-                        Qcur, KQ_mask, n_ctx, n_tokens, n_kv, hparams.f_max_alibi_bias, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                    Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+
+                    cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+                } else {
+                    Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+                    cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                            model.layers[il].wo, model.layers[il].bo,
+                            Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+                }
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
             }
 
             // Add the input
@@ -5479,13 +7831,13 @@ struct llm_build_context {
             {
                 cur = llm_build_norm(ctx0, ffn_inp, hparams,
                         model.layers[il].ffn_norm,
-                        NULL,
+                        model.layers[il].ffn_norm_b,
                         LLM_NORM, cb, il);
                 cb(cur, "ffn_norm", il);
                 cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   NULL,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
                         NULL,                      NULL,
-                        model.layers[il].ffn_down, NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
                         model.layers[il].ffn_act,
                         LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                 cb(cur, "ffn_out", il);
@@ -5502,7 +7854,7 @@ struct llm_build_context {
 
         cur = llm_build_norm(ctx0, cur, hparams,
                 model.output_norm,
-                NULL,
+                model.output_norm_b,
                 LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
 
@@ -5523,21 +7875,13 @@ struct llm_build_context {
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, hparams.n_rot, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * inpSA = inpL;
@@ -5554,33 +7898,49 @@ struct llm_build_context {
                 // compute Q and K and RoPE them
                 struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
 
                 struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
 
                 struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
 
                 Qcur = ggml_rope_custom(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
-                    hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_custom(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
-
-                cur = llm_build_kqv(ctx0, model, hparams, kv_self,
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                         model.layers[il].wo, NULL,
-                        Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
             }
 
             struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
@@ -5636,21 +7996,13 @@ struct llm_build_context {
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * inpSA = inpL;
@@ -5681,23 +8033,27 @@ struct llm_build_context {
 
                 // using mode = 2 for neox mode
                 Qcur = ggml_rope_custom(
-                    ctx0, Qcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx,
+                    ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_custom(
-                    ctx0, Kcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx,
+                    ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
-
-                cur = llm_build_kqv(ctx0, model, hparams, kv_self,
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                         model.layers[il].wo, NULL,
-                        Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
             }
 
             struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
@@ -5741,6 +8097,124 @@ struct llm_build_context {
 
         return gf;
     }
+
+    struct ggml_cgraph * build_qwen2() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                cb(Qcur, "Qcur", il);
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                cb(Kcur, "Kcur", il);
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                cb(Vcur, "Vcur", il);
+
+                // these nodes are added to the graph together so that they are not reordered
+                // by doing so, the number of splits in the graph is reduced
+                ggml_build_forward_expand(gf, Qcur);
+                ggml_build_forward_expand(gf, Kcur);
+                ggml_build_forward_expand(gf, Vcur);
+
+                Qcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = llm_build_ffn(ctx0, cur,
+                    model.layers[il].ffn_up,   NULL,
+                    model.layers[il].ffn_gate, NULL,
+                    model.layers[il].ffn_down, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
     struct ggml_cgraph * build_phi2() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
@@ -5753,21 +8227,13 @@ struct llm_build_context {
         struct ggml_tensor * ffn_output;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
             attn_norm_output = llm_build_norm(ctx0, inpL, hparams,
@@ -5778,15 +8244,25 @@ struct llm_build_context {
 
             // self-attention
             {
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output);
-                cb(cur, "wqkv", il);
+                struct ggml_tensor * Qcur = nullptr;
+                struct ggml_tensor * Kcur = nullptr;
+                struct ggml_tensor * Vcur = nullptr;
 
-                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-                cb(cur, "bqkv", il);
+                if (model.layers[il].wqkv) {
+                    cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output);
+                    cb(cur, "wqkv", il);
 
-                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
-                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
-                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+                    cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                    cb(cur, "bqkv", il);
+
+                    Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+                    Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+                    Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+                } else {
+                    Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
+                    Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
+                    Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
+                }
 
                 cb(Qcur, "Qcur", il);
                 cb(Kcur, "Kcur", il);
@@ -5796,7 +8272,7 @@ struct llm_build_context {
                 Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 
                 Qcur = ggml_rope_custom(
-                    ctx0, Qcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
+                    ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
@@ -5807,17 +8283,22 @@ struct llm_build_context {
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_custom(
-                    ctx0, Kcur, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
+                    ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
 
-                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
-
-                cur = llm_build_kqv(ctx0, model, hparams, kv_self,
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f, cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur              = ggml_get_rows(ctx0,              cur, inp_out_ids);
+                inpL             = ggml_get_rows(ctx0,             inpL, inp_out_ids);
+                attn_norm_output = ggml_get_rows(ctx0, attn_norm_output, inp_out_ids);
             }
 
             // FF
@@ -5862,25 +8343,18 @@ struct llm_build_context {
 
         const int64_t n_embd_head = hparams.n_embd_head_v;
         GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
 
         struct ggml_tensor * cur;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-        cb(KQ_mask, "KQ_mask", -1);
-
-        // shift the entire K-cache if needed
-        if (do_rope_shift) {
-            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb);
-        }
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
 
@@ -5905,28 +8379,33 @@ struct llm_build_context {
                 cb(Vcur, "Vcur", il);
 
                 Qcur = ggml_rope_custom(
-                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
-                        n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale,
+                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_rot, n_head,    n_tokens), inp_pos,
+                        n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_custom(
-                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                        n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale,
+                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens), inp_pos,
+                        n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Kcur, "Kcur", il);
 
-                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
-
-                cur = llm_build_kqv(ctx0, model, hparams, kv_self,
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                         model.layers[il].wo, NULL,
-                        Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
             struct ggml_tensor * sa_out = cur;
 
             cur = attention_norm;
 
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur    = ggml_get_rows(ctx0,    cur, inp_out_ids);
+                sa_out = ggml_get_rows(ctx0, sa_out, inp_out_ids);
+                inpL   = ggml_get_rows(ctx0,   inpL, inp_out_ids);
+            }
+
             // feed-forward network
             {
                 cur = llm_build_ffn(ctx0, cur,
@@ -5975,16 +8454,13 @@ struct llm_build_context {
         struct ggml_tensor * pos;
         struct ggml_tensor * inpL;
 
-        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
-        cb(inpL, "inp_embd", -1);
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
         // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-        cb(inp_pos, "inp_pos", -1);
+        struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
-        cb(KQ_mask, "KQ_mask", -1);
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
         cb(pos, "pos_embd", -1);
@@ -6017,12 +8493,16 @@ struct llm_build_context {
 
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
-
-                cur = llm_build_kqv(ctx0, model, hparams, kv_self,
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
-                        Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-                cb(cur, "kqv_out", il);
+                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
             }
 
             // add the input
@@ -6063,219 +8543,1047 @@ struct llm_build_context {
 
         return gf;
     }
-};
 
-//
-// tensor offloading helpers
-//
-// TODO: will be removed with backend v2
+    struct ggml_cgraph * build_codeshell() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
-enum llm_offload_func_e {
-    OFFLOAD_FUNC_NOP,
-    OFFLOAD_FUNC,
-    OFFLOAD_FUNC_FRC, // force offload
-    OFFLOAD_FUNC_KQV,
-    OFFLOAD_FUNC_NR,
-    OFFLOAD_FUNC_EMB, // embeddings
-    OFFLOAD_FUNC_OUT,
-};
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
 
-// TODO: will be removed with backend v2
-struct llm_offload_trie {
-    struct node {
-        ~node() {
-            for (int i = 0; i < 256; ++i) {
-                if (children[i]) {
-                    delete children[i];
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm,
+                    model.layers[il].attn_norm_b,
+                    LLM_NORM, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
+
+                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                cb(cur, "bqkv", il);
+
+                struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+                struct ggml_tensor * tmpk = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+                cb(tmpq, "tmpq", il);
+                cb(tmpk, "tmpk", il);
+                cb(Vcur, "Vcur", il);
+
+                struct ggml_tensor * Qcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head,    n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                struct ggml_tensor * Kcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            }
+
+            // add the input
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // FF
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm,
+                        model.layers[il].ffn_norm_b,
+                        LLM_NORM, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
+                        NULL,                      NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                        NULL,
+                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+                cb(cur, "ffn_out", il);
+            }
+
+            inpL = ggml_add(ctx0, cur, ffn_inp);
+            cb(inpL, "l_out", il);
+        }
+
+        cur = llm_build_norm(ctx0, inpL, hparams,
+                model.output_norm,
+                model.output_norm_b,
+                LLM_NORM, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_orion() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, model.layers[il].attn_norm_b,
+                    LLM_NORM, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                // if (model.layers[il].bq) {
+                //     Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                //     cb(Qcur, "Qcur", il);
+                // }
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                // if (model.layers[il].bk) {
+                //     Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                //     cb(Kcur, "Kcur", il);
+                // }
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                // if (model.layers[il].bv) {
+                //     Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                //     cb(Vcur, "Vcur", il);
+                // }
+
+                Qcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                        model.layers[il].wo, NULL,
+                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                    model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
+                    LLM_NORM, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = llm_build_ffn(ctx0, cur,
+                    model.layers[il].ffn_up,   NULL,
+                    model.layers[il].ffn_gate, NULL,
+                    model.layers[il].ffn_down, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, model.output_norm_b,
+                LLM_NORM, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_internlm2() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
                 }
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                Qcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = llm_build_ffn(ctx0, cur,
+                    model.layers[il].ffn_up,   NULL,
+                    model.layers[il].ffn_gate, NULL,
+                    model.layers[il].ffn_down, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
         }
 
-        node * children[256] = { nullptr };
-        llm_offload_func_e func = OFFLOAD_FUNC_NOP;
-    };
+        cur = inpL;
 
-    llm_offload_trie() {
-        root = new node;
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
     }
 
-    llm_offload_trie(const std::unordered_map<const char *, llm_offload_func_e> & map) {
-        root = new node;
+    // ref: https://arxiv.org/abs/2203.03466
+    //      https://github.com/ggerganov/llama.cpp/issues/5276#issuecomment-1925774738
+    // based on the original build_llama() function
+    struct ggml_cgraph * build_minicpm() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
-        for (const auto & kv : map) {
-            add(kv.first, kv.second);
-        }
-    }
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
 
-    ~llm_offload_trie() {
-        delete root;
-    }
+        const int64_t n_embd = hparams.n_embd;
+        //TODO: if the model varies, these parameters need to be read from the model
+        const int64_t n_embd_base = 256;
+        const float scale_embd  = 12.0f;
+        const float scale_depth = 1.4f;
 
-    void add(const char * name, llm_offload_func_e func) {
-        node * cur = root;
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
 
-        for (int i = 0; ; ++i) {
-            const uint8_t c = name[i];
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
 
-            if (!c) {
-                break;
+        // scale the input embeddings
+        inpL = ggml_scale(ctx0, inpL, scale_embd);
+        cb(inpL, "inp_scaled", -1);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                Qcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
 
-            if (!cur->children[c]) {
-                cur->children[c] = new node;
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
             }
 
-            cur = cur->children[c];
+            // scale_res - scale the hidden states for residual connection
+            const float scale_res = scale_depth/sqrtf(float(n_layer));
+            cur = ggml_scale(ctx0, cur, scale_res);
+            cb(cur, "hidden_scaled", -1);
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   NULL,
+                        model.layers[il].ffn_gate, NULL,
+                        model.layers[il].ffn_down, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            }
+
+            // scale the hidden states for residual connection
+            cur = ggml_scale(ctx0, cur, scale_res);
+            cb(cur, "hidden_scaled_ffn", -1);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
         }
 
-        cur->func = func;
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head scaling
+        const float scale_lmhead = float(n_embd_base)/float(n_embd);
+        cur = ggml_scale(ctx0, cur, scale_lmhead);
+        cb(cur, "lmhead_scaling", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.tok_embd, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
     }
 
-    llm_offload_func_e find(const char * name) const {
-        const node * cur = root;
+    struct ggml_cgraph * build_gemma() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
-        for (int i = 0; ; ++i) {
-            const uint8_t c = name[i];
+        const int64_t n_embd_head_k = hparams.n_embd_head_k;
 
-            if (!c) {
-                break;
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
+        cb(inpL, "inp_scaled", -1);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_rope_custom(
+                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head,    n_tokens), inp_pos,
+                        n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
+                cb(Qcur, "Qcur", il);
+
+                Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));
+                cb(Qcur, "Qcur_scaled", il);
+
+                Kcur = ggml_rope_custom(
+                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos,
+                        n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                        model.layers[il].wo, NULL,
+                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il);
             }
 
-            if (!cur->children[c]) {
-                return OFFLOAD_FUNC_NOP;
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
             }
 
-            cur = cur->children[c];
+            struct ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
+            cb(sa_out, "sa_out", il);
+
+            cur = llm_build_norm(ctx0, sa_out, hparams,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            // feed-forward network
+            {
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up, NULL,
+                        model.layers[il].ffn_gate, NULL,
+                        model.layers[il].ffn_down, NULL,
+                        NULL,
+                        LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            }
+
+            cur = ggml_add(ctx0, cur, sa_out);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
         }
 
-        return cur->func;
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
     }
 
-    node * root = nullptr;
+    struct ggml_cgraph * build_starcoder2() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, model.layers[il].attn_norm_b,
+                    LLM_NORM, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                Qcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                    model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
+                    LLM_NORM, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
+                        NULL,                      NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                        NULL,
+                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+            cb(cur, "ffn_out", il);
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, model.output_norm_b,
+                LLM_NORM, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_mamba() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t d_model = n_embd;
+        const int64_t d_conv  = hparams.ssm_d_conv;
+        const int64_t d_inner = hparams.ssm_d_inner;
+        GGML_ASSERT(2 * d_model == d_inner);
+        const int64_t d_state = hparams.ssm_d_state;
+        const int64_t dt_rank = hparams.ssm_dt_rank;
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        // {n_embd, n_tokens}
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        struct ggml_tensor * state_mask = build_inp_s_mask();
+        struct ggml_tensor * state_seq  = build_inp_s_seq();
+
+        for (int il = 0; il < n_layer; ++il) {
+            // (ab)using the KV cache to store the states
+            struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size);
+            struct ggml_tensor * ssm_states  = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size);
+
+            // clear states of sequences which are starting at the beginning of this batch
+            {
+                conv_states = ggml_mul(ctx0,
+                    ggml_view_2d(ctx0, conv_states, conv_states->ne[0], n_kv, conv_states->nb[1], kv_head*conv_states->nb[1]),
+                    state_mask);
+                ssm_states  = ggml_mul(ctx0,
+                    ggml_view_2d(ctx0, ssm_states, ssm_states->ne[0], n_kv, ssm_states->nb[1], kv_head*ssm_states->nb[1]),
+                    state_mask);
+            }
+
+            conv_states = ggml_reshape_3d(ctx0, conv_states, d_conv - 1, d_inner, n_kv);
+            ssm_states  = ggml_reshape_3d(ctx0,  ssm_states,    d_state, d_inner, n_kv);
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // {n_embd, 2*d_inner} * {n_embd, n_tokens} => {2*d_inner, n_tokens}
+            struct ggml_tensor * xz = ggml_mul_mat(ctx0, model.layers[il].ssm_in, cur);
+            // split the above in two
+            // => {d_inner, n_tokens}
+            struct ggml_tensor * x = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], 0);
+            struct ggml_tensor * z = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], ggml_element_size(xz)*d_inner);
+
+            // conv
+            {
+                // Custom operator which is needed only to ease simultaneous sequence processing.
+                // For a single sequence, the equivalent is to concatenate the columns of conv_states and x,
+                // then make a self-overlapping view of that over d_conv columns at each stride in the 3rd dimension,
+                // then element-wise multiply that with the conv1d weigth,
+                // then sum the elements of each row,
+                // (the last two steps are a dot product over rows (also doable with mul_mat))
+                // then permute away the ne[0] dimension,
+                // and then you're left with the resulting x tensor.
+                // The new conv_states is the last (d_conv - 1) columns
+                // of the last 3rd dimensional "layer" of the self-overlapping view.
+                // For simultaneous sequences, it's more complicated.
+                struct ggml_tensor * x_conv = ggml_ssm_conv(ctx0, conv_states, x, model.layers[il].ssm_conv1d, state_seq);
+
+                // store last (d_conv - 1) columns of the conv_state part of x_conv back into the KV cache
+                ggml_build_forward_expand(gf,
+                    ggml_cpy(ctx0,
+                        ggml_view_2d(ctx0, x_conv, d_conv - 1, d_inner*n_kv, d_conv*ggml_element_size(x_conv), (1+d_inner*n_tokens)*ggml_element_size(x_conv)),
+                        ggml_view_1d(ctx0, kv_self.k_l[il], (d_conv - 1)*(d_inner)*(n_kv), kv_head*(d_conv - 1)*(d_inner)*ggml_element_size(x_conv))));
+
+                // extract x from x_conv
+                x = ggml_view_2d(ctx0, x_conv, d_inner, n_tokens, d_inner*ggml_element_size(x_conv), 0);
+
+                // bias
+                x = ggml_add(ctx0, x, model.layers[il].ssm_conv1d_b);
+
+                x = ggml_silu(ctx0, x);
+            }
+
+            // ssm
+            {
+                // {d_inner, dt_rank + 2*d_state} * {d_inner, n_tokens} => {dt_rank + 2*d_state, n_tokens}
+                struct ggml_tensor * x_db = ggml_mul_mat(ctx0, model.layers[il].ssm_x, x);
+                // split
+                struct ggml_tensor * dt = ggml_view_2d(ctx0, x_db, dt_rank, n_tokens, x_db->nb[1], 0);
+                struct ggml_tensor * B  = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*dt_rank);
+                struct ggml_tensor * C  = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*(dt_rank+d_state));
+
+                // {dt_rank, d_inner} * {dt_rank, n_tokens} => {d_inner, n_tokens}
+                dt = ggml_mul_mat(ctx0, model.layers[il].ssm_dt, dt);
+                dt = ggml_add(ctx0, dt, model.layers[il].ssm_dt_b);
+
+                // Custom operator to optimize the parallel associative scan
+                // as described in the Annex D of the Mamba paper.
+                // => {d_inner, n_tokens} and {d_state, d_inner, n_kv} combined,
+                // because only a single tensor can be returned.
+                struct ggml_tensor * y_ssm_states = ggml_ssm_scan(ctx0, ssm_states, x, dt, model.layers[il].ssm_a, B, C, state_seq);
+
+                // store last states (the second part of y_ssm_states)
+                ggml_build_forward_expand(gf,
+                    ggml_cpy(ctx0,
+                        ggml_view_1d(ctx0, y_ssm_states, d_state*d_inner*n_kv, d_inner*n_tokens*ggml_element_size(y_ssm_states)),
+                        ggml_view_1d(ctx0, kv_self.v_l[il], d_state*d_inner*n_kv, kv_head*d_state*d_inner*ggml_element_size(ssm_states))));
+
+                struct ggml_tensor * y = ggml_view_2d(ctx0, y_ssm_states, d_inner, n_tokens, d_inner*ggml_element_size(y_ssm_states), 0);
+
+                if (il == n_layer - 1) {
+                    // skip computing output for unused tokens
+                    struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                    x    = ggml_get_rows(ctx0,    x, inp_out_ids);
+                    y    = ggml_get_rows(ctx0,    y, inp_out_ids);
+                    z    = ggml_get_rows(ctx0,    z, inp_out_ids);
+                    inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+                }
+
+                // {d_inner, n_tokens} * {d_inner} => {d_inner, n_tokens}
+                y = ggml_add(ctx0, y, ggml_mul(ctx0, x, model.layers[il].ssm_d));
+                y = ggml_mul(ctx0, y, ggml_silu(ctx0, z));
+
+                // {d_inner, n_embd} * {d_inner, n_tokens} => {n_embd, n_tokens}
+                cur = ggml_mul_mat(ctx0, model.layers[il].ssm_out, y);
+            }
+
+            // residual
+            cur = ggml_add(ctx0, cur, inpL);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        // final rmsnorm
+        cur = llm_build_norm(ctx0, inpL, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_command_r() {
+
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        const float f_logit_scale = hparams.f_logit_scale;
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM, cb, il);
+            cb(cur, "attn_norm", il);
+            struct ggml_tensor * ffn_inp = cur;
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                    cb(Qcur, "Qcur", il);
+                }
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                    cb(Kcur, "Kcur", il);
+                }
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                    cb(Vcur, "Vcur", il);
+                }
+
+                Qcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_custom(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur     = ggml_get_rows(ctx0,     cur, inp_out_ids);
+                inpL    = ggml_get_rows(ctx0,    inpL, inp_out_ids);
+                ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
+            }
+
+            struct ggml_tensor * attn_out = cur;
+
+            // feed-forward network
+            {
+                cur = llm_build_ffn(ctx0, ffn_inp,
+                        model.layers[il].ffn_up,   NULL,
+                        model.layers[il].ffn_gate, NULL,
+                        model.layers[il].ffn_down, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            }
+
+            // add together residual + FFN + self-attention
+            cur = ggml_add(ctx0, cur, inpL);
+            cur = ggml_add(ctx0, cur, attn_out);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+
+        if (f_logit_scale) {
+            cur = ggml_scale(ctx0, cur, f_logit_scale);
+        }
+
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+
+    }
 };
 
-// TODO: will be removed with backend v2
-static const std::unordered_map<const char *, llm_offload_func_e> k_offload_map = {
-  //{ "inp_tokens",                 OFFLOAD_FUNC_NR  }, // TODO: missing K-quants get_rows kernel
-  //{ "inp_embd",                   OFFLOAD_FUNC_NR  }, // TODO: missing K-quants get_rows kernel
-    { "pos_embd",                   OFFLOAD_FUNC_NR  },
+static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
+    llama_batch dummy;
+    dummy.n_tokens = 0;
 
-    { "inp_pos",                    OFFLOAD_FUNC_FRC }, // this is often used for KQ ops (e.g. rope)
-    { "KQ_mask",                    OFFLOAD_FUNC_FRC },
-    { "K_shift",                    OFFLOAD_FUNC_FRC },
+    llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
 
-    { "K_shifted",                  OFFLOAD_FUNC     },
+    struct llm_build_context llm(lctx, dummy, cb, false);
 
-    { "inp_norm",                   OFFLOAD_FUNC_NR  },
-    { "inp_norm_w",                 OFFLOAD_FUNC_NR  },
-    { "inp_norm_wb",                OFFLOAD_FUNC_NR  },
+    llm.init();
 
-    { "norm",                       OFFLOAD_FUNC     },
-    { "norm_w",                     OFFLOAD_FUNC     },
-    { "norm_wb",                    OFFLOAD_FUNC     },
+    struct ggml_cgraph * result = llm.build_defrag(ids);
 
-    { "attn_norm",                  OFFLOAD_FUNC     },
-    { "attn_norm_2",                OFFLOAD_FUNC     },
+    llm.free();
 
-    { "wqkv",                       OFFLOAD_FUNC_KQV },
-    { "bqkv",                       OFFLOAD_FUNC_KQV },
-    { "wqkv_clamped",               OFFLOAD_FUNC_KQV },
+    return result;
+}
 
-    { "tmpk",                       OFFLOAD_FUNC_KQV },
-    { "tmpq",                       OFFLOAD_FUNC_KQV },
-    { "tmpv",                       OFFLOAD_FUNC_KQV },
-    { "Kcur",                       OFFLOAD_FUNC_KQV },
-    { "Qcur",                       OFFLOAD_FUNC_KQV },
-    { "Vcur",                       OFFLOAD_FUNC_KQV },
+static struct ggml_cgraph * llama_build_graph_k_shift(llama_context & lctx) {
+    llama_batch dummy;
+    dummy.n_tokens = 0;
 
-    { "krot",                       OFFLOAD_FUNC_KQV },
-    { "qrot",                       OFFLOAD_FUNC_KQV },
-    { "kpass",                      OFFLOAD_FUNC_KQV },
-    { "qpass",                      OFFLOAD_FUNC_KQV },
-    { "krotated",                   OFFLOAD_FUNC_KQV },
-    { "qrotated",                   OFFLOAD_FUNC_KQV },
+    llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
 
-    { "q",                          OFFLOAD_FUNC_KQV },
-    { "k",                          OFFLOAD_FUNC_KQV },
-    { "kq",                         OFFLOAD_FUNC_KQV },
-    { "kq_scaled",                  OFFLOAD_FUNC_KQV },
-    { "kq_scaled_alibi",            OFFLOAD_FUNC_KQV },
-    { "kq_masked",                  OFFLOAD_FUNC_KQV },
-    { "kq_soft_max",                OFFLOAD_FUNC_KQV },
-    { "kq_soft_max_ext",            OFFLOAD_FUNC_KQV },
-    { "v",                          OFFLOAD_FUNC_KQV },
-    { "kqv",                        OFFLOAD_FUNC_KQV },
-    { "kqv_merged",                 OFFLOAD_FUNC_KQV },
-    { "kqv_merged_cont",            OFFLOAD_FUNC_KQV },
-    { "kqv_wo",                     OFFLOAD_FUNC_KQV },
-    { "kqv_out",                    OFFLOAD_FUNC_KQV },
+    struct llm_build_context llm(lctx, dummy, cb, false);
 
-    { "ffn_inp",                    OFFLOAD_FUNC     },
-    { "ffn_norm",                   OFFLOAD_FUNC     },
+    llm.init();
 
-    { "ffn_up",                     OFFLOAD_FUNC     },
-    { "ffn_up_b",                   OFFLOAD_FUNC     },
-    { "ffn_gate",                   OFFLOAD_FUNC     },
-    { "ffn_gate_b",                 OFFLOAD_FUNC     },
-    { "ffn_gate_par",               OFFLOAD_FUNC     },
-    { "ffn_act",                    OFFLOAD_FUNC     },
-    { "ffn_down",                   OFFLOAD_FUNC     },
-    { "ffn_down_b",                 OFFLOAD_FUNC     },
-    { "ffn_out",                    OFFLOAD_FUNC     },
+    struct ggml_cgraph * result = llm.build_k_shift();
 
-    { "ffn_silu",                   OFFLOAD_FUNC     },
-    { "ffn_gelu",                   OFFLOAD_FUNC     },
-    { "ffn_relu",                   OFFLOAD_FUNC     },
-    { "ffn_sqr(relu)",              OFFLOAD_FUNC     },
+    llm.free();
 
-    { "ffn_moe_logits",             OFFLOAD_FUNC     },
-    { "ffn_moe_probs",              OFFLOAD_FUNC     },
-    { "ffn_moe_argsort",            OFFLOAD_FUNC     },
-    { "ffn_moe_weights",            OFFLOAD_FUNC     },
-    { "ffn_moe_weights_sum",        OFFLOAD_FUNC     },
-    { "ffn_moe_weights_norm",       OFFLOAD_FUNC     },
-    { "ffn_moe_weighted",           OFFLOAD_FUNC     },
-    { "ffn_moe_up",                 OFFLOAD_FUNC     },
-    { "ffn_moe_gate",               OFFLOAD_FUNC     },
-    { "ffn_moe_silu",               OFFLOAD_FUNC     },
-    { "ffn_moe_gate_par",           OFFLOAD_FUNC     },
-    { "ffn_moe_down",               OFFLOAD_FUNC     },
-    { "ffn_moe_out",                OFFLOAD_FUNC     },
+    return result;
+}
 
-    { "l_out",                      OFFLOAD_FUNC     },
+static struct ggml_cgraph * llama_build_graph_s_copy(llama_context & lctx) {
+    llama_batch dummy;
+    dummy.n_tokens = 0;
 
-    { "result_norm",                OFFLOAD_FUNC_EMB },
-    { "result_output_no_bias",      OFFLOAD_FUNC_EMB },
-    { "result_output",              OFFLOAD_FUNC_OUT },
-};
+    llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
 
-static llm_offload_trie k_offload_func_trie(k_offload_map);
+    struct llm_build_context llm(lctx, dummy, cb, false);
+
+    llm.init();
+
+    struct ggml_cgraph * result = llm.build_s_copy();
+
+    llm.free();
+
+    return result;
+}
 
 static struct ggml_cgraph * llama_build_graph(
          llama_context & lctx,
-     const llama_batch & batch) {
+     const llama_batch & batch,
+                  bool   worst_case) {
     const auto & model = lctx.model;
 
-    // check if we should build the worst-case graph (for memory measurement)
-    const bool worst_case = ggml_allocr_is_measure(lctx.alloc);
-
-    // keep track of the input that has already been allocated
-    bool alloc_inp_tokens   = false;
-    bool alloc_inp_embd     = false;
-    bool alloc_inp_pos      = false;
-    bool alloc_inp_KQ_mask  = false;
-    bool alloc_inp_K_shift  = false;
-
-#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
-    const bool do_offload = true;
-#else
-    const bool do_offload = true; // TODO: set to false after finishing refactoring
-#endif
-
-    int n_non_view = 0; // number of non-view tensors that have been processed by the callback
-
     // this callback allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.)
-    // TODO: will be removed with backend v2
     llm_build_cb cb = [&](struct ggml_tensor * cur, const char * name, int il) {
         if (il >= 0) {
             ggml_format_name(cur, "%s-%d", name, il);
@@ -6283,244 +9591,26 @@ static struct ggml_cgraph * llama_build_graph(
             ggml_set_name(cur, name);
         }
 
-        //
-        // allocate input tensors and set input data
-        //
-        // TODO: will be removed with backend v2
-
-        if (!alloc_inp_tokens && strcmp(name, "inp_tokens") == 0) {
-            ggml_allocr_alloc(lctx.alloc, cur);
-
-            if (!ggml_allocr_is_measure(lctx.alloc) && batch.token) {
-                const int64_t n_tokens = cur->ne[0];
-
-                ggml_backend_tensor_set(cur, batch.token, 0, n_tokens*ggml_element_size(cur));
+        if (!lctx.cparams.offload_kqv) {
+            if (strcmp(name, "kqv_merged_cont") == 0) {
+                // all nodes between the KV store and the attention output are run on the CPU
+                ggml_backend_sched_set_tensor_backend(lctx.sched, cur, lctx.backend_cpu);
             }
-
-            alloc_inp_tokens = true;
         }
 
-        if (!alloc_inp_embd && strcmp(name, "inp_embd") == 0) {
-            ggml_allocr_alloc(lctx.alloc, cur);
-
-            if (!ggml_allocr_is_measure(lctx.alloc) && batch.embd) {
-                const int64_t n_embd   = cur->ne[0];
-                const int64_t n_tokens = cur->ne[1];
-
-                ggml_backend_tensor_set(cur, batch.embd, 0, n_tokens*n_embd*ggml_element_size(cur));
-            }
-
-            alloc_inp_embd = true;
-        }
-
-        if (!alloc_inp_pos && strcmp(name, "inp_pos") == 0) {
-            ggml_allocr_alloc(lctx.alloc, cur);
-
-            if (!ggml_allocr_is_measure(lctx.alloc) && batch.pos) {
-                const int64_t n_tokens = cur->ne[0];
-
-                static_assert(std::is_same<llama_pos, int32_t>::value, "llama_pos must be int32_t");
-                ggml_backend_tensor_set(cur, batch.pos, 0, n_tokens*ggml_element_size(cur));
-            }
-
-            alloc_inp_pos = true;
-        }
-
-        if (!alloc_inp_KQ_mask && strcmp(name, "KQ_mask") == 0) {
-            ggml_allocr_alloc(lctx.alloc, cur);
-
-            if (!ggml_allocr_is_measure(lctx.alloc)) {
-                const int64_t n_kv     = cur->ne[0];
-                const int64_t n_tokens = cur->ne[1];
-
-                float * data;
-                if (ggml_backend_buffer_is_host(cur->buffer)) {
-                    data = (float *) cur->data;
-                } else {
-                    lctx.buf_copy.resize(ggml_nbytes(cur));
-                    data = (float *) lctx.buf_copy.data();
-                }
-
-                for (int h = 0; h < 1; ++h) {
-                    for (int j = 0; j < n_tokens; ++j) {
-                        const llama_pos    pos    = batch.pos[j];
-                        const llama_seq_id seq_id = batch.seq_id[j][0];
-
-                        for (int i = 0; i < n_kv; ++i) {
-                            float f;
-                            if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) {
-                                f = -INFINITY;
-                            } else {
-                                f = 0;
-                            }
-                            data[h*(n_kv*n_tokens) + j*n_kv + i] = f;
-                        }
+        // norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends
+        // FIXME: fix in ggml_backend_sched
+        const bool full_offload = lctx.model.n_gpu_layers > (int)lctx.model.hparams.n_layer;
+        if (batch.n_tokens < 32 || full_offload) {
+            if (il != -1 && strcmp(name, "norm") == 0) {
+                for (auto * backend : lctx.backends) {
+                    if (ggml_backend_buft_supports_backend(lctx.model.buft_layer[il].buft, backend)) {
+                        ggml_backend_sched_set_tensor_backend(lctx.sched, cur, backend);
+                        break;
                     }
                 }
-
-                if (data != cur->data) {
-                    ggml_backend_tensor_set(cur, data, 0, ggml_nbytes(cur));
-                }
             }
-
-            alloc_inp_KQ_mask = true;
         }
-
-        if (!alloc_inp_K_shift && strcmp(name, "K_shift") == 0) {
-            ggml_allocr_alloc(lctx.alloc, cur);
-
-            if (!ggml_allocr_is_measure(lctx.alloc)) {
-                const int64_t n_ctx = cur->ne[0];
-
-                int32_t * data;
-                if (ggml_backend_buffer_is_host(cur->buffer)) {
-                    data = (int32_t *) cur->data;
-                } else {
-                    lctx.buf_copy.resize(ggml_nbytes(cur));
-                    data = (int32_t *) lctx.buf_copy.data();
-                }
-
-                for (int i = 0; i < n_ctx; ++i) {
-                    data[i] = lctx.kv_self.cells[i].delta;
-                }
-
-                if (data != cur->data) {
-                    ggml_backend_tensor_set(cur, data, 0, ggml_nbytes(cur));
-                }
-            }
-
-            alloc_inp_K_shift = true;
-        }
-
-        // view tensors are not processed further
-        if (cur->view_src != nullptr) {
-            return;
-        }
-
-        if (cur->op != GGML_OP_NONE) {
-            n_non_view++;
-        }
-
-        //
-        // offload layers
-        //
-        // TODO: will be removed with backend v2
-
-//#define LLAMA_OFFLOAD_DEBUG
-
-        if (!do_offload) {
-            return;
-        }
-
-        const int n_layer = model.hparams.n_layer;
-
-        const int n_gpu_layers = model.n_gpu_layers;
-        const int i_gpu_start  = n_layer - n_gpu_layers;
-
-        // should we offload the final norm? yes if we are not computing embeddings
-        const bool offload_emb = lctx.embedding.empty();
-
-        static const std::unordered_map<llm_offload_func_e, std::string, std::hash<int>> k_offload_func_name = {
-            { OFFLOAD_FUNC_NOP, "CPU" },
-            { OFFLOAD_FUNC_OUT, "CPU" },
-#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
-            { OFFLOAD_FUNC,     "GPU (CUDA)"     },
-            { OFFLOAD_FUNC_FRC, "GPU (CUDA) FRC" },
-            { OFFLOAD_FUNC_KQV, "GPU (CUDA) KQV" },
-            { OFFLOAD_FUNC_NR,  "GPU (CUDA) NR"  },
-            { OFFLOAD_FUNC_EMB, "GPU (CUDA) EMB" },
-#else
-            { OFFLOAD_FUNC,     "CPU" },
-            { OFFLOAD_FUNC_FRC, "CPU" },
-            { OFFLOAD_FUNC_KQV, "CPU" },
-            { OFFLOAD_FUNC_NR,  "CPU" },
-            { OFFLOAD_FUNC_EMB, "CPU" },
-#endif // GGML_USE_CUBLAS
-        };
-
-        // check the global map for what offload function to use for this tensor
-        llm_offload_func_e func_e = k_offload_func_trie.find(name);
-
-        if (func_e == OFFLOAD_FUNC_NOP) {
-#ifdef LLAMA_OFFLOAD_DEBUG
-            // if a tensor hasn't been offloaded, we warn the user
-            if (worst_case) {
-                LLAMA_LOG_WARN("%s: %32s: not offloaded (ref: %s)\n", __func__,
-                        cur->name, "https://github.com/ggerganov/llama.cpp/pull/3837");
-            }
-#endif
-
-            return;
-        }
-
-        // count the number of layers and respect the provided n_gpu_layers
-        switch (func_e) {
-            case OFFLOAD_FUNC_NOP:
-            case OFFLOAD_FUNC_OUT:
-                break;
-            case OFFLOAD_FUNC:
-                if (n_gpu_layers < n_layer) {
-                    if (il < i_gpu_start) {
-                        func_e = OFFLOAD_FUNC_NOP;
-                    }
-                }
-                break;
-            case OFFLOAD_FUNC_FRC:
-                if (!lctx.cparams.offload_kqv) {
-                    func_e = OFFLOAD_FUNC_NOP;
-                } break;
-            case OFFLOAD_FUNC_KQV:
-                if (!lctx.cparams.offload_kqv) {
-                    func_e = OFFLOAD_FUNC_NOP;
-                } else {
-                    if (n_gpu_layers < n_layer) {
-                        if (il < i_gpu_start) {
-                            func_e = OFFLOAD_FUNC_NOP;
-                        }
-                    }
-                }
-                break;
-            case OFFLOAD_FUNC_NR:
-                if (n_gpu_layers <= n_layer + 0) {
-                    func_e = OFFLOAD_FUNC_NOP;
-                }
-                break;
-            case OFFLOAD_FUNC_EMB:
-                if (!offload_emb || n_gpu_layers < n_layer) {
-                    func_e = OFFLOAD_FUNC_NOP;
-                }
-                break;
-            default: GGML_ASSERT(false);
-        }
-
-        offload_func_t func = ggml_offload_nop;
-
-        // this is needed for compatibility with Metal for example
-#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
-        static offload_func_t ggml_offload_gpu = ggml_cuda_assign_buffers_no_alloc;
-#else
-        static offload_func_t ggml_offload_gpu = ggml_offload_nop;
-#endif
-
-        switch (func_e) {
-            case OFFLOAD_FUNC_NOP:
-            case OFFLOAD_FUNC_OUT: func = ggml_offload_nop; break;
-            case OFFLOAD_FUNC:
-            case OFFLOAD_FUNC_KQV:
-            case OFFLOAD_FUNC_FRC:
-            case OFFLOAD_FUNC_NR:
-            case OFFLOAD_FUNC_EMB: func = ggml_offload_gpu; break;
-            default: GGML_ASSERT(false);
-        }
-
-        // apply offload function to the tensor
-        func(cur);
-
-#ifdef LLAMA_OFFLOAD_DEBUG
-        if (worst_case) {
-            LLAMA_LOG_INFO("%s: %32s: %s\n", __func__, cur->name, k_offload_func_name.at(func_e).c_str());
-        }
-#endif
     };
 
     struct ggml_cgraph * result = NULL;
@@ -6542,6 +9632,10 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_falcon();
             } break;
+        case LLM_ARCH_GROK:
+            {
+                result = llm.build_grok();
+            } break;
         case LLM_ARCH_STARCODER:
             {
                 result = llm.build_starcoder();
@@ -6554,6 +9648,11 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_refact();
             } break;
+        case LLM_ARCH_BERT:
+        case LLM_ARCH_NOMIC_BERT:
+            {
+                result = llm.build_bert();
+            } break;
         case LLM_ARCH_BLOOM:
             {
                 result = llm.build_bloom();
@@ -6570,6 +9669,10 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_qwen();
             } break;
+        case LLM_ARCH_QWEN2:
+            {
+                result = llm.build_qwen2();
+            } break;
         case LLM_ARCH_PHI2:
             {
                 result = llm.build_phi2();
@@ -6582,34 +9685,409 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_gpt2();
             } break;
+        case LLM_ARCH_CODESHELL:
+            {
+                result = llm.build_codeshell();
+            } break;
+        case LLM_ARCH_ORION:
+            {
+                result = llm.build_orion();
+            } break;
+        case LLM_ARCH_INTERNLM2:
+            {
+                result = llm.build_internlm2();
+            } break;
+        case LLM_ARCH_MINICPM:
+            {
+                result = llm.build_minicpm();
+            } break;
+        case LLM_ARCH_GEMMA:
+            {
+                result = llm.build_gemma();
+            } break;
+        case LLM_ARCH_STARCODER2:
+            {
+                result = llm.build_starcoder2();
+            } break;
+        case LLM_ARCH_MAMBA:
+            {
+                result = llm.build_mamba();
+            } break;
+        case LLM_ARCH_XVERSE:
+            {
+                result = llm.build_xverse();
+            } break;
+        case LLM_ARCH_COMMAND_R:
+            {
+                result = llm.build_command_r();
+            } break;
         default:
             GGML_ASSERT(false);
     }
 
     llm.free();
 
-    if (worst_case) {
-        int n_non_view_total = 0;
+    return result;
+}
 
-        for (int i = 0; i < result->n_nodes; ++i) {
-            if (result->nodes[i]->view_src == nullptr) {
-                n_non_view_total++;
+static void llama_set_k_shift(llama_context & lctx) {
+    const int64_t kv_size = lctx.kv_self.size;
+
+    assert(ggml_backend_buffer_is_host(lctx.inp_K_shift->buffer));
+
+    int32_t * data = (int32_t *) lctx.inp_K_shift->data;
+
+    for (int i = 0; i < kv_size; ++i) {
+        data[i] = lctx.kv_self.cells[i].delta;
+    }
+}
+
+static void llama_set_s_copy(llama_context & lctx) {
+    const int64_t kv_size = lctx.kv_self.size;
+
+    assert(ggml_backend_buffer_is_host(lctx.inp_s_copy->buffer));
+
+    int32_t * data = (int32_t *) lctx.inp_s_copy->data;
+
+    for (int i = 0; i < kv_size; ++i) {
+        data[i] = lctx.kv_self.cells[i].src;
+    }
+}
+
+static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
+    //
+    // set input data
+    //
+
+    const auto & hparams = lctx.model.hparams;
+    const auto & cparams = lctx.cparams;
+    const auto & kv_self = lctx.kv_self;
+
+    if (batch.token) {
+        const int64_t n_tokens = batch.n_tokens;
+
+        ggml_backend_tensor_set(lctx.inp_tokens, batch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens));
+    }
+
+    if (batch.embd) {
+        const int64_t n_embd   = hparams.n_embd;
+        const int64_t n_tokens = batch.n_tokens;
+
+        ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
+    }
+
+    if (batch.pos && lctx.inp_pos) {
+        const int64_t n_tokens = batch.n_tokens;
+
+        ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos));
+    }
+
+    if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) {
+        GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs");
+        const int64_t n_tokens = batch.n_tokens;
+
+        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_out_ids->buffer));
+        int32_t * data = (int32_t *) lctx.inp_out_ids->data;
+
+        if (lctx.n_outputs == n_tokens) {
+            for (int i = 0; i < n_tokens; ++i) {
+                data[i] = i;
             }
-        }
-
-        LLAMA_LOG_INFO("%s: non-view tensors processed: %d/%d\n", __func__, n_non_view, n_non_view_total);
-
-        if (n_non_view != n_non_view_total) {
-            LLAMA_LOG_WARN("%s: ****************************************************************\n", __func__);
-            LLAMA_LOG_WARN("%s: not all non-view tensors have been processed with a callback\n",     __func__);
-            LLAMA_LOG_WARN("%s: this can indicate an inefficiency in the graph implementation\n",    __func__);
-            LLAMA_LOG_WARN("%s: build with LLAMA_OFFLOAD_DEBUG for more info\n",                     __func__);
-            LLAMA_LOG_WARN("%s: ref: https://github.com/ggerganov/llama.cpp/pull/3837\n",            __func__);
-            LLAMA_LOG_WARN("%s: ****************************************************************\n", __func__);
+        } else if (batch.logits) {
+            int32_t n_outputs = 0;
+            for (int i = 0; i < n_tokens; ++i) {
+                if (batch.logits[i]) {
+                    data[n_outputs++] = i;
+                }
+            }
+            // the graph needs to have been passed the correct number of outputs
+            GGML_ASSERT(lctx.n_outputs == n_outputs);
+        } else if (lctx.n_outputs == 1) {
+            // only keep last output
+            data[0] = n_tokens - 1;
+        } else {
+            GGML_ASSERT(lctx.n_outputs == 0);
         }
     }
 
-    return result;
+    GGML_ASSERT(
+        // (!a || b) is a logical implication (a -> b)
+        // !hparams.causal_attn -> !cparams.causal_attn
+        (hparams.causal_attn || !cparams.causal_attn) &&
+        "causal attention with embedding models is not supported"
+    );
+
+    if (lctx.inp_KQ_mask) {
+        // NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache.
+        if (cparams.causal_attn) {
+            const int64_t n_kv     = kv_self.n;
+            const int64_t n_tokens = batch.n_tokens;
+
+            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
+
+            float * data = (float *) lctx.inp_KQ_mask->data;
+
+            // For causal attention, use only the previous KV cells
+            // of the correct sequence for each token of the batch.
+            // It's assumed that if a token in the batch has multiple sequences, they are equivalent.
+            for (int h = 0; h < 1; ++h) {
+                for (int j = 0; j < n_tokens; ++j) {
+                    const llama_pos    pos    = batch.pos[j];
+                    const llama_seq_id seq_id = batch.seq_id[j][0];
+
+                    for (int i = 0; i < n_kv; ++i) {
+                        float f;
+                        if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) {
+                            f = -INFINITY;
+                        } else {
+                            f = 0.0f;
+                        }
+                        data[h*(n_kv*n_tokens) + j*n_kv + i] = f;
+                    }
+                }
+            }
+        } else {
+            // when using kv cache, the mask needs to match the kv cache size
+            const int64_t n_tokens = batch.n_tokens;
+            const int64_t n_stride = hparams.causal_attn ? kv_self.n : n_tokens;
+
+            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
+
+            float * data = (float *) lctx.inp_KQ_mask->data;
+
+            for (int h = 0; h < 1; ++h) {
+                for (int j = 0; j < n_tokens; ++j) {
+                    const llama_seq_id seq_id = batch.seq_id[j][0];
+
+                    for (int i = 0; i < n_tokens; ++i) {
+                        float f = -INFINITY;
+                        for (int s = 0; s < batch.n_seq_id[i]; ++s) {
+                            if (batch.seq_id[i][s] == seq_id) {
+                                f = 0.0f;
+                                break;
+                            }
+                        }
+
+                        data[h*(n_tokens*n_tokens) + j*n_stride + i] = f;
+                    }
+
+                    for (int i = n_tokens; i < n_stride; ++i) {
+                        data[h*(n_tokens*n_tokens) + j*n_stride + i] = -INFINITY;
+                    }
+                }
+            }
+        }
+    }
+
+    if (hparams.need_kq_pos) {
+        const int64_t n_kv = kv_self.n;
+
+        GGML_ASSERT(lctx.inp_KQ_pos);
+        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_pos->buffer));
+
+        float * data = (float *) lctx.inp_KQ_pos->data;
+
+        for (int i = 0; i < n_kv; ++i) {
+            data[i] = float(lctx.kv_self.cells[i].pos);
+        }
+    }
+
+    if (cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) {
+        const int64_t n_tokens = batch.n_tokens;
+
+        GGML_ASSERT(lctx.inp_mean);
+        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer));
+
+        float * data = (float *) lctx.inp_mean->data;
+        memset(lctx.inp_mean->data, 0, n_tokens * n_tokens * ggml_element_size(lctx.inp_mean));
+
+        std::vector<uint64_t> sum(n_tokens, 0);
+        for (int i = 0; i < n_tokens; ++i) {
+            const llama_seq_id seq_id = batch.seq_id[i][0];
+
+            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN");
+
+            sum[seq_id] += 1;
+        }
+
+        std::vector<float> div(n_tokens, 0.0f);
+        for (int i = 0; i < n_tokens; ++i) {
+            const uint64_t s = sum[i];
+            if (s > 0) {
+                div[i] = 1.0f/float(s);
+            }
+        }
+
+        for (int i = 0; i < n_tokens; ++i) {
+            const llama_seq_id seq_id = batch.seq_id[i][0];
+            data[seq_id*n_tokens + i] = div[seq_id];
+        }
+    }
+
+    if (cparams.pooling_type == LLAMA_POOLING_TYPE_CLS) {
+        const int64_t n_tokens = batch.n_tokens;
+
+        GGML_ASSERT(lctx.inp_cls);
+        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
+
+        uint32_t * data = (uint32_t *) lctx.inp_cls->data;
+        memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls));
+
+        for (int i = 0; i < n_tokens; ++i) {
+            const llama_seq_id seq_id = batch.seq_id[i][0];
+            const llama_pos    pos    = batch.pos[i];
+
+            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS");
+
+            if (pos == 0) {
+                data[seq_id] = i;
+            }
+        }
+    }
+
+    if (kv_self.recurrent) {
+        const int64_t n_kv = kv_self.n;
+
+        if (lctx.inp_s_mask) {
+            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_mask->buffer));
+            float * data = (float *) lctx.inp_s_mask->data;
+
+            // states which are not affected by the current batch are left untouched
+            for (int i = 0; i < n_kv; ++i) {
+                llama_seq_id    seq_id       = i + lctx.kv_self.head;
+                llama_kv_cell & kv_cell      = lctx.kv_self.cells[seq_id];
+                bool            has_self_seq = kv_cell.has_seq_id(seq_id);
+
+                data[i] = (float) has_self_seq;
+
+                // ensure current sequences will be kept
+                if (!has_self_seq && kv_cell.pos >= 0) {
+                    kv_cell.seq_id.insert(seq_id);
+                }
+            }
+        }
+        // For Mamba (and other recurrent architectures),
+        // update the correct state(s)/sequence(s) for each token of the batch.
+        // Like with the KQ_mask, if a token in the batch has multiple sequences,
+        // they are assumed to be equivalent (not here, but in ggml_ssm_scan and ggml_ssm_conv).
+        if (lctx.inp_s_seq) {
+            const int64_t n_tokens = batch.n_tokens;
+
+            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_seq->buffer));
+            int32_t * data = (int32_t *) lctx.inp_s_seq->data;
+
+            for (int j = 0; j < n_tokens; ++j) {
+                const int32_t n_seq = batch.n_seq_id[j];
+                GGML_ASSERT(0 < n_seq); // a token should be part of at least 1 sequence
+
+                for (int i = 0; i < n_kv; ++i) {
+                    if (i < n_seq) {
+                        // for this type of model, the head is the minimum seq_id of the batch
+                        data[j*n_kv + i] = batch.seq_id[j][i] - kv_self.head;
+                    } else {
+                        data[j*n_kv + i] = -1;
+                    }
+                }
+            }
+        }
+    }
+}
+
+// Make sure enough space is available for outputs.
+// Returns max number of outputs for which space was reserved.
+static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) {
+    const auto & cparams = lctx.cparams;
+    const auto & hparams = lctx.model.hparams;
+
+    const size_t n_outputs_max = std::max(n_outputs, (size_t) cparams.n_seq_max);
+
+    const auto n_batch = cparams.n_batch;
+    const auto n_vocab = hparams.n_vocab;
+    const auto n_embd  = hparams.n_embd;
+
+    // TODO: use a per-batch flag for logits presence instead
+    const bool has_logits = cparams.causal_attn;
+    const bool has_embd   = cparams.embeddings && (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
+
+    const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0;
+    const size_t embd_size   = has_embd   ?  n_embd*n_outputs_max : 0;
+
+    if (lctx.output_ids.empty()) {
+        // init, never resized afterwards
+        lctx.output_ids.resize(n_batch);
+    }
+
+    const size_t prev_size = lctx.buf_output ? ggml_backend_buffer_get_size(lctx.buf_output) : 0;
+    const size_t new_size  = (logits_size + embd_size) * sizeof(float);
+
+    // alloc only when more than the current capacity is required
+    // TODO: also consider shrinking the buffer
+    if (!lctx.buf_output || prev_size < new_size) {
+        if (lctx.buf_output) {
+#ifndef NDEBUG
+            // This doesn't happen often, but may be annoying in some cases (like the HellaSwag benchmark)
+            LLAMA_LOG_INFO("%s: reallocating output buffer from size %.02f MiB to %.02f MiB\n", __func__, prev_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
+#endif
+            ggml_backend_buffer_free(lctx.buf_output);
+            lctx.buf_output = nullptr;
+            lctx.logits = nullptr;
+            lctx.embd = nullptr;
+        }
+
+        lctx.buf_output = ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(true), new_size);
+        if (lctx.buf_output == nullptr) {
+            LLAMA_LOG_ERROR("%s: failed to allocate output buffer of size %.2f MiB\n", __func__, new_size / (1024.0 * 1024.0));
+            return 0;
+        }
+    }
+
+    float * output_base = (float *) ggml_backend_buffer_get_base(lctx.buf_output);
+
+    lctx.logits = has_logits ? output_base               : nullptr;
+    lctx.embd   = has_embd   ? output_base + logits_size : nullptr;
+
+    lctx.output_size = n_outputs_max;
+    lctx.logits_size = logits_size;
+    lctx.embd_size   = embd_size;
+
+    // set all ids as invalid (negative)
+    std::fill(lctx.output_ids.begin(), lctx.output_ids.end(), -1);
+
+    ggml_backend_buffer_clear(lctx.buf_output, 0);
+
+    lctx.n_outputs = 0;
+
+    return n_outputs_max;
+}
+
+
+static void llama_graph_compute(
+        llama_context & lctx,
+          ggml_cgraph * gf,
+                  int   n_threads) {
+#ifdef GGML_USE_MPI
+    const int64_t n_layer = lctx.model.hparams.n_layer;
+    ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer);
+#endif
+
+#ifdef GGML_USE_METAL
+    if (ggml_backend_is_metal(lctx.backend_metal)) {
+        ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads);
+    }
+#endif
+
+    if (lctx.backend_cpu != nullptr) {
+        ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads);
+        ggml_backend_cpu_set_abort_callback(lctx.backend_cpu, lctx.abort_callback, lctx.abort_callback_data);
+    }
+
+    ggml_backend_sched_graph_compute_async(lctx.sched, gf);
+
+    // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched));
+
+#ifdef GGML_USE_MPI
+    ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer);
+#endif
 }
 
 // decode a batch of tokens by evaluating the transformer
@@ -6623,10 +10101,11 @@ static struct ggml_cgraph * llama_build_graph(
 //
 static int llama_decode_internal(
          llama_context & lctx,
-           llama_batch   batch) {
-    const uint32_t n_tokens = batch.n_tokens;
+           llama_batch   batch_all) { // TODO: rename back to batch
 
-    if (n_tokens == 0) {
+    const uint32_t n_tokens_all = batch_all.n_tokens;
+
+    if (n_tokens_all == 0) {
         LLAMA_LOG_ERROR("%s: n_tokens == 0", __func__);
         return -1;
     }
@@ -6635,14 +10114,16 @@ static int llama_decode_internal(
     const auto & hparams = model.hparams;
     const auto & cparams = lctx.cparams;
 
-    const auto n_batch = cparams.n_batch;
+    GGML_ASSERT((!batch_all.token && batch_all.embd) || (batch_all.token && !batch_all.embd)); // NOLINT
 
-    GGML_ASSERT(n_tokens <= n_batch);
+    GGML_ASSERT(n_tokens_all <= cparams.n_batch);
 
-    int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
-    GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
+    GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch >= n_tokens_all) && "non-causal attention requires n_ubatch >= n_tokens");
 
-    const int64_t t_start_us = ggml_time_us();
+    if (lctx.t_compute_start_us == 0) {
+        lctx.t_compute_start_us = ggml_time_us();
+    }
+    lctx.n_queued_tokens += n_tokens_all;
 
 #ifdef GGML_USE_MPI
     // TODO: needs fix after #3228
@@ -6650,236 +10131,604 @@ static int llama_decode_internal(
     //ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
 #endif
 
-    GGML_ASSERT(n_threads > 0);
-
     auto & kv_self = lctx.kv_self;
 
-    GGML_ASSERT(!!kv_self.ctx);
-
     const int64_t n_embd  = hparams.n_embd;
     const int64_t n_vocab = hparams.n_vocab;
 
-    // helpers for smoother batch API transition
-    // after deprecating the llama_eval calls, these will be removed
-    std::vector<llama_pos> pos;
+    uint32_t n_outputs = 0;
+    uint32_t n_outputs_prev = 0;
 
+    const auto n_ubatch = cparams.n_ubatch;
+
+    std::vector<llama_pos> pos;
     std::vector<int32_t>                   n_seq_id;
     std::vector<llama_seq_id *>            seq_id_arr;
     std::vector<std::vector<llama_seq_id>> seq_id;
 
-    if (batch.pos == nullptr) {
-        pos.resize(n_tokens);
-        for (uint32_t i = 0; i < n_tokens; i++) {
-            pos[i] = batch.all_pos_0 + i*batch.all_pos_1;
+    // count outputs
+    if (batch_all.logits) {
+        for (uint32_t i = 0; i < n_tokens_all; ++i) {
+            n_outputs += batch_all.logits[i] != 0;
         }
-
-        batch.pos = pos.data();
+    } else if (lctx.logits_all || (cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE)) {
+        n_outputs = n_tokens_all;
+    } else {
+        // keep last output only
+        n_outputs = 1;
     }
 
-    if (batch.seq_id == nullptr) {
-        n_seq_id.resize(n_tokens);
-        seq_id.resize(n_tokens);
-        seq_id_arr.resize(n_tokens);
-        for (uint32_t i = 0; i < n_tokens; i++) {
-            n_seq_id[i] = 1;
-            seq_id[i].resize(1);
-            seq_id[i][0] = batch.all_seq_id;
-            seq_id_arr[i] = seq_id[i].data();
+    // reserve output buffer
+    if (llama_output_reserve(lctx, n_outputs) < n_outputs) {
+        LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_outputs);
+        return -2;
+    };
+
+    // set output mappings
+    if (batch_all.logits) {
+        int32_t i_logits = 0;
+        for (uint32_t i = 0; i < n_tokens_all; ++i) {
+            if (batch_all.logits[i]) {
+                lctx.output_ids[i] = i_logits++;
+            }
         }
-
-        batch.n_seq_id = n_seq_id.data();
-        batch.seq_id = seq_id_arr.data();
-    }
-
-    // if we have enough unused cells before the current head ->
-    //   better to start searching from the beginning of the cache, hoping to fill it
-    if (kv_self.head > kv_self.used + 2*n_tokens) {
-        kv_self.head = 0;
-    }
-
-    if (!llama_kv_cache_find_slot(kv_self, batch)) {
-        return 1;
-    }
-
-    // a heuristic, to avoid attending the full cache if it is not yet utilized
-    // after enough generations, the benefit from this heuristic disappears
-    // if we start defragmenting the cache, the benefit from this will be more important
-    kv_self.n = std::min((int32_t) cparams.n_ctx, std::max(32, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32)));
-    //kv_self.n = llama_kv_cache_cell_max(kv_self);
-
-    //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head);
-
-    ggml_allocr_reset(lctx.alloc);
-
-    ggml_cgraph * gf = llama_build_graph(lctx, batch);
-
-    ggml_allocr_alloc_graph(lctx.alloc, gf);
-
-    // the output is always the last tensor in the graph
-    struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
-    GGML_ASSERT(strcmp(res->name, "result_output") == 0);
-
-    // the embeddings could be the second to last tensor, or the third to last tensor
-    struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2];
-    if (strcmp(embeddings->name, "result_norm") != 0) {
-        embeddings = gf->nodes[gf->n_nodes - 3];
-        GGML_ASSERT(strcmp(embeddings->name, "result_norm") == 0);
-    }
-
-#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
-    char * buf_alloc_base = (char *)ggml_backend_buffer_get_base(lctx.buf_alloc);
-    for (int i = 0; i < gf->n_leafs; i++) {
-        ggml_tensor * node = gf->leafs[i];
-        if (node->backend == GGML_BACKEND_GPU && node->extra == NULL) {
-            ggml_cuda_assign_scratch_offset(node, (char *)node->data - buf_alloc_base);
-            ggml_cuda_copy_to_device(node);
+    } else {
+        for (uint32_t i = 0; i < n_outputs; ++i) {
+            lctx.output_ids[i] = i;
         }
     }
 
-    for (int i = 0; i < gf->n_nodes; i++) {
-        ggml_tensor * node = gf->nodes[i];
-        if (node->backend == GGML_BACKEND_GPU && node->extra == NULL) {
-            ggml_cuda_assign_scratch_offset(node, (char *)node->data - buf_alloc_base);
+    for (uint32_t cur_token = 0; cur_token < n_tokens_all; cur_token += n_ubatch) {
+        const uint32_t n_tokens = std::min(n_ubatch, n_tokens_all - cur_token);
+        llama_batch u_batch = {
+            /* .n_tokens   = */ (int32_t) n_tokens,
+            /* .token      = */ batch_all.token     ? batch_all.token    + cur_token        : nullptr,
+            /* .embd       = */ batch_all.embd      ? batch_all.embd     + cur_token*n_embd : nullptr,
+            /* .pos        = */ batch_all.pos       ? batch_all.pos      + cur_token        : nullptr,
+            /* .n_seq_id   = */ batch_all.n_seq_id  ? batch_all.n_seq_id + cur_token        : nullptr,
+            /* .seq_id     = */ batch_all.seq_id    ? batch_all.seq_id   + cur_token        : nullptr,
+            /* .logits     = */ batch_all.logits    ? batch_all.logits   + cur_token        : nullptr,
+            /* .all_pos_0  = */ batch_all.all_pos_0 + (llama_pos) cur_token*batch_all.all_pos_1,
+            /* .all_pos_1  = */ batch_all.all_pos_1,
+            /* .all_seq_id = */ batch_all.all_seq_id,
+        };
+
+        // count the outputs in this u_batch
+        {
+            int32_t n_outputs_new = 0;
+
+            if (u_batch.logits) {
+                for (uint32_t i = 0; i < n_tokens; i++) {
+                    n_outputs_new += u_batch.logits[i] != 0;
+                }
+            } else if (n_outputs == n_tokens_all) {
+                n_outputs_new = n_tokens;
+            } else {
+                // keep last output only
+                if (cur_token + n_tokens >= n_tokens_all) {
+                    n_outputs_new = 1;
+                }
+            }
+
+            // needs to happen before the graph is built
+            lctx.n_outputs = n_outputs_new;
+        }
+
+        int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
+        GGML_ASSERT(n_threads > 0);
+
+        // helpers for smoother batch API transition
+        // after deprecating the llama_eval calls, these will be removed
+        if (u_batch.pos == nullptr) {
+            pos.resize(n_tokens);
+            for (uint32_t i = 0; i < n_tokens; i++) {
+                pos[i] = u_batch.all_pos_0 + i*u_batch.all_pos_1;
+            }
+
+            u_batch.pos = pos.data();
+        }
+
+        if (u_batch.seq_id == nullptr) {
+            n_seq_id.resize(n_tokens);
+            seq_id.resize(n_tokens);
+            seq_id_arr.resize(n_tokens);
+            for (uint32_t i = 0; i < n_tokens; i++) {
+                n_seq_id[i] = 1;
+                seq_id[i].resize(1);
+                seq_id[i][0] = u_batch.all_seq_id;
+                seq_id_arr[i] = seq_id[i].data();
+            }
+
+            u_batch.n_seq_id = n_seq_id.data();
+            u_batch.seq_id = seq_id_arr.data();
+        }
+
+        // non-causal masks do not use the KV cache
+        if (hparams.causal_attn) {
+            llama_kv_cache_update(&lctx);
+
+            // if we have enough unused cells before the current head ->
+            //   better to start searching from the beginning of the cache, hoping to fill it
+            if (kv_self.head > kv_self.used + 2*n_tokens) {
+                kv_self.head = 0;
+            }
+
+            if (!llama_kv_cache_find_slot(kv_self, u_batch)) {
+                return 1;
+            }
+
+            if (!kv_self.recurrent) {
+                // a heuristic, to avoid attending the full cache if it is not yet utilized
+                // after enough generations, the benefit from this heuristic disappears
+                // if we start defragmenting the cache, the benefit from this will be more important
+                kv_self.n = std::min(kv_self.size, std::max(32u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32)));
+                //kv_self.n = llama_kv_cache_cell_max(kv_self);
+            }
+        }
+
+        //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head);
+
+        ggml_backend_sched_reset(lctx.sched);
+        ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data);
+
+        ggml_cgraph * gf = llama_build_graph(lctx, u_batch, false);
+
+        // the output is always the last tensor in the graph
+        struct ggml_tensor * res  = gf->nodes[gf->n_nodes - 1];
+        struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 2];
+
+        if (lctx.n_outputs == 0) {
+            // no output
+            res  = nullptr;
+            embd = nullptr;
+        } else if (!hparams.causal_attn) {
+            res = nullptr; // do not extract logits for embedding models such as BERT
+
+            // token or sequence embeddings
+            embd = gf->nodes[gf->n_nodes - 1];
+
+            GGML_ASSERT(strcmp(embd->name, "result_embd") == 0 || strcmp(embd->name, "result_embd_pooled") == 0);
+        } else if (cparams.embeddings) {
+            // the embeddings could be in the second to last tensor, or any of the previous tensors
+            int i_embd = gf->n_nodes - 2;
+            for (int i = 3; strcmp(embd->name, "result_norm") != 0; ++i) {
+                i_embd = gf->n_nodes - i;
+                if (i_embd < 0) { break; }
+                embd = gf->nodes[i_embd];
+            }
+            GGML_ASSERT(i_embd >= 0 && "missing result_norm tensor");
+
+            // TODO: use a per-batch flag to know when to skip logits while keeping embeddings
+            if (!cparams.causal_attn) {
+                res = nullptr; // do not extract logits when not needed
+                // skip computing logits
+                // TODO: is this safe?
+                gf->n_nodes = i_embd + 1;
+            }
+        } else {
+            embd = nullptr; // do not extract embeddings when not needed
+            GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor");
+        }
+        // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
+
+        // for big prompts, if BLAS is enabled, it is better to use only one thread
+        // otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
+        // TODO: this is mostly important for Apple Silicon where CBLAS is still performing very well
+        //       we still need some threads to process all non-mul_mat ops, but not too much to avoid interfering
+        //       with the BLAS calls. need a better solution
+        // MoE Special Case: This logic applies when hparams.n_expert == 0, i.e. the model is NOT an MoE model. When an MoE is
+        //                   being processed then Accelerate/BLAS will not be involved, so capping would limit performance.
+        if (n_tokens >= 32 && hparams.n_expert == 0 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) {
+            n_threads = std::min(4, n_threads);
+        }
+
+        ggml_backend_sched_alloc_graph(lctx.sched, gf);
+
+        llama_set_inputs(lctx, u_batch);
+
+        llama_graph_compute(lctx, gf, n_threads);
+
+        // update the kv ring buffer
+        {
+            kv_self.head += n_tokens;
+
+            // Ensure kv cache head points to a valid index.
+            if (kv_self.head >= kv_self.size) {
+                kv_self.head = 0;
+            }
+        }
+
+#ifdef GGML_PERF
+        // print timing information per ggml operation (for debugging purposes)
+        // requires GGML_PERF to be defined
+        ggml_graph_print(gf);
+#endif
+
+        // plot the computation graph in dot format (for debugging purposes)
+        //if (n_past%100 == 0) {
+        //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
+        //}
+
+        // extract logits
+        if (res) {
+            ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(lctx.sched, res);
+            GGML_ASSERT(backend_res != nullptr);
+            GGML_ASSERT(lctx.logits != nullptr);
+
+            float * logits_out = lctx.logits + n_outputs_prev*n_vocab;
+            const int32_t n_outputs_new = lctx.n_outputs;
+
+            if (n_outputs_new) {
+                GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs);
+                GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_vocab <= (int64_t) lctx.logits_size);
+                ggml_backend_tensor_get_async(backend_res, res, logits_out, 0, n_outputs_new*n_vocab*sizeof(float));
+            }
+        }
+
+        // extract embeddings
+        if (embd) {
+            ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd);
+            GGML_ASSERT(backend_embd != nullptr);
+
+            switch (cparams.pooling_type) {
+                case LLAMA_POOLING_TYPE_NONE:
+                    {
+                        // extract token embeddings
+                        GGML_ASSERT(lctx.embd != nullptr);
+                        float * embd_out = lctx.embd + n_outputs_prev*n_embd;
+                        const int32_t n_outputs_new = lctx.n_outputs;
+
+                        if (n_outputs_new) {
+                            GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs);
+                            GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_embd <= (int64_t) lctx.embd_size);
+                            ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_outputs_new*n_embd*sizeof(float));
+                        }
+                    } break;
+                case LLAMA_POOLING_TYPE_CLS:
+                case LLAMA_POOLING_TYPE_MEAN:
+                    {
+                        GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0);
+
+                        // extract sequence embeddings
+                        auto & embd_seq_out = lctx.embd_seq;
+                        embd_seq_out.clear();
+
+                        for (uint32_t i = 0; i < n_tokens; i++) {
+                            const llama_seq_id seq_id = u_batch.seq_id[i][0];
+                            if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
+                                continue;
+                            }
+                            embd_seq_out[seq_id].resize(n_embd);
+                            ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
+                        }
+                    } break;
+                case LLAMA_POOLING_TYPE_UNSPECIFIED:
+                    {
+                        GGML_ASSERT(false && "unknown pooling type");
+                    } break;
+            }
+        }
+        n_outputs_prev += lctx.n_outputs;
+    }
+
+    // wait for the computation to finish (automatically done when obtaining the model output)
+    //llama_synchronize(&lctx);
+
+    // decide if we need to defrag the kv cache
+    if (cparams.causal_attn && cparams.defrag_thold >= 0.0f) {
+        const float fragmentation = kv_self.n >= 128 ? 1.0f - float(kv_self.used)/float(kv_self.n) : 0.0f;
+
+        // queue defragmentation for next llama_kv_cache_update
+        if (fragmentation > cparams.defrag_thold) {
+            //LLAMA_LOG_INFO("fragmentation: %.2f\n", fragmentation);
+
+            llama_kv_cache_defrag(kv_self);
         }
     }
 
-    // HACK: ggml-alloc may change the tensor backend when reusing a parent, so force output to be on the CPU here if needed
-    if (!lctx.embedding.empty()) {
-        embeddings->backend = GGML_BACKEND_CPU;
+    return 0;
+}
+
+
+// find holes from the beginning of the KV cache and fill them by moving data from the end of the cache
+static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
+    auto & kv_self = lctx.kv_self;
+
+    const auto & hparams = lctx.model.hparams;
+
+    const uint32_t n_layer = hparams.n_layer;
+
+    const uint32_t n_kv   = llama_kv_cache_cell_max(kv_self);
+    const uint32_t n_used = kv_self.used;
+
+    assert(n_used <= n_kv);
+
+    //const int64_t t_start = ggml_time_us();
+
+    // number of cells moved
+    uint32_t n_moves = 0;
+
+    // each move requires 6*n_layer tensors (see build_defrag)
+    //   - source view, destination view, copy operation
+    //   - x2 for keys and values
+    const uint32_t max_moves = LLAMA_MAX_NODES/(6*n_layer);
+
+    // determine which KV cells to move where
+    //
+    //  cell i moves to ids[i]
+    //
+    //  if ids[i] == i || ids[i] == n_kv, then cell i is not moved
+    //
+    std::vector<uint32_t> ids(n_kv, n_kv);
+
+    for (uint32_t i0 = 0; i0 < n_used; ++i0) {
+        const auto & cell0 = kv_self.cells[i0];
+
+        if (!cell0.is_empty()) {
+            ids[i0] = i0;
+
+            continue;
+        }
+
+        // found a hole - fill it with data from the end of the cache
+
+        uint32_t nh = 1;
+
+        // determine the size of the hole
+        while (i0 + nh < n_used && kv_self.cells[i0 + nh].is_empty()) {
+            nh++;
+        }
+
+        uint32_t nf = 0;
+        uint32_t is = n_kv - 1;
+
+        // starting from the end, find nh non-empty cells
+        for (; is > i0; --is) {
+            const auto & cell1 = kv_self.cells[is];
+
+            if (cell1.is_empty() || ids[is] != n_kv) {
+                continue;
+            }
+
+            // non-empty cell which is not yet moved
+            nf++;
+
+            if (nf == nh) {
+                break;
+            }
+        }
+
+        // this can only happen if `n_used` is not accurate, which would be a bug
+        GGML_ASSERT(nf == nh && "KV defrag bug: nf != nh");
+
+        nf = 0;
+
+        uint32_t i1 = is;
+
+        // are we moving a continuous block of memory?
+        bool cont = false;
+
+        // should we stop searching for the next move?
+        bool stop = false;
+
+        // go back and move the nf cells to the hole
+        for (; i1 < n_kv; ++i1) {
+            auto & cell1 = kv_self.cells[i1];
+
+            if (cell1.is_empty() || ids[i1] != n_kv) {
+                if (n_moves == max_moves) {
+                    stop = true;
+                    break;
+                }
+
+                cont = false;
+                continue;
+            }
+
+            // this cell goes to (i0 + nf)
+            ids[i1] = i0 + nf;
+
+            // move the cell meta data
+            kv_self.cells[i0 + nf] = cell1;
+
+            // clear the old cell and move the head there
+            cell1 = llama_kv_cell();
+            kv_self.head = n_used;
+
+            if (!cont) {
+                n_moves++;
+                cont = true;
+            }
+
+            nf++;
+
+            if (nf == nh) {
+                break;
+            }
+        }
+
+        if (stop || n_moves == max_moves) {
+            break;
+        }
+
+        //LLAMA_LOG_INFO("(tmp log) KV defrag: move [%u, %u) to [%u, %u)\n", is, i1 + 1, i0, i0 + nh);
+
+        i0 += nh - 1;
     }
-    res->backend = GGML_BACKEND_CPU;
+
+    if (n_moves == 0) {
+        return;
+    }
+
+    //LLAMA_LOG_INFO("(tmp log) KV defrag cell moves: %u\n", n_moves);
+
+    //LLAMA_LOG_INFO("expected gf nodes: %u\n", 6*n_moves*n_layer);
+
+#if 0
+    // CPU defrag
+    //
+    // TODO: optimizations are possible:
+    //       - multiple threads
+    //       - avoid copying to the host memory when already there
+    //
+    // likely not worth the effort, as we have ggml_graph based defrag
+    //
+
+    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa();
+    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
+
+    const uint32_t kv_size = kv_self.size;
+
+    std::vector<uint8_t> buf_k;
+    std::vector<uint8_t> buf_v;
+
+    for (uint32_t il = 0; il < n_layer; ++il) {
+        const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
+        const size_t k_size     = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_size);
+
+        const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
+        const size_t v_size    = ggml_row_size (kv_self.v_l[il]->type, n_embd_v_gqa*kv_size);
+
+        buf_k.resize(k_size);
+        buf_v.resize(v_size);
+
+        ggml_backend_tensor_get(kv_self.k_l[il], buf_k.data(), 0, buf_k.size());
+        ggml_backend_tensor_get(kv_self.v_l[il], buf_v.data(), 0, buf_v.size());
+
+        // batch move [i, i+nm) to [id, id+nm)
+        // note: cells can move only to a lower index
+        for (uint32_t i = 0; i < n_kv; ++i) {
+            const uint32_t id = ids[i];
+
+            if (i == id || id == n_kv) {
+                continue;
+            }
+
+            uint32_t nm = 1;
+
+            while (i + nm < n_kv && ids[i + nm] == id + nm) {
+                nm++;
+            }
+
+            // move keys
+            {
+                const int64_t os =  i*k_size_row;
+                const int64_t od = id*k_size_row;
+
+                memcpy(buf_k.data() + od, buf_k.data() + os, nm*k_size_row);
+            }
+
+            // move values (note: they are transposed)
+            {
+                const int64_t os =  i;
+                const int64_t od = id;
+
+                for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+                    memcpy(buf_v.data() + (od + j*kv_size)*v_size_el, buf_v.data() + (os + j*kv_size)*v_size_el, nm*v_size_el);
+                }
+            }
+
+            i += nm - 1;
+        }
+
+        ggml_backend_tensor_set(kv_self.k_l[il], buf_k.data(), 0, buf_k.size());
+        ggml_backend_tensor_set(kv_self.v_l[il], buf_v.data(), 0, buf_v.size());
+    }
+#else
+    // ggml_graph defrag
+
+    ggml_backend_sched_reset(lctx.sched);
+
+    ggml_cgraph * gf = llama_build_graph_defrag(lctx, ids);
+
+    llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
 #endif
 
-    // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
+    //const int64_t t_end = ggml_time_us();
 
-    // for big prompts, if BLAS is enabled, it is better to use only one thread
-    // otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
-    // TODO: this is mostly important for Apple Silicon where CBLAS is still performing very well
-    //       we still need some threads to process all non-mul_mat ops, but not too much to avoid interfering
-    //       with the BLAS calls. need a better solution
-    if (n_tokens >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) {
-        n_threads = std::min(4, n_threads);
-    }
+    //LLAMA_LOG_INFO("(tmp log) KV defrag time: %.3f ms\n", (t_end - t_start)/1000.0);
+}
 
-    const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 1;
-    if (ggml_cpu_has_cublas() && fully_offloaded) {
-        n_threads = 1;
-    }
+static void llama_kv_cache_update_internal(struct llama_context & lctx) {
+    bool need_reserve = false;
 
-#ifdef GGML_USE_MPI
-    const int64_t n_layer = hparams.n_layer;
-    ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer);
-#endif
+    // apply K-shift if needed
+    if (lctx.model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE && lctx.kv_self.has_shift) {
+        {
+            ggml_backend_sched_reset(lctx.sched);
 
-#ifdef GGML_USE_METAL
-    if (ggml_backend_is_metal(lctx.backend)) {
-        ggml_backend_metal_set_n_cb(lctx.backend, n_threads);
-    }
-#endif
+            ggml_cgraph * gf = llama_build_graph_k_shift(lctx);
 
-    if (ggml_backend_is_cpu(lctx.backend)) {
-        ggml_backend_cpu_set_n_threads(lctx.backend, n_threads);
-    }
-    ggml_backend_graph_compute(lctx.backend, gf);
+            ggml_backend_sched_alloc_graph(lctx.sched, gf);
 
-#ifdef GGML_USE_MPI
-    ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer);
-#endif
+            llama_set_k_shift(lctx);
+
+            llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
+
+            need_reserve = true;
+        }
+
+        {
+            auto & kv_self = lctx.kv_self;
 
-    // update the kv ring buffer
-    {
-        if (kv_self.has_shift) {
             kv_self.has_shift = false;
+
             for (uint32_t i = 0; i < kv_self.size; ++i) {
                 kv_self.cells[i].delta = 0;
             }
         }
-
-        kv_self.head += n_tokens;
-
-        // Ensure kv cache head points to a valid index.
-        if (kv_self.head >= kv_self.size) {
-            kv_self.head = 0;
-        }
     }
 
-#ifdef GGML_PERF
-    // print timing information per ggml operation (for debugging purposes)
-    // requires GGML_PERF to be defined
-    ggml_graph_print(gf);
-#endif
+    if (lctx.kv_self.recurrent && lctx.kv_self.do_copy) {
+        {
+            ggml_backend_sched_reset(lctx.sched);
 
-    // plot the computation graph in dot format (for debugging purposes)
-    //if (n_past%100 == 0) {
-    //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
-    //}
+            ggml_cgraph * gf = llama_build_graph_s_copy(lctx);
 
-    // extract logits
-    // TODO: do not compute and extract logits if only embeddings are needed
-    //       need to update the graphs to skip "result_output"
-    {
-        auto & logits_out = lctx.logits;
+            ggml_backend_sched_alloc_graph(lctx.sched, gf);
 
-#ifndef NDEBUG
-        auto & logits_valid = lctx.logits_valid;
-        logits_valid.clear();
-        logits_valid.resize(n_tokens);
+            llama_set_s_copy(lctx);
 
-        logits_out.clear();
-#endif
+            llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
 
-        if (batch.logits) {
-            logits_out.resize(n_vocab * n_tokens);
-            for (uint32_t i = 0; i < n_tokens; i++) {
-                if (batch.logits[i] == 0) {
-                    continue;
-                }
-                ggml_backend_tensor_get(res, logits_out.data() + (n_vocab*i), (n_vocab*i)*sizeof(float), n_vocab*sizeof(float));
-#ifndef NDEBUG
-                logits_valid[i] = true;
-#endif
+            need_reserve = true;
+        }
+
+        {
+            auto & kv_self = lctx.kv_self;
+
+            kv_self.do_copy = false;
+
+            for (uint32_t i = 0; i < kv_self.size; ++i) {
+                kv_self.cells[i].src = i;
             }
-        } else if (lctx.logits_all) {
-            logits_out.resize(n_vocab * n_tokens);
-            ggml_backend_tensor_get(res, logits_out.data(), 0, n_vocab*n_tokens*sizeof(float));
-#ifndef NDEBUG
-            std::fill(logits_valid.begin(), logits_valid.end(), true);
-#endif
-        } else {
-            logits_out.resize(n_vocab);
-            ggml_backend_tensor_get(res, logits_out.data(), (n_vocab*(n_tokens - 1))*sizeof(float), n_vocab*sizeof(float));
-#ifndef NDEBUG
-            logits_valid[0] = true;
-#endif
         }
     }
 
-    // extract embeddings
-    if (!lctx.embedding.empty()) {
-        auto & embedding_out = lctx.embedding;
+    // defragment the KV cache if needed
+    if (lctx.kv_self.do_defrag) {
+        llama_kv_cache_defrag_internal(lctx);
 
-        embedding_out.resize(n_embd);
-        ggml_backend_tensor_get(embeddings, embedding_out.data(), (n_embd*(n_tokens - 1))*sizeof(float), n_embd*sizeof(float));
+        need_reserve = true;
+
+        lctx.kv_self.do_defrag = false;
     }
 
-    // measure the performance only for the single-token evals
-    if (n_tokens == 1) {
-        lctx.t_eval_us += ggml_time_us() - t_start_us;
-        lctx.n_eval++;
-    }
-    else if (n_tokens > 1) {
-        lctx.t_p_eval_us += ggml_time_us() - t_start_us;
-        lctx.n_p_eval += n_tokens;
-    }
+    // reserve a worst case graph again
+    if (need_reserve) {
+        // TODO: extract to a function
+        // build worst-case graph
+        int n_tokens = (int)std::min(lctx.cparams.n_ctx, lctx.cparams.n_ubatch);
+        int n_past = lctx.cparams.n_ctx - n_tokens;
+        llama_token token = llama_token_bos(&lctx.model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
+        ggml_cgraph * gf = llama_build_graph(lctx, llama_batch_get_one(&token, n_tokens, n_past, 0), true);
 
-    // get a more accurate load time, upon first eval
-    // TODO: fix this
-    if (!lctx.has_evaluated_once) {
-        lctx.t_load_us = ggml_time_us() - lctx.t_start_us;
-        lctx.has_evaluated_once = true;
+        // initialize scheduler with the worst-case graph
+        ggml_backend_sched_reset(lctx.sched);
+        if (!ggml_backend_sched_reserve(lctx.sched, gf)) {
+            LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
+        }
     }
-
-    return 0;
 }
 
 //
@@ -6891,54 +10740,71 @@ static enum llama_vocab_type llama_vocab_get_type(const llama_vocab & vocab) {
 }
 
 static bool llama_is_normal_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
     return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL;
 }
 
 static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
     return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_UNKNOWN;
 }
 
 static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
     return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_CONTROL;
 }
 
 static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
     return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_BYTE;
 }
 
 static bool llama_is_user_defined_token(const llama_vocab& vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
     return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_USER_DEFINED;
 }
 
 static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) {
+    GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
     GGML_ASSERT(llama_is_byte_token(vocab, id));
     const auto& token_data = vocab.id_to_token.at(id);
     switch (llama_vocab_get_type(vocab)) {
-    case LLAMA_VOCAB_TYPE_SPM: {
-        auto buf = token_data.text.substr(3, 2);
-        return strtol(buf.c_str(), NULL, 16);
-    }
-    case LLAMA_VOCAB_TYPE_BPE: {
-        GGML_ASSERT(false);
-        return unicode_to_bytes_bpe(token_data.text);
-    }
-    default:
-        GGML_ASSERT(false);
+        case LLAMA_VOCAB_TYPE_SPM: {
+            auto buf = token_data.text.substr(3, 2);
+            return strtol(buf.c_str(), NULL, 16);
+        }
+        case LLAMA_VOCAB_TYPE_BPE: {
+            GGML_ASSERT(false);
+            return unicode_utf8_to_byte(token_data.text);
+        }
+        case LLAMA_VOCAB_TYPE_WPM: {
+            GGML_ASSERT(false);
+        }
+        default:
+            GGML_ASSERT(false);
     }
 }
 
 static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
+    GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
     static const char * hex = "0123456789ABCDEF";
     switch (llama_vocab_get_type(vocab)) {
-    case LLAMA_VOCAB_TYPE_SPM: {
-        const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 };
-        return vocab.token_to_id.at(buf);
-    }
-    case LLAMA_VOCAB_TYPE_BPE: {
-        return vocab.token_to_id.at(bytes_to_unicode_bpe(ch));
-    }
-    default:
-        GGML_ASSERT(false);
+        case LLAMA_VOCAB_TYPE_SPM: {
+            const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 };
+            auto token = vocab.token_to_id.find(buf);
+            if (token != vocab.token_to_id.end()) {
+                return (*token).second;
+            }
+            // Try to fall back to just the byte as a string
+            const char buf2[2] = { (char)ch, 0 };
+            return vocab.token_to_id.at(buf2);
+        }
+        case LLAMA_VOCAB_TYPE_WPM:
+        case LLAMA_VOCAB_TYPE_BPE: {
+            return vocab.token_to_id.at(unicode_byte_to_utf8(ch));
+        }
+        default:
+            GGML_ASSERT(false);
     }
 }
 
@@ -6979,7 +10845,7 @@ struct llm_bigram_spm {
 };
 
 struct llm_tokenizer_spm {
-    llm_tokenizer_spm(const llama_vocab & vocab): vocab(vocab) {}
+    llm_tokenizer_spm(const llama_vocab & vocab) : vocab(vocab) {}
 
     void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
         // split string into utf8 chars
@@ -7054,6 +10920,7 @@ private:
 
         if (p == rev_merge.end()) {
             // output any symbols that did not form tokens as bytes.
+            output.reserve(output.size() + symbol.n);
             for (int j = 0; j < (int)symbol.n; ++j) {
                 llama_vocab::id token_id = llama_byte_to_token(vocab, symbol.text[j]);
                 output.push_back(token_id);
@@ -7274,9 +11141,9 @@ private:
         bpe_words.reserve(text.size());
         bpe_encoded_words.reserve(text.size());
 
-        auto cps = codepoints_from_utf8(text);
-        for (size_t i = 0; i < cps.size(); ++i)
-            text_utf.emplace_back(codepoint_to_utf8(cps[i]));
+        const auto cpts = unicode_cpts_from_utf8(text);
+        for (size_t i = 0; i < cpts.size(); ++i)
+            text_utf.emplace_back(unicode_cpt_to_utf8(cpts[i]));
 
         for (int i = 0; i < (int)text_utf.size(); i++) {
             const std::string & utf_char = text_utf[i];
@@ -7326,40 +11193,40 @@ private:
             }
 
             if (!split_condition && !collecting) {
-                if (codepoint_type(utf_char) == CODEPOINT_TYPE_LETTER || (!token.size() && utf_char == " " && codepoint_type(utf_char_next) == CODEPOINT_TYPE_LETTER)) {
+                if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || (!token.size() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER)) {
                     collecting_letter = true;
                     collecting = true;
                 }
-                else if (codepoint_type(utf_char) == CODEPOINT_TYPE_DIGIT || (!token.size() && utf_char == " " && codepoint_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) {
+                else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_DIGIT || (!token.size() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) {
                     collecting_numeric = true;
                     collecting = true;
                 }
                 else if (
-                    ((codepoint_type(utf_char) != CODEPOINT_TYPE_LETTER && codepoint_type(utf_char) != CODEPOINT_TYPE_DIGIT) && (codepoint_type(utf_char) != CODEPOINT_TYPE_WHITESPACE)) ||
-                    (!token.size() && utf_char == " " && codepoint_type(utf_char_next) != CODEPOINT_TYPE_LETTER && codepoint_type(utf_char_next) != CODEPOINT_TYPE_DIGIT && codepoint_type(utf_char_next) != CODEPOINT_TYPE_WHITESPACE)
+                    ((unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_DIGIT) && (unicode_cpt_type(utf_char) != CODEPOINT_TYPE_WHITESPACE)) ||
+                    (!token.size() && utf_char == " " && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_DIGIT && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_WHITESPACE)
                     ) {
                     collecting_special = true;
                     collecting = true;
                 }
-                else if (codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE && codepoint_type(utf_char_next) == CODEPOINT_TYPE_WHITESPACE) {
+                else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_WHITESPACE) {
                     collecting_whitespace_lookahead = true;
                     collecting = true;
                 }
-                else if (codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE) {
+                else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE) {
                     split_condition = true;
                 }
             }
             else if (!split_condition && collecting) {
-                if (collecting_letter && codepoint_type(utf_char) != CODEPOINT_TYPE_LETTER) {
+                if (collecting_letter && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER) {
                     split_condition = true;
                 }
-                else if (collecting_numeric && codepoint_type(utf_char) != CODEPOINT_TYPE_DIGIT) {
+                else if (collecting_numeric && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_DIGIT) {
                     split_condition = true;
                 }
-                else if (collecting_special && (codepoint_type(utf_char) == CODEPOINT_TYPE_LETTER || codepoint_type(utf_char) == CODEPOINT_TYPE_DIGIT || codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE)) {
+                else if (collecting_special && (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_DIGIT || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE)) {
                     split_condition = true;
                 }
-                else if (collecting_whitespace_lookahead && (codepoint_type(utf_char_next) == CODEPOINT_TYPE_LETTER || codepoint_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) {
+                else if (collecting_whitespace_lookahead && (unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) {
                     split_condition = true;
                 }
             }
@@ -7388,7 +11255,7 @@ private:
         for (std::string & word : bpe_words) {
             std::string encoded_token = "";
             for (char & c : word) {
-                encoded_token += bytes_to_unicode_bpe(c);
+                encoded_token += unicode_byte_to_utf8(c);
             }
             bpe_encoded_words.emplace_back(encoded_token);
         }
@@ -7404,29 +11271,159 @@ private:
     llm_bigram_bpe::queue work_queue;
 };
 
-typedef enum FRAGMENT_BUFFER_VARIANT_TYPE{
+struct llm_tokenizer_wpm {
+    llm_tokenizer_wpm(const llama_vocab & vocab): vocab(vocab) {}
+
+    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
+        auto * token_map = &vocab.token_to_id;
+
+        // normalize and split by whitespace
+        std::vector<std::string> words = preprocess(text);
+
+        // bos token prepended already
+
+        // find the longest tokens that form the words
+        for (const std::string &word : words) {
+            // skip empty words
+            if (word.size() == 0) {
+                continue;
+            }
+
+            // prepend phantom space
+            std::string word1 = "\xe2\x96\x81" + word;
+            int n = word1.size();
+
+            // we're at the start of a new word
+            int i = 0;
+            bool match_any = false;
+
+            // move through character position in word
+            while (i < n) {
+                // loop through possible match length
+                bool match = false;
+                for (int j = n; j > i; j--) {
+                    auto it = token_map->find(word1.substr(i, j - i));
+                    if (it != token_map->end()) {
+                        output.push_back(it->second);
+                        match = true;
+                        match_any = true;
+                        i = j;
+                        break;
+                    }
+                }
+
+                // must be an unknown character
+                if (!match) {
+                    i++;
+                }
+            }
+
+            // we didn't find any matches for this word
+            if (!match_any) {
+                output.push_back(vocab.special_unk_id);
+            }
+        }
+
+        // append eos token
+        output.push_back(vocab.special_eos_id);
+    }
+
+    std::vector<std::string> preprocess(const std::string & text) {
+        std::vector<uint32_t> cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));
+
+        // strip accents, strip control, uniformize whitespace,
+        // to lowercase, pad chinese characters, pad punctuation
+        std::string new_str = "";
+        for (uint32_t code : cpts_nfd) {
+            int type = unicode_cpt_type(code);
+            if (type == CODEPOINT_TYPE_ACCENT_MARK || type == CODEPOINT_TYPE_CONTROL) {
+                continue;
+            }
+            code = unicode_tolower(code);
+            if (type == CODEPOINT_TYPE_WHITESPACE) {
+                code = ' ';
+            }
+            std::string s = unicode_cpt_to_utf8(code);
+            if (type == CODEPOINT_TYPE_PUNCTUATION || is_ascii_punct(code) || is_chinese_char(code)) {
+                new_str += " ";
+                new_str += s;
+                new_str += " ";
+            } else {
+                new_str += s;
+            }
+        }
+
+        // split by whitespace
+        uint64_t l = 0;
+        uint64_t r = 0;
+        std::vector<std::string> words;
+        while (r < new_str.size()) {
+            // if is whitespace
+            if (isspace(new_str[r], std::locale::classic())) {
+                if (r > l) words.push_back(new_str.substr(l, (r - l)));
+                l = r + 1;
+                r = l;
+            } else {
+                r += 1;
+            }
+        }
+        if (r > l) {
+            words.push_back(new_str.substr(l, (r - l)));
+        }
+        return words;
+    }
+
+    bool is_ascii_punct(uint32_t code) {
+        if (code > 0xFF) {
+            return false;
+        }
+        auto c = char(static_cast<unsigned char>(code));
+        return ispunct(c, std::locale::classic());
+    }
+
+    bool is_chinese_char(uint32_t cpt) {
+        if ((cpt >= 0x4E00  && cpt <= 0x9FFF)  ||
+            (cpt >= 0x3400  && cpt <= 0x4DBF)  ||
+            (cpt >= 0x20000 && cpt <= 0x2A6DF) ||
+            (cpt >= 0x2A700 && cpt <= 0x2B73F) ||
+            (cpt >= 0x2B740 && cpt <= 0x2B81F) ||
+            (cpt >= 0x2B920 && cpt <= 0x2CEAF) || // this should be 0x2B820 but in hf rust code it is 0x2B920
+            (cpt >= 0xF900  && cpt <= 0xFAFF)  ||
+            (cpt >= 0x2F800 && cpt <= 0x2FA1F) ||
+            (cpt >= 0x3000  && cpt <= 0x303F)  ||
+            (cpt >= 0xFF00  && cpt <= 0xFFEF)) {
+            return true; // NOLINT
+        }
+        return false;
+    }
+
+    const llama_vocab & vocab;
+};
+
+typedef enum FRAGMENT_BUFFER_VARIANT_TYPE {
     FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN,
     FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT
 } FRAGMENT_BUFFER_VARIANT_TYPE;
 
-struct fragment_buffer_variant{
+struct fragment_buffer_variant {
     fragment_buffer_variant(llama_vocab::id _token)
     :
         type(FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN),
         token(_token),
         raw_text(_dummy),
         offset(0),
-        length(0){}
+        length(0) {}
+
     fragment_buffer_variant(const std::string & _raw_text, int64_t _offset, int64_t _length)
     :
         type(FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT),
-        token((llama_vocab::id)-1),
+        token((llama_vocab::id) - 1),
         raw_text(_raw_text),
         offset(_offset),
         length(_length){
-            GGML_ASSERT( _offset >= 0 );
-            GGML_ASSERT( _length >= 1 );
-            GGML_ASSERT( offset + length <= raw_text.length() );
+            GGML_ASSERT(_offset >= 0);
+            GGML_ASSERT(_length >= 1);
+            GGML_ASSERT(offset + length <= raw_text.length());
         }
 
     const FRAGMENT_BUFFER_VARIANT_TYPE type;
@@ -7439,8 +11436,7 @@ struct fragment_buffer_variant{
 
 // #define PRETOKENIZERDEBUG
 
-static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer)
-{
+static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer) {
     // for each special token
     for (const auto & st: vocab.special_tokens_cache) {
         const auto & special_token = st.first;
@@ -7472,7 +11468,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
                     if (match + special_token.length() > raw_text_base_offset + raw_text_base_length) break;
 
 #ifdef PRETOKENIZERDEBUG
-                    fprintf(stderr, "FF: (%ld %ld %ld) '%s'\n", raw_text->length(), raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
+                    LLAMA_LOG_WARN("FF: (%ld %ld %ld) '%s'\n", raw_text->length(), raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
 #endif
                     auto source = std::distance(buffer.begin(), it);
 
@@ -7485,7 +11481,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
                         buffer.emplace_after(it, (*raw_text), left_reminder_offset, left_reminder_length);
 
 #ifdef PRETOKENIZERDEBUG
-                        fprintf(stderr, "FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str());
+                        LLAMA_LOG_WARN("FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str());
 #endif
                         it++;
                     }
@@ -7501,7 +11497,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
                         buffer.emplace_after(it, (*raw_text), right_reminder_offset, right_reminder_length);
 
 #ifdef PRETOKENIZERDEBUG
-                        fprintf(stderr, "FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str());
+                        LLAMA_LOG_WARN("FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str());
 #endif
 
                         it++;
@@ -7517,7 +11513,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
                         raw_text_base_length = right_reminder_length;
 
 #ifdef PRETOKENIZERDEBUG
-                        fprintf(stderr, "RR: (%ld %ld) '%s'\n", raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
+                        LLAMA_LOG_WARN("RR: (%ld %ld) '%s'\n", raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
 #endif
                     } else {
                         if (source == 0) {
@@ -7551,17 +11547,15 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
     }
 
     std::forward_list<fragment_buffer_variant> fragment_buffer;
-    fragment_buffer.emplace_front( raw_text, 0, raw_text.length() );
+    fragment_buffer.emplace_front(raw_text, 0, raw_text.length());
 
-    if (special) tokenizer_st_partition( vocab, fragment_buffer );
+    if (special) tokenizer_st_partition(vocab, fragment_buffer);
 
     switch (vocab.type) {
         case LLAMA_VOCAB_TYPE_SPM:
             {
-                for (const auto & fragment: fragment_buffer)
-                {
-                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT)
-                    {
+                for (const auto & fragment : fragment_buffer) {
+                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
                         // without adding this leading whitespace, we do not get the same results as the original tokenizer
 
                         // TODO: It's likely possible to get rid of this string copy entirely
@@ -7570,42 +11564,56 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
                         //
                         auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
                         if (&fragment == &fragment_buffer.front()) {
-                            raw_text = " " + raw_text; // prefix with space if the first token is not special
+                            if (vocab.add_space_prefix) {
+                                raw_text = " " + raw_text; // prefix with space if the first token is not special
+                            }
                         }
 
 #ifdef PRETOKENIZERDEBUG
-                        fprintf(stderr,"TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
+                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
 #endif
                         llm_tokenizer_spm tokenizer(vocab);
                         llama_escape_whitespace(raw_text);
                         tokenizer.tokenize(raw_text, output);
-                    }
-                    else // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
-                    {
+                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
                         output.push_back(fragment.token);
                     }
                 }
             } break;
         case LLAMA_VOCAB_TYPE_BPE:
             {
-                for (const auto & fragment: fragment_buffer)
-                {
-                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT)
-                    {
+                for (const auto & fragment : fragment_buffer) {
+                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
                         auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
 
 #ifdef PRETOKENIZERDEBUG
-                        fprintf(stderr,"TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
+                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
 #endif
                         llm_tokenizer_bpe tokenizer(vocab);
                         tokenizer.tokenize(raw_text, output);
-                    }
-                    else // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
-                    {
+                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
                         output.push_back(fragment.token);
                     }
                 }
             } break;
+        case LLAMA_VOCAB_TYPE_WPM:
+            {
+                for (const auto & fragment : fragment_buffer) {
+                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
+                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
+
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
+#endif
+                        llm_tokenizer_wpm tokenizer(vocab);
+                        tokenizer.tokenize(raw_text, output);
+                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
+                        output.push_back(fragment.token);
+                    }
+                }
+            } break;
+        case LLAMA_VOCAB_TYPE_NONE:
+            GGML_ASSERT(false);
     }
 
     return output;
@@ -7615,28 +11623,10 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
 // grammar - internal
 //
 
-struct llama_partial_utf8 {
-    uint32_t value;    // bit value so far (unshifted)
-    int      n_remain; // num bytes remaining; -1 indicates invalid sequence
-};
-
-struct llama_grammar {
-    const std::vector<std::vector<llama_grammar_element>>   rules;
-    std::vector<std::vector<const llama_grammar_element *>> stacks;
-
-    // buffer for partially generated UTF-8 sequence from accepted tokens
-    llama_partial_utf8                                      partial_utf8;
-};
-
-struct llama_grammar_candidate {
-    size_t               index;
-    const uint32_t     * code_points;
-    llama_partial_utf8   partial_utf8;
-};
 
 // Decodes a UTF-8 string which may end in an incomplete sequence. Adds a terminating 0 for use as
 // pointer. If an invalid sequence is encountered, returns `llama_partial_utf8.n_remain == -1`.
-static std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
+std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
         const std::string & src,
         llama_partial_utf8   partial_start) {
     static const int      lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 3, 4 };
@@ -7838,7 +11828,7 @@ static void llama_grammar_advance_stack(
 // be positioned at a character range (see `llama_grammar_advance_stack`), and
 // produces the N possible stacks if the given char is accepted at those
 // positions
-static std::vector<std::vector<const llama_grammar_element *>> llama_grammar_accept(
+std::vector<std::vector<const llama_grammar_element *>> llama_grammar_accept(
         const std::vector<std::vector<llama_grammar_element>>         & rules,
         const std::vector<std::vector<const llama_grammar_element *>> & stacks,
         const uint32_t                                                  chr) {
@@ -7962,7 +11952,7 @@ struct llama_grammar * llama_grammar_init(
 
     // loop over alternates of start rule to build initial stacks
     std::vector<std::vector<const llama_grammar_element *>> stacks;
-    pos = rules[start_rule_index];
+    pos = vec_rules[start_rule_index].data();
     do {
         std::vector<const llama_grammar_element *> stack;
         if (!llama_grammar_is_end_of_sequence(pos)) {
@@ -8049,8 +12039,17 @@ void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * c
 }
 
 void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int32_t k, size_t min_keep) {
+    // TODO: move bucket sort to separate function so that top_p/tail_free/typical/softmax first is equally fast
+    // if (k >= (int32_t)candidates->size) {
+    //     return;
+    // }
+
     const int64_t t_start_sample_us = ggml_time_us();
 
+    if (k <= 0) {
+        k = candidates->size;
+    }
+
     k = std::max(k, (int) min_keep);
     k = std::min(k, (int) candidates->size);
 
@@ -8059,10 +12058,57 @@ void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * can
         auto comp = [](const llama_token_data & a, const llama_token_data & b) {
             return a.logit > b.logit;
         };
-        if (k == (int) candidates->size) {
-            std::sort(candidates->data, candidates->data + candidates->size, comp);
-        } else {
+        if (k <= 128) {
             std::partial_sort(candidates->data, candidates->data + k, candidates->data + candidates->size, comp);
+        } else {
+            constexpr int   nbuckets     = 128;
+            constexpr float bucket_low   = -10.0f;
+            constexpr float bucket_high  =  10.0f;
+            constexpr float bucket_scale = nbuckets/(bucket_high - bucket_low);
+            constexpr float bucker_inter = -bucket_low * bucket_scale;
+
+            std::vector<int> bucket_idx(candidates->size);
+            std::vector<int> histo(nbuckets, 0);
+
+            for (int i = 0; i < (int)candidates->size; ++i) {
+                const float val = candidates->data[i].logit;
+                int ib = int(bucket_scale * val + bucker_inter); //nbuckets * (val - bucket_low) / (bucket_high - bucket_low);
+                ib = std::max(0, std::min(nbuckets-1, ib));
+                bucket_idx[i] = ib;
+                ++histo[ib];
+            }
+            int nhave = 0;
+            int ib = nbuckets - 1;
+            for ( ; ib >= 0; --ib) {
+                nhave += histo[ib];
+                if (nhave >= k) break;
+            }
+            std::vector<llama_token_data> tmp_tokens(nhave);
+            auto ptr = tmp_tokens.data();
+            std::vector<llama_token_data*> bucket_ptrs;
+            bucket_ptrs.reserve(nbuckets - ib);
+            for (int j = nbuckets - 1; j >= ib; --j) {
+                bucket_ptrs.push_back(ptr);
+                ptr += histo[j];
+            }
+            for (int i = 0; i < (int)candidates->size; ++i) {
+                int j = bucket_idx[i];
+                if (j >= ib) {
+                    *bucket_ptrs[nbuckets-1-j]++ = candidates->data[i];
+                }
+            }
+
+            ptr = tmp_tokens.data();
+            int ndone = 0;
+            for (int j = nbuckets-1; j > ib; --j) {
+                std::sort(ptr, ptr + histo[j], comp);
+                ptr += histo[j];
+                ndone += histo[j];
+            }
+            std::partial_sort(ptr, ptr + k - ndone, ptr + histo[ib], comp);
+
+            std::memcpy(candidates->data, tmp_tokens.data(), k*sizeof(llama_token_data));
+
         }
         candidates->sorted = true;
     }
@@ -8110,21 +12156,56 @@ void llama_sample_min_p(struct llama_context * ctx, llama_token_data_array * can
         return;
     }
 
-    llama_sample_softmax(ctx, candidates);
-
     const int64_t t_start_sample_us = ggml_time_us();
 
-    float scale = candidates->data[0].p; // scale by max prob
-    size_t i = 1; // first token always matches
+    bool min_p_applied = false;
 
-    for (; i < candidates->size; ++i) {
-        if (candidates->data[i].p < p * scale && i >= min_keep) {
-            break; // prob too small
+    // if the candidates aren't sorted, try the unsorted implementation first
+    if (!candidates->sorted) {
+        std::vector<llama_token_data> filtered_tokens;
+
+        float max_logit = -FLT_MAX;
+        for (size_t i = 0; i < candidates->size; ++i) {
+            max_logit = std::max(max_logit, candidates->data[i].logit);
+        }
+        const float min_logit = max_logit + logf(p); // min logit for p_i >= p * p_max
+
+        for (size_t i = 0; i < candidates->size; ++i) {
+            if (candidates->data[i].logit >= min_logit) {
+                filtered_tokens.push_back(candidates->data[i]);
+            }
+        }
+
+        // if we have enough values the operation was a success
+        if (filtered_tokens.size() >= min_keep) {
+            memcpy(candidates->data, filtered_tokens.data(), filtered_tokens.size()*sizeof(llama_token_data));
+            candidates->size = filtered_tokens.size();
+            min_p_applied = true;
         }
     }
 
-    // Resize the output vector to keep only the matching tokens
-    candidates->size = i;
+    // if the candidates are sorted or the unsorted implementation failed, use this implementation
+    if (!min_p_applied) {
+        // Sort the logits in descending order
+        if (!candidates->sorted) {
+            std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
+                return a.logit > b.logit;
+            });
+            candidates->sorted = true;
+        }
+
+        const float min_logit = candidates->data[0].logit + logf(p); // min logit for p_i >= p * p_max
+        size_t i = 1; // first token always matches
+
+        for (; i < candidates->size; ++i) {
+            if (candidates->data[i].logit < min_logit && i >= min_keep) {
+                break; // prob too small
+            }
+        }
+
+        // Resize the output vector to keep only the matching tokens
+        candidates->size = i;
+    }
 
     if (ctx) {
         ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
@@ -8254,6 +12335,73 @@ void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * c
     }
 }
 
+void llama_sample_entropy(struct llama_context * ctx, llama_token_data_array * candidates_p, float min_temp, float max_temp, float exponent_val) {
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    // no need to do anything if there is only one (or zero) candidates
+    if(candidates_p->size <= 1) {
+        return;
+    }
+
+    // Calculate maximum possible entropy
+    float max_entropy = -logf(1.0f / candidates_p->size);
+
+    llama_sample_softmax(nullptr, candidates_p);
+
+    // Calculate entropy of the softmax probabilities
+    float entropy = 0.0f;
+    for (size_t i = 0; i < candidates_p->size; ++i) {
+        float prob = candidates_p->data[i].p;
+        if (prob > 0.0f) { // Ensure no log(0)
+            entropy -= prob * logf(prob);
+        }
+    }
+
+    // Normalize the entropy (max_entropy cannot be 0 here because we checked candidates_p->size != 1 above)
+    float normalized_entropy = entropy / max_entropy;
+
+    // Map the normalized entropy to the desired temperature range using the power function
+    float dyn_temp = min_temp + (max_temp - min_temp) * powf(normalized_entropy, exponent_val);
+
+#ifdef DEBUG
+    LLAMA_LOG_INFO("Your text maxtemp value is: %f\n", max_temp);
+    LLAMA_LOG_INFO("Entropy: %f\n", entropy);
+    LLAMA_LOG_INFO("Max Possible Entropy: %f\n", max_entropy);
+    LLAMA_LOG_INFO("Normalized Entropy: %f\n", normalized_entropy);
+    LLAMA_LOG_INFO("Exponent: %f\n", exponent_val);
+    LLAMA_LOG_INFO("Dynamic Temperature (dyn_temp): %f\n", dyn_temp);
+#endif
+
+    // Apply the dynamically calculated temperature scaling
+    for (size_t i = 0; i < candidates_p->size; ++i) {
+        candidates_p->data[i].logit /= dyn_temp;
+    }
+
+    // Re-compute softmax probabilities after scaling logits with dynamic temperature
+    double max_l_double = candidates_p->data[0].logit;
+    double cum_sum_double = 0.0;
+    for (size_t i = 0; i < candidates_p->size; ++i) {
+        double p = exp(candidates_p->data[i].logit - max_l_double);
+        candidates_p->data[i].p = p; // Store the scaled probability
+        cum_sum_double += p;
+    }
+    for (size_t i = 0; i < candidates_p->size; ++i) {
+        candidates_p->data[i].p /= cum_sum_double; // Re-normalize the probabilities
+    }
+
+#ifdef DEBUG
+    // Print the updated top 25 probabilities after temperature scaling
+    LLAMA_LOG_INFO("\nUpdated Top 25 Probabilities After Dynamic Temperature Scaling (in percentages):\n");
+    for (size_t i = 0; i < 25 && i < candidates_p->size; ++i) {
+        LLAMA_LOG_INFO("Token %zu: %f%%\n", i + 1, candidates_p->data[i].p * 100.0f);
+    }
+#endif
+
+    if (ctx) {
+        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
 void llama_sample_temp(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
     const int64_t t_start_sample_us = ggml_time_us();
 
@@ -8266,10 +12414,6 @@ void llama_sample_temp(struct llama_context * ctx, llama_token_data_array * cand
     }
 }
 
-void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
-    llama_sample_temp(ctx, candidates_p, temp);
-}
-
 void llama_sample_repetition_penalties(
             struct llama_context * ctx,
           llama_token_data_array * candidates,
@@ -8373,39 +12517,27 @@ static void llama_log_softmax(float * array, size_t size) {
     }
 }
 
-void llama_sample_classifier_free_guidance(
+void llama_sample_apply_guidance(
           struct llama_context * ctx,
-        llama_token_data_array * candidates,
-          struct llama_context * guidance_ctx,
+                         float * logits,
+                         float * logits_guidance,
                          float   scale) {
-    int64_t t_start_sample_us = ggml_time_us();
-
     GGML_ASSERT(ctx);
 
-    auto n_vocab = llama_n_vocab(llama_get_model(ctx));
+    const auto t_start_sample_us = ggml_time_us();
+    const auto n_vocab = llama_n_vocab(llama_get_model(ctx));
 
-    GGML_ASSERT(n_vocab == (int)candidates->size);
-    GGML_ASSERT(!candidates->sorted);
-
-    std::vector<float> logits_base;
-    logits_base.reserve(candidates->size);
-    for (size_t i = 0; i < candidates->size; ++i) {
-        logits_base.push_back(candidates->data[i].logit);
-    }
-    llama_log_softmax(logits_base.data(), candidates->size);
-
-    float* logits_guidance = llama_get_logits(guidance_ctx);
+    llama_log_softmax(logits, n_vocab);
     llama_log_softmax(logits_guidance, n_vocab);
 
     for (int i = 0; i < n_vocab; ++i) {
-        float logit_guidance = logits_guidance[i];
-        float logit_base = logits_base[i];
-        candidates->data[i].logit = scale * (logit_base - logit_guidance) + logit_guidance;
+              auto & l = logits[i];
+        const auto & g = logits_guidance[i];
+
+        l = scale * (l - g) + g;
     }
 
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
+    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
 }
 
 llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) {
@@ -8822,20 +12954,29 @@ struct quantize_state_internal {
     const llama_model_quantize_params * params;
 
     int n_attention_wv    = 0;
-    int n_feed_forward_w2 = 0;
+    int n_ffn_down        = 0;
+    int n_ffn_gate        = 0;
+    int n_ffn_up          = 0;
     int i_attention_wv    = 0;
-    int i_feed_forward_w2 = 0;
+    int i_ffn_down        = 0;
+    int i_ffn_gate        = 0;
+    int i_ffn_up          = 0;
 
     int n_k_quantized     = 0;
     int n_fallback        = 0;
 
+    bool has_imatrix      = false;
+
+    // used to figure out if a model shares tok_embd with the output weight
+    bool has_output       = false;
+
     quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params)
         : model(model)
         , params(params)
         {}
 };
 
-static void llama_convert_tensor_internal(
+static void llama_tensor_dequantize_internal(
     struct ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
     const size_t nelements, const int nthread
 ) {
@@ -8896,7 +13037,7 @@ static void llama_convert_tensor_internal(
     workers.clear();
 }
 
-static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
+static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
     const std::string name = ggml_get_name(tensor);
 
     // TODO: avoid hardcoded tensor names - use the TN_* constants
@@ -8906,21 +13047,104 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
     auto use_more_bits = [](int i_layer, int num_layers) -> bool {
         return i_layer < num_layers/8 || i_layer >= 7*num_layers/8 || (i_layer - num_layers/8)%3 == 2;
     };
-
-    if (name == tn(LLM_TENSOR_OUTPUT, "weight")) {
-        int nx = tensor->ne[0];
-        if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
-            new_type = GGML_TYPE_Q8_0;
+    const int n_expert = std::max(1, (int)qs.model.hparams.n_expert);
+    auto layer_info = [n_expert] (int i_layer, int n_layer, const char * name) {
+        if (n_expert > 1) {
+            // Believe it or not, "experts" in the FFN of Mixtral-8x7B are not consecutive, but iccasionally randomly
+            // sprinkled in the model. Hence, simply dividing i_ffn_down by n_expert does not work
+            // for getting the current layer as I initially thought, and we need to resort to parsing the
+            // tensor name.
+            if (sscanf(name, "blk.%d.", &i_layer) != 1) {
+                throw std::runtime_error(format("Failed to determine layer for tensor %s", name));
+            }
+            if (i_layer < 0 || i_layer >= n_layer) {
+                throw std::runtime_error(format("Bad layer %d for tensor %s. Must be in [0, %d)", i_layer, name, n_layer));
+            }
         }
-        else if (new_type != GGML_TYPE_Q8_0) {
-            new_type = GGML_TYPE_Q6_K;
+        return std::make_pair(i_layer, n_layer);
+    };
+
+    // for arches that share the same tensor between the token embeddings and the output, we quantize the token embeddings
+    // with the quantization of the output tensor
+    if (name == tn(LLM_TENSOR_OUTPUT, "weight") || (!qs.has_output && name == tn(LLM_TENSOR_TOKEN_EMBD, "weight"))) {
+        if (qs.params->output_tensor_type < GGML_TYPE_COUNT) {
+            new_type = qs.params->output_tensor_type;
+        } else {
+            int nx = tensor->ne[0];
+            if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
+                new_type = GGML_TYPE_Q8_0;
+            }
+            else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
+                     ftype == LLAMA_FTYPE_MOSTLY_IQ1_S   || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S  || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M   ||
+                     ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
+                new_type = GGML_TYPE_Q5_K;
+            }
+            else if (new_type != GGML_TYPE_Q8_0) {
+                new_type = GGML_TYPE_Q6_K;
+            }
+        }
+    } else if (name == "token_embd.weight") {
+        if (qs.params->token_embedding_type < GGML_TYPE_COUNT) {
+            new_type = qs.params->token_embedding_type;
+        } else {
+            if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS ||
+                ftype == LLAMA_FTYPE_MOSTLY_IQ1_S   || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
+                new_type = GGML_TYPE_Q2_K;
+            }
+            else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
+                new_type = GGML_TYPE_IQ3_S;
+            }
+            else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+                new_type = GGML_TYPE_IQ3_S;
+            }
+        }
+    } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
+               ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M    || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
+        if (name.find("attn_v.weight") != std::string::npos) {
+            if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K;
+            else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
+            ++qs.i_attention_wv;
+        }
+        else if (qs.model.hparams.n_expert == 8 && name.find("attn_k.weight") != std::string::npos) {
+            new_type = GGML_TYPE_Q4_K;
+        }
+        else if (name.find("ffn_down") != std::string::npos) {
+            if (qs.i_ffn_down < qs.n_ffn_down/8) {
+                new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
+            }
+            ++qs.i_ffn_down;
+        }
+        else if (name.find("attn_output.weight") != std::string::npos) {
+            if (qs.model.hparams.n_expert == 8) {
+                new_type = GGML_TYPE_Q5_K;
+            } else {
+                if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ2_XXS;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_S;
+            }
         }
     } else if (name.find("attn_v.weight") != std::string::npos) {
-        if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
+        if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
+            new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) {
+            new_type = GGML_TYPE_Q4_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+            new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_IQ3_S : GGML_TYPE_IQ3_XXS;
+        }
+        else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S) && qs.model.hparams.n_gqa() >= 4) {
+            new_type = GGML_TYPE_Q4_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
+            new_type = GGML_TYPE_Q4_K;
+        }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
             new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
+        else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && qs.model.hparams.n_gqa() >= 4) {
+            new_type = GGML_TYPE_Q5_K;
+        }
         else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
                 use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K;
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
@@ -8944,46 +13168,114 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
             // TODO: explore better strategies
             new_type = GGML_TYPE_Q8_0;
         }
-    } else if (name.find("ffn_down.weight") != std::string::npos) {
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
+            new_type = GGML_TYPE_IQ3_XXS;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+            new_type = GGML_TYPE_IQ2_S;
+        }
+    } else if (name.find("attn_q.weight") != std::string::npos) {
+        if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
+            new_type = GGML_TYPE_IQ3_XXS;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+            new_type = GGML_TYPE_IQ2_S;
+        }
+    } else if (name.find("ffn_down") != std::string::npos) {
+        auto info = layer_info(qs.i_ffn_down, qs.n_ffn_down, name.c_str());
+        int i_layer = info.first, n_layer = info.second;
         if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) {
+            if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K;
+        }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && !qs.has_imatrix) {
+            new_type = i_layer < n_layer/8 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
+        }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
-            new_type = qs.i_feed_forward_w2 < 2 ? GGML_TYPE_Q5_K
-                     : arch != LLM_ARCH_FALCON || use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2) ? GGML_TYPE_Q4_K
+            new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K
+                     : arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K
                      : GGML_TYPE_Q3_K;
         }
+        else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M && (i_layer < n_layer/8 ||
+                    (qs.model.hparams.n_expert == 8 && use_more_bits(i_layer, n_layer)))) {
+            new_type = GGML_TYPE_Q4_K;
+        }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
             new_type = arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K;
         }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
             if (arch == LLM_ARCH_FALCON) {
-                new_type = qs.i_feed_forward_w2 < 2 ? GGML_TYPE_Q6_K :
-                           use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
+                new_type = i_layer < n_layer/16 ? GGML_TYPE_Q6_K :
+                           use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
             } else {
-                if (use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
+                if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
             }
         }
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
-        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && qs.i_feed_forward_w2 < 4) {
+        else if (i_layer < n_layer/8 && (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && !qs.has_imatrix) {
             new_type = GGML_TYPE_Q5_K;
         }
-        ++qs.i_feed_forward_w2;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
+        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && i_layer < n_layer/8) {
+            new_type = GGML_TYPE_Q5_K;
+        }
+        else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_0 || ftype == LLAMA_FTYPE_MOSTLY_Q5_0)
+                && qs.has_imatrix && i_layer < n_layer/8) {
+            // Guard against craziness in the first few ffn_down layers that can happen even with imatrix for Q4_0/Q5_0.
+            // We only do it when an imatrix is provided because a) we want to make sure that one can always get the
+            // same quantization as before imatrix stuff, and b) Q4_1/Q5_1 do go crazy on ffn_down without an imatrix.
+            new_type = ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ? GGML_TYPE_Q4_1 : GGML_TYPE_Q5_1;
+        }
+        ++qs.i_ffn_down;
     } else if (name.find("attn_output.weight") != std::string::npos) {
         if (arch != LLM_ARCH_FALCON) {
-            if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K  ) new_type = GGML_TYPE_Q3_K;
-            else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K;
-            else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
+            if (qs.model.hparams.n_expert == 8) {
+                if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K   || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
+                    ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL  ||
+                    ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S  ||
+                    ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) {
+                    new_type = GGML_TYPE_Q5_K;
+                }
+            } else {
+                if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K   ) new_type = GGML_TYPE_Q3_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_IQ3_S;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M ) new_type = GGML_TYPE_Q4_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L ) new_type = GGML_TYPE_Q5_K;
+                else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M  ) new_type = GGML_TYPE_Q4_K;
+            }
         } else {
             if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
         }
     }
     else if (name.find("attn_qkv.weight") != std::string::npos) {
-        if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
+        if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
+            new_type = GGML_TYPE_Q4_K;
+        }
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K;
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K;
     }
-    else if (name.find("ffn_gate.weight") != std::string::npos || name.find("ffn_up.weight") != std::string::npos) {
-        if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
+    else if (name.find("ffn_gate") != std::string::npos) {
+        auto info = layer_info(qs.i_ffn_gate, qs.n_ffn_gate, name.c_str());
+        int i_layer = info.first, n_layer = info.second;
+        if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
+            new_type = GGML_TYPE_IQ3_XXS;
+        }
+        ++qs.i_ffn_gate;
     }
+    else if (name.find("ffn_up") != std::string::npos) {
+        auto info = layer_info(qs.i_ffn_up, qs.n_ffn_up, name.c_str());
+        int i_layer = info.first, n_layer = info.second;
+        if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
+            new_type = GGML_TYPE_IQ3_XXS;
+        }
+        ++qs.i_ffn_up;
+    }
+
+    //    if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
+    //}
+    // IK: let's remove this, else Q2_K is almost the same as Q3_K_S
+    //else if (name.find("ffn_gate") != std::string::npos || name.find("ffn_up") != std::string::npos) {
+    //    if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
+    //}
     // This can be used to reduce the size of the Q5_K_S model.
     // The associated PPL increase is fully in line with the size reduction
     //else {
@@ -8991,7 +13283,10 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
     //}
     bool convert_incompatible_tensor = false;
     if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K ||
-        new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K) {
+        new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || new_type == GGML_TYPE_IQ4_XS ||
+        new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_S ||
+        new_type == GGML_TYPE_IQ3_XXS || new_type == GGML_TYPE_IQ1_S || new_type == GGML_TYPE_IQ3_S ||
+        new_type == GGML_TYPE_IQ1_M) {
         int nx = tensor->ne[0];
         int ny = tensor->ne[1];
         if (nx % QK_K != 0) {
@@ -9003,11 +13298,19 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
     }
     if (convert_incompatible_tensor) {
         switch (new_type) {
-            case GGML_TYPE_Q2_K: new_type = GGML_TYPE_Q4_0; break;
-            case GGML_TYPE_Q3_K: new_type = GGML_TYPE_Q4_1; break;
-            case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break;
-            case GGML_TYPE_Q5_K: new_type = GGML_TYPE_Q5_1; break;
-            case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break;
+            case GGML_TYPE_IQ2_XXS:
+            case GGML_TYPE_IQ2_XS:
+            case GGML_TYPE_IQ2_S:
+            case GGML_TYPE_IQ3_XXS:
+            case GGML_TYPE_IQ3_S:
+            case GGML_TYPE_IQ1_S:
+            case GGML_TYPE_IQ1_M:
+            case GGML_TYPE_Q2_K:
+            case GGML_TYPE_Q3_K:
+            case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break;
+            case GGML_TYPE_Q4_K:   new_type = GGML_TYPE_Q5_0;   break;
+            case GGML_TYPE_Q5_K:   new_type = GGML_TYPE_Q5_1;   break;
+            case GGML_TYPE_Q6_K:   new_type = GGML_TYPE_Q8_0;   break;
             default: throw std::runtime_error("\nUnsupported tensor size encountered\n");
         }
         LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type));
@@ -9017,30 +13320,77 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
     return new_type;
 }
 
+static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int chunk_size, int nrows, int n_per_row, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
+    std::mutex mutex;
+    int counter = 0;
+    size_t new_size = 0;
+    if (nthread < 2) {
+        // single-thread
+        return ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix);
+    }
+    auto compute = [&mutex, &counter, &new_size, new_type, f32_data, new_data, chunk_size,
+            nrows, n_per_row, imatrix]() {
+        const int nrows_per_chunk = chunk_size / n_per_row;
+        size_t local_size = 0;
+        while (true) {
+            std::unique_lock<std::mutex> lock(mutex);
+            int first_row = counter; counter += nrows_per_chunk;
+            if (first_row >= nrows) {
+                if (local_size > 0) {
+                    new_size += local_size;
+                }
+                break;
+            }
+            lock.unlock();
+            const int this_nrow = std::min(nrows - first_row, nrows_per_chunk);
+            local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first_row * n_per_row, this_nrow, n_per_row, imatrix);
+        }
+    };
+    for (int it = 0; it < nthread - 1; ++it) {
+        workers.emplace_back(compute);
+    }
+    compute();
+    for (auto & w : workers) { w.join(); }
+    workers.clear();
+    return new_size;
+}
+
 static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
-    ggml_type quantized_type;
+    ggml_type default_type;
     llama_ftype ftype = params->ftype;
 
     switch (params->ftype) {
-        case LLAMA_FTYPE_MOSTLY_Q4_0: quantized_type = GGML_TYPE_Q4_0; break;
-        case LLAMA_FTYPE_MOSTLY_Q4_1: quantized_type = GGML_TYPE_Q4_1; break;
-        case LLAMA_FTYPE_MOSTLY_Q5_0: quantized_type = GGML_TYPE_Q5_0; break;
-        case LLAMA_FTYPE_MOSTLY_Q5_1: quantized_type = GGML_TYPE_Q5_1; break;
-        case LLAMA_FTYPE_MOSTLY_Q8_0: quantized_type = GGML_TYPE_Q8_0; break;
-        case LLAMA_FTYPE_MOSTLY_F16:  quantized_type = GGML_TYPE_F16;  break;
-        case LLAMA_FTYPE_ALL_F32:     quantized_type = GGML_TYPE_F32;  break;
+        case LLAMA_FTYPE_MOSTLY_Q4_0: default_type = GGML_TYPE_Q4_0; break;
+        case LLAMA_FTYPE_MOSTLY_Q4_1: default_type = GGML_TYPE_Q4_1; break;
+        case LLAMA_FTYPE_MOSTLY_Q5_0: default_type = GGML_TYPE_Q5_0; break;
+        case LLAMA_FTYPE_MOSTLY_Q5_1: default_type = GGML_TYPE_Q5_1; break;
+        case LLAMA_FTYPE_MOSTLY_Q8_0: default_type = GGML_TYPE_Q8_0; break;
+        case LLAMA_FTYPE_MOSTLY_F16:  default_type = GGML_TYPE_F16;  break;
+        case LLAMA_FTYPE_ALL_F32:     default_type = GGML_TYPE_F32;  break;
 
         // K-quants
-        case LLAMA_FTYPE_MOSTLY_Q2_K:   quantized_type = GGML_TYPE_Q2_K; break;
+        case LLAMA_FTYPE_MOSTLY_Q2_K_S:
+        case LLAMA_FTYPE_MOSTLY_Q2_K:    default_type = GGML_TYPE_Q2_K;    break;
+        case LLAMA_FTYPE_MOSTLY_IQ3_XS:  default_type = GGML_TYPE_IQ3_S;   break;
         case LLAMA_FTYPE_MOSTLY_Q3_K_S:
         case LLAMA_FTYPE_MOSTLY_Q3_K_M:
-        case LLAMA_FTYPE_MOSTLY_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break;
+        case LLAMA_FTYPE_MOSTLY_Q3_K_L:  default_type = GGML_TYPE_Q3_K;    break;
         case LLAMA_FTYPE_MOSTLY_Q4_K_S:
-        case LLAMA_FTYPE_MOSTLY_Q4_K_M: quantized_type = GGML_TYPE_Q4_K; break;
+        case LLAMA_FTYPE_MOSTLY_Q4_K_M:  default_type = GGML_TYPE_Q4_K;    break;
         case LLAMA_FTYPE_MOSTLY_Q5_K_S:
-        case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break;
-        case LLAMA_FTYPE_MOSTLY_Q6_K:   quantized_type = GGML_TYPE_Q6_K; break;
-        case LLAMA_FTYPE_MOSTLY_IQ2_XXS:quantized_type = GGML_TYPE_IQ2_XXS; break;
+        case LLAMA_FTYPE_MOSTLY_Q5_K_M:  default_type = GGML_TYPE_Q5_K;    break;
+        case LLAMA_FTYPE_MOSTLY_Q6_K:    default_type = GGML_TYPE_Q6_K;    break;
+        case LLAMA_FTYPE_MOSTLY_IQ2_XXS: default_type = GGML_TYPE_IQ2_XXS; break;
+        case LLAMA_FTYPE_MOSTLY_IQ2_XS:  default_type = GGML_TYPE_IQ2_XS;  break;
+        case LLAMA_FTYPE_MOSTLY_IQ2_S:   default_type = GGML_TYPE_IQ2_XS;  break;
+        case LLAMA_FTYPE_MOSTLY_IQ2_M:   default_type = GGML_TYPE_IQ2_S;   break;
+        case LLAMA_FTYPE_MOSTLY_IQ3_XXS: default_type = GGML_TYPE_IQ3_XXS; break;
+        case LLAMA_FTYPE_MOSTLY_IQ1_S:   default_type = GGML_TYPE_IQ1_S;   break;
+        case LLAMA_FTYPE_MOSTLY_IQ1_M:   default_type = GGML_TYPE_IQ1_M;   break;
+        case LLAMA_FTYPE_MOSTLY_IQ4_NL:  default_type = GGML_TYPE_IQ4_NL;  break;
+        case LLAMA_FTYPE_MOSTLY_IQ4_XS:  default_type = GGML_TYPE_IQ4_XS;  break;
+        case LLAMA_FTYPE_MOSTLY_IQ3_S:   default_type = GGML_TYPE_IQ3_S;   break;
+        case LLAMA_FTYPE_MOSTLY_IQ3_M:   default_type = GGML_TYPE_IQ3_S;   break;
 
         default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
     }
@@ -9059,8 +13409,13 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
     constexpr bool use_mmap = false;
 #endif
 
-    llama_model_loader ml(fname_inp, use_mmap, NULL);
-    ml.init_mapping(false); // no prefetching?
+    llama_model_kv_override * kv_overrides = nullptr;
+    if (params->kv_overrides) {
+        auto v = (std::vector<llama_model_kv_override>*)params->kv_overrides;
+        kv_overrides = v->data();
+    }
+    llama_model_loader ml(fname_inp, use_mmap, kv_overrides);
+    ml.init_mappings(false); // no prefetching
 
     llama_model model;
     llm_load_arch(ml, model);
@@ -9071,40 +13426,62 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
     if (params->only_copy) {
         ftype = model.ftype;
     }
+    const std::unordered_map<std::string, std::vector<float>> * imatrix_data = nullptr;
+    if (params->imatrix) {
+        imatrix_data = static_cast<const std::unordered_map<std::string, std::vector<float>>*>(params->imatrix);
+        if (imatrix_data) {
+            LLAMA_LOG_INFO("================================ Have weights data with %d entries\n",int(imatrix_data->size()));
+            qs.has_imatrix = true;
+        }
+    }
 
     const size_t align = GGUF_DEFAULT_ALIGNMENT;
     struct gguf_context * ctx_out = gguf_init_empty();
 
     // copy the KV pairs from the input file
-    gguf_set_kv     (ctx_out, ml.ctx_gguf);
+    gguf_set_kv     (ctx_out, ml.meta);
     gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
     gguf_set_val_u32(ctx_out, "general.file_type", ftype);
 
+    if (params->kv_overrides) {
+        const std::vector<llama_model_kv_override> & overrides = *(const std::vector<llama_model_kv_override> *)params->kv_overrides;
+        for (auto & o : overrides) {
+            if (o.key[0] == 0) break;
+            if (o.tag == LLAMA_KV_OVERRIDE_TYPE_FLOAT) {
+                gguf_set_val_f32(ctx_out, o.key, o.float_value);
+            } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_INT) {
+                gguf_set_val_i32(ctx_out, o.key, o.int_value);
+            } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_BOOL) {
+                gguf_set_val_bool(ctx_out, o.key, o.bool_value);
+            } else {
+                LLAMA_LOG_WARN("%s: unknown KV override type for key %s\n", __func__, o.key);
+            }
+        }
+    }
+
     for (int i = 0; i < ml.n_tensors; ++i) {
-        struct ggml_tensor * meta = ml.get_tensor_meta(i);
+        const struct ggml_tensor * meta = ml.get_tensor_meta(i);
 
         const std::string name = ggml_get_name(meta);
 
         // TODO: avoid hardcoded tensor names - use the TN_* constants
         if (name.find("attn_v.weight") != std::string::npos || name.find("attn_qkv.weight") != std::string::npos) {
             ++qs.n_attention_wv;
-        }
-        else if (name.find("ffn_down.weight") != std::string::npos) {
-            ++qs.n_feed_forward_w2;
+        } else if (name == LLM_TN(model.arch)(LLM_TENSOR_OUTPUT, "weight")) {
+            qs.has_output = true;
         }
     }
-    if (qs.n_attention_wv != qs.n_feed_forward_w2 || (uint32_t)qs.n_attention_wv != model.hparams.n_layer) {
-        LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_feed_forward_w2 = %d, hparams.n_layer = %d\n",
-                __func__, qs.n_attention_wv, qs.n_feed_forward_w2, model.hparams.n_layer);
-    }
+
+    qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)model.hparams.n_layer;
+
+    // sanity checks
+    GGML_ASSERT(qs.n_attention_wv == (int)model.hparams.n_layer && "n_attention_wv != n_layer is unexpected");
 
     size_t total_size_org = 0;
     size_t total_size_new = 0;
-    std::vector<int64_t> hist_all(1 << 4, 0);
 
     std::vector<std::thread> workers;
     workers.reserve(nthread);
-    std::mutex mutex;
 
     int idx = 0;
 
@@ -9114,7 +13491,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
 
     // populate the original tensors so we get an initial meta data
     for (int i = 0; i < ml.n_tensors; ++i) {
-        struct ggml_tensor * meta = ml.get_tensor_meta(i);
+        const struct ggml_tensor * meta = ml.get_tensor_meta(i);
         gguf_add_tensor(ctx_out, meta);
     }
 
@@ -9128,6 +13505,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
     // placeholder for the meta data
     ::zeros(fout, meta_size);
 
+    const auto tn = LLM_TN(model.arch);
+
     for (int i = 0; i < ml.n_tensors; ++i) {
         struct ggml_tensor * tensor = ml.get_tensor_meta(i);
 
@@ -9150,28 +13529,48 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         // This used to be a regex, but <regex> has an extreme cost to compile times.
         bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?
 
-        // quantize only 2D tensors
-        quantize &= (ggml_n_dims(tensor) == 2);
+        // quantize only 2D and 3D tensors (experts)
+        quantize &= (ggml_n_dims(tensor) >= 2);
         quantize &= params->quantize_output_tensor || name != "output.weight";
         quantize &= !params->only_copy;
 
         // do not quantize expert gating tensors
+        // NOTE: can't use LLM_TN here because the layer number is not known
         quantize &= name.find("ffn_gate_inp.weight") == std::string::npos;
 
+        // do not quantize positional embeddings and token types (BERT)
+        quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_POS_EMBD,    "weight");
+        quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_TOKEN_TYPES, "weight");
+
+        // do not quantize Mamba's small yet 2D weights
+        // NOTE: can't use LLM_TN here because the layer number is not known
+        quantize &= name.find("ssm_conv1d.weight") == std::string::npos;
+        quantize &= name.find("ssm_x.weight")      == std::string::npos;
+        quantize &= name.find("ssm_dt.weight")     == std::string::npos;
+
         enum ggml_type new_type;
         void * new_data;
         size_t new_size;
 
         if (quantize) {
-            new_type = quantized_type;
-            if (!params->pure) {
-                new_type = get_k_quant_type(qs, new_type, tensor, ftype);
+            new_type = default_type;
+
+            // get more optimal quantization type based on the tensor shape, layer, etc.
+            if (!params->pure && ggml_is_quantized(default_type)) {
+                new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
+            }
+            else if (params->token_embedding_type < GGML_TYPE_COUNT && strcmp(tensor->name, "token_embd.weight") == 0) {
+                new_type = params->token_embedding_type;
+            }
+            else if (params->output_tensor_type < GGML_TYPE_COUNT && strcmp(tensor->name, "output.weight") == 0) {
+                new_type = params->output_tensor_type;
             }
 
             // If we've decided to quantize to the same type the tensor is already
             // in then there's nothing to do.
             quantize = tensor->type != new_type;
         }
+
         if (!quantize) {
             new_type = tensor->type;
             new_data = tensor->data;
@@ -9180,6 +13579,42 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         } else {
             const size_t nelements = ggml_nelements(tensor);
 
+            const float * imatrix = nullptr;
+            if (imatrix_data) {
+                auto it = imatrix_data->find(tensor->name);
+                if (it == imatrix_data->end()) {
+                    LLAMA_LOG_INFO("\n====== %s: did not find weights for %s\n", __func__, tensor->name);
+                } else {
+                    if (it->second.size() == (size_t)tensor->ne[0]*tensor->ne[2]) {
+                        imatrix = it->second.data();
+                    } else {
+                        LLAMA_LOG_INFO("\n====== %s: imatrix size %d is different from tensor size %d for %s\n", __func__,
+                                int(it->second.size()), int(tensor->ne[0]*tensor->ne[2]), tensor->name);
+
+                        // this can happen when quantizing an old mixtral model with split tensors with a new incompatible imatrix
+                        // this is a significant error and it may be good idea to abort the process if this happens,
+                        // since many people will miss the error and not realize that most of the model is being quantized without an imatrix
+                        // tok_embd should be ignored in this case, since it always causes this warning
+                        if (name != tn(LLM_TENSOR_TOKEN_EMBD, "weight")) {
+                            throw std::runtime_error(format("imatrix size %d is different from tensor size %d for %s",
+                                    int(it->second.size()), int(tensor->ne[0]*tensor->ne[2]), tensor->name));
+                        }
+                    }
+                }
+            }
+            if ((new_type == GGML_TYPE_IQ2_XXS ||
+                 new_type == GGML_TYPE_IQ2_XS  ||
+                 new_type == GGML_TYPE_IQ2_S   ||
+                 new_type == GGML_TYPE_IQ1_S   ||
+                (new_type == GGML_TYPE_IQ1_M && strcmp(tensor->name, "token_embd.weight") && strcmp(tensor->name, "output.weight"))  ||
+                (new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0)) && !imatrix) {
+                LLAMA_LOG_ERROR("\n\n============================================================\n");
+                LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name);
+                LLAMA_LOG_ERROR("The result will be garbage, so bailing out\n");
+                LLAMA_LOG_ERROR("============================================================\n\n");
+                throw std::runtime_error(format("Missing importance matrix for tensor %s in a very low-bit quantization", tensor->name));
+            }
+
             float * f32_data;
 
             if (tensor->type == GGML_TYPE_F32) {
@@ -9187,68 +13622,38 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
             } else if (ggml_is_quantized(tensor->type) && !params->allow_requantize) {
                 throw std::runtime_error(format("requantizing from type %s is disabled", ggml_type_name(tensor->type)));
             } else {
-                llama_convert_tensor_internal(tensor, f32_conv_buf, workers, nelements, nthread);
+                llama_tensor_dequantize_internal(tensor, f32_conv_buf, workers, nelements, nthread);
                 f32_data = (float *) f32_conv_buf.data();
             }
 
-            LLAMA_LOG_INFO("quantizing to %s .. ", ggml_type_name(new_type));
+            LLAMA_LOG_INFO("converting to %s .. ", ggml_type_name(new_type));
             fflush(stdout);
 
             if (work.size() < nelements * 4) {
                 work.resize(nelements * 4); // upper bound on size
             }
             new_data = work.data();
-            std::array<int64_t, 1 << 4> hist_cur = {};
 
-            static const int chunk_size = 32 * 512;
-            const int nchunk = (nelements + chunk_size - 1)/chunk_size;
+            const int n_per_row = tensor->ne[0];
+            const int nrows = tensor->ne[1];
+
+            static const int min_chunk_size = 32 * 512;
+            const int chunk_size = n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row);
+
+            const int nelements_matrix = tensor->ne[0] * tensor->ne[1];
+            const int nchunk = (nelements_matrix + chunk_size - 1)/chunk_size;
             const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1;
-            if (nthread_use < 2) {
-                new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nelements, hist_cur.data());
-            } else {
-                size_t counter = 0;
-                new_size = 0;
-                auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, nelements]() {
-                    std::array<int64_t, 1 << 4> local_hist = {};
-                    size_t local_size = 0;
-                    while (true) {
-                        std::unique_lock<std::mutex> lock(mutex);
-                        size_t first = counter; counter += chunk_size;
-                        if (first >= nelements) {
-                            if (local_size > 0) {
-                                for (int j=0; j<int(local_hist.size()); ++j) {
-                                    hist_cur[j] += local_hist[j];
-                                }
-                                new_size += local_size;
-                            }
-                            break;
-                        }
-                        lock.unlock();
-                        size_t last = std::min(nelements, first + chunk_size);
-                        local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first, last - first, local_hist.data());
-                    }
-                };
-                for (int it = 0; it < nthread_use - 1; ++it) {
-                    workers.emplace_back(compute);
-                }
-                compute();
-                for (auto & w : workers) { w.join(); }
-                workers.clear();
-            }
 
-            LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB | hist: ", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
-            int64_t tot_count = 0;
-            for (size_t i = 0; i < hist_cur.size(); i++) {
-                hist_all[i] += hist_cur[i];
-                tot_count += hist_cur[i];
-            }
+            // quantize each expert separately since they have different importance matrices
+            new_size = 0;
+            for (int64_t i03 = 0; i03 < tensor->ne[2]; ++i03) {
+                const float * f32_data_03 = f32_data + i03 * nelements_matrix;
+                void * new_data_03 = (char *)new_data + ggml_row_size(new_type, n_per_row) * i03 * nrows;
+                const float * imatrix_03 = imatrix ? imatrix + i03 * n_per_row : nullptr;
 
-            if (tot_count > 0) {
-                for (size_t i = 0; i < hist_cur.size(); i++) {
-                    LLAMA_LOG_INFO("%5.3f ", hist_cur[i] / float(nelements));
-                }
+                new_size += llama_tensor_quantize_internal(new_type, f32_data_03, new_data_03, chunk_size, nrows, n_per_row, imatrix_03, workers, nthread_use);
             }
-            LLAMA_LOG_INFO("\n");
+            LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB\n", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
         }
         total_size_org += ggml_nbytes(tensor);
         total_size_new += new_size;
@@ -9277,24 +13682,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
     LLAMA_LOG_INFO("%s: model size  = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
     LLAMA_LOG_INFO("%s: quant size  = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
 
-    // print histogram for all tensors
-    {
-        int64_t sum_all = 0;
-        for (size_t i = 0; i < hist_all.size(); i++) {
-            sum_all += hist_all[i];
-        }
-
-        if (sum_all > 0) {
-            LLAMA_LOG_INFO("%s: hist: ", __func__);
-            for (size_t i = 0; i < hist_all.size(); i++) {
-                LLAMA_LOG_INFO("%5.3f ", hist_all[i] / float(sum_all));
-            }
-            LLAMA_LOG_INFO("\n");
-        }
-    }
-
     if (qs.n_fallback > 0) {
-        LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) incompatible with k-quants and required fallback quantization\n",
+        LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) required fallback quantization\n",
                 __func__, qs.n_fallback, qs.n_k_quantized + qs.n_fallback);
     }
 }
@@ -9329,48 +13718,23 @@ static int llama_apply_lora_from_file_internal(
 
     LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);
 
-    // create a name -> tensor map of the model to accelerate lookups
-    // find the max tensor size to estimate the required temporary buffer size
-    size_t max_tensor_size = 0;
-    std::unordered_map<std::string, struct ggml_tensor*> model_tensors;
-    for (const auto & kv : model.tensors_by_name) {
-        model_tensors.insert(kv);
-        size_t f32_size = ggml_nelements(kv.second) * sizeof(float);
-        max_tensor_size = std::max(max_tensor_size, f32_size);
-    }
-
-    // create a temporary ggml context to store the lora tensors
-    // TODO: use ggml-alloc
-    size_t lora_ctx_size = max_tensor_size * 3;
-    LLAMA_LOG_INFO("%s: allocating %.f MB for lora temporary buffer\n", __func__, lora_ctx_size / 1024.0 / 1024.0);
-    std::vector<uint8_t> lora_buf(lora_ctx_size);
-
-    struct ggml_init_params params;
-    params.mem_size   = lora_buf.size();
-    params.mem_buffer = lora_buf.data();
-    params.no_alloc   = false;
-
-    using unique_context = std::unique_ptr<ggml_context, decltype(&ggml_free)>;
-
-    unique_context lora_ctx(nullptr, ggml_free);
-    lora_ctx.reset(ggml_init(params));
-    std::unordered_map<std::string, struct ggml_tensor *> lora_tensors;
-
     // load base model
     std::unique_ptr<llama_model_loader> ml;
-
-   if (path_base_model) {
+    if (path_base_model) {
         LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model);
         ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*kv_overrides*/ nullptr));
-        ml->init_mapping(false); // no prefetching
+        ml->init_mappings(/*prefetch*/ false); // no prefetching
     }
 
-    // read tensors and apply
-    bool warned = false;
-    int n_tensors = 0;
-
-    std::vector<uint8_t> work_buffer;
+    struct tensor_meta {
+        std::string name;
+        ggml_type type;
+        int32_t ne[2];
+        size_t offset;
+    };
+    std::map<std::string, tensor_meta> tensor_meta_map;
 
+    // load all tensor meta
     while (true) {
         if (fin.tell() == fin.size) {
             // eof
@@ -9383,7 +13747,7 @@ static int llama_apply_lora_from_file_internal(
 
         fin.read_raw(&n_dims, sizeof(n_dims));
         fin.read_raw(&name_len, sizeof(name_len));
-        fin.read_raw(&ftype,  sizeof(ftype));
+        fin.read_raw(&ftype, sizeof(ftype));
 
         if (n_dims != 1 && n_dims != 2) {
             LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
@@ -9397,31 +13761,23 @@ static int llama_apply_lora_from_file_internal(
 
         std::string name;
         {
-            GGML_ASSERT(name_len <= 1024);
-            char buf[1024];
+            GGML_ASSERT(name_len < GGML_MAX_NAME);
+            char buf[GGML_MAX_NAME];
             fin.read_raw(buf, name_len);
             name = std::string(buf, name_len);
         }
 
-        // check for lora suffix and get the type of tensor
-        const std::string lora_suffix = ".lora";
-        size_t pos = name.rfind(lora_suffix);
-        if (pos == std::string::npos) {
+        // check for lora suffix
+        std::string lora_suffix;
+        if (name.length() > 6) {
+            lora_suffix = name.substr(name.length() - 6);
+        }
+        if (lora_suffix != ".loraA" && lora_suffix != ".loraB") {
             LLAMA_LOG_ERROR("%s: error: '%s' is not a lora tensor\n", __func__, name.c_str());
             return 1;
         }
 
-        std::string lora_type = name.substr(pos + lora_suffix.length());
-        std::string base_name = name;
-        base_name.erase(pos);
-        // LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(), base_name.c_str(), lora_type.c_str());
-
-        if (model_tensors.find(base_name) == model_tensors.end()) {
-            LLAMA_LOG_ERROR("%s: unknown tensor '%s' in lora adapter\n", __func__, name.data());
-            return 1;
-        }
-
-        // create ggml tensor
+        // tensor type
         ggml_type wtype;
         switch (ftype) {
             case 0: wtype = GGML_TYPE_F32;  break;
@@ -9430,125 +13786,180 @@ static int llama_apply_lora_from_file_internal(
                     {
                         LLAMA_LOG_ERROR("%s: invalid tensor data type '%d'\n",
                                 __func__, ftype);
-                        return false;
+                        return 1;
                     }
         }
-        ggml_tensor * lora_tensor = ggml_new_tensor_2d(lora_ctx.get(), wtype, ne[0], ne[1]);
-        ggml_set_name(lora_tensor, name.c_str());
 
-        // load tensor data
+        // data offset
         size_t offset = fin.tell();
-        size_t tensor_data_size = ggml_nbytes(lora_tensor);
         offset = (offset + 31) & -32;
-        fin.seek(offset, SEEK_SET);
-        fin.read_raw(lora_tensor->data, tensor_data_size);
 
-        lora_tensors[name] = lora_tensor;
+        // skip tensor data
+        fin.seek(offset + ggml_row_size(wtype, ne[0]) * ne[1], SEEK_SET);
 
-        // check if we have both A and B tensors and apply
-        if (lora_tensors.find(base_name + ".loraA") != lora_tensors.end() &&
-            lora_tensors.find(base_name + ".loraB") != lora_tensors.end()) {
+        tensor_meta_map.emplace(name, tensor_meta{ name, wtype, { ne[0], ne[1] }, offset });
+    }
 
-            ggml_tensor * dest_t = model_tensors[base_name];
+    bool warned = false;
+    int n_tensors = 0;
 
-            offload_func_t offload_func               = ggml_offload_nop;
-            offload_func_t offload_func_force_inplace = ggml_offload_nop;
+    // apply
+    ggml_backend_t backend_cpu = ggml_backend_cpu_init();
+    if (backend_cpu == nullptr) {
+        LLAMA_LOG_ERROR("%s: error: failed to initialize cpu backend\n", __func__);
+        return 1;
+    }
+    ggml_backend_cpu_set_n_threads(backend_cpu, n_threads);
 
-#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
-            if (dest_t->backend == GGML_BACKEND_GPU || dest_t->backend == GGML_BACKEND_GPU_SPLIT) {
-                if (dest_t->type != GGML_TYPE_F16) {
-                    throw std::runtime_error(format(
-                        "%s: error: the simultaneous use of LoRAs and GPU acceleration is only supported for f16 models. dest_t->type: %d", __func__, dest_t->type));
-                }
-                offload_func = ggml_cuda_assign_buffers;
-                offload_func_force_inplace = ggml_cuda_assign_buffers_force_inplace;
-            }
-#endif // GGML_USE_CUBLAS
+    std::vector<no_init<uint8_t>> read_buf;
+    for (const auto & it : model.tensors_by_name) {
+        const std::string & base_name = it.first;
+        ggml_tensor * model_t = it.second;
 
-            ggml_tensor * base_t;
-            if (ml) {
-                struct gguf_context * ctx_gguf = ml->ctx_gguf;
+        if (tensor_meta_map.find(base_name + ".loraA") == tensor_meta_map.end() ||
+            tensor_meta_map.find(base_name + ".loraB") == tensor_meta_map.end()) {
+            continue;
+        }
 
-                // load from base model
-                if (gguf_find_tensor(ctx_gguf, base_name.c_str()) < 0) {
-                    LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
-                    return 1;
-                }
+        tensor_meta & metaA = tensor_meta_map.at(base_name + ".loraA");
+        tensor_meta & metaB = tensor_meta_map.at(base_name + ".loraB");
 
-                base_t = ml->get_tensor_meta(base_name.c_str());
-                ml->load_data_for(base_t);
-            } else {
-                base_t = dest_t;
-            }
+        ggml_init_params lora_init_params = {
+            /* .mem_size   */ ggml_tensor_overhead()*128 + ggml_graph_overhead(),
+            /* .mem_buffer */ nullptr,
+            /* .no_alloc   */ true,
+        };
+        ggml_context * lora_ctx = ggml_init(lora_init_params);
+        if (lora_ctx == nullptr) {
+            LLAMA_LOG_ERROR("%s: error: failed to initialize lora context\n", __func__);
+            ggml_backend_free(backend_cpu);
+            return 1;
+        }
 
-            if (ggml_is_quantized(base_t->type)) {
-                if (!warned) {
-                    LLAMA_LOG_WARN("%s: warning: using a lora adapter with a quantized model may result in poor quality, "
-                                   "use a f16 or f32 base model with --lora-base\n", __func__);
-                    warned = true;
-                }
-            }
+        // create tensors
+        ggml_tensor * loraA = ggml_new_tensor_2d(lora_ctx, metaA.type, metaA.ne[0], metaA.ne[1]);
+        ggml_tensor * loraB = ggml_new_tensor_2d(lora_ctx, metaB.type, metaB.ne[0], metaB.ne[1]);
+        ggml_set_name(loraA, metaA.name.c_str());
+        ggml_set_name(loraB, metaB.name.c_str());
 
-            ggml_tensor * loraA = lora_tensors[base_name + ".loraA"];
-            GGML_ASSERT(loraA->type == GGML_TYPE_F32);
-            ggml_set_name(loraA, "loraA");
-
-            ggml_tensor * loraB = lora_tensors[base_name + ".loraB"];
-            GGML_ASSERT(loraB->type == GGML_TYPE_F32);
-            ggml_set_name(loraB, "loraB");
-
-            if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
-                LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
-                                " are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]);
+        ggml_tensor * base_t;
+        if (ml) {
+            if (!ml->get_tensor_meta(base_name.c_str())) {
+                LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
                 return 1;
             }
+            base_t = ggml_dup_tensor(lora_ctx, ml->get_tensor_meta(base_name.c_str()));
+        } else {
+            base_t = ggml_dup_tensor(lora_ctx, model_t);
+        }
+        ggml_set_name(base_t, base_name.c_str());
 
+        // allocate in backend buffer
+        ggml_backend_buffer_t lora_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type());
+        if (lora_buf == nullptr) {
+            LLAMA_LOG_ERROR("%s: error: failed to allocate lora tensors\n", __func__);
+            return 1;
+        }
+
+        // load tensor data
+        auto load_tensor = [&read_buf, &fin](const tensor_meta & tensor_meta, ggml_tensor * tensor) {
+            read_buf.resize(ggml_nbytes(tensor));
+            fin.seek(tensor_meta.offset, SEEK_SET);
+            fin.read_raw(read_buf.data(), ggml_nbytes(tensor));
+            ggml_backend_tensor_set(tensor, read_buf.data(), 0, read_buf.size());
+        };
+        load_tensor(metaA, loraA);
+        load_tensor(metaB, loraB);
+
+        // load base model tensor data
+        if (ml) {
+            ml->load_data_for(base_t);
+        } else {
+            ggml_backend_tensor_copy(model_t, base_t);
+        }
+
+        if (ggml_is_quantized(base_t->type) && !warned) {
+            LLAMA_LOG_WARN("%s: warning: using a lora adapter with a quantized model may result in poor quality, "
+                            "use a f16 or f32 base model with --lora-base\n", __func__);
+            warned = true;
+        }
+
+        if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
+            LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
+                            " are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]);
+            ggml_free(lora_ctx);
+            ggml_backend_buffer_free(lora_buf);
+            ggml_backend_free(backend_cpu);
+            return 1;
+        }
+
+        auto build_lora_graph = [&]() {
             // w = w + BA*s
-            ggml_tensor * BA = ggml_mul_mat(lora_ctx.get(), loraA, loraB);
-            offload_func(BA);
+            ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
             ggml_set_name(BA, "BA");
 
             if (scaling != 1.0f) {
-                BA = ggml_scale_inplace(lora_ctx.get(), BA, scaling);
-                offload_func(BA);
+                BA = ggml_scale(lora_ctx, BA, scaling);
                 ggml_set_name(BA, "BA_scaled");
             }
 
             ggml_tensor * r;
-            if (base_t == dest_t) {
-                r = ggml_add_inplace(lora_ctx.get(), dest_t, BA);
-                offload_func_force_inplace(r);
-                ggml_set_name(r, "r_add_inplace");
-            }
-            else {
-                r = ggml_add(lora_ctx.get(), base_t, BA);
-                offload_func(r);
-                ggml_set_name(r, "r_add");
+            r = ggml_add_inplace(lora_ctx, base_t, BA);
+            ggml_set_name(r, "r_add");
 
-                r = ggml_cpy(lora_ctx.get(), r, dest_t);
-                offload_func(r);
-                ggml_set_name(r, "r_cpy");
+            if (base_t->type != model_t->type) {
+                // convert the result to the model type
+                r = ggml_cast(lora_ctx, r, model_t->type);
+                ggml_set_name(r, "r_cast");
             }
 
-            struct ggml_cgraph * gf = ggml_new_graph(lora_ctx.get());
-            ggml_build_forward_expand(gf, r);
+            return r;
+        };
 
-            ggml_graph_compute_helper(work_buffer, gf, n_threads);
+        ggml_cgraph * gf = ggml_new_graph(lora_ctx);
+        ggml_tensor * r = build_lora_graph();
+        ggml_build_forward_expand(gf, r);
 
-            // the tensors in the adapter must be sorted such that loraA and loraB of the same tensor are next to each other
-            GGML_ASSERT(lora_tensors.size() == 2);
+        ggml_backend_buffer_t graph_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type());
+        if (graph_buf == nullptr) {
+            LLAMA_LOG_ERROR("%s: error: failed to allocate graph tensors\n", __func__);
+            ggml_free(lora_ctx);
+            ggml_backend_buffer_free(lora_buf);
+            ggml_backend_free(backend_cpu);
+            return 1;
+        }
 
-            // we won't need these tensors again, reset the context to save memory
-            lora_ctx.reset(ggml_init(params));
-            lora_tensors.clear();
+        ggml_backend_graph_compute(backend_cpu, gf);
 
-            n_tensors++;
-            if (n_tensors % 4 == 0) {
-                LLAMA_LOG_INFO(".");
-            }
+        ggml_backend_tensor_set(model_t, r->data, 0, ggml_nbytes(r));
+
+#if 0
+        // TODO: use scheduler with fallback to CPU for less copies between CPU and GPU
+        //ggml_backend_sched_t sched = ggml_backend_sched_new(backends.data(), backends.size(), GGML_DEFAULT_GRAPH_SIZE);
+
+        // sched compute
+        ggml_build_forward_expand(gf, build_graph());
+        ggml_backend_sched_init_measure(sched, gf);
+
+        // create the graph again, since the previous one was destroyed by the measure
+        ggml_graph_clear(gf);
+        ggml_build_forward_expand(gf, build_graph());
+        ggml_backend_sched_graph_compute(sched, gf);
+        ggml_backend_sched_free(sched);
+#endif
+
+        ggml_backend_buffer_free(lora_buf);
+        ggml_backend_buffer_free(graph_buf);
+        ggml_free(lora_ctx);
+
+        n_tensors++;
+        if (n_tensors % 4 == 0) {
+            LLAMA_LOG_INFO(".");
         }
     }
 
+    ggml_backend_free(backend_cpu);
+
     const int64_t t_lora_us = ggml_time_us() - t_start_lora_us;
     LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0);
 
@@ -9561,6 +13972,7 @@ static int llama_apply_lora_from_file_internal(
 struct llama_model_params llama_model_default_params() {
     struct llama_model_params result = {
         /*.n_gpu_layers                =*/ 0,
+        /*.split_mode                  =*/ LLAMA_SPLIT_MODE_LAYER,
         /*.main_gpu                    =*/ 0,
         /*.tensor_split                =*/ nullptr,
         /*.progress_callback           =*/ nullptr,
@@ -9572,7 +13984,8 @@ struct llama_model_params llama_model_default_params() {
     };
 
 #ifdef GGML_USE_METAL
-    result.n_gpu_layers = 1;
+    // note: we usually have plenty of VRAM, so by default offload all layers to the GPU
+    result.n_gpu_layers = 999;
 #endif
 
     return result;
@@ -9582,10 +13995,13 @@ struct llama_context_params llama_context_default_params() {
     struct llama_context_params result = {
         /*.seed                        =*/ LLAMA_DEFAULT_SEED,
         /*.n_ctx                       =*/ 512,
-        /*.n_batch                     =*/ 512,
+        /*.n_batch                     =*/ 2048,
+        /*.n_ubatch                    =*/ 512,
+        /*.n_seq_max                   =*/ 1,
         /*.n_threads                   =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
         /*.n_threads_batch             =*/ GGML_DEFAULT_N_THREADS,
-        /*.rope_scaling_type           =*/ LLAMA_ROPE_SCALING_UNSPECIFIED,
+        /*.rope_scaling_type           =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
+        /*.pooling_type                =*/ LLAMA_POOLING_TYPE_UNSPECIFIED,
         /*.rope_freq_base              =*/ 0.0f,
         /*.rope_freq_scale             =*/ 0.0f,
         /*.yarn_ext_factor             =*/ -1.0f,
@@ -9593,12 +14009,16 @@ struct llama_context_params llama_context_default_params() {
         /*.yarn_beta_fast              =*/ 32.0f,
         /*.yarn_beta_slow              =*/ 1.0f,
         /*.yarn_orig_ctx               =*/ 0,
+        /*.defrag_thold                =*/ -1.0f,
+        /*.cb_eval                     =*/ nullptr,
+        /*.cb_eval_user_data           =*/ nullptr,
         /*.type_k                      =*/ GGML_TYPE_F16,
         /*.type_v                      =*/ GGML_TYPE_F16,
-        /*.mul_mat_q                   =*/ true,
         /*.logits_all                  =*/ false,
-        /*.embedding                   =*/ false,
+        /*.embeddings                  =*/ false,
         /*.offload_kqv                 =*/ true,
+        /*.abort_callback              =*/ nullptr,
+        /*.abort_callback_data         =*/ nullptr,
     };
 
     return result;
@@ -9608,28 +14028,52 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
     struct llama_model_quantize_params result = {
         /*.nthread                     =*/ 0,
         /*.ftype                       =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
+        /*.output_tensor_type          =*/ GGML_TYPE_COUNT,
+        /*.token_embedding_type        =*/ GGML_TYPE_COUNT,
         /*.allow_requantize            =*/ false,
         /*.quantize_output_tensor      =*/ true,
         /*.only_copy                   =*/ false,
         /*.pure                        =*/ false,
+        /*.imatrix                     =*/ nullptr,
+        /*.kv_overrides                =*/ nullptr,
     };
 
     return result;
 }
 
-int32_t llama_max_devices(void) {
-    return LLAMA_MAX_DEVICES;
+size_t llama_max_devices(void) {
+#if defined(GGML_USE_METAL)
+    return 1;
+#elif defined(GGML_USE_CUDA)
+    return GGML_CUDA_MAX_DEVICES;
+#elif defined(GGML_USE_SYCL)
+    return GGML_SYCL_MAX_DEVICES;
+#elif defined(GGML_USE_VULKAN)
+    return GGML_VK_MAX_DEVICES;
+#else
+    return 1;
+#endif
 }
 
-bool llama_mmap_supported(void) {
+bool llama_supports_mmap(void) {
     return llama_mmap::SUPPORTED;
 }
 
-bool llama_mlock_supported(void) {
+bool llama_supports_mlock(void) {
     return llama_mlock::SUPPORTED;
 }
 
-void llama_backend_init(bool numa) {
+bool llama_supports_gpu_offload(void) {
+#if defined(GGML_USE_CUDA) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \
+    defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE)
+    // Defined when llama.cpp is compiled with support for offloading model layers to GPU.
+    return true;
+#else
+    return false;
+#endif
+}
+
+void llama_backend_init(void) {
     ggml_time_init();
 
     // needed to initialize f16 tables
@@ -9639,19 +14083,22 @@ void llama_backend_init(bool numa) {
         ggml_free(ctx);
     }
 
-    if (numa) {
-        ggml_numa_init();
-    }
-
 #ifdef GGML_USE_MPI
     ggml_mpi_backend_init();
 #endif
 }
 
+void llama_numa_init(enum ggml_numa_strategy numa) {
+    if (numa != GGML_NUMA_STRATEGY_DISABLED) {
+        ggml_numa_init(numa);
+    }
+}
+
 void llama_backend_free(void) {
 #ifdef GGML_USE_MPI
     ggml_mpi_backend_free();
 #endif
+    ggml_quantize_free();
 }
 
 int64_t llama_time_us(void) {
@@ -9659,8 +14106,8 @@ int64_t llama_time_us(void) {
 }
 
 struct llama_model * llama_load_model_from_file(
-                             const char * path_model,
-              struct llama_model_params   params) {
+        const char * path_model,
+        struct llama_model_params   params) {
     ggml_time_init();
 
     llama_model * model = new llama_model;
@@ -9706,6 +14153,17 @@ struct llama_context * llama_new_context_with_model(
         struct llama_context_params   params) {
 
     if (!model) {
+        LLAMA_LOG_ERROR("%s: model cannot be NULL\n", __func__);
+        return nullptr;
+    }
+
+    if (params.n_batch == 0 && params.n_ubatch == 0) {
+        LLAMA_LOG_ERROR("%s: n_batch and n_ubatch cannot both be zero\n", __func__);
+        return nullptr;
+    }
+
+    if (params.n_ctx == 0 && model->hparams.n_ctx_train == 0) {
+        LLAMA_LOG_ERROR("%s: n_ctx and model->hparams.n_ctx_train cannot both be zero\n", __func__);
         return nullptr;
     }
 
@@ -9714,35 +14172,58 @@ struct llama_context * llama_new_context_with_model(
     const auto & hparams = model->hparams;
     auto       & cparams = ctx->cparams;
 
-    cparams.n_batch          = params.n_batch;
+    cparams.n_seq_max        = std::max(1u, params.n_seq_max);
     cparams.n_threads        = params.n_threads;
     cparams.n_threads_batch  = params.n_threads_batch;
     cparams.yarn_ext_factor  = params.yarn_ext_factor;
     cparams.yarn_attn_factor = params.yarn_attn_factor;
     cparams.yarn_beta_fast   = params.yarn_beta_fast;
     cparams.yarn_beta_slow   = params.yarn_beta_slow;
-    cparams.mul_mat_q        = params.mul_mat_q;
+    cparams.defrag_thold     = params.defrag_thold;
+    cparams.embeddings       = params.embeddings;
     cparams.offload_kqv      = params.offload_kqv;
+    cparams.pooling_type     = params.pooling_type;
 
     cparams.n_ctx            = params.n_ctx           == 0    ? hparams.n_ctx_train           : params.n_ctx;
     cparams.rope_freq_base   = params.rope_freq_base  == 0.0f ? hparams.rope_freq_base_train  : params.rope_freq_base;
     cparams.rope_freq_scale  = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale;
 
+    // this is necessary due to kv_self.n being padded later during inference
+    cparams.n_ctx = GGML_PAD(cparams.n_ctx, 32);
+
+    // with causal attention, the batch size is limited by the context size
+    cparams.n_batch          = hparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch;
+    cparams.n_ubatch         = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
+
+
     cparams.n_yarn_orig_ctx  = params.yarn_orig_ctx    != 0 ? params.yarn_orig_ctx    :
                                hparams.n_yarn_orig_ctx != 0 ? hparams.n_yarn_orig_ctx :
                                                               hparams.n_ctx_train;
 
+    cparams.cb_eval           = params.cb_eval;
+    cparams.cb_eval_user_data = params.cb_eval_user_data;
+
     auto rope_scaling_type = params.rope_scaling_type;
-    if (rope_scaling_type == LLAMA_ROPE_SCALING_UNSPECIFIED) {
+    if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED) {
         rope_scaling_type = hparams.rope_scaling_type_train;
     }
 
-    if (rope_scaling_type == LLAMA_ROPE_SCALING_NONE) {
+    if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_NONE) {
         cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none
     }
 
     if (cparams.yarn_ext_factor < 0.0f) { // negative indicates 'not set'
-        cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_YARN ? 1.0f : 0.0f;
+        cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f;
+    }
+
+    cparams.causal_attn = hparams.causal_attn;
+
+    if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
+        if (hparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
+            cparams.pooling_type = LLAMA_POOLING_TYPE_NONE;
+        } else {
+            cparams.pooling_type = hparams.pooling_type;
+        }
     }
 
     if (params.seed == LLAMA_DEFAULT_SEED) {
@@ -9750,53 +14231,137 @@ struct llama_context * llama_new_context_with_model(
     }
 
     LLAMA_LOG_INFO("%s: n_ctx      = %u\n",     __func__, cparams.n_ctx);
+    LLAMA_LOG_INFO("%s: n_batch    = %u\n",     __func__, cparams.n_batch);
+    LLAMA_LOG_INFO("%s: n_ubatch   = %u\n",     __func__, cparams.n_ubatch);
     LLAMA_LOG_INFO("%s: freq_base  = %.1f\n",   __func__, cparams.rope_freq_base);
     LLAMA_LOG_INFO("%s: freq_scale = %g\n",     __func__, cparams.rope_freq_scale);
 
-    ctx->rng = std::mt19937(params.seed);
-    ctx->logits_all = params.logits_all;
+    ctx->abort_callback      = params.abort_callback;
+    ctx->abort_callback_data = params.abort_callback_data;
 
-    const ggml_type type_k = params.type_k;
-    const ggml_type type_v = params.type_v;
+    ctx->rng                 = std::mt19937(params.seed);
+    ctx->logits_all          = params.logits_all;
+
+    uint32_t kv_size = cparams.n_ctx;
+    ggml_type type_k = params.type_k;
+    ggml_type type_v = params.type_v;
+
+    // Mamba only needs a constant number of KV cache cells per sequence
+    if (model->arch == LLM_ARCH_MAMBA) {
+        // Mamba needs at least as many KV cells as there are sequences kept at any time
+        kv_size = std::max((uint32_t) 1, params.n_seq_max);
+        // it's probably best to keep as much precision as possible for the states
+        type_k = GGML_TYPE_F32; // required by ggml_ssm_conv for Mamba's conv_states
+        type_v = GGML_TYPE_F32; // required by ggml_ssm_scan for Mamba's ssm_states
+    }
 
     GGML_ASSERT(hparams.n_embd_head_k % ggml_blck_size(type_k) == 0);
     GGML_ASSERT(hparams.n_embd_head_v % ggml_blck_size(type_v) == 0);
 
-    // reserve memory for context buffers
     if (!hparams.vocab_only) {
-        // initialize backend
+        // initialize backends
 #ifdef GGML_USE_METAL
         if (model->n_gpu_layers > 0) {
-            ctx->backend = ggml_backend_metal_init();
-            if (ctx->backend == nullptr) {
+            ctx->backend_metal = ggml_backend_metal_init();
+            if (ctx->backend_metal == nullptr) {
                 LLAMA_LOG_ERROR("%s: failed to initialize Metal backend\n", __func__);
+                llama_free(ctx);
+                return nullptr;
+            }
+            ctx->backends.push_back(ctx->backend_metal);
+        }
+#elif defined(GGML_USE_CUDA)
+        if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
+            // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
+            ggml_backend_t backend = ggml_backend_cuda_init(model->main_gpu);
+            if (backend == nullptr) {
+                LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, model->main_gpu);
+                llama_free(ctx);
+                return nullptr;
+            }
+            ctx->backends.push_back(backend);
+        } else {
+            // LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU
+            for (int device = 0; device < ggml_backend_cuda_get_device_count(); ++device) {
+                ggml_backend_t backend = ggml_backend_cuda_init(device);
+                if (backend == nullptr) {
+                    LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, device);
+                    llama_free(ctx);
+                    return nullptr;
+                }
+                ctx->backends.push_back(backend);
             }
         }
-#elif defined(GGML_USE_CUBLAS) && defined(LLAMA_GGML_BACKEND_CUDA_TEST)
-        // for testing only
-        if (model->n_gpu_layers > 0) {
-            ctx->backend = ggml_backend_cuda_init(0);
-            if (ctx->backend == nullptr) {
-                LLAMA_LOG_ERROR("%s: failed to initialize CUDA backend\n", __func__);
-            }
-        }
-#endif
-
-        if (ctx->backend == nullptr && ggml_backend_buffer_is_host(model->buf)) {
-            ctx->backend = ggml_backend_cpu_init();
-            if (ctx->backend == nullptr) {
-                LLAMA_LOG_ERROR("%s: failed to initialize CPU backend\n", __func__);
-            }
-        }
-
-        if (ctx->backend == nullptr) {
-            LLAMA_LOG_ERROR("%s: failed to initialize a backend\n", __func__);
-            delete ctx;
+#elif defined(GGML_USE_VULKAN)
+        if (model->split_mode == LLAMA_SPLIT_MODE_ROW) {
+            LLAMA_LOG_ERROR("%s: Row split not supported. Failed to initialize Vulkan backend\n", __func__);
+            llama_free(ctx);
             return nullptr;
         }
+        if (model->split_mode == LLAMA_SPLIT_MODE_NONE) {
+            ggml_backend_t backend = ggml_backend_vk_init(0);
+            if (backend == nullptr) {
+                LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__);
+                llama_free(ctx);
+                return nullptr;
+            }
+            ctx->backends.push_back(backend);
+        } else {
+            for (int device = 0; device < ggml_backend_vk_get_device_count(); ++device) {
+                ggml_backend_t backend = ggml_backend_vk_init(device);
+                if (backend == nullptr) {
+                    LLAMA_LOG_ERROR("%s: failed to initialize Vulkan%d backend\n", __func__, device);
+                    llama_free(ctx);
+                    return nullptr;
+                }
+                ctx->backends.push_back(backend);
+            }
+        }
+#elif defined(GGML_USE_SYCL)
+        // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
+        if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
+            ggml_backend_t backend = ggml_backend_sycl_init(model->main_gpu);
+            if (backend == nullptr) {
+                int main_gpu_id = ggml_backend_sycl_get_device_id(model->main_gpu);
+                LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d) backend\n", __func__, main_gpu_id, model->main_gpu);
+                llama_free(ctx);
+                return nullptr;
+            }
+            ctx->backends.push_back(backend);
+        } else {
+            // LLAMA_SPLIT_LAYER requires a backend for each GPU
+            for (int i = 0; i < ggml_backend_sycl_get_device_count(); ++i) {
+                ggml_backend_t backend = ggml_backend_sycl_init(i);
+                if (backend == nullptr) {
+                    int id_list[GGML_SYCL_MAX_DEVICES];
+                    ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES);
+                    LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d) backend\n", __func__, id_list[i], i);
+                    llama_free(ctx);
+                    return nullptr;
+                }
+                ctx->backends.push_back(backend);
+            }
+        }
+#elif defined(GGML_USE_KOMPUTE)
+        if (model->n_gpu_layers > 0) {
+            auto * backend = ggml_backend_kompute_init(model->main_gpu);
+            if (backend == nullptr) {
+                LLAMA_LOG_ERROR("%s: failed to initialize Kompute backend\n", __func__);
+                llama_free(ctx);
+                return nullptr;
+            }
+            ctx->backends.push_back(backend);
+        }
+#endif
+        ctx->backend_cpu = ggml_backend_cpu_init();
+        if (ctx->backend_cpu == nullptr) {
+            LLAMA_LOG_ERROR("%s: failed to initialize CPU backend\n", __func__);
+            llama_free(ctx);
+            return nullptr;
+        }
+        ctx->backends.push_back(ctx->backend_cpu);
 
-        if (!llama_kv_cache_init(ctx->model.hparams, ctx->kv_self, type_k, type_v,
-                cparams.n_ctx, model->n_gpu_layers, cparams.offload_kqv)) {
+        if (!llama_kv_cache_init(ctx->kv_self, ctx->model, type_k, type_v, kv_size, cparams.offload_kqv)) {
             LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__);
             llama_free(ctx);
             return nullptr;
@@ -9820,74 +14385,77 @@ struct llama_context * llama_new_context_with_model(
                 ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f));
         }
 
-        // resized during inference
-        if (params.logits_all) {
-            ctx->logits.reserve(cparams.n_ctx*hparams.n_vocab);
-        } else {
-            ctx->logits.reserve(hparams.n_vocab);
-        }
-
-        if (params.embedding){
-            ctx->embedding.resize(hparams.n_embd);
-        }
-
+        // graph outputs buffer
         {
-            // the compute buffer is used to store the tensor and graph structs, while the allocator buffer is used for the tensor data
-            ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead());
+            // resized during inference when a batch uses more outputs
+            if (llama_output_reserve(*ctx, params.n_seq_max) < params.n_seq_max) {
+                LLAMA_LOG_ERROR("%s: failed to reserve initial output buffer\n", __func__);
+                llama_free(ctx);
+                return nullptr;
+            }
 
-            // create measure allocator
-            ctx->alloc = ggml_allocr_new_measure_from_backend(ctx->backend);
+            LLAMA_LOG_INFO("%s: %10s  output buffer size = %8.2f MiB\n", __func__,
+                    ggml_backend_buffer_name(ctx->buf_output),
+                    ggml_backend_buffer_get_size(ctx->buf_output) / 1024.0 / 1024.0);
+        }
+
+        // scheduler and compute buffers
+        {
+            // buffer types used for the compute buffer of each backend
+            std::vector<ggml_backend_buffer_type_t> backend_buft;
+            for (auto * backend : ctx->backends) {
+                if (ggml_backend_is_cpu(backend)) {
+                    // use host buffers for the CPU backend compute buffer
+                    backend_buft.push_back(llama_default_buffer_type_cpu(true));
+                } else {
+                    backend_buft.push_back(ggml_backend_get_default_buffer_type(backend));
+                }
+            }
+
+            // buffer used to store the computation graph and the tensor meta data
+            ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead_custom(LLAMA_MAX_NODES, false));
+
+            // enabling pipeline parallelism in the scheduler increases memory usage, so it is only done when necessary
+            bool pipeline_parallel = llama_get_device_count() > 1 && model->n_gpu_layers > (int)model->hparams.n_layer && model->split_mode == LLAMA_SPLIT_MODE_LAYER;
+#ifndef GGML_USE_CUDA
+            // pipeline parallelism requires support for async compute and events
+            // currently this is only implemented in the CUDA backend
+            pipeline_parallel = false;
+#endif
+            ctx->sched = ggml_backend_sched_new(ctx->backends.data(), backend_buft.data(), ctx->backends.size(), LLAMA_MAX_NODES, pipeline_parallel);
+
+            if (pipeline_parallel) {
+                LLAMA_LOG_INFO("%s: pipeline parallelism enabled (n_copies=%d)\n", __func__, ggml_backend_sched_get_n_copies(ctx->sched));
+            }
 
             // build worst-case graph
-            int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_batch);
+            int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_ubatch);
             int n_past = cparams.n_ctx - n_tokens;
             llama_token token = llama_token_bos(&ctx->model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
-            ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, n_past, 0));
+            ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, n_past, 0), true);
 
-            // measure memory requirements for the graph
-            size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf);
-
-            LLAMA_LOG_INFO("%s: compute buffer total size = %.2f MiB\n", __func__, (ctx->buf_compute_meta.size() + alloc_size) / 1024.0 / 1024.0);
-
-            // create allocator again with exact memory requirements
-            ggml_allocr_free(ctx->alloc);
-
-            ctx->buf_alloc = ggml_backend_alloc_buffer(ctx->backend, alloc_size);
-            ctx->alloc = ggml_allocr_new_from_buffer(ctx->buf_alloc);
-#if defined(GGML_USE_CUBLAS) && !defined(LLAMA_GGML_BACKEND_CUDA_TEST)
-            if (model->n_gpu_layers > 0) {
-                // the CPU buffer adds this padding in case the malloc buffer is not aligned, so we need to do the same for the GPU buffer, since we use the same offsets
-                ggml_cuda_set_scratch_size(alloc_size + 64);
-                LLAMA_LOG_INFO("%s: VRAM scratch buffer: %.2f MiB\n", __func__, alloc_size / 1024.0 / 1024.0);
-
-                // calculate total VRAM usage
-                auto add_tensor = [](const ggml_tensor * t, size_t & size) {
-                    if (t->backend == GGML_BACKEND_GPU || t->backend == GGML_BACKEND_GPU_SPLIT) {
-                        size += ggml_nbytes(t);
-                    }
-                };
-                size_t model_vram_size = 0;
-                for (const auto & kv : model->tensors_by_name) {
-                    add_tensor(kv.second, model_vram_size);
-                }
-
-                size_t kv_vram_size = 0;
-                for (auto & k : ctx->kv_self.k_l) {
-                    add_tensor(k, kv_vram_size);
-                }
-                for (auto & v : ctx->kv_self.v_l) {
-                    add_tensor(v, kv_vram_size);
-                }
-
-                size_t ctx_vram_size = alloc_size + kv_vram_size;
-                size_t total_vram_size = model_vram_size + ctx_vram_size;
-
-                LLAMA_LOG_INFO("%s: total VRAM used: %.2f MiB (model: %.2f MiB, context: %.2f MiB)\n", __func__,
-                        total_vram_size / 1024.0 / 1024.0,
-                        model_vram_size / 1024.0 / 1024.0,
-                        ctx_vram_size   / 1024.0 / 1024.0);
+            // initialize scheduler with the worst-case graph
+            if (!ggml_backend_sched_reserve(ctx->sched, gf)) {
+                LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
+                llama_free(ctx);
+                return nullptr;
             }
-#endif
+
+            for (size_t i = 0; i < ctx->backends.size(); i++) {
+                ggml_backend_t backend = ctx->backends[i];
+                ggml_backend_buffer_type_t buft = backend_buft[i];
+                size_t size = ggml_backend_sched_get_buffer_size(ctx->sched, backend);
+                if (size > 1) {
+                    LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__,
+                            ggml_backend_buft_name(buft),
+                            size / 1024.0 / 1024.0);
+                }
+            }
+
+            // note: the number of splits during measure is higher than during inference due to the kv shift
+            int n_splits = ggml_backend_sched_get_n_splits(ctx->sched);
+            LLAMA_LOG_INFO("%s: graph nodes  = %d\n", __func__, gf->n_nodes);
+            LLAMA_LOG_INFO("%s: graph splits = %d\n", __func__, n_splits);
         }
     }
 
@@ -9924,12 +14492,68 @@ uint32_t llama_n_batch(const struct llama_context * ctx) {
     return ctx->cparams.n_batch;
 }
 
+uint32_t llama_n_ubatch(const struct llama_context * ctx) {
+    return ctx->cparams.n_ubatch;
+}
+
+uint32_t llama_n_seq_max(const struct llama_context * ctx) {
+    return ctx->kv_self.size;
+}
+
 enum llama_vocab_type llama_vocab_type(const struct llama_model * model) {
     return model->vocab.type;
 }
 
+enum llama_rope_type llama_rope_type(const struct llama_model * model) {
+    switch (model->arch) {
+        // these models do not use RoPE
+        case LLM_ARCH_GPT2:
+        case LLM_ARCH_GPTJ:
+        case LLM_ARCH_GPTNEOX:
+        case LLM_ARCH_MPT:
+        case LLM_ARCH_REFACT:
+        case LLM_ARCH_BLOOM:
+        case LLM_ARCH_MAMBA:
+            return LLAMA_ROPE_TYPE_NONE;
+
+        // use what we call a normal RoPE, operating on pairs of consecutive head values
+        case LLM_ARCH_LLAMA:
+        case LLM_ARCH_BAICHUAN:
+        case LLM_ARCH_STARCODER:
+        case LLM_ARCH_PLAMO:
+        case LLM_ARCH_CODESHELL:
+        case LLM_ARCH_ORION:
+        case LLM_ARCH_INTERNLM2:
+        case LLM_ARCH_MINICPM:
+        case LLM_ARCH_XVERSE:
+        case LLM_ARCH_COMMAND_R:
+            return LLAMA_ROPE_TYPE_NORM;
+
+        // the pairs of head values are offset by n_rot/2
+        case LLM_ARCH_FALCON:
+        case LLM_ARCH_GROK:
+        case LLM_ARCH_PERSIMMON:
+        case LLM_ARCH_BERT:
+        case LLM_ARCH_NOMIC_BERT:
+        case LLM_ARCH_STABLELM:
+        case LLM_ARCH_QWEN:
+        case LLM_ARCH_QWEN2:
+        case LLM_ARCH_PHI2:
+        case LLM_ARCH_GEMMA:
+        case LLM_ARCH_STARCODER2:
+            return LLAMA_ROPE_TYPE_NEOX;
+
+        // all model arches should be listed explicitly here
+        case LLM_ARCH_UNKNOWN:
+            GGML_ASSERT(false && "unknown architecture");
+            break;
+    }
+
+    return LLAMA_ROPE_TYPE_NONE;
+}
+
 int32_t llama_n_vocab(const struct llama_model * model) {
-    return model->vocab.id_to_token.size();
+    return model->hparams.n_vocab;
 }
 
 int32_t llama_n_ctx_train(const struct llama_model * model) {
@@ -9940,6 +14564,10 @@ int32_t llama_n_embd(const struct llama_model * model) {
     return model->hparams.n_embd;
 }
 
+int32_t llama_n_layer(const struct llama_model * model) {
+    return model->hparams.n_layer;
+}
+
 float llama_rope_freq_scale_train(const struct llama_model * model) {
     return model->hparams.rope_freq_scale_train;
 }
@@ -9984,9 +14612,8 @@ int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int3
 }
 
 int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
-    return snprintf(buf, buf_size, "%s %s%s %s",
-            llama_model_arch_name(model->arch).c_str(),
-            model->hparams.n_expert > 0 ? (std::to_string(model->hparams.n_expert) + "x").c_str() : "",
+    return snprintf(buf, buf_size, "%s %s %s",
+            llama_model_arch_name(model->arch),
             llama_model_type_name(model->type),
             llama_model_ftype_name(model->ftype).c_str());
 }
@@ -10008,7 +14635,14 @@ uint64_t llama_model_n_params(const struct llama_model * model) {
 }
 
 struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name) {
-    return ggml_get_tensor(model->ctx, name);
+    auto it = std::find_if(model->tensors_by_name.begin(), model->tensors_by_name.end(),
+            [name](const std::pair<std::string, struct ggml_tensor *> & it) {
+                return it.first == name;
+            });
+    if (it == model->tensors_by_name.end()) {
+        return nullptr;
+    }
+    return it->second;
 }
 
 uint32_t llama_model_quantize(
@@ -10024,15 +14658,6 @@ uint32_t llama_model_quantize(
     }
 }
 
-int32_t llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) {
-    try {
-        return llama_apply_lora_from_file_internal(ctx->model, path_lora, scale, path_base_model, n_threads);
-    } catch (const std::exception & err) {
-        LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
-        return 1;
-    }
-}
-
 int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) {
     try {
         return llama_apply_lora_from_file_internal(*model, path_lora, scale, path_base_model, n_threads);
@@ -10042,10 +14667,100 @@ int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const
     }
 }
 
-struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_max_seq) {
+static bool llama_control_vector_init(struct llama_control_vector & cvec, const llama_model & model) {
+    GGML_ASSERT(cvec.tensors.empty());
+    GGML_ASSERT(cvec.ctxs.empty());
+    GGML_ASSERT(cvec.bufs.empty());
+
+    // count layer buffer types
+    std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
+    for (int64_t i = 0; i < model.hparams.n_layer; i++) {
+        buft_layer_count[model.buft_layer[i].buft]++;
+    }
+
+    // allocate contexts
+    std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
+    for (auto & it : buft_layer_count) {
+        int n_layers = it.second;
+        struct ggml_init_params params = {
+            /*.mem_size   =*/ n_layers * ggml_tensor_overhead(),
+            /*.mem_buffer =*/ NULL,
+            /*.no_alloc   =*/ true,
+        };
+        ggml_context * ctx = ggml_init(params);
+        if (!ctx) {
+            LLAMA_LOG_ERROR("%s: failed to allocate context for control vector\n", __func__);
+            return 1;
+        }
+        ctx_map[it.first] = ctx;
+    }
+
+    // make tensors
+    cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0
+    for (size_t il = 1; il < model.hparams.n_layer; il++) {
+        struct ggml_context * ctx = ctx_map.at(model.buft_layer[il].buft);
+        ggml_tensor * tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
+        cvec.tensors.push_back(tensor);
+    }
+
+    // allocate tensors / buffers and zero
+    for (auto it : ctx_map) {
+        ggml_backend_buffer_type_t buft = it.first;
+        ggml_context * ctx = it.second;
+        ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
+        if (!buf) {
+            LLAMA_LOG_ERROR("%s: failed to allocate buffer for control vector\n", __func__);
+            return false;
+        }
+        ggml_backend_buffer_clear(buf, 0);
+        cvec.ctxs.push_back(ctx);
+        cvec.bufs.push_back(buf);
+    }
+
+    return true;
+}
+
+int32_t llama_control_vector_apply(struct llama_context * lctx, const float * data, size_t len, int32_t n_embd, int32_t il_start, int32_t il_end) {
+    const llama_model & model = lctx->model;
+    llama_control_vector & cvec = lctx->cvec;
+
+    if (data == nullptr) {
+        // disable the current control vector (but leave allocated for later)
+        cvec.layer_start = -1;
+        cvec.layer_end   = -1;
+        return 0;
+    }
+
+    if (n_embd != (int) model.hparams.n_embd) {
+        LLAMA_LOG_ERROR("%s: control vector n_embd does not match model\n", __func__);
+        return 1;
+    }
+
+    if (cvec.tensors.empty()) {
+        if (!llama_control_vector_init(cvec, model)) {
+            return 1;
+        }
+    }
+
+    cvec.layer_start = il_start;
+    cvec.layer_end   = il_end;
+
+    for (size_t il = 1; il < model.hparams.n_layer; il++) {
+        assert(cvec.tensors[il] != nullptr);
+
+        const size_t off = n_embd * (il - 1); // buffer doesn't have data for layer 0, since it's never present
+        if (off + n_embd <= len) {
+            ggml_backend_tensor_set(cvec.tensors[il], data + off, 0, n_embd * ggml_element_size(cvec.tensors[il]));
+        }
+    }
+
+    return 0;
+}
+
+struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max) {
     struct llama_kv_cache_view result = {
         /*.n_cells            = */ 0,
-        /*.n_max_seq          = */ n_max_seq,
+        /*.n_seq_max          = */ n_seq_max,
         /*.token_count        = */ 0,
         /*.used_cells         = */ llama_get_kv_cache_used_cells(ctx),
         /*.max_contiguous     = */ 0,
@@ -10073,7 +14788,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k
         void * p = realloc(view->cells, sizeof(struct llama_kv_cache_view_cell) * view->n_cells);
         GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells");
         view->cells = (struct llama_kv_cache_view_cell *)p;
-        p = realloc(view->cells_sequences, sizeof(llama_seq_id) * view->n_max_seq * view->n_cells);
+        p = realloc(view->cells_sequences, sizeof(llama_seq_id) * view->n_seq_max * view->n_cells);
         GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells sequences");
         view->cells_sequences = (llama_seq_id *)p;
     }
@@ -10087,7 +14802,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k
     uint32_t max_contig = 0;
     int32_t max_contig_idx = -1;
 
-    for (int32_t i = 0; i < int32_t(ctx->kv_self.size); i++, c_curr++, cs_curr += view->n_max_seq) {
+    for (int32_t i = 0; i < int32_t(ctx->kv_self.size); i++, c_curr++, cs_curr += view->n_seq_max) {
         const size_t curr_size = kv_cells[i].seq_id.size();
         token_count += curr_size;
         c_curr->pos = kv_cells[i].pos + kv_cells[i].delta;
@@ -10104,7 +14819,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k
 
         int seq_idx = 0;
         for (const llama_seq_id it : kv_cells[i].seq_id) {
-            if (seq_idx >= view->n_max_seq) {
+            if (seq_idx >= view->n_seq_max) {
                 break;
             }
             cs_curr[seq_idx] = it;
@@ -10113,7 +14828,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k
         if (seq_idx != 0) {
             used_cells++;
         }
-        for (; seq_idx < view->n_max_seq; seq_idx++) {
+        for (; seq_idx < view->n_seq_max; seq_idx++) {
             cs_curr[seq_idx] = -1;
         }
     }
@@ -10149,8 +14864,8 @@ void llama_kv_cache_clear(struct llama_context * ctx) {
     llama_kv_cache_clear(ctx->kv_self);
 }
 
-void llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
-    llama_kv_cache_seq_rm(ctx->kv_self, seq_id, p0, p1);
+bool llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
+    return llama_kv_cache_seq_rm(ctx->kv_self, seq_id, p0, p1);
 }
 
 void llama_kv_cache_seq_cp(struct llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
@@ -10164,12 +14879,12 @@ void llama_kv_cache_seq_keep(struct llama_context * ctx, llama_seq_id seq_id) {
     llama_kv_cache_seq_keep(ctx->kv_self, seq_id);
 }
 
-void llama_kv_cache_seq_shift(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
+void llama_kv_cache_seq_add(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
     if (delta == 0) {
         return;
     }
 
-    llama_kv_cache_seq_shift(ctx->kv_self, seq_id, p0, p1, delta);
+    llama_kv_cache_seq_add(ctx->kv_self, seq_id, p0, p1, delta);
 }
 
 void llama_kv_cache_seq_div(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
@@ -10180,32 +14895,58 @@ void llama_kv_cache_seq_div(struct llama_context * ctx, llama_seq_id seq_id, lla
     llama_kv_cache_seq_div(ctx->kv_self, seq_id, p0, p1, d);
 }
 
+llama_pos llama_kv_cache_seq_pos_max(struct llama_context * ctx, llama_seq_id seq_id) {
+    return llama_kv_cache_seq_pos_max(ctx->kv_self, seq_id);
+}
+
+void llama_kv_cache_defrag(struct llama_context * ctx) {
+    llama_kv_cache_defrag(ctx->kv_self);
+}
+
+void llama_kv_cache_update(struct llama_context * ctx) {
+    llama_kv_cache_update_internal(*ctx);
+}
+
+
 // Returns the *maximum* size of the state
 size_t llama_get_state_size(const struct llama_context * ctx) {
+    const auto & cparams = ctx->cparams;
+    const auto & hparams = ctx->model.hparams;
+
     // we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
     // for reference, std::mt19937(1337) serializes to 6701 bytes.
     const size_t s_rng_size        = sizeof(size_t);
     const size_t s_rng             = LLAMA_MAX_RNG_STATE;
-    const size_t s_logits_capacity = sizeof(size_t);
+    const size_t s_n_outputs       = sizeof(size_t);
+    // assume worst case for outputs although only currently set ones are serialized
+    const size_t s_output_pos      = ctx->cparams.n_batch * sizeof(int32_t);
     const size_t s_logits_size     = sizeof(size_t);
-    const size_t s_logits          = ctx->logits.capacity() * sizeof(float);
+    const size_t s_logits          = ctx->logits_size ? cparams.n_batch * hparams.n_vocab * sizeof(float) : 0;
     const size_t s_embedding_size  = sizeof(size_t);
-    const size_t s_embedding       = ctx->embedding.size() * sizeof(float);
-    const size_t s_kv_size         = sizeof(size_t);
-    const size_t s_kv_ntok         = sizeof(int);
-    const size_t s_kv              = ggml_backend_buffer_get_size(ctx->kv_self.buf);
+    const size_t s_embedding       = ctx->embd_size   ? cparams.n_batch * hparams.n_embd  * sizeof(float) : 0;
+    const size_t s_kv_buf_size     = sizeof(size_t);
+    const size_t s_kv_head         = sizeof(uint32_t);
+    const size_t s_kv_size         = sizeof(uint32_t);
+    const size_t s_kv_used         = sizeof(uint32_t);
+    const size_t s_kv              = ctx->kv_self.total_size();
+    const size_t s_kv_cell         = sizeof(llama_pos) + sizeof(size_t) + cparams.n_seq_max*sizeof(llama_seq_id);
+    const size_t s_kv_cells        = ctx->kv_self.size * s_kv_cell;
 
     const size_t s_total = (
         + s_rng_size
         + s_rng
-        + s_logits_capacity
+        + s_n_outputs
+        + s_output_pos
         + s_logits_size
         + s_logits
         + s_embedding_size
         + s_embedding
+        + s_kv_buf_size
+        + s_kv_head
         + s_kv_size
-        + s_kv_ntok
+        + s_kv_used
         + s_kv
+        + s_kv_cells
     );
 
     return s_total;
@@ -10267,47 +15008,73 @@ struct llama_data_file_context : llama_data_context {
 static void llama_copy_state_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) {
     // copy rng
     {
-        std::stringstream rng_ss;
+        std::ostringstream rng_ss;
         rng_ss << ctx->rng;
 
-        const size_t rng_size = rng_ss.str().size();
-        char rng_buf[LLAMA_MAX_RNG_STATE];
+        const std::string & rng_str  = rng_ss.str();
+        const size_t        rng_size = rng_str.size();
 
-        memset(&rng_buf[0], 0, LLAMA_MAX_RNG_STATE);
-        memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size());
+        GGML_ASSERT(rng_size <= LLAMA_MAX_RNG_STATE);
 
-        data_ctx->write(&rng_size,   sizeof(rng_size));
-        data_ctx->write(&rng_buf[0], LLAMA_MAX_RNG_STATE);
+        data_ctx->write(&rng_size,      sizeof(rng_size));
+        data_ctx->write(rng_str.data(), rng_size);
     }
 
-    // copy logits
+    // copy outputs
     {
-        const size_t logits_cap  = ctx->logits.capacity();
-        const size_t logits_size = ctx->logits.size();
+        // Can't use ctx->n_outputs because it's not for the
+        // entire last batch when n_ubatch is smaller than n_batch
+        size_t n_outputs = 0;
 
-        data_ctx->write(&logits_cap,  sizeof(logits_cap));
-        data_ctx->write(&logits_size, sizeof(logits_size));
+        // copy output ids
+        {
+            std::vector<int32_t> output_pos;
 
-        if (logits_size) {
-            data_ctx->write(ctx->logits.data(), logits_size * sizeof(float));
+            const size_t    n_batch = ctx->cparams.n_batch;
+            const auto & output_ids = ctx->output_ids;
+
+            output_pos.resize(ctx->output_size);
+
+            // build a more compact representation of the output ids
+            for (size_t i = 0; i < n_batch; ++i) {
+                // map an output id to a position in the batch
+                int32_t pos = output_ids[i];
+                if (pos >= 0) {
+                    if ((size_t) pos >= n_outputs) {
+                        n_outputs = pos + 1;
+                    }
+                    GGML_ASSERT((size_t) pos < ctx->output_size);
+                    output_pos[pos] = i;
+                }
+            }
+
+            data_ctx->write(&n_outputs, sizeof(n_outputs));
+
+            if (n_outputs) {
+                data_ctx->write(output_pos.data(), n_outputs * sizeof(int32_t));
+            }
         }
 
-        // If there is a gap between the size and the capacity, write padding
-        size_t padding_size = (logits_cap - logits_size) * sizeof(float);
-        if (padding_size > 0) {
-            std::vector<uint8_t> padding(padding_size, 0); // Create a buffer filled with zeros
-            data_ctx->write(padding.data(), padding_size);
+        // copy logits
+        {
+            const size_t logits_size = std::min(ctx->logits_size, n_outputs * ctx->model.hparams.n_vocab);
+
+            data_ctx->write(&logits_size, sizeof(logits_size));
+
+            if (logits_size) {
+                data_ctx->write(ctx->logits, logits_size * sizeof(float));
+            }
         }
-    }
 
-    // copy embeddings
-    {
-        const size_t embedding_size = ctx->embedding.size();
+        // copy embeddings
+        {
+            const size_t embeddings_size = std::min(ctx->embd_size, n_outputs * ctx->model.hparams.n_embd);
 
-        data_ctx->write(&embedding_size, sizeof(embedding_size));
+            data_ctx->write(&embeddings_size, sizeof(embeddings_size));
 
-        if (embedding_size) {
-            data_ctx->write(ctx->embedding.data(), embedding_size * sizeof(float));
+            if (embeddings_size) {
+                data_ctx->write(ctx->embd, embeddings_size * sizeof(float));
+            }
         }
     }
 
@@ -10315,16 +15082,15 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat
     {
         const auto & kv_self = ctx->kv_self;
         const auto & hparams = ctx->model.hparams;
-        const auto & cparams = ctx->cparams;
 
-        const auto   n_layer      = hparams.n_layer;
-        const auto   n_embd_k_gqa = hparams.n_embd_k_gqa();
-        const auto   n_embd_v_gqa = hparams.n_embd_v_gqa();
-        const auto   n_ctx        = cparams.n_ctx;
+        const uint32_t n_layer      = hparams.n_layer;
+        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
+        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
 
-        const size_t   kv_buf_size = ggml_backend_buffer_get_size(kv_self.buf);
-        const uint32_t kv_head     = kv_self.head;
+        // NOTE: kv_size and kv_buf_size are mostly used for sanity checks
+        const uint32_t kv_head     = llama_kv_cache_cell_max(kv_self);
         const uint32_t kv_size     = kv_self.size;
+        const size_t   kv_buf_size = kv_self.total_size() / (kv_size ? kv_size : 1) * kv_head;
         const uint32_t kv_used     = kv_self.used;
 
         data_ctx->write(&kv_buf_size, sizeof(kv_buf_size));
@@ -10333,51 +15099,41 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat
         data_ctx->write(&kv_used,     sizeof(kv_used));
 
         if (kv_buf_size) {
-            const size_t elt_size = ggml_element_size(kv_self.k_l[0]);
-
-            ggml_context * cpy_ctx = ggml_init({ 6*n_layer*ggml_tensor_overhead() + ggml_graph_overhead(), NULL, /* no_alloc */ true });
-            ggml_cgraph * gf = ggml_new_graph(cpy_ctx);
-
-            std::vector<struct ggml_tensor *> kout2d(n_layer);
-            std::vector<struct ggml_tensor *> vout2d(n_layer);
-
-            for (int il = 0; il < (int) n_layer; ++il) {
-                kout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd_k_gqa, kv_head);
-                vout2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd_v_gqa);
-
-                ggml_tensor * k2d = ggml_view_2d(cpy_ctx, kv_self.k_l[il],
-                        n_embd_k_gqa, kv_head,
-                        elt_size*n_embd_k_gqa, 0);
-
-                ggml_tensor * v2d = ggml_view_2d(cpy_ctx, kv_self.v_l[il],
-                        kv_head, n_embd_v_gqa,
-                        elt_size*n_ctx, 0);
-
-                ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, k2d, kout2d[il]));
-                ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, v2d, vout2d[il]));
-            }
-
-            ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(cpy_ctx, ctx->backend);
-
-            ggml_backend_graph_compute(ctx->backend, gf);
+            const size_t pre_kv_buf_size = data_ctx->get_size_written();
 
             std::vector<uint8_t> tmp_buf;
             for (int il = 0; il < (int) n_layer; ++il) {
-                tmp_buf.resize(ggml_nbytes(kout2d[il]));
-                ggml_backend_tensor_get(kout2d[il], tmp_buf.data(), 0, tmp_buf.size());
+                const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);
+
+                tmp_buf.resize(k_size);
+                ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), 0, tmp_buf.size());
                 data_ctx->write(tmp_buf.data(), tmp_buf.size());
 
-                tmp_buf.resize(ggml_nbytes(vout2d[il]));
-                ggml_backend_tensor_get(vout2d[il], tmp_buf.data(), 0, tmp_buf.size());
-                data_ctx->write(tmp_buf.data(), tmp_buf.size());
+                if (kv_self.recurrent) {
+                    // v is contiguous for recurrent models
+                    // TODO: use other tensors for state models than k and v
+                    const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head);
+
+                    tmp_buf.resize(v_size);
+                    ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), 0, tmp_buf.size());
+                    data_ctx->write(tmp_buf.data(), tmp_buf.size());
+                    continue;
+                }
+
+                // v is not contiguous, copy row by row
+                const size_t v_row_size   = ggml_row_size(kv_self.v_l[il]->type, kv_head);
+                const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_size);
+
+                tmp_buf.resize(v_row_size);
+                for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) {
+                    ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), ir*v_row_stride, tmp_buf.size());
+                    data_ctx->write(tmp_buf.data(), tmp_buf.size());
+                }
             }
-
-            ggml_free(cpy_ctx);
-
-            ggml_backend_buffer_free(buf);
+            GGML_ASSERT(kv_buf_size == data_ctx->get_size_written() - pre_kv_buf_size);
         }
 
-        for (uint32_t i = 0; i < kv_size; ++i) {
+        for (uint32_t i = 0; i < kv_head; ++i) {
             const auto & cell = kv_self.cells[i];
 
             const llama_pos pos         = cell.pos;
@@ -10401,53 +15157,71 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
 }
 
 // Sets the state reading from the specified source address
-size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
-    uint8_t * inp = src;
+size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
+    const uint8_t * inp = src;
 
     // set rng
     {
         size_t rng_size;
-        char   rng_buf[LLAMA_MAX_RNG_STATE];
+        memcpy(&rng_size, inp, sizeof(rng_size)); inp += sizeof(rng_size);
 
-        memcpy(&rng_size,   inp, sizeof(rng_size));    inp += sizeof(rng_size);
-        memcpy(&rng_buf[0], inp, LLAMA_MAX_RNG_STATE); inp += LLAMA_MAX_RNG_STATE;
+        GGML_ASSERT(rng_size <= LLAMA_MAX_RNG_STATE);
 
-        std::stringstream rng_ss;
-        rng_ss.str(std::string(&rng_buf[0], rng_size));
+        std::string rng_str((const char *)inp, rng_size); inp += rng_size;
+
+        std::istringstream rng_ss(rng_str);
         rng_ss >> ctx->rng;
 
         GGML_ASSERT(!rng_ss.fail());
     }
 
+    // set output ids
+    {
+        size_t n_outputs;
+        std::vector<int32_t> output_pos;
+
+        memcpy(&n_outputs, inp, sizeof(n_outputs)); inp += sizeof(n_outputs);
+
+        GGML_ASSERT(n_outputs <= llama_output_reserve(*ctx, n_outputs));
+
+        if (n_outputs) {
+            output_pos.resize(n_outputs);
+            memcpy(output_pos.data(), inp, n_outputs * sizeof(int32_t));
+            inp += n_outputs * sizeof(int32_t);
+
+            for (int32_t i = 0; i < (int32_t) output_pos.size(); ++i) {
+                int32_t id = output_pos[i];
+                GGML_ASSERT((uint32_t) id < ctx->cparams.n_batch);
+                ctx->output_ids[id] = i;
+            }
+        }
+    }
+
     // set logits
     {
-        size_t logits_cap;
         size_t logits_size;
 
-        memcpy(&logits_cap,  inp, sizeof(logits_cap));  inp += sizeof(logits_cap);
         memcpy(&logits_size, inp, sizeof(logits_size)); inp += sizeof(logits_size);
 
-        GGML_ASSERT(ctx->logits.capacity() == logits_cap);
+        GGML_ASSERT(ctx->logits_size >= logits_size);
 
         if (logits_size) {
-            ctx->logits.resize(logits_size);
-            memcpy(ctx->logits.data(), inp, logits_size * sizeof(float));
+            memcpy(ctx->logits, inp, logits_size * sizeof(float));
+            inp += logits_size * sizeof(float);
         }
-
-        inp += logits_cap * sizeof(float);
     }
 
     // set embeddings
     {
-        size_t embedding_size;
+        size_t embeddings_size;
 
-        memcpy(&embedding_size, inp, sizeof(embedding_size)); inp += sizeof(embedding_size);
+        memcpy(&embeddings_size, inp, sizeof(embeddings_size)); inp += sizeof(embeddings_size);
 
-        GGML_ASSERT(ctx->embedding.capacity() == embedding_size);
+        GGML_ASSERT(ctx->embd_size >= embeddings_size);
 
-        if (embedding_size) {
-            memcpy(ctx->embedding.data(), inp, embedding_size * sizeof(float));
-            inp += embedding_size * sizeof(float);
+        if (embeddings_size) {
+            memcpy(ctx->embd, inp, embeddings_size * sizeof(float));
+            inp += embeddings_size * sizeof(float);
         }
     }
 
@@ -10455,12 +15229,10 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
     {
         const auto & kv_self = ctx->kv_self;
         const auto & hparams = ctx->model.hparams;
-        const auto & cparams = ctx->cparams;
 
-        const int    n_layer      = hparams.n_layer;
-        const int    n_embd_k_gqa = hparams.n_embd_k_gqa();
-        const int    n_embd_v_gqa = hparams.n_embd_v_gqa();
-        const int    n_ctx        = cparams.n_ctx;
+        const uint32_t n_layer      = hparams.n_layer;
+        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
+        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
 
         size_t   kv_buf_size;
         uint32_t kv_head;
@@ -10472,58 +15244,53 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
         memcpy(&kv_size,     inp, sizeof(kv_size));     inp += sizeof(kv_size);
         memcpy(&kv_used,     inp, sizeof(kv_used));     inp += sizeof(kv_used);
 
-        if (kv_buf_size) {
-            GGML_ASSERT(ggml_backend_buffer_get_size(kv_self.buf) == kv_buf_size);
+        if (kv_self.size != kv_size) {
+            // the KV cache needs to be big enough to load all the KV cells from the saved state
+            GGML_ASSERT(kv_self.size >= kv_head);
 
-            const size_t elt_size = ggml_element_size(kv_self.k_l[0]);
-
-            ggml_context * cpy_ctx = ggml_init({ 6*n_layer*ggml_tensor_overhead() + ggml_graph_overhead(), NULL, /* no_alloc */ true });
-            ggml_cgraph * gf = ggml_new_graph(cpy_ctx);
-
-            std::vector<struct ggml_tensor *> kin2d(n_layer);
-            std::vector<struct ggml_tensor *> vin2d(n_layer);
-
-            for (int il = 0; il < n_layer; ++il) {
-                kin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.k_l[il]->type, n_embd_k_gqa, kv_head);
-                vin2d[il] = ggml_new_tensor_2d(cpy_ctx, kv_self.v_l[il]->type, kv_head, n_embd_v_gqa);
-
-                ggml_tensor * k2d = ggml_view_2d(cpy_ctx, kv_self.k_l[il],
-                    n_embd_k_gqa, kv_head,
-                    elt_size*n_embd_k_gqa, 0);
-
-                ggml_tensor * v2d = ggml_view_2d(cpy_ctx, kv_self.v_l[il],
-                    kv_head, n_embd_v_gqa,
-                    elt_size*n_ctx, 0);
-
-                ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, kin2d[il], k2d));
-                ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, vin2d[il], v2d));
-            }
-
-            ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(cpy_ctx, ctx->backend);
-
-            // load data into the tensors
-            for (int il = 0; il < n_layer; ++il) {
-                ggml_backend_tensor_set(kin2d[il], inp, 0, ggml_nbytes(kin2d[il]));
-                inp += ggml_nbytes(kin2d[il]);
-
-                ggml_backend_tensor_set(vin2d[il], inp, 0, ggml_nbytes(vin2d[il]));
-                inp += ggml_nbytes(vin2d[il]);
-            }
-
-            ggml_backend_graph_compute(ctx->backend, gf);
-
-            ggml_free(cpy_ctx);
-
-            ggml_backend_buffer_free(buf);
+            LLAMA_LOG_INFO("%s: state contains %d KV cells, was saved with kv_size=%d, but is loaded with kv_size=%d (fine, but different)\n",
+                __func__, kv_head, kv_size, kv_self.size);
         }
 
+        if (kv_buf_size) {
+            const size_t pre_kv_buf_size = inp - src;
+
+            GGML_ASSERT(kv_self.total_size() >= kv_buf_size);
+
+            for (int il = 0; il < (int) n_layer; ++il) {
+                const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);
+
+                ggml_backend_tensor_set(kv_self.k_l[il], inp, 0, k_size);
+                inp += k_size;
+
+                if (kv_self.recurrent) {
+                    // v is contiguous for recurrent models
+                    // TODO: use other tensors for state models than k and v
+                    const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head);
+
+                    ggml_backend_tensor_set(kv_self.v_l[il], inp, 0, v_size);
+                    inp += v_size;
+                    continue;
+                }
+
+                // v is not contiguous, copy row by row
+                const size_t v_row_size   = ggml_row_size(kv_self.v_l[il]->type, kv_head);
+                const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_self.size);
+
+                for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) {
+                    ggml_backend_tensor_set(kv_self.v_l[il], inp, ir*v_row_stride, v_row_size);
+                    inp += v_row_size;
+                }
+            }
+            GGML_ASSERT(kv_buf_size == inp - src - pre_kv_buf_size);
+        }
+
+        llama_kv_cache_clear(ctx);
+
         ctx->kv_self.head = kv_head;
-        ctx->kv_self.size = kv_size;
         ctx->kv_self.used = kv_used;
 
-        ctx->kv_self.cells.resize(kv_size);
-
-        for (uint32_t i = 0; i < kv_size; ++i) {
+        for (uint32_t i = 0; i < kv_head; ++i) {
             llama_pos pos;
             size_t    seq_id_size;
 
@@ -10631,43 +15398,20 @@ bool llama_save_session_file(struct llama_context * ctx, const char * path_sessi
     return true;
 }
 
-int llama_eval(
-        struct llama_context * ctx,
-                 llama_token * tokens,
-                     int32_t   n_tokens,
-                     int32_t   n_past) {
-    llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1);
-
-    const int ret = llama_decode_internal(*ctx, llama_batch_get_one(tokens, n_tokens, n_past, 0));
-    if (ret < 0) {
-        LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
-    }
-
-    return ret;
-}
-
-int llama_eval_embd(
-            struct llama_context * ctx,
-                           float * embd,
-                         int32_t   n_tokens,
-                         int32_t   n_past) {
-    llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1);
-
-    llama_batch batch = { n_tokens, nullptr, embd, nullptr, nullptr, nullptr, nullptr, n_past, 1, 0, };
-
-    const int ret = llama_decode_internal(*ctx, batch);
-    if (ret < 0) {
-        LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
-    }
-
-    return ret;
-}
-
 void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch) {
     ctx->cparams.n_threads       = n_threads;
     ctx->cparams.n_threads_batch = n_threads_batch;
 }
 
+void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback)(void * data), void * abort_callback_data) {
+    ctx->abort_callback      = abort_callback;
+    ctx->abort_callback_data = abort_callback_data;
+}
+
+void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) {
+    ctx->cparams.causal_attn = causal_attn;
+}
+
 struct llama_batch llama_batch_get_one(
              llama_token * tokens,
                  int32_t   n_tokens,
@@ -10687,22 +15431,24 @@ struct llama_batch llama_batch_get_one(
     };
 }
 
-struct llama_batch llama_batch_init(int32_t n_tokens, int32_t embd, int32_t n_seq_max) {
+struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_t n_seq_max) {
     llama_batch batch = { 0, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, 0, 0, 0, };
 
     if (embd) {
-        batch.embd = (float *) malloc(sizeof(float) * n_tokens * embd);
+        batch.embd = (float *) malloc(sizeof(float) * n_tokens_alloc * embd);
     } else {
-        batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens);
+        batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens_alloc);
     }
 
-    batch.pos      = (llama_pos *)     malloc(sizeof(llama_pos)      * n_tokens);
-    batch.n_seq_id = (int32_t *)       malloc(sizeof(int32_t)        * n_tokens);
-    batch.seq_id   = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * n_tokens);
-    for (int i = 0; i < n_tokens; ++i) {
+    batch.pos      = (llama_pos *)     malloc(sizeof(llama_pos)      * n_tokens_alloc);
+    batch.n_seq_id = (int32_t *)       malloc(sizeof(int32_t)        * n_tokens_alloc);
+    batch.seq_id   = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * (n_tokens_alloc + 1));
+    for (int i = 0; i < n_tokens_alloc; ++i) {
         batch.seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max);
     }
-    batch.logits   = (int8_t *)        malloc(sizeof(int8_t)         * n_tokens);
+    batch.seq_id[n_tokens_alloc] = nullptr;
+
+    batch.logits   = (int8_t *)        malloc(sizeof(int8_t)         * n_tokens_alloc);
 
     return batch;
 }
@@ -10713,7 +15459,7 @@ void llama_batch_free(struct llama_batch batch) {
     if (batch.pos)      free(batch.pos);
     if (batch.n_seq_id) free(batch.n_seq_id);
     if (batch.seq_id) {
-        for (int i = 0; i < batch.n_tokens; ++i) {
+        for (int i = 0; batch.seq_id[i] != nullptr; ++i) {
             free(batch.seq_id[i]);
         }
         free(batch.seq_id);
@@ -10732,28 +15478,127 @@ int32_t llama_decode(
     return ret;
 }
 
+void llama_synchronize(struct llama_context * ctx) {
+    ggml_backend_sched_synchronize(ctx->sched);
+
+    // FIXME: if multiple single tokens are evaluated without a synchronization,
+    // the stats will be added to the prompt evaluation stats
+    // this should only happen when using batch size 1 to evaluate a batch
+
+    // add the evaluation to the stats
+    if (ctx->n_queued_tokens == 1) {
+        ctx->t_eval_us += ggml_time_us() - ctx->t_compute_start_us;
+        ctx->n_eval++;
+    } else if (ctx->n_queued_tokens > 1) {
+        ctx->t_p_eval_us += ggml_time_us() - ctx->t_compute_start_us;
+        ctx->n_p_eval += ctx->n_queued_tokens;
+    }
+
+    // get a more accurate load time, upon first eval
+    if (ctx->n_queued_tokens > 0 && !ctx->has_evaluated_once) {
+        ctx->t_load_us = ggml_time_us() - ctx->t_start_us;
+        ctx->has_evaluated_once = true;
+    }
+
+    ctx->n_queued_tokens = 0;
+    ctx->t_compute_start_us = 0;
+}
+
 float * llama_get_logits(struct llama_context * ctx) {
-    return ctx->logits.data();
+    llama_synchronize(ctx);
+
+    return ctx->logits;
 }
 
 float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) {
-    assert(ctx->logits_valid.at(i));
-    return ctx->logits.data() + i*ctx->model.hparams.n_vocab;
+    llama_synchronize(ctx);
+
+    try {
+        if (ctx->logits == nullptr) {
+            throw std::runtime_error("no logits");
+        }
+        if ((size_t) i >= ctx->output_ids.size()) {
+            throw std::runtime_error(format("out of range [0, %lu)", ctx->output_ids.size()));
+        }
+        const int32_t j = ctx->output_ids[i];
+
+        if (j < 0) {
+            throw std::runtime_error(format("batch.logits[%d] != true", i));
+        }
+        if ((size_t) j >= ctx->output_size) {
+            // This should not happen
+            throw std::runtime_error(format("corrupt output buffer (j=%d, output_size=%lu)", j, ctx->output_size));
+        }
+
+        return ctx->logits + j*ctx->model.hparams.n_vocab;
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: invalid logits id %d, reason: %s\n", __func__, i, err.what());
+#ifndef NDEBUG
+        GGML_ASSERT(false);
+#endif
+        return nullptr;
+    }
 }
 
 float * llama_get_embeddings(struct llama_context * ctx) {
-    return ctx->embedding.data();
+    llama_synchronize(ctx);
+
+    return ctx->embd;
+}
+
+float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i) {
+    llama_synchronize(ctx);
+
+    try {
+        if (ctx->embd == nullptr) {
+            throw std::runtime_error("no embeddings");
+        }
+        if ((size_t) i >= ctx->output_ids.size()) {
+            throw std::runtime_error(format("out of range [0, %lu)", ctx->output_ids.size()));
+        }
+        const int32_t j = ctx->output_ids[i];
+
+        if (j < 0) {
+            throw std::runtime_error(format("batch.logits[%d] != true", i));
+        }
+        if ((size_t) j >= ctx->output_size) {
+            // This should not happen
+            throw std::runtime_error(format("corrupt output buffer (j=%d, output_size=%lu)", j, ctx->output_size));
+        }
+
+        return ctx->embd + j*ctx->model.hparams.n_embd;
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("%s: invalid embeddings id %d, reason: %s\n", __func__, i, err.what());
+#ifndef NDEBUG
+        GGML_ASSERT(false);
+#endif
+        return nullptr;
+    }
+}
+
+float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id) {
+    llama_synchronize(ctx);
+
+    auto it = ctx->embd_seq.find(seq_id);
+    if (it == ctx->embd_seq.end()) {
+        return nullptr;
+    }
+
+    return it->second.data();
 }
 
 const char * llama_token_get_text(const struct llama_model * model, llama_token token) {
+    GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
     return model->vocab.id_to_token[token].text.c_str();
 }
 
 float llama_token_get_score(const struct llama_model * model, llama_token token) {
+    GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
     return model->vocab.id_to_token[token].score;
 }
 
 llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token) {
+    GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
     return model->vocab.id_to_token[token].type;
 }
 
@@ -10798,12 +15643,12 @@ int32_t llama_tokenize(
                   const char * text,
                      int32_t   text_len,
                  llama_token * tokens,
-                     int32_t   n_max_tokens,
+                     int32_t   n_tokens_max,
                         bool   add_bos,
                         bool   special) {
     auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_bos, special);
 
-    if (n_max_tokens < (int) res.size()) {
+    if (n_tokens_max < (int) res.size()) {
         // LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
         return -((int) res.size());
     }
@@ -10817,9 +15662,9 @@ int32_t llama_tokenize(
 
 static std::string llama_decode_text(const std::string & text) {
     std::string decoded_text;
-    auto unicode_sequences = codepoints_from_utf8(text);
-    for (auto& unicode_sequence : unicode_sequences) {
-        decoded_text += unicode_to_bytes_bpe(codepoint_to_utf8(unicode_sequence));
+    auto unicode_sequences = unicode_cpts_from_utf8(text);
+    for (auto & unicode_sequence : unicode_sequences) {
+        decoded_text += unicode_utf8_to_byte(unicode_cpt_to_utf8(unicode_sequence));
     }
 
     return decoded_text;
@@ -10829,7 +15674,10 @@ static std::string llama_decode_text(const std::string & text) {
 int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length) {
     if (0 <= token && token < llama_n_vocab(model)) {
         switch (llama_vocab_get_type(model->vocab)) {
+        case LLAMA_VOCAB_TYPE_WPM:
         case LLAMA_VOCAB_TYPE_SPM: {
+            // NOTE: we accept all unsupported token types,
+            // suppressing them like CONTROL tokens.
             if (llama_is_normal_token(model->vocab, token)) {
                 std::string result = model->vocab.id_to_token[token].text;
                 llama_unescape_whitespace(result);
@@ -10838,6 +15686,13 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
                 }
                 memcpy(buf, result.c_str(), result.length());
                 return result.length();
+            } else if (llama_is_user_defined_token(model->vocab, token)) {
+                std::string result = model->vocab.id_to_token[token].text;
+                if (length < (int) result.length()) {
+                    return -(int) result.length();
+                }
+                memcpy(buf, result.c_str(), result.length());
+                return result.length();
             } else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT
                 if (length < 3) {
                     return -3;
@@ -10852,14 +15707,12 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
                 }
                 buf[0] = llama_token_to_byte(model->vocab, token);
                 return 1;
-            } else {
-                // TODO: for now we accept all unsupported token types,
-                // suppressing them like CONTROL tokens.
-                // GGML_ASSERT(false);
             }
             break;
         }
         case LLAMA_VOCAB_TYPE_BPE: {
+            // NOTE: we accept all unsupported token types,
+            // suppressing them like CONTROL tokens.
             if (llama_is_normal_token(model->vocab, token)) {
                 std::string result = model->vocab.id_to_token[token].text;
                 result = llama_decode_text(result);
@@ -10868,12 +15721,15 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
                 }
                 memcpy(buf, result.c_str(), result.length());
                 return result.length();
+            } else if (llama_is_user_defined_token(model->vocab, token)) {
+                std::string result = model->vocab.id_to_token[token].text;
+                if (length < (int) result.length()) {
+                    return -(int) result.length();
+                }
+                memcpy(buf, result.c_str(), result.length());
+                return result.length();
             } else if (llama_is_control_token(model->vocab, token)) {
                 ;
-            } else {
-                // TODO: for now we accept all unsupported token types,
-                // suppressing them like CONTROL tokens.
-                // GGML_ASSERT(false);
             }
             break;
         }
@@ -10884,6 +15740,251 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
     return 0;
 }
 
+// trim whitespace from the beginning and end of a string
+static std::string trim(const std::string & str) {
+    size_t start = 0;
+    size_t end = str.size();
+    while (start < end && isspace(str[start])) {
+        start += 1;
+    }
+    while (end > start && isspace(str[end - 1])) {
+        end -= 1;
+    }
+    return str.substr(start, end - start);
+}
+
+// Simple version of "llama_apply_chat_template" that only works with strings
+// This function uses heuristic checks to determine commonly used template. It is not a jinja parser.
+static int32_t llama_chat_apply_template_internal(
+    const std::string & tmpl,
+    const std::vector<const llama_chat_message *> & chat,
+    std::string & dest, bool add_ass) {
+    // Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527
+    std::stringstream ss;
+    if (tmpl == "chatml" || tmpl.find("<|im_start|>") != std::string::npos) {
+        // chatml template
+        for (auto message : chat) {
+            ss << "<|im_start|>" << message->role << "\n" << message->content << "<|im_end|>\n";
+        }
+        if (add_ass) {
+            ss << "<|im_start|>assistant\n";
+        }
+    } else if (tmpl == "llama2" || tmpl.find("[INST]") != std::string::npos) {
+        // llama2 template and its variants
+        // [variant] support system message
+        bool support_system_message = tmpl.find("<<SYS>>") != std::string::npos;
+        // [variant] space before + after response
+        bool space_around_response = tmpl.find("' ' + eos_token") != std::string::npos;
+        // [variant] add BOS inside history
+        bool add_bos_inside_history = tmpl.find("bos_token + '[INST]") != std::string::npos;
+        // [variant] trim spaces from the input message
+        bool strip_message = tmpl.find("content.strip()") != std::string::npos;
+        // construct the prompt
+        bool is_inside_turn = true; // skip BOS at the beginning
+        ss << "[INST] ";
+        for (auto message : chat) {
+            std::string content = strip_message ? trim(message->content) : message->content;
+            std::string role(message->role);
+            if (!is_inside_turn) {
+                is_inside_turn = true;
+                ss << (add_bos_inside_history ? "<s>[INST] " : "[INST] ");
+            }
+            if (role == "system") {
+                if (support_system_message) {
+                    ss << "<<SYS>>\n" << content << "\n<</SYS>>\n\n";
+                } else {
+                    // if the model does not support system message, we still include it in the first message, but without <<SYS>>
+                    ss << content << "\n";
+                }
+            } else if (role == "user") {
+                ss << content << " [/INST]";
+            } else {
+                ss << (space_around_response ? " " : "") << content << (space_around_response ? " " : "") << "</s>";
+                is_inside_turn = false;
+            }
+        }
+        // llama2 templates seem to not care about "add_generation_prompt"
+    } else if (tmpl == "zephyr" || tmpl.find("<|user|>") != std::string::npos) {
+        // zephyr template
+        for (auto message : chat) {
+            ss << "<|" << message->role << "|>" << "\n" << message->content << "<|endoftext|>\n";
+        }
+        if (add_ass) {
+            ss << "<|assistant|>\n";
+        }
+    } else if (tmpl == "monarch" || tmpl.find("bos_token + message['role']") != std::string::npos) {
+        // mlabonne/AlphaMonarch-7B template (the <s> is included inside history)
+        for (auto message : chat) {
+            std::string bos = (message == chat.front()) ? "" : "<s>"; // skip BOS for first message
+            ss << bos << message->role << "\n" << message->content << "</s>\n";
+        }
+        if (add_ass) {
+            ss << "<s>assistant\n";
+        }
+    } else if (tmpl == "gemma" || tmpl.find("<start_of_turn>") != std::string::npos) {
+        // google/gemma-7b-it
+        std::string system_prompt = "";
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                // there is no system message for gemma, but we will merge it with user prompt, so nothing is broken
+                system_prompt = trim(message->content);
+                continue;
+            }
+            // in gemma, "assistant" is "model"
+            role = role == "assistant" ? "model" : message->role;
+            ss << "<start_of_turn>" << role << "\n";
+            if (!system_prompt.empty() && role != "model") {
+                ss << system_prompt << "\n\n";
+                system_prompt = "";
+            }
+            ss << trim(message->content) << "<end_of_turn>\n";
+        }
+        if (add_ass) {
+            ss << "<start_of_turn>model\n";
+        }
+    } else if (tmpl == "orion" || tmpl.find("'\\n\\nAssistant: ' + eos_token") != std::string::npos) {
+        // OrionStarAI/Orion-14B-Chat
+        std::string system_prompt = "";
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                // there is no system message support, we will merge it with user prompt
+                system_prompt = message->content;
+                continue;
+            } else if (role == "user") {
+                ss << "Human: ";
+                if (!system_prompt.empty()) {
+                    ss << system_prompt << "\n\n";
+                    system_prompt = "";
+                }
+                ss << message->content << "\n\nAssistant: </s>";
+            } else {
+                ss << message->content << "</s>";
+            }
+        }
+    } else if (tmpl == "openchat" || tmpl.find("GPT4 Correct ") != std::string::npos) {
+        // openchat/openchat-3.5-0106,
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                ss << message->content << "<|end_of_turn|>";
+            } else {
+                role[0] = toupper(role[0]);
+                ss << "GPT4 Correct " << role << ": " << message->content << "<|end_of_turn|>";
+            }
+        }
+        if (add_ass) {
+            ss << "GPT4 Correct Assistant:";
+        }
+    } else if (tmpl == "vicuna" || tmpl == "vicuna-orca" || (tmpl.find("USER: ") != std::string::npos && tmpl.find("ASSISTANT: ") != std::string::npos)) {
+        // eachadea/vicuna-13b-1.1 (and Orca variant)
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                // Orca-Vicuna variant uses a system prefix
+                if (tmpl == "vicuna-orca" || tmpl.find("SYSTEM: ") != std::string::npos) {
+                    ss << "SYSTEM: " << message->content << "\n";
+                } else {
+                    ss << message->content << "\n\n";
+                }
+            } else if (role == "user") {
+                ss << "USER: " << message->content << "\n";
+            } else if (role == "assistant") {
+                ss << "ASSISTANT: " << message->content << "</s>\n";
+            }
+        }
+        if (add_ass) {
+            ss << "ASSISTANT:";
+        }
+    } else if (tmpl == "deepseek" || (tmpl.find("### Instruction:") != std::string::npos && tmpl.find("<|EOT|>") != std::string::npos)) {
+        // deepseek-ai/deepseek-coder-33b-instruct
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                ss << message->content;
+            } else if (role == "user") {
+                ss << "### Instruction:\n" << message->content << "\n";
+            } else if (role == "assistant") {
+                ss << "### Response:\n" << message->content << "\n<|EOT|>\n";
+            }
+        }
+        if (add_ass) {
+            ss << "### Response:\n";
+        }
+    } else {
+        // template not supported
+        return -1;
+    }
+    dest = ss.str();
+    return dest.size();
+}
+
+LLAMA_API int32_t llama_chat_apply_template(
+                const struct llama_model * model,
+                              const char * tmpl,
+         const struct llama_chat_message * chat,
+                                  size_t   n_msg,
+                                    bool   add_ass,
+                                    char * buf,
+                                 int32_t   length) {
+    std::string curr_tmpl(tmpl == nullptr ? "" : tmpl);
+    if (tmpl == nullptr) {
+        GGML_ASSERT(model != nullptr);
+        // load template from model
+        std::vector<char> model_template(2048, 0); // longest known template is about 1200 bytes
+        std::string template_key = "tokenizer.chat_template";
+        int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size());
+        if (res < 0) {
+            // worst case: there is no information about template, we will use chatml by default
+            curr_tmpl = "chatml"; // see llama_chat_apply_template_internal
+        } else {
+            curr_tmpl = std::string(model_template.data(), model_template.size());
+        }
+    }
+
+    // format the chat to string
+    std::vector<const llama_chat_message *> chat_vec;
+    chat_vec.resize(n_msg);
+    for (size_t i = 0; i < n_msg; i++) {
+        chat_vec[i] = &chat[i];
+    }
+
+    std::string formatted_chat;
+    int32_t res = llama_chat_apply_template_internal(curr_tmpl, chat_vec, formatted_chat, add_ass);
+    if (res < 0) {
+        return res;
+    }
+    if (buf && length > 0) {
+        strncpy(buf, formatted_chat.c_str(), length);
+    }
+    return res;
+}
+
+LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count) {
+    static const char * const SPLIT_PATH_FORMAT = "%s-%05d-of-%05d.gguf";
+    if (snprintf(split_path, maxlen, SPLIT_PATH_FORMAT, path_prefix, split_no + 1, split_count)) {
+        return strlen(split_path);
+    }
+    return 0;
+}
+
+int llama_split_prefix(char * dest, size_t maxlen, const char * split_path, int split_no, int split_count) {
+    std::string str_split_path(split_path);
+    char postfix[32];
+    snprintf(postfix, 32, "-%05d-of-%05d.gguf", split_no + 1, split_count);
+    std::string str_postfix(postfix);
+
+    // check if dest ends with postfix
+    int size_prefix = str_split_path.size() - str_postfix.size();
+    if (size_prefix > 0 && str_split_path.find(str_postfix, size_prefix) != std::string::npos) {
+        snprintf(dest, std::min((size_t) size_prefix + 1, maxlen), "%s", split_path);
+        return size_prefix;
+    }
+
+    return 0;
+}
+
 struct llama_timings llama_get_timings(struct llama_context * ctx) {
     struct llama_timings result = {
         /*.t_start_ms  =*/ 1e-3 * ctx->t_start_us,
@@ -10912,7 +16013,7 @@ void llama_print_timings(struct llama_context * ctx) {
             __func__, timings.t_p_eval_ms, timings.n_p_eval, timings.t_p_eval_ms / timings.n_p_eval, 1e3 / timings.t_p_eval_ms * timings.n_p_eval);
     LLAMA_LOG_INFO("%s:        eval time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
             __func__, timings.t_eval_ms, timings.n_eval, timings.t_eval_ms / timings.n_eval, 1e3 / timings.t_eval_ms * timings.n_eval);
-    LLAMA_LOG_INFO("%s:       total time = %10.2f ms\n", __func__, (timings.t_end_ms - timings.t_start_ms));
+    LLAMA_LOG_INFO("%s:       total time = %10.2f ms / %5d tokens\n", __func__, (timings.t_end_ms - timings.t_start_ms), (timings.n_p_eval + timings.n_eval));
 }
 
 void llama_reset_timings(struct llama_context * ctx) {
@@ -10942,6 +16043,7 @@ const char * llama_print_system_info(void) {
     s += "SSE3 = "        + std::to_string(ggml_cpu_has_sse3())        + " | ";
     s += "SSSE3 = "       + std::to_string(ggml_cpu_has_ssse3())       + " | ";
     s += "VSX = "         + std::to_string(ggml_cpu_has_vsx())         + " | ";
+    s += "MATMUL_INT8 = " + std::to_string(ggml_cpu_has_matmul_int8()) + " | ";
 
     return s.c_str();
 }
@@ -10985,7 +16087,7 @@ void llama_log_set(ggml_log_callback log_callback, void * user_data) {
     g_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
     g_state.log_callback_user_data = user_data;
 #ifdef GGML_USE_METAL
-    ggml_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
+    ggml_backend_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
 #endif
 }
 
diff --git a/llama.h b/llama.h
index c11075bbc..036b32685 100644
--- a/llama.h
+++ b/llama.h
@@ -2,12 +2,8 @@
 #define LLAMA_H
 
 #include "ggml.h"
-#ifdef GGML_USE_CUBLAS
-#include "ggml-cuda.h"
-#define LLAMA_MAX_DEVICES GGML_CUDA_MAX_DEVICES
-#else
-#define LLAMA_MAX_DEVICES 1
-#endif // GGML_USE_CUBLAS
+#include "ggml-backend.h"
+
 #include <stddef.h>
 #include <stdint.h>
 #include <stdio.h>
@@ -43,12 +39,7 @@
 #define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
 
 #define LLAMA_SESSION_MAGIC   LLAMA_FILE_MAGIC_GGSN
-#define LLAMA_SESSION_VERSION 3
-
-#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL)
-// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
-#define LLAMA_SUPPORTS_GPU_OFFLOAD
-#endif
+#define LLAMA_SESSION_VERSION 5
 
 #ifdef __cplusplus
 extern "C" {
@@ -68,8 +59,19 @@ extern "C" {
     typedef int32_t llama_seq_id;
 
     enum llama_vocab_type {
-        LLAMA_VOCAB_TYPE_SPM = 0, // SentencePiece
-        LLAMA_VOCAB_TYPE_BPE = 1, // Byte Pair Encoding
+        LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab
+        LLAMA_VOCAB_TYPE_SPM  = 1, // LLaMA tokenizer based on byte-level BPE with byte fallback
+        LLAMA_VOCAB_TYPE_BPE  = 2, // GPT-2 tokenizer based on byte-level BPE
+        LLAMA_VOCAB_TYPE_WPM  = 3, // BERT tokenizer based on WordPiece
+    };
+
+    // note: these values should be synchronized with ggml_rope
+    // TODO: maybe move this enum to ggml.h (ggml_rope_type)
+    enum llama_rope_type {
+        LLAMA_ROPE_TYPE_NONE = -1,
+        LLAMA_ROPE_TYPE_NORM =  0,
+        LLAMA_ROPE_TYPE_NEOX =  2,
+        LLAMA_ROPE_TYPE_GLM  =  4,
     };
 
     enum llama_token_type {
@@ -104,16 +106,41 @@ extern "C" {
         LLAMA_FTYPE_MOSTLY_Q5_K_M        = 17, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_Q6_K          = 18, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_IQ2_XXS       = 19, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ2_XS        = 20, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q2_K_S        = 21, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ3_XS        = 22, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ3_XXS       = 23, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ1_S         = 24, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ4_NL        = 25, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ3_S         = 26, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ3_M         = 27, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ2_S         = 28, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ2_M         = 29, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ4_XS        = 30, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_IQ1_M         = 31, // except 1d tensors
 
         LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
     };
 
     enum llama_rope_scaling_type {
-        LLAMA_ROPE_SCALING_UNSPECIFIED = -1,
-        LLAMA_ROPE_SCALING_NONE        = 0,
-        LLAMA_ROPE_SCALING_LINEAR      = 1,
-        LLAMA_ROPE_SCALING_YARN        = 2,
-        LLAMA_ROPE_SCALING_MAX_VALUE   = LLAMA_ROPE_SCALING_YARN,
+        LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED = -1,
+        LLAMA_ROPE_SCALING_TYPE_NONE        = 0,
+        LLAMA_ROPE_SCALING_TYPE_LINEAR      = 1,
+        LLAMA_ROPE_SCALING_TYPE_YARN        = 2,
+        LLAMA_ROPE_SCALING_TYPE_MAX_VALUE   = LLAMA_ROPE_SCALING_TYPE_YARN,
+    };
+
+    enum llama_pooling_type {
+        LLAMA_POOLING_TYPE_UNSPECIFIED = -1,
+        LLAMA_POOLING_TYPE_NONE = 0,
+        LLAMA_POOLING_TYPE_MEAN = 1,
+        LLAMA_POOLING_TYPE_CLS  = 2,
+    };
+
+    enum llama_split_mode {
+        LLAMA_SPLIT_MODE_NONE    = 0, // single GPU
+        LLAMA_SPLIT_MODE_LAYER   = 1, // split layers and KV across GPUs
+        LLAMA_SPLIT_MODE_ROW     = 2, // split rows across GPUs
     };
 
     typedef struct llama_token_data {
@@ -138,7 +165,7 @@ extern "C" {
     // - embd   : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
     // - pos    : the positions of the respective token in the sequence
     // - seq_id : the sequence to which the respective token belongs
-    // - logits : if zero, the logits for the respective token will not be output
+    // - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output
     //
     typedef struct llama_batch {
         int32_t n_tokens;
@@ -148,7 +175,7 @@ extern "C" {
         llama_pos    *  pos;
         int32_t      *  n_seq_id;
         llama_seq_id ** seq_id;
-        int8_t       *  logits;
+        int8_t       *  logits; // TODO: rename this to "output"
 
         // NOTE: helpers for smooth API transition - can be deprecated in the future
         //       for future-proof code, use the above fields instead and ignore everything below
@@ -161,9 +188,9 @@ extern "C" {
     } llama_batch;
 
     enum llama_model_kv_override_type {
-        LLAMA_KV_OVERRIDE_INT,
-        LLAMA_KV_OVERRIDE_FLOAT,
-        LLAMA_KV_OVERRIDE_BOOL,
+        LLAMA_KV_OVERRIDE_TYPE_INT,
+        LLAMA_KV_OVERRIDE_TYPE_FLOAT,
+        LLAMA_KV_OVERRIDE_TYPE_BOOL,
     };
 
     struct llama_model_kv_override {
@@ -178,8 +205,16 @@ extern "C" {
 
     struct llama_model_params {
         int32_t n_gpu_layers; // number of layers to store in VRAM
-        int32_t main_gpu;     // the GPU that is used for scratch and small tensors
-        const float * tensor_split; // how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES)
+        enum llama_split_mode split_mode; // how to split the model across multiple GPUs
+
+        // main_gpu interpretation depends on split_mode:
+        // LLAMA_SPLIT_NONE: the GPU that is used for the entire model
+        // LLAMA_SPLIT_ROW: the GPU that is used for small tensors and intermediate results
+        // LLAMA_SPLIT_LAYER: ignored
+        int32_t main_gpu;
+
+        // proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices()
+        const float * tensor_split;
 
         // Called with a progress value between 0.0 and 1.0. Pass NULL to disable.
         // If the provided progress_callback returns true, model loading continues.
@@ -201,10 +236,15 @@ extern "C" {
     struct llama_context_params {
         uint32_t seed;              // RNG seed, -1 for random
         uint32_t n_ctx;             // text context, 0 = from model
-        uint32_t n_batch;           // prompt processing maximum batch size
+        uint32_t n_batch;           // logical maximum batch size that can be submitted to llama_decode
+        uint32_t n_ubatch;          // physical maximum batch size
+        uint32_t n_seq_max;         // max number of sequences (i.e. distinct states for recurrent models)
         uint32_t n_threads;         // number of threads to use for generation
         uint32_t n_threads_batch;   // number of threads to use for batch processing
-        int8_t   rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
+
+        enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
+        enum llama_pooling_type      pooling_type;      // whether to pool (sum) embedding results by sequence id
+                                                        // (ignored if no pooling layer)
 
         // ref: https://github.com/ggerganov/llama.cpp/pull/2054
         float    rope_freq_base;   // RoPE base frequency, 0 = from model
@@ -214,25 +254,38 @@ extern "C" {
         float    yarn_beta_fast;   // YaRN low correction dim
         float    yarn_beta_slow;   // YaRN high correction dim
         uint32_t yarn_orig_ctx;    // YaRN original context size
+        float    defrag_thold;     // defragment the KV cache if holes/size > thold, < 0 disabled (default)
+
+        ggml_backend_sched_eval_callback cb_eval;
+        void * cb_eval_user_data;
 
         enum ggml_type type_k; // data type for K cache
         enum ggml_type type_v; // data type for V cache
 
         // Keep the booleans together to avoid misalignment during copy-by-value.
-        bool mul_mat_q;   // if true, use experimental mul_mat_q kernels (DEPRECATED - always true)
-        bool logits_all;  // the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
-        bool embedding;   // embedding mode only
+        bool logits_all;  // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
+        bool embeddings;  // if true, extract embeddings (together with logits)
         bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
+
+        // Abort callback
+        // if it returns true, execution of llama_decode() will be aborted
+        // currently works only with CPU execution
+        ggml_abort_callback abort_callback;
+        void *              abort_callback_data;
     };
 
     // model quantization parameters
     typedef struct llama_model_quantize_params {
-        int32_t nthread;             // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
-        enum llama_ftype ftype;      // quantize to this llama_ftype
-        bool allow_requantize;       // allow quantizing non-f32/f16 tensors
-        bool quantize_output_tensor; // quantize output.weight
-        bool only_copy;              // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
-        bool pure;                   // disable k-quant mixtures and quantize all tensors to the same type
+        int32_t nthread;                     // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
+        enum llama_ftype ftype;              // quantize to this llama_ftype
+        enum ggml_type output_tensor_type;   // output tensor type
+        enum ggml_type token_embedding_type; // itoken embeddings tensor type
+        bool allow_requantize;               // allow quantizing non-f32/f16 tensors
+        bool quantize_output_tensor;         // quantize output.weight
+        bool only_copy;                      // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
+        bool pure;                           // quantize all tensors to the default type
+        void * imatrix;                      // pointer to importance matrix data
+        void * kv_overrides;                 // pointer to vector containing overrides
     } llama_model_quantize_params;
 
     // grammar types
@@ -283,6 +336,12 @@ extern "C" {
         int32_t n_eval;
     };
 
+    // used in chat template
+    typedef struct llama_chat_message {
+        const char * role;
+        const char * content;
+    } llama_chat_message;
+
     // Helpers for getting default parameters
     LLAMA_API struct llama_model_params llama_model_default_params(void);
     LLAMA_API struct llama_context_params llama_context_default_params(void);
@@ -291,7 +350,10 @@ extern "C" {
     // Initialize the llama + ggml backend
     // If numa is true, use NUMA optimizations
     // Call once at the start of the program
-    LLAMA_API void llama_backend_init(bool numa);
+    LLAMA_API void llama_backend_init(void);
+
+    //optional:
+    LLAMA_API void llama_numa_init(enum ggml_numa_strategy numa);
 
     // Call once at the end of the program - currently only used for MPI
     LLAMA_API void llama_backend_free(void);
@@ -311,20 +373,26 @@ extern "C" {
 
     LLAMA_API int64_t llama_time_us(void);
 
-    LLAMA_API int32_t  llama_max_devices(void);
-    LLAMA_API bool llama_mmap_supported (void);
-    LLAMA_API bool llama_mlock_supported(void);
+    LLAMA_API size_t llama_max_devices(void);
+
+    LLAMA_API bool llama_supports_mmap       (void);
+    LLAMA_API bool llama_supports_mlock      (void);
+    LLAMA_API bool llama_supports_gpu_offload(void);
 
     LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
 
     LLAMA_API uint32_t llama_n_ctx      (const struct llama_context * ctx);
     LLAMA_API uint32_t llama_n_batch    (const struct llama_context * ctx);
+    LLAMA_API uint32_t llama_n_ubatch   (const struct llama_context * ctx);
+    LLAMA_API uint32_t llama_n_seq_max  (const struct llama_context * ctx);
 
     LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
+    LLAMA_API enum llama_rope_type  llama_rope_type (const struct llama_model * model);
 
     LLAMA_API int32_t llama_n_vocab    (const struct llama_model * model);
     LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
     LLAMA_API int32_t llama_n_embd     (const struct llama_model * model);
+    LLAMA_API int32_t llama_n_layer    (const struct llama_model * model);
 
     // Get the model's RoPE frequency scaling factor
     LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
@@ -370,20 +438,26 @@ extern "C" {
     // The model needs to be reloaded before applying a new adapter, otherwise the adapter
     // will be applied on top of the previous one
     // Returns 0 on success
-    LLAMA_API DEPRECATED(int32_t llama_apply_lora_from_file(
-            struct llama_context * ctx,
-                      const char * path_lora,
-                           float   scale,
-                      const char * path_base_model,
-                         int32_t   n_threads),
-            "use llama_model_apply_lora_from_file instead");
-
     LLAMA_API int32_t llama_model_apply_lora_from_file(
             const struct llama_model * model,
-                      const char * path_lora,
-                           float   scale,
-                      const char * path_base_model,
-                         int32_t   n_threads);
+                          const char * path_lora,
+                               float   scale,
+                          const char * path_base_model,
+                             int32_t   n_threads);
+
+    // Apply a loaded control vector to a llama_context, or if data is NULL, clear
+    // the currently loaded vector.
+    // n_embd should be the size of a single layer's control, and data should point
+    // to an n_embd x n_layers buffer starting from layer 1.
+    // il_start and il_end are the layer range the vector should apply to (both inclusive)
+    // See llama_control_vector_load in common to load a control vector.
+    LLAMA_API int32_t llama_control_vector_apply(
+            struct llama_context * lctx,
+                     const float * data,
+                          size_t   len,
+                         int32_t   n_embd,
+                         int32_t   il_start,
+                         int32_t   il_end);
 
     //
     // KV cache
@@ -404,7 +478,7 @@ extern "C" {
         // Maximum number of sequences that can exist in a cell. It's not an error
         // if there are more sequences in a cell than this value, however they will
         // not be visible in the view cells_sequences.
-        int32_t n_max_seq;
+        int32_t n_seq_max;
 
         // Number of tokens in the cache. For example, if there are two populated
         // cells, the first with 1 sequence id in it and the second with 2 sequence
@@ -424,12 +498,12 @@ extern "C" {
         // Information for an individual cell.
         struct llama_kv_cache_view_cell * cells;
 
-        // The sequences for each cell. There will be n_max_seq items per cell.
+        // The sequences for each cell. There will be n_seq_max items per cell.
         llama_seq_id * cells_sequences;
     };
 
     // Create an empty KV cache view. (use only for debugging purposes)
-    LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_max_seq);
+    LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max);
 
     // Free a KV cache view. (use only for debugging purposes)
     LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view);
@@ -452,7 +526,7 @@ extern "C" {
     // seq_id < 0 : match any sequence
     // p0 < 0     : [0,  p1]
     // p1 < 0     : [p0, inf)
-    LLAMA_API void llama_kv_cache_seq_rm(
+    LLAMA_API bool llama_kv_cache_seq_rm(
             struct llama_context * ctx,
                     llama_seq_id   seq_id,
                        llama_pos   p0,
@@ -475,10 +549,12 @@ extern "C" {
                     llama_seq_id   seq_id);
 
     // Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
-    // If the KV cache is RoPEd, the KV data is updated accordingly
+    // If the KV cache is RoPEd, the KV data is updated accordingly:
+    //   - lazily on next llama_decode()
+    //   - explicitly with llama_kv_cache_update()
     // p0 < 0 : [0,  p1]
     // p1 < 0 : [p0, inf)
-    LLAMA_API void llama_kv_cache_seq_shift(
+    LLAMA_API void llama_kv_cache_seq_add(
             struct llama_context * ctx,
                     llama_seq_id   seq_id,
                        llama_pos   p0,
@@ -486,7 +562,9 @@ extern "C" {
                        llama_pos   delta);
 
     // Integer division of the positions by factor of `d > 1`
-    // If the KV cache is RoPEd, the KV data is updated accordingly
+    // If the KV cache is RoPEd, the KV data is updated accordingly:
+    //   - lazily on next llama_decode()
+    //   - explicitly with llama_kv_cache_update()
     // p0 < 0 : [0,  p1]
     // p1 < 0 : [p0, inf)
     LLAMA_API void llama_kv_cache_seq_div(
@@ -496,6 +574,20 @@ extern "C" {
                        llama_pos   p1,
                              int   d);
 
+    // Returns the largest position present in the KV cache for the specified sequence
+    LLAMA_API llama_pos llama_kv_cache_seq_pos_max(
+            struct llama_context * ctx,
+                    llama_seq_id   seq_id);
+
+    // Defragment the KV cache
+    // This will be applied:
+    //   - lazily on next llama_decode()
+    //   - explicitly with llama_kv_cache_update()
+    LLAMA_API void llama_kv_cache_defrag(struct llama_context * ctx);
+
+    // Apply the KV cache updates (such as K-shifts, defragmentation, etc.)
+    LLAMA_API void llama_kv_cache_update(struct llama_context * ctx);
+
     //
     // State / sessions
     //
@@ -515,7 +607,7 @@ extern "C" {
     // Returns the number of bytes read
     LLAMA_API size_t llama_set_state_data(
             struct llama_context * ctx,
-                         uint8_t * src);
+                   const uint8_t * src);
 
     // Save/load session file
     LLAMA_API bool llama_load_session_file(
@@ -535,27 +627,6 @@ extern "C" {
     // Decoding
     //
 
-    // Run the llama inference to obtain the logits and probabilities for the next token(s).
-    // tokens + n_tokens is the provided batch of new tokens to process
-    // n_past is the number of tokens to use from previous eval calls
-    // Returns 0 on success
-    // DEPRECATED: use llama_decode() instead
-    LLAMA_API DEPRECATED(int llama_eval(
-            struct llama_context * ctx,
-                     llama_token * tokens,
-                         int32_t   n_tokens,
-                         int32_t   n_past),
-            "use llama_decode() instead");
-
-    // Same as llama_eval, but use float matrix input directly.
-    // DEPRECATED: use llama_decode() instead
-    LLAMA_API DEPRECATED(int llama_eval_embd(
-            struct llama_context * ctx,
-                           float * embd,
-                         int32_t   n_tokens,
-                         int32_t   n_past),
-            "use llama_decode() instead");
-
     // Return batch for single sequence of tokens starting at pos_0
     //
     // NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it
@@ -594,21 +665,49 @@ extern "C" {
     // n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens)
     LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch);
 
-    // Token logits obtained from the last call to llama_eval()
-    // The logits for the last token are stored in the last row
-    // Logits for which llama_batch.logits[i] == 0 are undefined
-    // Rows: n_tokens provided with llama_batch
+    // Set whether to use causal attention or not
+    // If set to true, the model will only attend to the past tokens
+    LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn);
+
+    // Set abort callback
+    LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data);
+
+    // Wait until all computations are finished
+    // This is automatically done when using one of the functions below to obtain the computation results
+    // and is not necessary to call it explicitly in most cases
+    LLAMA_API void llama_synchronize(struct llama_context * ctx);
+
+    // Token logits obtained from the last call to llama_decode()
+    // The logits for which llama_batch.logits[i] != 0 are stored contiguously
+    // in the order they have appeared in the batch.
+    // Rows: number of tokens for which llama_batch.logits[i] != 0
     // Cols: n_vocab
     LLAMA_API float * llama_get_logits(struct llama_context * ctx);
 
     // Logits for the ith token. Equivalent to:
-    // llama_get_logits(ctx) + i*n_vocab
+    // llama_get_logits(ctx) + ctx->output_ids[i]*n_vocab
+    // returns NULL for invalid ids.
     LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i);
 
-    // Get the embeddings for the input
-    // shape: [n_embd] (1-dimensional)
+    // Get all output token embeddings.
+    // when pooling_type == LLAMA_POOLING_TYPE_NONE or when using a generative model,
+    // the embeddings for which llama_batch.logits[i] != 0 are stored contiguously
+    // in the order they have appeared in the batch.
+    // shape: [n_outputs*n_embd]
+    // Otherwise, returns NULL.
     LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
 
+    // Get the embeddings for the ith token. Equivalent to:
+    // llama_get_embeddings(ctx) + ctx->output_ids[i]*n_embd
+    // shape: [n_embd] (1-dimensional)
+    // returns NULL for invalid ids.
+    LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i);
+
+    // Get the embeddings for a sequence id
+    // Returns NULL if pooling_type is LLAMA_POOLING_TYPE_NONE
+    // shape: [n_embd] (1-dimensional)
+    LLAMA_API float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id);
+
     //
     // Vocab
     //
@@ -642,7 +741,7 @@ extern "C" {
 
     /// @details Convert the provided text into tokens.
     /// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
-    /// @return Returns the number of tokens on success, no more than n_max_tokens
+    /// @return Returns the number of tokens on success, no more than n_tokens_max
     /// @return Returns a negative number on failure - the number of tokens that would have been returned
     /// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
     ///                Does not insert a leading space.
@@ -651,7 +750,7 @@ extern "C" {
                       const char * text,
                          int32_t   text_len,
                      llama_token * tokens,
-                         int32_t   n_max_tokens,
+                         int32_t   n_tokens_max,
                             bool   add_bos,
                             bool   special);
 
@@ -665,6 +764,25 @@ extern "C" {
                                   char * buf,
                                int32_t   length);
 
+    /// Apply chat template. Inspired by hf apply_chat_template() on python.
+    /// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model"
+    /// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
+    /// @param tmpl A Jinja template to use for this chat. If this is nullptr, the model’s default chat template will be used instead.
+    /// @param chat Pointer to a list of multiple llama_chat_message
+    /// @param n_msg Number of llama_chat_message in this chat
+    /// @param add_ass Whether to end the prompt with the token(s) that indicate the start of an assistant message.
+    /// @param buf A buffer to hold the output formatted prompt. The recommended alloc size is 2 * (total number of characters of all messages)
+    /// @param length The size of the allocated buffer
+    /// @return The total number of bytes of the formatted prompt. If is it larger than the size of buffer, you may need to re-alloc it and then re-apply the template.
+    LLAMA_API int32_t llama_chat_apply_template(
+              const struct llama_model * model,
+                            const char * tmpl,
+       const struct llama_chat_message * chat,
+                                size_t   n_msg,
+                                  bool   add_ass,
+                                  char * buf,
+                               int32_t   length);
+
     //
     // Grammar
     //
@@ -697,13 +815,13 @@ extern "C" {
                            float   penalty_present);
 
     /// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806
-    /// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted.
-    /// @params guidance_ctx A separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.
-    /// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
-    LLAMA_API void llama_sample_classifier_free_guidance(
+    /// @param logits Logits extracted from the original generation context.
+    /// @param logits_guidance Logits extracted from a separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.
+    /// @param scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
+    LLAMA_API void llama_sample_apply_guidance(
               struct llama_context * ctx,
-            llama_token_data_array * candidates,
-              struct llama_context * guidance_ctx,
+                             float * logits,
+                             float * logits_guidance,
                              float   scale);
 
     /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
@@ -746,17 +864,19 @@ extern "C" {
                            float   p,
                           size_t   min_keep);
 
+    /// @details Dynamic temperature implementation described in the paper https://arxiv.org/abs/2309.02772.
+    LLAMA_API void llama_sample_entropy(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates_p,
+                           float   min_temp,
+                           float   max_temp,
+                           float   exponent_val);
+
     LLAMA_API void llama_sample_temp(
             struct llama_context * ctx,
           llama_token_data_array * candidates,
                            float   temp);
 
-    LLAMA_API DEPRECATED(void llama_sample_temperature(
-                struct llama_context * ctx,
-              llama_token_data_array * candidates,
-                               float   temp),
-            "use llama_sample_temp instead");
-
     /// @details Apply constraints from grammar
     LLAMA_API void llama_sample_grammar(
             struct llama_context * ctx,
@@ -850,6 +970,16 @@ extern "C" {
                                 int32_t   n_past,
                                 int32_t   n_predict);
 
+    /// @details Build a split GGUF final path for this chunk.
+    ///          llama_split_path(split_path, sizeof(split_path), "/models/ggml-model-q4_0", 2, 4) => split_path = "/models/ggml-model-q4_0-00002-of-00004.gguf"
+    //  Returns the split_path length.
+    LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count);
+
+    /// @details Extract the path prefix from the split_path if and only if the split_no and split_count match.
+    ///          llama_split_prefix(split_prefix, 64, "/models/ggml-model-q4_0-00002-of-00004.gguf", 2, 4) => split_prefix = "/models/ggml-model-q4_0"
+    //  Returns the split_prefix length.
+    LLAMA_API int llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int split_no, int split_count);
+
     // Performance information
     LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
 
@@ -877,10 +1007,38 @@ extern "C" {
 
 struct ggml_tensor;
 
+struct llama_partial_utf8 {
+    uint32_t value;    // bit value so far (unshifted)
+    int      n_remain; // num bytes remaining; -1 indicates invalid sequence
+};
+
+struct llama_grammar {
+    const std::vector<std::vector<llama_grammar_element>>   rules;
+    std::vector<std::vector<const llama_grammar_element *>> stacks;
+
+    // buffer for partially generated UTF-8 sequence from accepted tokens
+    llama_partial_utf8                                      partial_utf8;
+};
+
+struct llama_grammar_candidate {
+    size_t               index;
+    const uint32_t     * code_points;
+    llama_partial_utf8   partial_utf8;
+};
+
 const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
     struct llama_context * ctx
 );
 
+std::vector<std::vector<const llama_grammar_element *>> llama_grammar_accept(
+        const std::vector<std::vector<llama_grammar_element>>         & rules,
+        const std::vector<std::vector<const llama_grammar_element *>> & stacks,
+        const uint32_t                                                  chr);
+
+std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
+        const std::string & src,
+        llama_partial_utf8   partial_start);
+
 #endif // LLAMA_API_INTERNAL
 
 #endif // LLAMA_H
diff --git a/mypy.ini b/mypy.ini
index 7215a05dd..e51910ca7 100644
--- a/mypy.ini
+++ b/mypy.ini
@@ -4,3 +4,4 @@ allow_untyped_calls = true
 allow_untyped_defs = true
 allow_incomplete_defs = true
 disable_error_code = import-untyped
+warn_return_any = false
diff --git a/pocs/vdot/q8dot.cpp b/pocs/vdot/q8dot.cpp
index 111770d55..1a52ff5e9 100644
--- a/pocs/vdot/q8dot.cpp
+++ b/pocs/vdot/q8dot.cpp
@@ -156,8 +156,8 @@ int main(int argc, char** argv) {
 
         t1 = std::chrono::high_resolution_clock::now();
         float fs;
-        if (type == 0) funcs.vec_dot(kVecSize * QK4_1, &fs, x40.data(), y.data());
-        else funcs.vec_dot(kVecSize * QK4_1, &fs, x41.data(), y.data());
+        if (type == 0) funcs.vec_dot(kVecSize * QK4_1, &fs, 0, x40.data(), 0, y.data(), 0, 1);
+        else funcs.vec_dot(kVecSize * QK4_1, &fs, 0, x41.data(), 0, y.data(), 0, 1);
         t2 = std::chrono::high_resolution_clock::now();
         t = 1e-3*std::chrono::duration_cast<std::chrono::nanoseconds>(t2-t1).count();
         if (iloop > 3) ggml.addResult(fs, t);
diff --git a/pocs/vdot/vdot.cpp b/pocs/vdot/vdot.cpp
index e96372c4b..17e9e4482 100644
--- a/pocs/vdot/vdot.cpp
+++ b/pocs/vdot/vdot.cpp
@@ -243,7 +243,6 @@ int main(int argc, char** argv) {
     if (useQ4_1) q41.resize(n4);
     else q40.resize(n4);
     std::vector<block_q8_0> q8(n8);
-    std::vector<int64_t> H(16, 0);
     double sumt = 0, sumt2 = 0, maxt = 0;
     double sumqt = 0, sumqt2 = 0, maxqt = 0;
     double sum = 0, sumq = 0, exactSum = 0;
@@ -285,8 +284,8 @@ int main(int argc, char** argv) {
         else {
             auto vdot = ggml_internal_get_type_traits(funcs.vec_dot_type);
             vdot.from_float(y1.data(), q8.data(), kVecSize);
-            if (useQ4_1) funcs.vec_dot(kVecSize, &result, q41.data(), q8.data());
-            else funcs.vec_dot(kVecSize, &result, q40.data(), q8.data());
+            if (useQ4_1) funcs.vec_dot(kVecSize, &result, 0, q41.data(), 0, q8.data(), 0, 1);
+            else funcs.vec_dot(kVecSize, &result, 0, q40.data(), 0, q8.data(), 0, 1);
         }
         sumq += result;
         t2 = std::chrono::high_resolution_clock::now();
diff --git a/requirements/requirements-convert-hf-to-gguf.txt b/requirements/requirements-convert-hf-to-gguf.txt
index 6ac402610..6ce840d73 100644
--- a/requirements/requirements-convert-hf-to-gguf.txt
+++ b/requirements/requirements-convert-hf-to-gguf.txt
@@ -1,2 +1,3 @@
 -r ./requirements-convert.txt
 torch~=2.1.1
+einops~=0.7.0
diff --git a/scripts/LlamaConfig.cmake.in b/scripts/LlamaConfig.cmake.in
index 6a6d8e39e..f842c7137 100644
--- a/scripts/LlamaConfig.cmake.in
+++ b/scripts/LlamaConfig.cmake.in
@@ -3,7 +3,7 @@ set(LLAMA_BUILD_COMMIT @LLAMA_BUILD_COMMIT@)
 set(LLAMA_BUILD_NUMBER @LLAMA_BUILD_NUMBER@)
 set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@)
 set(LLAMA_BLAS @LLAMA_BLAS@)
-set(LLAMA_CUBLAS @LLAMA_CUBLAS@)
+set(LLAMA_CUDA @LLAMA_CUDA@)
 set(LLAMA_METAL @LLAMA_METAL@)
 set(LLAMA_MPI @LLAMA_MPI@)
 set(LLAMA_CLBLAST @LLAMA_CLBLAST@)
@@ -27,7 +27,7 @@ if (LLAMA_BLAS)
     find_package(BLAS REQUIRED)
 endif()
 
-if (LLAMA_CUBLAS)
+if (LLAMA_CUDA)
     find_package(CUDAToolkit REQUIRED)
 endif()
 
diff --git a/scripts/ci-run.sh b/scripts/ci-run.sh
new file mode 100755
index 000000000..06b5d9c6e
--- /dev/null
+++ b/scripts/ci-run.sh
@@ -0,0 +1,50 @@
+#!/bin/bash
+set -euo pipefail
+this=$(realpath "$0"); readonly this
+cd "$(dirname "$this")"
+shellcheck "$this"
+
+if (( $# != 1 && $# != 2  )); then
+    cat >&2 <<'EOF'
+usage:
+    ci-run.sh <tmp_dir> [<cache_dir>]
+
+This script wraps ci/run.sh:
+* If <tmp_dir> is a ramdisk, you can reduce writes to your SSD. If <tmp_dir> is not a ramdisk, keep in mind that total writes will increase by the size of <cache_dir>.
+    (openllama_3b_v2: quantized models are about 30GB)
+* Persistent model and data files are synced to and from <cache_dir>,
+    excluding generated .gguf files.
+    (openllama_3b_v2: persistent files are about 6.6GB)
+* <cache_dir> defaults to  ~/.cache/llama.cpp
+EOF
+    exit 1
+fi
+
+cd .. # => llama.cpp repo root
+
+tmp="$1"
+mkdir -p "$tmp"
+tmp=$(realpath "$tmp")
+echo >&2 "Using tmp=$tmp"
+
+cache="${2-$HOME/.cache/llama.cpp}"
+mkdir -p "$cache"
+cache=$(realpath "$cache")
+echo >&2 "Using cache=$cache"
+
+_sync() {
+    local from="$1"; shift
+    local to="$1"; shift
+
+    echo >&2 "Syncing from $from to $to"
+    mkdir -p "$from" "$to"
+    rsync -a "$from" "$to" --delete-during "$@"
+}
+
+_sync "$(realpath .)/" "$tmp/llama.cpp"
+_sync "$cache/ci-mnt/models/" "$tmp/llama.cpp/ci-mnt/models/"
+
+cd "$tmp/llama.cpp"
+bash ci/run.sh ci-out ci-mnt
+
+_sync 'ci-mnt/models/' "$cache/ci-mnt/models/" --exclude='*.gguf' -P
diff --git a/scripts/compare-commits.sh b/scripts/compare-commits.sh
new file mode 100755
index 000000000..d1272506c
--- /dev/null
+++ b/scripts/compare-commits.sh
@@ -0,0 +1,37 @@
+#!/bin/bash
+
+if [ $# -lt 2 ]; then
+    echo "usage: ./scripts/compare-commits.sh <commit1> <commit2> [additional llama-bench arguments]"
+    exit 1
+fi
+
+set -e
+set -x
+
+bench_args="${@:3}"
+
+rm -f llama-bench.sqlite
+
+backend="cpu"
+
+if [[ "$OSTYPE" == "darwin"* ]]; then
+    backend="metal"
+elif command -v nvcc &> /dev/null; then
+    backend="cuda"
+fi
+
+make_opts=""
+
+if [[ "$backend" == "cuda" ]]; then
+    make_opts="LLAMA_CUDA=1"
+fi
+
+git checkout $1
+make clean && make -j32 $make_opts llama-bench
+./llama-bench -o sql $bench_args | tee /dev/tty | sqlite3 llama-bench.sqlite
+
+git checkout $2
+make clean && make -j32 $make_opts llama-bench
+./llama-bench -o sql $bench_args | tee /dev/tty | sqlite3 llama-bench.sqlite
+
+./scripts/compare-llama-bench.py -b $1 -c $2
diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py
new file mode 100755
index 000000000..ef7f19ecb
--- /dev/null
+++ b/scripts/compare-llama-bench.py
@@ -0,0 +1,377 @@
+#!/usr/bin/env python3
+
+import argparse
+import heapq
+import sys
+import os
+from glob import glob
+import sqlite3
+
+try:
+    import git
+    from tabulate import tabulate
+except ImportError as e:
+    print("ERROR: the following Python libraries are required: GitPython, tabulate.")
+    raise e
+
+# Properties by which to differentiate results per commit:
+KEY_PROPERTIES = [
+    "cpu_info", "gpu_info", "n_gpu_layers", "main_gpu", "cuda", "opencl", "metal", "gpu_blas",
+    "blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads",
+    "type_k", "type_v", "no_kv_offload", "tensor_split", "n_prompt", "n_gen"
+]
+
+# Properties that are boolean and are converted to Yes/No for the table:
+BOOL_PROPERTIES = ["cuda", "opencl", "metal", "gpu_blas", "blas"]
+
+# Header names for the table:
+PRETTY_NAMES = {
+    "cuda": "CUDA", "opencl": "OpenCL", "metal": "Metal", "gpu_blas": "GPU BLAS", "blas": "BLAS",
+    "cpu_info": "CPU", "gpu_info": "GPU", "model_filename": "File", "model_type": "Model",
+    "model_size": "Model Size [GiB]", "model_n_params": "Num. of Parameters",
+    "n_batch": "Batch size", "n_threads": "Threads", "type_k": "K type", "type_v": "V type",
+    "n_gpu_layers": "GPU layers", "main_gpu": "Main GPU", "no_kv_offload": "NKVO",
+    "tensor_split": "Tensor split"
+}
+
+DEFAULT_SHOW = ["model_type"]  # Always show these properties by default.
+DEFAULT_HIDE = ["model_filename"]  # Always hide these properties by default.
+GPU_NAME_STRIP = ["NVIDIA GeForce ", "Tesla ", "AMD Radeon "]  # Strip prefixes for smaller tables.
+MODEL_SUFFIX_REPLACE = {" - Small": "_S", " - Medium": "_M", " - Large": "_L"}
+
+DESCRIPTION = """Creates tables from llama-bench data written to an SQLite database. Example usage (Linux):
+
+$ git checkout master
+$ make clean && make llama-bench
+$ ./llama-bench -o sql | sqlite3 llama-bench.sqlite
+$ git checkout some_branch
+$ make clean && make llama-bench
+$ ./llama-bench -o sql | sqlite3 llama-bench.sqlite
+$ ./scripts/compare-llama-bench.py
+
+Performance numbers from multiple runs per commit are averaged WITHOUT being weighted by the --repetitions parameter of llama-bench.
+"""
+
+parser = argparse.ArgumentParser(
+    description=DESCRIPTION, formatter_class=argparse.RawDescriptionHelpFormatter)
+help_b = (
+    "The baseline commit to compare performance to. "
+    "Accepts either a branch name, tag name, or commit hash. "
+    "Defaults to latest master commit with data."
+)
+parser.add_argument("-b", "--baseline", help=help_b)
+help_c = (
+    "The commit whose performance is to be compared to the baseline. "
+    "Accepts either a branch name, tag name, or commit hash. "
+    "Defaults to the non-master commit for which llama-bench was run most recently."
+)
+parser.add_argument("-c", "--compare", help=help_c)
+help_i = (
+    "Input SQLite file for comparing commits. "
+    "Defaults to 'llama-bench.sqlite' in the current working directory. "
+    "If no such file is found and there is exactly one .sqlite file in the current directory, "
+    "that file is instead used as input."
+)
+parser.add_argument("-i", "--input", help=help_i)
+help_o = (
+    "Output format for the table. "
+    "Defaults to 'pipe' (GitHub compatible). "
+    "Also supports e.g. 'latex' or 'mediawiki'. "
+    "See tabulate documentation for full list."
+)
+parser.add_argument("-o", "--output", help=help_o, default="pipe")
+help_s = (
+    "Columns to add to the table. "
+    "Accepts a comma-separated list of values. "
+    f"Legal values: {', '.join(KEY_PROPERTIES[:-2])}. "
+    "Defaults to model name (model_type) and CPU and/or GPU name (cpu_info, gpu_info) "
+    "plus any column where not all data points are the same. "
+    "If the columns are manually specified, then the results for each unique combination of the "
+    "specified values are averaged WITHOUT weighing by the --repetitions parameter of llama-bench."
+)
+parser.add_argument("-s", "--show", help=help_s)
+
+known_args, unknown_args = parser.parse_known_args()
+
+if unknown_args:
+    print(f"ERROR: Received unknown args: {unknown_args}.")
+    print()
+    parser.print_help()
+    sys.exit(1)
+
+input_file = known_args.input
+if input_file is None and os.path.exists("./llama-bench.sqlite"):
+    input_file = "llama-bench.sqlite"
+if input_file is None:
+    sqlite_files = glob("*.sqlite")
+    if len(sqlite_files) == 1:
+        input_file = sqlite_files[0]
+
+if input_file is None:
+    print("ERROR: Cannot find a suitable input file, please provide one.")
+    print()
+    parser.print_help()
+    sys.exit(1)
+
+connection = sqlite3.connect(input_file)
+cursor = connection.cursor()
+builds = cursor.execute("SELECT DISTINCT build_commit FROM test;").fetchall()
+
+try:
+    repo = git.Repo(".", search_parent_directories=True)
+except git.exc.InvalidGitRepositoryError:
+    repo = None
+
+
+def find_parent_in_data(commit):
+    """Helper function to find the most recent parent measured in number of commits for which there is data."""
+    heap = [(0, commit)]
+    seen_hexsha8 = set()
+    while heap:
+        depth, current_commit = heapq.heappop(heap)
+        current_hexsha8 = commit.hexsha[:8]
+        if (current_hexsha8,) in builds:
+            return current_hexsha8
+        for parent in commit.parents:
+            parent_hexsha8 = parent.hexsha[:8]
+            if parent_hexsha8 not in seen_hexsha8:
+                seen_hexsha8.add(parent_hexsha8)
+                heapq.heappush(heap, (depth + 1, parent))
+    return None
+
+
+def get_all_parent_hexsha8s(commit):
+    """Helper function to recursively get hexsha8 values for all parents of a commit."""
+    unvisited = [commit]
+    visited   = []
+
+    while unvisited:
+        current_commit = unvisited.pop(0)
+        visited.append(current_commit.hexsha[:8])
+        for parent in current_commit.parents:
+            if parent.hexsha[:8] not in visited:
+                unvisited.append(parent)
+
+    return visited
+
+
+def get_commit_name(hexsha8):
+    """Helper function to find a human-readable name for a commit if possible."""
+    if repo is None:
+        return hexsha8
+    for h in repo.heads:
+        if h.commit.hexsha[:8] == hexsha8:
+            return h.name
+    for t in repo.tags:
+        if t.commit.hexsha[:8] == hexsha8:
+            return t.name
+    return hexsha8
+
+
+def get_commit_hexsha8(name):
+    """Helper function to search for a commit given a human-readable name."""
+    if repo is None:
+        return None
+    for h in repo.heads:
+        if h.name == name:
+            return h.commit.hexsha[:8]
+    for t in repo.tags:
+        if t.name == name:
+            return t.commit.hexsha[:8]
+    for c in repo.iter_commits("--all"):
+        if c.hexsha[:8] == name[:8]:
+            return c.hexsha[:8]
+    return None
+
+
+hexsha8_baseline = name_baseline = None
+
+# If the user specified a baseline, try to find a commit for it:
+if known_args.baseline is not None:
+    if (known_args.baseline,) in builds:
+        hexsha8_baseline = known_args.baseline
+    if hexsha8_baseline is None:
+        hexsha8_baseline = get_commit_hexsha8(known_args.baseline)
+        name_baseline = known_args.baseline
+    if hexsha8_baseline is None:
+        print(f"ERROR: cannot find data for baseline={known_args.baseline}.")
+        sys.exit(1)
+# Otherwise, search for the most recent parent of master for which there is data:
+elif repo is not None:
+    hexsha8_baseline = find_parent_in_data(repo.heads.master.commit)
+
+    if hexsha8_baseline is None:
+        print("ERROR: No baseline was provided and did not find data for any master branch commits.")
+        print()
+        parser.print_help()
+        sys.exit(1)
+else:
+    print(
+        "ERROR: No baseline was provided and the current working directory "
+        "is not part of a git repository from which a baseline could be inferred."
+    )
+    print()
+    parser.print_help()
+    sys.exit(1)
+
+
+name_baseline = get_commit_name(hexsha8_baseline)
+
+hexsha8_compare = name_compare = None
+
+# If the user has specified a compare value, try to find a corresponding commit:
+if known_args.compare is not None:
+    if (known_args.compare,) in builds:
+        hexsha8_compare = known_args.compare
+    if hexsha8_compare is None:
+        hexsha8_compare = get_commit_hexsha8(known_args.compare)
+        name_compare = known_args.compare
+    if hexsha8_compare is None:
+        print(f"ERROR: cannot find data for compare={known_args.compare}.")
+        sys.exit(1)
+# Otherwise, search for the commit for llama-bench was most recently run
+# and that is not a parent of master:
+elif repo is not None:
+    hexsha8s_master = get_all_parent_hexsha8s(repo.heads.master.commit)
+    builds_timestamp = cursor.execute(
+        "SELECT build_commit, test_time FROM test ORDER BY test_time;").fetchall()
+    for (hexsha8, _) in reversed(builds_timestamp):
+        if hexsha8 not in hexsha8s_master:
+            hexsha8_compare = hexsha8
+            break
+
+    if hexsha8_compare is None:
+        print("ERROR: No compare target was provided and did not find data for any non-master commits.")
+        print()
+        parser.print_help()
+        sys.exit(1)
+else:
+    print(
+        "ERROR: No compare target was provided and the current working directory "
+        "is not part of a git repository from which a compare target could be inferred."
+    )
+    print()
+    parser.print_help()
+    sys.exit(1)
+
+name_compare = get_commit_name(hexsha8_compare)
+
+
+def get_rows(properties):
+    """
+    Helper function that gets table rows for some list of properties.
+    Rows are created by combining those where all provided properties are equal.
+    The resulting rows are then grouped by the provided properties and the t/s values are averaged.
+    The returned rows are unique in terms of property combinations.
+    """
+    select_string = ", ".join(
+        [f"tb.{p}" for p in properties] + ["tb.n_prompt", "tb.n_gen", "AVG(tb.avg_ts)", "AVG(tc.avg_ts)"])
+    equal_string = " AND ".join(
+        [f"tb.{p} = tc.{p}" for p in KEY_PROPERTIES] + [
+            f"tb.build_commit = '{hexsha8_baseline}'", f"tc.build_commit = '{hexsha8_compare}'"]
+    )
+    group_order_string = ", ".join([f"tb.{p}" for p in properties] + ["tb.n_gen", "tb.n_prompt"])
+    query = (f"SELECT {select_string} FROM test tb JOIN test tc ON {equal_string} "
+             f"GROUP BY {group_order_string} ORDER BY {group_order_string};")
+    return cursor.execute(query).fetchall()
+
+
+# If the user provided columns to group the results by, use them:
+if known_args.show is not None:
+    show = known_args.show.split(",")
+    unknown_cols = []
+    for prop in show:
+        if prop not in KEY_PROPERTIES[:-2]:  # Last two values are n_prompt, n_gen.
+            unknown_cols.append(prop)
+    if unknown_cols:
+        print(f"ERROR: Unknown values for --show: {', '.join(unknown_cols)}")
+        print()
+        parser.print_usage()
+        sys.exit(1)
+    rows_show = get_rows(show)
+# Otherwise, select those columns where the values are not all the same:
+else:
+    rows_full = get_rows(KEY_PROPERTIES)
+    properties_different = []
+    for i, kp_i in enumerate(KEY_PROPERTIES):
+        if kp_i in DEFAULT_SHOW or kp_i == "n_prompt" or kp_i == "n_gen":
+            continue
+        for row_full in rows_full:
+            if row_full[i] != rows_full[0][i]:
+                properties_different.append(kp_i)
+                break
+
+    show = []
+    # Show CPU and/or GPU by default even if the hardware for all results is the same:
+    if "gpu_blas" not in properties_different and "n_gpu_layers" not in properties_different:
+        gpu_blas = bool(rows_full[0][KEY_PROPERTIES.index("gpu_blas")])
+        ngl = int(rows_full[0][KEY_PROPERTIES.index("n_gpu_layers")])
+
+        if not gpu_blas or ngl != 99 and "cpu_info" not in properties_different:
+            show.append("cpu_info")
+        if gpu_blas and "gpu_info" not in properties_different:
+            show.append("gpu_info")
+
+    show += properties_different
+
+    index_default = 0
+    for prop in ["cpu_info", "gpu_info", "n_gpu_layers", "main_gpu"]:
+        if prop in show:
+            index_default += 1
+    show = show[:index_default] + DEFAULT_SHOW + show[index_default:]
+    for prop in DEFAULT_HIDE:
+        try:
+            show.remove(prop)
+        except ValueError:
+            pass
+    rows_show = get_rows(show)
+
+table = []
+for row in rows_show:
+    n_prompt = int(row[-4])
+    n_gen    = int(row[-3])
+    assert n_prompt == 0 or n_gen == 0
+    test_name = f"tg{n_gen}" if n_prompt == 0 else f"pp{n_prompt}"
+    #           Regular columns    test name    avg t/s values              Speedup
+    #            VVVVVVVVVVVVV     VVVVVVVVV    VVVVVVVVVVVVVV              VVVVVVV
+    table.append(list(row[:-4]) + [test_name] + list(row[-2:]) + [float(row[-1]) / float(row[-2])])
+
+# Some a-posteriori fixes to make the table contents prettier:
+for bool_property in BOOL_PROPERTIES:
+    if bool_property in show:
+        ip = show.index(bool_property)
+        for row_table in table:
+            row_table[ip] = "Yes" if int(row_table[ip]) == 1 else "No"
+
+if "model_type" in show:
+    ip = show.index("model_type")
+    for (old, new) in MODEL_SUFFIX_REPLACE.items():
+        for row_table in table:
+            row_table[ip] = row_table[ip].replace(old, new)
+
+if "model_size" in show:
+    ip = show.index("model_size")
+    for row_table in table:
+        row_table[ip] = float(row_table[ip]) / 1024 ** 3
+
+if "gpu_info" in show:
+    ip = show.index("gpu_info")
+    for row_table in table:
+        for gns in GPU_NAME_STRIP:
+            row_table[ip] = row_table[ip].replace(gns, "")
+
+        gpu_names = row_table[ip].split("/")
+        num_gpus = len(gpu_names)
+        all_names_the_same = len(set(gpu_names)) == 1
+        if len(gpu_names) >= 2 and all_names_the_same:
+            row_table[ip] = f"{num_gpus}x {gpu_names[0]}"
+
+headers  = [PRETTY_NAMES[p] for p in show]
+headers += ["Test", f"t/s {name_baseline}", f"t/s {name_compare}", "Speedup"]
+
+print(tabulate(
+    table,
+    headers=headers,
+    floatfmt=".2f",
+    tablefmt=known_args.output
+))
diff --git a/scripts/get-flags.mk b/scripts/get-flags.mk
index 596d7ead1..a742766d1 100644
--- a/scripts/get-flags.mk
+++ b/scripts/get-flags.mk
@@ -1,6 +1,6 @@
 ifeq '' '$(findstring clang,$(shell $(GF_CC) --version))'
 	GF_CC_IS_GCC = 1
-	GF_CC_VER := $(shell { $(GF_CC) -dumpfullversion 2>/dev/null || $(GF_CC) -dumpversion; } | awk -F. '{ printf("%02d%02d%02d", $$1, $$2, $$3) }')
+	GF_CC_VER := $(shell { $(GF_CC) -dumpfullversion 2>/dev/null; echo; $(GF_CC) -dumpversion; } | awk -F. '/./ { printf("%02d%02d%02d", $$1, $$2, $$3); exit }')
 else
 	GF_CC_IS_CLANG = 1
 	ifeq '' '$(findstring Apple,$(shell $(GF_CC) --version))'
diff --git a/scripts/get-hellaswag.sh b/scripts/get-hellaswag.sh
new file mode 100755
index 000000000..121979fe2
--- /dev/null
+++ b/scripts/get-hellaswag.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+
+wget https://raw.githubusercontent.com/klosax/hellaswag_text_data/main/hellaswag_val_full.txt
+
+echo "Usage:"
+echo ""
+echo "  ./perplexity -m model.gguf -f hellaswag_val_full.txt --hellaswag [--hellaswag-tasks N] [other params]"
+echo ""
+
+exit 0
diff --git a/scripts/get-wikitext-103.sh b/scripts/get-wikitext-103.sh
new file mode 100755
index 000000000..880dd5cbe
--- /dev/null
+++ b/scripts/get-wikitext-103.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+
+wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip
+
+echo "Usage:"
+echo ""
+echo "  ./perplexity -m model.gguf -f wiki.test.raw [other params]"
+echo ""
+
+exit 0
diff --git a/scripts/get-wikitext-2.sh b/scripts/get-wikitext-2.sh
index 98aec3e3e..7ca760fa6 100755
--- a/scripts/get-wikitext-2.sh
+++ b/scripts/get-wikitext-2.sh
@@ -1,3 +1,10 @@
 #!/bin/bash
 
-wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip
+wget https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
+
+echo "Usage:"
+echo ""
+echo "  ./perplexity -m model.gguf -f wiki.test.raw [other params]"
+echo ""
+
+exit 0
diff --git a/scripts/get-winogrande.sh b/scripts/get-winogrande.sh
new file mode 100755
index 000000000..5f234468e
--- /dev/null
+++ b/scripts/get-winogrande.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+
+wget https://huggingface.co/datasets/ikawrakow/winogrande-eval-for-llama.cpp/raw/main/winogrande-debiased-eval.csv
+
+echo "Usage:"
+echo ""
+echo "  ./perplexity -m model.gguf -f winogrande-debiased-eval.csv --winogrande [--winogrande-tasks N] [other params]"
+echo ""
+
+exit 0
diff --git a/scripts/hf.sh b/scripts/hf.sh
new file mode 100755
index 000000000..1e9e5a6ea
--- /dev/null
+++ b/scripts/hf.sh
@@ -0,0 +1,107 @@
+#!/bin/bash
+#
+# Shortcut for downloading HF models
+#
+# Usage:
+#   ./main -m $(./examples/hf.sh https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
+#   ./main -m $(./examples/hf.sh --url https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/blob/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
+#   ./main -m $(./examples/hf.sh --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf)
+#
+
+# all logs go to stderr
+function log {
+    echo "$@" 1>&2
+}
+
+function usage {
+    log "Usage: $0 [[--url] <url>] [--repo <repo>] [--file <file>] [-h|--help]"
+    exit 1
+}
+
+# check for curl or wget
+function has_cmd {
+    if ! [ -x "$(command -v $1)" ]; then
+        return 1
+    fi
+}
+
+if has_cmd wget; then
+    cmd="wget -q --show-progress -c -O %s %s"
+elif has_cmd curl; then
+    cmd="curl -C - -f -o %s -L %s"
+else
+    log "[E] curl or wget not found"
+    exit 1
+fi
+
+url=""
+repo=""
+file=""
+
+# parse args
+while [[ $# -gt 0 ]]; do
+    case "$1" in
+        --url)
+            url="$2"
+            shift 2
+            ;;
+        --repo)
+            repo="$2"
+            shift 2
+            ;;
+        --file)
+            file="$2"
+            shift 2
+            ;;
+        -h|--help)
+            usage
+            ;;
+        *)
+            url="$1"
+            shift
+            ;;
+    esac
+done
+
+if [ -n "$repo" ] && [ -n "$file" ]; then
+    url="https://huggingface.co/$repo/resolve/main/$file"
+fi
+
+if [ -z "$url" ]; then
+    log "[E] missing --url"
+    usage
+fi
+
+# check if the URL is a HuggingFace model, and if so, try to download it
+is_url=false
+
+if [[ ${#url} -gt 22 ]]; then
+    if [[ ${url:0:22} == "https://huggingface.co" ]]; then
+        is_url=true
+    fi
+fi
+
+if [ "$is_url" = false ]; then
+    log "[E] invalid URL, must start with https://huggingface.co"
+    exit 0
+fi
+
+# replace "blob/main" with "resolve/main"
+url=${url/blob\/main/resolve\/main}
+
+basename=$(basename $url)
+
+log "[+] attempting to download $basename"
+
+if [ -n "$cmd" ]; then
+    cmd=$(printf "$cmd" "$basename" "$url")
+    log "[+] $cmd"
+    if $cmd; then
+        echo $basename
+        exit 0
+    fi
+fi
+
+log "[-] failed to download"
+
+exit 1
diff --git a/scripts/install-oneapi.bat b/scripts/install-oneapi.bat
new file mode 100644
index 000000000..e99bef14a
--- /dev/null
+++ b/scripts/install-oneapi.bat
@@ -0,0 +1,19 @@
+::  MIT license
+::  Copyright (C) 2024 Intel Corporation
+::  SPDX-License-Identifier: MIT
+
+
+set URL=%1
+set COMPONENTS=%2
+
+curl.exe --output %TEMP%\webimage.exe --url %URL% --retry 5 --retry-delay 5
+start /b /wait %TEMP%\webimage.exe -s -x -f webimage_extracted --log extract.log
+del %TEMP%\webimage.exe
+if "%COMPONENTS%"=="" (
+  webimage_extracted\bootstrapper.exe -s --action install --eula=accept -p=NEED_VS2017_INTEGRATION=0 -p=NEED_VS2019_INTEGRATION=0 -p=NEED_VS2022_INTEGRATION=0 --log-dir=.
+) else (
+  webimage_extracted\bootstrapper.exe -s --action install --components=%COMPONENTS% --eula=accept -p=NEED_VS2017_INTEGRATION=0 -p=NEED_VS2019_INTEGRATION=0 -p=NEED_VS2022_INTEGRATION=0 --log-dir=.
+)
+set installer_exit_code=%ERRORLEVEL%
+rd /s/q "webimage_extracted"
+exit /b %installer_exit_code%
diff --git a/scripts/pod-llama.sh b/scripts/pod-llama.sh
new file mode 100644
index 000000000..2058ceabf
--- /dev/null
+++ b/scripts/pod-llama.sh
@@ -0,0 +1,213 @@
+#!/bin/bash
+#
+# Use this script only on fresh pods (runpod.io)!
+# Otherwise, it can break your environment!
+#
+
+if [ -z "$1" ]; then
+    echo "Usage: $0 <data>"
+    echo "  0: no models"
+    echo "  1: tinyllama-1b"
+    echo "  2: codellama-7b"
+    echo "  3: codellama-13b"
+    echo "  4: codellama-34b"
+    echo "  5: codellama-7b-instruct"
+    echo "  6: codellama-13b-instruct"
+    echo "  7: codellama-34b-instruct"
+
+    exit 1
+fi
+
+set -x
+
+# setup deps
+apt-get update
+apt-get install -y git-lfs cmake cmake-curses-gui vim ruby
+git-lfs install
+
+if [ ! -d "/workspace" ]; then
+    ln -sfn $(pwd) /workspace
+fi
+
+# download data
+cd /workspace
+
+# this is useful to git clone repos without doubling the disk size due to .git
+git clone https://github.com/iboB/git-lfs-download
+ln -sfn /workspace/git-lfs-download/git-lfs-download /usr/local/bin/git-lfs-download
+
+# llama.cpp
+cd /workspace
+git clone https://github.com/ggerganov/llama.cpp
+
+cd llama.cpp
+
+LLAMA_CUDA=1 make -j
+
+ln -sfn /workspace/TinyLlama-1.1B-Chat-v0.3  ./models/tinyllama-1b
+ln -sfn /workspace/CodeLlama-7b-hf           ./models/codellama-7b
+ln -sfn /workspace/CodeLlama-13b-hf          ./models/codellama-13b
+ln -sfn /workspace/CodeLlama-34b-hf          ./models/codellama-34b
+ln -sfn /workspace/CodeLlama-7b-Instruct-hf  ./models/codellama-7b-instruct
+ln -sfn /workspace/CodeLlama-13b-Instruct-hf ./models/codellama-13b-instruct
+ln -sfn /workspace/CodeLlama-34b-Instruct-hf ./models/codellama-34b-instruct
+
+pip install -r requirements.txt
+
+# cmake
+cd /workspace/llama.cpp
+
+mkdir build-cublas
+cd build-cublas
+
+cmake -DLLAMA_CUDA=1 ../
+make -j
+
+if [ "$1" -eq "0" ]; then
+    exit 0
+fi
+
+# more models
+if [ "$1" -eq "1" ]; then
+    cd /workspace
+
+    git-lfs-download https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3
+
+    cd /workspace/llama.cpp
+
+    python3 convert.py ./models/tinyllama-1b  --outfile ./models/tinyllama-1b/ggml-model-f16.gguf  --outtype f16
+
+    ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_0.gguf q4_0
+    ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_k.gguf q4_k
+    ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q8_0.gguf q8_0
+fi
+
+if [ "$1" -eq "2" ]; then
+    cd /workspace
+
+    git-lfs-download https://huggingface.co/codellama/CodeLlama-7b-hf  --without *safetensors*
+    rm -v ./CodeLlama-7b-hf/*safetensors*
+
+    cd /workspace/llama.cpp
+
+    python3 convert.py ./models/codellama-7b  --outfile ./models/codellama-7b/ggml-model-f16.gguf  --outtype f16
+
+    ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_0.gguf q4_0
+    ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_k.gguf q4_k
+    ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q8_0.gguf q8_0
+fi
+
+if [ "$1" -eq "3" ]; then
+    cd /workspace
+
+    git-lfs-download https://huggingface.co/codellama/CodeLlama-13b-hf --without *safetensors*
+    rm -v ./CodeLlama-13b-hf/*safetensors*
+
+    cd /workspace/llama.cpp
+
+    python3 convert.py ./models/codellama-13b --outfile ./models/codellama-13b/ggml-model-f16.gguf --outtype f16
+
+    ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_0.gguf q4_0
+    ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_k.gguf q4_k
+    ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q8_0.gguf q8_0
+fi
+
+if [ "$1" -eq "4" ]; then
+    cd /workspace
+
+    git-lfs-download https://huggingface.co/codellama/CodeLlama-34b-hf --without *safetensors*
+    rm -v ./CodeLlama-34b-hf/*safetensors*
+
+    cd /workspace/llama.cpp
+
+    python3 convert.py ./models/codellama-34b --outfile ./models/codellama-34b/ggml-model-f16.gguf --outtype f16
+
+    ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_0.gguf q4_0
+    ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_k.gguf q4_k
+    ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q8_0.gguf q8_0
+fi
+
+if [ "$1" -eq "5" ]; then
+    cd /workspace
+
+    git-lfs-download https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf  --without *safetensors*
+    rm -v ./CodeLlama-7b-Instruct-hf/*safetensors*
+
+    cd /workspace/llama.cpp
+
+    python3 convert.py ./models/codellama-7b-instruct  --outfile ./models/codellama-7b-instruct/ggml-model-f16.gguf  --outtype f16
+
+    ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_0.gguf q4_0
+    ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_k.gguf q4_k
+    ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q8_0.gguf q8_0
+fi
+
+if [ "$1" -eq "6" ]; then
+    cd /workspace
+
+    git-lfs-download https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf --without *safetensors*
+    rm -v ./CodeLlama-13b-Instruct-hf/*safetensors*
+
+    cd /workspace/llama.cpp
+
+    python3 convert.py ./models/codellama-13b-instruct --outfile ./models/codellama-13b-instruct/ggml-model-f16.gguf --outtype f16
+
+    ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_0.gguf q4_0
+    ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_k.gguf q4_k
+    ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q8_0.gguf q8_0
+fi
+
+if [ "$1" -eq "7" ]; then
+    cd /workspace
+
+    git-lfs-download https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf --without *safetensors*
+    rm -v ./CodeLlama-34b-Instruct-hf/*safetensors*
+
+    cd /workspace/llama.cpp
+
+    python3 convert.py ./models/codellama-34b-instruct --outfile ./models/codellama-34b-instruct/ggml-model-f16.gguf --outtype f16
+
+    ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_0.gguf q4_0
+    ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_k.gguf q4_k
+    ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q8_0.gguf q8_0
+fi
+
+if [ "$1" -eq "1" ]; then
+    # perf + perplexity
+    cd /workspace/llama.cpp/build-cublas
+
+    make -j && ../scripts/run-all-perf.sh tinyllama-1b "f16" "-ngl 99 -t 1 -p 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,32,64,128,256,512,1024,2048 -n 128"
+
+    ../scripts/get-wikitext-2.sh
+    unzip wikitext-2-raw-v1.zip
+
+    make -j && ./bin/perplexity -m ../models/tinyllama-1b/ggml-model-f16.gguf -f ./wikitext-2-raw/wiki.test.raw -ngl 100 --chunks 32
+
+    # batched
+    cd /workspace/llama.cpp
+
+    LLAMA_CUDA=1 make -j && ./batched ./models/tinyllama-1b/ggml-model-f16.gguf "Hello, my name is" 8 128 999
+
+    # batched-bench
+    cd /workspace/llama.cpp
+
+    LLAMA_CUDA=1 make -j && ./batched-bench ./models/tinyllama-1b/ggml-model-f16.gguf 4608 1 99 0 512 128 1,2,3,4,5,6,7,8,16,32
+
+    # parallel
+    cd /workspace/llama.cpp
+
+    LLAMA_CUDA=1 make -j && ./parallel -m ./models/tinyllama-1b/ggml-model-f16.gguf -t 1 -ngl 100 -c 4096 -b 512 -s 1 -np 8 -ns 128 -n 100 -cb
+
+fi
+
+# speculative
+#if [ "$1" -eq "7" ]; then
+#    cd /workspace/llama.cpp
+#
+#    LLAMA_CUDA=1 make -j && ./speculative -m ./models/codellama-34b-instruct/ggml-model-f16.gguf -md ./models/codellama-7b-instruct/ggml-model-q4_0.gguf -p "# Dijkstra's shortest path algorithm in Python (4 spaces indentation) + complexity analysis:\n\n" -e -ngl 999 -ngld 999 -t 4 -n 512 -c 4096 -s 21 --draft 16 -np 1 --temp 0.0
+#fi
+
+# more benches
+#LLAMA_CUDA=1 make -j && ./batched-bench ./models/codellama-7b/ggml-model-q4_k.gguf  4096 1 99 1 512,3200 128,128,800 1
+#LLAMA_CUDA=1 make -j && ./batched-bench ./models/codellama-13b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1
+
diff --git a/run_with_preset.py b/scripts/run-with-preset.py
similarity index 98%
rename from run_with_preset.py
rename to scripts/run-with-preset.py
index 9b4d7ecbe..a18252730 100755
--- a/run_with_preset.py
+++ b/scripts/run-with-preset.py
@@ -46,7 +46,7 @@ Formatting considerations:
 - To define multiple "reverse_prompt" properties simultaneously the expected format is a list of strings.
 - To define a tensor split, pass a list of floats.
 """
-usage = "run_with_preset.py [-h] [yaml_files ...] [--<ARG_NAME> <ARG_VALUE> ...]"
+usage = "run-with-preset.py [-h] [yaml_files ...] [--<ARG_NAME> <ARG_VALUE> ...]"
 epilog = ("  --<ARG_NAME> specify additional CLI ars to be passed to the binary (override all preset files). "
           "Unknown args will be ignored.")
 
diff --git a/scripts/server-llm.sh b/scripts/server-llm.sh
index 7bf0929bb..eb6ce458e 100644
--- a/scripts/server-llm.sh
+++ b/scripts/server-llm.sh
@@ -14,16 +14,17 @@
 # - Might be unstable!
 #
 # Usage:
-#   ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose]
+#   ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose] [-non-interactive]
 #
-#   --port:       port number, default is 8888
-#   --repo:       path to a repo containing GGUF model files
-#   --wtype:      weights type (f16, q8_0, q4_0, q4_1), default is user-input
-#   --backend:    cpu, cuda, metal, opencl, depends on the OS
-#   --gpu-id:     gpu id, default is 0
-#   --n-parallel: number of parallel requests, default is 8
-#   --n-kv:       KV cache size, default is 4096
-#   --verbose:    verbose output
+#   --port:            port number, default is 8888
+#   --repo:            path to a repo containing GGUF model files
+#   --wtype:           weights type (f16, q8_0, q4_0, q4_1), default is user-input
+#   --backend:         cpu, cuda, metal, opencl, depends on the OS
+#   --gpu-id:          gpu id, default is 0
+#   --n-parallel:      number of parallel requests, default is 8
+#   --n-kv:            KV cache size, default is 4096
+#   --verbose:         verbose output
+#   --non-interactive: run without asking a permission to run
 #
 # Example:
 #
@@ -47,6 +48,7 @@ if ! command -v make &> /dev/null; then
 fi
 
 # parse arguments
+is_interactive=1
 port=8888
 repo=""
 wtype=""
@@ -66,15 +68,16 @@ verbose=0
 
 function print_usage {
     printf "Usage:\n"
-    printf "  ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose]\n\n"
-    printf "  --port:       port number, default is 8888\n"
-    printf "  --repo:       path to a repo containing GGUF model files\n"
-    printf "  --wtype:      weights type (f16, q8_0, q4_0, q4_1), default is user-input\n"
-    printf "  --backend:    cpu, cuda, metal, opencl, depends on the OS\n"
-    printf "  --gpu-id:     gpu id, default is 0\n"
-    printf "  --n-parallel: number of parallel requests, default is 8\n"
-    printf "  --n-kv:       KV cache size, default is 4096\n"
-    printf "  --verbose:    verbose output\n\n"
+    printf "  ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose] [-non-interactive]\n\n"
+    printf "  --port:             port number, default is 8888\n"
+    printf "  --repo:             path to a repo containing GGUF model files\n"
+    printf "  --wtype:            weights type (f16, q8_0, q4_0, q4_1), default is user-input\n"
+    printf "  --backend:          cpu, cuda, metal, opencl, depends on the OS\n"
+    printf "  --gpu-id:           gpu id, default is 0\n"
+    printf "  --n-parallel:       number of parallel requests, default is 8\n"
+    printf "  --n-kv:             KV cache size, default is 4096\n"
+    printf "  --verbose:          verbose output\n\n"
+    printf "  --non-interactive:  run without asking a permission to run\n"
     printf "Example:\n\n"
     printf '  bash -c "$(curl -s https://ggml.ai/server-llm.sh)"\n\n'
 }
@@ -82,6 +85,10 @@ function print_usage {
 while [[ $# -gt 0 ]]; do
     key="$1"
     case $key in
+        --non-interactive)
+            is_interactive=0
+            shift
+            ;;
         --port)
             port="$2"
             shift
@@ -141,6 +148,28 @@ for wt in "${wtypes[@]}"; do
     wfiles+=("")
 done
 
+# map wtype input to index
+if [[ ! -z "$wtype" ]]; then
+    iw=-1
+    is=0
+    for wt in "${wtypes[@]}"; do
+        # uppercase
+        uwt=$(echo "$wt" | tr '[:lower:]' '[:upper:]')
+        if [[ "$uwt" == "$wtype" ]]; then
+            iw=$is
+            break
+        fi
+        is=$((is+1))
+    done
+
+    if [[ $iw -eq -1 ]]; then
+        printf "[-] Invalid weight type: %s\n" "$wtype"
+        exit 1
+    fi
+
+    wtype="$iw"
+fi
+
 # sample repos
 repos=(
     "https://huggingface.co/TheBloke/Llama-2-7B-GGUF"
@@ -154,31 +183,32 @@ repos=(
     "https://huggingface.co/TheBloke/OpenHermes-2-Mistral-7B-GGUF"
     "https://huggingface.co/TheBloke/CausalLM-7B-GGUF"
 )
+if [ $is_interactive -eq 1 ]; then
+    printf "\n"
+    printf "[I] This is a helper script for deploying llama.cpp's server on this machine.\n\n"
+    printf "    Based on the options that follow, the script might download a model file\n"
+    printf "    from the internet, which can be a few GBs in size. The script will also\n"
+    printf "    build the latest llama.cpp source code from GitHub, which can be unstable.\n"
+    printf "\n"
+    printf "    Upon success, an HTTP server will be started and it will serve the selected\n"
+    printf "    model using llama.cpp for demonstration purposes.\n"
+    printf "\n"
+    printf "    Please note:\n"
+    printf "\n"
+    printf "    - All new data will be stored in the current folder\n"
+    printf "    - The server will be listening on all network interfaces\n"
+    printf "    - The server will run with default settings which are not always optimal\n"
+    printf "    - Do not judge the quality of a model based on the results from this script\n"
+    printf "    - Do not use this script to benchmark llama.cpp\n"
+    printf "    - Do not use this script in production\n"
+    printf "    - This script is only for demonstration purposes\n"
+    printf "\n"
+    printf "    If you don't know what you are doing, please press Ctrl-C to abort now\n"
+    printf "\n"
+    printf "    Press Enter to continue ...\n\n"
 
-printf "\n"
-printf "[I] This is a helper script for deploying llama.cpp's server on this machine.\n\n"
-printf "    Based on the options that follow, the script might download a model file\n"
-printf "    from the internet, which can be a few GBs in size. The script will also\n"
-printf "    build the latest llama.cpp source code from GitHub, which can be unstable.\n"
-printf "\n"
-printf "    Upon success, an HTTP server will be started and it will serve the selected\n"
-printf "    model using llama.cpp for demonstration purposes.\n"
-printf "\n"
-printf "    Please note:\n"
-printf "\n"
-printf "    - All new data will be stored in the current folder\n"
-printf "    - The server will be listening on all network interfaces\n"
-printf "    - The server will run with default settings which are not always optimal\n"
-printf "    - Do not judge the quality of a model based on the results from this script\n"
-printf "    - Do not use this script to benchmark llama.cpp\n"
-printf "    - Do not use this script in production\n"
-printf "    - This script is only for demonstration purposes\n"
-printf "\n"
-printf "    If you don't know what you are doing, please press Ctrl-C to abort now\n"
-printf "\n"
-printf "    Press Enter to continue ...\n\n"
-
-read
+    read
+fi
 
 if [[ -z "$repo" ]]; then
     printf "[+] No repo provided from the command line\n"
@@ -252,8 +282,10 @@ for file in $model_files; do
     printf "    %2d) %s %s\n" $iw "$have" "$file"
 done
 
+wfile="${wfiles[$wtype]}"
+
 # ask for weights type until provided and available
-while [[ -z "$wtype" ]]; do
+while [[ -z "$wfile" ]]; do
     printf "\n"
     read -p "[+] Select weight type: " wtype
     wfile="${wfiles[$wtype]}"
@@ -348,7 +380,7 @@ fi
 
 if [[ "$backend" == "cuda" ]]; then
     printf "[+] Building with CUDA backend\n"
-    LLAMA_CUBLAS=1 make -j server $log
+    LLAMA_CUDA=1 make -j server $log
 elif [[ "$backend" == "cpu" ]]; then
     printf "[+] Building with CPU backend\n"
     make -j server $log
diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh
index 248cf1023..3003290f6 100755
--- a/scripts/sync-ggml-am.sh
+++ b/scripts/sync-ggml-am.sh
@@ -5,7 +5,7 @@
 # Usage:
 #
 #   $ cd /path/to/llama.cpp
-#   $ ./scripts/sync-ggml-am.sh
+#   $ ./scripts/sync-ggml-am.sh -skip hash0,hash1,hash2...
 #
 
 set -e
@@ -24,6 +24,11 @@ fi
 lc=$(cat $SRC_LLAMA/scripts/sync-ggml.last)
 echo "Syncing ggml changes since commit $lc"
 
+to_skip=""
+if [ "$1" == "-skip" ]; then
+    to_skip=$2
+fi
+
 cd $SRC_GGML
 
 git log --oneline $lc..HEAD
@@ -40,6 +45,13 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
 fi
 
 while read c; do
+    if [ -n "$to_skip" ]; then
+        if [[ $to_skip == *"$c"* ]]; then
+            echo "Skipping $c"
+            continue
+        fi
+    fi
+
     git format-patch -k $c~1..$c --stdout -- \
         include/ggml/ggml*.h \
         src/ggml*.h \
@@ -82,9 +94,13 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
     # src/ggml-alloc.c            -> ggml-alloc.c
     # src/ggml-backend-impl.h     -> ggml-backend-impl.h
     # src/ggml-backend.c          -> ggml-backend.c
+    # src/ggml-common.h           -> ggml-common.h
+    # src/ggml-cuda/*             -> ggml-cuda/
     # src/ggml-cuda.cu            -> ggml-cuda.cu
     # src/ggml-cuda.h             -> ggml-cuda.h
     # src/ggml-impl.h             -> ggml-impl.h
+    # src/ggml-kompute.cpp        -> ggml-kompute.cpp
+    # src/ggml-kompute.h          -> ggml-kompute.h
     # src/ggml-metal.h            -> ggml-metal.h
     # src/ggml-metal.m            -> ggml-metal.m
     # src/ggml-mpi.h              -> ggml-mpi.h
@@ -93,6 +109,10 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
     # src/ggml-opencl.h           -> ggml-opencl.h
     # src/ggml-quants.c           -> ggml-quants.c
     # src/ggml-quants.h           -> ggml-quants.h
+    # src/ggml-sycl.cpp           -> ggml-sycl.cpp
+    # src/ggml-sycl.h             -> ggml-sycl.h
+    # src/ggml-vulkan.cpp         -> ggml-vulkan.cpp
+    # src/ggml-vulkan.h           -> ggml-vulkan.h
     # include/ggml/ggml.h         -> ggml.h
     # include/ggml/ggml-alloc.h   -> ggml-alloc.h
     # include/ggml/ggml-backend.h -> ggml-backend.h
@@ -108,9 +128,13 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
         -e 's/src\/ggml-alloc\.c/ggml-alloc.c/g' \
         -e 's/src\/ggml-backend-impl\.h/ggml-backend-impl.h/g' \
         -e 's/src\/ggml-backend\.c/ggml-backend.c/g' \
+        -e 's/src\/ggml-common\.h/ggml-common.h/g' \
+        -e 's/src\/ggml-cuda\//ggml-cuda\//g' \
         -e 's/src\/ggml-cuda\.cu/ggml-cuda.cu/g' \
         -e 's/src\/ggml-cuda\.h/ggml-cuda.h/g' \
         -e 's/src\/ggml-impl\.h/ggml-impl.h/g' \
+        -e 's/src\/ggml-kompute\.cpp/ggml-kompute.cpp/g' \
+        -e 's/src\/ggml-kompute\.h/ggml-kompute.h/g' \
         -e 's/src\/ggml-metal\.h/ggml-metal.h/g' \
         -e 's/src\/ggml-metal\.m/ggml-metal.m/g' \
         -e 's/src\/ggml-mpi\.h/ggml-mpi.h/g' \
@@ -119,6 +143,10 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
         -e 's/src\/ggml-opencl\.h/ggml-opencl.h/g' \
         -e 's/src\/ggml-quants\.c/ggml-quants.c/g' \
         -e 's/src\/ggml-quants\.h/ggml-quants.h/g' \
+        -e 's/src\/ggml-sycl\.cpp/ggml-sycl.cpp/g' \
+        -e 's/src\/ggml-sycl\.h/ggml-sycl.h/g' \
+        -e 's/src\/ggml-vulkan\.cpp/ggml-vulkan.cpp/g' \
+        -e 's/src\/ggml-vulkan\.h/ggml-vulkan.h/g' \
         -e 's/include\/ggml\/ggml\.h/ggml.h/g' \
         -e 's/include\/ggml\/ggml-alloc\.h/ggml-alloc.h/g' \
         -e 's/include\/ggml\/ggml-backend\.h/ggml-backend.h/g' \
diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index fe7f3202f..7c30162e2 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-f96711108d55bdbbd277e6be07204dce6a94fb93
+43a6d4af1971ee2912ff7bc2404011ff327b6a60
diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh
index 0097db435..d8fdaadf7 100755
--- a/scripts/sync-ggml.sh
+++ b/scripts/sync-ggml.sh
@@ -4,9 +4,13 @@ cp -rpv ../ggml/src/ggml.c                  ./ggml.c
 cp -rpv ../ggml/src/ggml-alloc.c            ./ggml-alloc.c
 cp -rpv ../ggml/src/ggml-backend-impl.h     ./ggml-backend-impl.h
 cp -rpv ../ggml/src/ggml-backend.c          ./ggml-backend.c
+cp -rpv ../ggml/src/ggml-common.h           ./ggml-common.h
+cp -rpv ../ggml/src/ggml-cuda/*             ./ggml-cuda/
 cp -rpv ../ggml/src/ggml-cuda.cu            ./ggml-cuda.cu
 cp -rpv ../ggml/src/ggml-cuda.h             ./ggml-cuda.h
 cp -rpv ../ggml/src/ggml-impl.h             ./ggml-impl.h
+cp -rpv ../ggml/src/ggml-kompute.cpp        ./ggml-kompute.cpp
+cp -rpv ../ggml/src/ggml-kompute.h          ./ggml-kompute.h
 cp -rpv ../ggml/src/ggml-metal.h            ./ggml-metal.h
 cp -rpv ../ggml/src/ggml-metal.m            ./ggml-metal.m
 cp -rpv ../ggml/src/ggml-metal.metal        ./ggml-metal.metal
@@ -16,6 +20,10 @@ cp -rpv ../ggml/src/ggml-opencl.cpp         ./ggml-opencl.cpp
 cp -rpv ../ggml/src/ggml-opencl.h           ./ggml-opencl.h
 cp -rpv ../ggml/src/ggml-quants.c           ./ggml-quants.c
 cp -rpv ../ggml/src/ggml-quants.h           ./ggml-quants.h
+cp -rpv ../ggml/src/ggml-sycl.cpp           ./ggml-sycl.cpp
+cp -rpv ../ggml/src/ggml-sycl.h             ./ggml-sycl.h
+cp -rpv ../ggml/src/ggml-vulkan.cpp         ./ggml-vulkan.cpp
+cp -rpv ../ggml/src/ggml-vulkan.h           ./ggml-vulkan.h
 cp -rpv ../ggml/include/ggml/ggml.h         ./ggml.h
 cp -rpv ../ggml/include/ggml/ggml-alloc.h   ./ggml-alloc.h
 cp -rpv ../ggml/include/ggml/ggml-backend.h ./ggml-backend.h
diff --git a/spm-headers/ggml-alloc.h b/spm-headers/ggml-alloc.h
new file mode 120000
index 000000000..a49d385a1
--- /dev/null
+++ b/spm-headers/ggml-alloc.h
@@ -0,0 +1 @@
+../ggml-alloc.h
\ No newline at end of file
diff --git a/spm-headers/ggml-backend.h b/spm-headers/ggml-backend.h
new file mode 120000
index 000000000..17c2cf14f
--- /dev/null
+++ b/spm-headers/ggml-backend.h
@@ -0,0 +1 @@
+../ggml-backend.h
\ No newline at end of file
diff --git a/tests/.gitignore b/tests/.gitignore
new file mode 100644
index 000000000..620a48ee4
--- /dev/null
+++ b/tests/.gitignore
@@ -0,0 +1,4 @@
+*
+!*.*
+*.o
+ggml-common.h
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 7c932240d..a43439aed 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -1,56 +1,75 @@
-function(llama_build_executable source)
-    get_filename_component(TEST_TARGET ${source} NAME_WE)
-    add_executable(${TEST_TARGET} ${source})
+# Builds and runs a test source file.
+# Optional args:
+# - NAME: name of the executable & test target (defaults to the source file name without extension)
+# - LABEL: label for the test (defaults to main)
+# - ARGS: arguments to pass to the test executable
+# - WORKING_DIRECTORY
+function(llama_test source)
+    include(CMakeParseArguments)
+    set(options)
+    set(oneValueArgs NAME LABEL WORKING_DIRECTORY)
+    set(multiValueArgs ARGS)
+    cmake_parse_arguments(LLAMA_TEST "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+
+    if (NOT DEFINED LLAMA_TEST_LABEL)
+        set(LLAMA_TEST_LABEL "main")
+    endif()
+    if (NOT DEFINED LLAMA_TEST_WORKING_DIRECTORY)
+        set(LLAMA_TEST_WORKING_DIRECTORY .)
+    endif()
+    if (DEFINED LLAMA_TEST_NAME)
+        set(TEST_TARGET ${LLAMA_TEST_NAME})
+    else()
+        get_filename_component(TEST_TARGET ${source} NAME_WE)
+    endif()
+
+    add_executable(${TEST_TARGET} ${source} get-model.cpp)
     install(TARGETS ${TEST_TARGET} RUNTIME)
-    target_link_libraries(${TEST_TARGET} PRIVATE common)
+    target_link_libraries(${TEST_TARGET} PRIVATE common json-schema-to-grammar)
+    add_test(
+        NAME ${TEST_TARGET}
+        WORKING_DIRECTORY ${LLAMA_TEST_WORKING_DIRECTORY}
+        COMMAND $<TARGET_FILE:${TEST_TARGET}>
+        ${LLAMA_TEST_ARGS})
+
+    set_property(TEST ${TEST_TARGET} PROPERTY LABELS ${LLAMA_TEST_LABEL})
 endfunction()
 
-function(llama_test_executable name source)
-    get_filename_component(TEST_TARGET ${source} NAME_WE)
-    add_test(NAME ${name} COMMAND $<TARGET_FILE:${TEST_TARGET}> ${ARGN})
-endfunction()
+# llama_test(test-double-float.cpp) # SLOW
+llama_test(test-quantize-fns.cpp)
+llama_test(test-quantize-perf.cpp)
+llama_test(test-sampling.cpp)
+llama_test(test-chat-template.cpp)
 
-function(llama_build_and_test_executable source)
-    get_filename_component(TEST_TARGET ${source} NAME_WE)
-    add_executable(${TEST_TARGET} ${source})
-    install(TARGETS ${TEST_TARGET} RUNTIME)
-    target_link_libraries(${TEST_TARGET} PRIVATE common)
-    add_test(NAME ${TEST_TARGET} COMMAND $<TARGET_FILE:${TEST_TARGET}> ${ARGN})
-endfunction()
+llama_test(test-tokenizer-0-llama.cpp  NAME test-tokenizer-0-llama            ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
+llama_test(test-tokenizer-0-falcon.cpp NAME test-tokenizer-0-falcon           ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
 
-# llama_build_and_test_executable(test-double-float.cpp) # SLOW
-llama_build_and_test_executable(test-quantize-fns.cpp)
-llama_build_and_test_executable(test-quantize-perf.cpp)
-llama_build_and_test_executable(test-sampling.cpp)
+llama_test(test-tokenizer-1-llama.cpp  NAME test-tokenizer-1-llama            ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
+llama_test(test-tokenizer-1-llama.cpp  NAME test-tokenizer-1-baichuan         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf)
 
-llama_build_executable(test-tokenizer-0-llama.cpp)
-llama_test_executable (test-tokenizer-0-llama test-tokenizer-0-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
+llama_test(test-tokenizer-1-bpe.cpp    NAME test-tokenizer-1-falcon           ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
+llama_test(test-tokenizer-1-bpe.cpp    NAME test-tokenizer-1-aquila           ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
+llama_test(test-tokenizer-1-bpe.cpp    NAME test-tokenizer-1-mpt              ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
+llama_test(test-tokenizer-1-bpe.cpp    NAME test-tokenizer-1-stablelm-3b-4e1t ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-stablelm-3b-4e1t.gguf)
+llama_test(test-tokenizer-1-bpe.cpp    NAME test-tokenizer-1-gpt-neox         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf)
+llama_test(test-tokenizer-1-bpe.cpp    NAME test-tokenizer-1-refact           ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
+llama_test(test-tokenizer-1-bpe.cpp    NAME test-tokenizer-1-starcoder        ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
+llama_test(test-tokenizer-1-bpe.cpp    NAME test-tokenizer-1-gpt2             ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt2.gguf)
+#llama_test(test-tokenizer-1-bpe.cpp    NAME test-tokenizer-1-bloom            ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bloom.gguf) # BIG
 
-llama_build_executable(test-tokenizer-0-falcon.cpp)
-llama_test_executable (test-tokenizer-0-falcon test-tokenizer-0-falcon.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
+llama_test(test-grammar-parser.cpp)
+llama_test(test-llama-grammar.cpp)
+llama_test(test-grad0.cpp)
+# llama_test(test-opt.cpp) # SLOW
+llama_test(test-backend-ops.cpp)
 
-llama_build_executable(test-tokenizer-1-llama.cpp)
-llama_test_executable (test-tokenizer-1-llama    test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
-llama_test_executable (test-tokenizer-1-baichuan test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf)
+llama_test(test-rope.cpp)
 
-llama_build_executable(test-tokenizer-1-bpe.cpp)
-llama_test_executable (test-tokenizer-1-falcon           test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
-llama_test_executable (test-tokenizer-1-aquila           test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
-llama_test_executable (test-tokenizer-1-mpt              test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
-llama_test_executable (test-tokenizer-1-stablelm-3b-4e1t test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-stablelm-3b-4e1t.gguf)
-llama_test_executable (test-tokenizer-1-gpt-neox         test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf)
-llama_test_executable (test-tokenizer-1-refact           test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
-llama_test_executable (test-tokenizer-1-starcoder        test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
-llama_test_executable (test-tokenizer-1-gpt2             test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt2.gguf)
-# llama_test_executable (test-tokenizer-1-bloom test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bloom.gguf) # BIG
+llama_test(test-model-load-cancel.cpp  LABEL "model")
+llama_test(test-autorelease.cpp        LABEL "model")
 
-llama_build_and_test_executable(test-grammar-parser.cpp)
-llama_build_and_test_executable(test-llama-grammar.cpp)
-llama_build_and_test_executable(test-grad0.cpp)
-# llama_build_and_test_executable(test-opt.cpp) # SLOW
-llama_build_and_test_executable(test-backend-ops.cpp)
-
-llama_build_and_test_executable(test-rope.cpp)
+llama_test(test-json-schema-to-grammar.cpp   WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..)
+target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../examples/server)
 
 # dummy executable - not installed
 get_filename_component(TEST_TARGET test-c.c NAME_WE)
diff --git a/tests/get-model.cpp b/tests/get-model.cpp
new file mode 100644
index 000000000..4edb685f0
--- /dev/null
+++ b/tests/get-model.cpp
@@ -0,0 +1,21 @@
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+
+#include "get-model.h"
+
+char * get_model_or_exit(int argc, char *argv[]) {
+    char * model_path;
+    if (argc > 1) {
+        model_path = argv[1];
+
+    } else {
+        model_path = getenv("LLAMACPP_TEST_MODELFILE");
+        if (!model_path || strlen(model_path) == 0) {
+            fprintf(stderr, "\033[33mWARNING: No model file provided. Skipping this test. Set LLAMACPP_TEST_MODELFILE=<gguf_model_path> to silence this warning and run this test.\n\033[0m");
+            exit(EXIT_SUCCESS);
+        }
+    }
+
+    return model_path;
+}
diff --git a/tests/get-model.h b/tests/get-model.h
new file mode 100644
index 000000000..81a3a0fef
--- /dev/null
+++ b/tests/get-model.h
@@ -0,0 +1,2 @@
+#pragma once
+char * get_model_or_exit(int, char*[]);
diff --git a/tests/run-json-schema-to-grammar.mjs b/tests/run-json-schema-to-grammar.mjs
new file mode 100644
index 000000000..71bf62ed3
--- /dev/null
+++ b/tests/run-json-schema-to-grammar.mjs
@@ -0,0 +1,10 @@
+import { readFileSync } from "fs"
+import { SchemaConverter } from "../examples/server/public/json-schema-to-grammar.mjs"
+
+const [, , file] = process.argv
+const url = `file://${file}`
+let schema = JSON.parse(readFileSync(file, "utf8"));
+const converter = new SchemaConverter({})
+schema = await converter.resolveRefs(schema, url)
+converter.visit(schema, '')
+console.log(converter.formatGrammar())
diff --git a/tests/test-autorelease.cpp b/tests/test-autorelease.cpp
new file mode 100644
index 000000000..57fa00011
--- /dev/null
+++ b/tests/test-autorelease.cpp
@@ -0,0 +1,24 @@
+// ref: https://github.com/ggerganov/llama.cpp/issues/4952#issuecomment-1892864763
+
+#include <cstdio>
+#include <string>
+#include <thread>
+
+#include "llama.h"
+#include "get-model.h"
+
+// This creates a new context inside a pthread and then tries to exit cleanly.
+int main(int argc, char ** argv) {
+    auto * model_path = get_model_or_exit(argc, argv);
+
+    std::thread([&model_path]() {
+        llama_backend_init();
+        auto * model = llama_load_model_from_file(model_path, llama_model_default_params());
+        auto * ctx = llama_new_context_with_model(model, llama_context_default_params());
+        llama_free(ctx);
+        llama_free_model(model);
+        llama_backend_free();
+    }).join();
+
+    return 0;
+}
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 7a60d7743..51b3487b2 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -16,47 +16,53 @@
 #include <vector>
 
 static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float max = 1.0f) {
+    // static RNG initialization (revisit if n_threads stops being constant)
+    static const size_t n_threads = std::thread::hardware_concurrency();
+    static std::vector<std::default_random_engine> generators = []() {
+        std::random_device rd;
+        std::vector<std::default_random_engine> vec;
+        vec.reserve(n_threads);
+        //for (size_t i = 0; i < n_threads; i++) { vec.emplace_back(1234 + i); } // fixed seed
+        for (size_t i = 0; i < n_threads; i++) { vec.emplace_back(rd()); }
+        return vec;
+    }();
+
     size_t size = ggml_nelements(tensor);
     std::vector<float> data(size);
 
-#if 0
-    static std::default_random_engine generator(1234);
-    std::uniform_real_distribution<float> distribution(min, max);
-
-    for (size_t i = 0; i < size; i++) {
-        data[i] = distribution(generator);
-    }
-#else
-    auto init_thread = [&](size_t start, size_t end) {
-        std::random_device rd;
-        std::default_random_engine generator(rd());
+    auto init_thread = [&](size_t ith, size_t start, size_t end) {
         std::uniform_real_distribution<float> distribution(min, max);
-
         for (size_t i = start; i < end; i++) {
-            data[i] = distribution(generator);
+            data[i] = distribution(generators[ith]);
         }
     };
 
-    size_t n_threads = std::thread::hardware_concurrency();
     std::vector<std::thread> threads;
     threads.reserve(n_threads);
     for (size_t i = 0; i < n_threads; i++) {
         size_t start =     i*size/n_threads;
         size_t end   = (i+1)*size/n_threads;
-        threads.emplace_back(init_thread, start, end);
+        threads.emplace_back(init_thread, i, start, end);
     }
     for (auto & t : threads) {
         t.join();
     }
-#endif
 
     if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) {
         ggml_backend_tensor_set(tensor, data.data(), 0, size * sizeof(float));
     } else if (ggml_is_quantized(tensor->type) || tensor->type == GGML_TYPE_F16) {
         GGML_ASSERT(size % ggml_blck_size(tensor->type) == 0);
         std::vector<uint8_t> dataq(ggml_row_size(tensor->type, size));
-        int64_t hist[16];
-        ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size, hist);
+        std::vector<float> imatrix(tensor->ne[0], 1.0f); // dummy importance matrix
+        const float * im = imatrix.data();
+        if (!ggml_quantize_requires_imatrix(tensor->type)) {
+            // when the imatrix is optional, we want to test both quantization with and without imatrix
+            // use one of the random numbers to decide
+            if (data[0] > 0.5f*(min + max)) {
+                im = nullptr;
+            }
+        }
+        ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], im);
         ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size());
     } else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) {
         // This is going to create some weird integers though.
@@ -95,7 +101,6 @@ static std::vector<float> tensor_to_float(const ggml_tensor * t) {
                     } else if (t->type == GGML_TYPE_I8) {
                         tv.push_back((float)*(int8_t *) &buf[i]);
                     } else if (quantized) {
-                        std::vector<float> vq(ggml_blck_size(t->type));
                         tt.to_float(&buf[i], vq.data(), ggml_blck_size(t->type));
                         tv.insert(tv.end(), vq.begin(), vq.end());
                     } else {
@@ -221,6 +226,14 @@ static std::string var_to_str(ggml_type type) {
     return ggml_type_name(type);
 }
 
+static std::string var_to_str(ggml_op_pool pool) {
+    switch (pool) {
+        case GGML_OP_POOL_AVG:  return "avg";
+        case GGML_OP_POOL_MAX:  return "max";
+        default:                return std::to_string(pool);
+    }
+}
+
 #define VARS_TO_STR1(a) VAR_TO_STR(a)
 #define VARS_TO_STR2(a, b) VAR_TO_STR(a) + "," + VAR_TO_STR(b)
 #define VARS_TO_STR3(a, b, c) VAR_TO_STR(a) + "," + VARS_TO_STR2(b, c)
@@ -232,11 +245,19 @@ static std::string var_to_str(ggml_type type) {
 #define VARS_TO_STR9(a, b, c, d, e, f, g, h, i) VAR_TO_STR(a) + "," + VARS_TO_STR8(b, c, d, e, f, g, h, i)
 #define VARS_TO_STR10(a, b, c, d, e, f, g, h, i, j) VAR_TO_STR(a) + "," + VARS_TO_STR9(b, c, d, e, f, g, h, i, j)
 #define VARS_TO_STR11(a, b, c, d, e, f, g, h, i, j, k) VAR_TO_STR(a) + "," + VARS_TO_STR10(b, c, d, e, f, g, h, i, j, k)
+#define VARS_TO_STR12(a, b, c, d, e, f, g, h, i, j, k, l) VAR_TO_STR(a) + "," + VARS_TO_STR11(b, c, d, e, f, g, h, i, j, k, l)
 
+#ifdef GGML_USE_SYCL
+static bool inline _isinf(float f) {
+    return (*(uint32_t *)&f & 0x7fffffff) == 0x7f800000;
+}
+#else
+static bool inline _isinf(float f) { return std::isinf(f); }
+#endif
 
 // accept FLT_MAX as infinity
 static bool isinf_or_max(float f) {
-    return std::isinf(f) || f == FLT_MAX || f == -FLT_MAX;
+    return _isinf(f) || f == FLT_MAX || f == -FLT_MAX;
 }
 
 static bool ggml_is_view_op(enum ggml_op op) {
@@ -357,12 +378,15 @@ struct test_case {
         printf("  %s(%s): ", op_desc(out).c_str(), vars().c_str());
         fflush(stdout);
 
-        // check if backends support op
+        // check if the backends support the ops
         bool supported = true;
         for (ggml_backend_t backend : {backend1, backend2}) {
-            if (!ggml_backend_supports_op(backend, out)) {
-                printf("not supported [%s] ", ggml_backend_name(backend));
-                supported = false;
+            for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+                if (!ggml_backend_supports_op(backend, t)) {
+                    printf("not supported [%s] ", ggml_backend_name(backend));
+                    supported = false;
+                    break;
+                }
             }
         }
         if (!supported) {
@@ -376,6 +400,11 @@ struct test_case {
 
         // allocate
         ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, backend1);
+        if (buf == NULL) {
+            printf("failed to allocate tensors [%s] ", ggml_backend_name(backend1));
+            ggml_free(ctx);
+            return false;
+        }
 
         // build graph
         ggml_build_forward_expand(gf, out);
@@ -463,19 +492,23 @@ struct test_case {
             GGML_UNUSED(index);
         };
 
-        ggml_backend_compare_graph_backend(backend1, backend2, gf, callback, &ud);
+        const bool cmp_ok = ggml_backend_compare_graph_backend(backend1, backend2, gf, callback, &ud);
 
-        if (ud.ok) {
-            printf("\033[1;32mOK\033[0m\n");
-        } else {
-            printf("\033[1;31mFAIL\033[0m\n");
+        if (!cmp_ok) {
+            printf("compare failed ");
         }
 
         ggml_backend_buffer_free(buf);
 
         ggml_free(ctx);
 
-        return ud.ok;
+        if (ud.ok && cmp_ok) {
+            printf("\033[1;32mOK\033[0m\n");
+            return true;
+        }
+
+        printf("\033[1;31mFAIL\033[0m\n");
+        return false;
     }
 
     bool eval_perf(ggml_backend_t backend, const char * op_name) {
@@ -519,6 +552,11 @@ struct test_case {
 
         // allocate
         ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, backend);
+        if (buf == NULL) {
+            printf("failed to allocate tensors\n");
+            ggml_free(ctx);
+            return false;
+        }
 
         // randomize tensors
         initialize_tensors(ctx);
@@ -599,6 +637,13 @@ struct test_unary : public test_case {
         ggml_tensor * out = ggml_unary(ctx, in, op);
         return out;
     }
+
+    void initialize_tensors(ggml_context * ctx) override {
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+            // test extended range of values to check for NaNs in GELU
+            init_tensor_uniform(t, -150.f, 150.f);
+        }
+    }
 };
 
 // GGML_OP_GET_ROWS
@@ -934,17 +979,13 @@ struct test_mul_mat_id : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         // C^T = A * B^T: (k, m) * (k, n) => (m, n)
-        std::vector<ggml_tensor *> mats;
-        for (int i = 0; i < n_mats; i++) {
-            ggml_tensor * a = ggml_new_tensor_2d(ctx, type_a, k, m);
-            mats.push_back(a);
-        }
+        ggml_tensor * mats = ggml_new_tensor_3d(ctx, type_a, k, m, n_mats);
         ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_mats, n);
         if (v) {
             ids = ggml_view_2d(ctx, ids, n_mats/2, ids->ne[1], ids->nb[1], 0);
         }
         ggml_tensor * b = ggml_new_tensor_2d(ctx, type_b, k, n);
-        ggml_tensor * out = ggml_mul_mat_id(ctx, mats.data(), n_mats, ids, v ? id/2 : id, b);
+        ggml_tensor * out = ggml_mul_mat_id(ctx, mats, ids, v ? id/2 : id, b);
         return out;
     }
 
@@ -1039,18 +1080,32 @@ struct test_diag_mask_inf : public test_case {
 struct test_soft_max : public test_case {
     const ggml_type type;
     const std::array<int64_t, 4> ne;
+    const bool mask;
+    const float scale;
+    const float max_bias;
 
     std::string vars() override {
-        return VARS_TO_STR2(type, ne);
+        return VARS_TO_STR5(type, ne, mask, scale, max_bias);
     }
 
     test_soft_max(ggml_type type = GGML_TYPE_F32,
-            std::array<int64_t, 4> ne = {10, 10, 10, 10})
-        : type(type), ne(ne) {}
+            std::array<int64_t, 4> ne = {10, 10, 10, 10},
+            bool mask = false,
+            float scale = 1.0f,
+            float max_bias = 0.0f)
+        : type(type), ne(ne), mask(mask), scale(scale), max_bias(max_bias) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_tensor * out = ggml_soft_max(ctx, a);
+        ggml_tensor * mask = nullptr;
+        if (this->mask) {
+            mask = ggml_new_tensor_2d(ctx, type, ne[0], ne[1]);
+        }
+        ggml_tensor * pos = nullptr;
+        if (max_bias > 0.0f) {
+            pos = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, ne[0]);
+        }
+        ggml_tensor * out = ggml_soft_max_ext(ctx, a, mask, pos, scale, max_bias);
         return out;
     }
 };
@@ -1095,26 +1150,36 @@ struct test_rope : public test_case {
     }
 };
 
-// GGML_OP_ALIBI
-struct test_alibi : public test_case {
-    const ggml_type type;
-    const std::array<int64_t, 4> ne;
-    int n_past;
-    int n_head;
-    float bias_max;
+// GGML_OP_POOL2D
+struct test_pool2d : public test_case {
+    enum ggml_op_pool pool_type;
+    const ggml_type type_input;
+    const std::array<int64_t, 4> ne_input;
+    // kernel size
+    const int k0;
+    const int k1;
+    // stride
+    const int s0;
+    const int s1;
+    // padding
+    const int p0;
+    const int p1;
 
     std::string vars() override {
-        return VARS_TO_STR5(type, ne, n_past, n_head, bias_max);
+        return VARS_TO_STR9(pool_type, type_input, ne_input, k0, k1, s0, s1, p0, p1);
     }
 
-    test_alibi(ggml_type type = GGML_TYPE_F32,
-            std::array<int64_t, 4> ne = {10, 10, 10, 10},
-            int n_past = 512, int n_head = 10, float bias_max = 0.5f)
-        : type(type), ne(ne), n_past(n_past), n_head(n_head), bias_max(bias_max) {}
+    test_pool2d(ggml_op_pool pool_type = GGML_OP_POOL_AVG,
+            ggml_type type_input = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1]
+            int k0 = 3, int k1 = 3,
+            int s0 = 1, int s1 = 1,
+            int p0 = 1, int p1 = 1)
+        : pool_type(pool_type), type_input(type_input), ne_input(ne_input), k0(k0), k1(k1), s0(s0), s1(s1), p0(p0), p1(p1) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
-        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_tensor * out = ggml_alibi(ctx, a, n_past, n_head, bias_max);
+        ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data());
+        ggml_tensor * out = ggml_pool_2d(ctx, input, pool_type, k0, k1, s0, s1, p0, p1);
         return out;
     }
 };
@@ -1123,6 +1188,7 @@ struct test_alibi : public test_case {
 struct test_im2col : public test_case {
     const ggml_type type_input;
     const ggml_type type_kernel;
+    const ggml_type dst_type;
     const std::array<int64_t, 4> ne_input;
     const std::array<int64_t, 4> ne_kernel;
     // stride
@@ -1138,22 +1204,22 @@ struct test_im2col : public test_case {
     const bool is_2D;
 
     std::string vars() override {
-        return VARS_TO_STR11(type_input, type_kernel, ne_input, ne_kernel, s0, s1, p0, p1, d0, d1, is_2D);
+        return VARS_TO_STR12(type_input, type_kernel, dst_type, ne_input, ne_kernel, s0, s1, p0, p1, d0, d1, is_2D);
     }
 
-    test_im2col(ggml_type type_input = GGML_TYPE_F32, ggml_type type_kernel = GGML_TYPE_F16,
+    test_im2col(ggml_type type_input = GGML_TYPE_F32, ggml_type type_kernel = GGML_TYPE_F16, ggml_type dst_type = GGML_TYPE_F32,
             std::array<int64_t, 4> ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1]
             std::array<int64_t, 4> ne_kernel = {3, 3, 3, 1}, // [kernel_width, kernel_height, input_channels, 1]
             int s0 = 1, int s1 = 1,
             int p0 = 1, int p1 = 1,
             int d0 = 1, int d1 = 1,
             bool is_2D = true)
-        : type_input(type_input), type_kernel(type_kernel), ne_input(ne_input), ne_kernel(ne_kernel), s0(s0), s1(s1), p0(p0), p1(p1), d0(d0), d1(d1), is_2D(is_2D) {}
+        : type_input(type_input), type_kernel(type_kernel), dst_type(dst_type), ne_input(ne_input), ne_kernel(ne_kernel), s0(s0), s1(s1), p0(p0), p1(p1), d0(d0), d1(d1), is_2D(is_2D) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data());
         ggml_tensor * kernel = ggml_new_tensor(ctx, type_kernel, 4, ne_kernel.data());
-        ggml_tensor * out = ggml_im2col(ctx, kernel, input, s0, s1, p0, p1, d0, d1, is_2D);
+        ggml_tensor * out = ggml_im2col(ctx, kernel, input, s0, s1, p0, p1, d0, d1, is_2D, dst_type);
         return out;
     }
 };
@@ -1193,7 +1259,7 @@ struct test_argsort : public test_case {
 
     test_argsort(ggml_type type = GGML_TYPE_F32,
             std::array<int64_t, 4> ne = {16, 10, 10, 10},
-            ggml_sort_order order = GGML_SORT_ASC)
+            ggml_sort_order order = GGML_SORT_ORDER_ASC)
         : type(type), ne(ne), order(order) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
@@ -1341,6 +1407,50 @@ struct test_pad : public test_case {
     }
 };
 
+// GGML_OP_ARANGE
+struct test_arange : public test_case {
+    const ggml_type type;
+    const float start;
+    const float stop;
+    const float step;
+
+    std::string vars() override {
+        return VARS_TO_STR4(type, start, stop, step);
+    }
+
+    test_arange(ggml_type type = GGML_TYPE_F32,
+            float start = 0.f, float stop = 10.f, float step = 1.f)
+        : type(type), start(start), stop(stop), step(step)  {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * out = ggml_arange(ctx, start, stop, step);
+        return out;
+    }
+};
+
+// GGML_OP_TIMESTEP_EMBEDDING
+struct test_timestep_embedding : public test_case {
+    const ggml_type type;
+    const std::array<int64_t, 4> ne_a;
+    const int dim;
+    const int max_period;
+
+    std::string vars() override {
+        return VARS_TO_STR4(type, ne_a, dim, max_period);
+    }
+
+    test_timestep_embedding(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne_a = {2, 1, 1, 1},
+            int dim = 320, int max_period=10000)
+        : type(type), ne_a(ne_a), dim(dim), max_period(max_period)  {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
+        ggml_tensor * out = ggml_timestep_embedding(ctx, a, dim, max_period);
+        return out;
+    }
+};
+
 // GGML_OP_LEAKY_RELU
 struct test_leaky_relu : public test_case {
     const ggml_type type;
@@ -1363,85 +1473,386 @@ struct test_leaky_relu : public test_case {
     }
 };
 
-// Mixtral MOE
-struct test_moe : public test_case {
-    const int n_experts;
-    const int n_experts_per_tok;
-    const int n_tokens;
-    const int n_embd;
-    const int n_ff;
+enum llm_norm_type {
+    LLM_NORM,
+    LLM_NORM_RMS,
+};
+
+struct llama_hparams {
+    uint32_t n_vocab;
+    uint32_t n_embd;
+    uint32_t n_head;
+    uint32_t n_head_kv;
+    static constexpr uint32_t n_layer = 1;
+    uint32_t n_rot;
+    uint32_t n_embd_head; // dimension of values (d_v)
+    uint32_t n_ff;
+
+    float f_norm_eps;
+    float f_norm_rms_eps;
+
+    // cparams
+    static constexpr uint32_t n_ctx = 512; // user-specified context size
+    static constexpr uint32_t n_orig_ctx = n_ctx;
+
+    // batch
+    int32_t n_tokens;
+
+    // llm_build_context
+    static constexpr int32_t n_kv    = 32; // size of KV cache to consider (n_kv <= n_ctx
+    static constexpr int32_t kv_head = 1;  // index of where we store new KV data in the cache
+
+    uint32_t n_embd_gqa() const { // dimension of key embeddings across all k-v heads
+        return n_embd_head * n_head_kv;
+    }
+};
+
+// LLM base class
+struct test_llm : public test_case {
+    llama_hparams hp;
+
+protected:
+    test_llm(llama_hparams hp)
+        : hp(std::move(hp)) {
+    }
+
+public:
+    struct ggml_tensor * llm_build_norm(
+            struct ggml_context * ctx,
+             struct ggml_tensor * cur,
+             struct ggml_tensor * mw,
+             struct ggml_tensor * mb,
+                  llm_norm_type   type) {
+        switch (type) {
+            case LLM_NORM:     cur = ggml_norm    (ctx, cur, hp.f_norm_eps); break;
+            case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, hp.f_norm_rms_eps); break;
+        }
+        cur = ggml_mul(ctx, cur, mw);
+        if (mb) {
+            cur = ggml_add(ctx, cur, mb);
+        }
+        return cur;
+    }
+
+    void llm_build_kv_store(
+            struct ggml_context * ctx,
+             struct ggml_tensor * k_l,
+             struct ggml_tensor * v_l,
+             struct ggml_tensor * k_cur,
+             struct ggml_tensor * v_cur) {
+        // compute the transposed [n_tokens, n_embd] V matrix
+        struct ggml_tensor * v_cur_t = ggml_transpose(ctx, ggml_reshape_2d(ctx, v_cur, hp.n_embd_gqa(), hp.n_tokens));
+
+        struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, k_l, hp.n_tokens*hp.n_embd_gqa(),
+                (ggml_row_size(k_l->type, hp.n_embd_gqa()))*hp.kv_head);
+
+        struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, v_l, hp.n_tokens, hp.n_embd_gqa(),
+                (  hp.n_ctx)*ggml_element_size(v_l),
+                (hp.kv_head)*ggml_element_size(v_l));
+
+        // important: storing RoPE-ed version of K in the KV cache!
+        ggml_cpy(ctx, k_cur,   k_cache_view);
+        ggml_cpy(ctx, v_cur_t, v_cache_view);
+    }
+
+    struct ggml_tensor * llm_build_kqv(
+            struct ggml_context * ctx,
+             struct ggml_tensor * k_l,
+             struct ggml_tensor * v_l,
+             struct ggml_tensor * q_cur,
+             struct ggml_tensor * kq_mask,
+                        float     kq_scale) {
+        struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3);
+
+        struct ggml_tensor * k =
+            ggml_view_3d(ctx, k_l,
+                    hp.n_embd_head, hp.n_kv, hp.n_head_kv,
+                    ggml_row_size(k_l->type, hp.n_embd_gqa()),
+                    ggml_row_size(k_l->type, hp.n_embd_head),
+                    0);
+
+        struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
+
+        kq = ggml_soft_max_ext(ctx, kq, kq_mask, nullptr, kq_scale, 0.0f);
+
+        // split cached v into n_head heads
+        struct ggml_tensor * v =
+            ggml_view_3d(ctx, v_l,
+                    hp.n_kv, hp.n_embd_head, hp.n_head_kv,
+                    ggml_element_size(v_l)*hp.n_ctx,
+                    ggml_element_size(v_l)*hp.n_ctx*hp.n_embd_head,
+                    0);
+
+        struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq);
+
+        struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3);
+
+        struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, hp.n_embd_head*hp.n_head, hp.n_tokens);
+
+        struct ggml_tensor * wo = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd);
+        cur = ggml_mul_mat(ctx, wo, cur);
+
+        return cur;
+    }
+
+    void initialize_tensors(ggml_context * ctx) override {
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+            if (t->type == GGML_TYPE_I32) {
+                // pos
+                std::vector<int> data(hp.n_tokens);
+                for (int i = 0; i < hp.n_tokens; i++) {
+                    data[i] = rand() % hp.n_ctx;
+                }
+                ggml_backend_tensor_set(t, data.data(), 0, hp.n_tokens * sizeof(int));
+            } else {
+                init_tensor_uniform(t);
+            }
+        }
+    }
+};
+
+
+// Llama
+struct test_llama : public test_llm {
+    static constexpr float freq_base = 10000.0f;
+    static constexpr float freq_scale = 1.0f;
+    static constexpr float ext_factor = 0.0f;
+    static constexpr float attn_factor = 1.0f;
+    static constexpr float beta_fast = 32.0f;
+    static constexpr float beta_slow = 1.0f;
 
     std::string op_desc(ggml_tensor * t) override {
-        return "MOE";
-
         GGML_UNUSED(t);
+        return "LLAMA";
     }
 
     std::string vars() override {
-        return VARS_TO_STR5(n_experts, n_experts_per_tok, n_tokens, n_embd, n_ff);
+        auto n_tokens = hp.n_tokens;
+        return VARS_TO_STR1(n_tokens);
     }
 
-    test_moe(int n_experts = 8, int n_experts_per_tok = 2, int n_tokens = 1, int n_embd = 4096, int n_ff = 14336)
-        : n_experts(n_experts), n_experts_per_tok(n_experts_per_tok), n_tokens(n_tokens), n_embd(n_embd), n_ff(n_ff) {
+    double max_nmse_err() override {
+        return 2e-3;
+    }
+
+    test_llama(int n_tokens = 1)
+        : test_llm({
+            /*n_vocab        =*/ 32000,
+            /*n_embd         =*/ 3200,
+            /*n_head         =*/ 32,
+            /*n_head_kv      =*/ 32,
+            /*n_rot          =*/ 100,
+            /*n_embd_head    =*/ 100,
+            /*n_ff           =*/ 8640,
+            /*f_norm_eps     =*/ 0.f,
+            /*f_norm_rms_eps =*/ 1e-5f,
+            /*n_tokens       =*/ n_tokens,
+        }) {
     }
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
-        ggml_tensor * ffn_gate_inp = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_experts);
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
 
-        std::vector<ggml_tensor *> ffn_up_exp(n_experts);
-        std::vector<ggml_tensor *> ffn_gate_exp(n_experts);
-        std::vector<ggml_tensor *> ffn_down_exp(n_experts);
+        inpL = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, hp.n_embd, hp.n_tokens);
 
-        for (int i = 0; i < n_experts; ++i) {
-            ffn_up_exp[i] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
-            ffn_gate_exp[i] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
-            ffn_down_exp[i] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd);
-        }
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, hp.n_tokens);
 
-        ggml_tensor * cur = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_tokens);
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hp.n_kv, hp.n_tokens, 1);
 
-        ggml_tensor * logits = ggml_mul_mat(ctx, ffn_gate_inp, cur);
-        ggml_tensor * probs = ggml_soft_max_ext(ctx, logits, nullptr, 1.0f/sqrtf(n_embd));
+        ggml_tensor * k_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
+        ggml_tensor * v_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
 
-        // select experts
-        ggml_tensor * selected_experts = ggml_top_k(ctx, probs, n_experts_per_tok);
+        for (uint32_t il = 0; il < hp.n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
 
-        ggml_tensor * weights = ggml_get_rows(ctx,
-                ggml_reshape_3d(ctx, probs, 1, n_experts, n_tokens), selected_experts);
+            // norm
+            ggml_tensor * attn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
+            cur = llm_build_norm(ctx, inpL, attn_norm, nullptr, LLM_NORM_RMS);
 
-        weights = ggml_reshape_2d(ctx, weights, n_experts_per_tok, n_tokens);
+            // self-attention
+            {
+                ggml_tensor * wq = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd);
+                ggml_tensor * wk = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd_gqa());
+                ggml_tensor * wv = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd_gqa());
 
-        ggml_tensor * weights_sum = ggml_sum_rows(ctx, weights);
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx, wq, cur);
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx, wk, cur);
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx, wv, cur);
 
-        weights = ggml_div(ctx, weights, weights_sum);
+                Qcur = ggml_rope_custom(
+                    ctx, ggml_reshape_3d(ctx, Qcur, hp.n_embd_head, hp.n_head,    hp.n_tokens), inp_pos,
+                    hp.n_rot, 0, 0, hp.n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
 
-        // compute expert outputs
-        ggml_tensor * moe_out = nullptr;
+                Kcur = ggml_rope_custom(
+                    ctx, ggml_reshape_3d(ctx, Kcur, hp.n_embd_head, hp.n_head_kv, hp.n_tokens), inp_pos,
+                    hp.n_rot, 0, 0, hp.n_orig_ctx, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
 
-        for (int i = 0; i < n_experts_per_tok; ++i) {
-            ggml_tensor * cur_expert;
+                llm_build_kv_store(ctx, k_l, v_l, Kcur, Vcur);
 
-            ggml_tensor * cur_up = ggml_mul_mat_id(ctx, ffn_up_exp.data(), n_experts, selected_experts, i, cur);
-
-            ggml_tensor * cur_gate = ggml_mul_mat_id(ctx, ffn_gate_exp.data(), n_experts, selected_experts, i, cur);
-
-            cur_gate = ggml_silu(ctx, cur_gate);
-
-            cur_expert = ggml_mul(ctx, cur_up, cur_gate);
-
-            cur_expert = ggml_mul_mat_id(ctx, ffn_down_exp.data(), n_experts, selected_experts, i, cur_expert);
-
-            cur_expert = ggml_mul(ctx, cur_expert,
-                    ggml_view_2d(ctx, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
-
-            if (i == 0) {
-                moe_out = cur_expert;
-            } else {
-                moe_out = ggml_add(ctx, moe_out, cur_expert);
+                cur = llm_build_kqv(ctx, k_l, v_l, Qcur, KQ_mask, 1.0f/sqrtf(float(hp.n_embd_head)));
             }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx, cur, inpSA);
+
+            // feed-forward network
+            ggml_tensor * ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
+            cur = llm_build_norm(ctx, ffn_inp, ffn_norm, nullptr, LLM_NORM_RMS);
+
+            ggml_tensor * ffn_gate = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_ff);
+            ggml_tensor * ffn_down = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_ff,   hp.n_embd);
+            ggml_tensor * ffn_up   = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_ff);
+            struct ggml_tensor * tmp = ggml_mul_mat(ctx, ffn_up, cur);
+            cur = ggml_mul_mat(ctx, ffn_gate, cur);
+            cur = ggml_silu(ctx, cur);
+            cur = ggml_mul(ctx, cur, tmp);
+            cur = ggml_mul_mat(ctx, ffn_down, cur);
+
+            cur = ggml_add(ctx, cur, ffn_inp);
+
+            // input for next layer
+            inpL = cur;
         }
 
-        cur = moe_out;
+        cur = inpL;
+
+        ggml_tensor * output_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
+        cur = llm_build_norm(ctx, cur, output_norm, nullptr, LLM_NORM_RMS);
+
+        // lm_head
+        ggml_tensor * output = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_vocab);
+        cur = ggml_mul_mat(ctx, output, cur);
+
+        return cur;
+    }
+};
+
+// Falcon
+struct test_falcon : public test_llm {
+    static constexpr float freq_base = 10000.0f;
+    static constexpr float freq_scale = 1.0f;
+    static constexpr float ext_factor = 0.0f;
+    static constexpr float attn_factor = 1.0f;
+    static constexpr float beta_fast = 32.0f;
+    static constexpr float beta_slow = 1.0f;
+
+    std::string op_desc(ggml_tensor * t) override {
+        GGML_UNUSED(t);
+        return "FALCON";
+    }
+
+    std::string vars() override {
+        auto n_tokens = hp.n_tokens;
+        return VARS_TO_STR1(n_tokens);
+    }
+
+    double max_nmse_err() override {
+        return 2e-3;
+    }
+
+    test_falcon(int n_tokens = 1)
+        : test_llm({
+            /*n_vocab        =*/ 32000,
+            /*n_embd         =*/ 3200,
+            /*n_head         =*/ 50,
+            /*n_head_kv      =*/ 1,
+            /*n_rot          =*/ 64,
+            /*n_embd_head    =*/ 64,
+            /*n_ff           =*/ 8640,
+            /*f_norm_eps     =*/ 1e-5f,
+            /*f_norm_rms_eps =*/ 0.f,
+            /*n_tokens       =*/ n_tokens,
+        }) {
+    }
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, hp.n_embd, hp.n_tokens);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, hp.n_tokens);
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hp.n_kv, hp.n_tokens, 1);
+
+        ggml_tensor * k_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
+        ggml_tensor * v_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
+
+        for (uint32_t il = 0; il < hp.n_layer; ++il) {
+            // norm
+            ggml_tensor * attn_norm_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
+            ggml_tensor * attn_norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
+            ggml_tensor * attn_norm = llm_build_norm(ctx, inpL, attn_norm_w, attn_norm_b, LLM_NORM);
+
+            // self-attention
+            {
+                cur = attn_norm;
+
+                ggml_tensor * wqkv = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd + 2*hp.n_embd_gqa());
+
+                cur = ggml_mul_mat(ctx, wqkv, cur);
+
+                struct ggml_tensor * Qcur = ggml_cont(ctx, ggml_view_2d(ctx, cur, hp.n_embd,     hp.n_tokens, cur->nb[1], 0*sizeof(float)*(hp.n_embd)));
+                struct ggml_tensor * Kcur = ggml_cont(ctx, ggml_view_2d(ctx, cur, hp.n_embd_gqa(), hp.n_tokens, cur->nb[1], 1*sizeof(float)*(hp.n_embd)));
+                struct ggml_tensor * Vcur = ggml_cont(ctx, ggml_view_2d(ctx, cur, hp.n_embd_gqa(), hp.n_tokens, cur->nb[1], 1*sizeof(float)*(hp.n_embd + hp.n_embd_gqa())));
+
+                Qcur = ggml_reshape_3d(ctx, Qcur, hp.n_embd_head, hp.n_head,    hp.n_tokens);
+                Kcur = ggml_reshape_3d(ctx, Kcur, hp.n_embd_head, hp.n_head_kv, hp.n_tokens);
+
+                // using mode = 2 for neox mode
+                Qcur = ggml_rope_custom(
+                    ctx, Qcur, inp_pos, hp.n_rot, 2, 0, hp.n_orig_ctx,
+                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+                );
+
+                Kcur = ggml_rope_custom(
+                    ctx, Kcur, inp_pos, hp.n_rot, 2, 0, hp.n_orig_ctx,
+                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+                );
+
+                llm_build_kv_store(ctx, k_l, v_l, Kcur, Vcur);
+
+                cur = llm_build_kqv(ctx, k_l, v_l, Qcur, KQ_mask, 1.0f/sqrtf(float(hp.n_embd_head)));
+            }
+
+            struct ggml_tensor * ffn_inp = cur;
+
+            // feed forward
+            {
+                ggml_tensor * ffn_up   = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_ff);
+                ggml_tensor * ffn_down = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_ff, hp.n_embd);
+                cur = attn_norm;
+                cur = ggml_mul_mat(ctx, ffn_up, cur);
+                cur = ggml_gelu(ctx, cur);
+                cur = ggml_mul_mat(ctx, ffn_down, cur);
+            }
+
+            cur = ggml_add(ctx, cur, ffn_inp);
+
+            cur = ggml_add(ctx, cur, inpL);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        ggml_tensor * output_norm   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
+        ggml_tensor * output_norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd);
+        cur = llm_build_norm(ctx, cur, output_norm, output_norm_b, LLM_NORM);
+
+        // lm_head
+        ggml_tensor * output = ggml_new_tensor_2d(ctx, GGML_TYPE_Q8_0, hp.n_embd, hp.n_vocab);
+        cur = ggml_mul_mat(ctx, output, cur);
 
         return cur;
     }
@@ -1458,7 +1869,10 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
         GGML_TYPE_Q8_0,
         GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
         GGML_TYPE_Q4_K, GGML_TYPE_Q5_K,
-        GGML_TYPE_Q6_K
+        GGML_TYPE_Q6_K,
+        GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S,
+        GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,
+        GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS,
     };
 
     // unary ops
@@ -1480,6 +1894,27 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
         }
     }
 
+    for (ggml_type type_input : {GGML_TYPE_F32}) {
+        for (ggml_op_pool pool_type : {GGML_OP_POOL_AVG, GGML_OP_POOL_MAX}) {
+            for (int k0 : {1, 3}) {
+                for (int k1 : {1, 3}) {
+                    for (int s0 : {1, 2}) {
+                        for (int s1 : {1, 2}) {
+                            for (int p0 : {0, 1}) {
+                                for (int p1 : {0, 1}) {
+                                    test_cases.emplace_back(new test_pool2d(pool_type, type_input, {10, 10, 3, 1}, k0, k1, s0, s1, p0, p1));
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+
+    test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32));
+    test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16));
+
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 1}));
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {2, 1, 1, 1}));
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 2, 1, 1}));
@@ -1495,8 +1930,10 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
     test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {0, 2, 1, 3}));
     test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {1, 2, 0, 3}));
 
-    for (ggml_type type : all_types) {
-       test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, type, {256, 10, 10, 1}));
+    for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) {
+        for (ggml_type type_dst : all_types) {
+           test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4}));
+        }
     }
 
     test_cases.emplace_back(new test_cont());
@@ -1565,6 +2002,13 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
         }
     }
 
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  64, 2,  128, { 8,  1}, {1, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  83, 2,  128, { 8,  1}, {4, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  64, 2,   64, { 8,  1}, {4, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  83, 2,   64, { 8,  1}, {4, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  64, 45, 128, { 8,  1}, {4, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 45,  64, { 8,  1}, {4, 1}));
+
     for (ggml_type type_a : all_types) {
         for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) {
             for (int n_mats : {2, 4, 8}) {
@@ -1584,6 +2028,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
     test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 10,  1}, 5));
     test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 10, 10}, 5));
 
+#if 0
     std::uniform_int_distribution<> dist_ne1(1, 50);
     int exponent = 1;
     while (exponent < (1 << 17)) {
@@ -1592,11 +2037,29 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
         for (int n = 0; n < 10; ++n) {
             int64_t ne0 = dist_ne0(rng);
             int64_t ne1 = dist_ne1(rng);
-            test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 1}));
+            test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 1}, n/2 == 0, 0.1f, ne0 < 1000 ? 4.0f : 0.0f));
         }
 
         exponent <<= 1;
     }
+#endif
+    for (bool mask : {false, true}) {
+        for (float max_bias : {0.0f, 8.0f}) {
+            for (float scale : {1.0f, 0.1f}) {
+                for (int64_t ne0 : {16, 1024}) {
+                    for (int64_t ne1 : {16, 1024}) {
+                        test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 1}, mask, scale, max_bias));
+                        test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0-1, ne1-1, 1, 1}, mask, scale, max_bias));
+                    }
+                }
+            }
+        }
+    }
+
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, false, 0.1f, 0.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  0.1f, 0.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, false, 0.1f, 8.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  0.1f, 8.0f));
 
     for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
         test_cases.emplace_back(new test_rope(type, {128,  32, 10, 1}, 128, 0, 512)); // llama 7B
@@ -1611,14 +2074,13 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
         test_cases.emplace_back(new test_rope(type, { 80,  32, 10, 1},  32, 2, 512)); // neox (phi-2)
     }
 
-    test_cases.emplace_back(new test_alibi());
-    test_cases.emplace_back(new test_im2col());
     test_cases.emplace_back(new test_concat(GGML_TYPE_F32));
     test_cases.emplace_back(new test_concat(GGML_TYPE_I32));
 
-    for (ggml_sort_order order : {GGML_SORT_ASC, GGML_SORT_DESC}) {
+    for (ggml_sort_order order : {GGML_SORT_ORDER_ASC, GGML_SORT_ORDER_DESC}) {
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order));
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {16, 10, 10, 10}, order));
+        test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {60, 10, 10, 10}, order)); // qwen
     }
 
     test_cases.emplace_back(new test_sum_rows());
@@ -1626,12 +2088,16 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
     test_cases.emplace_back(new test_group_norm());
     test_cases.emplace_back(new test_acc());
     test_cases.emplace_back(new test_pad());
+    test_cases.emplace_back(new test_arange());
+    test_cases.emplace_back(new test_timestep_embedding());
     test_cases.emplace_back(new test_leaky_relu());
 
-#if !defined(__SANITIZE_THREAD__)
-    // FIXME: these tests use too much memory with thread sanitizer
-    test_cases.emplace_back(new test_moe(8, 2, 1, 4096, 8*1024));
-    //test_cases.emplace_back(new test_moe(8, 2, 8, 4096, 14336));
+    // these tests are disabled to save execution time, but they can be handy for debugging
+#if 0
+    test_cases.emplace_back(new test_llama(1));
+    test_cases.emplace_back(new test_llama(2));
+    test_cases.emplace_back(new test_falcon(1));
+    test_cases.emplace_back(new test_falcon(2));
 #endif
 
     // run tests
@@ -1670,8 +2136,8 @@ static void usage(char ** argv) {
 
 int main(int argc, char ** argv) {
     test_mode mode = MODE_TEST;
-    const char * op_name = NULL;
-    const char * backend = NULL;
+    const char * op_name_filter = NULL;
+    const char * backend_filter = NULL;
 
     for (int i = 1; i < argc; i++) {
         if (strcmp(argv[i], "test") == 0) {
@@ -1680,14 +2146,14 @@ int main(int argc, char ** argv) {
             mode = MODE_PERF;
         } else if (strcmp(argv[i], "-o") == 0) {
             if (i + 1 < argc) {
-                op_name = argv[++i];
+                op_name_filter = argv[++i];
             } else {
                 usage(argv);
                 return 1;
             }
         } else if (strcmp(argv[i], "-b") == 0) {
             if (i + 1 < argc) {
-                backend = argv[++i];
+                backend_filter = argv[++i];
             } else {
                 usage(argv);
                 return 1;
@@ -1706,7 +2172,7 @@ int main(int argc, char ** argv) {
     for (size_t i = 0; i < ggml_backend_reg_get_count(); i++) {
         printf("Backend %zu/%zu (%s)\n", i + 1, ggml_backend_reg_get_count(), ggml_backend_reg_get_name(i));
 
-        if (backend != NULL && strcmp(backend, ggml_backend_reg_get_name(i)) != 0) {
+        if (backend_filter != NULL && strcmp(backend_filter, ggml_backend_reg_get_name(i)) != 0) {
             printf("  Skipping\n");
             n_ok++;
             continue;
@@ -1714,9 +2180,17 @@ int main(int argc, char ** argv) {
 
         ggml_backend_t backend = ggml_backend_reg_init_backend(i, NULL);
         GGML_ASSERT(backend != NULL);
+
+        if (backend_filter == NULL && ggml_backend_is_cpu(backend)) {
+            printf("  Skipping CPU backend\n");
+            ggml_backend_free(backend);
+            n_ok++;
+            continue;
+        }
+
         printf("  Backend name: %s\n", ggml_backend_name(backend));
 
-        bool ok = test_backend(backend, mode, op_name);
+        bool ok = test_backend(backend, mode, op_name_filter);
 
         printf("  Backend %s: ", ggml_backend_name(backend));
         if (ok) {
@@ -1738,6 +2212,8 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
+    ggml_quantize_free();
+
     printf("\033[1;32mOK\033[0m\n");
     return 0;
 }
diff --git a/tests/test-c.c b/tests/test-c.c
index a05071080..95ba73df3 100644
--- a/tests/test-c.c
+++ b/tests/test-c.c
@@ -1,3 +1,7 @@
 #include "llama.h"
 
+#ifdef GGML_USE_KOMPUTE
+#include "ggml-kompute.h"
+#endif
+
 int main(void) {}
diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp
new file mode 100644
index 000000000..73c3536fd
--- /dev/null
+++ b/tests/test-chat-template.cpp
@@ -0,0 +1,99 @@
+#include <iostream>
+#include <string>
+#include <vector>
+#include <sstream>
+
+#undef NDEBUG
+#include <cassert>
+
+#include "llama.h"
+
+int main(void) {
+    llama_chat_message conversation[] = {
+        {"system", "You are a helpful assistant"},
+        {"user", "Hello"},
+        {"assistant", "Hi there"},
+        {"user", "Who are you"},
+        {"assistant", "   I am an assistant   "},
+        {"user", "Another question"},
+    };
+    size_t message_count = 6;
+    std::vector<std::string> templates = {
+        // teknium/OpenHermes-2.5-Mistral-7B
+        "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}",
+        // mistralai/Mistral-7B-Instruct-v0.2
+        "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
+        // TheBloke/FusionNet_34Bx2_MoE-AWQ
+        "{%- for idx in range(0, messages|length) -%}\\n{%- if messages[idx]['role'] == 'user' -%}\\n{%- if idx > 1 -%}\\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\\n{%- else -%}\\n{{- messages[idx]['content'] + ' [/INST]' -}}\\n{%- endif -%}\\n{% elif messages[idx]['role'] == 'system' %}\\n{{- '[INST] <<SYS>>\\\\n' + messages[idx]['content'] + '\\\\n<</SYS>>\\\\n\\\\n' -}}\\n{%- elif messages[idx]['role'] == 'assistant' -%}\\n{{- ' '  + messages[idx]['content'] + ' ' + eos_token -}}\\n{% endif %}\\n{% endfor %}",
+        // bofenghuang/vigogne-2-70b-chat
+        "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif true == true and not '<<SYS>>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez autant que vous le pouvez.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\\\n' + system_message + '\\\\n<</SYS>>\\\\n\\\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<<SYS>>\\\\n' + content.strip() + '\\\\n<</SYS>>\\\\n\\\\n' }}{% elif message['role'] == 'assistant' %}{{ ' '  + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
+        // mlabonne/AlphaMonarch-7B
+        "{% for message in messages %}{{bos_token + message['role'] + '\\n' + message['content'] + eos_token + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ bos_token + 'assistant\\n' }}{% endif %}",
+        // google/gemma-7b-it
+        "{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\\n' + message['content'] | trim + '<end_of_turn>\\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\\n'}}{% endif %}",
+        // OrionStarAI/Orion-14B-Chat
+        "{% for message in messages %}{% if loop.first %}{{ bos_token }}{% endif %}{% if message['role'] == 'user' %}{{ 'Human: ' + message['content'] + '\\n\\nAssistant: ' + eos_token }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token }}{% endif %}{% endfor %}",
+        // openchat/openchat-3.5-0106
+        // The included chat_template differs from the author's suggestions here: https://huggingface.co/openchat/openchat_3.5/discussions/5#65448109b4a3f3a2f486fd9d
+        // So we match against the included template but implement the suggested version.
+        "{{ bos_token }}{% for message in messages %}{{ 'GPT4 Correct ' + message['role'].title() + ': ' + message['content'] + '<|end_of_turn|>'}}{% endfor %}{% if add_generation_prompt %}{{ 'GPT4 Correct Assistant:' }}{% endif %}",
+        // deepseek-ai/deepseek-coder-33b-instruct
+        "{% if not add_generation_prompt is defined %}\n{% set add_generation_prompt = false %}\n{% endif %}\n{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n    {%- if message['role'] == 'system' -%}\n        {%- set ns.found = true -%}\n    {%- endif -%}\n{%- endfor -%}\n{{bos_token}}{%- if not ns.found -%}\n{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\\n'}}\n{%- endif %}\n{%- for message in messages %}\n    {%- if message['role'] == 'system' %}\n{{ message['content'] }}\n    {%- else %}\n        {%- if message['role'] == 'user' %}\n{{'### Instruction:\\n' + message['content'] + '\\n'}}\n        {%- else %}\n{{'### Response:\\n' + message['content'] + '\\n<|EOT|>\\n'}}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{% if add_generation_prompt %}\n{{'### Response:'}}\n{% endif %}",
+        // eachadea/vicuna-13b-1.1
+        // No template included in tokenizer_config.json, so this template likely needs to be manually set.
+        "{%- for message in messages %}{%- if message['role'] == 'system' -%}{{- '' + message['content'] + '\n\n' -}}{%- else -%}{%- if message['role'] == 'user' -%}{{-'USER: ' + message['content'] + '\n'-}}{%- else -%}{{-'ASSISTANT: ' + message['content'] + '</s>\n' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{-'ASSISTANT:'-}}{%- endif -%}",
+        // Orca-Vicuna
+        // No template included in tokenizer_config.json, so this template likely needs to be manually set.
+        "{%- for message in messages %}{%- if message['role'] == 'system' -%}{{-'SYSTEM: ' + message['content'] + '\n' -}}{%- else -%}{%- if message['role'] == 'user' -%}{{-'USER: ' + message['content'] + '\n'-}}{%- else -%}{{-'ASSISTANT: ' + message['content'] + '</s>\n' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{-'ASSISTANT:'-}}{%- endif -%}",
+    };
+    std::vector<std::string> expected_output = {
+        // teknium/OpenHermes-2.5-Mistral-7B
+        "<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\nHi there<|im_end|>\n<|im_start|>user\nWho are you<|im_end|>\n<|im_start|>assistant\n   I am an assistant   <|im_end|>\n<|im_start|>user\nAnother question<|im_end|>\n<|im_start|>assistant\n",
+        // mistralai/Mistral-7B-Instruct-v0.2
+        "[INST] You are a helpful assistant\nHello [/INST]Hi there</s>[INST] Who are you [/INST]   I am an assistant   </s>[INST] Another question [/INST]",
+        // TheBloke/FusionNet_34Bx2_MoE-AWQ
+        "[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST] Hi there </s><s>[INST] Who are you [/INST]    I am an assistant    </s><s>[INST] Another question [/INST]",
+        // bofenghuang/vigogne-2-70b-chat
+        "[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST] Hi there </s>[INST] Who are you [/INST] I am an assistant </s>[INST] Another question [/INST]",
+        // mlabonne/AlphaMonarch-7B
+        "system\nYou are a helpful assistant</s>\n<s>user\nHello</s>\n<s>assistant\nHi there</s>\n<s>user\nWho are you</s>\n<s>assistant\n   I am an assistant   </s>\n<s>user\nAnother question</s>\n<s>assistant\n",
+        // google/gemma-7b-it
+        "<start_of_turn>user\nYou are a helpful assistant\n\nHello<end_of_turn>\n<start_of_turn>model\nHi there<end_of_turn>\n<start_of_turn>user\nWho are you<end_of_turn>\n<start_of_turn>model\nI am an assistant<end_of_turn>\n<start_of_turn>user\nAnother question<end_of_turn>\n<start_of_turn>model\n",
+        // OrionStarAI/Orion-14B-Chat
+        "Human: You are a helpful assistant\n\nHello\n\nAssistant: </s>Hi there</s>Human: Who are you\n\nAssistant: </s>   I am an assistant   </s>Human: Another question\n\nAssistant: </s>",
+        // openchat/openchat-3.5-0106
+        "You are a helpful assistant<|end_of_turn|>GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi there<|end_of_turn|>GPT4 Correct User: Who are you<|end_of_turn|>GPT4 Correct Assistant:    I am an assistant   <|end_of_turn|>GPT4 Correct User: Another question<|end_of_turn|>GPT4 Correct Assistant:",
+        // deepseek-ai/deepseek-coder-33b-instruct
+        "You are a helpful assistant### Instruction:\nHello\n### Response:\nHi there\n<|EOT|>\n### Instruction:\nWho are you\n### Response:\n   I am an assistant   \n<|EOT|>\n### Instruction:\nAnother question\n### Response:\n",
+        // eachadea/vicuna-13b-1.1
+        "You are a helpful assistant\n\nUSER: Hello\nASSISTANT: Hi there</s>\nUSER: Who are you\nASSISTANT:    I am an assistant   </s>\nUSER: Another question\nASSISTANT:",
+        // Orca-Vicuna
+        "SYSTEM: You are a helpful assistant\nUSER: Hello\nASSISTANT: Hi there</s>\nUSER: Who are you\nASSISTANT:    I am an assistant   </s>\nUSER: Another question\nASSISTANT:",
+    };
+    std::vector<char> formatted_chat(1024);
+    int32_t res;
+
+    // test invalid chat template
+    res = llama_chat_apply_template(nullptr, "INVALID TEMPLATE", conversation, message_count, true, formatted_chat.data(), formatted_chat.size());
+    assert(res < 0);
+
+    for (size_t i = 0; i < templates.size(); i++) {
+        std::string custom_template = templates[i];
+        std::string expected = expected_output[i];
+        formatted_chat.resize(1024);
+        res = llama_chat_apply_template(
+            nullptr,
+            custom_template.c_str(),
+            conversation,
+            message_count,
+            true,
+            formatted_chat.data(),
+            formatted_chat.size()
+        );
+        formatted_chat.resize(res);
+        std::string output(formatted_chat.data(), formatted_chat.size());
+        std::cout << output << "\n-------------------------\n";
+        assert(output == expected);
+    }
+    return 0;
+}
diff --git a/tests/test-grammar-parser.cpp b/tests/test-grammar-parser.cpp
index a0b5b043d..91939e276 100644
--- a/tests/test-grammar-parser.cpp
+++ b/tests/test-grammar-parser.cpp
@@ -38,8 +38,8 @@ term  ::= [0-9]+)""";
         // pretty print error message before asserting
         if (expected_pair.first != key || expected_pair.second != value)
         {
-            fprintf(stderr, "expected_pair: %s, %d\n", expected_pair.first.c_str(), expected_pair.second);
-            fprintf(stderr, "actual_pair: %s, %d\n", key.c_str(), value);
+            fprintf(stderr, "expected_pair: %s, %u\n", expected_pair.first.c_str(), expected_pair.second);
+            fprintf(stderr, "actual_pair: %s, %u\n", key.c_str(), value);
             fprintf(stderr, "expected_pair != actual_pair\n");
         }
 
@@ -96,9 +96,9 @@ term  ::= [0-9]+)""";
             // pretty print error message before asserting
             if (expected_element.type != element.type || expected_element.value != element.value)
             {
-                fprintf(stderr, "index: %d\n", index);
-                fprintf(stderr, "expected_element: %d, %d\n", expected_element.type, expected_element.value);
-                fprintf(stderr, "actual_element: %d, %d\n", element.type, element.value);
+                fprintf(stderr, "index: %u\n", index);
+                fprintf(stderr, "expected_element: %d, %u\n", expected_element.type, expected_element.value);
+                fprintf(stderr, "actual_element: %d, %u\n", element.type, element.value);
                 fprintf(stderr, "expected_element != actual_element\n");
             }
 
@@ -144,8 +144,8 @@ term  ::= [0-9]+)""";
         // pretty print error message before asserting
         if (expected_pair.first != key || expected_pair.second != value)
         {
-            fprintf(stderr, "expected_pair: %s, %d\n", expected_pair.first.c_str(), expected_pair.second);
-            fprintf(stderr, "actual_pair: %s, %d\n", key.c_str(), value);
+            fprintf(stderr, "expected_pair: %s, %u\n", expected_pair.first.c_str(), expected_pair.second);
+            fprintf(stderr, "actual_pair: %s, %u\n", key.c_str(), value);
             fprintf(stderr, "expected_pair != actual_pair\n");
         }
 
@@ -235,9 +235,9 @@ term  ::= [0-9]+)""";
             // pretty print error message before asserting
             if (expected_element.type != element.type || expected_element.value != element.value)
             {
-                fprintf(stderr, "index: %d\n", index);
-                fprintf(stderr, "expected_element: %d, %d\n", expected_element.type, expected_element.value);
-                fprintf(stderr, "actual_element: %d, %d\n", element.type, element.value);
+                fprintf(stderr, "index: %u\n", index);
+                fprintf(stderr, "expected_element: %d, %u\n", expected_element.type, expected_element.value);
+                fprintf(stderr, "actual_element: %d, %u\n", element.type, element.value);
                 fprintf(stderr, "expected_element != actual_element\n");
             }
 
diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp
new file mode 100755
index 000000000..4d110cf5a
--- /dev/null
+++ b/tests/test-json-schema-to-grammar.cpp
@@ -0,0 +1,842 @@
+#ifdef NDEBUG
+#undef NDEBUG
+#endif
+
+#include <fstream>
+#include <sstream>
+#include <regex>
+
+#include "json-schema-to-grammar.h"
+#include "grammar-parser.h"
+
+static std::string trim(const std::string & source) {
+    std::string s(source);
+    s.erase(0,s.find_first_not_of(" \n\r\t"));
+    s.erase(s.find_last_not_of(" \n\r\t")+1);
+    return std::regex_replace(s, std::regex("(^|\n)[ \t]+"), "$1");
+}
+
+enum TestCaseStatus {
+    SUCCESS,
+    FAILURE
+};
+
+struct TestCase {
+    TestCaseStatus expected_status;
+    std::string name;
+    std::string schema;
+    std::string expected_grammar;
+
+    void _print_failure_header() const {
+        fprintf(stderr, "#\n# Test '%s' failed.\n#\n%s\n", name.c_str(), schema.c_str());
+    }
+    void verify(const std::string & actual_grammar) const {
+        if (trim(actual_grammar) != trim(expected_grammar)) {
+        _print_failure_header();
+        fprintf(stderr, "# EXPECTED:\n%s\n# ACTUAL:\n%s\n", expected_grammar.c_str(), actual_grammar.c_str());
+        assert(false);
+        }
+    }
+    void verify_expectation_parseable() const {
+        try {
+            auto state = grammar_parser::parse(expected_grammar.c_str());
+            if (state.symbol_ids.find("root") == state.symbol_ids.end()) {
+                throw std::runtime_error("Grammar failed to parse:\n" + expected_grammar);
+            }
+        } catch (const std::runtime_error & ex) {
+            _print_failure_header();
+            fprintf(stderr, "# GRAMMAR ERROR: %s\n", ex.what());
+            assert(false);
+        }
+    }
+    void verify_status(TestCaseStatus status) const {
+        if (status != expected_status) {
+            _print_failure_header();
+            fprintf(stderr, "# EXPECTED STATUS: %s\n", expected_status == SUCCESS ? "SUCCESS" : "FAILURE");
+            fprintf(stderr, "# ACTUAL STATUS: %s\n", status == SUCCESS ? "SUCCESS" : "FAILURE");
+            assert(false);
+        }
+    }
+};
+
+static void write(const std::string & file, const std::string & content) {
+    std::ofstream f;
+    f.open(file.c_str());
+    f << content.c_str();
+    f.close();
+}
+
+static std::string read(const std::string & file) {
+    std::ostringstream actuals;
+    actuals << std::ifstream(file.c_str()).rdbuf();
+    return actuals.str();
+}
+
+static void test_all(const std::string & lang, std::function<void(const TestCase &)> runner) {
+    fprintf(stderr, "#\n# Testing JSON schema conversion (%s)\n#\n", lang.c_str());
+    auto test = [&](const TestCase & tc) {
+        fprintf(stderr, "- %s%s\n", tc.name.c_str(), tc.expected_status == FAILURE ? " (failure expected)" : "");
+        runner(tc);
+    };
+
+    test({
+        FAILURE,
+        "unknown type",
+        R"""({
+            "type": "kaboom"
+        })""",
+        ""
+    });
+
+    test({
+        FAILURE,
+        "invalid type",
+        R"""({
+            "type": 123
+        })""",
+        ""
+    });
+
+    test({
+        SUCCESS,
+        "empty schema (object)",
+        "{}",
+        R"""(
+            array ::= "[" space ( value ("," space value)* )? "]" space
+            boolean ::= ("true" | "false") space
+            null ::= "null" space
+            number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space
+            object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
+            root ::= object
+            space ::= " "?
+            string ::=  "\"" (
+                    [^"\\] |
+                    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+                    )* "\"" space
+            value ::= object | array | string | number | boolean
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "exotic formats",
+        R"""({
+            "items": [
+                { "format": "date" },
+                { "format": "uuid" },
+                { "format": "time" },
+                { "format": "date-time" }
+            ]
+        })""",
+        R"""(
+            date ::= [0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( "0" [1-9] | [1-2] [0-9] | "3" [0-1] )
+            date-string ::= "\"" date "\"" space
+            date-time ::= date "T" time
+            date-time-string ::= "\"" date-time "\"" space
+            root ::= "[" space date-string "," space uuid "," space time-string "," space date-time-string "]" space
+            space ::= " "?
+            time ::= ([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )
+            time-string ::= "\"" time "\"" space
+            uuid ::= "\"" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "-" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "-" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "-" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "-" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "string",
+        R"""({
+            "type": "string"
+        })""",
+        R"""(
+            root ::=  "\"" (
+                    [^"\\] |
+                    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+                    )* "\"" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "boolean",
+        R"""({
+            "type": "boolean"
+        })""",
+        R"""(
+            root ::= ("true" | "false") space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "integer",
+        R"""({
+            "type": "integer"
+        })""",
+        R"""(
+            root ::= ("-"? ([0-9] | [1-9] [0-9]*)) space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "string const",
+        R"""({
+            "const": "foo"
+        })""",
+        R"""(
+            root ::= "\"foo\""
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "non-string const",
+        R"""({
+            "const": 123
+        })""",
+        R"""(
+            root ::= "123"
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "non-string enum",
+        R"""({
+            "enum": ["red", "amber", "green", null, 42, ["foo"]]
+        })""",
+        R"""(
+            root ::= "\"red\"" | "\"amber\"" | "\"green\"" | "null" | "42" | "[\"foo\"]"
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "tuple1",
+        R"""({
+            "prefixItems": [{ "type": "string" }]
+        })""",
+        R"""(
+            root ::= "[" space string "]" space
+            space ::= " "?
+            string ::=  "\"" (
+                    [^"\\] |
+                    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+                    )* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "tuple2",
+        R"""({
+            "prefixItems": [{ "type": "string" }, { "type": "number" }]
+        })""",
+        R"""(
+            number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space
+            root ::= "[" space string "," space number "]" space
+            space ::= " "?
+            string ::=  "\"" (
+                    [^"\\] |
+                    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+                    )* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "number",
+        R"""({
+            "type": "number"
+        })""",
+        R"""(
+            root ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "minItems",
+        R"""({
+            "items": {
+                "type": "boolean"
+            },
+            "minItems": 2
+        })""",
+        R"""(
+            boolean ::= ("true" | "false") space
+            root ::= "[" space boolean ( "," space boolean )( "," space boolean )* "]" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "maxItems 1",
+        R"""({
+            "items": {
+                "type": "boolean"
+            },
+            "maxItems": 1
+        })""",
+        R"""(
+            boolean ::= ("true" | "false") space
+            root ::= "[" space ( boolean  )? "]" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "maxItems 2",
+        R"""({
+            "items": {
+                "type": "boolean"
+            },
+            "maxItems": 2
+        })""",
+        R"""(
+            boolean ::= ("true" | "false") space
+            root ::= "[" space ( boolean ( "," space boolean )? )? "]" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "min + maxItems",
+        R"""({
+            "items": {
+                "type": ["number", "integer"]
+            },
+            "minItems": 3,
+            "maxItems": 5
+        })""",
+        R"""(
+            integer ::= ("-"? ([0-9] | [1-9] [0-9]*)) space
+            item ::= number | integer
+            number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space
+            root ::= "[" space item ( "," space item )( "," space item )( "," space item )?( "," space item )? "]" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "simple regexp",
+        R"""({
+            "type": "string",
+            "pattern": "^abc?d*efg+(hij)?kl$"
+        })""",
+        R"""(
+            root ::= "\"" "ab" "c"? "d"* "ef" "g"+ ("hij")? "kl" "\"" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "regexp escapes",
+        R"""({
+            "type": "string",
+            "pattern": "^\\[\\]\\{\\}\\(\\)\\|\\+\\*\\?$"
+        })""",
+        R"""(
+            root ::= "\"" "[]{}()|+*?" "\"" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "regexp quote",
+        R"""({
+            "type": "string",
+            "pattern": "^\"$"
+        })""",
+        R"""(
+            root ::= "\"" "\"" "\"" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "regexp",
+        R"""({
+            "type": "string",
+            "pattern": "^(\\([0-9]{1,3}\\))?[0-9]{3}-[0-9]{4} and...$"
+        })""",
+        R"""(
+            dot ::= [\U00000000-\x09\x0B\x0C\x0E-\U0010FFFF]
+            root ::= "\"" ("(" root-1 root-1? root-1? ")")? root-1 root-1 root-1 "-" root-1 root-1 root-1 root-1 " and" dot dot dot "\"" space
+            root-1 ::= [0-9]
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "required props in original order",
+        R"""({
+            "type": "object",
+            "properties": {
+                "b": {"type": "string"},
+                "c": {"type": "string"},
+                "a": {"type": "string"}
+            },
+            "required": [
+                "a",
+                "b",
+                "c"
+            ],
+            "additionalProperties": false,
+            "definitions": {}
+        })""",
+        R"""(
+            a-kv ::= "\"a\"" space ":" space string
+            b-kv ::= "\"b\"" space ":" space string
+            c-kv ::= "\"c\"" space ":" space string
+            root ::= "{" space b-kv "," space c-kv "," space a-kv "}" space
+            space ::= " "?
+            string ::=  "\"" (
+                    [^"\\] |
+                    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+                    )* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "1 optional prop",
+        R"""({
+            "properties": {
+                "a": {
+                "type": "string"
+                }
+            },
+            "additionalProperties": false
+        })""",
+        R"""(
+            a-kv ::= "\"a\"" space ":" space string
+            root ::= "{" space  (a-kv )? "}" space
+            space ::= " "?
+            string ::=  "\"" (
+                    [^"\\] |
+                    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+                    )* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "N optional props",
+        R"""({
+            "properties": {
+                "a": {"type": "string"},
+                "b": {"type": "string"},
+                "c": {"type": "string"}
+            },
+            "additionalProperties": false
+        })""",
+        R"""(
+            a-kv ::= "\"a\"" space ":" space string
+            a-rest ::= ( "," space b-kv )? b-rest
+            b-kv ::= "\"b\"" space ":" space string
+            b-rest ::= ( "," space c-kv )?
+            c-kv ::= "\"c\"" space ":" space string
+            root ::= "{" space  (a-kv a-rest | b-kv b-rest | c-kv )? "}" space
+            space ::= " "?
+            string ::=  "\"" (
+                    [^"\\] |
+                    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+                    )* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "required + optional props each in original order",
+        R"""({
+            "properties": {
+                "b": {"type": "string"},
+                "a": {"type": "string"},
+                "d": {"type": "string"},
+                "c": {"type": "string"}
+            },
+            "required": ["a", "b"],
+            "additionalProperties": false
+        })""",
+        R"""(
+            a-kv ::= "\"a\"" space ":" space string
+            b-kv ::= "\"b\"" space ":" space string
+            c-kv ::= "\"c\"" space ":" space string
+            d-kv ::= "\"d\"" space ":" space string
+            d-rest ::= ( "," space c-kv )?
+            root ::= "{" space b-kv "," space a-kv ( "," space ( d-kv d-rest | c-kv ) )? "}" space
+            space ::= " "?
+            string ::=  "\"" (
+                    [^"\\] |
+                    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+                )* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "additional props",
+        R"""({
+            "type": "object",
+            "additionalProperties": {"type": "array", "items": {"type": "number"}}
+        })""",
+        R"""(
+            additional-kv ::= string ":" space additional-value
+            additional-kvs ::= additional-kv ( "," space additional-kv )*
+            additional-value ::= "[" space ( number ( "," space number )* )? "]" space
+            number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space
+            root ::= "{" space  (additional-kvs )? "}" space
+            space ::= " "?
+            string ::=  "\"" (
+                    [^"\\] |
+                    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+                    )* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "additional props (true)",
+        R"""({
+            "type": "object",
+            "additionalProperties": true
+        })""",
+        R"""(
+            array ::= "[" space ( value ("," space value)* )? "]" space
+            boolean ::= ("true" | "false") space
+            null ::= "null" space
+            number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space
+            object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
+            root ::= object
+            space ::= " "?
+            string ::=  "\"" (
+                    [^"\\] |
+                    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+                    )* "\"" space
+            value ::= object | array | string | number | boolean
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "additional props (implicit)",
+        R"""({
+            "type": "object"
+        })""",
+        R"""(
+            array ::= "[" space ( value ("," space value)* )? "]" space
+            boolean ::= ("true" | "false") space
+            null ::= "null" space
+            number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space
+            object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
+            root ::= object
+            space ::= " "?
+            string ::=  "\"" (
+                    [^"\\] |
+                    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+                    )* "\"" space
+            value ::= object | array | string | number | boolean
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "empty w/o additional props",
+        R"""({
+            "type": "object",
+            "additionalProperties": false
+        })""",
+        R"""(
+            root ::= "{" space  "}" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "required + additional props",
+        R"""({
+            "type": "object",
+            "properties": {
+                "a": {"type": "number"}
+            },
+            "required": ["a"],
+            "additionalProperties": {"type": "string"}
+        })""",
+        R"""(
+            a-kv ::= "\"a\"" space ":" space number
+            additional-kv ::= string ":" space string
+            additional-kvs ::= additional-kv ( "," space additional-kv )*
+            number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space
+            root ::= "{" space a-kv ( "," space ( additional-kvs ) )? "}" space
+            space ::= " "?
+            string ::=  "\"" (
+                    [^"\\] |
+                    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+                    )* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "optional + additional props",
+        R"""({
+            "type": "object",
+            "properties": {
+                "a": {"type": "number"}
+            },
+            "additionalProperties": {"type": "number"}
+        })""",
+        R"""(
+            a-kv ::= "\"a\"" space ":" space number
+            a-rest ::= additional-kvs
+            additional-kv ::= string ":" space number
+            additional-kvs ::= additional-kv ( "," space additional-kv )*
+            number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space
+            root ::= "{" space  (a-kv a-rest | additional-kvs )? "}" space
+            space ::= " "?
+            string ::=  "\"" (
+                    [^"\\] |
+                    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+                    )* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "required + optional + additional props",
+        R"""({
+            "type": "object",
+            "properties": {
+                "a": {"type": "number"},
+                "b": {"type": "number"}
+            },
+            "required": ["a"],
+            "additionalProperties": {"type": "number"}
+        })""",
+        R"""(
+            a-kv ::= "\"a\"" space ":" space number
+            additional-kv ::= string ":" space number
+            additional-kvs ::= additional-kv ( "," space additional-kv )*
+            b-kv ::= "\"b\"" space ":" space number
+            b-rest ::= additional-kvs
+            number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space
+            root ::= "{" space a-kv ( "," space ( b-kv b-rest | additional-kvs ) )? "}" space
+            space ::= " "?
+            string ::=  "\"" (
+                    [^"\\] |
+                    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+                    )* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "top-level $ref",
+        R"""({
+            "$ref": "#/definitions/MyType",
+            "definitions": {
+                "MyType": {
+                    "type": "object",
+                    "properties": {
+                        "a": {
+                            "type": "string"
+                        }
+                    },
+                    "required": [
+                        "a"
+                    ],
+                    "additionalProperties": false
+                }
+            }
+        })""",
+        R"""(
+            MyType ::= "{" space MyType-a-kv "}" space
+            MyType-a-kv ::= "\"a\"" space ":" space string
+            root ::= MyType
+            space ::= " "?
+            string ::=  "\"" (
+                    [^"\\] |
+                    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+                    )* "\"" space
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "anyOf",
+        R"""({
+            "anyOf": [
+                {"$ref": "#/definitions/foo"},
+                {"$ref": "#/definitions/bar"}
+            ],
+            "definitions": {
+                "foo": {
+                    "properties": {"a": {"type": "number"}}
+                },
+                "bar": {
+                    "properties": {"b": {"type": "number"}}
+                }
+            },
+            "type": "object"
+        })""",
+        R"""(
+            alternative-0 ::= foo
+            alternative-1 ::= bar
+            bar ::= "{" space  (bar-b-kv )? "}" space
+            bar-b-kv ::= "\"b\"" space ":" space number
+            foo ::= "{" space  (foo-a-kv )? "}" space
+            foo-a-kv ::= "\"a\"" space ":" space number
+            number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space
+            root ::= alternative-0 | alternative-1
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "mix of allOf, anyOf and $ref (similar to https://json.schemastore.org/tsconfig.json)",
+        R"""({
+            "allOf": [
+                {"$ref": "#/definitions/foo"},
+                {"$ref": "#/definitions/bar"},
+                {
+                "anyOf": [
+                    {"$ref": "#/definitions/baz"},
+                    {"$ref": "#/definitions/bam"}
+                ]
+                }
+            ],
+            "definitions": {
+                "foo": {
+                    "properties": {"a": {"type": "number"}}
+                },
+                "bar": {
+                    "properties": {"b": {"type": "number"}}
+                },
+                "bam": {
+                    "properties": {"c": {"type": "number"}}
+                },
+                "baz": {
+                    "properties": {"d": {"type": "number"}}
+                }
+            },
+            "type": "object"
+        })""",
+        R"""(
+            a-kv ::= "\"a\"" space ":" space number
+            b-kv ::= "\"b\"" space ":" space number
+            c-kv ::= "\"c\"" space ":" space number
+            d-kv ::= "\"d\"" space ":" space number
+            d-rest ::= ( "," space c-kv )?
+            number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space
+            root ::= "{" space a-kv "," space b-kv ( "," space ( d-kv d-rest | c-kv ) )? "}" space
+            space ::= " "?
+        )"""
+    });
+
+    test({
+        SUCCESS,
+        "conflicting names",
+        R"""({
+            "type": "object",
+            "properties": {
+                "number": {
+                "type": "object",
+                "properties": {
+                    "number": {
+                    "type": "object",
+                        "properties": {
+                            "root": {
+                                "type": "number"
+                            }
+                        },
+                        "required": [
+                            "root"
+                        ],
+                        "additionalProperties": false
+                    }
+                },
+                "required": [
+                    "number"
+                ],
+                "additionalProperties": false
+                }
+            },
+            "required": [
+                "number"
+            ],
+            "additionalProperties": false,
+            "definitions": {}
+        })""",
+        R"""(
+            number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space
+            number- ::= "{" space number-number-kv "}" space
+            number-kv ::= "\"number\"" space ":" space number-
+            number-number ::= "{" space number-number-root-kv "}" space
+            number-number-kv ::= "\"number\"" space ":" space number-number
+            number-number-root-kv ::= "\"root\"" space ":" space number
+            root ::= "{" space number-kv "}" space
+            space ::= " "?
+        )"""
+    });
+}
+
+int main() {
+    fprintf(stderr, "LLAMA_NODE_AVAILABLE = %s\n", getenv("LLAMA_NODE_AVAILABLE") ? "true" : "false");
+    fprintf(stderr, "LLAMA_PYTHON_AVAILABLE = %s\n", getenv("LLAMA_PYTHON_AVAILABLE") ? "true" : "false");
+
+    test_all("C++", [](const TestCase & tc) {
+        try {
+            tc.verify(json_schema_to_grammar(nlohmann::ordered_json::parse(tc.schema)));
+            tc.verify_status(SUCCESS);
+        } catch (const std::runtime_error & ex) {
+            fprintf(stderr, "Error: %s\n", ex.what());
+            tc.verify_status(FAILURE);
+        }
+    });
+
+    if (getenv("LLAMA_PYTHON_AVAILABLE") || (std::system("python --version") == 0)) {
+        test_all("Python", [](const TestCase & tc) {
+            write("test-json-schema-input.tmp", tc.schema);
+            tc.verify_status(std::system(
+                "python ./examples/json-schema-to-grammar.py test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE);
+            tc.verify(read("test-grammar-output.tmp"));
+        });
+    } else {
+        fprintf(stderr, "\033[33mWARNING: Python not found, skipping Python JSON schema -> grammar tests.\n\033[0m");
+    }
+
+    if (getenv("LLAMA_NODE_AVAILABLE") || (std::system("node --version") == 0)) {
+        test_all("JavaScript", [](const TestCase & tc) {
+            write("test-json-schema-input.tmp", tc.schema);
+            tc.verify_status(std::system(
+                "node ./tests/run-json-schema-to-grammar.mjs test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE);
+            tc.verify(read("test-grammar-output.tmp"));
+        });
+    } else {
+        fprintf(stderr, "\033[33mWARNING: Node not found, skipping JavaScript JSON schema -> grammar tests.\n\033[0m");
+    }
+
+    test_all("Check Expectations Validity", [](const TestCase & tc) {
+        if (tc.expected_status == SUCCESS) {
+            tc.verify_expectation_parseable();
+        }
+    });
+}
diff --git a/tests/test-llama-grammar.cpp b/tests/test-llama-grammar.cpp
index 73dd33dd2..27ca4d265 100644
--- a/tests/test-llama-grammar.cpp
+++ b/tests/test-llama-grammar.cpp
@@ -105,7 +105,7 @@ int main()
 
     for (auto rule : expected_rules)
     {
-        parsed_grammar.rules.push_back({});
+        parsed_grammar.rules.emplace_back();
         for (auto element : rule)
         {
             parsed_grammar.rules.back().push_back(element);
@@ -180,8 +180,8 @@ int main()
             if (expected_element.type != element->type || expected_element.value != element->value)
             {
                 fprintf(stderr, "index: %d\n", index);
-                fprintf(stderr, "expected_element: %d, %d\n", expected_element.type, expected_element.value);
-                fprintf(stderr, "actual_element: %d, %d\n", element->type, element->value);
+                fprintf(stderr, "expected_element: %d, %u\n", expected_element.type, expected_element.value);
+                fprintf(stderr, "actual_element: %d, %u\n", element->type, element->value);
                 fprintf(stderr, "expected_element != actual_element\n");
             }
 
@@ -190,7 +190,6 @@ int main()
         index++;
     }
 
-    std::vector<std::vector<const llama_grammar_element *>> next_stacks;
     std::vector<llama_grammar_candidate> next_candidates;
     next_candidates.resize(24);
 
diff --git a/tests/test-model-load-cancel.cpp b/tests/test-model-load-cancel.cpp
new file mode 100644
index 000000000..858535c3c
--- /dev/null
+++ b/tests/test-model-load-cancel.cpp
@@ -0,0 +1,27 @@
+#include "llama.h"
+#include "get-model.h"
+
+#include <cstdlib>
+
+int main(int argc, char *argv[] ) {
+    auto * model_path = get_model_or_exit(argc, argv);
+    auto * file = fopen(model_path, "r");
+    if (file == nullptr) {
+        fprintf(stderr, "no model at '%s' found\n", model_path);
+        return EXIT_FAILURE;
+    }
+
+    fprintf(stderr, "using '%s'\n", model_path);
+    fclose(file);
+
+    llama_backend_init();
+    auto params = llama_model_params{};
+    params.use_mmap = false;
+    params.progress_callback = [](float progress, void * ctx){
+        (void) ctx;
+        return progress > 0.50;
+    };
+    auto * model = llama_load_model_from_file(model_path, params);
+    llama_backend_free();
+    return model == nullptr ? EXIT_SUCCESS : EXIT_FAILURE;
+}
diff --git a/tests/test-opt.cpp b/tests/test-opt.cpp
index 2c9997fca..546ca230b 100644
--- a/tests/test-opt.cpp
+++ b/tests/test-opt.cpp
@@ -118,7 +118,7 @@ int main(void) {
     const float fe = ggml_get_f32_1d(e, 0);
     printf("%s: e = %.4f\n", __func__, fe);
 
-    struct ggml_opt_params opt_params = ggml_opt_default_params(GGML_OPT_ADAM);
+    struct ggml_opt_params opt_params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
 
     ggml_opt(ctx, opt_params, e);
 
diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp
index cee712618..e690ac6c8 100644
--- a/tests/test-quantize-fns.cpp
+++ b/tests/test-quantize-fns.cpp
@@ -17,7 +17,9 @@ constexpr float MAX_QUANTIZATION_REFERENCE_ERROR = 0.0001f;
 constexpr float MAX_QUANTIZATION_TOTAL_ERROR = 0.002f;
 constexpr float MAX_QUANTIZATION_TOTAL_ERROR_2BITS = 0.0075f;
 constexpr float MAX_QUANTIZATION_TOTAL_ERROR_3BITS = 0.0040f;
+constexpr float MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS = 0.0050f;
 constexpr float MAX_DOT_PRODUCT_ERROR = 0.02f;
+constexpr float MAX_DOT_PRODUCT_ERROR_LOWBIT = 0.04f;
 
 static const char* RESULT_STR[] = {"ok", "FAILED"};
 
@@ -85,7 +87,7 @@ static float dot_product_error(
     vdot.from_float(test_data2, tmp_q2.data(), test_size);
 
     float result = INFINITY;
-    qfns.vec_dot(test_size, &result, tmp_q1.data(), tmp_q2.data());
+    qfns.vec_dot(test_size, &result, 0, tmp_q1.data(), 0, tmp_q2.data(), 0, 1);
 
     const float dot_ref = dot_product(test_data1, test_data2, test_size);
 
@@ -134,18 +136,19 @@ int main(int argc, char * argv[]) {
             continue;
         }
 
-        if ((ggml_type)i == GGML_TYPE_IQ2_XXS) {
-            printf("Skip %s due to missing quantization functionality\n", ggml_type_name((ggml_type) i));
-            continue;
-        }
+        const ggml_type ei = (ggml_type)i;
 
         printf("Testing %s\n", ggml_type_name((ggml_type) i));
+        ggml_quantize_init(ei);
 
         if (qfns.from_float && qfns.to_float) {
             const float total_error = total_quantization_error(qfns, test_size, test_data.data());
             const float max_quantization_error =
-                type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
-                type == GGML_TYPE_Q3_K ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS : MAX_QUANTIZATION_TOTAL_ERROR;
+                type == GGML_TYPE_Q2_K    ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
+                type == GGML_TYPE_IQ2_S   ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
+                type == GGML_TYPE_Q3_K    ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS :
+                type == GGML_TYPE_IQ3_S   ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS :
+                type == GGML_TYPE_IQ3_XXS ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS : MAX_QUANTIZATION_TOTAL_ERROR;
             failed = !(total_error < max_quantization_error);
             num_failed += failed;
             if (failed || verbose) {
@@ -160,7 +163,11 @@ int main(int argc, char * argv[]) {
             }
 
             const float vec_dot_error = dot_product_error(qfns, test_size, test_data.data(), test_data2.data());
-            failed = !(vec_dot_error < MAX_DOT_PRODUCT_ERROR);
+            const float max_allowed_error = type == GGML_TYPE_Q2_K || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ2_XXS ||
+                                            type == GGML_TYPE_IQ3_XXS || type == GGML_TYPE_IQ3_S || type == GGML_TYPE_IQ2_S
+                                          ? MAX_DOT_PRODUCT_ERROR_LOWBIT
+                                          : MAX_DOT_PRODUCT_ERROR;
+            failed = !(vec_dot_error < max_allowed_error);
             num_failed += failed;
             if (failed || verbose) {
                 printf("%5s dot product error:              %s (%f)\n", ggml_type_name(type), RESULT_STR[failed], vec_dot_error);
diff --git a/tests/test-quantize-perf.cpp b/tests/test-quantize-perf.cpp
index 09d410b7f..48d9fae3d 100644
--- a/tests/test-quantize-perf.cpp
+++ b/tests/test-quantize-perf.cpp
@@ -278,6 +278,8 @@ int main(int argc, char * argv[]) {
         if (qfns.from_float && qfns.to_float) {
             printf("%s\n", ggml_type_name(type));
 
+            ggml_quantize_init(type);
+
             if (params.op_quantize_row_q_reference) {
                 printf("  quantize_row_q_reference\n");
                 for (size_t size : params.test_sizes) {
@@ -344,7 +346,7 @@ int main(int argc, char * argv[]) {
                     printf("    %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
                     auto quantize_fn = [&](void) -> float {
                         float result;
-                        qfns.vec_dot(size, &result, test_q1, test_q2);
+                        qfns.vec_dot(size, &result, 0, test_q1, 0, test_q2, 0, 1);
                         return result;
                     };
                     size_t quantized_size = ggml_row_size(type, size);
diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp
index 32e58941c..6374958fe 100644
--- a/tests/test-sampling.cpp
+++ b/tests/test-sampling.cpp
@@ -5,11 +5,10 @@
 #undef NDEBUG
 #endif
 
-#include <cmath>
-#include <numeric>
-#include <cassert>
-#include <vector>
 #include <algorithm>
+#include <cmath>
+#include <string>
+#include <vector>
 
 static void dump(const llama_token_data_array * candidates) {
     for (size_t i = 0; i < candidates->size; i++) {
@@ -20,11 +19,11 @@ static void dump(const llama_token_data_array * candidates) {
 #define DUMP(__candidates) do { printf("%s:%d (%s)\n", __FILE__, __LINE__, __func__); dump((__candidates)); printf("-\n"); } while(0)
 
 static void test_top_k(const std::vector<float> & probs, const std::vector<float> & expected_probs, int k) {
-    size_t n_vocab = probs.size();
+    const size_t n_vocab = probs.size();
     std::vector<llama_token_data> candidates;
     candidates.reserve(n_vocab);
     for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
-        float logit = log(probs[token_id]);
+        const float logit = logf(probs[token_id]);
         candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
     }
 
@@ -41,11 +40,11 @@ static void test_top_k(const std::vector<float> & probs, const std::vector<float
 }
 
 static void test_top_p(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p) {
-    size_t n_vocab = probs.size();
+    const size_t n_vocab = probs.size();
     std::vector<llama_token_data> candidates;
     candidates.reserve(n_vocab);
     for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
-        float logit = log(probs[token_id]);
+        const float logit = logf(probs[token_id]);
         candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
     }
 
@@ -62,11 +61,11 @@ static void test_top_p(const std::vector<float> & probs, const std::vector<float
 }
 
 static void test_tfs(const std::vector<float> & probs, const std::vector<float> & expected_probs, float z) {
-    size_t n_vocab = probs.size();
+    const size_t n_vocab = probs.size();
     std::vector<llama_token_data> candidates;
     candidates.reserve(n_vocab);
     for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
-        float logit = log(probs[token_id]);
+        const float logit = logf(probs[token_id]);
         candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
     }
 
@@ -81,12 +80,33 @@ static void test_tfs(const std::vector<float> & probs, const std::vector<float>
     }
 }
 
-static void test_typical(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p) {
-    size_t n_vocab = probs.size();
+static void test_min_p(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p) {
+    const size_t n_vocab = probs.size();
     std::vector<llama_token_data> candidates;
     candidates.reserve(n_vocab);
     for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
-        float logit = log(probs[token_id]);
+        const float logit = logf(probs[token_id]);
+        candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
+    }
+
+    llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+    DUMP(&candidates_p);
+    llama_sample_min_p(nullptr, &candidates_p, p, 1);
+    DUMP(&candidates_p);
+    llama_sample_softmax(nullptr, &candidates_p);
+
+    GGML_ASSERT(candidates_p.size == expected_probs.size());
+    for (size_t i = 0; i < candidates_p.size; i++) {
+        GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
+    }
+}
+
+static void test_typical(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p) {
+    const size_t n_vocab = probs.size();
+    std::vector<llama_token_data> candidates;
+    candidates.reserve(n_vocab);
+    for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
+        const float logit = logf(probs[token_id]);
         candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
     }
 
@@ -107,11 +127,11 @@ static void test_repetition_penalties(
 ) {
     GGML_ASSERT(probs.size() == expected_probs.size());
 
-    size_t n_vocab = probs.size();
+    const size_t n_vocab = probs.size();
     std::vector<llama_token_data> candidates;
     candidates.reserve(n_vocab);
     for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
-        float logit = log(probs[token_id]);
+        const float logit = logf(probs[token_id]);
         candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
     }
 
@@ -128,17 +148,110 @@ static void test_repetition_penalties(
     }
 }
 
+static void test_sampler_queue(
+    const size_t n_vocab, const std::string samplers_sequence, const int top_k, const float top_p, const float min_p
+) {
+    std::vector<llama_token_data> candidates;
+    candidates.reserve(n_vocab);
+    for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
+        const float logit = logf(token_id);
+        candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
+    }
+
+    llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+
+          llama_token min_token_id = 0;
+    const llama_token max_token_id = n_vocab-1;
+
+    for (auto s : samplers_sequence) {
+        switch (s){
+            case 'k': llama_sample_top_k    (nullptr, &candidates_p, top_k, 1); break;
+            case 'f': GGML_ASSERT(false && "tail_free test not implemented");   break;
+            case 'y': GGML_ASSERT(false && "typical test not implemented");     break;
+            case 'p': llama_sample_top_p    (nullptr, &candidates_p, top_p, 1); break;
+            case 'm': llama_sample_min_p    (nullptr, &candidates_p, min_p, 1); break;
+            case 't': GGML_ASSERT(false && "temperature test not implemented"); break;
+            default : GGML_ASSERT(false && "Unknown sampler");                  break;
+        }
+
+        llama_sample_softmax(nullptr, &candidates_p); // make sure tokens are sorted for tests
+
+        const int size = candidates_p.size;
+
+        if (s == 'k') {
+            const int expected_size = std::min(size, top_k);
+            min_token_id = std::max(min_token_id, (llama_token)(n_vocab - top_k));
+
+            GGML_ASSERT(size == expected_size);
+            GGML_ASSERT(candidates_p.data[0].id == max_token_id);
+            GGML_ASSERT(candidates_p.data[expected_size-1].id == min_token_id);
+        } else if (s == 'p') {
+            const int softmax_divisor = n_vocab * (n_vocab-1) / 2 - min_token_id * (min_token_id-1) / 2;
+            const int softmax_numerator_target = ceilf(top_p * softmax_divisor);
+
+                min_token_id  = n_vocab;
+            int expected_size = 0;
+            int cumsum        = 0;
+            do { // do-while because always at least one token is sampled
+                min_token_id--;
+                expected_size++;
+
+                cumsum += min_token_id;
+            } while (cumsum < softmax_numerator_target);
+
+            // token 0 has p == 0, need special consideration for cumsum because top_p immediately returns
+            if (min_token_id == 1) {
+                min_token_id--;
+                expected_size += 1;
+            }
+
+            GGML_ASSERT(size == expected_size);
+            GGML_ASSERT(candidates_p.data[0].id == max_token_id);
+            GGML_ASSERT(candidates_p.data[expected_size-1].id == min_token_id);
+        } else if (s == 'm') {
+            int expected_size = ceilf((1.0f-min_p) * n_vocab);
+            expected_size = std::max(expected_size, 1);
+            expected_size = std::min(expected_size, size);
+
+            min_token_id = floorf(min_p * n_vocab);
+            min_token_id = std::max(min_token_id, 1);
+            min_token_id = std::max(min_token_id, (llama_token)(n_vocab - size));
+            min_token_id = std::min(min_token_id, (llama_token)(n_vocab - 1));
+
+            GGML_ASSERT(size == expected_size);
+            GGML_ASSERT(candidates_p.data[0].id == max_token_id);
+            GGML_ASSERT(candidates_p.data[expected_size-1].id == min_token_id);
+        } else {
+            GGML_ASSERT(false);
+        }
+    }
+
+    printf("Sampler queue %3s OK with n_vocab=%05ld top_k=%05d top_p=%f min_p=%f\n",
+           samplers_sequence.c_str(), n_vocab, top_k, top_p, min_p);
+}
+
 int main(void) {
     ggml_time_init();
 
     test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 1);
     test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f}, 3);
+    test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 4);
+    test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 0);
 
     test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 0);
     test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f}, 0.7f);
     test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f}, 0.8f);
     test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1);
 
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/1.0f, 0.3f/1.0f, 0.2f/1.0f, 0.1f/1.0f}, 0.00f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/1.0f, 0.3f/1.0f, 0.2f/1.0f, 0.1f/1.0f}, 0.24f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.9f, 0.3f/0.9f, 0.2f/0.9f},            0.26f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.9f, 0.3f/0.9f, 0.2f/0.9f},            0.49f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.7f, 0.3f/0.7f},                       0.51f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.7f, 0.3f/0.7f},                       0.74f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f},                                  0.76f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f},                                  1.00f);
+
     test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f}, 0.25f);
     test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.75f);
     test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.99f);
@@ -154,6 +267,34 @@ int main(void) {
     test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2},       {0.499966f, 0.499966f, 0.000023f, 0.000023f, 0.000023f}, 1.0f, 5.0f, 5.0f);
     test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.499977f, 0.499977f, 0.000023f, 0.000023f, 0.000000f}, 1.0f, 5.0f, 5.0f);
 
+    test_sampler_queue(10000, "k", 10000, 1.0f, 1.0f);
+    test_sampler_queue(10000, "k",     1, 1.0f, 1.0f);
+    test_sampler_queue(10000, "p", 10000, 1.0f, 1.0f);
+    test_sampler_queue(10000, "p", 10000, 0.0f, 1.0f);
+    test_sampler_queue(10000, "m", 10000, 1.0f, 1.0f);
+    test_sampler_queue(10000, "m", 10000, 1.0f, 1e-12);
+
+    test_sampler_queue(10000, "k",   100, 1.0000f, 1.0f);
+    test_sampler_queue(10000, "p", 10000, 0.0002f, 1.0f);
+    test_sampler_queue(10000, "p", 10000, 0.8000f, 1.0f);
+    test_sampler_queue(10000, "m", 10000, 1.0000f, 9997.9f/9999.0f);
+    test_sampler_queue(10000, "m", 10000, 1.0000f, 0.1f);
+
+    test_sampler_queue(10000, "kp", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "km", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "pk", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "pm", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "mk", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "mp", 100, 0.8f, 9997.9f/9999.0f);
+    test_sampler_queue(10000, "mp", 100, 0.8f, 0.1f);
+
+    test_sampler_queue(10000, "kpm", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "kmp", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "pkm", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "pmk", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "mkp", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "mpk", 100, 0.8f, 0.1f);
+
     printf("OK\n");
 
     return 0;
diff --git a/tests/test-tokenizer-0-falcon.cpp b/tests/test-tokenizer-0-falcon.cpp
index a4e9d2b91..472b0b3a8 100644
--- a/tests/test-tokenizer-0-falcon.cpp
+++ b/tests/test-tokenizer-0-falcon.cpp
@@ -61,7 +61,7 @@ int main(int argc, char **argv) {
     llama_model * model;
     llama_context * ctx;
 
-    llama_backend_init(false);
+    llama_backend_init();
 
     // load the vocab
     {
diff --git a/tests/test-tokenizer-0-llama.cpp b/tests/test-tokenizer-0-llama.cpp
index 39c8d188c..0a16cd7eb 100644
--- a/tests/test-tokenizer-0-llama.cpp
+++ b/tests/test-tokenizer-0-llama.cpp
@@ -60,7 +60,7 @@ int main(int argc, char **argv) {
     llama_model * model;
     llama_context * ctx;
 
-    llama_backend_init(false);
+    llama_backend_init();
 
     // load the vocab
     {
diff --git a/tests/test-tokenizer-1-bpe.cpp b/tests/test-tokenizer-1-bpe.cpp
index 386530f23..a0e2caf94 100644
--- a/tests/test-tokenizer-1-bpe.cpp
+++ b/tests/test-tokenizer-1-bpe.cpp
@@ -4,13 +4,13 @@
 #include "console.h"
 
 #include <cassert>
+#include <codecvt>
 #include <cstdio>
 #include <cstring>
-#include <string>
-#include <codecvt>
-#include <map>
-#include <vector>
 #include <locale>
+#include <string>
+#include <thread>
+#include <vector>
 
 int main(int argc, char **argv) {
     if (argc < 2) {
@@ -25,7 +25,7 @@ int main(int argc, char **argv) {
     llama_model * model;
     llama_context * ctx;
 
-    llama_backend_init(false);
+    llama_backend_init();
 
     // load the vocab
     {
@@ -64,7 +64,7 @@ int main(int argc, char **argv) {
     for (int i = 0; i < n_vocab; ++i) {
         std::string str = llama_detokenize_bpe(ctx, std::vector<int>(1, i));
         try {
-            auto cps = codepoints_from_utf8(str);
+            auto cps = unicode_cpts_from_utf8(str);
             std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
             std::string check = llama_detokenize_bpe(ctx, tokens);
             if (check != str) {
@@ -74,45 +74,46 @@ int main(int argc, char **argv) {
             }
         }
         catch (const std::invalid_argument &) {
-            fprintf(stderr, "%s : info: utf8 conversion %d '%s'\n", __func__, i, str.c_str());
+            //fprintf(stderr, "%s : info: utf8 conversion %d '%s'\n", __func__, i, str.c_str());
         }
     }
 
-    for (uint32_t cp = 0x0000; cp < 0xffff; ++cp) {
-        // NOTE: these exceptions seem to be necessary, because the GPT2 tokenizer doesn't want to interfere with some ASCII control characters
-        if ((cp < 0x03 || cp > 0x05) && cp != 0x0b && cp != 0x11 && (cp < 0x13 || cp > 0x17) && cp != 0x19 && (cp < 0x1c || cp > 0x1e) && (cp < 0xd800 || cp > 0xdfff)) {
-            std::string str = " " + codepoint_to_utf8(cp);
-            std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
-            std::string check = llama_detokenize_bpe(ctx, tokens);
-            if (str != check) {
-                fprintf(stderr, "%s : error: codepoint %x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n",
-                    __func__, cp, check.c_str(), check.length(), str.c_str(), str.length());
-                return 3;
-            }
-        }
-    }
-    // Restrict to assigned unicode planes
-    // for (uint32_t cp = 0x10000; cp < 0x0010ffff; ++cp) {
-    for (uint32_t cp = 0x10000; cp < 0x00040000; ++cp) {
-        std::string str = codepoint_to_utf8(cp);
-        std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
-        std::string check = llama_detokenize_bpe(ctx, tokens);
-        if (str != check) {
-            fprintf(stderr, "%s : error: codepoint %x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n",
-                __func__, cp, check.c_str(), check.length(), str.c_str(), str.length());
-            return 4;
-        }
-    }
-    for (uint32_t cp = 0x000e0000; cp < 0x0010ffff; ++cp) {
-        std::string str = codepoint_to_utf8(cp);
-        std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
-        std::string check = llama_detokenize_bpe(ctx, tokens);
-        if (str != check) {
-            fprintf(stderr, "%s : error: codepoint %x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n",
-                __func__, cp, check.c_str(), check.length(), str.c_str(), str.length());
-            return 4;
+    // unicode
+    {
+        const int nthread = std::thread::hardware_concurrency();
+
+        std::vector<std::thread> threads(nthread);
+
+        for (int i = 0; i < nthread; ++i) {
+            threads[i] = std::thread([i, nthread, ctx]() {
+                for (uint32_t cp = i; cp < 0x0010ffff; cp += nthread) {
+                    if (!( // NOLINT
+                                (cp < 0x03       || cp >  0x05)   && cp != 0x0b && cp != 0x11 &&
+                                (cp < 0x13       || cp >  0x17)   && cp != 0x19 &&
+                                (cp < 0x1c       || cp >  0x1e)   &&
+                                (cp < 0xd800     || cp >  0xdfff) &&
+                                (cp < 0x00040000 || cp >= 0x000e0000)
+                        )) {
+                        continue;
+                    }
+
+                    std::string str = unicode_cpt_to_utf8(cp);
+                    std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
+                    std::string check = llama_detokenize_bpe(ctx, tokens);
+                    if (cp != 9601 && str != check) {
+                        fprintf(stderr, "error: codepoint %x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n",
+                                cp, check.c_str(), check.length(), str.c_str(), str.length());
+                        std::exit(3);
+                    }
+                }
+            });
+        }
+
+        for (auto & t : threads) {
+            t.join();
         }
     }
+
     llama_free_model(model);
     llama_free(ctx);
 
diff --git a/tests/test-tokenizer-1-llama.cpp b/tests/test-tokenizer-1-llama.cpp
index 4b58fe495..8caf0b24e 100644
--- a/tests/test-tokenizer-1-llama.cpp
+++ b/tests/test-tokenizer-1-llama.cpp
@@ -4,13 +4,13 @@
 #include "console.h"
 
 #include <cassert>
+#include <codecvt>
 #include <cstdio>
 #include <cstring>
-#include <string>
-#include <codecvt>
-#include <map>
-#include <vector>
 #include <locale>
+#include <string>
+#include <thread>
+#include <vector>
 
 int main(int argc, char **argv) {
     if (argc < 2) {
@@ -25,7 +25,7 @@ int main(int argc, char **argv) {
     llama_model * model;
     llama_context * ctx;
 
-    llama_backend_init(false);
+    llama_backend_init();
 
     // load the vocab
     {
@@ -72,26 +72,33 @@ int main(int argc, char **argv) {
         }
     }
 
-    for (uint32_t cp = 0x0000; cp < 0xffff; ++cp) {
-        if (cp < 0xd800 || cp > 0xdfff) {
-            std::string str = codepoint_to_utf8(cp);
-            std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
-            std::string check = llama_detokenize_spm(ctx, tokens);
-            if (cp != 9601 && str != check) {
-                fprintf(stderr, "%s : error: codepoint %d detokenizes to '%s'(%zu) instead of '%s'(%zu)\n",
-                    __func__, cp, check.c_str(), check.length(), str.c_str(), str.length());
-                return 3;
-            }
+    // unicode
+    {
+        const int nthread = std::thread::hardware_concurrency();
+
+        std::vector<std::thread> threads(nthread);
+
+        for (int i = 0; i < nthread; ++i) {
+            threads[i] = std::thread([i, nthread, ctx]() {
+                for (uint32_t cp = i; cp < 0x0010ffff; cp += nthread) {
+                    if (cp >= 0xd800 && cp <= 0xdfff) {
+                        continue;
+                    }
+
+                    std::string str = unicode_cpt_to_utf8(cp);
+                    std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
+                    std::string check = llama_detokenize_spm(ctx, tokens);
+                    if (cp != 9601 && str != check) {
+                        fprintf(stderr, "error: codepoint %x detokenizes to '%s'(%zu) instead of '%s'(%zu)\n",
+                                cp, check.c_str(), check.length(), str.c_str(), str.length());
+                        std::exit(3);
+                    }
+                }
+            });
         }
-    }
-    for (uint32_t cp = 0x10000; cp < 0x0010ffff; ++cp) {
-        std::string str = codepoint_to_utf8(cp);
-        std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
-        std::string check = llama_detokenize_spm(ctx, tokens);
-        if (str != check) {
-            fprintf(stderr, "%s : error: codepoint %d detokenizes to '%s'(%zu) instead of '%s'(%zu)\n",
-                __func__, cp, check.c_str(), check.length(), str.c_str(), str.length());
-            return 4;
+
+        for (auto & t : threads) {
+            t.join();
         }
     }
 
diff --git a/unicode-data.cpp b/unicode-data.cpp
new file mode 100644
index 000000000..22f8b0f0b
--- /dev/null
+++ b/unicode-data.cpp
@@ -0,0 +1,1651 @@
+#include "unicode-data.h"
+
+#include <cstdint>
+#include <map>
+#include <utility>
+#include <vector>
+
+const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_digit = {
+{0x00000030, 0x00000039}, {0x000000B2, 0x000000B3}, {0x000000B9, 0x000000B9}, {0x00000660, 0x00000669},
+{0x000006F0, 0x000006F9}, {0x000007C0, 0x000007C9}, {0x00000966, 0x0000096F}, {0x000009E6, 0x000009EF},
+{0x00000A66, 0x00000A6F}, {0x00000AE6, 0x00000AEF}, {0x00000B66, 0x00000B6F}, {0x00000BE6, 0x00000BEF},
+{0x00000C66, 0x00000C6F}, {0x00000CE6, 0x00000CEF}, {0x00000D66, 0x00000D6F}, {0x00000DE6, 0x00000DEF},
+{0x00000E50, 0x00000E59}, {0x00000ED0, 0x00000ED9}, {0x00000F20, 0x00000F29}, {0x00001040, 0x00001049},
+{0x00001090, 0x00001099}, {0x00001369, 0x00001371}, {0x000017E0, 0x000017E9}, {0x00001810, 0x00001819},
+{0x00001946, 0x0000194F}, {0x000019D0, 0x000019DA}, {0x00001A80, 0x00001A89}, {0x00001A90, 0x00001A99},
+{0x00001B50, 0x00001B59}, {0x00001BB0, 0x00001BB9}, {0x00001C40, 0x00001C49}, {0x00001C50, 0x00001C59},
+{0x00002070, 0x00002070}, {0x00002074, 0x00002079}, {0x00002080, 0x00002089}, {0x00002460, 0x00002468},
+{0x00002474, 0x0000247C}, {0x00002488, 0x00002490}, {0x000024EA, 0x000024EA}, {0x000024F5, 0x000024FD},
+{0x000024FF, 0x000024FF}, {0x00002776, 0x0000277E}, {0x00002780, 0x00002788}, {0x0000278A, 0x00002792},
+{0x0000A620, 0x0000A629}, {0x0000A8D0, 0x0000A8D9}, {0x0000A900, 0x0000A909}, {0x0000A9D0, 0x0000A9D9},
+{0x0000A9F0, 0x0000A9F9}, {0x0000AA50, 0x0000AA59}, {0x0000ABF0, 0x0000ABF9}, {0x0000FF10, 0x0000FF19},
+{0x000104A0, 0x000104A9}, {0x00010A40, 0x00010A43}, {0x00010D30, 0x00010D39}, {0x00010E60, 0x00010E68},
+{0x00011052, 0x0001105A}, {0x00011066, 0x0001106F}, {0x000110F0, 0x000110F9}, {0x00011136, 0x0001113F},
+{0x000111D0, 0x000111D9}, {0x000112F0, 0x000112F9}, {0x00011450, 0x00011459}, {0x000114D0, 0x000114D9},
+{0x00011650, 0x00011659}, {0x000116C0, 0x000116C9}, {0x00011730, 0x00011739}, {0x000118E0, 0x000118E9},
+{0x00011950, 0x00011959}, {0x00011C50, 0x00011C59}, {0x00011D50, 0x00011D59}, {0x00011DA0, 0x00011DA9},
+{0x00016A60, 0x00016A69}, {0x00016B50, 0x00016B59}, {0x0001D7CE, 0x0001D7FF}, {0x0001E140, 0x0001E149},
+{0x0001E2F0, 0x0001E2F9}, {0x0001E950, 0x0001E959}, {0x0001F100, 0x0001F10A}, {0x0001FBF0, 0x0001FBF9},
+};
+
+const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_letter = {
+{0x00000041, 0x0000005A}, {0x00000061, 0x0000007A}, {0x000000AA, 0x000000AA}, {0x000000B5, 0x000000B5},
+{0x000000BA, 0x000000BA}, {0x000000C0, 0x000000D6}, {0x000000D8, 0x000000F6}, {0x000000F8, 0x000002C1},
+{0x000002C6, 0x000002D1}, {0x000002E0, 0x000002E4}, {0x000002EC, 0x000002EC}, {0x000002EE, 0x000002EE},
+{0x00000370, 0x00000374}, {0x00000376, 0x00000377}, {0x0000037A, 0x0000037D}, {0x0000037F, 0x0000037F},
+{0x00000386, 0x00000386}, {0x00000388, 0x0000038A}, {0x0000038C, 0x0000038C}, {0x0000038E, 0x000003A1},
+{0x000003A3, 0x000003F5}, {0x000003F7, 0x00000481}, {0x0000048A, 0x0000052F}, {0x00000531, 0x00000556},
+{0x00000559, 0x00000559}, {0x00000560, 0x00000588}, {0x000005D0, 0x000005EA}, {0x000005EF, 0x000005F2},
+{0x00000620, 0x0000064A}, {0x0000066E, 0x0000066F}, {0x00000671, 0x000006D3}, {0x000006D5, 0x000006D5},
+{0x000006E5, 0x000006E6}, {0x000006EE, 0x000006EF}, {0x000006FA, 0x000006FC}, {0x000006FF, 0x000006FF},
+{0x00000710, 0x00000710}, {0x00000712, 0x0000072F}, {0x0000074D, 0x000007A5}, {0x000007B1, 0x000007B1},
+{0x000007CA, 0x000007EA}, {0x000007F4, 0x000007F5}, {0x000007FA, 0x000007FA}, {0x00000800, 0x00000815},
+{0x0000081A, 0x0000081A}, {0x00000824, 0x00000824}, {0x00000828, 0x00000828}, {0x00000840, 0x00000858},
+{0x00000860, 0x0000086A}, {0x000008A0, 0x000008B4}, {0x000008B6, 0x000008C7}, {0x00000904, 0x00000939},
+{0x0000093D, 0x0000093D}, {0x00000950, 0x00000950}, {0x00000958, 0x00000961}, {0x00000971, 0x00000980},
+{0x00000985, 0x0000098C}, {0x0000098F, 0x00000990}, {0x00000993, 0x000009A8}, {0x000009AA, 0x000009B0},
+{0x000009B2, 0x000009B2}, {0x000009B6, 0x000009B9}, {0x000009BD, 0x000009BD}, {0x000009CE, 0x000009CE},
+{0x000009DC, 0x000009DD}, {0x000009DF, 0x000009E1}, {0x000009F0, 0x000009F1}, {0x000009FC, 0x000009FC},
+{0x00000A05, 0x00000A0A}, {0x00000A0F, 0x00000A10}, {0x00000A13, 0x00000A28}, {0x00000A2A, 0x00000A30},
+{0x00000A32, 0x00000A33}, {0x00000A35, 0x00000A36}, {0x00000A38, 0x00000A39}, {0x00000A59, 0x00000A5C},
+{0x00000A5E, 0x00000A5E}, {0x00000A72, 0x00000A74}, {0x00000A85, 0x00000A8D}, {0x00000A8F, 0x00000A91},
+{0x00000A93, 0x00000AA8}, {0x00000AAA, 0x00000AB0}, {0x00000AB2, 0x00000AB3}, {0x00000AB5, 0x00000AB9},
+{0x00000ABD, 0x00000ABD}, {0x00000AD0, 0x00000AD0}, {0x00000AE0, 0x00000AE1}, {0x00000AF9, 0x00000AF9},
+{0x00000B05, 0x00000B0C}, {0x00000B0F, 0x00000B10}, {0x00000B13, 0x00000B28}, {0x00000B2A, 0x00000B30},
+{0x00000B32, 0x00000B33}, {0x00000B35, 0x00000B39}, {0x00000B3D, 0x00000B3D}, {0x00000B5C, 0x00000B5D},
+{0x00000B5F, 0x00000B61}, {0x00000B71, 0x00000B71}, {0x00000B83, 0x00000B83}, {0x00000B85, 0x00000B8A},
+{0x00000B8E, 0x00000B90}, {0x00000B92, 0x00000B95}, {0x00000B99, 0x00000B9A}, {0x00000B9C, 0x00000B9C},
+{0x00000B9E, 0x00000B9F}, {0x00000BA3, 0x00000BA4}, {0x00000BA8, 0x00000BAA}, {0x00000BAE, 0x00000BB9},
+{0x00000BD0, 0x00000BD0}, {0x00000C05, 0x00000C0C}, {0x00000C0E, 0x00000C10}, {0x00000C12, 0x00000C28},
+{0x00000C2A, 0x00000C39}, {0x00000C3D, 0x00000C3D}, {0x00000C58, 0x00000C5A}, {0x00000C60, 0x00000C61},
+{0x00000C80, 0x00000C80}, {0x00000C85, 0x00000C8C}, {0x00000C8E, 0x00000C90}, {0x00000C92, 0x00000CA8},
+{0x00000CAA, 0x00000CB3}, {0x00000CB5, 0x00000CB9}, {0x00000CBD, 0x00000CBD}, {0x00000CDE, 0x00000CDE},
+{0x00000CE0, 0x00000CE1}, {0x00000CF1, 0x00000CF2}, {0x00000D04, 0x00000D0C}, {0x00000D0E, 0x00000D10},
+{0x00000D12, 0x00000D3A}, {0x00000D3D, 0x00000D3D}, {0x00000D4E, 0x00000D4E}, {0x00000D54, 0x00000D56},
+{0x00000D5F, 0x00000D61}, {0x00000D7A, 0x00000D7F}, {0x00000D85, 0x00000D96}, {0x00000D9A, 0x00000DB1},
+{0x00000DB3, 0x00000DBB}, {0x00000DBD, 0x00000DBD}, {0x00000DC0, 0x00000DC6}, {0x00000E01, 0x00000E30},
+{0x00000E32, 0x00000E33}, {0x00000E40, 0x00000E46}, {0x00000E81, 0x00000E82}, {0x00000E84, 0x00000E84},
+{0x00000E86, 0x00000E8A}, {0x00000E8C, 0x00000EA3}, {0x00000EA5, 0x00000EA5}, {0x00000EA7, 0x00000EB0},
+{0x00000EB2, 0x00000EB3}, {0x00000EBD, 0x00000EBD}, {0x00000EC0, 0x00000EC4}, {0x00000EC6, 0x00000EC6},
+{0x00000EDC, 0x00000EDF}, {0x00000F00, 0x00000F00}, {0x00000F40, 0x00000F47}, {0x00000F49, 0x00000F6C},
+{0x00000F88, 0x00000F8C}, {0x00001000, 0x0000102A}, {0x0000103F, 0x0000103F}, {0x00001050, 0x00001055},
+{0x0000105A, 0x0000105D}, {0x00001061, 0x00001061}, {0x00001065, 0x00001066}, {0x0000106E, 0x00001070},
+{0x00001075, 0x00001081}, {0x0000108E, 0x0000108E}, {0x000010A0, 0x000010C5}, {0x000010C7, 0x000010C7},
+{0x000010CD, 0x000010CD}, {0x000010D0, 0x000010FA}, {0x000010FC, 0x00001248}, {0x0000124A, 0x0000124D},
+{0x00001250, 0x00001256}, {0x00001258, 0x00001258}, {0x0000125A, 0x0000125D}, {0x00001260, 0x00001288},
+{0x0000128A, 0x0000128D}, {0x00001290, 0x000012B0}, {0x000012B2, 0x000012B5}, {0x000012B8, 0x000012BE},
+{0x000012C0, 0x000012C0}, {0x000012C2, 0x000012C5}, {0x000012C8, 0x000012D6}, {0x000012D8, 0x00001310},
+{0x00001312, 0x00001315}, {0x00001318, 0x0000135A}, {0x00001380, 0x0000138F}, {0x000013A0, 0x000013F5},
+{0x000013F8, 0x000013FD}, {0x00001401, 0x0000166C}, {0x0000166F, 0x0000167F}, {0x00001681, 0x0000169A},
+{0x000016A0, 0x000016EA}, {0x000016F1, 0x000016F8}, {0x00001700, 0x0000170C}, {0x0000170E, 0x00001711},
+{0x00001720, 0x00001731}, {0x00001740, 0x00001751}, {0x00001760, 0x0000176C}, {0x0000176E, 0x00001770},
+{0x00001780, 0x000017B3}, {0x000017D7, 0x000017D7}, {0x000017DC, 0x000017DC}, {0x00001820, 0x00001878},
+{0x00001880, 0x00001884}, {0x00001887, 0x000018A8}, {0x000018AA, 0x000018AA}, {0x000018B0, 0x000018F5},
+{0x00001900, 0x0000191E}, {0x00001950, 0x0000196D}, {0x00001970, 0x00001974}, {0x00001980, 0x000019AB},
+{0x000019B0, 0x000019C9}, {0x00001A00, 0x00001A16}, {0x00001A20, 0x00001A54}, {0x00001AA7, 0x00001AA7},
+{0x00001B05, 0x00001B33}, {0x00001B45, 0x00001B4B}, {0x00001B83, 0x00001BA0}, {0x00001BAE, 0x00001BAF},
+{0x00001BBA, 0x00001BE5}, {0x00001C00, 0x00001C23}, {0x00001C4D, 0x00001C4F}, {0x00001C5A, 0x00001C7D},
+{0x00001C80, 0x00001C88}, {0x00001C90, 0x00001CBA}, {0x00001CBD, 0x00001CBF}, {0x00001CE9, 0x00001CEC},
+{0x00001CEE, 0x00001CF3}, {0x00001CF5, 0x00001CF6}, {0x00001CFA, 0x00001CFA}, {0x00001D00, 0x00001DBF},
+{0x00001E00, 0x00001F15}, {0x00001F18, 0x00001F1D}, {0x00001F20, 0x00001F45}, {0x00001F48, 0x00001F4D},
+{0x00001F50, 0x00001F57}, {0x00001F59, 0x00001F59}, {0x00001F5B, 0x00001F5B}, {0x00001F5D, 0x00001F5D},
+{0x00001F5F, 0x00001F7D}, {0x00001F80, 0x00001FB4}, {0x00001FB6, 0x00001FBC}, {0x00001FBE, 0x00001FBE},
+{0x00001FC2, 0x00001FC4}, {0x00001FC6, 0x00001FCC}, {0x00001FD0, 0x00001FD3}, {0x00001FD6, 0x00001FDB},
+{0x00001FE0, 0x00001FEC}, {0x00001FF2, 0x00001FF4}, {0x00001FF6, 0x00001FFC}, {0x00002071, 0x00002071},
+{0x0000207F, 0x0000207F}, {0x00002090, 0x0000209C}, {0x00002102, 0x00002102}, {0x00002107, 0x00002107},
+{0x0000210A, 0x00002113}, {0x00002115, 0x00002115}, {0x00002119, 0x0000211D}, {0x00002124, 0x00002124},
+{0x00002126, 0x00002126}, {0x00002128, 0x00002128}, {0x0000212A, 0x0000212D}, {0x0000212F, 0x00002139},
+{0x0000213C, 0x0000213F}, {0x00002145, 0x00002149}, {0x0000214E, 0x0000214E}, {0x00002183, 0x00002184},
+{0x00002C00, 0x00002C2E}, {0x00002C30, 0x00002C5E}, {0x00002C60, 0x00002CE4}, {0x00002CEB, 0x00002CEE},
+{0x00002CF2, 0x00002CF3}, {0x00002D00, 0x00002D25}, {0x00002D27, 0x00002D27}, {0x00002D2D, 0x00002D2D},
+{0x00002D30, 0x00002D67}, {0x00002D6F, 0x00002D6F}, {0x00002D80, 0x00002D96}, {0x00002DA0, 0x00002DA6},
+{0x00002DA8, 0x00002DAE}, {0x00002DB0, 0x00002DB6}, {0x00002DB8, 0x00002DBE}, {0x00002DC0, 0x00002DC6},
+{0x00002DC8, 0x00002DCE}, {0x00002DD0, 0x00002DD6}, {0x00002DD8, 0x00002DDE}, {0x00002E2F, 0x00002E2F},
+{0x00003005, 0x00003006}, {0x00003031, 0x00003035}, {0x0000303B, 0x0000303C}, {0x00003041, 0x00003096},
+{0x0000309D, 0x0000309F}, {0x000030A1, 0x000030FA}, {0x000030FC, 0x000030FF}, {0x00003105, 0x0000312F},
+{0x00003131, 0x0000318E}, {0x000031A0, 0x000031BF}, {0x000031F0, 0x000031FF}, {0x00003400, 0x00004DBF},
+{0x00004E00, 0x00009FFC}, {0x0000A000, 0x0000A48C}, {0x0000A4D0, 0x0000A4FD}, {0x0000A500, 0x0000A60C},
+{0x0000A610, 0x0000A61F}, {0x0000A62A, 0x0000A62B}, {0x0000A640, 0x0000A66E}, {0x0000A67F, 0x0000A69D},
+{0x0000A6A0, 0x0000A6E5}, {0x0000A717, 0x0000A71F}, {0x0000A722, 0x0000A788}, {0x0000A78B, 0x0000A7BF},
+{0x0000A7C2, 0x0000A7CA}, {0x0000A7F5, 0x0000A801}, {0x0000A803, 0x0000A805}, {0x0000A807, 0x0000A80A},
+{0x0000A80C, 0x0000A822}, {0x0000A840, 0x0000A873}, {0x0000A882, 0x0000A8B3}, {0x0000A8F2, 0x0000A8F7},
+{0x0000A8FB, 0x0000A8FB}, {0x0000A8FD, 0x0000A8FE}, {0x0000A90A, 0x0000A925}, {0x0000A930, 0x0000A946},
+{0x0000A960, 0x0000A97C}, {0x0000A984, 0x0000A9B2}, {0x0000A9CF, 0x0000A9CF}, {0x0000A9E0, 0x0000A9E4},
+{0x0000A9E6, 0x0000A9EF}, {0x0000A9FA, 0x0000A9FE}, {0x0000AA00, 0x0000AA28}, {0x0000AA40, 0x0000AA42},
+{0x0000AA44, 0x0000AA4B}, {0x0000AA60, 0x0000AA76}, {0x0000AA7A, 0x0000AA7A}, {0x0000AA7E, 0x0000AAAF},
+{0x0000AAB1, 0x0000AAB1}, {0x0000AAB5, 0x0000AAB6}, {0x0000AAB9, 0x0000AABD}, {0x0000AAC0, 0x0000AAC0},
+{0x0000AAC2, 0x0000AAC2}, {0x0000AADB, 0x0000AADD}, {0x0000AAE0, 0x0000AAEA}, {0x0000AAF2, 0x0000AAF4},
+{0x0000AB01, 0x0000AB06}, {0x0000AB09, 0x0000AB0E}, {0x0000AB11, 0x0000AB16}, {0x0000AB20, 0x0000AB26},
+{0x0000AB28, 0x0000AB2E}, {0x0000AB30, 0x0000AB5A}, {0x0000AB5C, 0x0000AB69}, {0x0000AB70, 0x0000ABE2},
+{0x0000AC00, 0x0000D7A3}, {0x0000D7B0, 0x0000D7C6}, {0x0000D7CB, 0x0000D7FB}, {0x0000F900, 0x0000FA6D},
+{0x0000FA70, 0x0000FAD9}, {0x0000FB00, 0x0000FB06}, {0x0000FB13, 0x0000FB17}, {0x0000FB1D, 0x0000FB1D},
+{0x0000FB1F, 0x0000FB28}, {0x0000FB2A, 0x0000FB36}, {0x0000FB38, 0x0000FB3C}, {0x0000FB3E, 0x0000FB3E},
+{0x0000FB40, 0x0000FB41}, {0x0000FB43, 0x0000FB44}, {0x0000FB46, 0x0000FBB1}, {0x0000FBD3, 0x0000FD3D},
+{0x0000FD50, 0x0000FD8F}, {0x0000FD92, 0x0000FDC7}, {0x0000FDF0, 0x0000FDFB}, {0x0000FE70, 0x0000FE74},
+{0x0000FE76, 0x0000FEFC}, {0x0000FF21, 0x0000FF3A}, {0x0000FF41, 0x0000FF5A}, {0x0000FF66, 0x0000FFBE},
+{0x0000FFC2, 0x0000FFC7}, {0x0000FFCA, 0x0000FFCF}, {0x0000FFD2, 0x0000FFD7}, {0x0000FFDA, 0x0000FFDC},
+{0x00010000, 0x0001000B}, {0x0001000D, 0x00010026}, {0x00010028, 0x0001003A}, {0x0001003C, 0x0001003D},
+{0x0001003F, 0x0001004D}, {0x00010050, 0x0001005D}, {0x00010080, 0x000100FA}, {0x00010280, 0x0001029C},
+{0x000102A0, 0x000102D0}, {0x00010300, 0x0001031F}, {0x0001032D, 0x00010340}, {0x00010342, 0x00010349},
+{0x00010350, 0x00010375}, {0x00010380, 0x0001039D}, {0x000103A0, 0x000103C3}, {0x000103C8, 0x000103CF},
+{0x00010400, 0x0001049D}, {0x000104B0, 0x000104D3}, {0x000104D8, 0x000104FB}, {0x00010500, 0x00010527},
+{0x00010530, 0x00010563}, {0x00010600, 0x00010736}, {0x00010740, 0x00010755}, {0x00010760, 0x00010767},
+{0x00010800, 0x00010805}, {0x00010808, 0x00010808}, {0x0001080A, 0x00010835}, {0x00010837, 0x00010838},
+{0x0001083C, 0x0001083C}, {0x0001083F, 0x00010855}, {0x00010860, 0x00010876}, {0x00010880, 0x0001089E},
+{0x000108E0, 0x000108F2}, {0x000108F4, 0x000108F5}, {0x00010900, 0x00010915}, {0x00010920, 0x00010939},
+{0x00010980, 0x000109B7}, {0x000109BE, 0x000109BF}, {0x00010A00, 0x00010A00}, {0x00010A10, 0x00010A13},
+{0x00010A15, 0x00010A17}, {0x00010A19, 0x00010A35}, {0x00010A60, 0x00010A7C}, {0x00010A80, 0x00010A9C},
+{0x00010AC0, 0x00010AC7}, {0x00010AC9, 0x00010AE4}, {0x00010B00, 0x00010B35}, {0x00010B40, 0x00010B55},
+{0x00010B60, 0x00010B72}, {0x00010B80, 0x00010B91}, {0x00010C00, 0x00010C48}, {0x00010C80, 0x00010CB2},
+{0x00010CC0, 0x00010CF2}, {0x00010D00, 0x00010D23}, {0x00010E80, 0x00010EA9}, {0x00010EB0, 0x00010EB1},
+{0x00010F00, 0x00010F1C}, {0x00010F27, 0x00010F27}, {0x00010F30, 0x00010F45}, {0x00010FB0, 0x00010FC4},
+{0x00010FE0, 0x00010FF6}, {0x00011003, 0x00011037}, {0x00011083, 0x000110AF}, {0x000110D0, 0x000110E8},
+{0x00011103, 0x00011126}, {0x00011144, 0x00011144}, {0x00011147, 0x00011147}, {0x00011150, 0x00011172},
+{0x00011176, 0x00011176}, {0x00011183, 0x000111B2}, {0x000111C1, 0x000111C4}, {0x000111DA, 0x000111DA},
+{0x000111DC, 0x000111DC}, {0x00011200, 0x00011211}, {0x00011213, 0x0001122B}, {0x00011280, 0x00011286},
+{0x00011288, 0x00011288}, {0x0001128A, 0x0001128D}, {0x0001128F, 0x0001129D}, {0x0001129F, 0x000112A8},
+{0x000112B0, 0x000112DE}, {0x00011305, 0x0001130C}, {0x0001130F, 0x00011310}, {0x00011313, 0x00011328},
+{0x0001132A, 0x00011330}, {0x00011332, 0x00011333}, {0x00011335, 0x00011339}, {0x0001133D, 0x0001133D},
+{0x00011350, 0x00011350}, {0x0001135D, 0x00011361}, {0x00011400, 0x00011434}, {0x00011447, 0x0001144A},
+{0x0001145F, 0x00011461}, {0x00011480, 0x000114AF}, {0x000114C4, 0x000114C5}, {0x000114C7, 0x000114C7},
+{0x00011580, 0x000115AE}, {0x000115D8, 0x000115DB}, {0x00011600, 0x0001162F}, {0x00011644, 0x00011644},
+{0x00011680, 0x000116AA}, {0x000116B8, 0x000116B8}, {0x00011700, 0x0001171A}, {0x00011800, 0x0001182B},
+{0x000118A0, 0x000118DF}, {0x000118FF, 0x00011906}, {0x00011909, 0x00011909}, {0x0001190C, 0x00011913},
+{0x00011915, 0x00011916}, {0x00011918, 0x0001192F}, {0x0001193F, 0x0001193F}, {0x00011941, 0x00011941},
+{0x000119A0, 0x000119A7}, {0x000119AA, 0x000119D0}, {0x000119E1, 0x000119E1}, {0x000119E3, 0x000119E3},
+{0x00011A00, 0x00011A00}, {0x00011A0B, 0x00011A32}, {0x00011A3A, 0x00011A3A}, {0x00011A50, 0x00011A50},
+{0x00011A5C, 0x00011A89}, {0x00011A9D, 0x00011A9D}, {0x00011AC0, 0x00011AF8}, {0x00011C00, 0x00011C08},
+{0x00011C0A, 0x00011C2E}, {0x00011C40, 0x00011C40}, {0x00011C72, 0x00011C8F}, {0x00011D00, 0x00011D06},
+{0x00011D08, 0x00011D09}, {0x00011D0B, 0x00011D30}, {0x00011D46, 0x00011D46}, {0x00011D60, 0x00011D65},
+{0x00011D67, 0x00011D68}, {0x00011D6A, 0x00011D89}, {0x00011D98, 0x00011D98}, {0x00011EE0, 0x00011EF2},
+{0x00011FB0, 0x00011FB0}, {0x00012000, 0x00012399}, {0x00012480, 0x00012543}, {0x00013000, 0x0001342E},
+{0x00014400, 0x00014646}, {0x00016800, 0x00016A38}, {0x00016A40, 0x00016A5E}, {0x00016AD0, 0x00016AED},
+{0x00016B00, 0x00016B2F}, {0x00016B40, 0x00016B43}, {0x00016B63, 0x00016B77}, {0x00016B7D, 0x00016B8F},
+{0x00016E40, 0x00016E7F}, {0x00016F00, 0x00016F4A}, {0x00016F50, 0x00016F50}, {0x00016F93, 0x00016F9F},
+{0x00016FE0, 0x00016FE1}, {0x00016FE3, 0x00016FE3}, {0x00017000, 0x000187F7}, {0x00018800, 0x00018CD5},
+{0x00018D00, 0x00018D08}, {0x0001B000, 0x0001B11E}, {0x0001B150, 0x0001B152}, {0x0001B164, 0x0001B167},
+{0x0001B170, 0x0001B2FB}, {0x0001BC00, 0x0001BC6A}, {0x0001BC70, 0x0001BC7C}, {0x0001BC80, 0x0001BC88},
+{0x0001BC90, 0x0001BC99}, {0x0001D400, 0x0001D454}, {0x0001D456, 0x0001D49C}, {0x0001D49E, 0x0001D49F},
+{0x0001D4A2, 0x0001D4A2}, {0x0001D4A5, 0x0001D4A6}, {0x0001D4A9, 0x0001D4AC}, {0x0001D4AE, 0x0001D4B9},
+{0x0001D4BB, 0x0001D4BB}, {0x0001D4BD, 0x0001D4C3}, {0x0001D4C5, 0x0001D505}, {0x0001D507, 0x0001D50A},
+{0x0001D50D, 0x0001D514}, {0x0001D516, 0x0001D51C}, {0x0001D51E, 0x0001D539}, {0x0001D53B, 0x0001D53E},
+{0x0001D540, 0x0001D544}, {0x0001D546, 0x0001D546}, {0x0001D54A, 0x0001D550}, {0x0001D552, 0x0001D6A5},
+{0x0001D6A8, 0x0001D6C0}, {0x0001D6C2, 0x0001D6DA}, {0x0001D6DC, 0x0001D6FA}, {0x0001D6FC, 0x0001D714},
+{0x0001D716, 0x0001D734}, {0x0001D736, 0x0001D74E}, {0x0001D750, 0x0001D76E}, {0x0001D770, 0x0001D788},
+{0x0001D78A, 0x0001D7A8}, {0x0001D7AA, 0x0001D7C2}, {0x0001D7C4, 0x0001D7CB}, {0x0001E100, 0x0001E12C},
+{0x0001E137, 0x0001E13D}, {0x0001E14E, 0x0001E14E}, {0x0001E2C0, 0x0001E2EB}, {0x0001E800, 0x0001E8C4},
+{0x0001E900, 0x0001E943}, {0x0001E94B, 0x0001E94B}, {0x0001EE00, 0x0001EE03}, {0x0001EE05, 0x0001EE1F},
+{0x0001EE21, 0x0001EE22}, {0x0001EE24, 0x0001EE24}, {0x0001EE27, 0x0001EE27}, {0x0001EE29, 0x0001EE32},
+{0x0001EE34, 0x0001EE37}, {0x0001EE39, 0x0001EE39}, {0x0001EE3B, 0x0001EE3B}, {0x0001EE42, 0x0001EE42},
+{0x0001EE47, 0x0001EE47}, {0x0001EE49, 0x0001EE49}, {0x0001EE4B, 0x0001EE4B}, {0x0001EE4D, 0x0001EE4F},
+{0x0001EE51, 0x0001EE52}, {0x0001EE54, 0x0001EE54}, {0x0001EE57, 0x0001EE57}, {0x0001EE59, 0x0001EE59},
+{0x0001EE5B, 0x0001EE5B}, {0x0001EE5D, 0x0001EE5D}, {0x0001EE5F, 0x0001EE5F}, {0x0001EE61, 0x0001EE62},
+{0x0001EE64, 0x0001EE64}, {0x0001EE67, 0x0001EE6A}, {0x0001EE6C, 0x0001EE72}, {0x0001EE74, 0x0001EE77},
+{0x0001EE79, 0x0001EE7C}, {0x0001EE7E, 0x0001EE7E}, {0x0001EE80, 0x0001EE89}, {0x0001EE8B, 0x0001EE9B},
+{0x0001EEA1, 0x0001EEA3}, {0x0001EEA5, 0x0001EEA9}, {0x0001EEAB, 0x0001EEBB}, {0x00020000, 0x0002A6DD},
+{0x0002A700, 0x0002B734}, {0x0002B740, 0x0002B81D}, {0x0002B820, 0x0002CEA1}, {0x0002CEB0, 0x0002EBE0},
+{0x0002F800, 0x0002FA1D}, {0x00030000, 0x0003134A},
+};
+
+const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_whitespace = {
+{0x00000009, 0x0000000D}, {0x0000001C, 0x00000020}, {0x00000085, 0x00000085}, {0x000000A0, 0x000000A0},
+{0x00001680, 0x00001680}, {0x00002000, 0x0000200A}, {0x00002028, 0x00002029}, {0x0000202F, 0x0000202F},
+{0x0000205F, 0x0000205F}, {0x00003000, 0x00003000},
+};
+
+const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_accent_mark = {
+{0x00000300, 0x0000036F}, {0x00000483, 0x00000489}, {0x00000591, 0x000005BD}, {0x000005BF, 0x000005BF},
+{0x000005C1, 0x000005C2}, {0x000005C4, 0x000005C5}, {0x000005C7, 0x000005C7}, {0x00000610, 0x0000061A},
+{0x0000064B, 0x0000065F}, {0x00000670, 0x00000670}, {0x000006D6, 0x000006DC}, {0x000006DF, 0x000006E4},
+{0x000006E7, 0x000006E8}, {0x000006EA, 0x000006ED}, {0x00000711, 0x00000711}, {0x00000730, 0x0000074A},
+{0x000007A6, 0x000007B0}, {0x000007EB, 0x000007F3}, {0x000007FD, 0x000007FD}, {0x00000816, 0x00000819},
+{0x0000081B, 0x00000823}, {0x00000825, 0x00000827}, {0x00000829, 0x0000082D}, {0x00000859, 0x0000085B},
+{0x000008D3, 0x000008E1}, {0x000008E3, 0x00000903}, {0x0000093A, 0x0000093C}, {0x0000093E, 0x0000094F},
+{0x00000951, 0x00000957}, {0x00000962, 0x00000963}, {0x00000981, 0x00000983}, {0x000009BC, 0x000009BC},
+{0x000009BE, 0x000009C4}, {0x000009C7, 0x000009C8}, {0x000009CB, 0x000009CD}, {0x000009D7, 0x000009D7},
+{0x000009E2, 0x000009E3}, {0x000009FE, 0x000009FE}, {0x00000A01, 0x00000A03}, {0x00000A3C, 0x00000A3C},
+{0x00000A3E, 0x00000A42}, {0x00000A47, 0x00000A48}, {0x00000A4B, 0x00000A4D}, {0x00000A51, 0x00000A51},
+{0x00000A70, 0x00000A71}, {0x00000A75, 0x00000A75}, {0x00000A81, 0x00000A83}, {0x00000ABC, 0x00000ABC},
+{0x00000ABE, 0x00000AC5}, {0x00000AC7, 0x00000AC9}, {0x00000ACB, 0x00000ACD}, {0x00000AE2, 0x00000AE3},
+{0x00000AFA, 0x00000AFF}, {0x00000B01, 0x00000B03}, {0x00000B3C, 0x00000B3C}, {0x00000B3E, 0x00000B44},
+{0x00000B47, 0x00000B48}, {0x00000B4B, 0x00000B4D}, {0x00000B55, 0x00000B57}, {0x00000B62, 0x00000B63},
+{0x00000B82, 0x00000B82}, {0x00000BBE, 0x00000BC2}, {0x00000BC6, 0x00000BC8}, {0x00000BCA, 0x00000BCD},
+{0x00000BD7, 0x00000BD7}, {0x00000C00, 0x00000C04}, {0x00000C3E, 0x00000C44}, {0x00000C46, 0x00000C48},
+{0x00000C4A, 0x00000C4D}, {0x00000C55, 0x00000C56}, {0x00000C62, 0x00000C63}, {0x00000C81, 0x00000C83},
+{0x00000CBC, 0x00000CBC}, {0x00000CBE, 0x00000CC4}, {0x00000CC6, 0x00000CC8}, {0x00000CCA, 0x00000CCD},
+{0x00000CD5, 0x00000CD6}, {0x00000CE2, 0x00000CE3}, {0x00000D00, 0x00000D03}, {0x00000D3B, 0x00000D3C},
+{0x00000D3E, 0x00000D44}, {0x00000D46, 0x00000D48}, {0x00000D4A, 0x00000D4D}, {0x00000D57, 0x00000D57},
+{0x00000D62, 0x00000D63}, {0x00000D81, 0x00000D83}, {0x00000DCA, 0x00000DCA}, {0x00000DCF, 0x00000DD4},
+{0x00000DD6, 0x00000DD6}, {0x00000DD8, 0x00000DDF}, {0x00000DF2, 0x00000DF3}, {0x00000E31, 0x00000E31},
+{0x00000E34, 0x00000E3A}, {0x00000E47, 0x00000E4E}, {0x00000EB1, 0x00000EB1}, {0x00000EB4, 0x00000EBC},
+{0x00000EC8, 0x00000ECD}, {0x00000F18, 0x00000F19}, {0x00000F35, 0x00000F35}, {0x00000F37, 0x00000F37},
+{0x00000F39, 0x00000F39}, {0x00000F3E, 0x00000F3F}, {0x00000F71, 0x00000F84}, {0x00000F86, 0x00000F87},
+{0x00000F8D, 0x00000F97}, {0x00000F99, 0x00000FBC}, {0x00000FC6, 0x00000FC6}, {0x0000102B, 0x0000103E},
+{0x00001056, 0x00001059}, {0x0000105E, 0x00001060}, {0x00001062, 0x00001064}, {0x00001067, 0x0000106D},
+{0x00001071, 0x00001074}, {0x00001082, 0x0000108D}, {0x0000108F, 0x0000108F}, {0x0000109A, 0x0000109D},
+{0x0000135D, 0x0000135F}, {0x00001712, 0x00001714}, {0x00001732, 0x00001734}, {0x00001752, 0x00001753},
+{0x00001772, 0x00001773}, {0x000017B4, 0x000017D3}, {0x000017DD, 0x000017DD}, {0x0000180B, 0x0000180D},
+{0x00001885, 0x00001886}, {0x000018A9, 0x000018A9}, {0x00001920, 0x0000192B}, {0x00001930, 0x0000193B},
+{0x00001A17, 0x00001A1B}, {0x00001A55, 0x00001A5E}, {0x00001A60, 0x00001A7C}, {0x00001A7F, 0x00001A7F},
+{0x00001AB0, 0x00001AC0}, {0x00001B00, 0x00001B04}, {0x00001B34, 0x00001B44}, {0x00001B6B, 0x00001B73},
+{0x00001B80, 0x00001B82}, {0x00001BA1, 0x00001BAD}, {0x00001BE6, 0x00001BF3}, {0x00001C24, 0x00001C37},
+{0x00001CD0, 0x00001CD2}, {0x00001CD4, 0x00001CE8}, {0x00001CED, 0x00001CED}, {0x00001CF4, 0x00001CF4},
+{0x00001CF7, 0x00001CF9}, {0x00001DC0, 0x00001DF9}, {0x00001DFB, 0x00001DFF}, {0x000020D0, 0x000020F0},
+{0x00002CEF, 0x00002CF1}, {0x00002D7F, 0x00002D7F}, {0x00002DE0, 0x00002DFF}, {0x0000302A, 0x0000302F},
+{0x00003099, 0x0000309A}, {0x0000A66F, 0x0000A672}, {0x0000A674, 0x0000A67D}, {0x0000A69E, 0x0000A69F},
+{0x0000A6F0, 0x0000A6F1}, {0x0000A802, 0x0000A802}, {0x0000A806, 0x0000A806}, {0x0000A80B, 0x0000A80B},
+{0x0000A823, 0x0000A827}, {0x0000A82C, 0x0000A82C}, {0x0000A880, 0x0000A881}, {0x0000A8B4, 0x0000A8C5},
+{0x0000A8E0, 0x0000A8F1}, {0x0000A8FF, 0x0000A8FF}, {0x0000A926, 0x0000A92D}, {0x0000A947, 0x0000A953},
+{0x0000A980, 0x0000A983}, {0x0000A9B3, 0x0000A9C0}, {0x0000A9E5, 0x0000A9E5}, {0x0000AA29, 0x0000AA36},
+{0x0000AA43, 0x0000AA43}, {0x0000AA4C, 0x0000AA4D}, {0x0000AA7B, 0x0000AA7D}, {0x0000AAB0, 0x0000AAB0},
+{0x0000AAB2, 0x0000AAB4}, {0x0000AAB7, 0x0000AAB8}, {0x0000AABE, 0x0000AABF}, {0x0000AAC1, 0x0000AAC1},
+{0x0000AAEB, 0x0000AAEF}, {0x0000AAF5, 0x0000AAF6}, {0x0000ABE3, 0x0000ABEA}, {0x0000ABEC, 0x0000ABED},
+{0x0000FB1E, 0x0000FB1E}, {0x0000FE00, 0x0000FE0F}, {0x0000FE20, 0x0000FE2F}, {0x000101FD, 0x000101FD},
+{0x000102E0, 0x000102E0}, {0x00010376, 0x0001037A}, {0x00010A01, 0x00010A03}, {0x00010A05, 0x00010A06},
+{0x00010A0C, 0x00010A0F}, {0x00010A38, 0x00010A3A}, {0x00010A3F, 0x00010A3F}, {0x00010AE5, 0x00010AE6},
+{0x00010D24, 0x00010D27}, {0x00010EAB, 0x00010EAC}, {0x00010F46, 0x00010F50}, {0x00011000, 0x00011002},
+{0x00011038, 0x00011046}, {0x0001107F, 0x00011082}, {0x000110B0, 0x000110BA}, {0x00011100, 0x00011102},
+{0x00011127, 0x00011134}, {0x00011145, 0x00011146}, {0x00011173, 0x00011173}, {0x00011180, 0x00011182},
+{0x000111B3, 0x000111C0}, {0x000111C9, 0x000111CC}, {0x000111CE, 0x000111CF}, {0x0001122C, 0x00011237},
+{0x0001123E, 0x0001123E}, {0x000112DF, 0x000112EA}, {0x00011300, 0x00011303}, {0x0001133B, 0x0001133C},
+{0x0001133E, 0x00011344}, {0x00011347, 0x00011348}, {0x0001134B, 0x0001134D}, {0x00011357, 0x00011357},
+{0x00011362, 0x00011363}, {0x00011366, 0x0001136C}, {0x00011370, 0x00011374}, {0x00011435, 0x00011446},
+{0x0001145E, 0x0001145E}, {0x000114B0, 0x000114C3}, {0x000115AF, 0x000115B5}, {0x000115B8, 0x000115C0},
+{0x000115DC, 0x000115DD}, {0x00011630, 0x00011640}, {0x000116AB, 0x000116B7}, {0x0001171D, 0x0001172B},
+{0x0001182C, 0x0001183A}, {0x00011930, 0x00011935}, {0x00011937, 0x00011938}, {0x0001193B, 0x0001193E},
+{0x00011940, 0x00011940}, {0x00011942, 0x00011943}, {0x000119D1, 0x000119D7}, {0x000119DA, 0x000119E0},
+{0x000119E4, 0x000119E4}, {0x00011A01, 0x00011A0A}, {0x00011A33, 0x00011A39}, {0x00011A3B, 0x00011A3E},
+{0x00011A47, 0x00011A47}, {0x00011A51, 0x00011A5B}, {0x00011A8A, 0x00011A99}, {0x00011C2F, 0x00011C36},
+{0x00011C38, 0x00011C3F}, {0x00011C92, 0x00011CA7}, {0x00011CA9, 0x00011CB6}, {0x00011D31, 0x00011D36},
+{0x00011D3A, 0x00011D3A}, {0x00011D3C, 0x00011D3D}, {0x00011D3F, 0x00011D45}, {0x00011D47, 0x00011D47},
+{0x00011D8A, 0x00011D8E}, {0x00011D90, 0x00011D91}, {0x00011D93, 0x00011D97}, {0x00011EF3, 0x00011EF6},
+{0x00016AF0, 0x00016AF4}, {0x00016B30, 0x00016B36}, {0x00016F4F, 0x00016F4F}, {0x00016F51, 0x00016F87},
+{0x00016F8F, 0x00016F92}, {0x00016FE4, 0x00016FE4}, {0x00016FF0, 0x00016FF1}, {0x0001BC9D, 0x0001BC9E},
+{0x0001D165, 0x0001D169}, {0x0001D16D, 0x0001D172}, {0x0001D17B, 0x0001D182}, {0x0001D185, 0x0001D18B},
+{0x0001D1AA, 0x0001D1AD}, {0x0001D242, 0x0001D244}, {0x0001DA00, 0x0001DA36}, {0x0001DA3B, 0x0001DA6C},
+{0x0001DA75, 0x0001DA75}, {0x0001DA84, 0x0001DA84}, {0x0001DA9B, 0x0001DA9F}, {0x0001DAA1, 0x0001DAAF},
+{0x0001E000, 0x0001E006}, {0x0001E008, 0x0001E018}, {0x0001E01B, 0x0001E021}, {0x0001E023, 0x0001E024},
+{0x0001E026, 0x0001E02A}, {0x0001E130, 0x0001E136}, {0x0001E2EC, 0x0001E2EF}, {0x0001E8D0, 0x0001E8D6},
+{0x0001E944, 0x0001E94A}, {0x000E0100, 0x000E01EF},
+};
+
+const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_punctuation = {
+{0x00000021, 0x00000023}, {0x00000025, 0x0000002A}, {0x0000002C, 0x0000002F}, {0x0000003A, 0x0000003B},
+{0x0000003F, 0x00000040}, {0x0000005B, 0x0000005D}, {0x0000005F, 0x0000005F}, {0x0000007B, 0x0000007B},
+{0x0000007D, 0x0000007D}, {0x000000A1, 0x000000A1}, {0x000000A7, 0x000000A7}, {0x000000AB, 0x000000AB},
+{0x000000B6, 0x000000B7}, {0x000000BB, 0x000000BB}, {0x000000BF, 0x000000BF}, {0x0000037E, 0x0000037E},
+{0x00000387, 0x00000387}, {0x0000055A, 0x0000055F}, {0x00000589, 0x0000058A}, {0x000005BE, 0x000005BE},
+{0x000005C0, 0x000005C0}, {0x000005C3, 0x000005C3}, {0x000005C6, 0x000005C6}, {0x000005F3, 0x000005F4},
+{0x00000609, 0x0000060A}, {0x0000060C, 0x0000060D}, {0x0000061B, 0x0000061B}, {0x0000061E, 0x0000061F},
+{0x0000066A, 0x0000066D}, {0x000006D4, 0x000006D4}, {0x00000700, 0x0000070D}, {0x000007F7, 0x000007F9},
+{0x00000830, 0x0000083E}, {0x0000085E, 0x0000085E}, {0x00000964, 0x00000965}, {0x00000970, 0x00000970},
+{0x000009FD, 0x000009FD}, {0x00000A76, 0x00000A76}, {0x00000AF0, 0x00000AF0}, {0x00000C77, 0x00000C77},
+{0x00000C84, 0x00000C84}, {0x00000DF4, 0x00000DF4}, {0x00000E4F, 0x00000E4F}, {0x00000E5A, 0x00000E5B},
+{0x00000F04, 0x00000F12}, {0x00000F14, 0x00000F14}, {0x00000F3A, 0x00000F3D}, {0x00000F85, 0x00000F85},
+{0x00000FD0, 0x00000FD4}, {0x00000FD9, 0x00000FDA}, {0x0000104A, 0x0000104F}, {0x000010FB, 0x000010FB},
+{0x00001360, 0x00001368}, {0x00001400, 0x00001400}, {0x0000166E, 0x0000166E}, {0x0000169B, 0x0000169C},
+{0x000016EB, 0x000016ED}, {0x00001735, 0x00001736}, {0x000017D4, 0x000017D6}, {0x000017D8, 0x000017DA},
+{0x00001800, 0x0000180A}, {0x00001944, 0x00001945}, {0x00001A1E, 0x00001A1F}, {0x00001AA0, 0x00001AA6},
+{0x00001AA8, 0x00001AAD}, {0x00001B5A, 0x00001B60}, {0x00001BFC, 0x00001BFF}, {0x00001C3B, 0x00001C3F},
+{0x00001C7E, 0x00001C7F}, {0x00001CC0, 0x00001CC7}, {0x00001CD3, 0x00001CD3}, {0x00002010, 0x00002027},
+{0x00002030, 0x00002043}, {0x00002045, 0x00002051}, {0x00002053, 0x0000205E}, {0x0000207D, 0x0000207E},
+{0x0000208D, 0x0000208E}, {0x00002308, 0x0000230B}, {0x00002329, 0x0000232A}, {0x00002768, 0x00002775},
+{0x000027C5, 0x000027C6}, {0x000027E6, 0x000027EF}, {0x00002983, 0x00002998}, {0x000029D8, 0x000029DB},
+{0x000029FC, 0x000029FD}, {0x00002CF9, 0x00002CFC}, {0x00002CFE, 0x00002CFF}, {0x00002D70, 0x00002D70},
+{0x00002E00, 0x00002E2E}, {0x00002E30, 0x00002E4F}, {0x00002E52, 0x00002E52}, {0x00003001, 0x00003003},
+{0x00003008, 0x00003011}, {0x00003014, 0x0000301F}, {0x00003030, 0x00003030}, {0x0000303D, 0x0000303D},
+{0x000030A0, 0x000030A0}, {0x000030FB, 0x000030FB}, {0x0000A4FE, 0x0000A4FF}, {0x0000A60D, 0x0000A60F},
+{0x0000A673, 0x0000A673}, {0x0000A67E, 0x0000A67E}, {0x0000A6F2, 0x0000A6F7}, {0x0000A874, 0x0000A877},
+{0x0000A8CE, 0x0000A8CF}, {0x0000A8F8, 0x0000A8FA}, {0x0000A8FC, 0x0000A8FC}, {0x0000A92E, 0x0000A92F},
+{0x0000A95F, 0x0000A95F}, {0x0000A9C1, 0x0000A9CD}, {0x0000A9DE, 0x0000A9DF}, {0x0000AA5C, 0x0000AA5F},
+{0x0000AADE, 0x0000AADF}, {0x0000AAF0, 0x0000AAF1}, {0x0000ABEB, 0x0000ABEB}, {0x0000FD3E, 0x0000FD3F},
+{0x0000FE10, 0x0000FE19}, {0x0000FE30, 0x0000FE52}, {0x0000FE54, 0x0000FE61}, {0x0000FE63, 0x0000FE63},
+{0x0000FE68, 0x0000FE68}, {0x0000FE6A, 0x0000FE6B}, {0x0000FF01, 0x0000FF03}, {0x0000FF05, 0x0000FF0A},
+{0x0000FF0C, 0x0000FF0F}, {0x0000FF1A, 0x0000FF1B}, {0x0000FF1F, 0x0000FF20}, {0x0000FF3B, 0x0000FF3D},
+{0x0000FF3F, 0x0000FF3F}, {0x0000FF5B, 0x0000FF5B}, {0x0000FF5D, 0x0000FF5D}, {0x0000FF5F, 0x0000FF65},
+{0x00010100, 0x00010102}, {0x0001039F, 0x0001039F}, {0x000103D0, 0x000103D0}, {0x0001056F, 0x0001056F},
+{0x00010857, 0x00010857}, {0x0001091F, 0x0001091F}, {0x0001093F, 0x0001093F}, {0x00010A50, 0x00010A58},
+{0x00010A7F, 0x00010A7F}, {0x00010AF0, 0x00010AF6}, {0x00010B39, 0x00010B3F}, {0x00010B99, 0x00010B9C},
+{0x00010EAD, 0x00010EAD}, {0x00010F55, 0x00010F59}, {0x00011047, 0x0001104D}, {0x000110BB, 0x000110BC},
+{0x000110BE, 0x000110C1}, {0x00011140, 0x00011143}, {0x00011174, 0x00011175}, {0x000111C5, 0x000111C8},
+{0x000111CD, 0x000111CD}, {0x000111DB, 0x000111DB}, {0x000111DD, 0x000111DF}, {0x00011238, 0x0001123D},
+{0x000112A9, 0x000112A9}, {0x0001144B, 0x0001144F}, {0x0001145A, 0x0001145B}, {0x0001145D, 0x0001145D},
+{0x000114C6, 0x000114C6}, {0x000115C1, 0x000115D7}, {0x00011641, 0x00011643}, {0x00011660, 0x0001166C},
+{0x0001173C, 0x0001173E}, {0x0001183B, 0x0001183B}, {0x00011944, 0x00011946}, {0x000119E2, 0x000119E2},
+{0x00011A3F, 0x00011A46}, {0x00011A9A, 0x00011A9C}, {0x00011A9E, 0x00011AA2}, {0x00011C41, 0x00011C45},
+{0x00011C70, 0x00011C71}, {0x00011EF7, 0x00011EF8}, {0x00011FFF, 0x00011FFF}, {0x00012470, 0x00012474},
+{0x00016A6E, 0x00016A6F}, {0x00016AF5, 0x00016AF5}, {0x00016B37, 0x00016B3B}, {0x00016B44, 0x00016B44},
+{0x00016E97, 0x00016E9A}, {0x00016FE2, 0x00016FE2}, {0x0001BC9F, 0x0001BC9F}, {0x0001DA87, 0x0001DA8B},
+{0x0001E95E, 0x0001E95F},
+};
+
+const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_symbol = {
+{0x00000024, 0x00000024}, {0x0000002B, 0x0000002B}, {0x0000003C, 0x0000003E}, {0x0000005E, 0x0000005E},
+{0x00000060, 0x00000060}, {0x0000007C, 0x0000007C}, {0x0000007E, 0x0000007E}, {0x000000A2, 0x000000A6},
+{0x000000A8, 0x000000A9}, {0x000000AC, 0x000000AC}, {0x000000AE, 0x000000B1}, {0x000000B4, 0x000000B4},
+{0x000000B8, 0x000000B8}, {0x000000D7, 0x000000D7}, {0x000000F7, 0x000000F7}, {0x000002C2, 0x000002C5},
+{0x000002D2, 0x000002DF}, {0x000002E5, 0x000002EB}, {0x000002ED, 0x000002ED}, {0x000002EF, 0x000002FF},
+{0x00000375, 0x00000375}, {0x00000384, 0x00000385}, {0x000003F6, 0x000003F6}, {0x00000482, 0x00000482},
+{0x0000058D, 0x0000058F}, {0x00000606, 0x00000608}, {0x0000060B, 0x0000060B}, {0x0000060E, 0x0000060F},
+{0x000006DE, 0x000006DE}, {0x000006E9, 0x000006E9}, {0x000006FD, 0x000006FE}, {0x000007F6, 0x000007F6},
+{0x000007FE, 0x000007FF}, {0x000009F2, 0x000009F3}, {0x000009FA, 0x000009FB}, {0x00000AF1, 0x00000AF1},
+{0x00000B70, 0x00000B70}, {0x00000BF3, 0x00000BFA}, {0x00000C7F, 0x00000C7F}, {0x00000D4F, 0x00000D4F},
+{0x00000D79, 0x00000D79}, {0x00000E3F, 0x00000E3F}, {0x00000F01, 0x00000F03}, {0x00000F13, 0x00000F13},
+{0x00000F15, 0x00000F17}, {0x00000F1A, 0x00000F1F}, {0x00000F34, 0x00000F34}, {0x00000F36, 0x00000F36},
+{0x00000F38, 0x00000F38}, {0x00000FBE, 0x00000FC5}, {0x00000FC7, 0x00000FCC}, {0x00000FCE, 0x00000FCF},
+{0x00000FD5, 0x00000FD8}, {0x0000109E, 0x0000109F}, {0x00001390, 0x00001399}, {0x0000166D, 0x0000166D},
+{0x000017DB, 0x000017DB}, {0x00001940, 0x00001940}, {0x000019DE, 0x000019FF}, {0x00001B61, 0x00001B6A},
+{0x00001B74, 0x00001B7C}, {0x00001FBD, 0x00001FBD}, {0x00001FBF, 0x00001FC1}, {0x00001FCD, 0x00001FCF},
+{0x00001FDD, 0x00001FDF}, {0x00001FED, 0x00001FEF}, {0x00001FFD, 0x00001FFE}, {0x00002044, 0x00002044},
+{0x00002052, 0x00002052}, {0x0000207A, 0x0000207C}, {0x0000208A, 0x0000208C}, {0x000020A0, 0x000020BF},
+{0x00002100, 0x00002101}, {0x00002103, 0x00002106}, {0x00002108, 0x00002109}, {0x00002114, 0x00002114},
+{0x00002116, 0x00002118}, {0x0000211E, 0x00002123}, {0x00002125, 0x00002125}, {0x00002127, 0x00002127},
+{0x00002129, 0x00002129}, {0x0000212E, 0x0000212E}, {0x0000213A, 0x0000213B}, {0x00002140, 0x00002144},
+{0x0000214A, 0x0000214D}, {0x0000214F, 0x0000214F}, {0x0000218A, 0x0000218B}, {0x00002190, 0x00002307},
+{0x0000230C, 0x00002328}, {0x0000232B, 0x00002426}, {0x00002440, 0x0000244A}, {0x0000249C, 0x000024E9},
+{0x00002500, 0x00002767}, {0x00002794, 0x000027C4}, {0x000027C7, 0x000027E5}, {0x000027F0, 0x00002982},
+{0x00002999, 0x000029D7}, {0x000029DC, 0x000029FB}, {0x000029FE, 0x00002B73}, {0x00002B76, 0x00002B95},
+{0x00002B97, 0x00002BFF}, {0x00002CE5, 0x00002CEA}, {0x00002E50, 0x00002E51}, {0x00002E80, 0x00002E99},
+{0x00002E9B, 0x00002EF3}, {0x00002F00, 0x00002FD5}, {0x00002FF0, 0x00002FFB}, {0x00003004, 0x00003004},
+{0x00003012, 0x00003013}, {0x00003020, 0x00003020}, {0x00003036, 0x00003037}, {0x0000303E, 0x0000303F},
+{0x0000309B, 0x0000309C}, {0x00003190, 0x00003191}, {0x00003196, 0x0000319F}, {0x000031C0, 0x000031E3},
+{0x00003200, 0x0000321E}, {0x0000322A, 0x00003247}, {0x00003250, 0x00003250}, {0x00003260, 0x0000327F},
+{0x0000328A, 0x000032B0}, {0x000032C0, 0x000033FF}, {0x00004DC0, 0x00004DFF}, {0x0000A490, 0x0000A4C6},
+{0x0000A700, 0x0000A716}, {0x0000A720, 0x0000A721}, {0x0000A789, 0x0000A78A}, {0x0000A828, 0x0000A82B},
+{0x0000A836, 0x0000A839}, {0x0000AA77, 0x0000AA79}, {0x0000AB5B, 0x0000AB5B}, {0x0000AB6A, 0x0000AB6B},
+{0x0000FB29, 0x0000FB29}, {0x0000FBB2, 0x0000FBC1}, {0x0000FDFC, 0x0000FDFD}, {0x0000FE62, 0x0000FE62},
+{0x0000FE64, 0x0000FE66}, {0x0000FE69, 0x0000FE69}, {0x0000FF04, 0x0000FF04}, {0x0000FF0B, 0x0000FF0B},
+{0x0000FF1C, 0x0000FF1E}, {0x0000FF3E, 0x0000FF3E}, {0x0000FF40, 0x0000FF40}, {0x0000FF5C, 0x0000FF5C},
+{0x0000FF5E, 0x0000FF5E}, {0x0000FFE0, 0x0000FFE6}, {0x0000FFE8, 0x0000FFEE}, {0x0000FFFC, 0x0000FFFD},
+{0x00010137, 0x0001013F}, {0x00010179, 0x00010189}, {0x0001018C, 0x0001018E}, {0x00010190, 0x0001019C},
+{0x000101A0, 0x000101A0}, {0x000101D0, 0x000101FC}, {0x00010877, 0x00010878}, {0x00010AC8, 0x00010AC8},
+{0x0001173F, 0x0001173F}, {0x00011FD5, 0x00011FF1}, {0x00016B3C, 0x00016B3F}, {0x00016B45, 0x00016B45},
+{0x0001BC9C, 0x0001BC9C}, {0x0001D000, 0x0001D0F5}, {0x0001D100, 0x0001D126}, {0x0001D129, 0x0001D164},
+{0x0001D16A, 0x0001D16C}, {0x0001D183, 0x0001D184}, {0x0001D18C, 0x0001D1A9}, {0x0001D1AE, 0x0001D1E8},
+{0x0001D200, 0x0001D241}, {0x0001D245, 0x0001D245}, {0x0001D300, 0x0001D356}, {0x0001D6C1, 0x0001D6C1},
+{0x0001D6DB, 0x0001D6DB}, {0x0001D6FB, 0x0001D6FB}, {0x0001D715, 0x0001D715}, {0x0001D735, 0x0001D735},
+{0x0001D74F, 0x0001D74F}, {0x0001D76F, 0x0001D76F}, {0x0001D789, 0x0001D789}, {0x0001D7A9, 0x0001D7A9},
+{0x0001D7C3, 0x0001D7C3}, {0x0001D800, 0x0001D9FF}, {0x0001DA37, 0x0001DA3A}, {0x0001DA6D, 0x0001DA74},
+{0x0001DA76, 0x0001DA83}, {0x0001DA85, 0x0001DA86}, {0x0001E14F, 0x0001E14F}, {0x0001E2FF, 0x0001E2FF},
+{0x0001ECAC, 0x0001ECAC}, {0x0001ECB0, 0x0001ECB0}, {0x0001ED2E, 0x0001ED2E}, {0x0001EEF0, 0x0001EEF1},
+{0x0001F000, 0x0001F02B}, {0x0001F030, 0x0001F093}, {0x0001F0A0, 0x0001F0AE}, {0x0001F0B1, 0x0001F0BF},
+{0x0001F0C1, 0x0001F0CF}, {0x0001F0D1, 0x0001F0F5}, {0x0001F10D, 0x0001F1AD}, {0x0001F1E6, 0x0001F202},
+{0x0001F210, 0x0001F23B}, {0x0001F240, 0x0001F248}, {0x0001F250, 0x0001F251}, {0x0001F260, 0x0001F265},
+{0x0001F300, 0x0001F6D7}, {0x0001F6E0, 0x0001F6EC}, {0x0001F6F0, 0x0001F6FC}, {0x0001F700, 0x0001F773},
+{0x0001F780, 0x0001F7D8}, {0x0001F7E0, 0x0001F7EB}, {0x0001F800, 0x0001F80B}, {0x0001F810, 0x0001F847},
+{0x0001F850, 0x0001F859}, {0x0001F860, 0x0001F887}, {0x0001F890, 0x0001F8AD}, {0x0001F8B0, 0x0001F8B1},
+{0x0001F900, 0x0001F978}, {0x0001F97A, 0x0001F9CB}, {0x0001F9CD, 0x0001FA53}, {0x0001FA60, 0x0001FA6D},
+{0x0001FA70, 0x0001FA74}, {0x0001FA78, 0x0001FA7A}, {0x0001FA80, 0x0001FA86}, {0x0001FA90, 0x0001FAA8},
+{0x0001FAB0, 0x0001FAB6}, {0x0001FAC0, 0x0001FAC2}, {0x0001FAD0, 0x0001FAD6}, {0x0001FB00, 0x0001FB92},
+{0x0001FB94, 0x0001FBCA},
+};
+
+const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_control = {
+{0x00000000, 0x00000008}, {0x0000000E, 0x0000001B}, {0x0000007F, 0x00000084}, {0x00000086, 0x0000009F},
+{0x000000AD, 0x000000AD}, {0x00000378, 0x00000379}, {0x00000380, 0x00000383}, {0x0000038B, 0x0000038B},
+{0x0000038D, 0x0000038D}, {0x000003A2, 0x000003A2}, {0x00000530, 0x00000530}, {0x00000557, 0x00000558},
+{0x0000058B, 0x0000058C}, {0x00000590, 0x00000590}, {0x000005C8, 0x000005CF}, {0x000005EB, 0x000005EE},
+{0x000005F5, 0x00000605}, {0x0000061C, 0x0000061D}, {0x000006DD, 0x000006DD}, {0x0000070E, 0x0000070F},
+{0x0000074B, 0x0000074C}, {0x000007B2, 0x000007BF}, {0x000007FB, 0x000007FC}, {0x0000082E, 0x0000082F},
+{0x0000083F, 0x0000083F}, {0x0000085C, 0x0000085D}, {0x0000085F, 0x0000085F}, {0x0000086B, 0x0000089F},
+{0x000008B5, 0x000008B5}, {0x000008C8, 0x000008D2}, {0x000008E2, 0x000008E2}, {0x00000984, 0x00000984},
+{0x0000098D, 0x0000098E}, {0x00000991, 0x00000992}, {0x000009A9, 0x000009A9}, {0x000009B1, 0x000009B1},
+{0x000009B3, 0x000009B5}, {0x000009BA, 0x000009BB}, {0x000009C5, 0x000009C6}, {0x000009C9, 0x000009CA},
+{0x000009CF, 0x000009D6}, {0x000009D8, 0x000009DB}, {0x000009DE, 0x000009DE}, {0x000009E4, 0x000009E5},
+{0x000009FF, 0x00000A00}, {0x00000A04, 0x00000A04}, {0x00000A0B, 0x00000A0E}, {0x00000A11, 0x00000A12},
+{0x00000A29, 0x00000A29}, {0x00000A31, 0x00000A31}, {0x00000A34, 0x00000A34}, {0x00000A37, 0x00000A37},
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+{0x0001E2FA, 0x0001E2FE}, {0x0001E300, 0x0001E7FF}, {0x0001E8C5, 0x0001E8C6}, {0x0001E8D7, 0x0001E8FF},
+{0x0001E94C, 0x0001E94F}, {0x0001E95A, 0x0001E95D}, {0x0001E960, 0x0001EC70}, {0x0001ECB5, 0x0001ED00},
+{0x0001ED3E, 0x0001EDFF}, {0x0001EE04, 0x0001EE04}, {0x0001EE20, 0x0001EE20}, {0x0001EE23, 0x0001EE23},
+{0x0001EE25, 0x0001EE26}, {0x0001EE28, 0x0001EE28}, {0x0001EE33, 0x0001EE33}, {0x0001EE38, 0x0001EE38},
+{0x0001EE3A, 0x0001EE3A}, {0x0001EE3C, 0x0001EE41}, {0x0001EE43, 0x0001EE46}, {0x0001EE48, 0x0001EE48},
+{0x0001EE4A, 0x0001EE4A}, {0x0001EE4C, 0x0001EE4C}, {0x0001EE50, 0x0001EE50}, {0x0001EE53, 0x0001EE53},
+{0x0001EE55, 0x0001EE56}, {0x0001EE58, 0x0001EE58}, {0x0001EE5A, 0x0001EE5A}, {0x0001EE5C, 0x0001EE5C},
+{0x0001EE5E, 0x0001EE5E}, {0x0001EE60, 0x0001EE60}, {0x0001EE63, 0x0001EE63}, {0x0001EE65, 0x0001EE66},
+{0x0001EE6B, 0x0001EE6B}, {0x0001EE73, 0x0001EE73}, {0x0001EE78, 0x0001EE78}, {0x0001EE7D, 0x0001EE7D},
+{0x0001EE7F, 0x0001EE7F}, {0x0001EE8A, 0x0001EE8A}, {0x0001EE9C, 0x0001EEA0}, {0x0001EEA4, 0x0001EEA4},
+{0x0001EEAA, 0x0001EEAA}, {0x0001EEBC, 0x0001EEEF}, {0x0001EEF2, 0x0001EFFF}, {0x0001F02C, 0x0001F02F},
+{0x0001F094, 0x0001F09F}, {0x0001F0AF, 0x0001F0B0}, {0x0001F0C0, 0x0001F0C0}, {0x0001F0D0, 0x0001F0D0},
+{0x0001F0F6, 0x0001F0FF}, {0x0001F1AE, 0x0001F1E5}, {0x0001F203, 0x0001F20F}, {0x0001F23C, 0x0001F23F},
+{0x0001F249, 0x0001F24F}, {0x0001F252, 0x0001F25F}, {0x0001F266, 0x0001F2FF}, {0x0001F6D8, 0x0001F6DF},
+{0x0001F6ED, 0x0001F6EF}, {0x0001F6FD, 0x0001F6FF}, {0x0001F774, 0x0001F77F}, {0x0001F7D9, 0x0001F7DF},
+{0x0001F7EC, 0x0001F7FF}, {0x0001F80C, 0x0001F80F}, {0x0001F848, 0x0001F84F}, {0x0001F85A, 0x0001F85F},
+{0x0001F888, 0x0001F88F}, {0x0001F8AE, 0x0001F8AF}, {0x0001F8B2, 0x0001F8FF}, {0x0001F979, 0x0001F979},
+{0x0001F9CC, 0x0001F9CC}, {0x0001FA54, 0x0001FA5F}, {0x0001FA6E, 0x0001FA6F}, {0x0001FA75, 0x0001FA77},
+{0x0001FA7B, 0x0001FA7F}, {0x0001FA87, 0x0001FA8F}, {0x0001FAA9, 0x0001FAAF}, {0x0001FAB7, 0x0001FABF},
+{0x0001FAC3, 0x0001FACF}, {0x0001FAD7, 0x0001FAFF}, {0x0001FB93, 0x0001FB93}, {0x0001FBCB, 0x0001FBEF},
+{0x0001FBFA, 0x0001FFFF}, {0x0002A6DE, 0x0002A6FF}, {0x0002B735, 0x0002B73F}, {0x0002B81E, 0x0002B81F},
+{0x0002CEA2, 0x0002CEAF}, {0x0002EBE1, 0x0002F7FF}, {0x0002FA1E, 0x0002FFFF}, {0x0003134B, 0x000E00FF},
+{0x000E01F0, 0x0010FFFF},
+};
+
+const std::multimap<uint32_t, uint32_t> unicode_map_nfd = {
+{0x000000C0, 0x00000041}, {0x000000C0, 0x00000300}, {0x000000C1, 0x00000041}, {0x000000C1, 0x00000301},
+{0x000000C2, 0x00000041}, {0x000000C2, 0x00000302}, {0x000000C3, 0x00000041}, {0x000000C3, 0x00000303},
+{0x000000C4, 0x00000041}, {0x000000C4, 0x00000308}, {0x000000C5, 0x00000041}, {0x000000C5, 0x0000030A},
+{0x000000C7, 0x00000043}, {0x000000C7, 0x00000327}, {0x000000C8, 0x00000045}, {0x000000C8, 0x00000300},
+{0x000000C9, 0x00000045}, {0x000000C9, 0x00000301}, {0x000000CA, 0x00000045}, {0x000000CA, 0x00000302},
+{0x000000CB, 0x00000045}, {0x000000CB, 0x00000308}, {0x000000CC, 0x00000049}, {0x000000CC, 0x00000300},
+{0x000000CD, 0x00000049}, {0x000000CD, 0x00000301}, {0x000000CE, 0x00000049}, {0x000000CE, 0x00000302},
+{0x000000CF, 0x00000049}, {0x000000CF, 0x00000308}, {0x000000D1, 0x0000004E}, {0x000000D1, 0x00000303},
+{0x000000D2, 0x0000004F}, {0x000000D2, 0x00000300}, {0x000000D3, 0x0000004F}, {0x000000D3, 0x00000301},
+{0x000000D4, 0x0000004F}, {0x000000D4, 0x00000302}, {0x000000D5, 0x0000004F}, {0x000000D5, 0x00000303},
+{0x000000D6, 0x0000004F}, {0x000000D6, 0x00000308}, {0x000000D9, 0x00000055}, {0x000000D9, 0x00000300},
+{0x000000DA, 0x00000055}, {0x000000DA, 0x00000301}, {0x000000DB, 0x00000055}, {0x000000DB, 0x00000302},
+{0x000000DC, 0x00000055}, {0x000000DC, 0x00000308}, {0x000000DD, 0x00000059}, {0x000000DD, 0x00000301},
+{0x000000E0, 0x00000061}, {0x000000E0, 0x00000300}, {0x000000E1, 0x00000061}, {0x000000E1, 0x00000301},
+{0x000000E2, 0x00000061}, {0x000000E2, 0x00000302}, {0x000000E3, 0x00000061}, {0x000000E3, 0x00000303},
+{0x000000E4, 0x00000061}, {0x000000E4, 0x00000308}, {0x000000E5, 0x00000061}, {0x000000E5, 0x0000030A},
+{0x000000E7, 0x00000063}, {0x000000E7, 0x00000327}, {0x000000E8, 0x00000065}, {0x000000E8, 0x00000300},
+{0x000000E9, 0x00000065}, {0x000000E9, 0x00000301}, {0x000000EA, 0x00000065}, {0x000000EA, 0x00000302},
+{0x000000EB, 0x00000065}, {0x000000EB, 0x00000308}, {0x000000EC, 0x00000069}, {0x000000EC, 0x00000300},
+{0x000000ED, 0x00000069}, {0x000000ED, 0x00000301}, {0x000000EE, 0x00000069}, {0x000000EE, 0x00000302},
+{0x000000EF, 0x00000069}, {0x000000EF, 0x00000308}, {0x000000F1, 0x0000006E}, {0x000000F1, 0x00000303},
+{0x000000F2, 0x0000006F}, {0x000000F2, 0x00000300}, {0x000000F3, 0x0000006F}, {0x000000F3, 0x00000301},
+{0x000000F4, 0x0000006F}, {0x000000F4, 0x00000302}, {0x000000F5, 0x0000006F}, {0x000000F5, 0x00000303},
+{0x000000F6, 0x0000006F}, {0x000000F6, 0x00000308}, {0x000000F9, 0x00000075}, {0x000000F9, 0x00000300},
+{0x000000FA, 0x00000075}, {0x000000FA, 0x00000301}, {0x000000FB, 0x00000075}, {0x000000FB, 0x00000302},
+{0x000000FC, 0x00000075}, {0x000000FC, 0x00000308}, {0x000000FD, 0x00000079}, {0x000000FD, 0x00000301},
+{0x000000FF, 0x00000079}, {0x000000FF, 0x00000308}, {0x00000100, 0x00000041}, {0x00000100, 0x00000304},
+{0x00000101, 0x00000061}, {0x00000101, 0x00000304}, {0x00000102, 0x00000041}, {0x00000102, 0x00000306},
+{0x00000103, 0x00000061}, {0x00000103, 0x00000306}, {0x00000104, 0x00000041}, {0x00000104, 0x00000328},
+{0x00000105, 0x00000061}, {0x00000105, 0x00000328}, {0x00000106, 0x00000043}, {0x00000106, 0x00000301},
+{0x00000107, 0x00000063}, {0x00000107, 0x00000301}, {0x00000108, 0x00000043}, {0x00000108, 0x00000302},
+{0x00000109, 0x00000063}, {0x00000109, 0x00000302}, {0x0000010A, 0x00000043}, {0x0000010A, 0x00000307},
+{0x0000010B, 0x00000063}, {0x0000010B, 0x00000307}, {0x0000010C, 0x00000043}, {0x0000010C, 0x0000030C},
+{0x0000010D, 0x00000063}, {0x0000010D, 0x0000030C}, {0x0000010E, 0x00000044}, {0x0000010E, 0x0000030C},
+{0x0000010F, 0x00000064}, {0x0000010F, 0x0000030C}, {0x00000112, 0x00000045}, {0x00000112, 0x00000304},
+{0x00000113, 0x00000065}, {0x00000113, 0x00000304}, {0x00000114, 0x00000045}, {0x00000114, 0x00000306},
+{0x00000115, 0x00000065}, {0x00000115, 0x00000306}, {0x00000116, 0x00000045}, {0x00000116, 0x00000307},
+{0x00000117, 0x00000065}, {0x00000117, 0x00000307}, {0x00000118, 0x00000045}, {0x00000118, 0x00000328},
+{0x00000119, 0x00000065}, {0x00000119, 0x00000328}, {0x0000011A, 0x00000045}, {0x0000011A, 0x0000030C},
+{0x0000011B, 0x00000065}, {0x0000011B, 0x0000030C}, {0x0000011C, 0x00000047}, {0x0000011C, 0x00000302},
+{0x0000011D, 0x00000067}, {0x0000011D, 0x00000302}, {0x0000011E, 0x00000047}, {0x0000011E, 0x00000306},
+{0x0000011F, 0x00000067}, {0x0000011F, 0x00000306}, {0x00000120, 0x00000047}, {0x00000120, 0x00000307},
+{0x00000121, 0x00000067}, {0x00000121, 0x00000307}, {0x00000122, 0x00000047}, {0x00000122, 0x00000327},
+{0x00000123, 0x00000067}, {0x00000123, 0x00000327}, {0x00000124, 0x00000048}, {0x00000124, 0x00000302},
+{0x00000125, 0x00000068}, {0x00000125, 0x00000302}, {0x00000128, 0x00000049}, {0x00000128, 0x00000303},
+{0x00000129, 0x00000069}, {0x00000129, 0x00000303}, {0x0000012A, 0x00000049}, {0x0000012A, 0x00000304},
+{0x0000012B, 0x00000069}, {0x0000012B, 0x00000304}, {0x0000012C, 0x00000049}, {0x0000012C, 0x00000306},
+{0x0000012D, 0x00000069}, {0x0000012D, 0x00000306}, {0x0000012E, 0x00000049}, {0x0000012E, 0x00000328},
+{0x0000012F, 0x00000069}, {0x0000012F, 0x00000328}, {0x00000130, 0x00000049}, {0x00000130, 0x00000307},
+{0x00000134, 0x0000004A}, {0x00000134, 0x00000302}, {0x00000135, 0x0000006A}, {0x00000135, 0x00000302},
+{0x00000136, 0x0000004B}, {0x00000136, 0x00000327}, {0x00000137, 0x0000006B}, {0x00000137, 0x00000327},
+{0x00000139, 0x0000004C}, {0x00000139, 0x00000301}, {0x0000013A, 0x0000006C}, {0x0000013A, 0x00000301},
+{0x0000013B, 0x0000004C}, {0x0000013B, 0x00000327}, {0x0000013C, 0x0000006C}, {0x0000013C, 0x00000327},
+{0x0000013D, 0x0000004C}, {0x0000013D, 0x0000030C}, {0x0000013E, 0x0000006C}, {0x0000013E, 0x0000030C},
+{0x00000143, 0x0000004E}, {0x00000143, 0x00000301}, {0x00000144, 0x0000006E}, {0x00000144, 0x00000301},
+{0x00000145, 0x0000004E}, {0x00000145, 0x00000327}, {0x00000146, 0x0000006E}, {0x00000146, 0x00000327},
+{0x00000147, 0x0000004E}, {0x00000147, 0x0000030C}, {0x00000148, 0x0000006E}, {0x00000148, 0x0000030C},
+{0x0000014C, 0x0000004F}, {0x0000014C, 0x00000304}, {0x0000014D, 0x0000006F}, {0x0000014D, 0x00000304},
+{0x0000014E, 0x0000004F}, {0x0000014E, 0x00000306}, {0x0000014F, 0x0000006F}, {0x0000014F, 0x00000306},
+{0x00000150, 0x0000004F}, {0x00000150, 0x0000030B}, {0x00000151, 0x0000006F}, {0x00000151, 0x0000030B},
+{0x00000154, 0x00000052}, {0x00000154, 0x00000301}, {0x00000155, 0x00000072}, {0x00000155, 0x00000301},
+{0x00000156, 0x00000052}, {0x00000156, 0x00000327}, {0x00000157, 0x00000072}, {0x00000157, 0x00000327},
+{0x00000158, 0x00000052}, {0x00000158, 0x0000030C}, {0x00000159, 0x00000072}, {0x00000159, 0x0000030C},
+{0x0000015A, 0x00000053}, {0x0000015A, 0x00000301}, {0x0000015B, 0x00000073}, {0x0000015B, 0x00000301},
+{0x0000015C, 0x00000053}, {0x0000015C, 0x00000302}, {0x0000015D, 0x00000073}, {0x0000015D, 0x00000302},
+{0x0000015E, 0x00000053}, {0x0000015E, 0x00000327}, {0x0000015F, 0x00000073}, {0x0000015F, 0x00000327},
+{0x00000160, 0x00000053}, {0x00000160, 0x0000030C}, {0x00000161, 0x00000073}, {0x00000161, 0x0000030C},
+{0x00000162, 0x00000054}, {0x00000162, 0x00000327}, {0x00000163, 0x00000074}, {0x00000163, 0x00000327},
+{0x00000164, 0x00000054}, {0x00000164, 0x0000030C}, {0x00000165, 0x00000074}, {0x00000165, 0x0000030C},
+{0x00000168, 0x00000055}, {0x00000168, 0x00000303}, {0x00000169, 0x00000075}, {0x00000169, 0x00000303},
+{0x0000016A, 0x00000055}, {0x0000016A, 0x00000304}, {0x0000016B, 0x00000075}, {0x0000016B, 0x00000304},
+{0x0000016C, 0x00000055}, {0x0000016C, 0x00000306}, {0x0000016D, 0x00000075}, {0x0000016D, 0x00000306},
+{0x0000016E, 0x00000055}, {0x0000016E, 0x0000030A}, {0x0000016F, 0x00000075}, {0x0000016F, 0x0000030A},
+{0x00000170, 0x00000055}, {0x00000170, 0x0000030B}, {0x00000171, 0x00000075}, {0x00000171, 0x0000030B},
+{0x00000172, 0x00000055}, {0x00000172, 0x00000328}, {0x00000173, 0x00000075}, {0x00000173, 0x00000328},
+{0x00000174, 0x00000057}, {0x00000174, 0x00000302}, {0x00000175, 0x00000077}, {0x00000175, 0x00000302},
+{0x00000176, 0x00000059}, {0x00000176, 0x00000302}, {0x00000177, 0x00000079}, {0x00000177, 0x00000302},
+{0x00000178, 0x00000059}, {0x00000178, 0x00000308}, {0x00000179, 0x0000005A}, {0x00000179, 0x00000301},
+{0x0000017A, 0x0000007A}, {0x0000017A, 0x00000301}, {0x0000017B, 0x0000005A}, {0x0000017B, 0x00000307},
+{0x0000017C, 0x0000007A}, {0x0000017C, 0x00000307}, {0x0000017D, 0x0000005A}, {0x0000017D, 0x0000030C},
+{0x0000017E, 0x0000007A}, {0x0000017E, 0x0000030C}, {0x000001A0, 0x0000004F}, {0x000001A0, 0x0000031B},
+{0x000001A1, 0x0000006F}, {0x000001A1, 0x0000031B}, {0x000001AF, 0x00000055}, {0x000001AF, 0x0000031B},
+{0x000001B0, 0x00000075}, {0x000001B0, 0x0000031B}, {0x000001CD, 0x00000041}, {0x000001CD, 0x0000030C},
+{0x000001CE, 0x00000061}, {0x000001CE, 0x0000030C}, {0x000001CF, 0x00000049}, {0x000001CF, 0x0000030C},
+{0x000001D0, 0x00000069}, {0x000001D0, 0x0000030C}, {0x000001D1, 0x0000004F}, {0x000001D1, 0x0000030C},
+{0x000001D2, 0x0000006F}, {0x000001D2, 0x0000030C}, {0x000001D3, 0x00000055}, {0x000001D3, 0x0000030C},
+{0x000001D4, 0x00000075}, {0x000001D4, 0x0000030C}, {0x000001D5, 0x00000055}, {0x000001D5, 0x00000308},
+{0x000001D5, 0x00000304}, {0x000001D6, 0x00000075}, {0x000001D6, 0x00000308}, {0x000001D6, 0x00000304},
+{0x000001D7, 0x00000055}, {0x000001D7, 0x00000308}, {0x000001D7, 0x00000301}, {0x000001D8, 0x00000075},
+{0x000001D8, 0x00000308}, {0x000001D8, 0x00000301}, {0x000001D9, 0x00000055}, {0x000001D9, 0x00000308},
+{0x000001D9, 0x0000030C}, {0x000001DA, 0x00000075}, {0x000001DA, 0x00000308}, {0x000001DA, 0x0000030C},
+{0x000001DB, 0x00000055}, {0x000001DB, 0x00000308}, {0x000001DB, 0x00000300}, {0x000001DC, 0x00000075},
+{0x000001DC, 0x00000308}, {0x000001DC, 0x00000300}, {0x000001DE, 0x00000041}, {0x000001DE, 0x00000308},
+{0x000001DE, 0x00000304}, {0x000001DF, 0x00000061}, {0x000001DF, 0x00000308}, {0x000001DF, 0x00000304},
+{0x000001E0, 0x00000041}, {0x000001E0, 0x00000307}, {0x000001E0, 0x00000304}, {0x000001E1, 0x00000061},
+{0x000001E1, 0x00000307}, {0x000001E1, 0x00000304}, {0x000001E2, 0x000000C6}, {0x000001E2, 0x00000304},
+{0x000001E3, 0x000000E6}, {0x000001E3, 0x00000304}, {0x000001E6, 0x00000047}, {0x000001E6, 0x0000030C},
+{0x000001E7, 0x00000067}, {0x000001E7, 0x0000030C}, {0x000001E8, 0x0000004B}, {0x000001E8, 0x0000030C},
+{0x000001E9, 0x0000006B}, {0x000001E9, 0x0000030C}, {0x000001EA, 0x0000004F}, {0x000001EA, 0x00000328},
+{0x000001EB, 0x0000006F}, {0x000001EB, 0x00000328}, {0x000001EC, 0x0000004F}, {0x000001EC, 0x00000328},
+{0x000001EC, 0x00000304}, {0x000001ED, 0x0000006F}, {0x000001ED, 0x00000328}, {0x000001ED, 0x00000304},
+{0x000001EE, 0x000001B7}, {0x000001EE, 0x0000030C}, {0x000001EF, 0x00000292}, {0x000001EF, 0x0000030C},
+{0x000001F0, 0x0000006A}, {0x000001F0, 0x0000030C}, {0x000001F4, 0x00000047}, {0x000001F4, 0x00000301},
+{0x000001F5, 0x00000067}, {0x000001F5, 0x00000301}, {0x000001F8, 0x0000004E}, {0x000001F8, 0x00000300},
+{0x000001F9, 0x0000006E}, {0x000001F9, 0x00000300}, {0x000001FA, 0x00000041}, {0x000001FA, 0x0000030A},
+{0x000001FA, 0x00000301}, {0x000001FB, 0x00000061}, {0x000001FB, 0x0000030A}, {0x000001FB, 0x00000301},
+{0x000001FC, 0x000000C6}, {0x000001FC, 0x00000301}, {0x000001FD, 0x000000E6}, {0x000001FD, 0x00000301},
+{0x000001FE, 0x000000D8}, {0x000001FE, 0x00000301}, {0x000001FF, 0x000000F8}, {0x000001FF, 0x00000301},
+{0x00000200, 0x00000041}, {0x00000200, 0x0000030F}, {0x00000201, 0x00000061}, {0x00000201, 0x0000030F},
+{0x00000202, 0x00000041}, {0x00000202, 0x00000311}, {0x00000203, 0x00000061}, {0x00000203, 0x00000311},
+{0x00000204, 0x00000045}, {0x00000204, 0x0000030F}, {0x00000205, 0x00000065}, {0x00000205, 0x0000030F},
+{0x00000206, 0x00000045}, {0x00000206, 0x00000311}, {0x00000207, 0x00000065}, {0x00000207, 0x00000311},
+{0x00000208, 0x00000049}, {0x00000208, 0x0000030F}, {0x00000209, 0x00000069}, {0x00000209, 0x0000030F},
+{0x0000020A, 0x00000049}, {0x0000020A, 0x00000311}, {0x0000020B, 0x00000069}, {0x0000020B, 0x00000311},
+{0x0000020C, 0x0000004F}, {0x0000020C, 0x0000030F}, {0x0000020D, 0x0000006F}, {0x0000020D, 0x0000030F},
+{0x0000020E, 0x0000004F}, {0x0000020E, 0x00000311}, {0x0000020F, 0x0000006F}, {0x0000020F, 0x00000311},
+{0x00000210, 0x00000052}, {0x00000210, 0x0000030F}, {0x00000211, 0x00000072}, {0x00000211, 0x0000030F},
+{0x00000212, 0x00000052}, {0x00000212, 0x00000311}, {0x00000213, 0x00000072}, {0x00000213, 0x00000311},
+{0x00000214, 0x00000055}, {0x00000214, 0x0000030F}, {0x00000215, 0x00000075}, {0x00000215, 0x0000030F},
+{0x00000216, 0x00000055}, {0x00000216, 0x00000311}, {0x00000217, 0x00000075}, {0x00000217, 0x00000311},
+{0x00000218, 0x00000053}, {0x00000218, 0x00000326}, {0x00000219, 0x00000073}, {0x00000219, 0x00000326},
+{0x0000021A, 0x00000054}, {0x0000021A, 0x00000326}, {0x0000021B, 0x00000074}, {0x0000021B, 0x00000326},
+{0x0000021E, 0x00000048}, {0x0000021E, 0x0000030C}, {0x0000021F, 0x00000068}, {0x0000021F, 0x0000030C},
+{0x00000226, 0x00000041}, {0x00000226, 0x00000307}, {0x00000227, 0x00000061}, {0x00000227, 0x00000307},
+{0x00000228, 0x00000045}, {0x00000228, 0x00000327}, {0x00000229, 0x00000065}, {0x00000229, 0x00000327},
+{0x0000022A, 0x0000004F}, {0x0000022A, 0x00000308}, {0x0000022A, 0x00000304}, {0x0000022B, 0x0000006F},
+{0x0000022B, 0x00000308}, {0x0000022B, 0x00000304}, {0x0000022C, 0x0000004F}, {0x0000022C, 0x00000303},
+{0x0000022C, 0x00000304}, {0x0000022D, 0x0000006F}, {0x0000022D, 0x00000303}, {0x0000022D, 0x00000304},
+{0x0000022E, 0x0000004F}, {0x0000022E, 0x00000307}, {0x0000022F, 0x0000006F}, {0x0000022F, 0x00000307},
+{0x00000230, 0x0000004F}, {0x00000230, 0x00000307}, {0x00000230, 0x00000304}, {0x00000231, 0x0000006F},
+{0x00000231, 0x00000307}, {0x00000231, 0x00000304}, {0x00000232, 0x00000059}, {0x00000232, 0x00000304},
+{0x00000233, 0x00000079}, {0x00000233, 0x00000304}, {0x00000340, 0x00000300}, {0x00000341, 0x00000301},
+{0x00000343, 0x00000313}, {0x00000344, 0x00000308}, {0x00000344, 0x00000301}, {0x00000374, 0x000002B9},
+{0x0000037E, 0x0000003B}, {0x00000385, 0x000000A8}, {0x00000385, 0x00000301}, {0x00000386, 0x00000391},
+{0x00000386, 0x00000301}, {0x00000387, 0x000000B7}, {0x00000388, 0x00000395}, {0x00000388, 0x00000301},
+{0x00000389, 0x00000397}, {0x00000389, 0x00000301}, {0x0000038A, 0x00000399}, {0x0000038A, 0x00000301},
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+{0x0002FA0A, 0x00009B12}, {0x0002FA0B, 0x00009C40}, {0x0002FA0C, 0x00009CFD}, {0x0002FA0D, 0x00004CCE},
+{0x0002FA0E, 0x00004CED}, {0x0002FA0F, 0x00009D67}, {0x0002FA10, 0x0002A0CE}, {0x0002FA11, 0x00004CF8},
+{0x0002FA12, 0x0002A105}, {0x0002FA13, 0x0002A20E}, {0x0002FA14, 0x0002A291}, {0x0002FA15, 0x00009EBB},
+{0x0002FA16, 0x00004D56}, {0x0002FA17, 0x00009EF9}, {0x0002FA18, 0x00009EFE}, {0x0002FA19, 0x00009F05},
+{0x0002FA1A, 0x00009F0F}, {0x0002FA1B, 0x00009F16}, {0x0002FA1D, 0x0002A600},
+};
+
+const std::map<char32_t, char32_t> unicode_map_lowercase = {
+{0x00041, 0x00061}, {0x00042, 0x00062}, {0x00043, 0x00063}, {0x00044, 0x00064}, {0x00045, 0x00065}, {0x00046, 0x00066},
+{0x00047, 0x00067}, {0x00048, 0x00068}, {0x00049, 0x00069}, {0x0004A, 0x0006A}, {0x0004B, 0x0006B}, {0x0004C, 0x0006C},
+{0x0004D, 0x0006D}, {0x0004E, 0x0006E}, {0x0004F, 0x0006F}, {0x00050, 0x00070}, {0x00051, 0x00071}, {0x00052, 0x00072},
+{0x00053, 0x00073}, {0x00054, 0x00074}, {0x00055, 0x00075}, {0x00056, 0x00076}, {0x00057, 0x00077}, {0x00058, 0x00078},
+{0x00059, 0x00079}, {0x0005A, 0x0007A}, {0x000C0, 0x000E0}, {0x000C1, 0x000E1}, {0x000C2, 0x000E2}, {0x000C3, 0x000E3},
+{0x000C4, 0x000E4}, {0x000C5, 0x000E5}, {0x000C6, 0x000E6}, {0x000C7, 0x000E7}, {0x000C8, 0x000E8}, {0x000C9, 0x000E9},
+{0x000CA, 0x000EA}, {0x000CB, 0x000EB}, {0x000CC, 0x000EC}, {0x000CD, 0x000ED}, {0x000CE, 0x000EE}, {0x000CF, 0x000EF},
+{0x000D0, 0x000F0}, {0x000D1, 0x000F1}, {0x000D2, 0x000F2}, {0x000D3, 0x000F3}, {0x000D4, 0x000F4}, {0x000D5, 0x000F5},
+{0x000D6, 0x000F6}, {0x000D8, 0x000F8}, {0x000D9, 0x000F9}, {0x000DA, 0x000FA}, {0x000DB, 0x000FB}, {0x000DC, 0x000FC},
+{0x000DD, 0x000FD}, {0x000DE, 0x000FE}, {0x00100, 0x00101}, {0x00102, 0x00103}, {0x00104, 0x00105}, {0x00106, 0x00107},
+{0x00108, 0x00109}, {0x0010A, 0x0010B}, {0x0010C, 0x0010D}, {0x0010E, 0x0010F}, {0x00110, 0x00111}, {0x00112, 0x00113},
+{0x00114, 0x00115}, {0x00116, 0x00117}, {0x00118, 0x00119}, {0x0011A, 0x0011B}, {0x0011C, 0x0011D}, {0x0011E, 0x0011F},
+{0x00120, 0x00121}, {0x00122, 0x00123}, {0x00124, 0x00125}, {0x00126, 0x00127}, {0x00128, 0x00129}, {0x0012A, 0x0012B},
+{0x0012C, 0x0012D}, {0x0012E, 0x0012F}, {0x00130, 0x00069}, {0x00132, 0x00133}, {0x00134, 0x00135}, {0x00136, 0x00137},
+{0x00139, 0x0013A}, {0x0013B, 0x0013C}, {0x0013D, 0x0013E}, {0x0013F, 0x00140}, {0x00141, 0x00142}, {0x00143, 0x00144},
+{0x00145, 0x00146}, {0x00147, 0x00148}, {0x0014A, 0x0014B}, {0x0014C, 0x0014D}, {0x0014E, 0x0014F}, {0x00150, 0x00151},
+{0x00152, 0x00153}, {0x00154, 0x00155}, {0x00156, 0x00157}, {0x00158, 0x00159}, {0x0015A, 0x0015B}, {0x0015C, 0x0015D},
+{0x0015E, 0x0015F}, {0x00160, 0x00161}, {0x00162, 0x00163}, {0x00164, 0x00165}, {0x00166, 0x00167}, {0x00168, 0x00169},
+{0x0016A, 0x0016B}, {0x0016C, 0x0016D}, {0x0016E, 0x0016F}, {0x00170, 0x00171}, {0x00172, 0x00173}, {0x00174, 0x00175},
+{0x00176, 0x00177}, {0x00178, 0x000FF}, {0x00179, 0x0017A}, {0x0017B, 0x0017C}, {0x0017D, 0x0017E}, {0x00181, 0x00253},
+{0x00182, 0x00183}, {0x00184, 0x00185}, {0x00186, 0x00254}, {0x00187, 0x00188}, {0x00189, 0x00256}, {0x0018A, 0x00257},
+{0x0018B, 0x0018C}, {0x0018E, 0x001DD}, {0x0018F, 0x00259}, {0x00190, 0x0025B}, {0x00191, 0x00192}, {0x00193, 0x00260},
+{0x00194, 0x00263}, {0x00196, 0x00269}, {0x00197, 0x00268}, {0x00198, 0x00199}, {0x0019C, 0x0026F}, {0x0019D, 0x00272},
+{0x0019F, 0x00275}, {0x001A0, 0x001A1}, {0x001A2, 0x001A3}, {0x001A4, 0x001A5}, {0x001A6, 0x00280}, {0x001A7, 0x001A8},
+{0x001A9, 0x00283}, {0x001AC, 0x001AD}, {0x001AE, 0x00288}, {0x001AF, 0x001B0}, {0x001B1, 0x0028A}, {0x001B2, 0x0028B},
+{0x001B3, 0x001B4}, {0x001B5, 0x001B6}, {0x001B7, 0x00292}, {0x001B8, 0x001B9}, {0x001BC, 0x001BD}, {0x001C4, 0x001C6},
+{0x001C5, 0x001C6}, {0x001C7, 0x001C9}, {0x001C8, 0x001C9}, {0x001CA, 0x001CC}, {0x001CB, 0x001CC}, {0x001CD, 0x001CE},
+{0x001CF, 0x001D0}, {0x001D1, 0x001D2}, {0x001D3, 0x001D4}, {0x001D5, 0x001D6}, {0x001D7, 0x001D8}, {0x001D9, 0x001DA},
+{0x001DB, 0x001DC}, {0x001DE, 0x001DF}, {0x001E0, 0x001E1}, {0x001E2, 0x001E3}, {0x001E4, 0x001E5}, {0x001E6, 0x001E7},
+{0x001E8, 0x001E9}, {0x001EA, 0x001EB}, {0x001EC, 0x001ED}, {0x001EE, 0x001EF}, {0x001F1, 0x001F3}, {0x001F2, 0x001F3},
+{0x001F4, 0x001F5}, {0x001F6, 0x00195}, {0x001F7, 0x001BF}, {0x001F8, 0x001F9}, {0x001FA, 0x001FB}, {0x001FC, 0x001FD},
+{0x001FE, 0x001FF}, {0x00200, 0x00201}, {0x00202, 0x00203}, {0x00204, 0x00205}, {0x00206, 0x00207}, {0x00208, 0x00209},
+{0x0020A, 0x0020B}, {0x0020C, 0x0020D}, {0x0020E, 0x0020F}, {0x00210, 0x00211}, {0x00212, 0x00213}, {0x00214, 0x00215},
+{0x00216, 0x00217}, {0x00218, 0x00219}, {0x0021A, 0x0021B}, {0x0021C, 0x0021D}, {0x0021E, 0x0021F}, {0x00220, 0x0019E},
+{0x00222, 0x00223}, {0x00224, 0x00225}, {0x00226, 0x00227}, {0x00228, 0x00229}, {0x0022A, 0x0022B}, {0x0022C, 0x0022D},
+{0x0022E, 0x0022F}, {0x00230, 0x00231}, {0x00232, 0x00233}, {0x0023A, 0x02C65}, {0x0023B, 0x0023C}, {0x0023D, 0x0019A},
+{0x0023E, 0x02C66}, {0x00241, 0x00242}, {0x00243, 0x00180}, {0x00244, 0x00289}, {0x00245, 0x0028C}, {0x00246, 0x00247},
+{0x00248, 0x00249}, {0x0024A, 0x0024B}, {0x0024C, 0x0024D}, {0x0024E, 0x0024F}, {0x00370, 0x00371}, {0x00372, 0x00373},
+{0x00376, 0x00377}, {0x0037F, 0x003F3}, {0x00386, 0x003AC}, {0x00388, 0x003AD}, {0x00389, 0x003AE}, {0x0038A, 0x003AF},
+{0x0038C, 0x003CC}, {0x0038E, 0x003CD}, {0x0038F, 0x003CE}, {0x00391, 0x003B1}, {0x00392, 0x003B2}, {0x00393, 0x003B3},
+{0x00394, 0x003B4}, {0x00395, 0x003B5}, {0x00396, 0x003B6}, {0x00397, 0x003B7}, {0x00398, 0x003B8}, {0x00399, 0x003B9},
+{0x0039A, 0x003BA}, {0x0039B, 0x003BB}, {0x0039C, 0x003BC}, {0x0039D, 0x003BD}, {0x0039E, 0x003BE}, {0x0039F, 0x003BF},
+{0x003A0, 0x003C0}, {0x003A1, 0x003C1}, {0x003A3, 0x003C3}, {0x003A4, 0x003C4}, {0x003A5, 0x003C5}, {0x003A6, 0x003C6},
+{0x003A7, 0x003C7}, {0x003A8, 0x003C8}, {0x003A9, 0x003C9}, {0x003AA, 0x003CA}, {0x003AB, 0x003CB}, {0x003CF, 0x003D7},
+{0x003D8, 0x003D9}, {0x003DA, 0x003DB}, {0x003DC, 0x003DD}, {0x003DE, 0x003DF}, {0x003E0, 0x003E1}, {0x003E2, 0x003E3},
+{0x003E4, 0x003E5}, {0x003E6, 0x003E7}, {0x003E8, 0x003E9}, {0x003EA, 0x003EB}, {0x003EC, 0x003ED}, {0x003EE, 0x003EF},
+{0x003F4, 0x003B8}, {0x003F7, 0x003F8}, {0x003F9, 0x003F2}, {0x003FA, 0x003FB}, {0x003FD, 0x0037B}, {0x003FE, 0x0037C},
+{0x003FF, 0x0037D}, {0x00400, 0x00450}, {0x00401, 0x00451}, {0x00402, 0x00452}, {0x00403, 0x00453}, {0x00404, 0x00454},
+{0x00405, 0x00455}, {0x00406, 0x00456}, {0x00407, 0x00457}, {0x00408, 0x00458}, {0x00409, 0x00459}, {0x0040A, 0x0045A},
+{0x0040B, 0x0045B}, {0x0040C, 0x0045C}, {0x0040D, 0x0045D}, {0x0040E, 0x0045E}, {0x0040F, 0x0045F}, {0x00410, 0x00430},
+{0x00411, 0x00431}, {0x00412, 0x00432}, {0x00413, 0x00433}, {0x00414, 0x00434}, {0x00415, 0x00435}, {0x00416, 0x00436},
+{0x00417, 0x00437}, {0x00418, 0x00438}, {0x00419, 0x00439}, {0x0041A, 0x0043A}, {0x0041B, 0x0043B}, {0x0041C, 0x0043C},
+{0x0041D, 0x0043D}, {0x0041E, 0x0043E}, {0x0041F, 0x0043F}, {0x00420, 0x00440}, {0x00421, 0x00441}, {0x00422, 0x00442},
+{0x00423, 0x00443}, {0x00424, 0x00444}, {0x00425, 0x00445}, {0x00426, 0x00446}, {0x00427, 0x00447}, {0x00428, 0x00448},
+{0x00429, 0x00449}, {0x0042A, 0x0044A}, {0x0042B, 0x0044B}, {0x0042C, 0x0044C}, {0x0042D, 0x0044D}, {0x0042E, 0x0044E},
+{0x0042F, 0x0044F}, {0x00460, 0x00461}, {0x00462, 0x00463}, {0x00464, 0x00465}, {0x00466, 0x00467}, {0x00468, 0x00469},
+{0x0046A, 0x0046B}, {0x0046C, 0x0046D}, {0x0046E, 0x0046F}, {0x00470, 0x00471}, {0x00472, 0x00473}, {0x00474, 0x00475},
+{0x00476, 0x00477}, {0x00478, 0x00479}, {0x0047A, 0x0047B}, {0x0047C, 0x0047D}, {0x0047E, 0x0047F}, {0x00480, 0x00481},
+{0x0048A, 0x0048B}, {0x0048C, 0x0048D}, {0x0048E, 0x0048F}, {0x00490, 0x00491}, {0x00492, 0x00493}, {0x00494, 0x00495},
+{0x00496, 0x00497}, {0x00498, 0x00499}, {0x0049A, 0x0049B}, {0x0049C, 0x0049D}, {0x0049E, 0x0049F}, {0x004A0, 0x004A1},
+{0x004A2, 0x004A3}, {0x004A4, 0x004A5}, {0x004A6, 0x004A7}, {0x004A8, 0x004A9}, {0x004AA, 0x004AB}, {0x004AC, 0x004AD},
+{0x004AE, 0x004AF}, {0x004B0, 0x004B1}, {0x004B2, 0x004B3}, {0x004B4, 0x004B5}, {0x004B6, 0x004B7}, {0x004B8, 0x004B9},
+{0x004BA, 0x004BB}, {0x004BC, 0x004BD}, {0x004BE, 0x004BF}, {0x004C0, 0x004CF}, {0x004C1, 0x004C2}, {0x004C3, 0x004C4},
+{0x004C5, 0x004C6}, {0x004C7, 0x004C8}, {0x004C9, 0x004CA}, {0x004CB, 0x004CC}, {0x004CD, 0x004CE}, {0x004D0, 0x004D1},
+{0x004D2, 0x004D3}, {0x004D4, 0x004D5}, {0x004D6, 0x004D7}, {0x004D8, 0x004D9}, {0x004DA, 0x004DB}, {0x004DC, 0x004DD},
+{0x004DE, 0x004DF}, {0x004E0, 0x004E1}, {0x004E2, 0x004E3}, {0x004E4, 0x004E5}, {0x004E6, 0x004E7}, {0x004E8, 0x004E9},
+{0x004EA, 0x004EB}, {0x004EC, 0x004ED}, {0x004EE, 0x004EF}, {0x004F0, 0x004F1}, {0x004F2, 0x004F3}, {0x004F4, 0x004F5},
+{0x004F6, 0x004F7}, {0x004F8, 0x004F9}, {0x004FA, 0x004FB}, {0x004FC, 0x004FD}, {0x004FE, 0x004FF}, {0x00500, 0x00501},
+{0x00502, 0x00503}, {0x00504, 0x00505}, {0x00506, 0x00507}, {0x00508, 0x00509}, {0x0050A, 0x0050B}, {0x0050C, 0x0050D},
+{0x0050E, 0x0050F}, {0x00510, 0x00511}, {0x00512, 0x00513}, {0x00514, 0x00515}, {0x00516, 0x00517}, {0x00518, 0x00519},
+{0x0051A, 0x0051B}, {0x0051C, 0x0051D}, {0x0051E, 0x0051F}, {0x00520, 0x00521}, {0x00522, 0x00523}, {0x00524, 0x00525},
+{0x00526, 0x00527}, {0x00528, 0x00529}, {0x0052A, 0x0052B}, {0x0052C, 0x0052D}, {0x0052E, 0x0052F}, {0x00531, 0x00561},
+{0x00532, 0x00562}, {0x00533, 0x00563}, {0x00534, 0x00564}, {0x00535, 0x00565}, {0x00536, 0x00566}, {0x00537, 0x00567},
+{0x00538, 0x00568}, {0x00539, 0x00569}, {0x0053A, 0x0056A}, {0x0053B, 0x0056B}, {0x0053C, 0x0056C}, {0x0053D, 0x0056D},
+{0x0053E, 0x0056E}, {0x0053F, 0x0056F}, {0x00540, 0x00570}, {0x00541, 0x00571}, {0x00542, 0x00572}, {0x00543, 0x00573},
+{0x00544, 0x00574}, {0x00545, 0x00575}, {0x00546, 0x00576}, {0x00547, 0x00577}, {0x00548, 0x00578}, {0x00549, 0x00579},
+{0x0054A, 0x0057A}, {0x0054B, 0x0057B}, {0x0054C, 0x0057C}, {0x0054D, 0x0057D}, {0x0054E, 0x0057E}, {0x0054F, 0x0057F},
+{0x00550, 0x00580}, {0x00551, 0x00581}, {0x00552, 0x00582}, {0x00553, 0x00583}, {0x00554, 0x00584}, {0x00555, 0x00585},
+{0x00556, 0x00586}, {0x010A0, 0x02D00}, {0x010A1, 0x02D01}, {0x010A2, 0x02D02}, {0x010A3, 0x02D03}, {0x010A4, 0x02D04},
+{0x010A5, 0x02D05}, {0x010A6, 0x02D06}, {0x010A7, 0x02D07}, {0x010A8, 0x02D08}, {0x010A9, 0x02D09}, {0x010AA, 0x02D0A},
+{0x010AB, 0x02D0B}, {0x010AC, 0x02D0C}, {0x010AD, 0x02D0D}, {0x010AE, 0x02D0E}, {0x010AF, 0x02D0F}, {0x010B0, 0x02D10},
+{0x010B1, 0x02D11}, {0x010B2, 0x02D12}, {0x010B3, 0x02D13}, {0x010B4, 0x02D14}, {0x010B5, 0x02D15}, {0x010B6, 0x02D16},
+{0x010B7, 0x02D17}, {0x010B8, 0x02D18}, {0x010B9, 0x02D19}, {0x010BA, 0x02D1A}, {0x010BB, 0x02D1B}, {0x010BC, 0x02D1C},
+{0x010BD, 0x02D1D}, {0x010BE, 0x02D1E}, {0x010BF, 0x02D1F}, {0x010C0, 0x02D20}, {0x010C1, 0x02D21}, {0x010C2, 0x02D22},
+{0x010C3, 0x02D23}, {0x010C4, 0x02D24}, {0x010C5, 0x02D25}, {0x010C7, 0x02D27}, {0x010CD, 0x02D2D}, {0x013A0, 0x0AB70},
+{0x013A1, 0x0AB71}, {0x013A2, 0x0AB72}, {0x013A3, 0x0AB73}, {0x013A4, 0x0AB74}, {0x013A5, 0x0AB75}, {0x013A6, 0x0AB76},
+{0x013A7, 0x0AB77}, {0x013A8, 0x0AB78}, {0x013A9, 0x0AB79}, {0x013AA, 0x0AB7A}, {0x013AB, 0x0AB7B}, {0x013AC, 0x0AB7C},
+{0x013AD, 0x0AB7D}, {0x013AE, 0x0AB7E}, {0x013AF, 0x0AB7F}, {0x013B0, 0x0AB80}, {0x013B1, 0x0AB81}, {0x013B2, 0x0AB82},
+{0x013B3, 0x0AB83}, {0x013B4, 0x0AB84}, {0x013B5, 0x0AB85}, {0x013B6, 0x0AB86}, {0x013B7, 0x0AB87}, {0x013B8, 0x0AB88},
+{0x013B9, 0x0AB89}, {0x013BA, 0x0AB8A}, {0x013BB, 0x0AB8B}, {0x013BC, 0x0AB8C}, {0x013BD, 0x0AB8D}, {0x013BE, 0x0AB8E},
+{0x013BF, 0x0AB8F}, {0x013C0, 0x0AB90}, {0x013C1, 0x0AB91}, {0x013C2, 0x0AB92}, {0x013C3, 0x0AB93}, {0x013C4, 0x0AB94},
+{0x013C5, 0x0AB95}, {0x013C6, 0x0AB96}, {0x013C7, 0x0AB97}, {0x013C8, 0x0AB98}, {0x013C9, 0x0AB99}, {0x013CA, 0x0AB9A},
+{0x013CB, 0x0AB9B}, {0x013CC, 0x0AB9C}, {0x013CD, 0x0AB9D}, {0x013CE, 0x0AB9E}, {0x013CF, 0x0AB9F}, {0x013D0, 0x0ABA0},
+{0x013D1, 0x0ABA1}, {0x013D2, 0x0ABA2}, {0x013D3, 0x0ABA3}, {0x013D4, 0x0ABA4}, {0x013D5, 0x0ABA5}, {0x013D6, 0x0ABA6},
+{0x013D7, 0x0ABA7}, {0x013D8, 0x0ABA8}, {0x013D9, 0x0ABA9}, {0x013DA, 0x0ABAA}, {0x013DB, 0x0ABAB}, {0x013DC, 0x0ABAC},
+{0x013DD, 0x0ABAD}, {0x013DE, 0x0ABAE}, {0x013DF, 0x0ABAF}, {0x013E0, 0x0ABB0}, {0x013E1, 0x0ABB1}, {0x013E2, 0x0ABB2},
+{0x013E3, 0x0ABB3}, {0x013E4, 0x0ABB4}, {0x013E5, 0x0ABB5}, {0x013E6, 0x0ABB6}, {0x013E7, 0x0ABB7}, {0x013E8, 0x0ABB8},
+{0x013E9, 0x0ABB9}, {0x013EA, 0x0ABBA}, {0x013EB, 0x0ABBB}, {0x013EC, 0x0ABBC}, {0x013ED, 0x0ABBD}, {0x013EE, 0x0ABBE},
+{0x013EF, 0x0ABBF}, {0x013F0, 0x013F8}, {0x013F1, 0x013F9}, {0x013F2, 0x013FA}, {0x013F3, 0x013FB}, {0x013F4, 0x013FC},
+{0x013F5, 0x013FD}, {0x01C90, 0x010D0}, {0x01C91, 0x010D1}, {0x01C92, 0x010D2}, {0x01C93, 0x010D3}, {0x01C94, 0x010D4},
+{0x01C95, 0x010D5}, {0x01C96, 0x010D6}, {0x01C97, 0x010D7}, {0x01C98, 0x010D8}, {0x01C99, 0x010D9}, {0x01C9A, 0x010DA},
+{0x01C9B, 0x010DB}, {0x01C9C, 0x010DC}, {0x01C9D, 0x010DD}, {0x01C9E, 0x010DE}, {0x01C9F, 0x010DF}, {0x01CA0, 0x010E0},
+{0x01CA1, 0x010E1}, {0x01CA2, 0x010E2}, {0x01CA3, 0x010E3}, {0x01CA4, 0x010E4}, {0x01CA5, 0x010E5}, {0x01CA6, 0x010E6},
+{0x01CA7, 0x010E7}, {0x01CA8, 0x010E8}, {0x01CA9, 0x010E9}, {0x01CAA, 0x010EA}, {0x01CAB, 0x010EB}, {0x01CAC, 0x010EC},
+{0x01CAD, 0x010ED}, {0x01CAE, 0x010EE}, {0x01CAF, 0x010EF}, {0x01CB0, 0x010F0}, {0x01CB1, 0x010F1}, {0x01CB2, 0x010F2},
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+{0x10415, 0x1043D}, {0x10416, 0x1043E}, {0x10417, 0x1043F}, {0x10418, 0x10440}, {0x10419, 0x10441}, {0x1041A, 0x10442},
+{0x1041B, 0x10443}, {0x1041C, 0x10444}, {0x1041D, 0x10445}, {0x1041E, 0x10446}, {0x1041F, 0x10447}, {0x10420, 0x10448},
+{0x10421, 0x10449}, {0x10422, 0x1044A}, {0x10423, 0x1044B}, {0x10424, 0x1044C}, {0x10425, 0x1044D}, {0x10426, 0x1044E},
+{0x10427, 0x1044F}, {0x104B0, 0x104D8}, {0x104B1, 0x104D9}, {0x104B2, 0x104DA}, {0x104B3, 0x104DB}, {0x104B4, 0x104DC},
+{0x104B5, 0x104DD}, {0x104B6, 0x104DE}, {0x104B7, 0x104DF}, {0x104B8, 0x104E0}, {0x104B9, 0x104E1}, {0x104BA, 0x104E2},
+{0x104BB, 0x104E3}, {0x104BC, 0x104E4}, {0x104BD, 0x104E5}, {0x104BE, 0x104E6}, {0x104BF, 0x104E7}, {0x104C0, 0x104E8},
+{0x104C1, 0x104E9}, {0x104C2, 0x104EA}, {0x104C3, 0x104EB}, {0x104C4, 0x104EC}, {0x104C5, 0x104ED}, {0x104C6, 0x104EE},
+{0x104C7, 0x104EF}, {0x104C8, 0x104F0}, {0x104C9, 0x104F1}, {0x104CA, 0x104F2}, {0x104CB, 0x104F3}, {0x104CC, 0x104F4},
+{0x104CD, 0x104F5}, {0x104CE, 0x104F6}, {0x104CF, 0x104F7}, {0x104D0, 0x104F8}, {0x104D1, 0x104F9}, {0x104D2, 0x104FA},
+{0x104D3, 0x104FB}, {0x10570, 0x10597}, {0x10571, 0x10598}, {0x10572, 0x10599}, {0x10573, 0x1059A}, {0x10574, 0x1059B},
+{0x10575, 0x1059C}, {0x10576, 0x1059D}, {0x10577, 0x1059E}, {0x10578, 0x1059F}, {0x10579, 0x105A0}, {0x1057A, 0x105A1},
+{0x1057C, 0x105A3}, {0x1057D, 0x105A4}, {0x1057E, 0x105A5}, {0x1057F, 0x105A6}, {0x10580, 0x105A7}, {0x10581, 0x105A8},
+{0x10582, 0x105A9}, {0x10583, 0x105AA}, {0x10584, 0x105AB}, {0x10585, 0x105AC}, {0x10586, 0x105AD}, {0x10587, 0x105AE},
+{0x10588, 0x105AF}, {0x10589, 0x105B0}, {0x1058A, 0x105B1}, {0x1058C, 0x105B3}, {0x1058D, 0x105B4}, {0x1058E, 0x105B5},
+{0x1058F, 0x105B6}, {0x10590, 0x105B7}, {0x10591, 0x105B8}, {0x10592, 0x105B9}, {0x10594, 0x105BB}, {0x10595, 0x105BC},
+{0x10C80, 0x10CC0}, {0x10C81, 0x10CC1}, {0x10C82, 0x10CC2}, {0x10C83, 0x10CC3}, {0x10C84, 0x10CC4}, {0x10C85, 0x10CC5},
+{0x10C86, 0x10CC6}, {0x10C87, 0x10CC7}, {0x10C88, 0x10CC8}, {0x10C89, 0x10CC9}, {0x10C8A, 0x10CCA}, {0x10C8B, 0x10CCB},
+{0x10C8C, 0x10CCC}, {0x10C8D, 0x10CCD}, {0x10C8E, 0x10CCE}, {0x10C8F, 0x10CCF}, {0x10C90, 0x10CD0}, {0x10C91, 0x10CD1},
+{0x10C92, 0x10CD2}, {0x10C93, 0x10CD3}, {0x10C94, 0x10CD4}, {0x10C95, 0x10CD5}, {0x10C96, 0x10CD6}, {0x10C97, 0x10CD7},
+{0x10C98, 0x10CD8}, {0x10C99, 0x10CD9}, {0x10C9A, 0x10CDA}, {0x10C9B, 0x10CDB}, {0x10C9C, 0x10CDC}, {0x10C9D, 0x10CDD},
+{0x10C9E, 0x10CDE}, {0x10C9F, 0x10CDF}, {0x10CA0, 0x10CE0}, {0x10CA1, 0x10CE1}, {0x10CA2, 0x10CE2}, {0x10CA3, 0x10CE3},
+{0x10CA4, 0x10CE4}, {0x10CA5, 0x10CE5}, {0x10CA6, 0x10CE6}, {0x10CA7, 0x10CE7}, {0x10CA8, 0x10CE8}, {0x10CA9, 0x10CE9},
+{0x10CAA, 0x10CEA}, {0x10CAB, 0x10CEB}, {0x10CAC, 0x10CEC}, {0x10CAD, 0x10CED}, {0x10CAE, 0x10CEE}, {0x10CAF, 0x10CEF},
+{0x10CB0, 0x10CF0}, {0x10CB1, 0x10CF1}, {0x10CB2, 0x10CF2}, {0x118A0, 0x118C0}, {0x118A1, 0x118C1}, {0x118A2, 0x118C2},
+{0x118A3, 0x118C3}, {0x118A4, 0x118C4}, {0x118A5, 0x118C5}, {0x118A6, 0x118C6}, {0x118A7, 0x118C7}, {0x118A8, 0x118C8},
+{0x118A9, 0x118C9}, {0x118AA, 0x118CA}, {0x118AB, 0x118CB}, {0x118AC, 0x118CC}, {0x118AD, 0x118CD}, {0x118AE, 0x118CE},
+{0x118AF, 0x118CF}, {0x118B0, 0x118D0}, {0x118B1, 0x118D1}, {0x118B2, 0x118D2}, {0x118B3, 0x118D3}, {0x118B4, 0x118D4},
+{0x118B5, 0x118D5}, {0x118B6, 0x118D6}, {0x118B7, 0x118D7}, {0x118B8, 0x118D8}, {0x118B9, 0x118D9}, {0x118BA, 0x118DA},
+{0x118BB, 0x118DB}, {0x118BC, 0x118DC}, {0x118BD, 0x118DD}, {0x118BE, 0x118DE}, {0x118BF, 0x118DF}, {0x16E40, 0x16E60},
+{0x16E41, 0x16E61}, {0x16E42, 0x16E62}, {0x16E43, 0x16E63}, {0x16E44, 0x16E64}, {0x16E45, 0x16E65}, {0x16E46, 0x16E66},
+{0x16E47, 0x16E67}, {0x16E48, 0x16E68}, {0x16E49, 0x16E69}, {0x16E4A, 0x16E6A}, {0x16E4B, 0x16E6B}, {0x16E4C, 0x16E6C},
+{0x16E4D, 0x16E6D}, {0x16E4E, 0x16E6E}, {0x16E4F, 0x16E6F}, {0x16E50, 0x16E70}, {0x16E51, 0x16E71}, {0x16E52, 0x16E72},
+{0x16E53, 0x16E73}, {0x16E54, 0x16E74}, {0x16E55, 0x16E75}, {0x16E56, 0x16E76}, {0x16E57, 0x16E77}, {0x16E58, 0x16E78},
+{0x16E59, 0x16E79}, {0x16E5A, 0x16E7A}, {0x16E5B, 0x16E7B}, {0x16E5C, 0x16E7C}, {0x16E5D, 0x16E7D}, {0x16E5E, 0x16E7E},
+{0x16E5F, 0x16E7F}, {0x1E900, 0x1E922}, {0x1E901, 0x1E923}, {0x1E902, 0x1E924}, {0x1E903, 0x1E925}, {0x1E904, 0x1E926},
+{0x1E905, 0x1E927}, {0x1E906, 0x1E928}, {0x1E907, 0x1E929}, {0x1E908, 0x1E92A}, {0x1E909, 0x1E92B}, {0x1E90A, 0x1E92C},
+{0x1E90B, 0x1E92D}, {0x1E90C, 0x1E92E}, {0x1E90D, 0x1E92F}, {0x1E90E, 0x1E930}, {0x1E90F, 0x1E931}, {0x1E910, 0x1E932},
+{0x1E911, 0x1E933}, {0x1E912, 0x1E934}, {0x1E913, 0x1E935}, {0x1E914, 0x1E936}, {0x1E915, 0x1E937}, {0x1E916, 0x1E938},
+{0x1E917, 0x1E939}, {0x1E918, 0x1E93A}, {0x1E919, 0x1E93B}, {0x1E91A, 0x1E93C}, {0x1E91B, 0x1E93D}, {0x1E91C, 0x1E93E},
+{0x1E91D, 0x1E93F}, {0x1E91E, 0x1E940}, {0x1E91F, 0x1E941}, {0x1E920, 0x1E942}, {0x1E921, 0x1E943},
+};
diff --git a/unicode-data.h b/unicode-data.h
new file mode 100644
index 000000000..b99500b8f
--- /dev/null
+++ b/unicode-data.h
@@ -0,0 +1,16 @@
+#pragma once
+
+#include <cstdint>
+#include <map>
+#include <utility>
+#include <vector>
+
+extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_digit;
+extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_letter;
+extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_whitespace;
+extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_accent_mark;
+extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_punctuation;
+extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_symbol;
+extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_control;
+extern const std::multimap<uint32_t, uint32_t> unicode_map_nfd;
+extern const std::map<char32_t, char32_t> unicode_map_lowercase;
diff --git a/unicode.cpp b/unicode.cpp
new file mode 100644
index 000000000..df8c5f581
--- /dev/null
+++ b/unicode.cpp
@@ -0,0 +1,277 @@
+#include "unicode.h"
+#include "unicode-data.h"
+
+#include <cassert>
+#include <cstddef>
+#include <cstdint>
+#include <map>
+#include <stdexcept>
+#include <string>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+static std::string unicode_cpts_to_utf8(const std::vector<uint32_t> & cps) {
+    std::string result;
+    for (size_t i = 0; i < cps.size(); ++i) {
+        result.append(unicode_cpt_to_utf8(cps[i]));
+    }
+    return result;
+}
+
+static uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset) {
+    assert(offset < utf8.size());
+    if (!(utf8[offset + 0] & 0x80)) {
+        auto result = utf8[offset + 0];
+        offset += 1;
+        return result;
+    }
+    if (!(utf8[offset + 0] & 0x40)) {
+        throw std::invalid_argument("invalid character");
+    }
+    if (!(utf8[offset + 0] & 0x20)) {
+        if (offset + 1 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80)) {
+            throw std::invalid_argument("invalid character");
+        }
+        auto result = ((utf8[offset + 0] & 0x1f) << 6) | (utf8[offset + 1] & 0x3f);
+        offset += 2;
+        return result;
+    }
+    if (!(utf8[offset + 0] & 0x10)) {
+        if (offset + 2 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80)) {
+            throw std::invalid_argument("invalid character");
+        }
+        auto result = ((utf8[offset + 0] & 0x0f) << 12) | ((utf8[offset + 1] & 0x3f) << 6) | (utf8[offset + 2] & 0x3f);
+        offset += 3;
+        return result;
+    }
+    if (!(utf8[offset + 0] & 0x08)) {
+        if (offset + 3 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80) || !((utf8[offset + 3] & 0xc0) == 0x80)) {
+            throw std::invalid_argument("invalid character");
+        }
+        auto result = ((utf8[offset + 0] & 0x07) << 18) | ((utf8[offset + 1] & 0x3f) << 12) | ((utf8[offset + 2] & 0x3f) << 6) | (utf8[offset + 3] & 0x3f);
+        offset += 4;
+        return result;
+    }
+    throw std::invalid_argument("invalid string");
+}
+
+static std::vector<uint16_t> unicode_cpt_to_utf16(uint32_t cp) {
+    std::vector<uint16_t> result;
+    if (/* 0x0000 <= cp && */ cp <= 0xffff) {
+        result.emplace_back(cp);
+    }
+    else if (0x10000 <= cp && cp <= 0x10ffff) {
+        result.emplace_back(0xd800 | ((cp - 0x10000) >> 10));
+        result.emplace_back(0xdc00 | ((cp - 0x10000) & 0x03ff));
+    }
+    else {
+        throw std::invalid_argument("invalid cpt");
+    }
+    return result;
+}
+
+//static std::vector<uint16_t> unicode_cpts_to_utf16(const std::vector<uint32_t> & cps) {
+//    std::vector<uint16_t> result;
+//    for (size_t i = 0; i < cps.size(); ++i) {
+//        auto temp = unicode_cpt_to_utf16(cps[i]);
+//        result.insert(result.end(), temp.begin(), temp.end());
+//    }
+//    return result;
+//}
+
+static uint32_t cpt_from_utf16(const std::vector<uint16_t> & utf16, size_t & offset) {
+    assert(offset < utf16.size());
+    if (((utf16[0] >> 10) << 10) != 0xd800) {
+        auto result = utf16[offset + 0];
+        offset += 1;
+        return result;
+    }
+
+    if (offset + 1 >= utf16.size() || !((utf16[1] & 0xdc00) == 0xdc00)) {
+        throw std::invalid_argument("invalid character");
+    }
+
+    auto result = 0x10000 + (((utf16[0] & 0x03ff) << 10) | (utf16[1] & 0x03ff));
+    offset += 2;
+    return result;
+}
+
+//static std::vector<uint32_t> unicode_cpts_from_utf16(const std::vector<uint16_t> & utf16) {
+//    std::vector<uint32_t> result;
+//    size_t offset = 0;
+//    while (offset < utf16.size()) {
+//        result.push_back(cpt_from_utf16(utf16, offset));
+//    }
+//    return result;
+//}
+
+static std::unordered_map<uint32_t, int> unicode_cpt_type_map() {
+    std::unordered_map<uint32_t, int> cpt_types;
+    for (auto p : unicode_ranges_digit) {
+        for (auto i = p.first; i <= p.second; ++ i) {
+            cpt_types[i] = CODEPOINT_TYPE_DIGIT;
+        }
+    }
+    for (auto p : unicode_ranges_letter) {
+        for (auto i = p.first; i <= p.second; ++ i) {
+            cpt_types[i] = CODEPOINT_TYPE_LETTER;
+        }
+    }
+    for (auto p : unicode_ranges_whitespace) {
+        for (auto i = p.first; i <= p.second; ++ i) {
+            cpt_types[i] = CODEPOINT_TYPE_WHITESPACE;
+        }
+    }
+    for (auto p : unicode_ranges_accent_mark) {
+        for (auto i = p.first; i <= p.second; ++ i) {
+            cpt_types[i] = CODEPOINT_TYPE_ACCENT_MARK;
+        }
+    }
+    for (auto p : unicode_ranges_punctuation) {
+        for (auto i = p.first; i <= p.second; ++ i) {
+            cpt_types[i] = CODEPOINT_TYPE_PUNCTUATION;
+        }
+    }
+    for  (auto p : unicode_ranges_symbol) {
+        for (auto i = p.first; i <= p.second; ++i) {
+            cpt_types[i] = CODEPOINT_TYPE_SYMBOL;
+        }
+    }
+    for (auto p : unicode_ranges_control) {
+        for (auto i = p.first; i <= p.second; ++ i) {
+            cpt_types[i] = CODEPOINT_TYPE_CONTROL;
+        }
+    }
+    return cpt_types;
+}
+
+static std::unordered_map<uint8_t, std::string> unicode_byte_to_utf8_map() {
+    std::unordered_map<uint8_t, std::string> map;
+    for (int ch = u'!'; ch <= u'~'; ++ch) {
+        assert(0 <= ch && ch < 256);
+        map[ch] = unicode_cpt_to_utf8(ch);
+    }
+    for (int ch = u'¡'; ch <= u'¬'; ++ch) {
+        assert(0 <= ch && ch < 256);
+        map[ch] = unicode_cpt_to_utf8(ch);
+    }
+    for (int ch = u'®'; ch <= u'ÿ'; ++ch) {
+        assert(0 <= ch && ch < 256);
+        map[ch] = unicode_cpt_to_utf8(ch);
+    }
+    auto n = 0;
+    for (int ch = 0; ch < 256; ++ch) {
+        if (map.find(ch) == map.end()) {
+            map[ch] = unicode_cpt_to_utf8(256 + n);
+            ++n;
+        }
+    }
+    return map;
+}
+
+static std::unordered_map<std::string, uint8_t> unicode_utf8_to_byte_map() {
+    std::unordered_map<std::string, uint8_t> map;
+    for (int ch = u'!'; ch <= u'~'; ++ch) {
+        assert(0 <= ch && ch < 256);
+        map[unicode_cpt_to_utf8(ch)] = ch;
+    }
+    for (int ch = u'¡'; ch <= u'¬'; ++ch) {
+        assert(0 <= ch && ch < 256);
+        map[unicode_cpt_to_utf8(ch)] = ch;
+    }
+    for (int ch = u'®'; ch <= u'ÿ'; ++ch) {
+        assert(0 <= ch && ch < 256);
+        map[unicode_cpt_to_utf8(ch)] = ch;
+    }
+    auto n = 0;
+    for (int ch = 0; ch < 256; ++ch) {
+        if (map.find(unicode_cpt_to_utf8(ch)) == map.end()) {
+            map[unicode_cpt_to_utf8(256 + n)] = ch;
+            ++n;
+        }
+    }
+    return map;
+}
+
+//
+// interface
+//
+
+std::string unicode_cpt_to_utf8(uint32_t cp) {
+    std::string result;
+    if (/* 0x00 <= cp && */ cp <= 0x7f) {
+        result.push_back(cp);
+    }
+    else if (0x80 <= cp && cp <= 0x7ff) {
+        result.push_back(0xc0 | ((cp >> 6) & 0x1f));
+        result.push_back(0x80 | (cp & 0x3f));
+    }
+    else if (0x800 <= cp && cp <= 0xffff) {
+        result.push_back(0xe0 | ((cp >> 12) & 0x0f));
+        result.push_back(0x80 | ((cp >> 6) & 0x3f));
+        result.push_back(0x80 | (cp & 0x3f));
+    }
+    else if (0x10000 <= cp && cp <= 0x10ffff) {
+        result.push_back(0xf0 | ((cp >> 18) & 0x07));
+        result.push_back(0x80 | ((cp >> 12) & 0x3f));
+        result.push_back(0x80 | ((cp >> 6) & 0x3f));
+        result.push_back(0x80 | (cp & 0x3f));
+    }
+    else {
+        throw std::invalid_argument("invalid codepoint");
+    }
+    return result;
+}
+
+std::vector<uint32_t> unicode_cpts_normalize_nfd(const std::vector<uint32_t> & cpts) {
+    std::vector<uint32_t> result;
+    result.reserve(cpts.size());
+    for (size_t i = 0; i < cpts.size(); ++i) {
+        auto it = unicode_map_nfd.find(cpts[i]);
+        if (it == unicode_map_nfd.end()) {
+            result.push_back(cpts[i]);
+        } else {
+            result.push_back(it->second);
+        }
+    }
+    return result;
+}
+
+std::vector<uint32_t> unicode_cpts_from_utf8(const std::string & utf8) {
+    std::vector<uint32_t> result;
+    size_t offset = 0;
+    while (offset < utf8.size()) {
+        result.push_back(unicode_cpt_from_utf8(utf8, offset));
+    }
+    return result;
+}
+
+int unicode_cpt_type(uint32_t cp) {
+    static std::unordered_map<uint32_t, int> cpt_types = unicode_cpt_type_map();
+    const auto it = cpt_types.find(cp);
+    return it == cpt_types.end() ? CODEPOINT_TYPE_UNIDENTIFIED : it->second;
+}
+
+int unicode_cpt_type(const std::string & utf8) {
+    if (utf8.length() == 0) {
+        return CODEPOINT_TYPE_UNIDENTIFIED;
+    }
+    size_t offset = 0;
+    return unicode_cpt_type(unicode_cpt_from_utf8(utf8, offset));
+}
+
+std::string unicode_byte_to_utf8(uint8_t byte) {
+    static std::unordered_map<uint8_t, std::string> map = unicode_byte_to_utf8_map();
+    return map.at(byte);
+}
+
+uint8_t unicode_utf8_to_byte(const std::string & utf8) {
+    static std::unordered_map<std::string, uint8_t> map = unicode_utf8_to_byte_map();
+    return map.at(utf8);
+}
+
+char32_t unicode_tolower(char32_t cp) {
+    auto it = unicode_map_lowercase.find(cp);
+    return it == unicode_map_lowercase.end() ? cp : it->second;
+}
diff --git a/unicode.h b/unicode.h
index aeca879ea..6a0be393a 100644
--- a/unicode.h
+++ b/unicode.h
@@ -1,462 +1,28 @@
-#pragma once
+#pragma once
 
-#include <cassert>
-#include <stdexcept>
+#include <cstdint>
+#include <string>
 #include <vector>
-#include <unordered_map>
-
-static const std::vector<std::pair<uint32_t, uint32_t>> digit_ranges = {
-{0x30, 0x39}, {0xB2, 0xB3}, {0xB9, 0xB9}, {0x660, 0x669}, {0x6F0, 0x6F9}, {0x7C0, 0x7C9}, {0x966, 0x96F}, {0x9E6, 0x9EF}, {0xA66, 0xA6F}, {0xAE6, 0xAEF}, {0xB66, 0xB6F}, {0xBE6, 0xBEF}, {0xC66, 0xC6F},
-{0xCE6, 0xCEF}, {0xD66, 0xD6F}, {0xDE6, 0xDEF}, {0xE50, 0xE59}, {0xED0, 0xED9}, {0xF20, 0xF29}, {0x1040, 0x1049}, {0x1090, 0x1099}, {0x1369, 0x1371}, {0x17E0, 0x17E9}, {0x1810, 0x1819}, {0x1946, 0x194F},
-{0x19D0, 0x19DA}, {0x1A80, 0x1A89}, {0x1A90, 0x1A99}, {0x1B50, 0x1B59}, {0x1BB0, 0x1BB9}, {0x1C40, 0x1C49}, {0x1C50, 0x1C59}, {0x2070, 0x2070}, {0x2074, 0x2079}, {0x2080, 0x2089}, {0x2460, 0x2468},
-{0x2474, 0x247C}, {0x2488, 0x2490}, {0x24EA, 0x24EA}, {0x24F5, 0x24FD}, {0x24FF, 0x24FF}, {0x2776, 0x277E}, {0x2780, 0x2788}, {0x278A, 0x2792}, {0xA620, 0xA629}, {0xA8D0, 0xA8D9}, {0xA900, 0xA909},
-{0xA9D0, 0xA9D9}, {0xA9F0, 0xA9F9}, {0xAA50, 0xAA59}, {0xABF0, 0xABF9}, {0xFF10, 0xFF19}, {0x104A0, 0x104A9}, {0x10A40, 0x10A43}, {0x10D30, 0x10D39}, {0x10E60, 0x10E68}, {0x11052, 0x1105A},
-{0x11066, 0x1106F}, {0x110F0, 0x110F9}, {0x11136, 0x1113F}, {0x111D0, 0x111D9}, {0x112F0, 0x112F9}, {0x11450, 0x11459}, {0x114D0, 0x114D9}, {0x11650, 0x11659}, {0x116C0, 0x116C9}, {0x11730, 0x11739},
-{0x118E0, 0x118E9}, {0x11950, 0x11959}, {0x11C50, 0x11C59}, {0x11D50, 0x11D59}, {0x11DA0, 0x11DA9}, {0x16A60, 0x16A69}, {0x16B50, 0x16B59}, {0x1D7CE, 0x1D7FF}, {0x1E140, 0x1E149}, {0x1E2F0, 0x1E2F9},
-{0x1E950, 0x1E959}, {0x1F100, 0x1F10A}, {0x1FBF0, 0x1FBF9},
-};
-
-static const std::vector<std::pair<uint32_t, uint32_t>> letter_ranges = {
-{0x41, 0x5A}, {0x61, 0x7A}, {0xAA, 0xAA}, {0xB5, 0xB5}, {0xBA, 0xBA}, {0xC0, 0xD6}, {0xD8, 0xF6}, {0xF8, 0x2C1}, {0x2C6, 0x2D1}, {0x2E0, 0x2E4}, {0x2EC, 0x2EC}, {0x2EE, 0x2EE}, {0x370, 0x374},
-{0x376, 0x377}, {0x37A, 0x37D}, {0x37F, 0x37F}, {0x386, 0x386}, {0x388, 0x38A}, {0x38C, 0x38C}, {0x38E, 0x3A1}, {0x3A3, 0x3F5}, {0x3F7, 0x481}, {0x48A, 0x52F}, {0x531, 0x556}, {0x559, 0x559},
-{0x560, 0x588}, {0x5D0, 0x5EA}, {0x5EF, 0x5F2}, {0x620, 0x64A}, {0x66E, 0x66F}, {0x671, 0x6D3}, {0x6D5, 0x6D5}, {0x6E5, 0x6E6}, {0x6EE, 0x6EF}, {0x6FA, 0x6FC}, {0x6FF, 0x6FF}, {0x710, 0x710},
-{0x712, 0x72F}, {0x74D, 0x7A5}, {0x7B1, 0x7B1}, {0x7CA, 0x7EA}, {0x7F4, 0x7F5}, {0x7FA, 0x7FA}, {0x800, 0x815}, {0x81A, 0x81A}, {0x824, 0x824}, {0x828, 0x828}, {0x840, 0x858}, {0x860, 0x86A},
-{0x8A0, 0x8B4}, {0x8B6, 0x8C7}, {0x904, 0x939}, {0x93D, 0x93D}, {0x950, 0x950}, {0x958, 0x961}, {0x971, 0x980}, {0x985, 0x98C}, {0x98F, 0x990}, {0x993, 0x9A8}, {0x9AA, 0x9B0}, {0x9B2, 0x9B2},
-{0x9B6, 0x9B9}, {0x9BD, 0x9BD}, {0x9CE, 0x9CE}, {0x9DC, 0x9DD}, {0x9DF, 0x9E1}, {0x9F0, 0x9F1}, {0x9FC, 0x9FC}, {0xA05, 0xA0A}, {0xA0F, 0xA10}, {0xA13, 0xA28}, {0xA2A, 0xA30}, {0xA32, 0xA33},
-{0xA35, 0xA36}, {0xA38, 0xA39}, {0xA59, 0xA5C}, {0xA5E, 0xA5E}, {0xA72, 0xA74}, {0xA85, 0xA8D}, {0xA8F, 0xA91}, {0xA93, 0xAA8}, {0xAAA, 0xAB0}, {0xAB2, 0xAB3}, {0xAB5, 0xAB9}, {0xABD, 0xABD},
-{0xAD0, 0xAD0}, {0xAE0, 0xAE1}, {0xAF9, 0xAF9}, {0xB05, 0xB0C}, {0xB0F, 0xB10}, {0xB13, 0xB28}, {0xB2A, 0xB30}, {0xB32, 0xB33}, {0xB35, 0xB39}, {0xB3D, 0xB3D}, {0xB5C, 0xB5D}, {0xB5F, 0xB61},
-{0xB71, 0xB71}, {0xB83, 0xB83}, {0xB85, 0xB8A}, {0xB8E, 0xB90}, {0xB92, 0xB95}, {0xB99, 0xB9A}, {0xB9C, 0xB9C}, {0xB9E, 0xB9F}, {0xBA3, 0xBA4}, {0xBA8, 0xBAA}, {0xBAE, 0xBB9}, {0xBD0, 0xBD0},
-{0xC05, 0xC0C}, {0xC0E, 0xC10}, {0xC12, 0xC28}, {0xC2A, 0xC39}, {0xC3D, 0xC3D}, {0xC58, 0xC5A}, {0xC60, 0xC61}, {0xC80, 0xC80}, {0xC85, 0xC8C}, {0xC8E, 0xC90}, {0xC92, 0xCA8}, {0xCAA, 0xCB3},
-{0xCB5, 0xCB9}, {0xCBD, 0xCBD}, {0xCDE, 0xCDE}, {0xCE0, 0xCE1}, {0xCF1, 0xCF2}, {0xD04, 0xD0C}, {0xD0E, 0xD10}, {0xD12, 0xD3A}, {0xD3D, 0xD3D}, {0xD4E, 0xD4E}, {0xD54, 0xD56}, {0xD5F, 0xD61},
-{0xD7A, 0xD7F}, {0xD85, 0xD96}, {0xD9A, 0xDB1}, {0xDB3, 0xDBB}, {0xDBD, 0xDBD}, {0xDC0, 0xDC6}, {0xE01, 0xE30}, {0xE32, 0xE33}, {0xE40, 0xE46}, {0xE81, 0xE82}, {0xE84, 0xE84}, {0xE86, 0xE8A},
-{0xE8C, 0xEA3}, {0xEA5, 0xEA5}, {0xEA7, 0xEB0}, {0xEB2, 0xEB3}, {0xEBD, 0xEBD}, {0xEC0, 0xEC4}, {0xEC6, 0xEC6}, {0xEDC, 0xEDF}, {0xF00, 0xF00}, {0xF40, 0xF47}, {0xF49, 0xF6C}, {0xF88, 0xF8C},
-{0x1000, 0x102A}, {0x103F, 0x103F}, {0x1050, 0x1055}, {0x105A, 0x105D}, {0x1061, 0x1061}, {0x1065, 0x1066}, {0x106E, 0x1070}, {0x1075, 0x1081}, {0x108E, 0x108E}, {0x10A0, 0x10C5}, {0x10C7, 0x10C7},
-{0x10CD, 0x10CD}, {0x10D0, 0x10FA}, {0x10FC, 0x1248}, {0x124A, 0x124D}, {0x1250, 0x1256}, {0x1258, 0x1258}, {0x125A, 0x125D}, {0x1260, 0x1288}, {0x128A, 0x128D}, {0x1290, 0x12B0}, {0x12B2, 0x12B5},
-{0x12B8, 0x12BE}, {0x12C0, 0x12C0}, {0x12C2, 0x12C5}, {0x12C8, 0x12D6}, {0x12D8, 0x1310}, {0x1312, 0x1315}, {0x1318, 0x135A}, {0x1380, 0x138F}, {0x13A0, 0x13F5}, {0x13F8, 0x13FD}, {0x1401, 0x166C},
-{0x166F, 0x167F}, {0x1681, 0x169A}, {0x16A0, 0x16EA}, {0x16F1, 0x16F8}, {0x1700, 0x170C}, {0x170E, 0x1711}, {0x1720, 0x1731}, {0x1740, 0x1751}, {0x1760, 0x176C}, {0x176E, 0x1770}, {0x1780, 0x17B3},
-{0x17D7, 0x17D7}, {0x17DC, 0x17DC}, {0x1820, 0x1878}, {0x1880, 0x1884}, {0x1887, 0x18A8}, {0x18AA, 0x18AA}, {0x18B0, 0x18F5}, {0x1900, 0x191E}, {0x1950, 0x196D}, {0x1970, 0x1974}, {0x1980, 0x19AB},
-{0x19B0, 0x19C9}, {0x1A00, 0x1A16}, {0x1A20, 0x1A54}, {0x1AA7, 0x1AA7}, {0x1B05, 0x1B33}, {0x1B45, 0x1B4B}, {0x1B83, 0x1BA0}, {0x1BAE, 0x1BAF}, {0x1BBA, 0x1BE5}, {0x1C00, 0x1C23}, {0x1C4D, 0x1C4F},
-{0x1C5A, 0x1C7D}, {0x1C80, 0x1C88}, {0x1C90, 0x1CBA}, {0x1CBD, 0x1CBF}, {0x1CE9, 0x1CEC}, {0x1CEE, 0x1CF3}, {0x1CF5, 0x1CF6}, {0x1CFA, 0x1CFA}, {0x1D00, 0x1DBF}, {0x1E00, 0x1F15}, {0x1F18, 0x1F1D},
-{0x1F20, 0x1F45}, {0x1F48, 0x1F4D}, {0x1F50, 0x1F57}, {0x1F59, 0x1F59}, {0x1F5B, 0x1F5B}, {0x1F5D, 0x1F5D}, {0x1F5F, 0x1F7D}, {0x1F80, 0x1FB4}, {0x1FB6, 0x1FBC}, {0x1FBE, 0x1FBE}, {0x1FC2, 0x1FC4},
-{0x1FC6, 0x1FCC}, {0x1FD0, 0x1FD3}, {0x1FD6, 0x1FDB}, {0x1FE0, 0x1FEC}, {0x1FF2, 0x1FF4}, {0x1FF6, 0x1FFC}, {0x2071, 0x2071}, {0x207F, 0x207F}, {0x2090, 0x209C}, {0x2102, 0x2102}, {0x2107, 0x2107},
-{0x210A, 0x2113}, {0x2115, 0x2115}, {0x2119, 0x211D}, {0x2124, 0x2124}, {0x2126, 0x2126}, {0x2128, 0x2128}, {0x212A, 0x212D}, {0x212F, 0x2139}, {0x213C, 0x213F}, {0x2145, 0x2149}, {0x214E, 0x214E},
-{0x2183, 0x2184}, {0x2C00, 0x2C2E}, {0x2C30, 0x2C5E}, {0x2C60, 0x2CE4}, {0x2CEB, 0x2CEE}, {0x2CF2, 0x2CF3}, {0x2D00, 0x2D25}, {0x2D27, 0x2D27}, {0x2D2D, 0x2D2D}, {0x2D30, 0x2D67}, {0x2D6F, 0x2D6F},
-{0x2D80, 0x2D96}, {0x2DA0, 0x2DA6}, {0x2DA8, 0x2DAE}, {0x2DB0, 0x2DB6}, {0x2DB8, 0x2DBE}, {0x2DC0, 0x2DC6}, {0x2DC8, 0x2DCE}, {0x2DD0, 0x2DD6}, {0x2DD8, 0x2DDE}, {0x2E2F, 0x2E2F}, {0x3005, 0x3006},
-{0x3031, 0x3035}, {0x303B, 0x303C}, {0x3041, 0x3096}, {0x309D, 0x309F}, {0x30A1, 0x30FA}, {0x30FC, 0x30FF}, {0x3105, 0x312F}, {0x3131, 0x318E}, {0x31A0, 0x31BF}, {0x31F0, 0x31FF}, {0x3400, 0x4DBF},
-{0x4E00, 0x9FFC}, {0xA000, 0xA48C}, {0xA4D0, 0xA4FD}, {0xA500, 0xA60C}, {0xA610, 0xA61F}, {0xA62A, 0xA62B}, {0xA640, 0xA66E}, {0xA67F, 0xA69D}, {0xA6A0, 0xA6E5}, {0xA717, 0xA71F}, {0xA722, 0xA788},
-{0xA78B, 0xA7BF}, {0xA7C2, 0xA7CA}, {0xA7F5, 0xA801}, {0xA803, 0xA805}, {0xA807, 0xA80A}, {0xA80C, 0xA822}, {0xA840, 0xA873}, {0xA882, 0xA8B3}, {0xA8F2, 0xA8F7}, {0xA8FB, 0xA8FB}, {0xA8FD, 0xA8FE},
-{0xA90A, 0xA925}, {0xA930, 0xA946}, {0xA960, 0xA97C}, {0xA984, 0xA9B2}, {0xA9CF, 0xA9CF}, {0xA9E0, 0xA9E4}, {0xA9E6, 0xA9EF}, {0xA9FA, 0xA9FE}, {0xAA00, 0xAA28}, {0xAA40, 0xAA42}, {0xAA44, 0xAA4B},
-{0xAA60, 0xAA76}, {0xAA7A, 0xAA7A}, {0xAA7E, 0xAAAF}, {0xAAB1, 0xAAB1}, {0xAAB5, 0xAAB6}, {0xAAB9, 0xAABD}, {0xAAC0, 0xAAC0}, {0xAAC2, 0xAAC2}, {0xAADB, 0xAADD}, {0xAAE0, 0xAAEA}, {0xAAF2, 0xAAF4},
-{0xAB01, 0xAB06}, {0xAB09, 0xAB0E}, {0xAB11, 0xAB16}, {0xAB20, 0xAB26}, {0xAB28, 0xAB2E}, {0xAB30, 0xAB5A}, {0xAB5C, 0xAB69}, {0xAB70, 0xABE2}, {0xAC00, 0xD7A3}, {0xD7B0, 0xD7C6}, {0xD7CB, 0xD7FB},
-{0xF900, 0xFA6D}, {0xFA70, 0xFAD9}, {0xFB00, 0xFB06}, {0xFB13, 0xFB17}, {0xFB1D, 0xFB1D}, {0xFB1F, 0xFB28}, {0xFB2A, 0xFB36}, {0xFB38, 0xFB3C}, {0xFB3E, 0xFB3E}, {0xFB40, 0xFB41}, {0xFB43, 0xFB44},
-{0xFB46, 0xFBB1}, {0xFBD3, 0xFD3D}, {0xFD50, 0xFD8F}, {0xFD92, 0xFDC7}, {0xFDF0, 0xFDFB}, {0xFE70, 0xFE74}, {0xFE76, 0xFEFC}, {0xFF21, 0xFF3A}, {0xFF41, 0xFF5A}, {0xFF66, 0xFFBE}, {0xFFC2, 0xFFC7},
-{0xFFCA, 0xFFCF}, {0xFFD2, 0xFFD7}, {0xFFDA, 0xFFDC}, {0x10000, 0x1000B}, {0x1000D, 0x10026}, {0x10028, 0x1003A}, {0x1003C, 0x1003D}, {0x1003F, 0x1004D}, {0x10050, 0x1005D}, {0x10080, 0x100FA},
-{0x10280, 0x1029C}, {0x102A0, 0x102D0}, {0x10300, 0x1031F}, {0x1032D, 0x10340}, {0x10342, 0x10349}, {0x10350, 0x10375}, {0x10380, 0x1039D}, {0x103A0, 0x103C3}, {0x103C8, 0x103CF}, {0x10400, 0x1049D},
-{0x104B0, 0x104D3}, {0x104D8, 0x104FB}, {0x10500, 0x10527}, {0x10530, 0x10563}, {0x10600, 0x10736}, {0x10740, 0x10755}, {0x10760, 0x10767}, {0x10800, 0x10805}, {0x10808, 0x10808}, {0x1080A, 0x10835},
-{0x10837, 0x10838}, {0x1083C, 0x1083C}, {0x1083F, 0x10855}, {0x10860, 0x10876}, {0x10880, 0x1089E}, {0x108E0, 0x108F2}, {0x108F4, 0x108F5}, {0x10900, 0x10915}, {0x10920, 0x10939}, {0x10980, 0x109B7},
-{0x109BE, 0x109BF}, {0x10A00, 0x10A00}, {0x10A10, 0x10A13}, {0x10A15, 0x10A17}, {0x10A19, 0x10A35}, {0x10A60, 0x10A7C}, {0x10A80, 0x10A9C}, {0x10AC0, 0x10AC7}, {0x10AC9, 0x10AE4}, {0x10B00, 0x10B35},
-{0x10B40, 0x10B55}, {0x10B60, 0x10B72}, {0x10B80, 0x10B91}, {0x10C00, 0x10C48}, {0x10C80, 0x10CB2}, {0x10CC0, 0x10CF2}, {0x10D00, 0x10D23}, {0x10E80, 0x10EA9}, {0x10EB0, 0x10EB1}, {0x10F00, 0x10F1C},
-{0x10F27, 0x10F27}, {0x10F30, 0x10F45}, {0x10FB0, 0x10FC4}, {0x10FE0, 0x10FF6}, {0x11003, 0x11037}, {0x11083, 0x110AF}, {0x110D0, 0x110E8}, {0x11103, 0x11126}, {0x11144, 0x11144}, {0x11147, 0x11147},
-{0x11150, 0x11172}, {0x11176, 0x11176}, {0x11183, 0x111B2}, {0x111C1, 0x111C4}, {0x111DA, 0x111DA}, {0x111DC, 0x111DC}, {0x11200, 0x11211}, {0x11213, 0x1122B}, {0x11280, 0x11286}, {0x11288, 0x11288},
-{0x1128A, 0x1128D}, {0x1128F, 0x1129D}, {0x1129F, 0x112A8}, {0x112B0, 0x112DE}, {0x11305, 0x1130C}, {0x1130F, 0x11310}, {0x11313, 0x11328}, {0x1132A, 0x11330}, {0x11332, 0x11333}, {0x11335, 0x11339},
-{0x1133D, 0x1133D}, {0x11350, 0x11350}, {0x1135D, 0x11361}, {0x11400, 0x11434}, {0x11447, 0x1144A}, {0x1145F, 0x11461}, {0x11480, 0x114AF}, {0x114C4, 0x114C5}, {0x114C7, 0x114C7}, {0x11580, 0x115AE},
-{0x115D8, 0x115DB}, {0x11600, 0x1162F}, {0x11644, 0x11644}, {0x11680, 0x116AA}, {0x116B8, 0x116B8}, {0x11700, 0x1171A}, {0x11800, 0x1182B}, {0x118A0, 0x118DF}, {0x118FF, 0x11906}, {0x11909, 0x11909},
-{0x1190C, 0x11913}, {0x11915, 0x11916}, {0x11918, 0x1192F}, {0x1193F, 0x1193F}, {0x11941, 0x11941}, {0x119A0, 0x119A7}, {0x119AA, 0x119D0}, {0x119E1, 0x119E1}, {0x119E3, 0x119E3}, {0x11A00, 0x11A00},
-{0x11A0B, 0x11A32}, {0x11A3A, 0x11A3A}, {0x11A50, 0x11A50}, {0x11A5C, 0x11A89}, {0x11A9D, 0x11A9D}, {0x11AC0, 0x11AF8}, {0x11C00, 0x11C08}, {0x11C0A, 0x11C2E}, {0x11C40, 0x11C40}, {0x11C72, 0x11C8F},
-{0x11D00, 0x11D06}, {0x11D08, 0x11D09}, {0x11D0B, 0x11D30}, {0x11D46, 0x11D46}, {0x11D60, 0x11D65}, {0x11D67, 0x11D68}, {0x11D6A, 0x11D89}, {0x11D98, 0x11D98}, {0x11EE0, 0x11EF2}, {0x11FB0, 0x11FB0},
-{0x12000, 0x12399}, {0x12480, 0x12543}, {0x13000, 0x1342E}, {0x14400, 0x14646}, {0x16800, 0x16A38}, {0x16A40, 0x16A5E}, {0x16AD0, 0x16AED}, {0x16B00, 0x16B2F}, {0x16B40, 0x16B43}, {0x16B63, 0x16B77},
-{0x16B7D, 0x16B8F}, {0x16E40, 0x16E7F}, {0x16F00, 0x16F4A}, {0x16F50, 0x16F50}, {0x16F93, 0x16F9F}, {0x16FE0, 0x16FE1}, {0x16FE3, 0x16FE3}, {0x17000, 0x187F7}, {0x18800, 0x18CD5}, {0x18D00, 0x18D08},
-{0x1B000, 0x1B11E}, {0x1B150, 0x1B152}, {0x1B164, 0x1B167}, {0x1B170, 0x1B2FB}, {0x1BC00, 0x1BC6A}, {0x1BC70, 0x1BC7C}, {0x1BC80, 0x1BC88}, {0x1BC90, 0x1BC99}, {0x1D400, 0x1D454}, {0x1D456, 0x1D49C},
-{0x1D49E, 0x1D49F}, {0x1D4A2, 0x1D4A2}, {0x1D4A5, 0x1D4A6}, {0x1D4A9, 0x1D4AC}, {0x1D4AE, 0x1D4B9}, {0x1D4BB, 0x1D4BB}, {0x1D4BD, 0x1D4C3}, {0x1D4C5, 0x1D505}, {0x1D507, 0x1D50A}, {0x1D50D, 0x1D514},
-{0x1D516, 0x1D51C}, {0x1D51E, 0x1D539}, {0x1D53B, 0x1D53E}, {0x1D540, 0x1D544}, {0x1D546, 0x1D546}, {0x1D54A, 0x1D550}, {0x1D552, 0x1D6A5}, {0x1D6A8, 0x1D6C0}, {0x1D6C2, 0x1D6DA}, {0x1D6DC, 0x1D6FA},
-{0x1D6FC, 0x1D714}, {0x1D716, 0x1D734}, {0x1D736, 0x1D74E}, {0x1D750, 0x1D76E}, {0x1D770, 0x1D788}, {0x1D78A, 0x1D7A8}, {0x1D7AA, 0x1D7C2}, {0x1D7C4, 0x1D7CB}, {0x1E100, 0x1E12C}, {0x1E137, 0x1E13D},
-{0x1E14E, 0x1E14E}, {0x1E2C0, 0x1E2EB}, {0x1E800, 0x1E8C4}, {0x1E900, 0x1E943}, {0x1E94B, 0x1E94B}, {0x1EE00, 0x1EE03}, {0x1EE05, 0x1EE1F}, {0x1EE21, 0x1EE22}, {0x1EE24, 0x1EE24}, {0x1EE27, 0x1EE27},
-{0x1EE29, 0x1EE32}, {0x1EE34, 0x1EE37}, {0x1EE39, 0x1EE39}, {0x1EE3B, 0x1EE3B}, {0x1EE42, 0x1EE42}, {0x1EE47, 0x1EE47}, {0x1EE49, 0x1EE49}, {0x1EE4B, 0x1EE4B}, {0x1EE4D, 0x1EE4F}, {0x1EE51, 0x1EE52},
-{0x1EE54, 0x1EE54}, {0x1EE57, 0x1EE57}, {0x1EE59, 0x1EE59}, {0x1EE5B, 0x1EE5B}, {0x1EE5D, 0x1EE5D}, {0x1EE5F, 0x1EE5F}, {0x1EE61, 0x1EE62}, {0x1EE64, 0x1EE64}, {0x1EE67, 0x1EE6A}, {0x1EE6C, 0x1EE72},
-{0x1EE74, 0x1EE77}, {0x1EE79, 0x1EE7C}, {0x1EE7E, 0x1EE7E}, {0x1EE80, 0x1EE89}, {0x1EE8B, 0x1EE9B}, {0x1EEA1, 0x1EEA3}, {0x1EEA5, 0x1EEA9}, {0x1EEAB, 0x1EEBB}, {0x20000, 0x2A6DD}, {0x2A700, 0x2B734},
-{0x2B740, 0x2B81D}, {0x2B820, 0x2CEA1}, {0x2CEB0, 0x2EBE0}, {0x2F800, 0x2FA1D}, {0x30000, 0x3134A},
-};
-
-static const std::vector<std::pair<uint32_t, uint32_t>> whitespace_ranges = {
-{0x9, 0xD}, {0x1C, 0x20}, {0x85, 0x85}, {0xA0, 0xA0}, {0x1680, 0x1680}, {0x2000, 0x200A}, {0x2028, 0x2029}, {0x202F, 0x202F}, {0x205F, 0x205F}, {0x3000, 0x3000},
-};
-
-static const std::vector<std::pair<uint32_t, uint32_t>> accent_mark_ranges = {
-{0x300, 0x36F}, {0x483, 0x489}, {0x591, 0x5BD}, {0x5BF, 0x5BF}, {0x5C1, 0x5C2}, {0x5C4, 0x5C5}, {0x5C7, 0x5C7}, {0x610, 0x61A}, {0x64B, 0x65F}, {0x670, 0x670}, {0x6D6, 0x6DC}, {0x6DF, 0x6E4},
-{0x6E7, 0x6E8}, {0x6EA, 0x6ED}, {0x711, 0x711}, {0x730, 0x74A}, {0x7A6, 0x7B0}, {0x7EB, 0x7F3}, {0x7FD, 0x7FD}, {0x816, 0x819}, {0x81B, 0x823}, {0x825, 0x827}, {0x829, 0x82D}, {0x859, 0x85B},
-{0x8D3, 0x8E1}, {0x8E3, 0x903}, {0x93A, 0x93C}, {0x93E, 0x94F}, {0x951, 0x957}, {0x962, 0x963}, {0x981, 0x983}, {0x9BC, 0x9BC}, {0x9BE, 0x9C4}, {0x9C7, 0x9C8}, {0x9CB, 0x9CD}, {0x9D7, 0x9D7},
-{0x9E2, 0x9E3}, {0x9FE, 0x9FE}, {0xA01, 0xA03}, {0xA3C, 0xA3C}, {0xA3E, 0xA42}, {0xA47, 0xA48}, {0xA4B, 0xA4D}, {0xA51, 0xA51}, {0xA70, 0xA71}, {0xA75, 0xA75}, {0xA81, 0xA83}, {0xABC, 0xABC},
-{0xABE, 0xAC5}, {0xAC7, 0xAC9}, {0xACB, 0xACD}, {0xAE2, 0xAE3}, {0xAFA, 0xAFF}, {0xB01, 0xB03}, {0xB3C, 0xB3C}, {0xB3E, 0xB44}, {0xB47, 0xB48}, {0xB4B, 0xB4D}, {0xB55, 0xB57}, {0xB62, 0xB63},
-{0xB82, 0xB82}, {0xBBE, 0xBC2}, {0xBC6, 0xBC8}, {0xBCA, 0xBCD}, {0xBD7, 0xBD7}, {0xC00, 0xC04}, {0xC3E, 0xC44}, {0xC46, 0xC48}, {0xC4A, 0xC4D}, {0xC55, 0xC56}, {0xC62, 0xC63}, {0xC81, 0xC83},
-{0xCBC, 0xCBC}, {0xCBE, 0xCC4}, {0xCC6, 0xCC8}, {0xCCA, 0xCCD}, {0xCD5, 0xCD6}, {0xCE2, 0xCE3}, {0xD00, 0xD03}, {0xD3B, 0xD3C}, {0xD3E, 0xD44}, {0xD46, 0xD48}, {0xD4A, 0xD4D}, {0xD57, 0xD57},
-{0xD62, 0xD63}, {0xD81, 0xD83}, {0xDCA, 0xDCA}, {0xDCF, 0xDD4}, {0xDD6, 0xDD6}, {0xDD8, 0xDDF}, {0xDF2, 0xDF3}, {0xE31, 0xE31}, {0xE34, 0xE3A}, {0xE47, 0xE4E}, {0xEB1, 0xEB1}, {0xEB4, 0xEBC},
-{0xEC8, 0xECD}, {0xF18, 0xF19}, {0xF35, 0xF35}, {0xF37, 0xF37}, {0xF39, 0xF39}, {0xF3E, 0xF3F}, {0xF71, 0xF84}, {0xF86, 0xF87}, {0xF8D, 0xF97}, {0xF99, 0xFBC}, {0xFC6, 0xFC6}, {0x102B, 0x103E},
-{0x1056, 0x1059}, {0x105E, 0x1060}, {0x1062, 0x1064}, {0x1067, 0x106D}, {0x1071, 0x1074}, {0x1082, 0x108D}, {0x108F, 0x108F}, {0x109A, 0x109D}, {0x135D, 0x135F}, {0x1712, 0x1714}, {0x1732, 0x1734},
-{0x1752, 0x1753}, {0x1772, 0x1773}, {0x17B4, 0x17D3}, {0x17DD, 0x17DD}, {0x180B, 0x180D}, {0x1885, 0x1886}, {0x18A9, 0x18A9}, {0x1920, 0x192B}, {0x1930, 0x193B}, {0x1A17, 0x1A1B}, {0x1A55, 0x1A5E},
-{0x1A60, 0x1A7C}, {0x1A7F, 0x1A7F}, {0x1AB0, 0x1AC0}, {0x1B00, 0x1B04}, {0x1B34, 0x1B44}, {0x1B6B, 0x1B73}, {0x1B80, 0x1B82}, {0x1BA1, 0x1BAD}, {0x1BE6, 0x1BF3}, {0x1C24, 0x1C37}, {0x1CD0, 0x1CD2},
-{0x1CD4, 0x1CE8}, {0x1CED, 0x1CED}, {0x1CF4, 0x1CF4}, {0x1CF7, 0x1CF9}, {0x1DC0, 0x1DF9}, {0x1DFB, 0x1DFF}, {0x20D0, 0x20F0}, {0x2CEF, 0x2CF1}, {0x2D7F, 0x2D7F}, {0x2DE0, 0x2DFF}, {0x302A, 0x302F},
-{0x3099, 0x309A}, {0xA66F, 0xA672}, {0xA674, 0xA67D}, {0xA69E, 0xA69F}, {0xA6F0, 0xA6F1}, {0xA802, 0xA802}, {0xA806, 0xA806}, {0xA80B, 0xA80B}, {0xA823, 0xA827}, {0xA82C, 0xA82C}, {0xA880, 0xA881},
-{0xA8B4, 0xA8C5}, {0xA8E0, 0xA8F1}, {0xA8FF, 0xA8FF}, {0xA926, 0xA92D}, {0xA947, 0xA953}, {0xA980, 0xA983}, {0xA9B3, 0xA9C0}, {0xA9E5, 0xA9E5}, {0xAA29, 0xAA36}, {0xAA43, 0xAA43}, {0xAA4C, 0xAA4D},
-{0xAA7B, 0xAA7D}, {0xAAB0, 0xAAB0}, {0xAAB2, 0xAAB4}, {0xAAB7, 0xAAB8}, {0xAABE, 0xAABF}, {0xAAC1, 0xAAC1}, {0xAAEB, 0xAAEF}, {0xAAF5, 0xAAF6}, {0xABE3, 0xABEA}, {0xABEC, 0xABED}, {0xFB1E, 0xFB1E},
-{0xFE00, 0xFE0F}, {0xFE20, 0xFE2F}, {0x101FD, 0x101FD}, {0x102E0, 0x102E0}, {0x10376, 0x1037A}, {0x10A01, 0x10A03}, {0x10A05, 0x10A06}, {0x10A0C, 0x10A0F}, {0x10A38, 0x10A3A}, {0x10A3F, 0x10A3F},
-{0x10AE5, 0x10AE6}, {0x10D24, 0x10D27}, {0x10EAB, 0x10EAC}, {0x10F46, 0x10F50}, {0x11000, 0x11002}, {0x11038, 0x11046}, {0x1107F, 0x11082}, {0x110B0, 0x110BA}, {0x11100, 0x11102}, {0x11127, 0x11134},
-{0x11145, 0x11146}, {0x11173, 0x11173}, {0x11180, 0x11182}, {0x111B3, 0x111C0}, {0x111C9, 0x111CC}, {0x111CE, 0x111CF}, {0x1122C, 0x11237}, {0x1123E, 0x1123E}, {0x112DF, 0x112EA}, {0x11300, 0x11303},
-{0x1133B, 0x1133C}, {0x1133E, 0x11344}, {0x11347, 0x11348}, {0x1134B, 0x1134D}, {0x11357, 0x11357}, {0x11362, 0x11363}, {0x11366, 0x1136C}, {0x11370, 0x11374}, {0x11435, 0x11446}, {0x1145E, 0x1145E},
-{0x114B0, 0x114C3}, {0x115AF, 0x115B5}, {0x115B8, 0x115C0}, {0x115DC, 0x115DD}, {0x11630, 0x11640}, {0x116AB, 0x116B7}, {0x1171D, 0x1172B}, {0x1182C, 0x1183A}, {0x11930, 0x11935}, {0x11937, 0x11938},
-{0x1193B, 0x1193E}, {0x11940, 0x11940}, {0x11942, 0x11943}, {0x119D1, 0x119D7}, {0x119DA, 0x119E0}, {0x119E4, 0x119E4}, {0x11A01, 0x11A0A}, {0x11A33, 0x11A39}, {0x11A3B, 0x11A3E}, {0x11A47, 0x11A47},
-{0x11A51, 0x11A5B}, {0x11A8A, 0x11A99}, {0x11C2F, 0x11C36}, {0x11C38, 0x11C3F}, {0x11C92, 0x11CA7}, {0x11CA9, 0x11CB6}, {0x11D31, 0x11D36}, {0x11D3A, 0x11D3A}, {0x11D3C, 0x11D3D}, {0x11D3F, 0x11D45},
-{0x11D47, 0x11D47}, {0x11D8A, 0x11D8E}, {0x11D90, 0x11D91}, {0x11D93, 0x11D97}, {0x11EF3, 0x11EF6}, {0x16AF0, 0x16AF4}, {0x16B30, 0x16B36}, {0x16F4F, 0x16F4F}, {0x16F51, 0x16F87}, {0x16F8F, 0x16F92},
-{0x16FE4, 0x16FE4}, {0x16FF0, 0x16FF1}, {0x1BC9D, 0x1BC9E}, {0x1D165, 0x1D169}, {0x1D16D, 0x1D172}, {0x1D17B, 0x1D182}, {0x1D185, 0x1D18B}, {0x1D1AA, 0x1D1AD}, {0x1D242, 0x1D244}, {0x1DA00, 0x1DA36},
-{0x1DA3B, 0x1DA6C}, {0x1DA75, 0x1DA75}, {0x1DA84, 0x1DA84}, {0x1DA9B, 0x1DA9F}, {0x1DAA1, 0x1DAAF}, {0x1E000, 0x1E006}, {0x1E008, 0x1E018}, {0x1E01B, 0x1E021}, {0x1E023, 0x1E024}, {0x1E026, 0x1E02A},
-{0x1E130, 0x1E136}, {0x1E2EC, 0x1E2EF}, {0x1E8D0, 0x1E8D6}, {0x1E944, 0x1E94A}, {0xE0100, 0xE01EF},
-};
-
-static const std::vector<std::pair<uint32_t, uint32_t>> punctuation_ranges = {
-{0x21, 0x23}, {0x25, 0x2A}, {0x2C, 0x2F}, {0x3A, 0x3B}, {0x3F, 0x40}, {0x5B, 0x5D}, {0x5F, 0x5F}, {0x7B, 0x7B}, {0x7D, 0x7D}, {0xA1, 0xA1}, {0xA7, 0xA7}, {0xAB, 0xAB}, {0xB6, 0xB7}, {0xBB, 0xBB},
-{0xBF, 0xBF}, {0x37E, 0x37E}, {0x387, 0x387}, {0x55A, 0x55F}, {0x589, 0x58A}, {0x5BE, 0x5BE}, {0x5C0, 0x5C0}, {0x5C3, 0x5C3}, {0x5C6, 0x5C6}, {0x5F3, 0x5F4}, {0x609, 0x60A}, {0x60C, 0x60D},
-{0x61B, 0x61B}, {0x61E, 0x61F}, {0x66A, 0x66D}, {0x6D4, 0x6D4}, {0x700, 0x70D}, {0x7F7, 0x7F9}, {0x830, 0x83E}, {0x85E, 0x85E}, {0x964, 0x965}, {0x970, 0x970}, {0x9FD, 0x9FD}, {0xA76, 0xA76},
-{0xAF0, 0xAF0}, {0xC77, 0xC77}, {0xC84, 0xC84}, {0xDF4, 0xDF4}, {0xE4F, 0xE4F}, {0xE5A, 0xE5B}, {0xF04, 0xF12}, {0xF14, 0xF14}, {0xF3A, 0xF3D}, {0xF85, 0xF85}, {0xFD0, 0xFD4}, {0xFD9, 0xFDA},
-{0x104A, 0x104F}, {0x10FB, 0x10FB}, {0x1360, 0x1368}, {0x1400, 0x1400}, {0x166E, 0x166E}, {0x169B, 0x169C}, {0x16EB, 0x16ED}, {0x1735, 0x1736}, {0x17D4, 0x17D6}, {0x17D8, 0x17DA}, {0x1800, 0x180A},
-{0x1944, 0x1945}, {0x1A1E, 0x1A1F}, {0x1AA0, 0x1AA6}, {0x1AA8, 0x1AAD}, {0x1B5A, 0x1B60}, {0x1BFC, 0x1BFF}, {0x1C3B, 0x1C3F}, {0x1C7E, 0x1C7F}, {0x1CC0, 0x1CC7}, {0x1CD3, 0x1CD3}, {0x2010, 0x2027},
-{0x2030, 0x2043}, {0x2045, 0x2051}, {0x2053, 0x205E}, {0x207D, 0x207E}, {0x208D, 0x208E}, {0x2308, 0x230B}, {0x2329, 0x232A}, {0x2768, 0x2775}, {0x27C5, 0x27C6}, {0x27E6, 0x27EF}, {0x2983, 0x2998},
-{0x29D8, 0x29DB}, {0x29FC, 0x29FD}, {0x2CF9, 0x2CFC}, {0x2CFE, 0x2CFF}, {0x2D70, 0x2D70}, {0x2E00, 0x2E2E}, {0x2E30, 0x2E4F}, {0x2E52, 0x2E52}, {0x3001, 0x3003}, {0x3008, 0x3011}, {0x3014, 0x301F},
-{0x3030, 0x3030}, {0x303D, 0x303D}, {0x30A0, 0x30A0}, {0x30FB, 0x30FB}, {0xA4FE, 0xA4FF}, {0xA60D, 0xA60F}, {0xA673, 0xA673}, {0xA67E, 0xA67E}, {0xA6F2, 0xA6F7}, {0xA874, 0xA877}, {0xA8CE, 0xA8CF},
-{0xA8F8, 0xA8FA}, {0xA8FC, 0xA8FC}, {0xA92E, 0xA92F}, {0xA95F, 0xA95F}, {0xA9C1, 0xA9CD}, {0xA9DE, 0xA9DF}, {0xAA5C, 0xAA5F}, {0xAADE, 0xAADF}, {0xAAF0, 0xAAF1}, {0xABEB, 0xABEB}, {0xFD3E, 0xFD3F},
-{0xFE10, 0xFE19}, {0xFE30, 0xFE52}, {0xFE54, 0xFE61}, {0xFE63, 0xFE63}, {0xFE68, 0xFE68}, {0xFE6A, 0xFE6B}, {0xFF01, 0xFF03}, {0xFF05, 0xFF0A}, {0xFF0C, 0xFF0F}, {0xFF1A, 0xFF1B}, {0xFF1F, 0xFF20},
-{0xFF3B, 0xFF3D}, {0xFF3F, 0xFF3F}, {0xFF5B, 0xFF5B}, {0xFF5D, 0xFF5D}, {0xFF5F, 0xFF65}, {0x10100, 0x10102}, {0x1039F, 0x1039F}, {0x103D0, 0x103D0}, {0x1056F, 0x1056F}, {0x10857, 0x10857},
-{0x1091F, 0x1091F}, {0x1093F, 0x1093F}, {0x10A50, 0x10A58}, {0x10A7F, 0x10A7F}, {0x10AF0, 0x10AF6}, {0x10B39, 0x10B3F}, {0x10B99, 0x10B9C}, {0x10EAD, 0x10EAD}, {0x10F55, 0x10F59}, {0x11047, 0x1104D},
-{0x110BB, 0x110BC}, {0x110BE, 0x110C1}, {0x11140, 0x11143}, {0x11174, 0x11175}, {0x111C5, 0x111C8}, {0x111CD, 0x111CD}, {0x111DB, 0x111DB}, {0x111DD, 0x111DF}, {0x11238, 0x1123D}, {0x112A9, 0x112A9},
-{0x1144B, 0x1144F}, {0x1145A, 0x1145B}, {0x1145D, 0x1145D}, {0x114C6, 0x114C6}, {0x115C1, 0x115D7}, {0x11641, 0x11643}, {0x11660, 0x1166C}, {0x1173C, 0x1173E}, {0x1183B, 0x1183B}, {0x11944, 0x11946},
-{0x119E2, 0x119E2}, {0x11A3F, 0x11A46}, {0x11A9A, 0x11A9C}, {0x11A9E, 0x11AA2}, {0x11C41, 0x11C45}, {0x11C70, 0x11C71}, {0x11EF7, 0x11EF8}, {0x11FFF, 0x11FFF}, {0x12470, 0x12474}, {0x16A6E, 0x16A6F},
-{0x16AF5, 0x16AF5}, {0x16B37, 0x16B3B}, {0x16B44, 0x16B44}, {0x16E97, 0x16E9A}, {0x16FE2, 0x16FE2}, {0x1BC9F, 0x1BC9F}, {0x1DA87, 0x1DA8B}, {0x1E95E, 0x1E95F},
-};
-
-static const std::vector<std::pair<uint32_t, uint32_t>> symbol_ranges = {
-{0x24, 0x24}, {0x2B, 0x2B}, {0x3C, 0x3E}, {0x5E, 0x5E}, {0x60, 0x60}, {0x7C, 0x7C}, {0x7E, 0x7E}, {0xA2, 0xA6}, {0xA8, 0xA9}, {0xAC, 0xAC}, {0xAE, 0xB1}, {0xB4, 0xB4}, {0xB8, 0xB8}, {0xD7, 0xD7},
-{0xF7, 0xF7}, {0x2C2, 0x2C5}, {0x2D2, 0x2DF}, {0x2E5, 0x2EB}, {0x2ED, 0x2ED}, {0x2EF, 0x2FF}, {0x375, 0x375}, {0x384, 0x385}, {0x3F6, 0x3F6}, {0x482, 0x482}, {0x58D, 0x58F}, {0x606, 0x608},
-{0x60B, 0x60B}, {0x60E, 0x60F}, {0x6DE, 0x6DE}, {0x6E9, 0x6E9}, {0x6FD, 0x6FE}, {0x7F6, 0x7F6}, {0x7FE, 0x7FF}, {0x9F2, 0x9F3}, {0x9FA, 0x9FB}, {0xAF1, 0xAF1}, {0xB70, 0xB70}, {0xBF3, 0xBFA},
-{0xC7F, 0xC7F}, {0xD4F, 0xD4F}, {0xD79, 0xD79}, {0xE3F, 0xE3F}, {0xF01, 0xF03}, {0xF13, 0xF13}, {0xF15, 0xF17}, {0xF1A, 0xF1F}, {0xF34, 0xF34}, {0xF36, 0xF36}, {0xF38, 0xF38}, {0xFBE, 0xFC5},
-{0xFC7, 0xFCC}, {0xFCE, 0xFCF}, {0xFD5, 0xFD8}, {0x109E, 0x109F}, {0x1390, 0x1399}, {0x166D, 0x166D}, {0x17DB, 0x17DB}, {0x1940, 0x1940}, {0x19DE, 0x19FF}, {0x1B61, 0x1B6A}, {0x1B74, 0x1B7C},
-{0x1FBD, 0x1FBD}, {0x1FBF, 0x1FC1}, {0x1FCD, 0x1FCF}, {0x1FDD, 0x1FDF}, {0x1FED, 0x1FEF}, {0x1FFD, 0x1FFE}, {0x2044, 0x2044}, {0x2052, 0x2052}, {0x207A, 0x207C}, {0x208A, 0x208C}, {0x20A0, 0x20BF},
-{0x2100, 0x2101}, {0x2103, 0x2106}, {0x2108, 0x2109}, {0x2114, 0x2114}, {0x2116, 0x2118}, {0x211E, 0x2123}, {0x2125, 0x2125}, {0x2127, 0x2127}, {0x2129, 0x2129}, {0x212E, 0x212E}, {0x213A, 0x213B},
-{0x2140, 0x2144}, {0x214A, 0x214D}, {0x214F, 0x214F}, {0x218A, 0x218B}, {0x2190, 0x2307}, {0x230C, 0x2328}, {0x232B, 0x2426}, {0x2440, 0x244A}, {0x249C, 0x24E9}, {0x2500, 0x2767}, {0x2794, 0x27C4},
-{0x27C7, 0x27E5}, {0x27F0, 0x2982}, {0x2999, 0x29D7}, {0x29DC, 0x29FB}, {0x29FE, 0x2B73}, {0x2B76, 0x2B95}, {0x2B97, 0x2BFF}, {0x2CE5, 0x2CEA}, {0x2E50, 0x2E51}, {0x2E80, 0x2E99}, {0x2E9B, 0x2EF3},
-{0x2F00, 0x2FD5}, {0x2FF0, 0x2FFB}, {0x3004, 0x3004}, {0x3012, 0x3013}, {0x3020, 0x3020}, {0x3036, 0x3037}, {0x303E, 0x303F}, {0x309B, 0x309C}, {0x3190, 0x3191}, {0x3196, 0x319F}, {0x31C0, 0x31E3},
-{0x3200, 0x321E}, {0x322A, 0x3247}, {0x3250, 0x3250}, {0x3260, 0x327F}, {0x328A, 0x32B0}, {0x32C0, 0x33FF}, {0x4DC0, 0x4DFF}, {0xA490, 0xA4C6}, {0xA700, 0xA716}, {0xA720, 0xA721}, {0xA789, 0xA78A},
-{0xA828, 0xA82B}, {0xA836, 0xA839}, {0xAA77, 0xAA79}, {0xAB5B, 0xAB5B}, {0xAB6A, 0xAB6B}, {0xFB29, 0xFB29}, {0xFBB2, 0xFBC1}, {0xFDFC, 0xFDFD}, {0xFE62, 0xFE62}, {0xFE64, 0xFE66}, {0xFE69, 0xFE69},
-{0xFF04, 0xFF04}, {0xFF0B, 0xFF0B}, {0xFF1C, 0xFF1E}, {0xFF3E, 0xFF3E}, {0xFF40, 0xFF40}, {0xFF5C, 0xFF5C}, {0xFF5E, 0xFF5E}, {0xFFE0, 0xFFE6}, {0xFFE8, 0xFFEE}, {0xFFFC, 0xFFFD}, {0x10137, 0x1013F},
-{0x10179, 0x10189}, {0x1018C, 0x1018E}, {0x10190, 0x1019C}, {0x101A0, 0x101A0}, {0x101D0, 0x101FC}, {0x10877, 0x10878}, {0x10AC8, 0x10AC8}, {0x1173F, 0x1173F}, {0x11FD5, 0x11FF1}, {0x16B3C, 0x16B3F},
-{0x16B45, 0x16B45}, {0x1BC9C, 0x1BC9C}, {0x1D000, 0x1D0F5}, {0x1D100, 0x1D126}, {0x1D129, 0x1D164}, {0x1D16A, 0x1D16C}, {0x1D183, 0x1D184}, {0x1D18C, 0x1D1A9}, {0x1D1AE, 0x1D1E8}, {0x1D200, 0x1D241},
-{0x1D245, 0x1D245}, {0x1D300, 0x1D356}, {0x1D6C1, 0x1D6C1}, {0x1D6DB, 0x1D6DB}, {0x1D6FB, 0x1D6FB}, {0x1D715, 0x1D715}, {0x1D735, 0x1D735}, {0x1D74F, 0x1D74F}, {0x1D76F, 0x1D76F}, {0x1D789, 0x1D789},
-{0x1D7A9, 0x1D7A9}, {0x1D7C3, 0x1D7C3}, {0x1D800, 0x1D9FF}, {0x1DA37, 0x1DA3A}, {0x1DA6D, 0x1DA74}, {0x1DA76, 0x1DA83}, {0x1DA85, 0x1DA86}, {0x1E14F, 0x1E14F}, {0x1E2FF, 0x1E2FF}, {0x1ECAC, 0x1ECAC},
-{0x1ECB0, 0x1ECB0}, {0x1ED2E, 0x1ED2E}, {0x1EEF0, 0x1EEF1}, {0x1F000, 0x1F02B}, {0x1F030, 0x1F093}, {0x1F0A0, 0x1F0AE}, {0x1F0B1, 0x1F0BF}, {0x1F0C1, 0x1F0CF}, {0x1F0D1, 0x1F0F5}, {0x1F10D, 0x1F1AD},
-{0x1F1E6, 0x1F202}, {0x1F210, 0x1F23B}, {0x1F240, 0x1F248}, {0x1F250, 0x1F251}, {0x1F260, 0x1F265}, {0x1F300, 0x1F6D7}, {0x1F6E0, 0x1F6EC}, {0x1F6F0, 0x1F6FC}, {0x1F700, 0x1F773}, {0x1F780, 0x1F7D8},
-{0x1F7E0, 0x1F7EB}, {0x1F800, 0x1F80B}, {0x1F810, 0x1F847}, {0x1F850, 0x1F859}, {0x1F860, 0x1F887}, {0x1F890, 0x1F8AD}, {0x1F8B0, 0x1F8B1}, {0x1F900, 0x1F978}, {0x1F97A, 0x1F9CB}, {0x1F9CD, 0x1FA53},
-{0x1FA60, 0x1FA6D}, {0x1FA70, 0x1FA74}, {0x1FA78, 0x1FA7A}, {0x1FA80, 0x1FA86}, {0x1FA90, 0x1FAA8}, {0x1FAB0, 0x1FAB6}, {0x1FAC0, 0x1FAC2}, {0x1FAD0, 0x1FAD6}, {0x1FB00, 0x1FB92}, {0x1FB94, 0x1FBCA},
-};
-
-static const std::vector<std::pair<uint32_t, uint32_t>> control_ranges = {
-{0x0, 0x8}, {0xE, 0x1B}, {0x7F, 0x84}, {0x86, 0x9F}, {0xAD, 0xAD}, {0x378, 0x379}, {0x380, 0x383}, {0x38B, 0x38B}, {0x38D, 0x38D}, {0x3A2, 0x3A2}, {0x530, 0x530}, {0x557, 0x558}, {0x58B, 0x58C},
-{0x590, 0x590}, {0x5C8, 0x5CF}, {0x5EB, 0x5EE}, {0x5F5, 0x605}, {0x61C, 0x61D}, {0x6DD, 0x6DD}, {0x70E, 0x70F}, {0x74B, 0x74C}, {0x7B2, 0x7BF}, {0x7FB, 0x7FC}, {0x82E, 0x82F}, {0x83F, 0x83F},
-{0x85C, 0x85D}, {0x85F, 0x85F}, {0x86B, 0x89F}, {0x8B5, 0x8B5}, {0x8C8, 0x8D2}, {0x8E2, 0x8E2}, {0x984, 0x984}, {0x98D, 0x98E}, {0x991, 0x992}, {0x9A9, 0x9A9}, {0x9B1, 0x9B1}, {0x9B3, 0x9B5},
-{0x9BA, 0x9BB}, {0x9C5, 0x9C6}, {0x9C9, 0x9CA}, {0x9CF, 0x9D6}, {0x9D8, 0x9DB}, {0x9DE, 0x9DE}, {0x9E4, 0x9E5}, {0x9FF, 0xA00}, {0xA04, 0xA04}, {0xA0B, 0xA0E}, {0xA11, 0xA12}, {0xA29, 0xA29},
-{0xA31, 0xA31}, {0xA34, 0xA34}, {0xA37, 0xA37}, {0xA3A, 0xA3B}, {0xA3D, 0xA3D}, {0xA43, 0xA46}, {0xA49, 0xA4A}, {0xA4E, 0xA50}, {0xA52, 0xA58}, {0xA5D, 0xA5D}, {0xA5F, 0xA65}, {0xA77, 0xA80},
-{0xA84, 0xA84}, {0xA8E, 0xA8E}, {0xA92, 0xA92}, {0xAA9, 0xAA9}, {0xAB1, 0xAB1}, {0xAB4, 0xAB4}, {0xABA, 0xABB}, {0xAC6, 0xAC6}, {0xACA, 0xACA}, {0xACE, 0xACF}, {0xAD1, 0xADF}, {0xAE4, 0xAE5},
-{0xAF2, 0xAF8}, {0xB00, 0xB00}, {0xB04, 0xB04}, {0xB0D, 0xB0E}, {0xB11, 0xB12}, {0xB29, 0xB29}, {0xB31, 0xB31}, {0xB34, 0xB34}, {0xB3A, 0xB3B}, {0xB45, 0xB46}, {0xB49, 0xB4A}, {0xB4E, 0xB54},
-{0xB58, 0xB5B}, {0xB5E, 0xB5E}, {0xB64, 0xB65}, {0xB78, 0xB81}, {0xB84, 0xB84}, {0xB8B, 0xB8D}, {0xB91, 0xB91}, {0xB96, 0xB98}, {0xB9B, 0xB9B}, {0xB9D, 0xB9D}, {0xBA0, 0xBA2}, {0xBA5, 0xBA7},
-{0xBAB, 0xBAD}, {0xBBA, 0xBBD}, {0xBC3, 0xBC5}, {0xBC9, 0xBC9}, {0xBCE, 0xBCF}, {0xBD1, 0xBD6}, {0xBD8, 0xBE5}, {0xBFB, 0xBFF}, {0xC0D, 0xC0D}, {0xC11, 0xC11}, {0xC29, 0xC29}, {0xC3A, 0xC3C},
-{0xC45, 0xC45}, {0xC49, 0xC49}, {0xC4E, 0xC54}, {0xC57, 0xC57}, {0xC5B, 0xC5F}, {0xC64, 0xC65}, {0xC70, 0xC76}, {0xC8D, 0xC8D}, {0xC91, 0xC91}, {0xCA9, 0xCA9}, {0xCB4, 0xCB4}, {0xCBA, 0xCBB},
-{0xCC5, 0xCC5}, {0xCC9, 0xCC9}, {0xCCE, 0xCD4}, {0xCD7, 0xCDD}, {0xCDF, 0xCDF}, {0xCE4, 0xCE5}, {0xCF0, 0xCF0}, {0xCF3, 0xCFF}, {0xD0D, 0xD0D}, {0xD11, 0xD11}, {0xD45, 0xD45}, {0xD49, 0xD49},
-{0xD50, 0xD53}, {0xD64, 0xD65}, {0xD80, 0xD80}, {0xD84, 0xD84}, {0xD97, 0xD99}, {0xDB2, 0xDB2}, {0xDBC, 0xDBC}, {0xDBE, 0xDBF}, {0xDC7, 0xDC9}, {0xDCB, 0xDCE}, {0xDD5, 0xDD5}, {0xDD7, 0xDD7},
-{0xDE0, 0xDE5}, {0xDF0, 0xDF1}, {0xDF5, 0xE00}, {0xE3B, 0xE3E}, {0xE5C, 0xE80}, {0xE83, 0xE83}, {0xE85, 0xE85}, {0xE8B, 0xE8B}, {0xEA4, 0xEA4}, {0xEA6, 0xEA6}, {0xEBE, 0xEBF}, {0xEC5, 0xEC5},
-{0xEC7, 0xEC7}, {0xECE, 0xECF}, {0xEDA, 0xEDB}, {0xEE0, 0xEFF}, {0xF48, 0xF48}, {0xF6D, 0xF70}, {0xF98, 0xF98}, {0xFBD, 0xFBD}, {0xFCD, 0xFCD}, {0xFDB, 0xFFF}, {0x10C6, 0x10C6}, {0x10C8, 0x10CC},
-{0x10CE, 0x10CF}, {0x1249, 0x1249}, {0x124E, 0x124F}, {0x1257, 0x1257}, {0x1259, 0x1259}, {0x125E, 0x125F}, {0x1289, 0x1289}, {0x128E, 0x128F}, {0x12B1, 0x12B1}, {0x12B6, 0x12B7}, {0x12BF, 0x12BF},
-{0x12C1, 0x12C1}, {0x12C6, 0x12C7}, {0x12D7, 0x12D7}, {0x1311, 0x1311}, {0x1316, 0x1317}, {0x135B, 0x135C}, {0x137D, 0x137F}, {0x139A, 0x139F}, {0x13F6, 0x13F7}, {0x13FE, 0x13FF}, {0x169D, 0x169F},
-{0x16F9, 0x16FF}, {0x170D, 0x170D}, {0x1715, 0x171F}, {0x1737, 0x173F}, {0x1754, 0x175F}, {0x176D, 0x176D}, {0x1771, 0x1771}, {0x1774, 0x177F}, {0x17DE, 0x17DF}, {0x17EA, 0x17EF}, {0x17FA, 0x17FF},
-{0x180E, 0x180F}, {0x181A, 0x181F}, {0x1879, 0x187F}, {0x18AB, 0x18AF}, {0x18F6, 0x18FF}, {0x191F, 0x191F}, {0x192C, 0x192F}, {0x193C, 0x193F}, {0x1941, 0x1943}, {0x196E, 0x196F}, {0x1975, 0x197F},
-{0x19AC, 0x19AF}, {0x19CA, 0x19CF}, {0x19DB, 0x19DD}, {0x1A1C, 0x1A1D}, {0x1A5F, 0x1A5F}, {0x1A7D, 0x1A7E}, {0x1A8A, 0x1A8F}, {0x1A9A, 0x1A9F}, {0x1AAE, 0x1AAF}, {0x1AC1, 0x1AFF}, {0x1B4C, 0x1B4F},
-{0x1B7D, 0x1B7F}, {0x1BF4, 0x1BFB}, {0x1C38, 0x1C3A}, {0x1C4A, 0x1C4C}, {0x1C89, 0x1C8F}, {0x1CBB, 0x1CBC}, {0x1CC8, 0x1CCF}, {0x1CFB, 0x1CFF}, {0x1DFA, 0x1DFA}, {0x1F16, 0x1F17}, {0x1F1E, 0x1F1F},
-{0x1F46, 0x1F47}, {0x1F4E, 0x1F4F}, {0x1F58, 0x1F58}, {0x1F5A, 0x1F5A}, {0x1F5C, 0x1F5C}, {0x1F5E, 0x1F5E}, {0x1F7E, 0x1F7F}, {0x1FB5, 0x1FB5}, {0x1FC5, 0x1FC5}, {0x1FD4, 0x1FD5}, {0x1FDC, 0x1FDC},
-{0x1FF0, 0x1FF1}, {0x1FF5, 0x1FF5}, {0x1FFF, 0x1FFF}, {0x200B, 0x200F}, {0x202A, 0x202E}, {0x2060, 0x206F}, {0x2072, 0x2073}, {0x208F, 0x208F}, {0x209D, 0x209F}, {0x20C0, 0x20CF}, {0x20F1, 0x20FF},
-{0x218C, 0x218F}, {0x2427, 0x243F}, {0x244B, 0x245F}, {0x2B74, 0x2B75}, {0x2B96, 0x2B96}, {0x2C2F, 0x2C2F}, {0x2C5F, 0x2C5F}, {0x2CF4, 0x2CF8}, {0x2D26, 0x2D26}, {0x2D28, 0x2D2C}, {0x2D2E, 0x2D2F},
-{0x2D68, 0x2D6E}, {0x2D71, 0x2D7E}, {0x2D97, 0x2D9F}, {0x2DA7, 0x2DA7}, {0x2DAF, 0x2DAF}, {0x2DB7, 0x2DB7}, {0x2DBF, 0x2DBF}, {0x2DC7, 0x2DC7}, {0x2DCF, 0x2DCF}, {0x2DD7, 0x2DD7}, {0x2DDF, 0x2DDF},
-{0x2E53, 0x2E7F}, {0x2E9A, 0x2E9A}, {0x2EF4, 0x2EFF}, {0x2FD6, 0x2FEF}, {0x2FFC, 0x2FFF}, {0x3040, 0x3040}, {0x3097, 0x3098}, {0x3100, 0x3104}, {0x3130, 0x3130}, {0x318F, 0x318F}, {0x31E4, 0x31EF},
-{0x321F, 0x321F}, {0x9FFD, 0x9FFF}, {0xA48D, 0xA48F}, {0xA4C7, 0xA4CF}, {0xA62C, 0xA63F}, {0xA6F8, 0xA6FF}, {0xA7C0, 0xA7C1}, {0xA7CB, 0xA7F4}, {0xA82D, 0xA82F}, {0xA83A, 0xA83F}, {0xA878, 0xA87F},
-{0xA8C6, 0xA8CD}, {0xA8DA, 0xA8DF}, {0xA954, 0xA95E}, {0xA97D, 0xA97F}, {0xA9CE, 0xA9CE}, {0xA9DA, 0xA9DD}, {0xA9FF, 0xA9FF}, {0xAA37, 0xAA3F}, {0xAA4E, 0xAA4F}, {0xAA5A, 0xAA5B}, {0xAAC3, 0xAADA},
-{0xAAF7, 0xAB00}, {0xAB07, 0xAB08}, {0xAB0F, 0xAB10}, {0xAB17, 0xAB1F}, {0xAB27, 0xAB27}, {0xAB2F, 0xAB2F}, {0xAB6C, 0xAB6F}, {0xABEE, 0xABEF}, {0xABFA, 0xABFF}, {0xD7A4, 0xD7AF}, {0xD7C7, 0xD7CA},
-{0xD7FC, 0xF8FF}, {0xFA6E, 0xFA6F}, {0xFADA, 0xFAFF}, {0xFB07, 0xFB12}, {0xFB18, 0xFB1C}, {0xFB37, 0xFB37}, {0xFB3D, 0xFB3D}, {0xFB3F, 0xFB3F}, {0xFB42, 0xFB42}, {0xFB45, 0xFB45}, {0xFBC2, 0xFBD2},
-{0xFD40, 0xFD4F}, {0xFD90, 0xFD91}, {0xFDC8, 0xFDEF}, {0xFDFE, 0xFDFF}, {0xFE1A, 0xFE1F}, {0xFE53, 0xFE53}, {0xFE67, 0xFE67}, {0xFE6C, 0xFE6F}, {0xFE75, 0xFE75}, {0xFEFD, 0xFF00}, {0xFFBF, 0xFFC1},
-{0xFFC8, 0xFFC9}, {0xFFD0, 0xFFD1}, {0xFFD8, 0xFFD9}, {0xFFDD, 0xFFDF}, {0xFFE7, 0xFFE7}, {0xFFEF, 0xFFFB}, {0xFFFE, 0xFFFF}, {0x1000C, 0x1000C}, {0x10027, 0x10027}, {0x1003B, 0x1003B},
-{0x1003E, 0x1003E}, {0x1004E, 0x1004F}, {0x1005E, 0x1007F}, {0x100FB, 0x100FF}, {0x10103, 0x10106}, {0x10134, 0x10136}, {0x1018F, 0x1018F}, {0x1019D, 0x1019F}, {0x101A1, 0x101CF}, {0x101FE, 0x1027F},
-{0x1029D, 0x1029F}, {0x102D1, 0x102DF}, {0x102FC, 0x102FF}, {0x10324, 0x1032C}, {0x1034B, 0x1034F}, {0x1037B, 0x1037F}, {0x1039E, 0x1039E}, {0x103C4, 0x103C7}, {0x103D6, 0x103FF}, {0x1049E, 0x1049F},
-{0x104AA, 0x104AF}, {0x104D4, 0x104D7}, {0x104FC, 0x104FF}, {0x10528, 0x1052F}, {0x10564, 0x1056E}, {0x10570, 0x105FF}, {0x10737, 0x1073F}, {0x10756, 0x1075F}, {0x10768, 0x107FF}, {0x10806, 0x10807},
-{0x10809, 0x10809}, {0x10836, 0x10836}, {0x10839, 0x1083B}, {0x1083D, 0x1083E}, {0x10856, 0x10856}, {0x1089F, 0x108A6}, {0x108B0, 0x108DF}, {0x108F3, 0x108F3}, {0x108F6, 0x108FA}, {0x1091C, 0x1091E},
-{0x1093A, 0x1093E}, {0x10940, 0x1097F}, {0x109B8, 0x109BB}, {0x109D0, 0x109D1}, {0x10A04, 0x10A04}, {0x10A07, 0x10A0B}, {0x10A14, 0x10A14}, {0x10A18, 0x10A18}, {0x10A36, 0x10A37}, {0x10A3B, 0x10A3E},
-{0x10A49, 0x10A4F}, {0x10A59, 0x10A5F}, {0x10AA0, 0x10ABF}, {0x10AE7, 0x10AEA}, {0x10AF7, 0x10AFF}, {0x10B36, 0x10B38}, {0x10B56, 0x10B57}, {0x10B73, 0x10B77}, {0x10B92, 0x10B98}, {0x10B9D, 0x10BA8},
-{0x10BB0, 0x10BFF}, {0x10C49, 0x10C7F}, {0x10CB3, 0x10CBF}, {0x10CF3, 0x10CF9}, {0x10D28, 0x10D2F}, {0x10D3A, 0x10E5F}, {0x10E7F, 0x10E7F}, {0x10EAA, 0x10EAA}, {0x10EAE, 0x10EAF}, {0x10EB2, 0x10EFF},
-{0x10F28, 0x10F2F}, {0x10F5A, 0x10FAF}, {0x10FCC, 0x10FDF}, {0x10FF7, 0x10FFF}, {0x1104E, 0x11051}, {0x11070, 0x1107E}, {0x110BD, 0x110BD}, {0x110C2, 0x110CF}, {0x110E9, 0x110EF}, {0x110FA, 0x110FF},
-{0x11135, 0x11135}, {0x11148, 0x1114F}, {0x11177, 0x1117F}, {0x111E0, 0x111E0}, {0x111F5, 0x111FF}, {0x11212, 0x11212}, {0x1123F, 0x1127F}, {0x11287, 0x11287}, {0x11289, 0x11289}, {0x1128E, 0x1128E},
-{0x1129E, 0x1129E}, {0x112AA, 0x112AF}, {0x112EB, 0x112EF}, {0x112FA, 0x112FF}, {0x11304, 0x11304}, {0x1130D, 0x1130E}, {0x11311, 0x11312}, {0x11329, 0x11329}, {0x11331, 0x11331}, {0x11334, 0x11334},
-{0x1133A, 0x1133A}, {0x11345, 0x11346}, {0x11349, 0x1134A}, {0x1134E, 0x1134F}, {0x11351, 0x11356}, {0x11358, 0x1135C}, {0x11364, 0x11365}, {0x1136D, 0x1136F}, {0x11375, 0x113FF}, {0x1145C, 0x1145C},
-{0x11462, 0x1147F}, {0x114C8, 0x114CF}, {0x114DA, 0x1157F}, {0x115B6, 0x115B7}, {0x115DE, 0x115FF}, {0x11645, 0x1164F}, {0x1165A, 0x1165F}, {0x1166D, 0x1167F}, {0x116B9, 0x116BF}, {0x116CA, 0x116FF},
-{0x1171B, 0x1171C}, {0x1172C, 0x1172F}, {0x11740, 0x117FF}, {0x1183C, 0x1189F}, {0x118F3, 0x118FE}, {0x11907, 0x11908}, {0x1190A, 0x1190B}, {0x11914, 0x11914}, {0x11917, 0x11917}, {0x11936, 0x11936},
-{0x11939, 0x1193A}, {0x11947, 0x1194F}, {0x1195A, 0x1199F}, {0x119A8, 0x119A9}, {0x119D8, 0x119D9}, {0x119E5, 0x119FF}, {0x11A48, 0x11A4F}, {0x11AA3, 0x11ABF}, {0x11AF9, 0x11BFF}, {0x11C09, 0x11C09},
-{0x11C37, 0x11C37}, {0x11C46, 0x11C4F}, {0x11C6D, 0x11C6F}, {0x11C90, 0x11C91}, {0x11CA8, 0x11CA8}, {0x11CB7, 0x11CFF}, {0x11D07, 0x11D07}, {0x11D0A, 0x11D0A}, {0x11D37, 0x11D39}, {0x11D3B, 0x11D3B},
-{0x11D3E, 0x11D3E}, {0x11D48, 0x11D4F}, {0x11D5A, 0x11D5F}, {0x11D66, 0x11D66}, {0x11D69, 0x11D69}, {0x11D8F, 0x11D8F}, {0x11D92, 0x11D92}, {0x11D99, 0x11D9F}, {0x11DAA, 0x11EDF}, {0x11EF9, 0x11FAF},
-{0x11FB1, 0x11FBF}, {0x11FF2, 0x11FFE}, {0x1239A, 0x123FF}, {0x1246F, 0x1246F}, {0x12475, 0x1247F}, {0x12544, 0x12FFF}, {0x1342F, 0x143FF}, {0x14647, 0x167FF}, {0x16A39, 0x16A3F}, {0x16A5F, 0x16A5F},
-{0x16A6A, 0x16A6D}, {0x16A70, 0x16ACF}, {0x16AEE, 0x16AEF}, {0x16AF6, 0x16AFF}, {0x16B46, 0x16B4F}, {0x16B5A, 0x16B5A}, {0x16B62, 0x16B62}, {0x16B78, 0x16B7C}, {0x16B90, 0x16E3F}, {0x16E9B, 0x16EFF},
-{0x16F4B, 0x16F4E}, {0x16F88, 0x16F8E}, {0x16FA0, 0x16FDF}, {0x16FE5, 0x16FEF}, {0x16FF2, 0x16FFF}, {0x187F8, 0x187FF}, {0x18CD6, 0x18CFF}, {0x18D09, 0x1AFFF}, {0x1B11F, 0x1B14F}, {0x1B153, 0x1B163},
-{0x1B168, 0x1B16F}, {0x1B2FC, 0x1BBFF}, {0x1BC6B, 0x1BC6F}, {0x1BC7D, 0x1BC7F}, {0x1BC89, 0x1BC8F}, {0x1BC9A, 0x1BC9B}, {0x1BCA0, 0x1CFFF}, {0x1D0F6, 0x1D0FF}, {0x1D127, 0x1D128}, {0x1D173, 0x1D17A},
-{0x1D1E9, 0x1D1FF}, {0x1D246, 0x1D2DF}, {0x1D2F4, 0x1D2FF}, {0x1D357, 0x1D35F}, {0x1D379, 0x1D3FF}, {0x1D455, 0x1D455}, {0x1D49D, 0x1D49D}, {0x1D4A0, 0x1D4A1}, {0x1D4A3, 0x1D4A4}, {0x1D4A7, 0x1D4A8},
-{0x1D4AD, 0x1D4AD}, {0x1D4BA, 0x1D4BA}, {0x1D4BC, 0x1D4BC}, {0x1D4C4, 0x1D4C4}, {0x1D506, 0x1D506}, {0x1D50B, 0x1D50C}, {0x1D515, 0x1D515}, {0x1D51D, 0x1D51D}, {0x1D53A, 0x1D53A}, {0x1D53F, 0x1D53F},
-{0x1D545, 0x1D545}, {0x1D547, 0x1D549}, {0x1D551, 0x1D551}, {0x1D6A6, 0x1D6A7}, {0x1D7CC, 0x1D7CD}, {0x1DA8C, 0x1DA9A}, {0x1DAA0, 0x1DAA0}, {0x1DAB0, 0x1DFFF}, {0x1E007, 0x1E007}, {0x1E019, 0x1E01A},
-{0x1E022, 0x1E022}, {0x1E025, 0x1E025}, {0x1E02B, 0x1E0FF}, {0x1E12D, 0x1E12F}, {0x1E13E, 0x1E13F}, {0x1E14A, 0x1E14D}, {0x1E150, 0x1E2BF}, {0x1E2FA, 0x1E2FE}, {0x1E300, 0x1E7FF}, {0x1E8C5, 0x1E8C6},
-{0x1E8D7, 0x1E8FF}, {0x1E94C, 0x1E94F}, {0x1E95A, 0x1E95D}, {0x1E960, 0x1EC70}, {0x1ECB5, 0x1ED00}, {0x1ED3E, 0x1EDFF}, {0x1EE04, 0x1EE04}, {0x1EE20, 0x1EE20}, {0x1EE23, 0x1EE23}, {0x1EE25, 0x1EE26},
-{0x1EE28, 0x1EE28}, {0x1EE33, 0x1EE33}, {0x1EE38, 0x1EE38}, {0x1EE3A, 0x1EE3A}, {0x1EE3C, 0x1EE41}, {0x1EE43, 0x1EE46}, {0x1EE48, 0x1EE48}, {0x1EE4A, 0x1EE4A}, {0x1EE4C, 0x1EE4C}, {0x1EE50, 0x1EE50},
-{0x1EE53, 0x1EE53}, {0x1EE55, 0x1EE56}, {0x1EE58, 0x1EE58}, {0x1EE5A, 0x1EE5A}, {0x1EE5C, 0x1EE5C}, {0x1EE5E, 0x1EE5E}, {0x1EE60, 0x1EE60}, {0x1EE63, 0x1EE63}, {0x1EE65, 0x1EE66}, {0x1EE6B, 0x1EE6B},
-{0x1EE73, 0x1EE73}, {0x1EE78, 0x1EE78}, {0x1EE7D, 0x1EE7D}, {0x1EE7F, 0x1EE7F}, {0x1EE8A, 0x1EE8A}, {0x1EE9C, 0x1EEA0}, {0x1EEA4, 0x1EEA4}, {0x1EEAA, 0x1EEAA}, {0x1EEBC, 0x1EEEF}, {0x1EEF2, 0x1EFFF},
-{0x1F02C, 0x1F02F}, {0x1F094, 0x1F09F}, {0x1F0AF, 0x1F0B0}, {0x1F0C0, 0x1F0C0}, {0x1F0D0, 0x1F0D0}, {0x1F0F6, 0x1F0FF}, {0x1F1AE, 0x1F1E5}, {0x1F203, 0x1F20F}, {0x1F23C, 0x1F23F}, {0x1F249, 0x1F24F},
-{0x1F252, 0x1F25F}, {0x1F266, 0x1F2FF}, {0x1F6D8, 0x1F6DF}, {0x1F6ED, 0x1F6EF}, {0x1F6FD, 0x1F6FF}, {0x1F774, 0x1F77F}, {0x1F7D9, 0x1F7DF}, {0x1F7EC, 0x1F7FF}, {0x1F80C, 0x1F80F}, {0x1F848, 0x1F84F},
-{0x1F85A, 0x1F85F}, {0x1F888, 0x1F88F}, {0x1F8AE, 0x1F8AF}, {0x1F8B2, 0x1F8FF}, {0x1F979, 0x1F979}, {0x1F9CC, 0x1F9CC}, {0x1FA54, 0x1FA5F}, {0x1FA6E, 0x1FA6F}, {0x1FA75, 0x1FA77}, {0x1FA7B, 0x1FA7F},
-{0x1FA87, 0x1FA8F}, {0x1FAA9, 0x1FAAF}, {0x1FAB7, 0x1FABF}, {0x1FAC3, 0x1FACF}, {0x1FAD7, 0x1FAFF}, {0x1FB93, 0x1FB93}, {0x1FBCB, 0x1FBEF}, {0x1FBFA, 0x1FFFF}, {0x2A6DE, 0x2A6FF}, {0x2B735, 0x2B73F},
-{0x2B81E, 0x2B81F}, {0x2CEA2, 0x2CEAF}, {0x2EBE1, 0x2F7FF}, {0x2FA1E, 0x2FFFF}, {0x3134B, 0xE00FF}, {0xE01F0, 0x10FFFF},
-};
-
-static std::string codepoint_to_utf8(uint32_t cp) {
-    std::string result;
-    if (/* 0x00 <= cp && */ cp <= 0x7f) {
-        result.push_back(cp);
-    }
-    else if (0x80 <= cp && cp <= 0x7ff) {
-        result.push_back(0xc0 | ((cp >> 6) & 0x1f));
-        result.push_back(0x80 | (cp & 0x3f));
-    }
-    else if (0x800 <= cp && cp <= 0xffff) {
-        result.push_back(0xe0 | ((cp >> 12) & 0x0f));
-        result.push_back(0x80 | ((cp >> 6) & 0x3f));
-        result.push_back(0x80 | (cp & 0x3f));
-    }
-    else if (0x10000 <= cp && cp <= 0x10ffff) {
-        result.push_back(0xf0 | ((cp >> 18) & 0x07));
-        result.push_back(0x80 | ((cp >> 12) & 0x3f));
-        result.push_back(0x80 | ((cp >> 6) & 0x3f));
-        result.push_back(0x80 | (cp & 0x3f));
-    }
-    else {
-        throw std::invalid_argument("invalid codepoint");
-    }
-    return result;
-}
-
-static std::string codepoints_to_utf8(const std::vector<uint32_t> & cps) {
-    std::string result;
-    for (size_t i = 0; i < cps.size(); ++i) {
-        result.append(codepoint_to_utf8(cps[i]));
-    }
-    return result;
-}
-
-static uint32_t codepoint_from_utf8(const std::string & utf8, size_t & offset) {
-    assert(offset < utf8.size());
-    if (!(utf8[offset + 0] & 0x80)) {
-        auto result = utf8[offset + 0];
-        offset += 1;
-        return result;
-    }
-    else if (!(utf8[offset + 0] & 0x40)) {
-        throw std::invalid_argument("invalid character");
-    }
-    else if (!(utf8[offset + 0] & 0x20)) {
-        if (offset + 1 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80))
-            throw std::invalid_argument("invalid character");
-        auto result = ((utf8[offset + 0] & 0x1f) << 6) | (utf8[offset + 1] & 0x3f);
-        offset += 2;
-        return result;
-    }
-    else if (!(utf8[offset + 0] & 0x10)) {
-        if (offset + 2 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80))
-            throw std::invalid_argument("invalid character");
-        auto result = ((utf8[offset + 0] & 0x0f) << 12) | ((utf8[offset + 1] & 0x3f) << 6) | (utf8[offset + 2] & 0x3f);
-        offset += 3;
-        return result;
-    }
-    else if (!(utf8[offset + 0] & 0x08)) {
-        if (offset + 3 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80) || !((utf8[offset + 3] & 0xc0) == 0x80))
-            throw std::invalid_argument("invalid character");
-        auto result = ((utf8[offset + 0] & 0x07) << 18) | ((utf8[offset + 1] & 0x3f) << 12) | ((utf8[offset + 2] & 0x3f) << 6) | (utf8[offset + 3] & 0x3f);
-        offset += 4;
-        return result;
-    }
-    throw std::invalid_argument("invalid string");
-}
-
-static std::vector<uint32_t> codepoints_from_utf8(const std::string & utf8) {
-    std::vector<uint32_t> result;
-    size_t offset = 0;
-    while (offset < utf8.size()) {
-        result.push_back(codepoint_from_utf8(utf8, offset));
-    }
-    return result;
-}
-
-static std::vector<uint16_t> codepoint_to_utf16(uint32_t cp) {
-    std::vector<uint16_t> result;
-    if (/* 0x0000 <= cp && */ cp <= 0xffff) {
-        result.emplace_back(cp);
-    }
-    else if (0x10000 <= cp && cp <= 0x10ffff) {
-        result.emplace_back(0xd800 | ((cp - 0x10000) >> 10));
-        result.emplace_back(0xdc00 | ((cp - 0x10000) & 0x03ff));
-    }
-    else {
-        throw std::invalid_argument("invalid codepoint");
-    }
-    return result;
-}
-
-static std::vector<uint16_t> codepoints_to_utf16(const std::vector<uint32_t> & cps) {
-    std::vector<uint16_t> result;
-    for (size_t i = 0; i < cps.size(); ++i) {
-        auto temp = codepoint_to_utf16(cps[i]);
-        result.insert(result.end(), temp.begin(), temp.end());
-    }
-    return result;
-}
-
-static uint32_t codepoint_from_utf16(const std::vector<uint16_t> & utf16, size_t & offset) {
-    assert(offset < utf16.size());
-    if (((utf16[0] >> 10) << 10) != 0xd800) {
-        auto result = utf16[offset + 0];
-        offset += 1;
-        return result;
-    }
-    else {
-        if (offset + 1 >= utf16.size() || !((utf16[1] & 0xdc00) == 0xdc00))
-            throw std::invalid_argument("invalid character");
-        auto result = 0x10000 + (((utf16[0] & 0x03ff) << 10) | (utf16[1] & 0x03ff));
-        offset += 2;
-        return result;
-    }
-    throw std::invalid_argument("invalid string");
-}
-
-static std::vector<uint32_t> codepoints_from_utf16(const std::vector<uint16_t> & utf16) {
-    std::vector<uint32_t> result;
-    size_t offset = 0;
-    while (offset < utf16.size())
-        result.push_back(codepoint_from_utf16(utf16, offset));
-    return result;
-}
 
 #define CODEPOINT_TYPE_UNIDENTIFIED 0
-#define CODEPOINT_TYPE_DIGIT 1
-#define CODEPOINT_TYPE_LETTER 2
-#define CODEPOINT_TYPE_WHITESPACE 3
-#define CODEPOINT_TYPE_ACCENT_MARK 4
-#define CODEPOINT_TYPE_PUNCTUATION 5
-#define CODEPOINT_TYPE_SYMBOL 6
-#define CODEPOINT_TYPE_CONTROL 7
+#define CODEPOINT_TYPE_DIGIT        1
+#define CODEPOINT_TYPE_LETTER       2
+#define CODEPOINT_TYPE_WHITESPACE   3
+#define CODEPOINT_TYPE_ACCENT_MARK  4
+#define CODEPOINT_TYPE_PUNCTUATION  5
+#define CODEPOINT_TYPE_SYMBOL       6
+#define CODEPOINT_TYPE_CONTROL      7
 
-static std::unordered_map<uint32_t, int> codepoint_type_map() {
-    std::unordered_map<uint32_t, int> codepoint_types;
-    for (auto p : digit_ranges) {
-        for(auto i = p.first; i <= p.second; ++ i)
-            codepoint_types[i] = CODEPOINT_TYPE_DIGIT;
-    }
-    for(auto p : letter_ranges) {
-        for(auto i = p.first; i <= p.second; ++ i)
-            codepoint_types[i] = CODEPOINT_TYPE_LETTER;
-    }
-    for(auto p : whitespace_ranges) {
-        for(auto i = p.first; i <= p.second; ++ i)
-            codepoint_types[i] = CODEPOINT_TYPE_WHITESPACE;
-    }
-    for(auto p : accent_mark_ranges) {
-        for(auto i = p.first; i <= p.second; ++ i)
-            codepoint_types[i] = CODEPOINT_TYPE_ACCENT_MARK;
-    }
-    for(auto p : punctuation_ranges) {
-        for(auto i = p.first; i <= p.second; ++ i)
-            codepoint_types[i] = CODEPOINT_TYPE_PUNCTUATION;
-    }
-    for (auto p : symbol_ranges) {
-        for (auto i = p.first; i <= p.second; ++i)
-            codepoint_types[i] = CODEPOINT_TYPE_SYMBOL;
-    }
-    for(auto p : control_ranges) {
-        for(auto i = p.first; i <= p.second; ++ i)
-            codepoint_types[i] = CODEPOINT_TYPE_CONTROL;
-    }
-    return codepoint_types;
-}
+std::string unicode_cpt_to_utf8(uint32_t cp);
+std::vector<uint32_t> unicode_cpts_from_utf8(const std::string & utf8);
 
-static int codepoint_type(uint32_t cp) {
-    static std::unordered_map<uint32_t, int> codepoint_types = codepoint_type_map();
-    return codepoint_types[cp];
-}
+std::vector<uint32_t> unicode_cpts_normalize_nfd(const std::vector<uint32_t> & cpts);
 
-static int codepoint_type(const std::string & utf8) {
-    if (utf8.length() == 0)
-        return CODEPOINT_TYPE_UNIDENTIFIED;
-    size_t offset = 0;
-    return codepoint_type(codepoint_from_utf8(utf8, offset));
-}
+int unicode_cpt_type(uint32_t cp);
+int unicode_cpt_type(const std::string & utf8);
 
-static std::unordered_map<uint8_t, std::string> bytes_to_unicode_map_bpe() {
-    std::unordered_map<uint8_t, std::string> map;
-    for (int ch = u'!'; ch <= u'~'; ++ch) {
-        assert(0 <= ch && ch < 256);
-        map[ch] = codepoint_to_utf8(ch);
-    }
-    for (int ch = u'¡'; ch <= u'¬'; ++ch) {
-        assert(0 <= ch && ch < 256);
-        map[ch] = codepoint_to_utf8(ch);
-    }
-    for (int ch = u'®'; ch <= u'ÿ'; ++ch) {
-        assert(0 <= ch && ch < 256);
-        map[ch] = codepoint_to_utf8(ch);
-    }
-    auto n = 0;
-    for (int ch = 0; ch < 256; ++ch) {
-        if (map.find(ch) == map.end()) {
-            map[ch] = codepoint_to_utf8(256 + n);
-            ++n;
-        }
-    }
-    return map;
-}
-
-static std::string bytes_to_unicode_bpe(uint8_t byte) {
-    static std::unordered_map<uint8_t, std::string> map = bytes_to_unicode_map_bpe();
-    return map.at(byte);
-}
-
-static std::unordered_map<std::string, uint8_t> unicode_to_bytes_map_bpe() {
-    std::unordered_map<std::string, uint8_t> map;
-    for (int ch = u'!'; ch <= u'~'; ++ch) {
-        assert(0 <= ch && ch < 256);
-        map[codepoint_to_utf8(ch)] = ch;
-    }
-    for (int ch = u'¡'; ch <= u'¬'; ++ch) {
-        assert(0 <= ch && ch < 256);
-        map[codepoint_to_utf8(ch)] = ch;
-    }
-    for (int ch = u'®'; ch <= u'ÿ'; ++ch) {
-        assert(0 <= ch && ch < 256);
-        map[codepoint_to_utf8(ch)] = ch;
-    }
-    auto n = 0;
-    for (int ch = 0; ch < 256; ++ch) {
-        if (map.find(codepoint_to_utf8(ch)) == map.end()) {
-            map[codepoint_to_utf8(256 + n)] = ch;
-            ++n;
-        }
-    }
-    return map;
-}
-
-static uint8_t unicode_to_bytes_bpe(const std::string & utf8) {
-    static std::unordered_map<std::string, uint8_t> map = unicode_to_bytes_map_bpe();
-    return map.at(utf8);
-}
+std::string unicode_byte_to_utf8(uint8_t byte);
+uint8_t unicode_utf8_to_byte(const std::string & utf8);
 
+// simple tolower that only implements one-to-one mapping, not one-to-many
+char32_t unicode_tolower(char32_t cp);